Article High-Resolution Mapping of Tile in Agricultural Fields Using Unmanned Aerial System (UAS)-Based Radiometric Thermal and Optical Sensors

Tewodros Tilahun and Wondwosen M. Seyoum *

Department of Geography, Geology and the Environment, Illinois State University, Normal, IL 61790, USA; [email protected] * Correspondence: [email protected]; Tel.: +1-309-438-2833

Abstract: With the growing concerns of water quality related to tile drainage in agricultural lands, developing an efficient and cost-effective method of mapping tile drainage is essential. This research aimed to establish mapping of tile drainage systems in agricultural fields using optical and radio- metric thermal sensors mounted on Unmanned Aerial System (UAS). The overarching hypothesis is that in a tile-drained land, spatial distribution of water content is affected by tile lines, therefore, contrasting soil temperature signals exist between areas along the tile lines and between the tile lines. Designated flights were conducted to assess the effectiveness of the UAS under various conditions such as rainfall, crop cover, crop maturity and time of the day. Image correction, mosaicking, im- age enhancements and map production were conducted using Agisoft and ENVI image analysis software. The results showed intermediate growth stage of soybean plants and rainfall helped delineating tile lines. In-situ soil temperature measurements revealed appropriate time of the day (14:00 to 18:00 h) for thermal image detection of the tile lines. The role of soil moisture and plant cover is not resolved, thus, further refinement of the approach considering these factors is necessary   to develop efficient mapping techniques of tile drainage using UAS thermal and optical sensors.

Citation: Tilahun, T.; Seyoum, W.M. Keywords: tile drainage; thermal sensor; optical sensor; image processing; UAS; remote sensing High-Resolution Mapping of Tile Drainage in Agricultural Fields Using Unmanned Aerial System (UAS)-Based Radiometric Thermal 1. Introduction and Optical Sensors. Hydrology 2021, 8, 2. https://doi.org/10.3390/ Modification of a watershed to improve productivity of agricultural lands is a com- hydrology8010002 mon practice around the globe. Installation of tile drainage systems, modification of surface drainage and retention ponds are among the few types of watershed modifica- Received: 5 November 2020 tion techniques [1]. Agricultural subsurface drainage also known as tile drainage [2,3] Accepted: 24 December 2020 (hereinafter referred to as ‘tile drainage’) is the most widely used conservation method Published: 28 December 2020 since the earliest civilizations of Mesopotamia and Iran before 4000 BC [4,5]. Tile drainage removes water from the soil, with poor drainage characteristics and found in a relatively Publisher’s Note: MDPI stays neu- flat to gentle-sloped watersheds, preventing flooding that hinders the functionality of the tral with regard to jurisdictional claims land [6,7]. As a result, people can utilize much of the land for recreation and agricultural in published maps and institutional purposes. In the Mid-western region of the , extensive agricultural land was affiliations. obtained through installation of tile drainage [8,9]. However, mostly there are no records indicating the location of tile drainage in much of the Mid-west, due to various reasons such as a change in ownership and lack of proper documentation. Tile drainage provides a tremendous advantage for agronomy by improving soil mois- Copyright: © 2020 by the authors. Li- censee MDPI, Basel, Switzerland. This ture conditions and inducing aeration, reducing total (flooding) and erosion article is an open access article distributed and increasing infiltration [10–13] and trafficability. The removal of excess water improves under the terms and conditions of the aeration and microbial activity, thereby creating suitable soil conditions to attain good Creative Commons Attribution (CC BY) crop productions [14]. Dry soil warms up faster than wet soil, tile drained soil surface dry license (https://creativecommons.org/ faster and allows earlier land preparation and early spring planting for the farmers [14]. licenses/by/4.0/). Though tile drainage has positive agronomic advantages, it presents a negative impact on

Hydrology 2021, 8, 2. https://doi.org/10.3390/hydrology8010002 https://www.mdpi.com/journal/hydrology Hydrology 2021, 8, 2 2 of 14

the environment. Tiles relatively quickly drains the water from the agricultural land into receiving water systems such as streams. As a result, tile drainage allows a direct flow path of nutrients from agricultural lands to streams, a potential water quality concern [12]. Gen- erally, tiles can deliver substantial amounts of dissolved nutrients (e.g., nitrate) into bodies [15]. In addition, tile drainage systems lower soil moisture in the soil and maintain a lower and potentially increase the volume of water draining from the field and reduce the water retention time in the soil zone. The faster the water drains and reduction in retention time results in faster washing of nutrients to the stream. Thus, high nutrient loss from agricultural lands degrade soil fertility and impair water quality of receiving waters. This has a huge impact on the environment creating algal blooms and hypoxic zones in receiving water bodies. For example, water quality problems associated with nutrient loss was observed in the Great Lakes, Gulf of Mexico, Baltic Sea and Lake Winnipeg, among others [16]. Locating tile drainage is perhaps important to the farmers because productivity is highly dependent on efficiency of the tile drainage. It is also essential to better under- stand environmental impacts associated with tile drainage. For example, estimates of the contribution of tile flow and load from farm fields and water quality and hydrological modeling of watersheds containing tile drainage requires knowledge of the location of tile lines and measurement of density and spacing between tile lines. Therefore, proper, cost- effective and efficient mapping techniques are necessary to locate tile lines. Furthermore, such techniques help identify and locate malfunctioning of tile drainage due to aging and prolonged utilization where the tiles are filled with sediment, mosses or dirt to the point that hinders infiltration and movement of water through them. Locating such tiles helps the farmers replace the old (malfunctioning) tiles which increases the overall productivity of the farmland. Various techniques, using geophysical instruments and remote sensing, have been developed to map tile lines. The Ground Penetrating Radar (GPR) is the most reliable method with high accuracy of delineating tile drainage [17–20]. However, GPR is costly, labor intensive and time consuming to map tile drainage over large fields. Remote sensing- based techniques through processing of aerial photography and using optical remote sensing satellites proved potential of mapping tile drainage [21]. The aerial photography techniques integrate additional information such as landcover, soil drainage class and surface slope to map tile drainage [16,21]. This technique is limited to determining the total percentage of land area that is tile drained and fails to detect individual tile locations. Satellite-based remote sensing data have been used for various hydrological and resources applications including mapping tile drainage [22]. The optical satellite technique compares reflectance values of imageries taken before and after rainfall based on the assumption that tile drainage drain water faster and have higher reflectance values compared to non- tile drained area. Limitations of satellite remote sensing method include lower spatial resolution and effect of cloud cover on getting good quality imagery taken before and after a rainfall [23–25]. Development of Unmanned Aerial Systems (UASs) piqued interest to the scientific community in wide areas of applications including precision [26], hydrol- ogy [27] and environmental sciences [28]. Improvement of sensor technology and pro- cessing capability provides opportunities for accurate measurement and mapping of land surface features. Compared to satellite remote sensing, UASs have advantages in terms of flexibility, high temporal and spatial resolution and cost. Unlike satellite remote sensing, the UAS is the best alternative for mapping with little or no effect of cloud cover. In this study, UAS-based tile lines mapping technique was employed to map tile lines in agricultural fields in central Illinois. Very limited studies conducted to map tile lines using sensors mounted on UAS. For example, Allred et al. [18] used thermal sensor on a corn field and found that thermal sensors are capable of mapping tile drainage effectively as compared to the visible-infrared and near-infrared sensors. The limitation of this research is that no thermal sensor was flown under different soil, rain and temperature conditions. Hydrology 2021, 8, x FOR PEER REVIEW 3 of 14

Hydrology 2021, 8, 2 3 of 14 as compared to the visible-infrared and near-infrared sensors. The limitation of this re- search is that no thermal sensor was flown under different soil, rain and temperature con- ditions. Woo, et al. [29] explored a small-scale experimental device simulating agricultural landWoo and etal. tile [29 drainage] explored and a small-scaleused temperature experimental sensors device to map simulating the tile lines agricultural and the land result and indicatedtile drainage a promising and used tool temperature to locate tile sensors drainage. to map The the objective tile lines of and this the research result indicatedis to map a tilepromising drainage tool using to locatethermal tile and drainage. optical Thesensor objectives mounted of this on research UAS and is toassess map the tile effect drainage of differentusing thermal field conditions and optical including sensors the mounted effect of on time UAS of the and day assess (temperature the effect variation), of different rainfall,field conditions crop cover including and growth the stage effect (height) of time of crops the day on (temperature the land were variation), evaluated rainfall,to es- tablishcrop coverappropriate and growth mapping stage conditions. (height) of crops on the land were evaluated to establish appropriateThe overarching mapping hypothesis conditions. of this research is based on the fact that tile drainage may produceThe distinct overarching spatial hypothesisdistribution of of this soil researchmoisture is on based the land on thesurface. fact thatDry tilesoils drainage warm upmay faster produce than wet distinct form spatial the distribution basis of this ofresearch. soil moisture The ability on the of landa dry surface. soil to store Dry and soils releasewarm heat up faster is different than wet from soils the form capacity the basis of a ofmoist this soil research. to store The and ability releases of a heat dry soil[30]. to Afterstore a andrainfall, release soils heat above is different the tile lines from get the quickly capacity dry of relative a moist to soil areas to storebetween and the releases tile linesheat (Figure [30]. After 1). This a rainfall, is because soils the above tiles the drain tile lineswater get from quickly the soil dry at relative a faster to rate areas above between the the tile lines (Figure1). This is because the tiles drain water from the soil at a faster rate tile lines compared to adjacent non-tiled region (between tile lines). Soil moisture being above the tile lines compared to adjacent non-tiled region (between tile lines). Soil moisture one of the factors affecting soil temperature, the temperature of the soil above the tile lines being one of the factors affecting soil temperature, the temperature of the soil above expected to be different from areas in-between tile lines. The lowering of soil moisture the tile lines expected to be different from areas in-between tile lines. The lowering above the tile lines decreases soil heat capacity thereby increasing in surface soil temper- of soil moisture above the tile lines decreases soil heat capacity thereby increasing in ature [29]. As a result, there would be a variation of thermal inertia, the degree of slowness surface soil temperature [29]. As a result, there would be a variation of thermal inertia, with which the temperature of a material approaches that of its surroundings, during di- the degree of slowness with which the temperature of a material approaches that of its urnal fluctuation of temperature in the soil. This assumption led to the testing of the ap- surroundings, during diurnal fluctuation of temperature in the soil. This assumption led to plicability of thermal sensor to map tile lines in this research. In addition, optical sensor, the testing of the applicability of thermal sensor to map tile lines in this research. In addition, which was used to produce visual images and to create Digital Surface Models (DSM), optical sensor, which was used to produce visual images and to create Digital Surface augmentedModels (DSM), the thermal augmented method. the thermal This is method.because Thistile lines is because produces tile linessurface produces depressions surface duedepressions to compaction due towhere compaction soil particles where make soil particles adjustment, make in adjustment, the modified in soils the modified due to ditchingsoils due and to installation ditching and of installation the tile lines. of theFurther, tile lines. tile drainage Further, tileimproves drainage soil improves conditions soil andconditions results in and crop results performance in crop performance variability in variability the field in which the field can which be detected can be by detected optical by and/oroptical thermal and/or sensors. thermal sensors.

Figure 1. Conceptual diagram (cross-section) showing tile drainage and assumed subsurface moisture Figure 1. Conceptual diagram (cross-section) showing tile drainage and assumed subsurface mois- variation along and between tile lines (blue shading indicates water saturation). ture variation along and between tile lines (blue shading indicates water saturation). 2. Study Site 2. Study Site The study site is located 18 km north of Normal town in central Illinois, USA. The lo- cationThe ofstudy the tilesite linesis located in the 18 study km north site is of known Normal and town existing in central tile lines Illinois, map USA. (Figure The2 ) locationis available, of the which tile lines can in be the used study to compare site is known and validate and existing against tile the lines results map from (Figure this study.2) is available,Two types which of tiles can installed be used into thecompare area; 10and cm validate diameter against tiles the that results serve asfrom tributaries this study. and Two20 cmtypes diameter of tiles main installed tile that in the empty area; into 10 ancm experimentaldiameter tiles that serve and as then tributaries to the nearby and 20creek cm diameter (Figure2 ).main The tile site that is an empty agricultural into an field experimental with alternating wetland soybean and then and to corn the grown nearby in creekthe area.(Figure The 2). site The has site an is area an ofagricultural 0.24 km2 (24 field ha) with and hasalternating nearly flat soybean topography and corn with grown rolling inhills the andarea. slopes The site ranging has an from area 0 of to 0.24 5%. Siltykm2 (24 clay ha) loam and and hassilty nearly clay flat characterize topography the with soils rollingin the hills area and which slopes is derived ranging from from glacial 0 to deposits.5%. Silty Theclay siteloam is and located silty on clay the characterize Bloomington theridge soils plain in the physiographic area which regionis derived of Illinois, from characterizedglacial deposits. by gentlyThe site sloping, is located broad on ridges the

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Bloomington ridge plain physiographic region of Illinois, characterized by gently sloping, formedbroad ridges by moraines formed andby moraines flat lacustrine and flat Plaines. lacustrine Specifically, Plaines. Specifically, the study site the is study located site on is Normallocated on and Normal Bloomington and Bloomington moraines thatmoraines are part that of are Batestown part of Batestown till member till member [31]. It also[31]. containsIt also contains the Cahoka the Cahoka formation formation which includes which depositsincludes ofdeposits glacial outwashof glacial along outwash the largeralong valleys,the larger alluvial valleys, sand, alluvial silt, clayssand, and silt,thin clays deposits and thin of deposits loess (windblown of loess (windblown silt). silt).

FigureFigure 2.2. LocationLocation mapmap of of the the study study site, site, existing existing tile tile lines lines map map (red (red lines), lines), location location of soil of soil temperature temper- probesature probes (yellow (yellow circles) circles) and background and background is orthomosaic is orthomosaic image image collected collected using using Unmanned Unmanned Aerial SystemAerial System (UAS). (UAS).

3.3. Methods andand DataData 3.1.3.1. In-SituIn-Situ SoilSoil TemperatureTemperature TheThe appropriateappropriate time time for for the the acquisition acquisition of of thermal thermal images images was was explored, explored, first, first, using using in- situin-situ soil soil temperature temperature measurements. measurements. The The objective objective is to is find to find a proper a proper time time of the of day the thatday maximizesthat maximizes the thermal the thermal contrast contrast between between areas ar closeeas close to tile to lines tile andlines in-between and in-between tile lines. tile Twolines. sets Two of sets decagons of decagons Em59G Em59G remote remote loggers lo withggers soil with temperature soil temperature and moisture and moisture probes wereprobes installed were installed right on right top on of thetop tileof the lines tile and lines in-between and in-between tile lines. tile lines. Each Each logger logger has 5has probes 5 probes installed installed at 1 at m 1 spacingm spacing parallel parallel to theto the axis axis of of the the tile tile line. line. The The average average depthdepth ofof thethe measurement is 5 5 cm. cm. Tile Tile spacing spacing betw betweeneen adjacent adjacent tile tile lines lines is isapproximately approximately 20 20m. m.The The spacing spacing between between the the two two sets sets of probes of probes was was 10 m, 10 m,one one set setinstalled installed right right above above the the tile line while the other mid-way (at 10 m) between two adjacent tiles lines (Figure2 ). tile line while the other mid-way (at 10 m) between two adjacent tiles lines (Figure 2). The The device was set to take a measurement every 5 min and collected data for 15 days. device was set to take a measurement every 5 min and collected data for 15 days. Tem- Temperature data were analyzed to get the time where there is a significant difference perature data were analyzed to get the time where there is a significant difference in tem- in temperature between the measurements taken on the tile line and areas in between perature between the measurements taken on the tile line and areas in between tile lines. tile lines.

3.2.3.2. SensorsSensors ThermalThermal andand OpticalOptical SensorsSensors TheThe thermalthermal sensor was used to identify th thee distinct distinct surficial surficial soil soil temperature temperature varia- vari- abilitybility caused caused by by tile tile lines. lines. Whereas Whereas the the optical optical sensor sensor was was helped helped visual visual interpretation interpretation and andused used to build to build Digital Digital Surface Surface Model Model (DSM) (DSM) [32,33], [32,33 ],which which is is the the equivalent equivalent of a DigitalDigital ElevationElevation Model (DEM) (DEM) that that gives gives a avery very high-resolution high-resolution (centimeter-scale) (centimeter-scale) 3D 3Drepresen- repre- sentationtation of the of thesurface surface drainage drainage pattern pattern and water and water flow. flow.During During the preliminary the preliminary data collec- data collectiontion from fromdifferent different farmlands farmlands outside outside the stud the studyy site, site,surface surface depressions depressions indicating indicating tile tiledrainage drainage location location were were observed observed using using a 10 a m 10 resolution m resolution DEM. DEM. Optical Optical sensors sensors help help ob- obtaintain centimeter-scale centimeter-scale resolution resolution and and alleviate alleviate thethe resolution resolution constraint constraint to improve to improve the thevis- visibilityibility of surface of surface features. features. TheThe Forward Forward Looking Looking InfraRed InfraRed (FLIR) (FLIR) Vue VuePro R Pro thermal R thermal cam- cameraera and andthe DJI the DJIoptical optical camera camera were were used. used. See SeeTable Table 1 for1 for detail detail specifications. specifications.

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Table 1. Thermal and optical camera specifications used for the data collection.

DJI FC 6510 ZENMUSE X4S DJI FC330 Phantom 4 Parameter FLIR Vue Pro R SPECS Quadcopter Spectral Range 7.5–13.5 µm 380–740 nm 380–740 nm Frame Rate 30 HZ 14 frames per second 30 frames per second Data Format Radiometric jpeg DNG, JPEG, DNG+JPEG JPEG, DNG RAW 16:9 (5472 × 3078), Sensor Resolution 640 × 512 12 MP (4000 × 3000) 4:3 (4864 × 3648) FOV 45◦ × 35◦ 84◦ 94◦ Thermal Sensitivity (NETD) 0.02 ◦C N/A N/A Focal length (mm) 13 24 20 Weight (g) 92–113 253 190

3.3. UAS Flight Planning All flight information, such as flight date and time, flight altitude, type of sensors used and weather conditions is summarized in Table2. A total of seven flight missions are accomplished. Ten Ground Control Points (GCPs) were collected using Tremble Geo7x GPS to make a geometric correction during image processing. The flights were designed to counter field conditions such as rainfall, crop cover and time of day with maximum thermal inertia between the tile lines and the adjacent zones.

Table 2. Detailed flight information during data collection, study site was covered with soybean crop for all flights except for the flight conducted after harvesting on 24 October 2019.

Type of Flight Flight Weather Condition (Max Flight Altitude No. of Images Flight Date Camera Started Ended T. ◦C/Min T. ◦C) ABGL (m) Collected 27/15 ◦C, partly cloudy, 22 July 2019 17:20:22 18:03:30 45 733 24.4 mm rain on 5/7/2019 31/18 ◦C, cloudy, 2.3 mm 29 July 2019 16:51:40 17:27:14 45 645 of rain on 27/7/2019 FLIR Vue Pro R 29/20 ◦C cloudy, 16.8 mm 640 30 July 2019 16:04:02 16:43:28 45 750 of rainfall the night before 13/5 ◦C, Cloudy, 3.6 mm 24 October 2019 17:05:24 17:50:34 45 628 rain on 21/10 18/19 ◦C, Partly cloudy, 6 August 2019 17:04:14 17:30:14 6.9 mm rainfall in the 100 348 DJI FC 6510 morning ZENMUSE X4S 13/5, Cloudy, 3.6 mm rain 24 October 2019 15:15:00 15:30:00 100 356 on 21/10 DJI FC330 30/19 ◦C, windy and phantom 4 15 July 2019 15:49:02 16:02:02 60 325 cloudy Quadcopter

3.4. Data Processing and Analysis 3.4.1. Visible Image Agisoft Metashape software (v 1.5.4) [34] is used to process the visible images taken by phantom 4 and Zenmuse X4S cameras. The Agisoft software uses photogrammetry and computer vision algorisms to generate high resolution georeferenced orthomosaics, digital surface model and textured polygonal models by transforming 2D maps to 3D maps and models [35]. The parameter setting called quality and depth filtering is set to Ultra-high and aggressive, respectively.

3.4.2. Thermal Image The lack of geographic information (due to the lack of communication between the sensor and drone) of the thermal images makes the processing complex. First, Geoset- Hydrology 2021, 8, 2 6 of 14

ter software (version 3.5.3) [36] was used to register each image’s geocoordinates by matching the time stamp of each image with flight log data. The timestamp of the image is used to synchronize the image timestamp with the timestamp of the GPS tracklog found in the flight log data. After getting the image coordinates, Pix4D software was used to create mosaic thermal images.

3.4.3. Data Analysis ENVI image analysis software (analysis were done using ENVI 5.5.1) [37] was em- ployed to perform image processing. First, image stretching, a linear percent stretch function, has been used. A linear percent stretching enables trimming extreme values from both ends of the histogram of the pixel values using a specified percentage. Ac- cordingly, the 5% linear stretching function was employed as it showed the best contrast. After stretching the images, convolution and morphology filters were used. Convolution filters operate based on the brightness value of a given pixel. A 5 × 5 kernel size was selected for the filter mask. The directional filter provided the best result for delineating the tile lines. The analysis was conducted by dividing the study site into four fields based on the direction of the tile lines as noticed from the temperature anomaly and pre-existing tile drainage map (Figure2). After directional filtering, supervised classification is conducted by training the data using two training classes based on the range of temperature values of each pixel. The clas- sification method was set to a minimum distance. A minimum distance uses mean vectors for each class and calculates the Euclidean distance from each unknown pixel to the mean vector for each class. Then the pixels are classified to the nearest class. Minimum distance classification calculates the Euclidean distance for each pixel in the image to each class: q T Di(x) = (x − mi) (x − mi), (1)

where: D = Euclidean distance i = the ith class x = n-dimensional data (where n is the number of bands) mi = mean vector of the class i The results from supervised classification were further enhanced to refine the classifica- tion result by using two methods, including enabling smoothing and enabling aggregation. Enabling smoothing helps to remove speckling noises created during classification. A 5 × 5 smooth kernel size was used for smoothing, whereas the aggregate minimum size is set to the default value of 9. Regions with a size of 9 or smaller are aggregated to an adjacent, larger region. The entire methodology followed in this study is outlined in Figure3. Cali- bration of thermal imaging camera using in-situ temperature data was not implemented as the FLIR Vue Pro R camera used in this study gather reliable, non-contact temperature measurements from an aerial perspective. Hydrology 2021, 8, 2 7 of 14

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Figure 3.3. Flow chart showing steps of the methodology. Figure 3. Flow chart showing steps of the methodology. 4. Results 4.4.1. Results In-Situ Soil Temperature 4.1. In-Situ Soil Temperature 4.1. In-SituFigure Soil4 showsTemperature the fluctuation of in-situ soil temperature differences collected from Figure 4 shows the fluctuation of in-situ soil temperature differences collected from nearFigure tile line 4 shows and in-betweenthe fluctuation tile of lines. in-situ The soil probestemperature installed differences along collected the tile linefrom showed a near tile line and in-between tile lines. The probes installed along the tile line showed a nearrelatively tile line higher and in-between temperature tile lines. during The the probes nighttime installed and along lower the tile temperatures line showed duringa the relatively higher temperature during the nighttime and lower temperatures during the relativelydaytime higher than the temperature probes installed during the between nighttime tile and lines lower caused temperatures by thermal during inertia. the Compar- daytimeingdaytime the than two than the temperature the probes probes installed installed measurements between between tile (onlines tile the caused lines tile caused lineby thermal and by area thermal inertia. between Comparing inertia. the Comparing tile lines), themaximumthe two two temperature temperature temperature measurements measurements contrasts (on are the (on generally tile the line tile and line observed area and between area in the between the afternoon tile lines), the tilebetween max- lines), 11:00max- imum temperature contrasts are generally observed in the afternoon between 11:00 and andimum 18:00 temperature h with peak contrasts differences are generally around observed 15:00 h (Figure in the4 afternoon). In the afternoon, between 11:00 the soil and 18:00 h with peak differences around 15:00 h (Figure 4). In the afternoon, the soil temper- temperature18:00 h with alongpeak differences the tile line around becomes 15:00 higher h (Figure than the 4). temperature In the afternoon, between the thesoil tile temper- lines. ature along the tile line becomes higher than the temperature between the tile lines. Thus, Thus,ature along all the the UAS tile thermalline becomes data collectionhigher than is the conducted temperature in the between afternoon the hourstile lines. between Thus, allall the the UAS UAS thermal thermal data data collection collection is conduc is conducted inted the inafternoon the afternoon hours between hours between14:00 and 14:00 and 18:00.14:00 and 18:00. 18:00.

FigureFigure 4. 4. PlotPlot of ofin-situ in-situ soil soiltemperature temperature difference difference between between soil temperature soil temperature measured measured in between in between the tile line and on the tile line, with time of the day in the x-axis. The highest temperature differ- the tile line and on the tile line, with time of the day in the x-axis. The highest temperature difference ence between the tile and non-tiled area occurred between 11:00 AM and 6:00 PM local time and peakedbetween (ΔT~3 the ° tileC) at and around non-tiled 3:00 PM area local occurred time. between 11:00 AM and 6:00 PM local time and peaked Figure◦ 4. Plot of in-situ soil temperature difference between soil temperature measured in between (the∆T~3 tile lineC) at and around on the 3:00 tile PM line, local with time. time of the day in the x-axis. The highest temperature differ- ence between the tile and non-tiled area occurred between 11:00 AM and 6:00 PM local time and peaked (ΔT~3 °C) at around 3:00 PM local time.

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4.2.4.2. UASUAS OpticalOptical ImageryImagery FigureFigure5 a,c,e5a,c,e are are orthomosiac orthomosiac maps maps created created from from imageries imageries collected collected using using optical optical sensorsensor atat different times times and and co conditions.nditions. Figure Figure 5a,c5a,c were were collected collected under under crop crop cover cover (soy- (soybean)bean) conditions, conditions, while while Figure Figure 5e5 ecollected collected af afterter harvesting harvesting (no cropcrop cover).cover). TheThe map map in in FigureFigure5 a5a was was taken taken at earlyat early growth growth stage stage of the of soybean.the soybean. As seen As seen in both in mapsboth mapsFigure Figure5a,c the5a,c vegetation the vegetation density density is different. is different. In FigureIn Figure5a, 5a, we we can can see see more more distinctive distinctive patterns patterns ofof growthgrowth ofof thethe soybeansoybean plants.plants. WhereasWhereas FigureFigure5 c5c represents represents more more matured matured soybeans soybeans andand hashas aa densedense populationpopulation ofof soybeans,soybeans, itit showsshows uniformuniform growthgrowth onon thethe leftleft partpart ofof the the studystudy sitesite whilewhile distinctdistinct patternspatterns areare visiblevisible onon thethe rightright sideside of of the the study study site. site. TheThe mapmap fromfrom 2424 October October 2019 2019 (Figure (Figure5 e)5e) with with no no crop crop cover cover exhibits exhibits no no patterns patterns unlike unlike the the other other orthomosaicorthomosaic maps.maps.

FigureFigure 5.5.Orthomosaic Orthomosaic maps maps and and Digital Digital Surface Surface Models Models (DSMs) (DSMs) from from UAS UAS data data collected collected at differentat differ- timesent times (a,c) ( ona,c 15) on July 15 2019,July 2019, (b,d) ( onb,d 6) Auguston 6 August 2019, (2019,c,e) on (c, 24e) on October 24 October 2019. The2019. maps The onmaps the on left the side left side are DSMs while on the right side are the corresponding orthomosaic maps. are DSMs while on the right side are the corresponding orthomosaic maps.

4.3.4.3. DigitalDigital SurfaceSurface ModelModel (DSM)(DSM) FigureFigure5 b,d,f5b,d,f display display the the DSMs DSMs of theof the study study site. site. The effectThe effect of the of crop the cover crop (soybeans)cover (soy- isbeans) recognized is recognized with distinct with distinct patterns patterns on the on DSMs the DSMs (Figure (Figure5b,d) as5b,d) compared as compared to the to no the coverno cover crop crop DSM DSM (Figure (Figure5f). The5f). patternThe pattern of the of taller the taller soybean soybean plants plants aligns aligns well withwell with the orientationthe orientation of the of the tile tile lines. lines. For For example, example, the the image image captured captured on on 6 6 August August (Figure (Figure5 b)5b) showsshows moremore distinctdistinct signaturessignatures ofof thethe tiletile lineline thanthan thethe imageimage capturedcaptured onon 1515 JulyJuly 20192019 (Figure(Figure5 a)5a) at at the the early early growth growth stage stage of of the the soybeans. soybeans. The The height height of of the the crop crop cover cover played played aa significantsignificant role.role. TheThe DSMDSM waswas expectedexpected toto showshow thethe depressionsdepressions parallelparallel toto tiletile lineslines caused by the compaction of the soil refill after the construction of the tiles. However, no

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caused by the compaction of the soil refill after the construction of the tiles. However, no significant feature indicating the tile lines were observed, especially from the DSM with Hydrology 2021, 8, x FOR PEER REVIEW 9 of 14 no crop cover except the showing the overall topography of the study site. significant feature indicating the tile lines were observed, especially from the DSM with 4.4.no crop Radiometric cover except Thermal the showing Imagery the overall topography of the study site. The radiometric thermal imageries collected on 22 July, 29 July, 30 July and 24 July 2019, among4.4. Radiometric which theThermal last threeImagery imageries are collected following a rainfall, were processed and analyzed.The radiometric The thermal thermal Maps imageries based on collected flights collectedon 22 July, after 29 July, rainfall 30 July were and able 24 July to show the tile2019, lines among well. which the last three imageries are collected following a rainfall, were pro- cessedFigure and analyzed.6b,c compared The thermal to the Maps thermal based on imageries flights collected collected after duringrainfall were dry able conditions (Figureto show6 thea) . tile Figure lines6 well.d was taken on 6 August 2019, with rainfall in the morning. The mo- saickingFigure for 6b,c this compared data was to incomplete the thermal due imageries to homogenous collected during nature dry of conditions the individual (Fig- thermal ure 6a). Figure 6d was taken on 6 August 2019, with rainfall in the morning. The mosaick- images, specifically in the southern part of the study site. Though Figure6d collected ing for this data was incomplete due to homogenous nature of the individual thermal afterimages, rain specifically event, maturity in the southern of the part soybean of the study plant, site. which Though we Figure believe 6d createdcollected homogenous after thermalrain event, signal, maturity interfered of the soybean with definingplant, which the we tile believe lines. created The dark-colored homogenous thermal patch of a line (indicatedsignal, interfered by white with arrows,defining seethe thetile line figuress. The below) dark-colored that runs patch from of a southline (indicated to north on the lowerby white right arrows, section see andthe figures upper below) left section that runs of the from study south site to (Figurenorth on6c) the aligns lower well right with the patternsection and of the upper line left observed section onof the visible study images site (Figure and DSM 6c) aligns (Figure well5). with Further, the pattern these signaturesof alignthe line well observed with the on existingvisible images surveyed and DSM map (Figure of the tile5). Further, lines. The these thermal signatures map align in Figure 6a (flightwell with on the22 Julyexisting 2019 surveyed), collected map under of the ti dryle lines. condition, The thermal shows map the in signature Figure 6a of (flight the tile lines mainlyon 22 July due 2019), to thecollected crop under cover, dry preferentially condition, shows the soybeanthe signature plants of the grown tile lines following mainly the tile lines.due to Onthe thecrop other cover, hand, preferentially the thermal the soyb imageryean plants from grown 24 October following 2019 the has tile no lines. soybean On cover andthe other displayed hand, the no thermal tile signature. imagery The from summary 24 October statistics 2019 has showedno soybean the cover ground and temperaturedis- played no tile signature. The summary statistics showed the ground temperature ranging ranging from 15 to 64 ◦C, with a mean temperature of about 33 ◦C and the interquartile from 15 to 64 °C, with a mean temperature of about 33 °C and the interquartile range range extending from 5 to 10 ◦C (Table3). Most of the soil temperature values were between extending from◦ 5 to 10 °C (Table 3). Most of the soil temperature values were between 25 25and and 45 °C 45 withC withsome outliers some outliers that mainly that come mainly from come objects from on the objects ground, on such the as ground, metals, such as metals,asphalt roads asphalt and roads equipment. and equipment.

FigureFigure 6. 6. ThermalThermal maps maps derived derived from radiometric from radiometric thermal images thermal collected images at different collected days: at different (a) days: dry condition, on 22 July 2019, (b) After rainfall, on 29 July 2019, (c) After rainfall, on 30 July 2019 (a) dry condition, on 22 July 2019,(b) After rainfall, on 29 July 2019, (c) After rainfall, on 30 July 2019 and (d) rain in the morning, on 6 August 2019. The actual ground temperature varies between 30 ◦ and°C to ( d45) rain°C. The in thewhite morning, arrows are on showing 6 August the 2019. dark The lower-temperature actual ground line temperature running parallel varies to between the 30 C ◦ totile 45 lines.C. The white arrows are showing the dark lower-temperature line running parallel to the tile lines.

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Table 3. Summary statistics of temperature values from the radiometric thermal imageries collected Table 3. Summary statistics of temperature values from the radiometric thermal imageries col- at different days. lected at different days.

Date CollectedDate Collected Statistical ParameterStatistical Parameter 22/7/201922/7/2019 29/7/2019 29/7/2019 30/7/201930/7/2019 Min. TemperatureMin. Temperature 15.01 15.01 20.82 20.82 15.03 15.03 1st Quartile1st Quartile 29.17 29.17 32.41 32.41 32.69 32.69 MedianMedian 31.1831.18 33.933.9 34.5934.59 MeanMean 31.4331.43 34.6634.66 36.0636.06 3rd Quartile3rd Quartile 33.41 33.41 36.18 36.18 38.27 38.27 Max TemperatureMax Temperature 49.95 49.95 59.02 59.02 63.94 63.94 IQR (interquartile IQR (interquartile Range)4.83 4.83 7.27 7.27 9.46 9.46 Range)

4.5. Image Processing 4.5. Image Processing Figure 7 produced through image processing including correction for sharpness, con- trastFigure and brightness,7 produced image through filtering, image supervised processing classification including correctionand image forsmoothing sharpness, and contrast and brightness, image filtering, supervised classification and image smoothing aggregation. For interpretation purposes, the area was divided into four zones, Zone I, and aggregation. For interpretation purposes, the area was divided into four zones, Zone I, Zone II, Zone III and Zone IV. The image processing results have greatly enhanced the Zone II, Zone III and Zone IV. The image processing results have greatly enhanced the visibility of tile drainage, especially in Zone II. Zone I and Zone III have shown less visi- visibility of tile drainage, especially in Zone II. Zone I and Zone III have shown less visibility bility to the tile lines. Further processing of the thermal imagery (e.g., after directional to the tile lines. Further processing of the thermal imagery (e.g., after directional filtering), filtering), the pixel values across the tile lines showed distinct signals right at the tile loca- the pixel values across the tile lines showed distinct signals right at the tile locations. tions.

FigureFigure 7. 7.Enhanced Enhanced thermal thermal imageries imageries showing showing tile tile lines lines in in four four sub-regions sub-regions (zones) (zones) in thein the study study site. site. 5. Discussion 5. DiscussionIn-situ soil temperature data depicted that significant contrast in ground temperature was observedIn-situ soil between temperature temperature data depicted measurements that significant conducted contrast near in tiles ground and temperature in-between tilewas lines. observed This wasbetween caused temperature by thermal measuremen inertia [38], thets conducted degree of slownessnear tiles withand in-between which the temperaturetile lines. This of was a body caused approaches by thermal that inerti of itsa [38], surroundings, the degree causedof slowness by the with variation which the in soiltemperature moisture of conditions a body approaches between these that of locations its surroundings, due to the caused presence by (orthe absence)variation of in tilesoil drainage.moisture conditions Further, this between variation these is governedlocations due by theto the time presence of the day,(or absence) where the of tile tempera- drain- tureage. differenceFurther, this between variation near is tiles governed and between by the tiletime lines of the becomes day, where the highest the temperature (Figure4 ). Thedifference largest between difference near in tiles temperature and between occurred tile lines in the becomes afternoon. the highest This helped (Figure identify 4). The largest difference in temperature occurred in the afternoon. This helped identify the opti- mal time where there is significant thermal contrast between the near tile and non-tiled locations.

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the optimal time where there is significant thermal contrast between the near tile and non-tiled locations. The optical imagery played a role in identifying the locations of the tile lines. For ex- ample, the image collected on 6 August 2019, displayed the soybean plants developed following linear patterns parallel to the tile lines. This could be due to optimal growth conditions (e.g., aeration, moisture flow and temperature) created by the tile drainage that favor the relatively healthier development of the plants (Figure5a,c). As the plant density increases, those linear patterns become less conspicuous from the optical imageries (Figure5c) . Figure5c collected at a later stage of growth compared to Figure5a. Further, some of those linear patterns are depicted by high resolution DSMs (Figure5b,d). Again, the ability of the DSMs showing the patterns is highly dependent on the crop cover. Similar to the optical imageries and DSMs, the thermal imagery was able to depict the drainage patterns. On the thermal imageries (Figure6), the tile lines are characterized by relatively low temperature zones (darker tones) while areas with sparse soybean plants (or bare soil) are characterized by higher temperature (brighter tones). Note that there are clear patterns of the lines depicted by the thermal images (Figure6b,c). The temper- ature anomaly near the tile lines is unexpected, somewhat contrary to our hypothesis, where dryer soils near the tile lines warm up faster. This is due to the soybean plant cover. The plant cover can change soil thermal properties [39] by shading the soil and helps reduce soil moisture evaporation. Considering the brighter regions (higher temperature), these are bare or sparsely plant covered areas soil evaporation is heightened. The thermal imagery was collected on 24 October 2019. (No plant cover) showed no distinct patterns of ground temperature. From this and reasons discussed above, we concluded that soybean plants cover played significant role in showing the drainage patterns. However, this neither proves nor disproves the need for plant cover on the land surface for this approach to apply. We believe the season was a factor in the bare soil map. The 24 October 2019 data was collected in the fall season. The weather condition is relatively colder and the air temperature is almost similar to subsurface water temperature and showed a homogenous thermal response (Figure8). The no plant cover (bare soil) hypothesis has to be tested in other seasons, for example, in late spring before planting season when the temperature is high. Figure8 shows thermal images from the same location that are used to compare the thermal signals before and after harvesting. The comparison indicates that crop covered images taken on 30 July 2019 (Figure8a,b) showed the tile lines compared to images col- lected on 24 October 2019 after harvesting (Figure8c). The homogeneity of the temperature data hindered the mosaicking (image stitching) process; the software could not find a tie point to connect the overlapping thermal images. The larger inter quantile range (~5 to 10 ◦C) of the temperature of the ground from the thermal imageries under plant cover scenarios (Table3) indicates a high variability of temperature across the field that helped for the visualization of the tile lines. The effect of rainfall on thermal mapping is inconclusive at this time. Based on the available data, it is tough to conclude that rainfall helped delineate the tile lines. How- ever, comparing the two thermal imageries collected on the 29 July 2019 and 30 July 2019 (Figure6b,c respectively), 16.8 mm rained in the morning of the 30th which is wetter than the 29th where rainfall occurred two days before (Table2), the thermal image collected on the 30th has better contrast than the imagery collected on the 29th. The rainfall, which af- fects moisture viability in the soil, has an effect on the soil thermal property along with other factors including plant cover, plant height and air temperature. The effect of crop maturity was assessed by comparing images collected at various stages. The development of soybean plant was not uniform across the farm field. where the soybean plants grow taller along the tile lines. More distinct linear pattern was observed at later stage of the soybean plant (Figure9). Comparing optical images from 15 July 2019 (early growth stage, Figure9a) with 6 August 2019 (later stage, Figure9b), both showed preferential growth parallel to the tile lines, however, the distinct tile signature is observed on the image from 6 August 2019. This interpretation is restricted to areas where there are Hydrology 2021, 8, x FOR PEER REVIEW 11 of 14

The optical imagery played a role in identifying the locations of the tile lines. For example, the image collected on 6 August 2019, displayed the soybean plants developed following linear patterns parallel to the tile lines. This could be due to optimal growth conditions (e.g., aeration, moisture flow and temperature) created by the tile drainage that favor the relatively healthier development of the plants (Figure 5a,c). As the plant density increases, those linear patterns become less conspicuous from the optical imageries (Fig- ure 5c). Figure 5c collected at a later stage of growth compared to Figure 5a. Further, some of those linear patterns are depicted by high resolution DSMs (Figure 5b,d). Again, the ability of the DSMs showing the patterns is highly dependent on the crop cover. Similar to the optical imageries and DSMs, the thermal imagery was able to depict the drainage patterns. On the thermal imageries (Figure 6), the tile lines are characterized by relatively low temperature zones (darker tones) while areas with sparse soybean plants (or bare soil) are characterized by higher temperature (brighter tones). Note that there are clear patterns of the lines depicted by the thermal images (Figure 6b,c). The temperature anomaly near the tile lines is unexpected, somewhat contrary to our hypothesis, where dryer soils near the tile lines warm up faster. This is due to the soybean plant cover. The plant cover can change soil thermal properties [39] by shading the soil and helps reduce soil moisture evaporation. Considering the brighter regions (higher temperature), these are bare or sparsely plant covered areas soil evaporation is heightened. The thermal im- agery was collected on 24 October 2019. (No plant cover) showed no distinct patterns of ground temperature. From this and reasons discussed above, we concluded that soybean plants cover played significant role in showing the drainage patterns. However, this nei- ther proves nor disproves the need for plant cover on the land surface for this approach to apply. We believe the season was a factor in the bare soil map. The 24 October 2019 data was collected in the fall season. The weather condition is relatively colder and the air tem- perature is almost similar to subsurface water temperature and showed a homogenous thermal response (Figure 8). The no plant cover (bare soil) hypothesis has to be tested in other seasons, for example, in late spring before planting season when the temperature is high. Figure 8 shows thermal images from the same location that are used to compare the thermal signals before and after harvesting. The comparison indicates that crop covered Hydrology 2021, 8, 2 images taken on 30 July 2019 (Figure 8a,b) showed the tile lines compared to images12 of col- 14 lected on 24 October 2019 after harvesting (Figure 8c). The homogeneity of the tempera- ture data hindered the mosaicking (image stitching) process; the software could not find a tie point to connect the overlapping thermal images. The larger inter quantile range (~5 observableto 10 °C) of patterns the temperature of the tile of lines. the ground For example, from the in thermal the lower imageries left part under of the plant study cover site (Figurescenarios5c), (Table dense 3) soybean indicates plants a high show variabilit less patternsy of temperature of the tile lines.across This the couldfield that be due helped to Hydrology 2021, 8, x FOR PEER REVIEWuniform development of the soybean plants in this part of the study site. 12 of 14 for the visualization of the tile lines. The effect of rainfall on thermal mapping is inconclusive at this time. Based on the available data, it is tough to conclude that rainfall helped delineate the tile lines. However, comparing the two thermal imageries collected on the 29 July 2019 and 30 July 2019 (Fig- ure 6b,c respectively), 16.8 mm rained in the morning of the 30th which is wetter than the 29th where rainfall occurred two days before (Table 2), the thermal image collected on the 30th has better contrast than the imagery collected on the 29th. The rainfall, which affects moisture viability in the soil, has an effect on the soil thermal property along with other factors including plant cover, plant height and air temperature. The effect of crop maturity was assessed by comparing images collected at various stages. The development of soybean plant was not uniform across the farm field. where the soybean plants grow taller along the tile lines. More distinct linear pattern was ob- served at later stage of the soybean plant (Figure 9). Comparing optical images from 15 July 2019 (early growth stage, Figure 9a) with 6 August 2019 (later stage, Figure 9b), both showed preferential growth parallel to the tile lines, however, the distinct tile signature is FigureobservedFigure 8.8. (( aona)) Non-mosaiced Non-mosaicedthe image from thermal thermal 6 August images, images, 2019. date date This collected: collected: interpretation 30 July 30 July 2019 is 2019 restricted(b) mosaiced (b) mosaiced to thermalareas thermal where im- images,thereages, collected are collected observable on on 30 30July Julypatterns 2019 2019 and andof (c the) ( cnot) nottile mosaiced mosaiced lines. thermalFor thermal example, images images infrom fromthe 24 lower 24October October left 2019 part 2019 (note: of (note: the difficultystudydifficulty site mosaickingmosaicking (Figure 5c), the the dense images images soybean collected collected plants on on this this show date date due dueless to to patterns large large temperature temperature of the tile variability). variability).lines. This could be due to uniform development of the soybean plants in this part of the study site.

FigureFigure 9. (a(a) )Showing Showing optical optical image image collected collected on 15 on July 15 July2019 2019(b) optical (b) optical image imagecollected collected on 6 Au- on 6gust August 2019. 2019. White White arrows arrows indicating indicating the location the location of tile of lines. tile lines.

6.6. ConclusionsConclusions ThisThis studystudy explored explored the the feasibility feasibility of of mapping mapping tile tile drainage drainage in agriculturalin agricultural fields fields using us- UASing UAS mounted mounted radiometric radiometric thermal thermal and optical and optical sensors. sensors. Multiple Multiple flights flights were conducted were con- toducted collect to data collect from data a site from with a site an existing with an tile exis drainageting tile locationdrainage map. location The flightmap. The planning flight integratedplanning integrated weather conditions weather conditions and land cover,and land including cover, including bare soil and bare different soil and growingdifferent stagegrowing of soybean stage of plant. soybean plant. WeWe foundfound thatthat variousvarious mapsmaps suchsuch asas thermal,thermal, DSMsDSMs andand orthomosaicsorthomosaics fromfrommultiple multiple sensorssensors providedprovided thethe tiletile lineslines signaturesignature inin mostmostof ofthe thestudy studysite. site. NoNo signaturesignatureof ofthe thetile tile lineslines waswas observedobserved whenwhen thethe landland surfacesurface waswas barebare soil.soil. ThisThis doesdoes notnot meanmean thatthat therethere mustmust bebe aa plantplant covercover onon thethe landland surface,surface, soso thisthis techniquetechniquehas hasto towork. work. FurtherFurther researchresearch isis neededneeded toto verifyverify thisthis hypothesis,hypothesis, specificallyspecifically inin warmwarm latelate springspring beforebefore plantingplanting season.season. TheThe mappingmapping performanceperformance ofof thethe UASUAS methodsmethods variesvaries acrossacross thethe field;field; clearclear patchpatch lineslines representingrepresenting thethe tiletile lineslines werewere observedobserved inin somesome partsparts ofof thethe studystudy sitesite whilewhile lessless conspicuousconspicuous inin otherother parts.parts. Generally,Generally, thethe mappingmapping isis clearclear inin thethe easterneastern partpart comparedcompared toto thethe westernwestern partpart ofof thethe studystudy site.site. ThisThis couldcould bebe duedue toto factorsfactors suchsuch asas topographytopography andand plant development. The western part of the area is relatively elevated and has a higher slope than the eastern part. This permits more runoff than infiltration compared with the lower elevated and flat part of the study site (eastern part). As a result, the soil can quickly dry and become homogeneous in terms of thermal signature. Due to the homogeneity of the thermal imageries, we have had difficulty stitching the images.

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plant development. The western part of the area is relatively elevated and has a higher slope than the eastern part. This permits more runoff than infiltration compared with the lower elevated and flat part of the study site (eastern part). As a result, the soil can quickly dry and become homogeneous in terms of thermal signature. Due to the homogeneity of the thermal imageries, we have had difficulty stitching the images. Further, considering the effect of plant development, it was very helpful in parts of the study site because there are clear patterns of preferential growth of the soybeans plant parallel to the tile lines. In other areas, where soybean plant growth was uniform, fewer tile line signatures were observed. The study demonstrated that UAS based thermal and optical sensors could be combined to produce map of tile lines. Further refinement of the technique would allow a better and cost-effective mapping of tile drainage.

Author Contributions: Conceptualization, T.T. and W.M.S., field measurements, T.T. and W.M.S., data processing, T.T., analysis and interpretation, T.T. and W.M.S., writing-original draft preparation, T.T., writing-review, W.M.S., editing, W.M.S. and data collection, T.T. and W.M.S., supervision, W.M.S.; project administration, W.M.S.; funding acquisition, T.T. and W.M.S. All authors have read and agreed to the published version of the manuscript. Funding: This research received no external funding. Institutional Review Board Statement: Not applicable. Informed Consent Statement: Not applicable. Data Availability Statement: Data is contained within the article. Acknowledgments: The authors would like to acknowledge the City of Bloomington, Illinois, Rick Twait for facilitaiting and providing access to the study site and resources, and the Office of Sustainability, Illinois State University for partially funding the project. Conflicts of Interest: The authors declares no conflict of interest.

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