remote sensing

Article Surface Reflectance and Sun-Induced Fluorescence Spectroscopy Measurements Using a Small Hyperspectral UAS

Roberto Garzonio *, Biagio Di Mauro, Roberto Colombo and Sergio Cogliati

Remote Sensing of Environmental Dynamics Laboratory. Department of Earth and Environmental Sciences, University of Milano-Bicocca, 20126 Milan, Italy; [email protected] (B.D.M.); [email protected] (R.C.); [email protected] (S.C.) * Correspondence: [email protected]; Tel.: +39-02-6448-2848

Academic Editors: Jose Moreno and Prasad Thenkabail Received: 25 January 2017; Accepted: 9 May 2017; Published: 12 May 2017

Abstract: This study describes the development of a small hyperspectral Unmanned Aircraft System (HyUAS) for measuring Visible and Near-Infrared (VNIR) surface reflectance and sun-induced fluorescence, co-registered with high-resolution RGB imagery, to support field spectroscopy surveys and calibration and validation of remote sensing products. The system, namely HyUAS, is based on a multirotor platform equipped with a cost-effective payload composed of a VNIR non-imaging spectrometer and an RGB camera. The spectrometer is connected to a custom entrance optics receptor developed to tune the instrument field-of-view and to obtain systematic measurements of instrument dark-current. The geometric, radiometric and spectral characteristics of the instruments were characterized and calibrated through dedicated laboratory tests. The overall accuracy of HyUAS data was evaluated during a flight campaign in which surface reflectance was compared with ground-based reference measurements. HyUAS data were used to estimate spectral indices and far-red fluorescence for different land covers. RGB images were processed as a high-resolution 3D surface model using structure from motion algorithms. The spectral measurements were accurately geo-located and projected on the digital surface model. The overall results show that: (i) rigorous calibration enabled radiance and reflectance spectra from HyUAS with RRMSE < 10% compared with ground measurements; (ii) the low-flying UAS setup allows retrieving fluorescence in absolute units; (iii) the accurate geo-location of spectra on the digital surface model greatly improves the overall interpretation of reflectance and fluorescence data. In general, the HyUAS was demonstrated to be a reliable system for supporting high-resolution field spectroscopy surveys allowing one to collect systematic measurements at very detailed spatial resolution with a valuable potential for vegetation monitoring studies. Furthermore, it can be considered a useful tool for collecting spatially-distributed observations of reflectance and fluorescence that can be further used for calibration and validation activities of airborne and satellite optical images in the context of the upcoming FLEX mission and the VNIR spectral bands of optical Earth observation missions (i.e., Landsat, Sentinel-2 and Sentinel-3).

Keywords: UAS; spectroscopy; reflectance signature; hyperspectral; sun-induced fluorescence; 3D surface model; calibration and validation; FLEX

1. Introduction Field spectroscopy is an essential technique to gain valuable insights on Earth’s surface optical properties. These types of data are important for characterizing natural surfaces, supporting calibration and validation of airborne and satellite images and providing essential information for upscaling measurements from the local to the regional scale [1–4]. In general, the common way to collect field

Remote Sens. 2017, 9, 472; doi:10.3390/rs9050472 www.mdpi.com/journal/remotesensing Remote Sens. 2017, 9, 472 2 of 24 spectroscopy data relies on close-range measurements using portable non-imaging spectrometers [5,6]. Characterizing the spatial distribution of bio-geophysical parameters in heterogeneous areas could be challenging and time consuming, and errors could arise by moving the spectrometer from place to place. This limits the exploitation of field spectroscopy to a broader range of studies [7]. For example, the collection of top-of-canopy spectral measurements on high forest trees generally requires dedicated installations such as scaffolding towers or hydraulic platforms [8–14], limiting its applicability. Analogous complications can arise when field spectroscopy data must be collected in areas that are difficult to access, such as dense vegetation forest, mountain glaciers, volcanoes or in-land water bodies. In the last decade, small Unmanned Aircraft Systems (UASs) are becoming widely-used tools for collecting data with a wide variety of sensors. The rapid growing of UASs is mainly motivated by the fact that they can provide rapid, flexible and cost-effective observations of the environment. Furthermore, UASs can facilitate a systematic sampling even for heterogeneous areas or areas that are difficult to access that can be integrated with traditional field spectroscopy surveys. Moreover, the flight maneuverability provided by UAS further motivates the deployment of scientific sensors on this type of system. In fact, platform flight parameters (e.g., altitude, speed, path, etc.) and payload characteristics (e.g., sensor sampling rate, viewing angle, etc.) can be easily tuned to fulfill specific observation requirements for different applications. For example, the spatial resolution can be modified by varying the flight altitude, enabling observation of either specific characteristics of individual targets rather than larger sampling areas in which details are averaged out. The combination of UAS with field spectroscopy constitutes a multi-scale approach that can be helpful for understanding the upscaling from in-field to airborne and satellite measurements, improving the analysis of large-scale environmental processes [15]. The capability of flying and recording data autonomously, according to a pre-defined plan, can improve the consistency of data collected in surveys repeated over time. Such a feature supports studies aimed at monitoring dynamic processes like crop yield or forest disease, which require frequent revisits during the entire growing season. It must be noted that UAS technology involves a wide range of platforms with very different technical characteristics suitable for different and specific applications. Usually, fixed-wing platforms are preferred to cover larger areas with an approach similar to airborne remote sensing. In comparison, rotary-wing systems usually fly at lower altitudes, allowing hovering over a target for a certain lapse of time to complete a series of specific measurements. Evidence of the widespread exploitation of UASs is provided by the growing number of recent scientific studies covering a broad range of environmental applications [16,17]. Several investigations, for example, show how to derive a high-resolution 3D surface model and orthophoto by using Structure from Motion (SfM) algorithms on overlapped RGB images series [18–22]. Typical examples involve glaciers, snow and landslide monitoring [23–31]. The 3D models derived by UAS images have been also used to obtain information about plants’ canopy architecture, such as tree height and crown diameter [21,32–34]. Instead, other applications exploit more in detail the surface spectral response, such as studies related to snow radiative properties and water quality monitoring [35,36]. UAS-based multispectral and hyperspectral sensors have been also used for vegetation monitoring in agriculture applications. For example, plants’ biophysical and biochemical parameters can be derived from spectral information for evaluating plant stress caused by water or nitrogen deficiency [37–40]. From this point of view, UASs provide improved spatial and temporal resolutions for crop management [41–44]. Currently, the major limits of small UASs concern payload technical requirements (e.g., weight, size, power supply and data storage/downlink) and legislative regulations. The limited payload available on small UAS makes it difficult or prohibitive to deploy some high-performance scientific instruments. On the other hand, sensor miniaturization often entails lower data quality in terms of signal-to-noise ratio and spatial/spectral resolving power. For these reasons, applications that make use of small UAS are mostly based on RGB or VNIR multispectral cameras [41,45]. Conversely, the exploitation of hyperspectral imaging instruments is still limited because instrumentation and data processing techniques are more complex and technically expensive. However, a few pioneering works Remote Sens. 2017, 9, 472 3 of 24

Remote Sens. 2017, 9, 472 3 of 25 show the great potential of spectral data with high spatial and temporal resolution, for example in monitoringprocessing agricultural techniques and are naturalmore complex vegetation and technically [19,37,41,43 expensive.,46,47]. In However, particular, a few Zarco-Tejada pioneering et al. (2012)works [37] show used the a fixed-winggreat potential UAS of spectral and imaging data with spectrometer high spatial toand collect temporal reflectance resolution, signature, for narrow-bandexample Photochemicalin monitoring agricultural Reflectance and Index natural (PRI) vegetation and Sun-Induced [19,37,41,43,46,47]. Fluorescence In particular, (SIF) to assess cropZarco-Tejada water stress. et The al. (2012) capability [37] used of collecting a fixed-wing high UAS spatial and imaging resolution spectrometer data from to collect UASs, reflectance repeated over time,signature, can strongly narrow-band support novel Photochemi scientificcal Reflectance approaches Index in monitoring (PRI) and Sun-Induced vegetation dynamics. Fluorescence Particularly, (SIF) this isto theassess case crop of spectralwater stress. indicators The capability such as of PRI collecting and SIF, high which spatial provide resolution valuable data from information UASs, on repeated over time, can strongly support novel scientific approaches in monitoring vegetation vegetation photosynthetic activity. These indicators, related to vegetation energy dissipation pathways, dynamics. Particularly, this is the case of spectral indicators such as PRI and SIF, which provide are characterized by strong diurnal and seasonal dynamics [48–50]. UASs, equipped with calibrated valuable information on vegetation photosynthetic activity. These indicators, related to vegetation spectrometers,energy dissipation could provide pathways, accurate are characterized retrieval of PRIby strong and SIF diurnal with and the necessaryseasonal dynamics temporal [48–50]. and spatial resolutionUASs, towardsequipped vegetation with calibrated stress spectrometers, detection and coul modelingd provide ofaccurate gross primaryretrieval of productivity PRI and SIF [with51–54 ]. Inthe this necessary context, temporal this study and spatial aims to resolution present atowa novelrds approachvegetation basedstress detection on a small and rotary-wing modeling of UAS, equippedgross withprimary a non-imaging productivity [51–54]. spectrometer and an RGB camera, for collecting spectral radiance and high-resolutionIn this imagerycontext, this (named study HyUAS). aims to present The geometric, a novel approach radiometric based and on a spectral small rotary-wing characteristics UAS, of the systemequipped were analyzed with a non-imaging through dedicated spectrometer laboratory and an tests.RGB camera, The reliability for collecting of surface spectral reflectance, radiance and derived spectralhigh-resolution indices and imagery far-red (named SIF was HyUAS). evaluated The geometri duringc, a radiometric dedicated flightand spectral campaign. characteristics The limitation of the system were analyzed through dedicated laboratory tests. The reliability of surface reflectance, of a non-imaging instrument can be overcome with the accurate geo-location of spectra on the 3D derived spectral indices and far-red SIF was evaluated during a dedicated flight campaign. The surface model derived by co-registered RGB imagery. The hyperspectral payload represents a compact limitation of a non-imaging instrument can be overcome with the accurate geo-location of spectra on and cost-effectivethe 3D surface solutionmodel derived that can by co-registered be easily integrated RGB imagery. on a The wide hyperspectral range of small payload UASs. represents This would promotea compact the UAS-based and cost-effective spectroscopy solution approachthat can be for easily vegetation integrated monitoring on a wide range and support of small calibrationUASs. and validationThis would ofpromote remote the sensing UAS-based products. spectroscopy The HyUAS approach system for vegetation would offer monitoring an improved and support version of the typicalcalibration field and spectroscopy validation of measurement remote sensing approach products. withThe HyUAS the aim system of providing would offer accurate an improved close-range measurementsversion of the (ground-truths) typical field spectr thatoscopy do not measurement require complex approach processing with the typical aim of ofproviding satellite, accurate airborne or high-altitudeclose-range UAS measurements (i.e., atmospheric (ground-truths) correction, that smile, do keystone).not require Moreover, complex processing HyUAS is onetypical of theof first systemssatellite, providing airborne high or spectralhigh-altitude resolution UAS (i.e., for retrievingatmospheric sun-induce correction, fluorescence;smile, keystone). therefore, Moreover, it could HyUAS is one of the first systems providing high spectral resolution for retrieving sun-induce be further employed for ground-based studies in the framework of upcoming European Space Agency fluorescence; therefore, it could be further employed for ground-based studies in the framework of (ESA) FLuorescence EXplorer (FLEX) mission [55]. upcoming European Space Agency (ESA) FLuorescence EXplorer (FLEX) mission [55].

2. The2. HyUASThe HyUAS System System

2.1. UAS2.1. UAS Platform Platform TheThe HyUAS HyUAS combines combines a a multirotor multirotor platformplatform (q (quadcopter)uadcopter) with with a specifically-developed a specifically-developed hyperspectralhyperspectral payload payload (Figure (Figure1). 1). The The Anteos Anteos platform platform (Aermatica S.p.A., S.p.A., Gironico, Gironico, CO, CO, Italy) Italy) is is poweredpowered by four by brushlessfour brushless electric electric rotors rotors that allowthat allow autonomous autonomous vertical vertical take-off take-off and landing,and landing, hovering overhovering a target andover high a target maneuverability and high maneuv duringerability in-flight during operations.in-flight operations.

FigureFigure 1. The 1. The HyUAS: HyUAS: (a) picture(a) picture of HyUASof HyUAS during during the the field field survey; survey; ((bb)) schematicschematic drawing drawing (rotors (rotors are maskedare masked because because they are they not are yet not patented). yet patented).

Anteos provides a total take-off weight of 9 kg, and it is able of carrying a maximum scientific Anteospayload providesof 2 kg for aa total flight take-off time weight of 20 min. of 9The kg, platform and it isfeatures able of a carryingwidth of 2 am, maximum a length of scientific2 m payloadwhen of the 2 kg blades for a of total the rotors flight are time unfolded of 20 min. and Thea height platform of 0.5 m. features Its maximum a width forward of 2 m, flight a length speed of 2 m when the blades of the rotors are unfolded and a height of 0.5 m. Its maximum forward flight speed is about 5 m/s. The platform uses the Global Positioning System coupled with the inertial measurement Remote Sens. 2017, 9, 472 4 of 25

is about 5 m/s. The platform uses the Global Positioning System coupled with the inertial measurement unit (GPS-IMU, MTi-G-710 GNSS/INS Xsens Technologies B.V., Enschede, NL) to accurately locate its position and attitude during flight. The GPS-IMU integrated solution offers speed and position values with the accuracy level significantly improved with respect to the accuracy of the GPS/GNSS receiver alone. In particular, the IMU provides accuracy of 0.3° for pitch and roll angles in dynamic mode and 1.0° for yaw. The device is installed on the HyUAS platform (Figure 2), and it serves for both HyUAS navigation and geolocation of measurements. This allows controlling flight maneuvers and accurately geolocating the scientific data payload. The system also comprises the Ground Control Station (GCS) that is essential to control the HyUAS in real time through a radio link connection.

2.2. Hyperspectral and RGB Sensors The payload is specifically developed for field spectroscopy surveys and combines a high-resolution spectrometer with an RGB digital camera (Figure 2). The USB4000 (Ocean Optics Inc., Dunedin, FL, U.S.) is a non-imaging fiber optic spectrometer (weight 0.190 kg), which measures 3648 spectral bands from 350 to 1000 nm with a Full Width at Half Maximum (FWHM) of about 1.5 nm (Figure 2A1). The spectrometer is connected to a specifically-developed Entrance Optic Receptor Remote(EOR) Sens. (Figure2017, 9, 2A2) 472 through a 0.1 m-long fiber optic. The EOR (Figure 2b) is composed of a series4 of of 24 lens tubes and an iris diaphragm (Thorlabs, Newton, NJ, U.S.) (Figure 2B2) that allow adapting the spectrometer Field-Of-View (FOV) in the range between ∼2.5° and ∼15° (Figure 2B4). Consequently, unitthe (GPS-IMU, dimension MTi-G-710of the area GNSS/INSsampled at the Xsens ground Technologies can vary between B.V., Enschede, 0.5 and NL)12 m to considering accurately a locate flight its positionaltitude and ranging attitude from during 5 to flight.50 m. This The GPS-IMUoptical setup, integrated commonly solution referred offers to speedas Gershun and position tube, has values a withtwo-fold the accuracy advantage level of significantly reducing the improved spectrometer with acceptance respect to theangle accuracy and providing of the GPS/GNSS a more uniform receiver ◦ alone.spot Inmeasurement. particular, the The IMU RDI131 provides shutter accuracy (Melles ofGrio 0.3t, Rochester,for pitch and NY, roll U.S.) angles (Figure in dynamic2B3), mounted mode at and ◦ 1.0thefor end yaw. of the The EOR, device allows is installed regular on and the systematic HyUAS platform measurement (Figure of 2the), and spectrometer it serves for Dark-Current both HyUAS navigation(DC). In fact, and the geolocation DC signal of can measurements. substantially Thischange allows during controlling flight data flight recording, maneuvers mainly and caused accurately by geolocatingchanges of the spectrometer scientific data Integration payload. TheTime system (IT) and also sensor comprises temperature, the Ground which Control can Station introduce (GCS) thatuncertainty is essential in toretrieving control theaccurate HyUAS surf inace real radiance time through and reflectance a radio spectra. link connection.

FigureFigure 2. 2.(a )(a Schematic) Schematic drawing drawing (not (not toto scale)scale) of different sensors sensors and and devices devices installed: installed: spectrometer spectrometer (A1),(A1), entrance entrance optic optic receptor receptor (A2), (A2), RGBRGB digitaldigital cameracamera (A3) (A3) mounted mounted on on the the stabilized stabilized support support (A4) (A4) andand GPS-IMU GPS-IMU installed installed on on the the UAS UAS platformplatform (A5); ( b)) detailed detailed drawing drawing of of the the entrance entrance optic optic receptor receptor (not(not to to scale): scale): filters’ filters’ holder holder (B1), (B1), lens lens tubes tubes withwith irisiris diaphragmsdiaphragms (B2) (B2) and and shutter shutter (B3). (B3).

The spectrometer IT can be either set manually or automatically during the flight in order to 2.2. Hyperspectral and RGB Sensors collect higher quality data at different light illumination conditions and surface albedo. The The payload is specifically developed for field spectroscopy surveys and combines a high-resolution spectrometer with an RGB digital camera (Figure2). The USB4000 (Ocean Optics Inc., Dunedin, FL, U.S.) is a non-imaging fiber optic spectrometer (weight 0.190 kg), which measures 3648 spectral bands from 350 to 1000 nm with a Full Width at Half Maximum (FWHM) of about 1.5 nm (Figure2A1). The spectrometer is connected to a specifically-developed Entrance Optic Receptor (EOR) (Figure2A2) through a 0.1 m-long fiber optic. The EOR (Figure2b) is composed of a series of lens tubes and an iris diaphragm (Thorlabs, Newton, NJ, U.S.) (Figure2B2) that allow adapting the spectrometer Field-Of-View (FOV) in the range between ~2.5◦ and ~15◦ (Figure2B4). Consequently, the dimension of the area sampled at the ground can vary between 0.5 and 12 m considering a flight altitude ranging from 5 to 50 m. This optical setup, commonly referred to as Gershun tube, has a two-fold advantage of reducing the spectrometer acceptance angle and providing a more uniform spot measurement. The RDI131 shutter (Melles Griot, Rochester, NY, U.S.) (Figure2B3), mounted at the end of the EOR, allows regular and systematic measurement of the spectrometer Dark-Current (DC). In fact, the DC signal can substantially change during flight data recording, mainly caused by changes of spectrometer Integration Time (IT) and sensor temperature, which can introduce uncertainty in retrieving accurate surface radiance and reflectance spectra. The spectrometer IT can be either set manually or automatically during the flight in order to collect higher quality data at different light illumination conditions and surface albedo. The Integration Time Optimization (ITO) algorithm [50] is implemented to set the IT automatically on the basis of the intensity of the surface reflected radiance observed at the different wavelengths. In this way, Remote Sens. 2017, 9, 472 5 of 25

RemoteIntegration Sens. 2017 Time, 9, 472 Optimization (ITO) algorithm [50] is implemented to set the IT automatically5 of on 24 the basis of the intensity of the surface reflected radiance observed at the different wavelengths. In this way, it is possible to maximize the spectrometer dynamic range and, consequently, the itsignal-to-noise is possible to maximizeratio. Furthermore, the spectrometer light intensity dynamic can range be optimized and, consequently, by placing the neutral signal-to-noise density filters ratio. Furthermore,(Thorlabs Inc., light Newton, intensity NJ, can U.S.) be optimized in the EOR by placing(Figure neutral 2B1). densityThese filters filters (Thorlabscan be used Inc., to Newton, avoid NJ,spectrometer U.S.) in the saturation EOR (Figure for 2veryB1). high These light filters intensit can beies used (i.e., tomeasurements avoid spectrometer over high saturation albedo forsurfaces very highsuch lightas snow-covered intensities (i.e., areas) measurements and to achieve over high opti albedomal spectrometer surfaces such IT as snow-coveredcompatible with areas) platform and to achievespeed during optimal data spectrometer collection. IT compatible with platform speed during data collection. The PowerShotPowerShot S100S100 (Canon, (Canon, Tokyo, Tokyo, JAP) JAP) (Figure (Figure2A3) 2A3) is a is compact a compact RGB RGB digital digital camera camera with with a 12.1 a Megapixel12.1 Megapixel CMOS CMOS sensor sensor that recordsthat records high-resolution high-resolution images images (4000 (4000× 3000 × 3000 pixels). pixels). The cameraThe camera FOV calculatedFOV calculated considering considering the minimum the minimum focal length focal of length the zoom of the lens zoom (5.2 mm)lens and(5.2 themm) physical and the dimension physical ofdimension the sensor of (7.5 the× sensor5.6 mm, (7.5 1.87 × 5.6µm mm, pixel 1.87 size) µm is 71.5 pixel× 56.6size) degrees. is 71.5 Therefore,× 56.6 degrees. the camera Therefore, captures the ancamera area ofcaptures 14.42 × an10.77 area mof with14.42 a × pixel 10.77 resolution m with a ofpixel 3.5 resolution mm flying of at 3.5 10 mmm over flying the target.at 10 m The over RGB the cameratarget. The is automatically RGB camera triggered is automatically (1 image/s) triggered during the(1 image/s) flight, ensuring during image the flight, overlapping ensuring of atimage least 80%.overlapping Both the of RGB at least camera 80%. and Both spectrometer the RGB arecamera installed and onspectrometer a stabilized are gimbal installed (Photohigher on a stabilized AV200, CARVECgimbal (Photohigher Systems, Hull, AV200, UK) CARVEC (Figure2A4) Systems, to reduce Hull the, UK) impact (Figure of the 2A4) mechanical to reduce vibrationsthe impact on of thethe scientificmechanical instruments. vibrations on the scientific instruments.

2.3. Mission Planning and Data Collection The missionmission planningplanning is is facilitated facilitated by by a dedicateda dedicated software software supporting supporting the matchingthe matching between between user observationuser observation requirements requirements and platform/payload and platform/payload technical technical parameters parameters (i.e., flight (i.e., altitude, flight sampledaltitude, areas,sampled overlapping, areas, overlapping, etc.). As the etc.). first step,As the reference first step images, reference of the investigatedimages of the area investigated (e.g., Google area Earth (e.g., or similar)Google Earth are uploaded or similar) into are the Graphicaluploaded Userinto Interfacethe Graphical (GUI), User and theInterface so-called (GUI), flight-space and the isso-called defined (i.e.,flight-space surface areais defined and maximum (i.e., surface flight area height). and Afterwards,maximum flight the overall height). flight-plan Afterwards, is automatically the overall calculatedflight-plan asis aautomatically series of Way-Points calculated (WPs), as a connectedseries of Way-Points by line segments, (WPs), covering connected the by entire line studysegments, area consideringcovering the a certainentire study level ofarea RGB considering images overlapping a certain (e.g.,level 80%). of RGB This images approach overlapping is particularly (e.g., helpful 80%). toThis achieve approach a systematic is particularly sampling helpful on to a achieve regular a grid systematic to produce sampling a 3D surfaceon a regular model, grid orthophoto to produce or a thematic3D surface maps. model, Once orthophoto the base flight-planor thematic for maps. RGB On imageryce the base has beenflight-plan defined, for the RGB specific imagery information has been relateddefined, to the spectrometer specific information measurements related can to be spectrometer edited. Two measurements approaches are can available be edited. (Figure Two3): (i)approaches in the way-point are available mode, (Figure the HyUAS 3): (i) hoversin the way-point over each WPmode, until the all HyUAS of the definedhovers over operation each WP are completeduntil all of the (i.e., defined ITO, DC operation and target are completed surface measurements); (i.e., ITO, DC (ii)and in target the transectsurface measurements); mode, the HyUAS (ii) collectsin the transect continuous mode, spectral the HyUAS measurements collects whilecontinuous it flies spectral along a spatialmeasurements transect betweenwhile it flies two definedalong a WPs.spatial The transect White between Reference two (WR) defined measurement, WPs. The Wh typicallyite Reference used to (WR) calculate measurement, surface reflectance, typically used can beto calculate collected surface including reflectance, WPs’ measurements can be collected over including a reference WPs’ tarp measurements (i.e., WP0 in Figure over a3 reference). A different tarp approach(i.e., WP0 toin collect Figure WR 3). wasA different developed approach measuring to collect the incoming WR was irradiance developed using measuring a second the spectrometer incoming operatedirradiance at using the ground a second (see spectrometer Section 3.1.2 operated for details). at the ground (see Section 2.4.1. for details).

Figure 3. SchematicSchematic representation representation ofof the the spectromet spectrometerer data data collection collection procedures: procedures: way-point way-point (a) and (a) andtransect transect (b) (modes.b) modes. The The basic basic spectral spectral measurements measurements:: Integration Integration Time Time Optimization Optimization (ITO), (ITO), Dark-Current (DC) (DC) and and target target Surface Surface (S) (S) can can be bespecified specified for each for eachway-point way-point independently. independently. WP, WP,Way-Point; Way-Point; WR, WR, White White Reference. Reference.

Flight operations can be monitored and controlled in real time by the mission control software Flight operations can be monitored and controlled in real time by the mission control software running on the ground control station. The GUI displays the current spectral signature, the RGB image running on the ground control station. The GUI displays the current spectral signature, the RGB image Remote Sens. 2017, 9, 472 6 of 24 Remote Sens. 2017, 9, 472 6 of 25 and the flight flight parameters (Figure4 4).). TheseThese datadata areare usefuluseful toto havehave anan overviewoverview ofof flightflight operationsoperations andand measurements. The The software provides also the po possibilityssibility to operate the HyUAS manually whenever needed. Each Each spectrum spectrum is isassociated associated with with the the corre correspondingsponding RBG RBG image image and anda set a of set navigation of navigation data thatdata is that used is used in the in thedata data processing. processing. In particular, In particular, the the following following data data are are stored stored in in a alog log file file (.csv): spectrum file file names, target identificationidentification name, GPSGPS data (latitude,(latitude, longitude, elevation), IMU data (relative position, roll, roll, pitch, pitch, yaw, yaw, vertical vertical and and horizontal horizontal speeds) speeds) and and RGB RGB filenames. filenames.

Figure 4. The mission control software GUI displays platformplatform flight flight parameters and payload data in real time.time. UpperUpper left left panel: panel: spectra spectra (raw (raw counts) counts and) and spectrometer spectrometer parameters. parameters. Lower Lower left panel: left panel: aerial aerialmap and map HyUAS and HyUAS position. position. Right panel: Right real-timepanel: re RGBal-time image RGB acquired image acquired from the from HyUAS, the GPSHyUAS, and IMUGPS parametersand IMU parameters (position, (position, attitude, speed). attitude, speed).

3. Material and Methods

3.1. Data Processing

3.1.1. 3D Surface Model and Geo-Location of Spectra RGB images werewere processedprocessed with with Structure Structure from from Motion Motion (SfM) (SfM) algorithm algorithm [18 [18]] implemented implemented in the in © Agisoftthe Agisoft PhotoScan PhotoScansoftware© software package. package. The 3D The point 3D cloud point was cloud created was exploiting created theexploiting overlapping the overlappingbetween repeated between RGB repeated images [RGB16]. This images point [16]. cloud This was point then cloud interpolated was then to interpolated create a Digital to create Surface a DigitalModel (DSM)Surface and Model a geo-referenced (DSM) and a orthophoto.geo-referenced orthophoto. The areaarea sampled sampled by by the the spectrometer spectrometer was identifiedwas identified for each for collected each collected spectrum. spectrum. This calculation This wascalculation performed was considering performed rollconsidering and pitch roll angles and and pitch the distanceangles and from the the distance target measured from the on target DSM. Afterwards,measured on the DSM. exact Afterwards, area sampled the byexact the area spectrometer sampled by was the projected spectrometer on the was orthophoto projected drapedon the orthophotoon the DSM. draped This process on the providesDSM. This an process accurate provid geo-locationes an accurate of spectra geo-location that is particularly of spectra relevant that is particularlyfor improving relevant the interpretation for improving of reflectance the interpreta signaturestion of (especiallyreflectance over signatures structurally (especially complex over or structurallyheterogeneous complex surfaces). or heterogeneous surfaces). 3.1.2. Retrieval of Surface Reflectance and Fluorescence 3.1.2. Retrieval of Surface Reflectance and Fluorescence A dedicated software package was developed for processing spectra in a systematic way. A dedicated software package was developed for processing spectra in a systematic way. The The routines, implemented in the MATLAB® language, include a set of standard operations to routines, implemented in the MATLAB® language, include a set of standard operations to process process the spectroscopy data: (i) convert spectrometer raw counts to radiance spectra following the spectroscopy data: (i) convert spectrometer raw counts to radiance spectra following standard standard processing methods (i.e., DC subtraction, non-linearity correction, radiometric and spectral processing methods (i.e., DC subtraction, non-linearity correction, radiometric and spectral calibrations); (ii) calculate surface reflectance, widely-used spectral indices and SIF. calibrations); (ii) calculate surface reflectance, widely-used spectral indices and SIF. Two methods were tested to estimate surface reflectance ρ(λ) from HyUAS (Figure 5). They mainly differ in the measurement of the incoming radiance term L(λ).

Remote Sens. 2017, 9, 472 7 of 24

Two methods were tested to estimate surface reflectance ρ(λ) from HyUAS (Figure5). They mainly Remote Sens. 2017, 9, 472 7 of 25 differ in the measurement of the incoming radiance term Linc(λ).

FigureFigure 5. 5.Different Different methodsmethods testedtested toto estimateestimate reflectance. reflectance. The The ρρ-tarp-tarp method method (a ()a )uses uses HyUAS HyUAS spectrometerspectrometer data data only; only; while while the theρ ρ-spec-spec methodmethod ( b) combines combines measurements measurements by by two two spectrometers. spectrometers. HH,HH, HandHeld. HandHeld.

TheThe first first approach approach (ρ -tarp)(ρ-tarp) uses uses only only the the HyUAS HyUAS spectrometer spectrometer measurements measurements (Figure (Figure5a). 5a). In fact,In fact, L (λ) is collected directly from UAS over a calibrated tarp (6 × 6 m) at least twice for each Linc(λ) is collected directly from UAS over a calibrated tarp (6 × 6 m) at least twice for each flight: immediatelyflight: immediately after take-off after and take-off after targetand after measurements target measurements (before landing). (before A linearlanding). interpolation A linear is interpolation is applied to estimate L(λ) exactly at the time of target radiance measurements applied to estimate Linc(λ) exactly at the time of target radiance measurements Lref(λ). This approach L (λ). This approach limits errors in retrieving surface reflectance due to slight changes of limits errors in retrieving surface reflectance due to slight changes of illumination that can be caused illumination that can be caused by variations of the Sun zenith angle. In case of vegetation, L (λ) by variations of the Sun zenith angle. In case of vegetation, L (λ) includes the additive contributions includes the additive contributions of canopy reflected radianceref and fluorescence. of canopy reflected radiance and fluorescence. The second approach (ρ-spec) relies on a dual-spectrometer method [56,57] (Figure 5b). L(λ) The second approach (ρ-spec) relies on a dual-spectrometer method [56,57] (Figure5b). L (λ) measurements are recorded simultaneously by a second spectrometer operated at the ground overinc a measurements are recorded® simultaneously by a second spectrometer operated at the ground typical Spectralon panel during HyUAS measurements. L(λ) is measured continuously (i.e., ® λ overevery a typical 0.5 s) by Spectralon the groundpanel FieldSpec during HandHeld HyUAS (H measurements.H) spectrometerLinc (ASD( ) isInc., measured Boulder, continuously CO, U.S.). (i.e.,Dark-current every 0.5 s) measurements by the ground FieldSpecare acquired HandHeld routinely (HH) every spectrometer few minutes (ASD through Inc., Boulder, the integrated CO, U.S.). Dark-currentshutter. We measurements synchronized the are acquiredtimestamp routinely of the tw everyo spectrometers few minutes before through the theflights. integrated The surface shutter. Wereflectance synchronized is thus the timestampretrieved by of thematching two spectrometers spectra collected before by the the flights. two instruments: The surface reflectanceL(λ) by is thusground retrieved spectrometer by matching and spectra L(λ) collected by HyUAS. by the twoThe instruments: spectral characteristicsLinc(λ) by ground of the spectrometer ground andspectrometerLref(λ) by HyUAS.(i.e., spectral The range, spectral sampling characteristics and FWHM) of the should ground be spectrometeras much as possible (i.e., spectral comparable range, samplingto those andof the FWHM) USB4000 should aboard be the as muchHyUAS as in possible order to comparable limit the formation to those of of signal the USB4000 artifacts aboard(i.e., thespikes). HyUAS Typically, in order these to limit errors the are formation more pronounced of signal around artifacts major (i.e., absorption spikes). Typically, features, such these as errorsthe areO more2-band pronouncedused to retrieve around fluorescence. major absorption Howeve features,r, we implemented such as the radiometric O2-band used and tospectral retrieve fluorescence.cross-calibration However, methods we implementedbetween the two radiometric spectromet anders spectral to limit cross-calibrationerrors on surface methodsreflectance between and thefluorescence two spectrometers as much to as limit possible. errors onIn surfacethe labo reflectanceratory, we and cross-calibrated fluorescence asthe much spectrometers as possible. Inradiometric the laboratory, responses we cross-calibrated considering a therange spectrometers of light intensities radiometric typical responses for clear-sky considering days during a range ofsummer. light intensities In the field, typical we forused clear-sky the empirical days co duringrrection summer. method Inproposed the field, in Bachmann we usedthe et al., empirical 2012 correction[58]. It consists method of proposeddetermining in the Bachmann actual spectral et al., transfer 2012 [58 function]. It consists between of determiningtwo spectrometers. the actual In practice, the spectra are resampled to a common wavelength-grid through linear interpolation; spectral transfer function between two spectrometers. In practice, the spectra are resampled to afterwards, Equation (1) is used to compute surface reflectance: a common wavelength-grid through linear interpolation; afterwards, Equation (1) is used to compute surface reflectance: HyUAS () gnd () L (λ) L (λ) ρ =ref inc , (1) ρspec = () () , (1) gnd HyUAS λ Linc (λ) Linc ( ) TheThe superscript superscript indicates indicates radiance radiance spectra spectra measured measured by the groundby the (gnd)ground or HyUAS(gnd) or spectrometer. HyUAS Thespectrometer. first term represents The first the term surface represents reflectance the computedsurface reflectance as the ratio computed between HyUASas the ratio targets between radiance HyUAS L gnd L LrefHyUASand targets ground-spectrometer radiance irradianceand ground-spectrometer measurements Lirradianceinc . These measurements spectra are acquired . These while thespectra UAS isare flying acquired over while different the UAS investigated is flying targets.over different The second investigated term representstargets. The the second instruments term represents the instruments transfer function derived as the ratio between simultaneous irradiance measurements collected by the two spectrometers at the moment of take-off. It must be remarked

Remote Sens. 2017, 9, 472 8 of 24 transfer function derived as the ratio between simultaneous irradiance measurements collected by the two spectrometers at the moment of take-off. It must be remarked that the instrument transfer function is valid for a short timeframe, until the absorption features’ depth in the irradiance spectra do not change much. Therefore, spectral measurements were collected around noon with clear-sky conditions, and the transfer function was frequently estimated. The same approach was also used to HyUAS correct radiance spectra Lref needed to retrieve sun-induced fluorescence. The reflectance signatures were used for calculating a number of widely-used vegetation indicators (Table1): (i) the Normalized Difference Vegetation Index (NDVI), related to vegetation greenness [59]; (ii) the MERIS Terrestrial Chlorophyll Index (MTCI), linked to chlorophyll content [60]; (iii) the Photochemical Reflectance Index (PRI) [61] and the SIF [62] ,related to canopy photosynthetic activity. In fact, fluorescence emission is directly released within the core of the photosystems; it represents the fraction of radiation absorbed by leaves that exceeds the photosynthetic demand. An alternative and competitive way to dissipate excess light energy is represented by the Non-Photochemical Quenching (NPQ) process through the xanthophyll cycle activation. The PRI index allows tracking the interconversion of the xanthophyll pigments’ pool (i.e., violaxanthin, antheraxanthin and zeaxanthin) based on reflectance changes at 531 nm.

Table 1. Spectral indicators derived from radiance and reflectance measurements collected by HyUAS.

Name Formula or Method Reference ρ − ρ Normalized Difference Vegetation Index NDVI = 800 670 [59] ρ + ρ ρ800 − ρ670 MERIS Terrestrial Chlorophyll Index MTCI = 753 708 [60] ρ − ρ ρ 708− ρ 681 Photochemical Reflectance Index PRI = 531 570 [61] ρ531 + ρ570 Sun-Induced Fluorescence SIF O2-A, 3FLD method [8,62,63]

The first three indices can be easily calculated from the reflectance signature, while SIF retrieval requires more complex techniques. The SIF retrieval methods are generally based on the analysis of the radiance spectra at the oxygen absorption bands located at 760 nm (O2-A) and 687 nm (O2-B) [62,64]. In particular, we computed the far-red fluorescence comparing Linc(λ) and Lref(λ) spectral radiance inside and outside the O2-A band, according to the widely-used 3FLD (Fraunhofer Line Depth) approach [8,63]. We limited our analysis to the O2-A band because the spectral resolution offered by the USB4000 spectrometer can still provide meaningful results at this broader spectral feature [8,65]; conversely, it is not suitable for retrieving fluorescence at the narrower O2-B band. Although the HyUAS measurement approach is very similar to the typical field spectroscopy method, we carried out radiative transfer simulations to assess the impact of atmospheric effects in the limited path length between the target surface and the HyUAS sensor (typically 10 m). The analysis was carried out based on surface-atmosphere-coupled radiative transfer simulations by means of atmospheric MODerate resolution TRANsmission (MODTRAN5) code [66] and the Soil Canopy Observation Photosynthesis Energy balance (SCOPE) model [67]. The 4-flux coupled surface-atmosphere scheme has been implemented as described in [64,68], considering a Lambertian surface. This approach includes direct and diffuse target radiance, atmospheric path radiance and contribution from the surroundings (adjacency). We considered a restricted number of atmospheric and canopy parameters to limit the total number of simulations. The “standard” case of 19 described in [64] was used as the baseline; thus, we considered different O2-A band depths and fluorescence values to compare a wider range of environmental conditions. In particular, Aerosol Optical Thickness (AOT) varying between 0.1 and 0.4, ground-altitude between sea surface level to 0.15 km (proxy of surface pressure) and solar zenith angles of 30◦, 45◦ and 60◦ were considered in MODTRAN. We include two fluorescence levels by changing the fluorescence quantum efficiency (fqe) from the default value of 0.002 to 0.004 within the SCOPE model. The reflectance was not changed because its spectral behavior around the O2-A band is smooth; therefore, its relative contribution to wavelengths used to retrieve fluorescence is Remote Sens. 2017, 9, 472 9 of 24 veryRemote limited. Sens. 2017 The, 9, simulations472 were carried out considering HyUAS flight heights: 0.050, 0.025,9 of 0.015, 25 0.010, 0.005, 0.003 km. The synthetic radiance spectra were resampled to HyUAS spectrometer bands synthetic radiance spectra were resampled to HyUAS spectrometer bands to provide more realistic to provide more realistic analysis for our experimental measurements. The Linc and Lref simulated at analysis for our experimental measurements. The L and L simulated at the surface level were the surface level were used to compute fluorescence as measured by a ground-based spectrometer used to compute fluorescence as measured by a ground-based spectrometer (free of an atmosphericHyUAS (free of an atmospheric effect). The ρ-tarp method was simulated considering canopy L and L refL effect). The ρ-tarpHyUAS method was simulated considering canopy and reference tarp referenceradiances tarp as measuredLinc radiances by HyUAS as at measured different altitudes by HyUAS from at the different surface. altitudesThe ρ-spec from approach the surface. was The ρ-spec approach was instead simulated by combining Linc at the ground (ground spectrometer) instead simulated by combining L at the ground (ground spectrometer) and L from HyUAS ρ ρ andHyUAS. Lref Thefrom fluorescence HyUAS. The computed fluorescence by means computed of the by meansρ-tarp ofand the ρ-spec-tarp methods and -spec at methodsdifferent at differentaltitudes altitudes was thus was compared thus compared to the reference to the reference values at valuesthe ground. at the ground.

3.2.3.2. Laboratory Laboratory Characterization Characterization and and Calibration Calibration DedicatedDedicated laboratory laboratory tests tests were were performed performed to characterize to characterize and calibrate and calibrate the geometric, the geometric, radiometric andradiometric spectral parameters and spectral of parameters the HyUAS. of The the HyUAS. geometric, The radiometric geometric, andradiometri spectralc and stability spectral were stability verified duringwere in-flightverified conditions.during in-flight In fact, conditions. platform In mechanical fact, platform vibrations mechanical can affect vibrations optical can interfaces affect optical and the spectrometerinterfaces and in termsthe spectrometer of band centers, in terms width of band and radiometriccenters, width response. and radiometric For this response. reason, a dedicatedFor this test-bedreason, was a dedicated realized totest-bed simulate was typical realized in-flight to simula conditionste typical (Figure in-flight6). This conditions setup enables (Figure turning 6). This on engines,setup enables allowing turning the platform on engines, toslightly allowing lift-off the platform from its to support slightly (i.e.,lift-off less from than its 1support cm) and (i.e., keeping less thethan payload 1 cm) centered and keeping on the the test-bed, payload duringcentered the on collectionthe test-bed, of spectralduring the measurements. collection of spectral This way, typicalmeasurements. flight maneuvers This way, can typical be simulated flight inmaneuvers the laboratory can be under simulated controlled in the conditions: laboratory taking-off under (i.e.,controlled maximum conditions: thrust), taking-off hovering (i.e., (i.e., maximum minimum thru thrust)st), hovering and flying (i.e., between minimum way-points. thrust) and Spectralflying measurementsbetween way-points. were acquired Spectral before, meas duringurements and were after acquired a series ofbefore, repeated during flight and simulations, after a series and of the repeated flight simulations, and the stability of spectrometer parameters was evaluated. stability of spectrometer parameters was evaluated.

FigureFigure 6. 6.Test-bed Test-bed used used during during laboratory laboratory teststests toto simulatesimulate in-flight in-flight conditions. conditions. The The HyUAS HyUAS (A) (A) is is attachedattached on on a supporta support (B) (B) that that permits permits thethe vehiclevehicle to lift-off keeping keeping the the payload; payload; (C) (C) centered centered in inthe the test-bed.test-bed. In In particular, particular, the the figure figure shows shows the the specific specific setup setup used used for for the the radiometric radiometric characterization: characterization: the halogenthe halogen light source; light source; (F) illuminates (F) illumi thenates Spectralon the Spectralon (R) panel; (R) panel; (D) used (D) forused cross-calibrating for cross-calibrating the HyUAS the withHyUAS the reference with the (calibrated)reference (calibrated) FieldSpec FieldSpec HH spectrometer HH spectrometer (E). (E).

TheThe geometric geometric test test aims aims toto characterizecharacterize the sp spectrometerectrometer FOV, FOV, in in terms terms of of acceptance acceptance angle angle degrees,degrees, and and to to identify identify the the center center ofof thethe sampledsampled area (footprint) in in the the co-registered co-registered RGB RGB image image (i.e.,(i.e., x, yx, pixel). y pixel). The The test test was was carried carried out out using using a specific a specific laboratory laboratory setup: setup: the the terminal terminal part part of theof the fiber fiber optic, generally connected to the spectrometer entrance, was temporally detached and optic, generally connected to the spectrometer entrance, was temporally detached and connected to connected to the HL2000 halogen light source (Ocean Optics Inc., Dunedin, FL, U.S.) through the the HL2000 halogen light source (Ocean Optics Inc., Dunedin, FL, U.S.) through the SMA905 interface. SMA905 interface. This allows feeding the optical system (i.e., light goes through fiber optic and This allows feeding the optical system (i.e., light goes through fiber optic and EOR), projecting FOV on EOR), projecting FOV on a flat and levelled surface placed at a 0.4-m distance. RGB images were a flat and levelled surface placed at a 0.4-m distance. RGB images were collected, and the diameter collected, and the diameter of the spectrometer sampling area (illuminated circle) was measured by ofcounting the spectrometer the number sampling of illuminate aread (illuminated pixels on the circle)RGB image. was measured This value bywas counting converted the to numberphysical of

Remote Sens. 2017, 9, 472 10 of 24 illuminated pixels on the RGB image. This value was converted to physical units by using a referenced chessboard to correct typical camera lens distortions, and the FOV center within the RGB image was estimated. The radiometric calibration required to convert raw digital counts to the radiance spectrum (W m−2 sr−1 nm−1) was achieved by cross-calibration with a reference, well-calibrated, FieldSpec HH spectrometer. Simultaneous measurements collected by the FieldSpec HH and HyUAS spectrometer were collected over a calibrated Lambertian Spectralon® panel placed in the test-bed and illuminated by a stabilized halogen lamp. The center (maximum wavelength) and width of the spectral bands detected by the HyUAS spectrometer were characterized according to standard laboratory methods pointing the CAL-2000 Mercury-Argon (Hg-Ar) calibrated light source (Ocean Optics Inc., Dunedin, FL, U.S.) directly at the entrance of the EOR optics. The CAL-2000 lamp emits very narrow and sable lines in the VNIR spectral domain, whose maximum wavelengths and widths are well known. The band center and the FWHM were derived following Cogliati et al. [50]; the method mainly relies on fitting Gaussian functions to the narrow spectral peaks observed in the HyUAS spectral measurements. For all of these tests, measurements were collected during repeated flight simulations to evaluate the sensitivity of instruments’ spectral and radiometric responses to possible misalignments of mechanical interfaces caused by platform vibrations.

3.3. Flight Campaign The flight campaign was performed on 18 July 2014 to test the overall performances of HyUAS in a realistic scenario. Data were collected on a small area (0.3 km2) close to Gironico (Como, Italy, Lat 45.7878◦, Lon 8.9840◦). The area was characterized by different anthropogenic and natural land cover types, such as asphalt roads, gravel and sand of a small pit, agricultural corn field, meadow and a small forest. The latter is a mixed forest with trees of different species: the dominant plant is Robinia pseudoacacia together with few trees of Quercus sp. Way-point and transect data collection modes were employed to record spectral data during three consecutive flight sessions. For comparing different measurement approaches, we flew the HyUAS above the corn field at two flight altitudes (i.e., 10 and 20 m) using the way-point mode and at 10 m using the transect mode. The HyUAS flights were conducted around midday (12:30 to 13:30 local time) with almost clear sky to have higher radiance levels (i.e., better signal-to-noise ratio) and to limit the variation of incoming irradiance caused by changes in solar zenith angle. The spectrometer FOV was set to 6◦. The absolute flight altitude from the ground level ranged between 10 and 35 m to keep almost the same sensor-surface distance of 10 m over the targets with different heights (e.g., meadow, asphalt and forest). In this configuration, the spectrometer samples an area of about 1.15 m, and the RGB camera captures an area of 14.42 m × 10.77 m with a pixel resolution of 3.5 mm. Simultaneous ground-based spectral measurements were recorded with the FieldSpec HH following typical field spectroscopy techniques [69]. These measurements were collected only over a small group of targets because it was not practical to acquire measurements over higher canopies, such as forest and corn. The accuracy of spectral data collected by the HyUAS were assessed comparing radiance and reflectance signatures against ground-truth spectra. The statistical indices considered in the comparison were the Root Mean Square Error (RMSE), which quantifies the differences between retrieved values (i.e., HyUAS data) and assumed ground-truth (i.e., FieldSpec HH data), and the Relative RMSE (RRMSE%), which represents the percentage of error.

4. Results and Discussions

4.1. Geometric, Radiometric and Spectral Characterization The geometric characterization was performed to quantify the spectrometer FOV, the footprint center and to assess the impact of platform vibrations on these parameters. Figure7 shows the Remote Sens. 2017, 9, 472 11 of 24 spectrometer sampling area (illuminated circle) as observed by the RGB camera. The mean and the standardRemote Sens. deviation 2017, 9, 472 values were calculated from a sample of four images collected for each of the11 three of 25 repeated flight simulations (Table2). The low standard deviation values demonstrate that platform platform vibrations during typical flight operations have a negligible effect on the geometric vibrations during typical flight operations have a negligible effect on the geometric parameters. parameters. The standard deviations observed translate to an uncertainty of the footprint size and The standard deviations observed translate to an uncertainty of the footprint size and center lower center lower than 10−2 m, considering a flight altitude equal to 10 m. Additionally, spatial transects of than 10−2 m, considering a flight altitude equal to 10 m. Additionally, spatial transects of blue, green blue, green and red channels were extracted from the RGB images along the x and y directions and red channels were extracted from the RGB images along the x and y directions (Figure7). The shape (Figure 7). The shape similar to a square wave indicates good performance of EOR; in fact, most of similar to a square wave indicates good performance of EOR; in fact, most of the light reaching the the light reaching the spectrometer comes from the area defined within the footprint (not from the spectrometer comes from the area defined within the footprint (not from the surroundings), and the surroundings), and the detected signal is almost uniform. detected signal is almost uniform.

Figure 7. RGB image of the spectrometer footprint area projected through the Entrance Optic Receptor Figure 7. RGB image of the spectrometer footprint area projected through the Entrance Optic (EOR) on a levelled surface. Plots represent spatial transects of RGB counts on the x and y coordinates. Receptor (EOR) on a levelled surface. Plots represent spatial transects of RGB counts on the x and y Tablecoordinates. 2. Mean and standard deviation (s.d.) values of the system geometric parameters in terms of footprint diameter (considering 0.4 m distance), field-of-view and position of footprint within the Table 2. Mean and standard deviation (s.d.) values of the system geometric parameters in terms of RGB image. footprint diameter (considering 0.4 m distance), field-of-view and position of footprint within the RGB image. Footprint Parameters RGB Image Coordinates FootprintDiameter Parameters (cm) FOV (◦) x (pixel) RGB Image y (pixel) Coordinates

mean (s.d.)Diameter 4.64(cm) (0.06) FOV 6.56 (°) (0.01) 2100 x (pixel) (5) 1894 (5) y (pixel) mean (s.d.) 4.64 (0.06) 6.56 (0.01) 2100 (5) 1894 (5) Data collected during the radiometric cross-calibration measurements were used to derive the radiometricData collected calibration during factors the used radiometric to convert cross-calib instrumentration raw measurements counts to radiance were units. used Furthermore,to derive the theradiometric impact of platformcalibration vibrations factors wasused evaluated to conver byt comparinginstrument signals raw measuredcounts to with radiance and without units. platformFurthermore, vibrations. the impact The signalof platform measured vibrations by the was HyUAS evaluated spectrometer by comparing during signals the in-flight measured condition with showsand without variations platform lower vibrations. than 10 digital The signal counts measured for USB4000 by the and HyUAS lower thanspectrometer 10−3 radiance during units the −3 (Win-flight m−2 sr condition−1 nm−1 )shows for FieldSpec variations HH. lower In particular, than 10 digital Figure 8counts shows for that USB4000 the two and signals lower are than linearly 10 −2 −1 −1 correlatedradiance units (R2 =(W 0.83). m sr Probably, nm ) for these FieldSpec small variations HH. In particular, are mostly Figure caused 8 shows by slight that fluctuationsthe two signals of 2 radianceare linearly emitted correlated by the light(R source= 0.83). and Probably, not by the these vibrations small ofvariations the rotor. are The mostly comparison caused was by made slight at 628fluctuations nm to exploit of radiance the maximum emitted dynamic by the rangelight ofsour thece spectrometer, and not by butthe similarvibrations results of werethe rotor. observed The forcomparison other wavelengths was made recorded at 628 nm by theto exploit spectrometer. the maximum dynamic range of the spectrometer, but similar results were observed for other wavelengths recorded by the spectrometer.

Remote Sens. 2017, 9, 472 12 of 24

Remote Sens. 2017, 9, 472 12 of 25

Figure 8. Scatterplot between USB4000 (digital counts) and ASD FieldSpec HH (radiance) spectrometers Figure 8. Scatterplot between USB4000 (digital counts) and ASD FieldSpec HH (radiance) at 628 nm. Blue, black and red/green dots refer to measurements acquired before, after and during spectrometers at 628 nm. Blue, black and red/green dots refer to measurements acquired before, after two flight simulations (i.e., vibrations 1 to 2). and during two flight simulations (i.e., vibrations 1 to 2).

TheThe applied applied spectral spectral characterization characterization methodologymethodology allowed allowed computing computing the the wavelength wavelength vector vector (i.e.,(i.e., absolute absolute wavelength wavelength for for each each spectral spectral band) band) and and to to characterize characterize the the instrument instrument FWHM.FWHM. FigureFigure9 a shows9a shows the narrow the narrow lines emittedlines emitted by the by CAL-2000 the CAL-200 light0 sourcelight source detected detected by the by HyUAS. the HyUAS. The absolute The differencesabsolute differences between maximum between maximum peaks’ wavelength peaks’ wave beforelength and before during and the during vibrations the vibrations are depicted are in Figuredepicted9b. The in Figure maximum 9b. The difference maximum found difference is about foun±0.1d is nm.about It was±0.1 observednm. It was at observed 404, 696 at and 404, 800 696 nm, andand it can800 nm, be considered and it can be negligible considered compared negligible with compared the spectrometer’s with the spectrometer’s FWHM of FWHM 1.5 nm. of This 1.5 nm. result demonstratesThis result demonstrates the good spectral the good stability spectral of the stabil HyUASity of because the HyUAS spectral because peaks spectral detected peaks during detected in-flight measurementsduring in-flight were measurements not significantly were shiftednot significan nor broadenedtly shifted bynor platform broadened vibrations. by platform vibrations.

FigureFigure 9. 9.Narrow Narrow spectral spectral peaks peaksdetected detected byby HyUASHyUAS with and and without without platform platform vibrations vibrations (a) ( aand) and peakpeak wavelength wavelength absolute absolute differences differences ( b(b).).

4.2.4.2. Analysis Analysis of of the the Retrieved Retrieved Reflectance Reflectance andand FluorescenceFluorescence SpectralSpectral signatures signatures of of different different land land cover cover classes classes were were collected collected during during a flight a flight campaign campaign to verify to theverify reliability the reliability of HyUAS of radiance,HyUAS radiance, reflectance reflectance spectra and spectra far-red and SIF far-red at 760 SIF nm. at The760 nm. overall The location overall of way-pointslocation of and way-points continuous and transect continuous measurements transect measurements in the study areain the are study depicted area in are Figure depicted 10. in FigureFigure 10. 11 shows the orthophoto draped on the DSM, derived by combining RGB images and platform navigation data of a subset area over the forest patch. The mixed forest looks well represented by the model: single trees can be clearly recognized, as well as their shape and their relative different

Remote Sens. 2017, 9, 472 13 of 24 heights. The height of the plants varies in the range of 13 to 22 m, and the crown size is about 5 m, as estimated from the model. The 3D model created with Photoscan indicates a camera location total error of about 0.60 m. It was calculated by the software as the differences between initial and camera positions projected after model calculations (x, y, z coordinates were considered for all of the images used to create the model). However, the accuracy of geographical coordinates in absolute terms is not critical for the purposes of our work. Rather, the spatial consistency between the 3D surface model and the spectral point measurements is much more relevant. This is strengthened by the fact that HyUAS navigation data are recorded with an acquisition rate at least 10-times higher than RGB camera and spectrometer sampling. The basic signal processing technique were applied to filter and integrate Remotehigh-frequency Sens. 2017, 9, measurements, 472 reducing uncertainties in geo-locating spectral measurements. 13 of 25

◦ ◦ Figure 10. StudyStudy area close to Gironico (Como, Italy,Italy, LatLat 45.787845.7878°,, Lon 8.98408.9840°)) characterized by different land cover types: asphalt, gr gravel,avel, sand, corn, meadow and forest ( Robinia pseudoacacia ,, Quercus sp.). Points show the location of way-point me measurements;asurements; continuous lines indicate transect mode measurements.

FigureThe attitude 11 shows of the the HyUAS orthophoto varies draped during on flightthe DSM, while derived spectra by measurements combining RGB were images collected. and platformIn particular, navigation pitch and data roll causedof a su abset variation area over of the the area forest sampled patch. by theThe spectrometer mixed forest at thelooks ground. well Werepresented observed by that the during model: the single way-point trees modecan be (i.e., clearly hovering), recognized, roll and as pitch well angles as their varied shape 1.4 and◦ (± 1.2their◦); relativetherefore, different the sampled heights. area The is veryheight close of the to, plants or completely varies in overlapped, the range of the 13 areato 22 viewed m, and fromthe crown nadir. sizeDifferently, is about roll 5 m, and as pitchestimated angles from show the variationsmodel. The of 3D 3◦ model(±2◦) duringcreated thewith transect Photoscan measurements indicates a withcamera larger location displacements total error ofof theabout area 0.60 sampled m. It bywas the calculated spectrometer. by the These software effects as accountthe differences for the betweenaforementioned initial and geo-location camera positions processing projected of spectral after measurements. model calculations Figure 11(x, shows y, z coordinates the spectrometer were consideredsampling area for all (circular of the shape)images projectedused to create over the model). different However, targets after the accuracy the geo-location of geographical process. coordinatesOver almost flatin absolute surfaces, terms the projected is not spectrometercritical for the FOV purposes is close toof a circle,our work. while Rather, on rugged thesurfaces spatial (e.g.,consistency rough canopybetween surface), the 3D thesurface sampled model area and is th displacede spectral as point an irregular measurements shape. Geo-locationis much more of relevant.spectral measurementThis is strengthened improved by considerably the fact that the HyUAS interpretation navigation of spectral data are signatures, recorded especially with an acquisitionduring continuous rate at measurements.least 10-times higher For example, than RGB knowing camera exactly and the spectrometer area sampled sampling. over the forestThe basic was signalfundamental processing in the technique interpretation were of applied spectral to measurement filter and integrate described high-frequency in the next paragraph. measurements, reducingThe uncertainties reflectance signatures in geo-locating estimated spectral by HyUASmeasurements. were compared with those collected at the groundThe by attitude using theof the FieldSpec HyUAS HH varies spectrometer during flight over while accessible spectra targets measurements (i.e., meadow, were sand,collected. gravel In particular,and asphalt), pitch hereafter and roll “ground-truth”. caused a variation Figure of th 12e areashows sampled a good by agreement the spectrometer between at ground-truth the ground. (greenWe observed line) and that reflectance during the signatures way-point estimated mode from(i.e., HyUAShovering), considering roll and bothpitch the anglesρ-tarp varied (blue line)1.4° (±1.2°); therefore, the sampled area is very close to, or completely overlapped, the area viewed from nadir. Differently, roll and pitch angles show variations of 3° (±2°) during the transect measurements with larger displacements of the area sampled by the spectrometer. These effects account for the aforementioned geo-location processing of spectral measurements. Figure 11 shows the spectrometer sampling area (circular shape) projected over the different targets after the geo-location process. Over almost flat surfaces, the projected spectrometer FOV is close to a circle, while on rugged surfaces (e.g., rough canopy surface), the sampled area is displaced as an irregular shape. Geo-location of spectral measurement improved considerably the interpretation of spectral signatures, especially during continuous measurements. For example, knowing exactly the area sampled over the forest was fundamental in the interpretation of spectral measurement described in the next paragraph.

Remote Sens. 2017, 9, 472 14 of 25

Remote Sens. 2017, 9, 472 14 of 24 and ρ-spec (red line) methods. In particular, spectral signatures collected by HyUAS show standard deviations comparable with ground-truth spectra. We found that, for all of the spectral bands and for all targets, reflectance measured with the HyUAS exhibits a high linear correlation (R2 = 0.98 for both methods)Remote Sens. compared 2017, 9, 472 to those measured at the ground with the FieldSpec HH (Figure 13). 14 of 25

Figure 11. Orthophoto image draped on the DSM, zoom in the forested area. The circles represent the projected sampled areas over the different targets measured.

The reflectance signatures estimated by HyUAS were compared with those collected at the ground by using the FieldSpec HH spectrometer over accessible targets (i.e., meadow, sand, gravel and asphalt), hereafter “ground-truth”. Figure 12 shows a good agreement between ground-truth (green line) and reflectance signatures estimated from HyUAS considering both the ρ-tarp (blue line) and ρ-spec (red line) methods. In particular, spectral signatures collected by HyUAS show standard deviations comparable with ground-truth spectra. We found that, for all of the spectral bands and for all targets, reflectance measured with the HyUAS exhibits a high linear correlation (R2 = 0.98 for FigureFigure 11.11. OrthophotoOrthophoto imageimage drapeddraped onon thethe DSM,DSM, zoomzoom inin thethe forestedforested area.area. TheThe circlescircles representrepresent thethe both methods) compared to those measured at the ground with the FieldSpec HH (Figure 13). projectedprojected sampledsampled areasareas overover thethe differentdifferent targets targets measured. measured.

The reflectance signatures estimated by HyUAS were compared with those collected at the ground by using the FieldSpec HH spectrometer over accessible targets (i.e., meadow, sand, gravel and asphalt), hereafter “ground-truth”. Figure 12 shows a good agreement between ground-truth (green line) and reflectance signatures estimated from HyUAS considering both the ρ-tarp (blue line) and ρ-spec (red line) methods. In particular, spectral signatures collected by HyUAS show standard deviations comparable with ground-truth spectra. We found that, for all of the spectral bands and for all targets, reflectance measured with the HyUAS exhibits a high linear correlation (R2 = 0.98 for both methods) compared to those measured at the ground with the FieldSpec HH (Figure 13).

Figure 12. Comparison between reflectance measured at the ground using the FieldSpec HH spectrometer (green line) and from HyUAS. The two methods employed to estimate reflectance were compared, ρ-tarp (blue line) and ρ-spec (red line). The continuous lines represent the average spectrum from different measurements, while the shaded area represents the standard deviation.

Remote Sens. 2017, 9, 472 15 of 25

Figure 12. Comparison between reflectance measured at the ground using the FieldSpec HH spectrometer (green line) and from HyUAS. The two methods employed to estimate reflectance were compared, ρ-tarp (blue line) and ρ-spec (red line). The continuous lines represent the average Remote Sens. 2017, 9, 472 15 of 24 spectrum from different measurements, while the shaded area represents the standard deviation.

FigureFigure 13. 13.Scatter Scatter plot plot of of reflectance reflectance measured measured from from HyUAS HyUAS and and from from FieldSpec FieldSpec for allfor targetsall targets and and all spectralall spectral bands. bands. Reflectance Reflectance measured measured with wi theth HyUAS the HyUAS exhibits exhibits a high a correlationhigh correlation (R2 = 0.98(R2 = for 0.98 both for methods)both methods) compared compared to those to measuredthose measured with the with FieldSpec the FieldSpec HH. HH.

The RMSEs and the RRMSEs values calculated between ground-truth and HyUAS spectra are The RMSEs and the RRMSEs values calculated between ground-truth and HyUAS spectra are shown in Table 3. Considering all targets, RRMSE values are lower than 15% and 10% for radiance shown in Table3. Considering all targets, RRMSE values are lower than 15% and 10% for radiance and reflectance, respectively. The ρ-spec method shows lower RMSE and RRMSE compared with the and reflectance, respectively. The ρ-spec method shows lower RMSE and RRMSE compared with the ρ-tarp methods. In particular, RRMSE values are equal to 7.88% and 9.62%, respectively, ρ-tarp methods. In particular, RRMSE values are equal to 7.88% and 9.62%, respectively, demonstrating demonstrating a good accuracy and the reliability of HyUAS spectral measurements. a good accuracy and the reliability of HyUAS spectral measurements.

Table 3. RMSE and RRMSE% of radiance and reflectance (ρ-tarp and ρ-spec methods) calculated Table 3. RMSE and RRMSE% of radiance and reflectance (ρ-tarp and ρ-spec methods) calculated from thefrom comparison the comparison between HyUASbetween data HyUAS and the data FieldSpec and th HHe FieldSpec ground-truth HH forground-truth all of the sampled for all targets. of the sampled targets. Reflectance Radiance HyUAS Reflectance Radiance HyUAS ρ ρ -tarpρ-tarp -specρ-spec

RMSE RRMSE%RRMSE% RMSERMSE RRMSE%RRMSE% RMSE RMSE RRMSE% RRMSE% MeadowMeadow 0.0050 19.44 19.44 0.00650.0065 7.997.99 0.00960.0096 6.78 6.78 Asphalt 0.0018 6.46 0.0319 15.88 0.0273 13.50 AsphaltGravel 0.00110.0018 4.13 6.46 0.0116 0.0319 6.9915.88 0.00790.0273 4.29 13.50 GravelSand 0.00770.0011 20.794.13 0.00520.0116 2.296.99 0.00210.0079 1.16 4.29 SandAll targets 0.00470.0077 14.7420.79 0.01740.0052 9.622.29 0.01500.0021 7.88 1.16 All targets 0.0047 14.74 0.0174 9.62 0.0150 7.88 Some practical considerations can be delineated in the process of collecting data and calculating reflectanceSome betweenpractical theconsiderationsρ-tarp and canρ-spec be delineated methods. in In the order process to collect of collecting incoming data solar and irradiancecalculating measurements,reflectance between the firstthe ρ method-tarp and required ρ-spec methods. a calibrated In order tarp to placed collect over incoming a flat solar terrain irradiance in the studymeasurements, area. This the requirement first method could required be difficulta calibrated to satisfytarp placed in certain over a scenariosflat terrain (e.g., in the surveys study area. in mountainThis requirement rugged areas).could be Furthermore, difficult to satisfy the mission-plan in certain scenarios is more complex(e.g., surveys because in mountain incoming rugged solar irradianceareas). Furthermore, must be frequently the mission-plan measured is to more assure complex reliable because reflectance incoming computations. solar irradiance Using the must dual be spectrometerfrequently measured approach, to continuous assure reliable measurements reflectance of solar computations. irradiance areUsing obtained the dual using spectrometer a calibrated Spectralonapproach,® continuouspanel. This measurements approach is of preferred solar irradi forance measurements are obtained during using a not calibrated completely Spectralon stable® illuminationpanel. This conditions.approach Onis preferred the other hand,for measuremen the ρ-spec methodts during requires not completely a fine spectral stable and radiometricillumination inter-calibrationconditions. On between the other the twohand, spectrometers the ρ-spec together method with requires the use a of fine the empiricalspectral transferand radiometric function asinter-calibration previously described between in Sectionthe two 3.1.2 spectrometers. together with the use of the empirical transfer functionThe analysis as previously of spectral described reflectance in Section collected 3.1.2. on the entire set of land cover types in the study area is shownThe inanalysis Figure of14 spectraland discussed reflectance in this collected section. on Asphalt the entire and set gravel of land show cover the expectedtypes in the spectral study behaviors,area is shown and thein Figure signatures 14 and observed discussed refer in to this different section. types Asphalt of gravel and characterizedgravel show the by differentexpected colors (e.g., mineral composition). The vegetated targets show the typical reflectance signatures characterized by: chlorophyll absorption in blue and red spectral regions, the characteristic sharp increase of reflectance in the red-edge and high scattering in the NIR. The peak caused by in-filling of Remote Sens. 2017, 9, 472 16 of 24

sun-induced fluorescence at the O2-A band is evident in the apparent surface reflectance of all vegetated targets. Corn, meadow and forest show their own different reflectance signatures. This proves the potential capability of HyUAS to identify different vegetation types. Furthermore, the corn field was observed with a different setup: in way-point mode from a 10- to 20-m distance above the canopy (blue and red line, respectively); along a spatial transect at 10 m above the canopy (green line). These approaches show similar average reflectance, but different standard deviations. In particular, the canopy reflectance spectra derived from 10 m, either by the way-point or transect measurement approach, have a larger standard deviation due to the fact that spectrometer FOV projected on the target (diameter = 1.15 m) covers a single crop row and part of the bare soil. From this distance, in fact, small variations of pitch and roll angles might cause larger variations on the relative amounts of canopy and bare soil. Instead, the area viewed at a 20-m distance from the canopy (diameter = 2.30 m) includes at least two crop rows and a smaller portion of bare soil between them. The soil contribution is lower in this case; therefore, the radiometric signal sampled during repeated measurements is less subject to small changes of the platform location, roll and pitch angles, and consequently, the standard deviation is lower. This example shows a practical opportunity offered by HyUAS; in fact, the capability of collecting measurements at different distances from the target surface permits optimizing the sampling area size to reduce effects caused by surface heterogeneity or canopy-specific arrangements (i.e., agricultural crops row, clumping, etc.). The reflectance signature of meadow shows higher values mostly due to a lower chlorophyll content (visible wavelengths) and the particular canopy structure, which limits multiple-scattering effects (near-infrared). The points measured over the forest show very different reflectance signatures for the two trees species. The average spectra and standard deviation of measurements collected on three different trees of Robinia pseudoacacia show very similar values. The variability of measurements on the Quercus sp. tree is larger; this could be due to the higher spatial heterogeneityRemote Sens. 2017,recorded 9, 472 through the transect mode. Despite such large variability in Quercus sp.17 data, of 25 the reflectance spectra related to these two plant species can be clearly distinguished.

Figure 14. Cont.

Figure 14. Surface reflectance signatures of different land covers as observed by the USB4000 spectrometer aboard the HyUAS. The continuous line represents the average reflectance signature, dashed lines the standard deviation.

The boxplots in Figure 15 show some of the spectral indicators widely used in vegetation studies. NDVI is mostly related to biomass and greenness; in fact, the values observed are close to zero for non-vegetated targets (i.e., asphalt and gravel), whereas higher values ranging between 0.8 and 0.9 were found for corn, meadow and forest. The canopy chlorophyll content as detected by MTCI shows the same general trend characterized by lower values for non-vegetation targets, but in this case, the values related to different vegetation types are much more diverse. The corn is characterized by the highest values; this is probably due to its higher chlorophyll content compared to natural ecosystems. In fact, agricultural practices have the objective of optimizing crop yield in terms of biomass and consequently chlorophyll content. The same pattern cannot be clearly observed by NDVI because of the well-known saturation effect and its limited sensitivity in dense vegetation [70,71]. It is worth noting that spectral indices derived from measurements collected on the corn field at different flight heights exhibit the same behavior described earlier for the reflectance signature. In fact, the median values are similar while the dispersion of data is larger for measurements collected at a 10-m distance. PRI is sensitive to the dissipation of the excess of light energy absorbed by chlorophyll through the activation of the xanthophyll cycle. In general, lower PRI values indicate a larger activation. The complete interpretation of this process is non-trivial because both the total pool of xanthophyll pigments and the canopy structure strongly affect the

Remote Sens. 2017, 9, 472 17 of 25

Remote Sens. 2017, 9, 472 17 of 24

Figure 14. Surface reflectance signatures of different land covers as observed by the USB4000 Figure 14. Surface reflectance signatures of different land covers as observed by the USB4000 spectrometer aboard the HyUAS. The continuous line represents the average reflectance signature, spectrometer aboard the HyUAS. The continuous line represents the average reflectance signature, dashed lines the standard deviation. dashed lines the standard deviation.

The boxplotsboxplots inin Figure Figure 15 15show show some some of the of spectralthe spectral indicators indicators widely widely used in used vegetation in vegetation studies. NDVIstudies. is NDVI mostly is relatedmostly torelated biomass to biomass and greenness; and greenness; in fact, in the fact, values the values observed observed are close are close to zero to forzero non-vegetated for non-vegetated targets targets (i.e., asphalt(i.e., asphalt and gravel),and gravel), whereas whereas higher higher values values ranging ranging between between 0.8 and 0.8 0.9and were 0.9 were found found for corn, for corn, meadow meadow and forest. and forest. The canopy The canopy chlorophyll chlorophyll content content as detected as detected by MTCI by showsMTCI shows the same the generalsame general trend trend characterized characterized by lower by lower values values for non-vegetation for non-vegetation targets, targets, but inbut this in case,this case, the values the values related related to different to different vegetation vegetation types are muchtypes moreare much diverse. more The diverse. corn ischaracterized The corn is bycharacterized the highest by values; the highest this is values; probably this dueis probably to its higher due to chlorophyll its higher chlorophyll content compared content tocompared natural ecosystems.to natural ecosystems. In fact, agricultural In fact, agricultural practices havepractice thes objectivehave the ofobjective optimizing of optimizing crop yield crop in termsyield ofin biomassterms of and biomass consequently and consequently chlorophyll content.chlorophyll The samecontent. pattern The cannot same be pattern clearly observedcannot be by clearly NDVI becauseobserved of by the NDVI well-known because saturationof the well-known effect and satura its limitedtion effect sensitivity and its inlimited dense sensitivity vegetation in [ 70dense,71]. Itvegetation is worth [70,71]. noting thatIt is worth spectral noting indices that derived spectral from indices measurements derived from collected measurements on the corncollected field on at differentthe corn field flight at heights different exhibit flight the heights same behaviorexhibit the described same behavior earlier fordescribed the reflectance earlier for signature. the reflectance In fact, thesignature. median In values fact, arethe similar median while values the dispersionare similar of while data isthe larger dispersion for measurements of data is collectedlarger for at ameasurements 10-m distance. collected PRI is sensitive at a 10-m to the distance. dissipation PRI ofis thesensitive excess to of the light dissipation energy absorbed of the byexcess chlorophyll of light throughenergy absorbed the activation by chlorophyll of the xanthophyll through cycle. the activation In general, of lower the PRIxanthophyll values indicate cycle. aIn larger general, activation. lower ThePRI completevalues indicate interpretation a larger of activation. this process The is non-trivialcomplete interpretation because both of the this total process pool of is xanthophyll non-trivial pigmentsbecause both and the the total canopy pool structure of xanthophyll strongly pigments affect the and overall the canopy magnitude structure of the strongly signal detected affect the at top-of-canopy. Therefore, an exhaustive and detailed interpretation of PRI is very challenging, and it is not the main aim of this study. However, it is interesting to underline how PRI values detected by HyUAS on different targets are diverse, opening perspectives of exploiting PRI detected by HyUAS in vegetation monitoring studies. SIF is considered a close proxy of photosynthetic activity because this signal arises from plant photosystems. The total magnitude of SIF is also driven by a number of factors, among which the overall amount of green biomass, the chlorophyll content and the incoming irradiance level. Therefore, an effective interpretation of SIF needs to consider the specific characteristics of the canopy and illumination conditions, thus complicating the comparison between different types of vegetation and measurement on different days. The SIF retrieval is much more difficult compared to reflectance-based indices, because the amount of fluorescence emitted by the plant is very small compared to the overall backscattered radiance in the NIR. In fact, SIF represents only a few percent of the total signal. Thus, even small errors in the retrieval algorithm generally translate into large bias on SIF (retrieved SIF could be orders of magnitude larger than the expected value). SIF of non-vegetated targets show very low values, which are almost close to zero. The residual error is lower than a few-tenths of mW m−2 sr−1 nm−1, and it is probably caused either by slight changes of the reference irradiance spectrum or errors introduced from the 3FLD method. The SIF values retrieved for vegetated targets are higher and fall in the range of values obtained by field spectroscopy measurements reported in the literature [9,48–50,65,72,73]. The obtained results strongly agree with the findings of Rossini et al. [73]. Remote Sens. 2017, 9, 472 18 of 24

In that paper, the authors provide a comprehensive review of ground-based fluorescence measurements on a wide range of vegetation species. In particular, our results are strongly in agreement with general statements drawn in the conclusions of their work: (i) SIF values are in the range of 0 to 3 mW m−2 sr−1 nm−1; (ii) the highest values were measured on crops and meadow followed by broadleaf plants; (iii) the absolute values and uncertainties are very similar, even though the differences in plant species prevent a direct quantitative comparison. Although SIF is a highly dynamic signal, the comparison of our results with those reported in [73] is motivated by the fact that in both cases, spectral measurements were collected around noon during typical clear-sky days in summer at mid-latitudes. In these conditions, the illumination can be considered almost comparable, at least within a short timeframe around noon. Moreover, Figure 15 shows that fluorescence retrieved values for different vegetation targets have diverse averages, and the interquartile ranges allow discriminating almost all of the different species. The Quercus sp. and Robinia are partially overlapped because the Quercus sp. sample shows a pretty large dispersion of measurements, likely due to strong canopy directional effects. The SIF values retrieved by HyUAS are consistent with the vegetation indices presented: high SIF values were found for targets with higher NDVI and MTCI because, as previously mentioned, SIF strongly depends on the amount of green biomass and leaf chlorophyll content. The latter statement is not general, for example corn has a larger chlorophyll content than meadow, but canopy fluorescence is lower. This highlights the added information content brought from SIF: it is largely driven by chlorophyll content, but it also sensitive to actual photosynthesis and canopy re-absorption and scattering processes. Understanding all of these effects together is very complex, and methodologies based on a synergic analysis of the reflectance signature and fluorescence are Remote Sens. 2017, 9, 472 19 of 25 under development.

FigureFigure 15.15. BoxplotsBoxplots ofof NDVI,NDVI, MTCI,MTCI, PRIPRI andand SIFSIF atat 760760 nmnm derivedderived fromfrom HyUASHyUAS reflectancereflectance andand radianceradiance spectra spectra for for the the different different targets targets investigated. investigated.

To better understand the overall accuracy of fluorescence values retrieved, we used radiative transfer simulations to analyze the impact of atmospheric transmittance along the short radiation path between top-of-canopy and sensor. Figure 16 shows relative deviations of fluorescence retrieved at different altitudes by means of the ρ-tarp and ρ-spec methods compared to the reference value at the ground (SIF diff). The ρ-tarp method shows lower error because canopy radiance absorbed within the O2-A band is partially compensated by reference measurement collected on the tarp at the same altitude. The remaining error is lower than a few percent at a 10-m distance. It is related to multiple scattering and adjacency effects in the forward radiative transfer simulations, which were not considered in the 3FLD retrieval method. The ρ-spec method shows larger errors because the transmittance between the top of the canopy and sensor is not considered. The errors at a 10-m distance range between 4% and 8% for different atmospheric and fluorescence values considered. This error budget is somehow acceptable for the current state of development because: (i) it has a similar magnitude of measurement uncertainty for a single target (interquartile boxes in Figure 15); (ii) it complies with the 10% error budget required for FLEX mission products. Basically, atmospheric correction could improve the fluorescence retrieval accuracy, but currently-available algorithms are not yet consolidated for this type of measurement. On the other hand, even small errors in retrieving atmospheric parameters and the successive convolution to instrument spectral bands could introduce significant errors. Therefore, low-flying UAS could offer a more practical approach to collect spatially-distributed fluorescence measurements within a reasonable level of accuracy. However, more dedicated studies are required to further develop the UAS-based measurement approach and practical atmospheric correction algorithms to improve fluorescence retrieval accuracy.

Remote Sens. 2017, 9, 472 19 of 24

To better understand the overall accuracy of fluorescence values retrieved, we used radiative transfer simulations to analyze the impact of atmospheric transmittance along the short radiation path between top-of-canopy and sensor. Figure 16 shows relative deviations of fluorescence retrieved at different altitudes by means of the ρ-tarp and ρ-spec methods compared to the reference value at the ground (SIF diff). The ρ-tarp method shows lower error because canopy radiance absorbed within the O2-A band is partially compensated by reference measurement collected on the tarp at the same altitude. The remaining error is lower than a few percent at a 10-m distance. It is related to multiple scattering and adjacency effects in the forward radiative transfer simulations, which were not considered in the 3FLD retrieval method. The ρ-spec method shows larger errors because the transmittance between the top of the canopy and sensor is not considered. The errors at a 10-m distance range between 4% and 8% for different atmospheric and fluorescence values considered. This error budget is somehow acceptable for the current state of development because: (i) it has a similar magnitude of measurement uncertainty for a single target (interquartile boxes in Figure 15); (ii) it complies with the 10% error budget required for FLEX mission products. Basically, atmospheric correction could improve the fluorescence retrieval accuracy, but currently-available algorithms are not yet consolidated for this type of measurement. On the other hand, even small errors in retrieving atmospheric parameters and the successive convolution to instrument spectral bands could introduce significant errors. Therefore, low-flying UAS could offer a more practical approach to collect spatially-distributed fluorescence measurements within a reasonable level of accuracy. However, more dedicated studies are required to further develop the UAS-based measurement approach and practical Remoteatmospheric Sens. 2017 correction, 9, 472 algorithms to improve fluorescence retrieval accuracy. 20 of 25

FigureFigure 16. 16. FluorescenceFluorescence relative relative error error (SIF (SIF diff) diff) for ρ for-tarpρ-tarp (left) (left)and ρ and-specρ (right-spec) (right)at different at different sensor altitudessensor altitudes above the above canopy the canopy(0.003 to (0.003 0.05 km) to 0.05 derive km)d derivedfrom radiative from radiative transfer transfersimulations. simulations. Colors representColors represent different differentatmospheric atmospheric parameters parameters and fluorescence and fluorescence values considered values in considered the simulations: in the Solarsimulations: Zenith SolarAngles Zenith (SZA), Angles Ground (SZA), altitude Ground (GRND), altitude (GRND), Aerosol AerosolOpticalOptical Thickness Thickness (AOD) (AOD) and fluorescenceand fluorescence quantum quantum efficiency efficiency (fqe). (fqe).

5.5. Conclusions Conclusions

ThisThis study presentedpresented a a novel novel UAS UAS to to measure measure VNIR VNIR surface surface reflectance reflectance and and far-red far-red SIF at SIF the at O the2-A Oband,2-A band, co-registered co-registered with RGB with images, RGB forimages, supporting for supporting field spectroscopy field spectroscopy surveys in the surveys framework in the of frameworkvegetation monitoringof vegetation and monitoring calibration/validation and calibratio activitiesn/validation of remote activities sensing of data. remote The exploitationsensing data. of TheHyUAS exploitation is facilitated of HyUAS by the softwareis facilitated applications by the specificallysoftware applications developed specifically for accomplishing developed mission for accomplishingplanning, data mission collection planning, and data data processing. collection The and characterization data processing. and The calibration characterization of geometric, and calibrationradiometric of and geometric, spectral radiomet parametersric and of the spectral instruments, parameters considering of the instruments, static and in-flight considering conditions, static andenabled in-flight accurate conditions, radiance, enabled reflectance accurate spectra radian and SIFce, at reflectance 760 nm. The spectra laboratory and tests SIF demonstrated at 760 nm. The that laboratoryin-flight platform tests demonstrated vibrations do that not in-flight significantly platform affect vibrations spectral measurements, do not significantly even for affect the retrievalspectral measurements,of the faint fluorescence even for the signal. retrieval The results of the faint obtained fluorescence during the signal. flight The test results showed obtained a good during agreement the flightbetween test HyUAS showed and a good ground-based agreement spectral between measurements HyUAS and ground-based for the investigated spectral land measurements covers. For both for the investigated land covers. For both the retrieval methods (ρ-tarp and ρ-spec), the RRMSE value was lower than 10%. The possibility of measuring spectral signals more related to the photosynthetic activity of plants, through PRI and SIF, has been demonstrated using this configuration. Although the estimation of SIF requires high-resolution spectrometers and accurate retrieval methods, the results obtained are promising to further develop this measurement approach. The radiative transfer simulations show that low-flying UAS allows measuring fluorescence with error lower than 10%. This fact opens relevant perspectives to employ similar UASs for monitoring vegetation photosynthesis characterized by high temporal dynamics. Further improvements will regard the much more difficult analysis of red fluorescence at the O2-B band, the use of advanced spectral fitting methods and the retrieval of biophysical products (i.e., leaf area index, chlorophyll content, etc.) needed for interpreting ecosystem productivity. In general, the HyUAS was demonstrated to be a reliable system for supporting field spectroscopy surveys enabling the collection of spectral measurements in a more systematic and rapid way even on inaccessible targets (e.g., forest trees). HyUAS is able to provide relevant environmental data (i.e., VNIR radiance/reflectance, fluorescence and 3D surface model) observing the heterogeneity of land covers at different spatial resolution. In the context of calibration and validation activities, HyUAS enables monitoring the spatial heterogeneity of dedicated measurement sites, including their temporal dynamics during the vegetation growing season. For example, regarding the FLEX mission operating at a spatial resolution of 300 m, HyUAS observations distributed in space and time can provide fluorescence data for evaluating the natural variability of a single vegetation type within the measurement area. Alternatively, HyUAS represents a useful and efficient tool for upscaling measurements whether the validation site is characterized by a mixture of different land cover types and different temporal dynamics.

Remote Sens. 2017, 9, 472 20 of 24 the retrieval methods (ρ-tarp and ρ-spec), the RRMSE value was lower than 10%. The possibility of measuring spectral signals more related to the photosynthetic activity of plants, through PRI and SIF, has been demonstrated using this configuration. Although the estimation of SIF requires high-resolution spectrometers and accurate retrieval methods, the results obtained are promising to further develop this measurement approach. The radiative transfer simulations show that low-flying UAS allows measuring fluorescence with error lower than 10%. This fact opens relevant perspectives to employ similar UASs for monitoring vegetation photosynthesis characterized by high temporal dynamics. Further improvements will regard the much more difficult analysis of red fluorescence at the O2-B band, the use of advanced spectral fitting methods and the retrieval of biophysical products (i.e., leaf area index, chlorophyll content, etc.) needed for interpreting ecosystem productivity. In general, the HyUAS was demonstrated to be a reliable system for supporting field spectroscopy surveys enabling the collection of spectral measurements in a more systematic and rapid way even on inaccessible targets (e.g., forest trees). HyUAS is able to provide relevant environmental data (i.e., VNIR radiance/reflectance, fluorescence and 3D surface model) observing the heterogeneity of land covers at different spatial resolution. In the context of calibration and validation activities, HyUAS enables monitoring the spatial heterogeneity of dedicated measurement sites, including their temporal dynamics during the vegetation growing season. For example, regarding the FLEX mission operating at a spatial resolution of 300 m, HyUAS observations distributed in space and time can provide fluorescence data for evaluating the natural variability of a single vegetation type within the measurement area. Alternatively, HyUAS represents a useful and efficient tool for upscaling measurements whether the validation site is characterized by a mixture of different land cover types and different temporal dynamics. The HyUAS concept privileged spectral and radiometric quality of measurements, essential to enable accurate retrieval of PRI and SIF for advanced vegetation studies. In comparison, compact hyperspectral imaging sensors for UAS applications offer a more systematic spatial sampling that simplifies the production of thematic maps. However, the spectral resolution and signal-to-noise ratio of currently-available instruments are not optimal to retrieve SIF. In addition, the very high spatial resolution (e.g., few centimeters) produces very complex reflectance anisotropy patterns (e.g., leaf orientation and shadows), which make the correct interpretation of vegetation indicators challenging. This could be overcome by flying the sensor at a larger distance from the canopy, but the need for atmospheric correction to derive SIF arises. Therefore, imaging systems do not provide necessarily better results considering the current state-of-art of instruments and retrieval methods. Concluding, the HyUAS constitutes a cost-effective instrument that can provide accurate, multi-scale and flexible spectral observations for vegetation monitoring studies. Furthermore, it could support the calibration and validation activities of the upcoming FLEX satellite, but it could be exploited also for assessing VNIR spectral bands of others optical satellite missions (i.e., Landsat, Sentinel-2 and Sentinel-3).

Acknowledgments: The research was supported by the SINOPIAE project funded by Regione Lombardia. The authors wish to thank the Aermatica S.p.A., P. Marras and B. Tattarletti for their collaboration, the Geomatic Laboratory (University of Milano-Bicocca, UNIMIB) and M. Celesti (UNIMIB) for the support in the field campaign, R. Gentili (UNIMIB) for the help in plant species identification and M. Anelli (UNIMIB). The authors are also grateful to the Editor and reviewers for their useful comments, improving the quality of the manuscript. Author Contributions: All of the authors conceived of, designed and performed the laboratory experiments and the flight campaign. R.G. and B.D.M. performed the data processing and analysis. S.C. calculated and interpreted the vegetation indicators and SIF. R.G. wrote the first draft of the paper, with significant revisions provided by B.D.M., R.C. and S.C. Conflicts of Interest: The authors declare no conflict of interest.

References

1. Masek, J.G.; Hayes, D.J.; Hughes, M.J.; Healey, S.P.; Turner, D.P. The role of remote sensing in process-scaling studies of managed forest ecosystems. For. Ecol. Manag. 2015, 355, 109–123. [CrossRef] Remote Sens. 2017, 9, 472 21 of 24

2. Morisette, J.T.; Privette, J.L.; Justice, C.O. Overview of Uav Activities in Wageningen Unmanned Aerial Remote Sensing Facility. Remote Sens. Environ. 2002, 83, 77–96. [CrossRef] 3. Schaepman, M.E.; Ustin, S.L.; Plaza, A.J.; Painter, T.H.; Verrelst, J.; Liang, S. Earth system science related imaging spectroscopy-An assessment. Remote Sens. Environ. 2009, 113, S123–S137. [CrossRef] 4. Meroni, M.; Colombo, R. 3S: A novel program for field spectroscopy. Comput. Geosci. 2009, 35, 1491–1496. [CrossRef] 5. Milton, E.J. Review Article Principles of field spectroscopy. Int. J. Remote Sens. 1987, 8, 1807–1827. [CrossRef] 6. Milton, E.J.; Schaepman, M.E.; Anderson, K.; Kneubühler, M.; Fox, N. Progress in field spectroscopy. Remote Sens. Environ. 2009, 113, S92–S109. [CrossRef] 7. Arthur, A.M.; Robinson, I. A critique of field spectroscopy and the challenges and opportunities it presents for remote sensing for agriculture, ecosystems, and hydrology. Proc. SPIE 2015, 9637, 963705. 8. Damm, A.; Erler, A.; Hillen, W.; Meroni, M.; Schaepman, M.E.; Verhoef, W.; Rascher, U. Modeling the impact of spectral sensor configurations on the FLD retrieval accuracy of sun-induced chlorophyll fluorescence. Remote Sens. Environ. 2011, 115, 1882–1892. [CrossRef] 9. Rossini, M.; Meroni, M.; Celesti, M.; Cogliati, S.; Julitta, T.; Panigada, C.; Rascher, U.; Van der Tol, C.; Colombo, R. Analysis of Red and Far-Red Sun-Induced Chlorophyll Fluorescence and Their Ratio in Different Canopies Based on Observed and Modeled Data. Remote Sens. 2016, 8, 412. [CrossRef] 10. Hilker, T.; Coops, N.C.; Nesic, Z.; Wulder, M.A.; Black, A.T. Instrumentation and approach for unattended year round tower based measurements of spectral reflectance. Comput. Electron. Agric. 2007, 56, 72–84. [CrossRef] 11. Hilker, T.; Coops, N.C.; Coggins, S.B.; Wulder, M.A.; Brownc, M.; Black, T.A.; Nesic, Z.; Lessard, D. Detection of foliage conditions and disturbance from multi-angular high spectral resolution remote sensing. Remote Sens. Environ. 2009, 113, 421–434. [CrossRef] 12. Drolet, G.; Wade, T.; Nichol, C.J.; MacLellan, C.; Levula, J.; Porcar-Castell, A.; Nikinmaa, E.; Vesala, T. A temperature-controlled spectrometer system for continuous and unattended measurements of canopy spectral radiance and reflectance. Int. J. Remote Sens. 2014, 35, 1769–1785. [CrossRef] 13. Sakowska, K.; Gianelle, D.; Zaldei, A.; MacArthur, A.; Carotenuto, F.; Miglietta, F.; Zampedri, R.; Cavagna, M.; Vescovo, L. WhiteRef: A New Tower-Based Hyperspectral System for Continuous Reflectance Measurements. Sensors 2015, 15, 1088–1105. [CrossRef][PubMed] 14. Pacheco-Labrador, J.; Martín, M.P.; Riaño, D.; Hilker, T.; Carrara, A. New approaches in multi-angular proximal sensing of vegetation: Accounting for spatial heterogeneity and diffuse radiation in directional reflectance distribution models. Remote Sens. Environ. 2016, 187, 447–457. [CrossRef] 15. Shugart, H.H.; Asner, G.P.; Fischer, R.; Huth, A.; Knapp, N.; Le Toan, T.; Shuman, J.K. Computer and remote-sensing infrastructure to enhance large-scale testing of individual-based forest models. Front. Ecol. Environ. 2015, 13, 503–511. [CrossRef] 16. Colomina, I.; Molina, P. Unmanned aerial systems for photogrammetry and remote sensing: A review. ISPRS J. Photogramm. Remote Sens. 2014, 92, 79–97. 17. Ma, L.; Li, M.; Tong, L.; Wang, Y.; Cheng, L. Using Unmanned Aerial Vehicle for Remote Sensing Application. In Proceedings of the 2013 21st International Conference on Geoinformatics, Kaifeng, China, 20–23 June 2013; pp. 1–5. 18. Westoby, M.J.; Brasington, J.; Glasser, N.F.; Hambrey, M.J.; Reynolds, J.M. “Structure-from-Motion” photogrammetry: A low-cost, effective tool for geoscience applications. Geomorphology 2012, 179, 300–314. [CrossRef] 19. Lucieer, A.; Turner, D.; King, D.H.; Robinson, S.A. Using an unmanned aerial vehicle (UAV) to capture micro-topography of antarctic moss beds. Int. J. Appl. Earth Obs. Geoinf. 2014, 27, 53–62. [CrossRef] 20. Turner, D.; Lucieer, A.; Wallace, L. Direct georeferencing of ultrahigh-resolution UAV imagery. IEEE Trans. Geosci. Remote Sens. 2014, 52, 2738–2745. [CrossRef] 21. Zarco-Tejada, P.J.; Diaz-Varela, R.; Angileri, V.; Loudjani, P. Tree height quantification using very high resolution imagery acquired from an unmanned aerial vehicle (UAV) and automatic 3D photo-reconstruction methods. Eur. J. Agron. 2014, 55, 89–99. [CrossRef] 22. Cunliffe, A.M.; Brazier, R.E.; Anderson, K. Ultra-fine grain landscape-scale quantification of dryland vegetation structure with drone-acquired structure-from-motion photogrammetry. Remote Sens. Environ. 2016, 183, 129–143. [CrossRef] Remote Sens. 2017, 9, 472 22 of 24

23. Niethammer, U.; James, M.R.; Rothmund, S.; Travelletti, J.; Joswig, M. UAV-based remote sensing of the Super-Sauze landslide: Evaluation and results. Eng. Geol. 2012, 128, 2–11. [CrossRef] 24. Turner, D.; Lucieer, A.; de Jong, S. Time Series Analysis of Landslide Dynamics Using an Unmanned Aerial Vehicle (UAV). Remote Sens. 2015, 7, 1736–1757. [CrossRef] 25. Immerzeel, W.W.; Kraaijenbrink, P.D.A.; Shea, J.M.; Shrestha, A.B.; Pellicciotti, F.; Bierkens, M.F.P.; De Jong, S.M. High-resolution monitoring of Himalayan glacier dynamics using unmanned aerial vehicles. Remote Sens. Environ. 2014, 150, 93–103. [CrossRef] 26. Whitehead, K.; Moorman, B.J.; Hugenholtz, C.H. Brief Communication: Low-cost, on-demand aerial photogrammetry for glaciological measurement. Cryosphere 2013, 7, 1879–1884. [CrossRef] 27. Fugazza, D.; Senese, A.; Azzoni, R.S.; Smiraglia, C.; Cernuschi, M.; Severi, D.; Diolaiuti, G.A. High resolution mapping of glacier surface features. The UAV survey of the Forni Glacier (Stelvio National Park, Italy). Geogr. Fis. E Din. Quat. 2015, 38, 25–33. 28. Vincent, C.; Wagnon, P.; Shea, J.M.; Immerzeel, W.W.; Kraaijenbrink, P.; Shrestha, D.; Soruco, A.; Arnaud, Y.; Brun, F.; Berthier, E.; et al. Reduced melt on debris-covered glaciers: Investigations from Changri Nup Glacier, Nepal. Cryosph. Discuss. 2016, 10, 1845–1858. [CrossRef] 29. Kraaijenbrink, P.D.A.; Shea, J.M.; Pellicciotti, F.; Jong, S.M.D.; Immerzeel, W.W. Object-based analysis of unmanned aerial vehicle imagery to map and characterise surface features on a debris-covered glacier. Remote Sens. Environ. 2016, 186, 581–595. [CrossRef] 30. De Michele, C.; Avanzi, F.; Passoni, D.; Barzaghi, R.; Pinto, L.; Dosso, P.; Ghezzi, A.; Gianatti, R.; Vedova, G.D. Using a fixed-wing UAS to map snow depth distribution: An evaluation at peak accumulation. Cryosphere 2016, 10, 511–522. [CrossRef] 31. Dall’Asta, E.; Forlani, G.; Roncella, R.; Santise, M.; Diotri, F.; Cella, U.M.d. Unmanned Aerial Systems and DSM matching for rock glacier monitoring. ISPRS J. Photogramm. Remote Sens. 2017, 127, 102–114. [CrossRef] 32. Wallace, L.; Lucieer, A.; Malenovský, Z.; Turner, D.; Vopˇenka,P. Assessment of Forest Structure Using Two UAV Techniques: A Comparison of Airborne Laser Scanning and Structure from Motion (SfM) Point Clouds. Forests 2016, 7, 62. [CrossRef] 33. Díaz-Varela, R.; Raúl, D.L.R.; León, L.; Zarco-Tejada, P. High-Resolution Airborne UAV Imagery to Assess Olive Tree Crown Parameters Using 3D Photo Reconstruction: Application in Breeding Trials. Remote Sens. 2015, 7, 4213–4232. [CrossRef] 34. Aasen, H.; Burkart, A.; Bolten, A.; Bareth, G. Generating 3D hyperspectral information with lightweight UAV snapshot cameras for vegetation monitoring: From camera calibration to quality assurance. ISPRS J. Photogramm. Remote Sens. 2015, 108, 245–259. [CrossRef] 35. Di Mauro, B.; Fava, F.; Ferrero, L.; Garzonio, R.; Baccolo, G.; Delmonte, B.; Colombo, R. Mineral dust impact on snow radiative properties in the European Alps combining ground, UAV, and satellite observations. J. Geophys. Res. Atmos. 2015, 120, 6080–6097. [CrossRef] 36. Su, T.-C.; Chou, H.-T. Application of Multispectral Sensors Carried on Unmanned Aerial Vehicle (UAV) to Trophic State Mapping of Small Reservoirs: A Case Study of Tain-Pu Reservoir in Kinmen, Taiwan. Remote Sens. 2015, 7, 10078–10097. [CrossRef] 37. Zarco-Tejada, P.J.; González-Dugo, V.; Berni, J.A.J. Fluorescence, temperature and narrow-band indices acquired from a UAV platform for water stress detection using a micro-hyperspectral imager and a thermal camera. Remote Sens. Environ. 2012, 117, 322–337. [CrossRef] 38. Bendig, J.; Yu, K.; Aasen, H.; Bolten, A.; Bennertz, S.; Broscheit, J.; Gnyp, M.L.; Bareth, G. Combining UAV-based plant height from crop surface models, visible, and near infrared vegetation indices for biomass monitoring in barley. Int. J. Appl. Earth Obs. Geoinf. 2015, 39, 79–87. [CrossRef] 39. Zheng, H.; Zhou, X.; Cheng, T.; Yao, X.; Tian, Y.; Cao, W.; Zhu, Y. Evaluation of a UAV-Based Hyperspectral Frame Camera for Monitoring the Leaf Nitrogen Concentration in Rice. In Proceedings of the 2016 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), Beijing, China, 10–15 July 2016; pp. 7350–7353. 40. Gonzalez-Dugo, V.; Hernandez, P.; Solis, I.; Zarco-Tejada, P. Using High-Resolution Hyperspectral and Thermal Airborne Imagery to Assess Physiological Condition in the Context of Wheat Phenotyping. Remote Sens. 2015, 7, 13586–13605. [CrossRef] Remote Sens. 2017, 9, 472 23 of 24

41. Berni, J.A.J.; Zarco-Tejada, P.J.; Suárez, L.; Fereres, E. Thermal and narrowband multispectral remote sensing for vegetation monitoring from an unmanned aerial vehicle. IEEE Trans. Geosci. Remote Sens. 2009, 47, 722–738. [CrossRef] 42. Zhang, C.; Kovacs, J.M. The application of small unmanned aerial systems for precision agriculture: A review. Precis. Agric. 2012, 13, 693–712. [CrossRef] 43. Jakob, S.; Zimmermann, R.; Gloaguen, R. The Need for Accurate Geometric and Radiometric Corrections of Drone-Borne Hyperspectral Data for Mineral Exploration: MEPHySTo—A Toolbox for Pre-Processing Drone-Borne Hyperspectral Data. Remote Sens. 2017, 9, 88. [CrossRef] 44. Burkart, A.; Hecht, V.L.; Kraska, T.; Rascher, U. Phenological analysis of unmanned aerial vehicle based time series of barley imagery with high temporal resolution. Precis. Agric. 2017, 1–13. [CrossRef] 45. Verger, A.; Vigneau, N.; Chéron, C.; Gilliot, J.-M.; Comar, A.; Baret, F. Green area index from an unmanned aerial system over wheat and rapeseed crops. Remote Sens. Environ. 2014, 152, 654–664. [CrossRef] 46. Misopolinos, L.; Zalidis, C.; Liakopoulos, V.; Stavridou, D.; Katsigiannis, P.; Alexandridis, T.K.; Zalidis, G. Development of A UAV System for VNIR-TIR Acquisitions in Precision Agriculture. In Proceedings of the SPIE 9535, 3rd International Conference on Remote Sensing and Geoinformation of the Environment, Paphos, Cyprus, 19 June 2015; Hadjimitsis, D.G., Themistocleous, K., Michaelides, S., Papadavid, G., Eds.; p. 95351H. 47. Bareth, G.; Aasen, H.; Bendig, J.; Gnyp, M.L.; Bolten, A.; Jung, A.; Michels, R.; Soukkamäki, J. Low-weight and UAV-based Hyperspectral Full-frame Cameras for Monitoring Crops: Spectral Comparison with Portable Spectroradiometer Measurements. Photogramm. Fernerkund. Geoinf. 2015, 2015, 69–79. [CrossRef] 48. Damm, A.; Guanter, L.; Laurent, V.C.E.; Schaepman, M.E.; Schickling, A.; Rascher, U. FLD-based retrieval of sun-induced chlorophyll fluorescence from medium spectral resolution airborne spectroscopy data. Remote Sens. Environ. 2014, 147, 256–266. [CrossRef] 49. Rossini, M.; Meroni, M.; Migliavacca, M.; Manca, G.; Cogliati, S.; Busetto, L.; Picchi, V.; Cescatti, A.; Seufert, G.; Colombo, R. High resolution field spectroscopy measurements for estimating gross ecosystem production in a rice field. Agric. For. Meteorol. 2010, 150, 1283–1296. [CrossRef] 50. Cogliati, S.; Rossini, M.; Julitta, T.; Meroni, M.; Schickling, A.; Burkart, A.; Pinto, F.; Rascher, U.; Colombo, R. Continuous and long-term measurements of reflectance and sun-induced chlorophyll fluorescence by using novel automated field spectroscopy systems. Remote Sens. Environ. 2015, 164, 270–281. [CrossRef] 51. Schickling, A.; Matveeva, M.; Damm, A.; Schween, J.; Wahner, A.; Graf, A.; Crewell, S.; Rascher, U. Combining Sun-Induced Chlorophyll Fluorescence and Photochemical Reflectance Index Improves Diurnal Modeling of Gross Primary Productivity. Remote Sens. 2016, 8, 574. [CrossRef] 52. Van Leeuwen, M.; Kremens, R.; van Aardt, J. Tracking Diurnal Variation in Photosynthetic Down-Regulation Using Low Cost Spectroscopic Instrumentation. Sensors 2015, 15, 10616–10630. [CrossRef][PubMed] 53. Rossini, M.; Panigada, C.; Cilia, C.; Meroni, M.; Busetto, L.; Cogliati, S.; Amaducci, S.; Colombo, R. Discriminating Irrigated and Rainfed Maize with Diurnal Fluorescence and Canopy Temperature Airborne Maps. ISPRS Int. J. Geo-Inf. 2015, 4, 626–646. [CrossRef] 54. Cheng, Y.-B.; Middleton, E.; Zhang, Q.; Huemmrich, K.; Campbell, P.; Corp, L.; Cook, B.; Kustas, W.; Daughtry, C. Integrating Solar Induced Fluorescence and the Photochemical Reflectance Index for Estimating Gross Primary Production in a Cornfield. Remote Sens. 2013, 5, 6857–6879. [CrossRef] 55. Drusch, M.; Moreno, J.; Bello, U.D.; Franco, R.; Goulas, Y.; Huth, A.; Kraft, S.; Middleton, E.M.; Miglietta, F.; Mohammed, G.; et al. The FLuorescence EXplorer Mission Concept-ESA’s Earth Explorer 8. IEEE Trans. Geosci. Remote Sens. 2016, 55, 1–12. [CrossRef] 56. Burkart, A.; Cogliati, S.; Schickling, A.; Rascher, U. A Novel UAV-Based Ultra-Light Weight Spectrometer for Field Spectroscopy. IEEE Sens. J. 2014, 14, 62–67. [CrossRef] 57. Anderson, K.; Milton, E.J.; Rollin, E.M. Calibration of dual-beam spectroradiometric data. Int. J. Remote Sens. 2006, 27, 975–986. [CrossRef] 58. Bachmann, C.M.; Montes, M.J.; Parrish, C.E.; Fusina, R.A.; Nichols, C.R.; Li, R.-R.; Hallenborg, E.; Jones, C.A.; Lee, K.; Sellars, J.; et al. A dual-spectrometer approach to reflectance measurements under sub-optimal sky conditions. Opt. Express 2012, 20, 8959–8973. [CrossRef][PubMed] 59. Rouse, J.W.; Haas, R.H.; Schell, J.A.; Deering, D.W. Monitoring vegetation systems in the great plains with ERTS. In Third Earth Resources Technology Satellite-1 Symposium—Volume I: Technical Presentations, NASA SP-351; Freden, S.C., Mercanti, E.P., Becker, M.A., Eds.; NASA: Washington, DC, USA, 1974; pp. 309–317. Remote Sens. 2017, 9, 472 24 of 24

60. Dash, J.; Curran, P.J. The MERIS terrestrial chlorophyll index. Int. J. Remote Sens. 2004, 25, 5403–5413. [CrossRef] 61. Gamon, J.A.; Peñuelas, J.; Field, C.B. A narrow-waveband spectral index that tracks diurnal changes in photosynthetic efficiency. Remote Sens. Environ. 1992, 41, 35–44. [CrossRef] 62. Meroni, M.; Rossini, M.; Guanter, L.; Alonso, L.; Rascher, U.; Colombo, R.; Moreno, J. Remote sensing of solar-induced chlorophyll fluorescence: Review of methods and applications. Remote Sens. Environ. 2009, 113, 2037–2051. [CrossRef] 63. Maier, S.W.; Gunther, K.P.; Stellmes, M. Remote Sensing and Modeling of Solar Induced Fluorescence. In Proceedings of the FLEX Workshop (ESA SP-527), Noordwijk, The Netherlands, 19–20 June 2002; pp. 24–25. 64. Cogliati, S.; Verhoef, W.; Kraft, S.; Sabater, N.; Alonso, L.; Vicent, J.; Moreno, J.; Drusch, M.; Colombo, R. Retrieval of sun-induced fluorescence using advanced spectral fitting methods. Remote Sens. Environ. 2015, 169, 344–357. [CrossRef] 65. Julitta, T.; Corp, L.; Rossini, M.; Burkart, A.; Cogliati, S.; Davies, N.; Hom, M.; Arthur, A.M.; Middleton, E.; Rascher, U.; et al. Comparison of Sun-Induced Chlorophyll Fluorescence Estimates Obtained from Four Portable Field Spectroradiometers. Remote Sens. 2016, 8, 122. [CrossRef] 66. Berk, A.; Anderson, G.P.; Acharya, P.K.; Shettle, E.P. MODTRAN 5.2.1 User’s Manual; Spectral Sciences Inc.: Burlington, MA, USA; Air Force Research Laboratory: Hanscom Air Force Base, MA, USA, 2011. 67. Tol, C.V.D.; Verhoef, W.; Timmermans, J.; Verhoef, A.; Su, Z. An integrated model of soil-canopy spectral radiances, photosynthesis, fluorescence, temperature and energy balance. Biogeosciences 2009, 6, 3109–3129. 68. Verhoef, W.; van der Tol, C.; Middleton, E.M. Vegetation Canopy Fluorescence and Reflectance Retrieval by Model Inversion Using Optimization. In Proceedings of the 5th International Workshop on Remote Sensing of Vegetation Fluorescence, Paris, France, 22–24 April 2014. 69. Milton, E.J.; Baret, F.; Rossello, P.; Anderson, E.; Rockall, E. A multistage database of field measurements and synoptic remotely sensed data to support model validation and testing in Earth observation. Comput. Geosci. 2011, 37, 1511–1514. [CrossRef] 70. Sellers, P.J. Canopy reflectance, photosynthesis and transpiration. Int. J. Remote Sens. 1985, 6, 1335–1372. [CrossRef] 71. Mutanga, O.; Skidmore, A.K. Narrow band vegetation indices overcome the saturation problem in biomass estimation. Int. J. Remote Sens. 2004, 25, 3999–4014. [CrossRef] 72. Meroni, M.; Rossini, M.; Picchi, V.; Panigada, C.; Cogliati, S.; Nali, C.; Colombo, R. Assessing Steady-state Fluorescence and PRI from Hyperspectral Proximal Sensing as Early Indicators of Plant Stress: The Case of Ozone Exposure. Sensors 2008, 8, 1740–1754. [CrossRef][PubMed] 73. Rossini, M.; Nedbal, L.; Guanter, L.; Aˇc,A.; Alonso, L.; Burkart, A.; Cogliati, S.; Colombo, R.; Damm, A.; Drusch, M.; et al. Red and far-red sun-induced chlorophyll fluorescence as a measure of plant photosynthesis. Geophys. Res. Lett. 2015, 42, 1632–1639. [CrossRef]

© 2017 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).