remote sensing

Article Radiometric Cross-Calibration of the Wide Field View Camera Onboard -6 in Multispectral Bands

Aixia Yang 1, Bo Zhong 1,* , Longfei Hu 1, Shanlong Wu 1, Zhaopeng Xu 2, Hongbo Wu 3, Junjun Wu 1, Xueshuang Gong 2, Haibo Wang 2 and Qinhuo Liu 1

1 State Key Laboratory of Remote Sensing Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100101, ; [email protected] (A.Y.); [email protected] (L.H.); [email protected] (S.W.); [email protected] (J.W.); [email protected] (Q.L.) 2 China Centre for Resources Satellite Data and Application, Beijing 100094, China; [email protected] (Z.X.); [email protected] (X.G.); [email protected] (H.W.) 3 Changchun Institute of Optics, Fine Mechanics and Physics, Chinese Academy of Sciences, Changchun 130033, China; [email protected] * Correspondence: [email protected]; Tel.: +86-10-64806256; Fax: +86-10-64806256

 Received: 10 March 2020; Accepted: 20 March 2020; Published: 24 March 2020 

Abstract: GaoFen6 (GF-6), successfully launched on June 2, 2018, is the sixth satellite of the High-Definition Earth observation system (HDEOS). Although GF-6 is the first high-resolution satellite in China to achieve precise agricultural observation, it will be widely used in many other domains, such as land survey, natural resources management, emergency management, ecological environment and so on. The GF-6 was not equipped with an onboard calibration instrument, so on-orbit radiometric calibration is essential. This paper aimed at the on-orbit radiometric calibration of the wide field of view camera (WFV) onboard GF-6 (GF-6/WFV) in multispectral bands. Firstly, the radiometric capability of GF-6/WFV is evaluated compared with the Operational Land Imager (OLI) onboard Landsat-8, Multi Spectral Instrument (MSI) onboard Sentinel-2 and Moderate Resolution Imaging Spectroradiometer (MODIS) onboard Terra, which shows that GF-6/WFV has an obvious attenuation. Consequently, instead of vicarious calibration once a year, more frequent calibration is required to guarantee its radiometric consistency. The cross-calibration method based on the Badain Jaran Desert site using the bi-directional reflectance distribution function (BRDF) model calculated by Landsat-8/OLI and ZY-3/Three-Line Camera (TLC) data is subsequently applied to GF-6/WFV and much higher frequencies of calibration are achieved. Finally, the cross-calibration results are validated using the synchronized ground measurements at Dunhuang test site and the uncertainty of the proposed method is analyzed. The validation shows that the relative difference of cross-calibration is less than 5% and it is satisfied with the requirements of cross-calibration.

Keywords: cross-calibration; GF-6/WFV; radiometric capability; Badain Jaran Desert; bidirectional reflectance distribution function (BRDF) model

1. Introduction On June 2, 2018, Gaofen-6 (GF-6) was successfully launched at Jiuquan Satellite Launch Center. GF-6 is a low-earth orbit optical remote sensing satellite and the first high-resolution satellite in China to achieve precise agricultural observation. So far, six satellites (GF-1~GF-6) have been launched based on the China High-Definition Earth observation system (HDEOS). GF-6 has the characteristics of high resolution, wide coverage and high-quality imaging. GF-6 loaded very similar imaging systems with GF-1, therefore networking of both GF-1 and GF-6 can achieve a higher frequent observation and it is

Remote Sens. 2020, 12, 1037; doi:10.3390/rs12061037 www.mdpi.com/journal/remotesensing Remote Sens. 2020, 12, 1037 2 of 21 therefore widely used in many other domains, such as natural resources, emergency management, ecological environment and so on. GF-6 is designed for an 8-year life time, which could ensure the continuous and stable supply capability of high spatio-temporal earth observation data. GF-6 is equipped with a panchromatic/multi-spectral high-resolution camera (PMS) and a multi-spectral medium resolution wide field view (WFV) camera. The observation width of the PMS is 90 km, while the WFV has a wide swath up to 800 km. GF-6/WFV with 16m resolution has 8 bands, including one costal band (400~450 nm), three visible bands (450~520 nm, 520~590 nm and 630~690 nm), one yellow band (590~630 nm), two red-edge bands (690~730 nm and 730~770 nm), one near-infrared (NIR) band (770–890 nm). GF-6/PMS contains one panchromatic band with 2 m resolution and four multispectral bands with 8 m resolution. The detail technical indicators of PMS and WFV are shown in Table1 (including spectral type, spectral range, spatial resolution, swath width and side swing). The revisiting period of GF-6 or GF-1 is 4 days, while the cooperation of the two satellites could cover the world once every 2 days in 16 m resolution. The details can be referred to in Table1.

Table 1. Technical indicators of GaoFen-6 (GF-6) payloads (panchromatic/multi-spectral high-resolution (PMS) and wide field of view (WFV)).

Spatial Swath Spectral Spectral Payload Spectral Type Resolution Width Side Swing Number Range (nm) (m) (km) 1 Panchromatic 450~900 2 2 Blue 450~520 PMS 3 Green 520~590 90 8 4 Red 630~690 5 Near infrared 770~890 Maneuvering side swing: 1 Blue 450~520 25 ; ± ◦ 2 Green 520~590 Emergency 3 Red 630~690 side swing: 4 Near infrared 770~890 WFV 16 800 35◦. 5 Costal 400~450 ± 6 Yellow 590~630 7 RedEdge1 690~730 8 Rededge2 730~770

For the first time, GF-6 has realized the localization of 8-band Complementary Metal Oxide Semiconductor (CMOS) detector in China, and it is also the first practice to add the “red-edge” bands on high-resolution sensor which can effectively capture the specific spectral characteristics of crops. GF-6 breaks through the single projection center ultra-large field of view imaging technology, resolves the problems of high-time imaging, overseas imaging, weak multi-target imaging ability, and improves the image quality of large field of view camera. The technical indicators of GF-6 in detection spectrum, image quantization bits and side swing angle have reached the leading position in China and advanced in the world. Like most land observation satellites in China (HJ-1, GF-1, GF-4, etc.), GF-6 also lacks on-board calibration equipment, so on-orbit vicarious calibration is the major way to do radiometric calibration. The Working Group on Calibration and Validation of the Committee on Earth Observation Satellites by the China Centre for Resources Satellite Data and Application (CRESDA) performs the vicarious calibration in Dunhuang calibration site for these sensors and publishes the calibration coefficients once a year through its official website (http://www.cresda.com). The vicarious calibration coefficients have been retrieved from an average of multiple observations during June to August of the year. Obviously, the radiometric performance of these sensors could be changed at any time of the year because attenuation in radiometric performance may occurs in the operation process in space. Thus, the annual vicarious calibration cannot meet the quantitative application requirements, and the calibration frequency of on-orbit sensors needs to be greatly increased. Remote Sens. 2020, 12, 1037 3 of 21

Zhong et al. developed a method in 2014 in order to cross-calibrate the charge-coupled device (CCD) onboard HJ (HJ-1/CCD) [1]. In this method, a new site, named Badain Jaran Desert site, is established. Multi-angle dataset is built with the Landsat Enhanced Thematic Mapper (TM) plus (ETM+) data and slope and aspect from the Advanced Spaceborne Thermal Emission and Reflection Radiometer Global Digital Elevation Model (ASTER GDEM) product. Then the site’s BRDF is built based on the multi-angle dataset [1]. The method was performed for HJ-1/CCD in 2009, and the results were verified less than 5% error using ground measurements. In the following years, Yang et al. [2] updated the method in 2015. In the updated version, the vertical observation data from Landsat-8/Operational Land Imager (OLI) images and slope and aspect from ZY-3/TLC are used for fitting the BRDF model of the Badain Jaran Desert site instead of the Landsat-TM/ETM+ and ASTER GDEM data [2]. The new BRDF model has a high accuracy and more details. Then the updated method was used to cross-calibrate the GF-1/WFV [2] and GF-4/PMS [3]. The calibration results were validated by cross-comparing with synchronized OLI images in transit with WFV at Dunhuang test site. The synchronized image pair should satisfy: (1) transit on the same day, (2) transit time difference within half an hour, (3) the weather of the observation day is good and the sky is clean, (4) the view zenith difference is less than 15◦. The validation results are proved to have a pretty high accuracy (less than 5% error), even better than the coefficients published by CRESDA [2,3]. This paper focuses on the on-orbit radiometric cross-calibration of GF-6/WFV in multispectral bands (1~4) (blue band, green band, red band and near infrared band). In order to show the necessity of cross calibration, radiometric capability of GF-6/WFV is evaluated by comparing with OLI onboard Landsat8, Multi Spectral Instrument (MSI) on Sentinel-2 and Moderate Resolution Imaging Spectroradiometer (MODIS) onboard Terra and Aqua. The cross-calibration method, which has been successfully applied to HJ-1/CCD, GF-1/WFV, GF-4/PMS [1–3], is employed to GF-6/WFV. This method still takes the Badain Jaran Desert as the calibration site and the nadir viewing Landsat-8/OLI and DEM products from ZY-3/Three-Line Camera (TLC) are combined to construct the calibration site’s bi-directional reflectance distribution function (BRDF). Based on the method, a much higher frequent cross-calibration is achieved finally and the cross-calibration results are validated using the synchronized ground measurements at Dunhuang test site. The validation shows that the relative difference of cross-calibration is less than 5% and it is satisfied with the requirements of cross-calibration.

2. Materials and Method

2.1. Study Area

2.1.1. Dunhuang Site Dunhuang site is chosen as the evaluation reference site. It is located on Danghe alluvial fan about 20 km northwest of Dunhuang City, Gansu Province (Figure1). Dunhuang site is the first national radiometric calibration site in China, and also an internationally calibration site recognized by the Committee on Earth Observation Satellites (CEOS) (https://calval.cr.usgs.gov/apps/test_sites_catalog). A flat ground without vegetation at an unpolluted region makes it highly stable and uniform (less than 2% variation over the 10 km * 10 km central region [4]). This site has been used to calibrate and validate many of China’s satellites, like FY series [5,6], CBERS series [7], HJ [8] and GF series [9,10]. In this study, Dunhuang site is only used for radiometric capability evaluation and validation purposes for two reasons: (1) routine ground campaigns have been carried out yearly during June to August and the synchronized ground measurements have subsequently been taken routinely; (2) the flat surface makes it impossible to construct BRDF characterization using nadir viewing Landsat8/OLI data. Remote Sens. 2020, 12, 1037 4 of 21

(a)

(b)

FigureFigure 1. 1. LocationLocation (a) (a) and and close close view view (b) (b) of of the the Dunhuang Dunhuang test test site site from from Google Google Earth. Earth.

2.1.2.2.1.2. Badain Badain Jaran Jaran Desert Desert site Site BadainBadain Jaran Jaran Desert Desert site site is chosen as the cross cross-calibration-calibration site. The The site site is is located located at at the Badain Jaran Desert in Central Inner Mongolia of Northern China (about 30 30 km or 1000 1000 pixels, Jaran Desert in Central Inner Mongolia of Northern China (about 30× * 30 km or 1000*1000× pixels, showshownn in Figure 2 2).). TheThe homogeneityhomogeneity andand stability stability of of this this area area are are assessed assessed and and proved proved qualified qualified as as a acalibration calibration site site [ 1[1].]. Furthermore, Furthermore, aa dozendozen uncontaminateduncontaminated lakeslakes are distributed in this area, which couldcould be be treated treated as as dark dark objects objects (DO) (DO) [ [11]] and used for for atmospheric correction correction to to obtain obtain the the aerosol aerosol opticaloptical depth (AOD) with highhigh accuracy.accuracy. AlthoughAlthough thisthis area area is is covered covered by by sand sand and and the the topography topography is ishilly, hilly, the the sand sand hills hills are are stable stable and and have have not no movedt moved in in decades, decades, which which means means the the BRDF BRDF of of this this area area is isstable. stable. The The abundant abundant slopes slopes and and aspects aspects from from the the numerous numerous sand sand hills hills could could be helpfulbe helpful in fittingin fitting the theBRDF BRDF model model of this of this area. area. Remote Sens. 2020, 12, 1037 5 of 21

FigureFigure 2.2. LocationLocation ((a)a) andand closeclose viewview ((b)b) ofof the BadainBadain JaranJaran DesertDesert sitesite fromfrom GoogleGoogle Earth.Earth. 2.2. Radiometric Capability Evaluation 2.2. Radiometric Capability Evaluation The radiometric capability of GF-6/WFV can be evaluated by comparing top of atmosphere (TOA) The radiometric capability of GF-6/WFV can be evaluated by comparing top of atmosphere reflectance in long time series with other sensors with good radiation performance. The MODIS (TOA) reflectance in long time series with other sensors with good radiation performance. The onboard Terra and Aqua satellites is recognized as a well calibrated instrument with an absolute MODIS onboard Terra and Aqua satellites is recognized as a well calibrated instrument with an accuracy better than 2% [12–15], and also used as reference for radiometric cross-calibration and absolute accuracy better than 2% [12–15], and also used as reference for radiometric cross-calibration performance evaluation of other sensors [16,17]. However, the spatial resolution between MODIS and performance evaluation of other sensors [16,17]. However, the spatial resolution between MODIS (1km) and GF-6/WFV (16m) is quite different, which may affect the evaluation result. Landsat-8/OLI (1km) and GF-6/WFV (16m) is quite different, which may affect the evaluation result. Landsat-8/OLI and Sentinel-2/MSI are two widely used medium resolution sensors. For Landsat-8/OLI, its drift and Sentinel-2/MSI are two widely used medium resolution sensors. For Landsat-8/OLI, its drift is is less than 0.1% per year measured by on-board calibrator lamps and diffusers, and the temporal less than 0.1% per year measured by on-board calibrator lamps and diffusers, and the temporal uncertainty is better than 0.5% by trending over vicarious targets, like Pseudo Invariant Calibration uncertainty is better than 0.5% by trending over vicarious targets, like Pseudo Invariant Calibration Sites (PICS) [18,19]. The radiometric performance of Landsat-8 is also proved better than the previous Sites (PICS) [18,19]. The radiometric performance of Landsat-8 is also proved better than the previous Landsat generations [19]. As to Sentinel-2/MSI, a robust light calibration strategy is used, which Landsat generations [19]. As to Sentinel-2/MSI, a robust light calibration strategy is used, which made made its radiometric absolute uncertainty is less than 5% and annual change estimate is less than its radiometric absolute uncertainty is less than 5% and annual change estimate is less than 1% [20,21]. 1% [20,21]. The Sentinel-2/MSI has a good radiometric performance on both accuracy and stability. The Sentinel-2/MSI has a good radiometric performance on both accuracy and stability. In this study, In this study, MODIS, OLI and MSI are all chosen as the references to assess the radiometric performance MODIS, OLI and MSI are all chosen as the references to assess the radiometric performance of GF- of GF-6/WFV. 6/WFV. The TOA reflectance of GF-6/WFV can be calculated using Equation (1) The TOA reflectance of GF-6/WFV can be calculated using Equation (1) 22 π휋L퐿λ휆d푑 ρλ휌휆== ((1)1) ESUN퐸푆푈푁λ휆cos⁡((θ휃SZ푆푍)

L = DN gain + bias (2) 퐿휆λ= 퐷푁 ××푔푎푖푛 + 푏푖푎푠 (2) where and L are the TOA reflectance and radiance of GF-6/WFV in band ; d is the distance where ρ휌λ휆 and 퐿휆λ are the TOA reflectance and radiance of GF-6/WFV in band λ휆; 푑 is the distance betweenbetween SunSun andand EarthEarth atat thethe observationobservation timetime expressedexpressed inin AUAU (Astronomical(Astronomical Unit)Unit) divideddivided byby thethe meanmean distancedistance between between Earth Earth and and SUN SUN (1 (1 AU), AU) and, and it isit ais dimensionless a dimensionless quantity quantity and and changes changes with with the image observation date; is the solar zenith; ESUN is the solar irradiance at the top of atmosphere. the image observation θ dateSZ ; 휃푆푍⁡is the solar zenithλ; 퐸푆푈푁휆 is the solar irradiance at the top of 2 1 2 1 Foratmosphere GF-6/WFV,. For the GFESUNs-6/WFV,are the 1969.61 퐸푆푈푁푠W arem 1969.61− µm −Win⋅ m the−2 ⋅ blueμm− band,1 in the 1852.80 blue band,W m 1852.80− µm W− ⋅inm the−2 ⋅ 2 1 · · 2 1 · · greenμm−1 band,in the 1557.53 green W band,m− 1557.53µm− Win the⋅ m− red2 ⋅ μ bandm−1 andin the 1073.29 red bandW m and− µ 1073.29m− in the W near⋅ m−2 infrared⋅ μm−1 in (NIR) the · · · 2· 1 1 band. Lλ can be calculated퐿 using Equation (2), and its unit is W m− sr− µmW− ⋅; mDN−2 is⋅ sr the−1 digital⋅ μm−1 near infrared (NIR) band. 휆 can be calculated using Equation (2),· and its· unit· is ; read퐷푁 is from the digital the GF-6 read/WFV from image; the GFgain-6/WandFVbias image;are the 푔푎푖푛 coe andfficients. 푏푖푎푠 Asare ofthe October coefficients. 31, 2019, As CRESDAof October has 31, 2019, CRESDA has published only one set of calibration coefficients on February 1, 2019. This set of coefficients comes from ground measurements in the summer of 2018. Remote Sens. 2020, 12, 1037 6 of 21 published only one set of calibration coefficients on February 1, 2019. This set of coefficients comes from ground measurements in the summer of 2018. The TOAThe TOA reflectance reflectance of of WFV, WFV, OLI, OLI, MSI MSI and and MODISMODIS are are plotted plotted in inFigure Figure 3. T3.he The spatial spatial resolution resolution of WFVof WFV (16 m), (16m), MSI MSI (10 (10m), m), MODIS MODIS (250 (250m/500m) m/500 m) and and OLI OLI (30m) (30 m)is different is different,, therefore therefore resampling resampling the the four sensors’ spatial resolution to 500m is required when doing cross comparison. In order to reduce four sensors’ spatial resolution to 500 m is required when doing cross comparison. In order to reduce the influence of angle, and obtain more data at the same time, only the data with the observation the influence of angle, and obtain more data at the same time, only the data with the observation zenith zenith angle less than 15 ° are included in the comparison. From this Figure, we can see that the OLI, angleMSI less and than MODIS 15◦ are show included better in consistency the comparison. and stability From than this Figure,WFV. Four we canstatistical see that indices the OLI, [22] MSIare and MODIScalculated show, betterincluding consistency slope of the and trend stability line, mean than value WFV., standard Four statistical deviation indices of the TOA [22] reflectance are calculated, includingvalues slope and relative of the error trend compared line, mean with value, WFV. standard The results deviation are listed in of Table the TOA 2. The reflectance slope of the values trend and relativeline error represents compared a drift with of calibration WFV. The during results the are long listed term inTable of a sensor.2. The The slope mean of the value trend indicates line represents the a driftaverage of calibration amount during of the sensor’s the long obtained term of aradiation sensor.. TheThe mean standard value deviation indicates indicates the average the sensor’ amount of the sensor’sradiometric obtained variation radiation.. The standard deviation indicates the sensor’ radiometric variation.

Figure 3. Cont. Remote Sens. 2020, 12, 1037 7 of 21

FigureFigure 3. Time 3. Time series series of theof the top top of of atmosphere atmosphere (TOA) (TOA) reflectancereflectance of of Multi Multi Spectral Spectral Instrument Instrument (MSI) (MSI),, OperationalOperational Land Land Imager Imager (OLI), (OLI), Moderate Moderate Resolution Resolution Imaging Imaging Spectroradiometer Spectroradiometer (MODIS (MODIS)) and WFV and in WFV in blueblue band band (A (A),), green green bandband (B), (B), red red band band (C) (C and) and near near infrared infrared band band (D), respectively. (D), respectively.

TableTable 2. The 2. statisticalThe statistical indices indices from from the the time time seriesseries TOA TOA reflectance reflectance of ofWFV, WFV, OLI, OLI, MSI MSIand MODIS and MODIS..

BandBand SensorSensor Slope Slope Mean Mean Standard Standard deviation Deviation MSI -4E-06 0.2001 0.0089 6 MSIOLI -5E4 -0610 − 0.19610.2001 0.0102 0.0089 Blue − × 6 MODISOLI -1E5 -0610 − 0.20320.1961 0.0082 0.0102 Blue − × 6 MODISWFV -4E1 -0510 − 0.20120.2032 0.0114 0.0082 − × 5 WFVMSI -9E4 -0610 − 0.20400.2012 0.0085 0.0114 OLI −-8E×-06 0.2034 0.0075 Green MSI 9 10 6 0.2040 0.0085 MODIS −-4E×-06 − 0.2122 0.0081 OLI 8 10 6 0.2034 0.0075 Green WFV −-6E×-05 − 0.2001 0.0130 MODISMSI -2E4 -0510 6 0.22570.2122 0.0090 0.0081 − × − WFVOLI -1E6 -0510 5 0.21930.2001 0.0086 0.0130 Red − × − MODIS -7E-06 5 0.2277 0.0094 MSI 2 10− 0.2257 0.0090 WFV −-7E×-05 5 0.2102 0.0138 OLIMSI -1E1 -0510 − 0.22470.2193 0.0101 0.0086 Red − × 6 MODISOLI -1E7 -0510 − 0.23000.2277 0.0098 0.0094 NIR − × MODISWFV -9E7 -0610 5 0.24460.2102 0.0102 0.0138 − × − WFV -9E-05 5 0.2075 0.0159 MSI 1 10− 0.2247 0.0101 − × 5 OLI 1 10− 0.2300 0.0098 In order to NIR clearly explain the evaluation− × result,6 two indicators are proposed: a) the relative MODIS 9 10− 0.2446 0.0102 − × 5 change in radiometric responseWFV during a year9 10 (Table− 3),0.2075 and b) the relative 0.0159 differences in radiometric response (Table 4). For the former indicator− × (푟), it is calculated using the equation 푟 = (푆푙표푝푒 ∗ 365)/푀푒푎푛, where the 푆푙표푝푒 and 푀푒푎푛 corresponding to columns 2 and 3, Table 2. For the second Inindicator order to, it clearlyis the relative explain difference the evaluation between result, WFV twoand indicatorsthe mean of are all proposed:other instruments (a) the. relative change in radiometric response during a year (Table3), and (b) the relative di fferences in radiometric response (Table4). For the formerTable indicator 3. The relative ( r), it change is calculated in radiometric using response the equation during ar year= ( Slope(%). 365)/Mean, where ∗ the Slope and Mean corresponding to columns 2 and 3, Table2. For the second indicator, it is the relative Band MSI OLI MODIS WFV difference between WFV and the mean of all other instruments. Blue -0.73 -0.93 -0.18 -7.26 Table 3. TheGreen relative change in-1.6 radiometric1 -1.44 response-0.69 during a- year10.94 (%). BandRed MSI-3.23 OLI-1.66 MODIS-1.12 WFV-12.16 NIRBlue -0.731.62 -0.931.59 0.18-1.34 7.26-15.83 − − − − Green 1.61 1.44 0.69 10.94 Mean of all bands −-1.80 −-1.40 − -0.83 − -11.55 Red 3.23 1.66 1.12 12.16 − − − − NIR 1.62 1.59 1.34 15.83 − − − − MeanTable of 4. all The bands relative difference1.80 in 1.40radiometric0.83 response (%)11.55. − − − − Remote Sens. 2020, 12, 1037 8 of 21

Table 4. The relative difference in radiometric response (%).

Bands WFV & MSI WFV & OLI WFV & MODIS WFV & Mean * Blue 0.55 2.53 0.99 0.70 − Green 1.95 1.65 6.05 3.22 − − − − Red 7.37 4.33 8.33 6.68 − − − − NIR 8.29 10.84 17.88 12.34 − − − − * Mean in this location is the mean value of MSI, OLI and MODIS in column 4, Table4.

From Tables2–4, the following conclusions are drawn: (1) WFV has the largest radiometric attenuation at all the four bands and they are all over 10% except blue band. MSI, OLI and MODIS all have different degrees of attenuation, but the attenuation is around 1%. MSI has the largest attenuation in the red band (3.23%) and the least attenuation in the blue band (0.73%); OLI has the largest attenuation in the red band (1.66%) and the least attenuation in the blue band (0.93%); MODIS has the largest attenuation in the near infrared band (1.34%) and the least attenuation in the blue band (0.18%); WFV has the largest attenuation in the near infrared band (15.83%) and the least attenuation in the blue band (7.26%). MODIS has the least attenuation at all the four bands. (2) WFV has lower TOA reflectance than other three sensors except in the blue band. The TOA reflectance between MSI, OLI, MODIS and WFV in four bands were compared, separately. For MSI and WFV, the maximum difference appears in the NIR band (8.29%), and the minimum difference appears in the blue band (0.55%). For OLI and WFV, the maximum difference appears in the NIR band (10.84%), and the minimum difference appears in the green band (1.65%). For MODIS and WFV, the maximum difference appears in the NIR band (17.88%), and the minimum difference appears in the blue band (0.99%). However, the TOA reflectance difference between WFV and the other sensors is much smaller at the beginning stage of calibration, which means the radiometric gap between WFV and the other sensor is induced by the larger radiometric attenuation of WFV. (3) WFV has the larger variation than MSI, OLI and MODIS in all these four bands. Comparing the corresponding bands of the four sensors, the blue band of MODIS is most stable, while the green band, red band and NIR band of OLI are most stable. For MSI, the most and least stable bands are blue band (0.0085) and NIR band (0.0101). For OLI, the most and least stable bands are the green band (0.0075) and the blue band (0.0102). For MODIS, the most and worst stable bands are the green band (0.0081) and the NIR band (0.0102). For WFV, the most and least stable bands are the blue band (0.0114) and the NIR band (0.0159). From the above, the TOA reflectance of WFV has much larger attenuation (over 10% except blue band), lower value and larger variation than that of OLI, MSI and MODIS. Although the difference of SRF and view angle may cause the above phenomenon to some extent, the main reason is that the yearly vicarious calibration coefficients is unable to correct changes in sensor performance timely, and cannot ensure the consistency of the TOA reflectance in a long time series. Thus, it is necessary and important to calibrate the radiation of on-orbit WFV at much higher frequency.

2.3. Cross-Calibration of WFV The cross-calibration procedure for GF-6/WFV used in this study is illustrated in Figure4. It contains the following main steps. Remote Sens. 2020, 12, 1037 9 of 21

Landsat- DEM 8/OLI derived from imagery ZY-3/TLC

BRDF characterization Solar illumnitions and of the calibration site view geometries

MODIS water Times series of GF-6/WFV surface vapor product MODIS data reflectance simulation (MOD05)

Radiative AODs for GF-6 image TOA radiance of transfer code date GF-6/WFV (L) (6S)

GF-6/WFV Calibration coefficients imageries (a & b) fitting: (DN) L=DN*a+b FigureFigure 4. 4. CrossCross-calibration-calibration procedure procedure for for GF GF-6-6/WFV/WFV used used in in this this paper. paper.

1)(1) T Thehe BRDF BRDF model model of of Badain Badain Jaran Jaran Desert Desert site site is is fitted fitted using using both both t thehe Landsat Landsat-8-8/OLI/OLI and and the the ZYZY-3-3/TLC/TLC data data.. The The radiometric performance performance of of Landsat Landsat-8-8/OLI/OLI is is r recognizedecognized as as exceptional exceptional,, which which makemakess it suitablesuitable forforcross-calibration cross-calibration as as the the reference. reference The. The TLC TLC camera camera onboard onboard ZY-3 ZY consists-3 consists of three of threepanchromatic panchromatic scanners: scanners: one with one nadir-viewing, with nadir-viewing one with, one forward-viewing with forward- andviewing one withand backwardone with backwardviewing. The viewing resolution. The ofresolution the three scannersof the three is 2.1 scanners m, 3.6 m is and 2.1 3.6m,m, 3.6m respectively. and 3.6m, The respectively. high resolution The highof ZY-3 resolution/TLC make of ZY the-3/TLC resolution make ofthe DEM resolution product of extractedDEM product from extracted it as high from as about it as 10m,high as with about the 10m,less thanwith 2the m errorless than [23, 242m]. error The BRDF[23,24] model. The BRDF is a 4-D model Look is Upa 4- TableD Look (LUT) Up Table including (LUT) solar including zenith, solarview zenith, zenith, view relative zenith, azimuth relative and azimuth surface and reflectance surface reflectance [2]. The records [2]. The from records the LUTfrom comethe LUT from come the fromstatistics the ofstatistics the selected of the OLI selected clean imagesOLI clean after images atmospheric after atmospheric correction. Amongcorrection that,. Among the solar that, zenith, the solarview zenith, zenith andview relative zenith azimuthand relative (absolute azimuth diff (aerencebsolute between difference solar between azimuth solar and azimuth view azimuth) and view are azimuthcome from) are the come multi-angle from the dataset multi- composedangle dataset by verticalcomposed angular by vertical information angular from information Landsat-8 from/OLI Landsatand slope-8/OLI information and slope of information DEM from of ZY-3 DEM/TLC. from The ZY solar-3/TLC. illuminations The solar illuminations (solar zenith and(solar azimuth zenith andangles) azimuth and view angles) geometries and view (viewing geometries zenith (viewing and azimuth zenith angles) and azimuth are varied angles) pixel are by pixelvaried because pixel by of pixelthe hilly because topography, of the hilly and topography only related, and to the only slope related and to aspect the slope once and the positionsaspect once of the positions sun and the of thesensor sun areand given. the sensor It should are given. be noted It should that the be coordinate noted that system the coordinate used by DEMsystem product used by (local DEM coordinate product (systemlocal coordinate with pixel system as origin) with is dipixelfferent as origin with that) is useddifferent by the with solar that illuminations used by the and solar view illu geometriesminations and(global view coordinate geometries system (global with coordinate pixel as system origin), with so coordinate pixel as origin system), so conversioncoordinate system between conversion local and betweenglobal is local required. and global The surface is required. reflectance The surface of each reflectance pixel in OLI of imageseach pixel is obtained in OLI images after atmospheric is obtained aftercorrection atmospheric using the correction DO method using [11 the] (taking DO m theethod clean [11 lakes] (taking in the the calibration clean lakes site in asthe the calibration dark objects). site asThe the accuracy dark objects). of BRDF The is accuracy verified, andof BRDF it showed is verified, a less and than it 2.2% showed error a [less2]. than 2.2% error [2]. 2)(2) T Thehe surface surface reflectance reflectance of of GF GF-6-6/WFV’s/WFV’s solar solar illumination illumination and and view view geometries geometries is is simulated usingusing the the retrieved retrieved BRDF. BRDF. The angle information information in in each each pixel pixel of of GF GF-6-6/WFV/WFV image image is is obtained obtained by by interpolatinginterpolating the the records records from from the the angle angle file file (*.nav) (*.nav) in in original original data data package. package. The The surface surface refle reflectancectance inin the the BRDF BRDF LUT LUT is is from from OLI, OLI, so so when simulating the the reflectance reflectance under under WFV’s WFV’s solar solar illumination illumination andand view view geometries, geometries, the the spectral spectral response response difference difference between between WFV WFV and and OLI OLI needs needs to to be be taken taken into into account.account. The The spectral response functionsfunctions (SRFs)(SRFs) ofof WFVWFV and and OLI OLI in in multispectral multispectral bands band ares are plotted plotted in inFigure Figure5. The 5. The SMFs SMFs between between OLI andOLI WFV and in WFV corresponding in corresponding bands are bands calculated are calculated using the using Equation the

Equation (3) [1–3,25]. The ground-measured spectrum of the Badain Jaran Desert site is used as 휌휆 (comes from the measurement on July,2012, and is shown in Figure 6). Remote Sens. 2020, 12, 1037 10 of 21

(3) [1–3,25]. The ground-measured spectrum of the Badain Jaran Desert site is used as ρλ (comes from 휆２푊퐹푉 휆2푂퐿퐼 the measurement on July, 2012, and is shown in Figure6). 푎 = ∫ 휌휆 ∗ 푓푊퐹푉(휆)푑휆 / ∫ 휌휆 ∗ 푓푂퐿퐼(휆)푑휆 (3) 휆２푊퐹푉 휆2푂퐿퐼 휆１ 휆 Z WFVWFV Z 1OLIOLI 푎 = ∫ λ2 휌휆 ∗ 푓푊퐹푉(휆)푑휆 / ∫λ2 휌휆 ∗ 푓푂퐿퐼(휆)푑휆 (3) = ( ) ( ) where 푓푤푓푣(휆) and 푓푂퐿퐼(휆a) are휆 the１WFV SRFsρ λof thefWFV WFVλ d andλ/ OLI,휆1OLI respectively.ρλ fOLI λ 휆d１λ~휆２ is the spectral range(3) λ ∗ λ ∗ of WFV and OLI. The TOA reflectance1WFV of WFV can be1OLI spectrally normalized using the spectral where 푓푤푓푣(휆) and 푓푂퐿퐼(휆) are the SRFs of the WFV and OLI, respectively. 휆１~휆２ is the spectral range ′ wherematchingof WFVfw and ffactors v(λ )OLI.and using TfheOLI the( TOAλ) equationare reflectance the SRFs휌푊퐹푉 of= theWFV휌푂퐿퐼 WFV can∗ 푎 and.be Figure spectral OLI, 7 respectively.showsly normalized an exampleλ1 ~usingλ of2 is the the the simulated spectralspectral rangesurface of reflectance WFV and OLI.on 5 July The TOA2019. reflectance′ of WFV can be spectrally normalized using the spectral matching factors using the equation 휌푊퐹푉 = 휌푂퐿퐼 ∗ 푎. Figure 7 shows an example of the simulated matching factors using the equation ρ = ρ a. Figure7 shows an example of the simulated surface reflectance on 5 July 2019. WFV0 OLI ∗ surface reflectance on 5 July 2019.

Figure 5. Spectral response functions (SRFs). (The red solid line represents the SRF of WFV, while the Figureblack dotted 5. Spectral line represents response functionsthe SRFs of (SRFs). OLI, respectively.) (The red solid. line represents the SRF of WFV, while the Figure 5. Spectral response functions (SRFs). (The red solid line represents the SRF of WFV, while the black dotted line represents the SRFs of OLI, respectively). black dotted line represents the SRFs of OLI, respectively.).

Figure 6. Ground-measured spectrum of the Badain Jaran Desert site. Figure 6. Ground-measured spectrum of the Badain Jaran Desert site. Figure 6. Ground-measured spectrum of the Badain Jaran Desert site. Remote Sens. 2020, 12, 1037 11 of 21

Figure 7. Example of the simulated surface reflectance of GF-6/WFV image on 5 July, 2019. Figure 7. Example of the simulated surface reflectance of GF-6/WFV image on 5 July, 2019. (3) The Aerosol Optical Depths (AODs) of the GF-6/WFV images are retrieved using times 3) The Aerosol Optical Depths (AODs) of the GF-6/WFV images are retrieved using times series series of MODIS data. The AOD retrieval algorithm is proposed by Liang et al. [26] and updated by of MODIS data. The AOD retrieval algorithm is proposed by Liang et al. [26] and updated by Zhong Zhong et al. [27]. Firstly, calculating the TOA reflectance of MODIS after radiometric calibration from et al. [27]. Firstly, calculating the TOA reflectance of MODIS after radiometric calibration from the the time series. Secondly, sorting these TOA reflectance values, and classifying them per 10 angle time series. Secondly, sorting these TOA reflectance values, and classifying them per 10 ° angle ◦range. range. Then selecting the TOA reflectance values as the “clearest” observation (with low aerosol and Then selecting the TOA reflectance values as the “clearest” observation (with low aerosol and water water vapor (WV), covered with no cloud and cloud shadow) during each pixel’s temporal window. vapor (WV), covered with no cloud and cloud shadow) during each pixel’s temporal window. The The AODs of the “clearest” observations could be obtained through high-resolution images like OLI AODs of the “clearest” observations could be obtained through high-resolution images like OLI using using DO method. Then the BRDF model in low-resolution can be fitted (the Staylor-Suttles BRDF DO method. Then the BRDF model in low-resolution can be fitted (the Staylor-Suttles BRDF model model [21] is selected in this algorithm). Once the BRDF model is fitted, the surface reflectance of each [21] is selected in this algorithm). Once the BRDF model is fitted, the surface reflectance of each pixel pixel could be calculated and the AOD of each pixel could be retrieved using atmospheric radiative could be calculated and the AOD of each pixel could be retrieved using atmospheric radiative transferring model (TOA reflectance is known) [27]. The AOD retrieval algorithm using MODIS data transferring model (TOA reflectance is known) [27]. The AOD retrieval algorithm using MODIS data ensures that AOD can be obtained every day. This method has been used to cross-calibrate reflective ensures that AOD can be obtained every day. This method has been used to cross-calibrate reflective bands of major moderate resolution remotely sensed data, and proved a high accuracy [28]. In this bands of major moderate resolution remotely sensed data, and proved a high accuracy [28]. In this paper, 12 WFV images are selected. Figure8 shows an example of the retrieved AOD on 5 July 2019. paper, 12 WFV images are selected. Figure 8 shows an example of the retrieved AOD on 5 July 2019. The acquisition time of these images and their retrieved AODs are listed in Table5. The acquisition time of these images and their retrieved AODs are listed in Table 5. Remote Sens. 2020, 12, 1037 12 of 21

Figure 8. Example of the Aerosol Optical Depth (AOD) of GF-6/WFV image on 5 July, 2019. Figure 8. Example of the Aerosol Optical Depth (AOD) of GF-6/WFV image on 5 July, 2019. Table 5. AODs of WFV images. Table 5. AODs of WFV images. Acquisition Time AOD (550 Acquisition Time AOD (550 Acquisition(YYYY Time/MM /DD) AODnm) (YYYYAcquisition/MM/DD) Timenm) AOD (YYYY/MM/DD)2019/03 /08(550nm) 0.2942 2019(YYYY/MM/DD)/07/25 0.4612 (550nm) 2019/03/082019 /03/200 0.0558.2942 2019/092/04019/07/25 0.4654 0.4612 2019/03/202019 /03/240 0.1358.0558 2019/092/25019/09/04 0.2703 0.4654 2019/03/242019 /04/140 0.0577.1358 2019/092/29019/09/25 0.3138 0.2703 2019/04/22 0.3198 2019/10/07 0.0995 2019/04/14 0.0577 2019/09/29 0.3138 2019/07/05 0.1625 2019/10/11 0.3514 2019/04/22 0.3198 2019/10/07 0.0995 2019/07/05 0.1625 2019/10/11 0.3514 (4)4) The TOATOA radianceradiance ofof WFVWFV isis calculatedcalculated usingusing thethe simulatedsimulated surfacesurface reflectance reflectance in in step step (2) 2) and and retrievedretrieved AOD AOD in in step step (3) 3) throughthrough thethe atmosphericatmospheric radiativeradiative transferring transferring model. model. AfterAfter thethe surface surface reflectancereflectance is is simulated simulated and and the the AOD AOD is is retrieved, retrieved, the the Second Second Simulation Simulation ofof a a Satellite Satellite Signal Signal in in the the SolarSolar Spectrum Spectrum (6S) (6S) model model is is chosen chosen [ 29[29]] to to calculate calculate the the TOA TOA radiance. radiance. This This computation computation of of radiative radiative transfertransfer model model needsneeds toto be performed in in each each pixel pixel of of each each WFV WFV image, image, which which is istime time consuming. consuming. In Inorder order to to improve improve the the efficiency efficiency of of calculation, calculation, a a 6S 6S LUT is built up.up. The water vaporvapor contentcontent usedused in in thisthis paper paper comes comes from from the the MODIS MODIS precipitable precipitable water water product product (MOD05). (MOD05). TheThe parametersparameters areare setset in in TableTable6. 6 Figure. Figure9 is9 anis an example example of of calculated calculated TOA TOA radiance radiance on on 5 July5 July 2019. 2019.

Table 6. 6S parameters. Table 6. 6S parameters.

Parameter NumberNumber of Parameters of Value Parameter Value Dateparameters 2 {June 1, December 1} SolarDate zenith 2 17{June 0~80,1, December interval 5 1} Solar azimuth 13 0~360, interval 30 ViewSolar zenithzenith 17 17 0~80, 0~80, interval interval 5 5 ViewSolar azimuthazimuth 13 13 0~360, 0~360, interval interval 30 30 WavelengthView zenith 17 40~80, Band1~band4 interval 5 SurfaceView reflectanceazimuth 13 11 0~360, 0~0.5, interval interval 30 0.05 WavelengthAOD set 4 9 {0, 0.01, 0.02,Band1~band4 0.05, 0.1, 0.2, 0.4, 0.6, 0.8} Surface 11 0~0.5, interval 0.05 reflectance {0, 0.01, 0.02, 0.05, 0.1, 0.2, 0.4, 0.6, AOD set 9 0.8}

Remote Sens. 2020, 12, 1037 13 of 21

FigureFigure 9.9. ExampleExample ofof thethe calculatedcalculated TOATOA radianceradiance ofof GF-6GF-6/WFV/WFV imageimage onon 55 July,July, 2019. 2019.

(5) The calibration coefficients are fitted using the calculated TOA radiance and the Digital Data 5) The calibration coefficients are fitted using the calculated TOA radiance and the Digital Data (DN) read from the GF-6/WFV image. (DN) read from the GF-6/WFV image. 3. Results 3. Results 3.1. Cross-calibration Results 3.1. Cross-calibration Results The calibration coefficients of each WFV image can be calculated using Equation (2). In this paper,The parameter calibrationbias coefficientsis used the of each pre-launch WFV image offset can value be calculated(0 for each using band). Equation The calibration (2). In this coepaper,fficients parameter of GF-6 푏푖푎푠/WFV is calculated used the preare-launch shown inoffset Table value7. The (0 for uncertainty each band). shown The here calibration is the valuecoefficients of standard of GF- deviation.6/WFV calculated are shown in Table 7. The uncertainty shown here is the value of standard deviation. Table 7. Calibration coefficients of GF-6/WFV. Table 7. Calibration coefficients of GF-6/WFV. Date Blue Band Green Band Red Band NIR Band (YYYYDate /MM/DD) Blue BandGain Green Gain Band GainRed Band GainNIR Band (YYYY/MM/DD) gain gain gain gain 2019/03/08 0.0606 0.0001 0.0511 0.0003 0.0521 0.0005 0.0335 0.0001 ± ± ± ± 2019/03/082019/03 /200.0606 0.0590±0.00010.0001 0.0511 0.0493 ±0.00010.0003 0.05080.05210.0001±0.0005 0.0329 0.03350.0001±0.0001 ± ± ± ± 2019/03/202019/03 /240.0590 0.0565±0.00010.0004 0.0493 0.0463 ±0.00040.0001 0.04630.05080.0002±0.0001 0.0302 0.03290.0002±0.0001 ± ± ± ± 2019/04/14 0.0655± 0.0007 0.0557 ±0.0006 0.0565 0.0003± 0.0362 0.0001± 2019/03/24 0.0565 0±.0004 0.0463± 0.0004 0.0463± 0.0002 0.0302± 0.0002 2019/04/22 0.0699 0.0005 0.0565 0.0003 0.0556 0.0002 0.0366 0.0003 2019/04/14 0.0655±0±.0007 0.0557± 0.0006 0.0565± ±0.0003 0.0362± ±0.0001 2019/07/05 0.0709 0.0009 0.0600 0.0006 0.0609 0.0003 0.0375 0.0002 ± ± ± ± ± ± ± 2019/04/222019/07 /250.0699 0.06090.00050.0007 0.0565 0.0481 0.00050.0003 0.04750.05560.00040.0002 0.0349 0.03660.00020.0003 ± ± ± ± 2019/07/052019/09 /040.0709 0.0689±0.00090.0007 0.0600 0.0549 ±0.00060.0006 0.05420.06090.0005±0.0003 0.0377 0.03750.0002±0.0002 ± ± ± ± 2019/07/252019/09 /250.0609 0.0779± 0.00070.0008 0.0481 0.0663 ±0.00070.0005 0.06890.04750.0006±0.0004 0.0433 0.03490.0002±0.0002 ± ± ± ± 2019/09/29 0.0714± 0.0005 0.0578 ±0.0004 0.0582 0.0003± 0.0366 0.0002± 2019/09/04 0.0689 0±.0007 0.0549 ± 0.0006 0.0542± 0.0005 0.0377± 0.0002 2019/10/07 0.0667 0.0002 0.0553 0.0003 0.0572 0.0001 0.0355 0.0001 2019/09/25 0.0779± 0.0008± 0.0663 ±±0.0007 0.0689± ±0.0006 0.0433± ±0.0002 2019/10/11 0.0689 0.0004 0.0543 0.0002 0.0539 0.0003 0.0356 0.0002 2019/09/29 0.0714± 0.0005± 0.0578± 0.0004 0.0582± ±0.0003 0.0366± ±0.0002 2019/10/07 0.0667±0.0002 0.0553±0.0003 0.0572±0.0001 0.0355±0.0001 2019/10/11 0.0689±0.0004 0.0543±0.0002 0.0539±0.0003 0.0356±0.0002

3.2. Validation Remote Sens. 2020, 12, 1037 14 of 21 For the purpose of validating the accuracy of the cross-calibration results obtained in this paper, the synchronized ground measurements with GF-6/WFV (ground measurements at Dunhuang site during3.2. Validation GF-6 overpasses) at Dunhuang test site are used. The measurements come from the ground campaigns for vicarious calibration carried by the CRESDA from June to August 2019 by the Working For the purpose of validating the accuracy of the cross-calibration results obtained in this paper, Group on Calibration and Validation of the Committee. The size of the validation area is 400m*400m. the synchronized ground measurements with GF-6/WFV (ground measurements at Dunhuang site A total of 4 groups of synchronized ground measurements data are obtained. The parameters of during GF-6 overpasses) at Dunhuang test site are used. The measurements come from the ground ground measurements include the land surface spectra, AOD, profile of temperature and humidity campaigns for vicarious calibration carried by the CRESDA from June to August 2019 by the Working and WV. Some parameters of synchronized ground measurements including the chosen WFV images Group on Calibration and Validation of the Committee. The size of the validation area is 400 m 400 m. and ground measurements are listed in Table 8. The validation procedure is illustrated in Figure× 10. A total of 4 groups of synchronized ground measurements data are obtained. The parameters of ground measurements include the land surface spectra, AOD, profile of temperature and humidity Table 8. Parameters of synchronized ground measurements at Dunhuang. and WV. Some parameters of synchronized ground measurements including the chosen WFV images WV and groundSatellite measurements transit Solar are listedAzimuth in TableSolar8. Zenith The validation View Azimuth procedure View is illustrated Zenith inAOD Figure 10. Date (g/c time (UTC) angle (°) angle (°) angle (°) angle (°) (550nm) m2) 2019 Table 8. Parameters of synchronized ground measurements at Dunhuang. 0.8 05:10:48 155.6110 22.3860 299.0170 26.7417 0.1950 0727 771 Satellite Solar Solar View View 2019 AOD WV1.0 Date 05Transit:03:53 Time 154.8450Azimuth 25.7393Zenith Azimuth305.8480 Zenith15.9232 0.2324 2 0808 (550 nm) (g/cm178) (UTC) Angle (◦) Angle (◦) Angle (◦) Angle (◦) 2019 0.7 05:01:35 154.8700 26.9710 312.2140 12.1461 0.2982 081220190727 05:10:48 155.6110 22.3860 299.0170 26.7417 0.1950 0.8771518 201920190808 05:03:53 154.8450 25.7393 305.8480 15.9232 0.2324 1.01780.6 05:54:38 155.4910 30.9459 35.8970 6.2175 0.3142 082420190812 05:01:35 154.8700 26.9710 312.2140 12.1461 0.2982 0.7518545 20190824 05:54:38 155.4910 30.9459 35.8970 6.2175 0.3142 0.6545

Synchronized Ground campaigns GF-6/WFV at Dunhuang test images at site Dunhuang site

Coefficients Land surface Atmospheric by cross- spectra parameters calibration

TOA radiance TOA radiance by ground by cross- measurements calibration

Comparisons

FigureFigure 10. 10. VValidationalidation procedure using synchronized ground measurements at Dunhuang test site.site.

TThehe TOA radiances are calculatedcalculated using parameters from synchronized ground measurements and cross cross-calibration-calibration coecoefficientsfficients retrievedretrieved inin thisthis study,study, respectively. respectively. The The relative relative errorerror comparedcompared with WFV is calculated using using the the following following equations: equations: ∆푎푏 푟푒푙푎푡푖푣푒⁡푒푟푟표푟 = ∆ab (4) relative error 푚푒푎푛= (푎, 푏) (4) mean(a, b) ∆푎푏 = 푎푏푠(푎 − 푏) ± √훿푎2 + 훿푏2 (5) p ∆ab = abs(a b) δa2 + δb2 (5) − ± where a is the TOA radiance from cross-calibration in this paper, b is the TOA radiance calculated from the ground measurements, ∆ab is the difference between a and b, mean(a, b) is the mean value of a and b, δa and δb are the standard deviation of a and b, respectively. Remote Sens. 2020, 12, 1037 15 of 21

The TOA radiance calculated from the cross-calibration and from the synchronized ground 푎 푏 measurementswhere is the and TOA the radiance relative from diff crosserence-calibration of the two in this calculations paper, is the for TOA each radiance band are calculated listed infrom Table 9. the ground measurements, ∆푎푏 is the difference between 푎 and 푏, 푚푒푎푛(푎, 푏) is the mean value of 푎 and Almost all relative differences of the two TOA radiance calculations are less than 5%. The largest 푏, 훿푎 and 훿푏 are the standard deviation of 푎 and 푏, respectively. differenceThe is 5.05% TOA and radiance it happens calculated at band from 1 on the August cross-calibration 8, 2019. Therefore, and from the the proposed synchronized cross-calibration ground methodmeasurements has the potential and the for relative improving difference the of radiometric the two calculations capability for of each GF-6 band/WFV are e fflistedectively in Table with 9 much. higherAlmost frequency all relative (monthly). differences of the two TOA radiance calculations are less than 5%. The largest difference is 5.05% and it happens at band 1 on August 8, 2019. Therefore, the proposed cross- calibration methodTable has 9. Validation the potential results for for improving GF-6/WFV’s the cross-calibration radiometric capability results. of GF-6/WFV effectively with much higher frequency (monthly). Value type TOA radiance calculated from cross-calibration coefficients DateTable 9. Validation Band1 results for GF Band2-6/WFV’s cross-calibration Band3 results. Band4 Value20190727 type 105.25TOA0.15 radiance 101.55 calculated0.15 from 87.14 ground0.13 measurements 60.32 0.09 ± ± ± ± Date20190808 111.33Band1 0.16 107.65Band20.15 92.23Band30.14 62.45Band40.09 ± ± ± ± 2019072720190812 105.25 102.24±0.150.15 101.55 9890 ±0.150.15 84.4987.14±0.130.13 57.4460.32±0.090.09 ± ± ± ± 2019080820190824 111. 104.3033±0.160.16 102.81107.65±00.15.15 88.9492.23±0.130.14 60.7162.45±0.090.09 ± ± ± ± ± ± ± ± 20190812Value type 102.24 TOA0 radiance.15 calculated9890 0.15 from cross-calibration84.49 0.13 coe57.4fficients4 0.09 20190824 104.30±0.16 102.81±0.15 88.94±0.13 60.71±0.09 ValueDate type TOA Band1 radiance calculated Band2 from cross Band3-calibration coefficients Band4 Date20190727 101.12Band1 0.20 100.89Band20.20 89.23Band30.18 61.48Band40.12 ± ± ± ± 2019072720190808 101. 106.7012±0.200.21 103.35100.89±00.20.20 91.0389.23±0.170.18 64.6161.48±0.130.12 ± ± ± ± 2019080820190812 106.70 98.45±0.210.19 103.35 98.05 ±0.200.20 85.6891.03±0.160.17 54.6164.61±0.110.13 ± ± ± ± 2019081220190824 98 105.62.45±0.190.21 103.5098.05±00.21.20 88.0285.68±0.180.16 58.5554.61±0.120.11 20190824 105.62±0±.21 103.50±0.21 88.02±±0.18 58.55± ±0.12 Value type Relative error (%) of the two TOA radiance calculations Value type Relative error (%) of the two TOA radiance calculations DateDate Band1 Band1 Band2 Band3Band3 Band4Band4 2019072720190727 4.00 4.00±0.240.24 0.650.65±00.25.25 2.372.37±0.250.25 1.901.90±0.250.25 ± ± ± ± 2019080820190808 4.25 4.25±0.240.24 4.084.08±00.24.24 1.311.31±0.240.24 3.403.40±0.250.25 ± ± ± ± 2019081220190812 3.78 3.78±0.250.25 0.860.86±0.25 1.401.40±0.240.24 5.055.05±0.250.25 ± ± ± ± 2019082420190824 1.26 1.26±0.250.25 0.670.67±00.24.24 1.041.04±0.250.25 3.623.62±0.250.25 ± ± ± ±

In order to observe the effect after cross-calibration, the TOA reflectance was compared before Incross order-calibration to observe (using the coefficients effect after published cross-calibration, by the CRESDA) the TOA and reflectance after cross- wascalibration compared (using before cross-calibrationcoefficients calculated (using coefficients in this paper) published from byMarch the CRESDA)1 to August and 31, after 2019 cross-calibration in Dunhuang site (using (Figure coefficients 11). calculatedFrom the in this figure, paper) we ca fromn see March the trend 1 to and August value31, of TOA 2019 reflectance in Dunhuang after site cross (Figure-calibration 11). From is closer the to figure, we canMODIS see the. In trend addition and, the value dispersion of TOA degree reflectance is decrease afterd cross-calibration in all the four bands is closer (the standard to MODIS. deviation In addition, the dispersionis less). degree is decreased in all the four bands (the standard deviation is less).

Figure 11. Cont. Remote Sens. 2020, 12, 1037 16 of 21

Figure 11. Comparison of the TOA reflectance between WFV and MODIS (before and after cross-calibration). Remote Sens. 2020, 12, 1037 17 of 21

3.3. Uncertainty Analysis Based on radiative transfer theory, the uncertainty in this cross-calibration procedure mainly comes from (1) 6S model, (2) DN, (3) sun irradiance, (4) view angle, (5) BRDF LUT, (6) AOD, (7) WV product and (8) spectral matching factor calculation. The uncertainty of each part and total are shown in Table 10. (1) 6S model. The uncertainty of 6S model is 1.6% according to the reference [29]. (2) DN. The standard deviations for bands blue, green, red and NIR are 1.87%, 3.23%, 6.17% and 6.47% [1]. Taking these standard deviations to simulate TOA reflectance using WFV bands, the uncertainty is shown in Table 10. (3) Sun irradiance. The TOA reflectance is calculated using Equation (1). Here the sun irradiance is calculated using oldkur.dat from MODTRAN. The RMSE is almost less than 5%. Take this RMSE to simulate TOA reflectance using WFV bands, the uncertainty is very small (less than 0.01%) and even can be ignored. (4) View angle. The angle range used in this paper is 0~15◦. Take this angle range to simulate TOA reflectance using WFV bands, the uncertainty is very small (less than 0.01%) and even can be ignored. (5) BRDF LUT. The difference errors of the BRDF are 1.82% for blue band, 2.10% for green band, 1.88% for red band and 1.94% for NIR band [1]. Take this RMSE to simulate TOA reflectance using WFV bands, the uncertainty is shown in Table 10. (6) AOD. We validate the AOD’s accuracy using the Dunhuang ground measurements data. Firstly, the BRDF model in Dunhuang site, coming from CRESDA, was used. Secondly, the AODs of Table8’s dates are simulated using the method introduced in Section 2.3. Thirdly, the accuracy is evaluated with the Dunhuang ground measurements result. The RMSE is 0.092. Take this RMSE to simulate TOA reflectance using WFV bands, the uncertainty is shown in Table 10. (7) WV product. According to the MODIS water vapor product’s ATBD, the standard deviation of MODIS WV product is 5%~15%. Here we selected 15% as the standard deviation, and take this to simulate TOA reflectance using WFV bands, the uncertainty is shown in Table 10. (8) Spectral matching factor calculation. The uncertainty of spectral matching factor comes from the ground-measured spectrum (2%). From the table, the total uncertainty is 3.35% for blue band, 3.56% for green band, 4.23% for red band and 4.60% for NIR band, all less than 5%.

Table 10. Uncertainty of the GF-6/WFV’s cross-calibration procedure.

Uncertainty Number Uncertainty Source Blue Green Red NIR

(1) 6S model (σ1) 1.6% 1.6% 1.6% 1.6% (2) DN (σ2) 0.01% 0.02% 0.04% 0.05% (3) Sun irradiance (σ3) <0.01% <0.01% <0.01% <0.01% (4) View Angle (σ4) <0.01% <0.01% <0.01% <0.01% (5) BRDF LUT (σ5) 2.13% 2.47% 3.36% 3.77% (6) AOD (σ6) 0.38% 0.20% 0.15% 0.22% (7) WV product (σ7) 0.02% 0.03% 0.13% 0.57% (8) Spectral matching factor calculation (σ8) 2.0% 2.0% 2.0% 2.0% Total uncertainty (σO) * 3.35% 3.56% 4.23% 4.60% q = 2 + 2 + 2 + 2 + 2 + 2 + 2 + 2 * σO σ1 σ2 σ3 σ4 σ5 σ6 σ7 σ8. Remote Sens. 2020, 12, 1037 18 of 21

4. Discussion The cross-calibration method used in this paper is based on a desert site (Badain Jaran Desert), The cross-calibration method used in this paper is based on a desert site (Badain Jaran Desert), and validated based on a Gobi site (Dunhuang site). The surface reflectance ranges of the two sites and validated based on a Gobi site (Dunhuang site). The surface reflectance ranges of the two sites (medium reflectivity, not too high and not too low) are similar, so the results obtained using the two (medium reflectivity, not too high and not too low) are similar, so the results obtained using the two sites can be cross-compared. However, we have no idea about whether the calibration result is also sites can be cross-compared. However, we have no idea about whether the calibration result is also applicable to high reflectivity surface or low reflectivity surface. If the WFV’s radiometric is not linear, applicable to high reflectivity surface or low reflectivity surface. If the WFV’s radiometric is not linear, then the results are not applicable to other types of surface. then the results are not applicable to other types of surface. We did a simple comparison between MSI and WFV using simultaneous nadir overpass (SNO) We did a simple comparison between MSI and WFV using simultaneous nadir overpass (SNO) method [30] on December 30, 2019. The results are showed in Figure 12. From the figure, the WFV method [30] on December 30, 2019. The results are showed in Figure 12. From the figure, the WFV shows a good linearity in blue band, green band, red band and NIR band, respectively. However, the shows a good linearity in blue band, green band, red band and NIR band, respectively. However, validation images are insufficient, further verification is still needed. The coefficients used here are the validation images are insufficient, further verification is still needed. The coefficients used here are from the new coefficients published by CRESDA in October, 2019. from the new coefficients published by CRESDA in October, 2019.

FigureFigure 12. 12. ComparisonComparison of of the the TOA TOA reflectance reflectance between between MSI andMSI WFV and using WFV SNO using method SNO on method December on December30, 2019. 30, 2019. 5. Conclusions 5. Conclusions GF-6, launched on June 2, 2018, is the sixth satellite of the HDEOS, and the first high-resolution GF-6, launched on June 2, 2018, is the sixth satellite of the HDEOS, and the first high-resolution satellite in China to achieve precise agricultural observation. As a very important camera onboard GF-6, satellite in China to achieve precise agricultural observation. As a very important camera onboard WFV’s radiometric performance is significant to the accuracy of quantitative application. Similar to the GF-6, WFV’s radiometric performance is significant to the accuracy of quantitative application. former HDEOS optical satellites (HJ-1, GF-1 and GF-4), GF-6 still lacks onboard calibration equipment, Similar to the former HDEOS optical satellites (HJ-1, GF-1 and GF-4), GF-6 still lacks onboard so on-orbit vicarious radiometric calibration is necessary for keeping the radiometric capability at a calibration equipment, so on-orbit vicarious radiometric calibration is necessary for keeping the high quality. The Working Group on Calibration and Validation of the Committee on Earth Observation radiometric capability at a high quality. The Working Group on Calibration and Validation of the Satellites from CRESDA has been taking synchronized ground measurements at the Dunhuang test Committee on Earth Observation Satellites from CRESDA has been taking synchronized ground site for years to update calibration coefficients once a year. However, due to the limitation of human measurements at the Dunhuang test site for years to update calibration coefficients once a year. and material resources, the coefficients only can be updated once a year. For GF-6/WFV, the official However, due to the limitation of human and material resources, the coefficients only can be updated once a year. For GF-6/WFV, the official calibration coefficients have been updated once until now. Therefore, cross-calibration becomes an alternative for updating the calibration coefficients with Remote Sens. 2020, 12, 1037 19 of 21 calibration coefficients have been updated once until now. Therefore, cross-calibration becomes an alternative for updating the calibration coefficients with lower cost, higher efficiency and higher frequency, which is very useful for keeping the radiometric consistency of GF-6/WFV in practices. The radiometric capability of GF-6/WFV is evaluated to prove the necessity of cross-calibration. Three referenced sensors are chosen, including Landsat-8/OLI, Sentinel-2/MSI and Terra/MODIS. Clear images in Dunhuang test site of these four sensors are selected from June 2, 2018 (launch date of GF-6) to October 30, 2019. The TOA reflectance of the four sensors are calculated and compared. Three statistical indices including slope of the trend line, mean value and standard deviation are used to evaluate the radiometric capability. The evaluation shows that: (1) WFV has the largest radiometric attenuation and they are over 10% except blue band (over 7%); (2) WFV has a lower TOA reflectance than other three sensors except in blue band; (3) WFV has a larger variation than MSI, OLI and MODIS at all four bands. The evaluation subsequently proved the necessity to cross-calibrate WFV. In this study, the approach firstly proposed by Zhong et al. to cross-calibrate HJ-1/CCDs and updated by Yang et al. to cross-calibrate GF-1/WFVs and GF-4/PMS is successfully applied to GF-6/WFV. Then, 12 sets of calibration coefficients are obtained from March 2019 to October 2019, which has achieved a much higher calibration frequency. Furthermore, the GF-6/WFV synchronized ground measurements at the Dunhuang test site are used to validate the cross-calibration results and less than 5% error has been achieved. In addition, a thorough uncertainty analysis shows that the total uncertainty is 3.35% for the blue band, 3.56% for the green band, 4.23% for the red band and 4.60% for the NIR band, all less than 5%.

Author Contributions: A.Y. was responsible for the data analysis and writing the manuscript. B.Z. contributed to the main research ideas. L.H. and S.W. wrote the program. Z.X. provided the GF-6 synchronized ground measurements data. H.W. (Hongbo Wu) provided technical information about the sensor. X.G. and H.W. (Haibo Wang) provided the GF-6 data. J.W. assisted in collating validation data. Q.L. reviewed the manuscript and provided valuable suggestions. All authors have read and agreed to the published version of the manuscript. Funding: This research received no external funding. Acknowledgments: This work was supported in part by the National Key R&D Program of China under Grant under Grant 2018YFA0605500, the GF6 Project under Grant 30-Y20A02-9003-17/18 and the National Key R&D Program of China under Grant under Grant 2017YFA0603000. The GF-6/WFV imagery used in this research is supported by the China Centre for Resources Satellite Data and Application (http://www.cresda.com). The Landsat-8/OLI data is downloaded from USGS website (http://landsat.usgs.gov). Conflicts of Interest: The authors declare no conflict of interest.

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