The present work aimed to identify the impacts of precipitation on the backscattering coefficient from Sentinel-1, in areas with agricultural landscape.Editores: Douglas Francisco Marcolino Gherardi, Ieda Del´Arco Sanches, Luiz Eduardo Oliveira e Cruz de Aragão

ANTUNES, J. F. G.

BUENO, I. T.

CAPUCCI, G.

COUTINHO, A. C.

ESQUERDO, J. C. D. M.

FIGUEIREDO, G. K. D. A.

LAMPARELLI, R. A. C.

MAGALHÃES, P. S. G.

OLIVEIRA JÚNIOR, J. G.

TORO, A. P. S. G. D.

WERNER, J. P. S.

Repository Open Access to Scientific Information from Embrapa

EFFECTS OF THE DIFFERENT PRECIPITATION LEVELS ON SAR DATAA.P.S.G.D.Toroa,1, G. Capuccia,2, J.P.S. Wernera,3„ I.T. Buenob,4, J.C.D.M. Esquerdoa,b,5, J.F.G. Antunesb,6, A.C.Coutinhob,7,R.A.C. Lamparellic,8, P.S.G. Magalhãesc,,J. G. Oliveira Júniora,9, G.K.D.A. Figueiredoa,10a School of Agricultural Engineering - FEAGRI, University of Campinas - UNICAMP, 13083-875 Campinas, SP, Brazilb Embrapa Digital Agriculture, Brazilian Agricultural Research Corporation - EMBRAPA, 13083-886, Campinas, SP, Brazilc Interdisciplinary Center of Energy Planning - NIPE, University of Campinas, SP, Brazil1a265622@dac.unicamp.br,2g167803@dac.unicamp.br,3j164880@dac.unicamp.br,4ibueno@unicamp.br,5julio.esquerdo@embrapa.br,6joao.antunes@embrapa.br,7alex.coutinho@embrapa.br,8lamparel@unicamp.br,9j164880@dac.unicamp.br,10gleyce@unicamp.brABSTRACTAgricultural monitoring systems based on optical satelliteimagery aim to provide up-to-date information regardinglarge scale food production. However, the frequent presenceof clouds during the annual crop cycle in tropical regionshampers image acquisition and leads to the use of imagesfrom active sensors (Radar), which are less affected bymeteorological conditions such as precipitation. The presentwork aimed to identify the impacts of precipitation on thebackscattering coefficient from Sentinel-1, in areas withagricultural landscape. We found no correlation between theprecipitation and backscattering. In terms of classificationaccuracy, looks more advantageous to keep all crop images,given the greater accuracy and the shorter time in theprocessing.Key words – SAR, Precipitation, Classification, NDVI.1. INTRODUCTIONIn the current population growth scenario , an increase of70% in food production will be needed by 2050 comparedto 2005 [1]. To achieve this goal, it is necessary toimprove agricultural monitoring systems for better landuse management and sustainable food production. Remotesensing techniques play a pivotal role in agriculturalmonitoring systems due to their ability to cover largeareas [2]. However, most of these assessments are carriedout with optical sensors, which are limited to the presenceof clouds in tropical regions [3]. FFor this reason, the use ofsynthetic aperture radar (SAR) data has been exploited, sincethey are less affected by cloud interference [4], [5].Despite the growing interest in using SAR data foragriculture, some factors directly affect its properties, suchas the dielectric constant, which is related to humidityand precipitation [6]. Nevertheless, there is no consensusabout how precipitation interferes with the backscatteringcoefficient.Thus, this work aims to evaluate the use of SAR data foragricultural monitoring during periods of precipitation. Fromthe results obtained it will be possible to infer the impactof precipitation on the backscattering coefficient in differentcropsand phenological stages.2. MATERIAL AND METHODS2.1. Study areaThe study area is located in Sinop - Mato Grosso state (Figure1). The region has climate type Am according to the Köppenclassification, with short dry season [7]. The average annualprecipitation ranges from 1800 to 2300 mm (Figure 2). Thelandscape of the region is composed of forest, pasture, andcrops.At the end of the crop year from 2020 to 2021, landuse and land cover information were collected through fieldcampaigns (Figure 1).Figure 1: Study Area in Sinop region, Mato Grosso, Brazil.Figure 2: Monthly accumulated precipitation in the Sinopregion, Mato Grosso, Brazil. Source: NASA POWERhttps://proceedings.science/p/164853?lang=pt-br 30882.2. Precipitation DataThe precipitation data were obtained fromthe MERRA-2Satellite provided by the Prediction Of Worldwide EnergyResources (NASA POWER) platform. This product has aspatial resolution of 200 meters and precipitation informationof millimeters per day. We acquired daily precipitation fromSeptember 2020 to August 2021 (one agricultural year), forthe study area.2.3. SAR dataWe acquired SAR images from Sentinel 1 (A/B) satellites,which use a C band, with a frequency of 5.495 GHz,spatial resolution of 10 meters, and temporal resolutionof 12 days (ESA, 2021). We acquired the acquisitionmode Interferometric Wide (IW), which provides doublepolarization (vertical-vertical - VV and vertical-horizontal -VH). The other features of the images are processing Level1, Single look complex (SLC), and Ground Range Detect(GRD). A total of 23 images were downloaded.In addition, the preprocessing step was performed inthe Sentinel Application Platform (SNAP) software. Thepreprocessing consisted of calibrating the images to obtainthe backscatter coefficients, conversion of backscatter todecibels (Db), noise reduction using the Lee filter, and aterrain correction to correct geometric distortions [8] [9].In addition to the VV and VH backscatter coefficients,coherence products were also acquired throughInterferometry techniques (InSAR). InSAR is a techniquethat explores the phase difference between two SAR images,acquired under the same geometric conditions [10]. Thesimilarity between both images works as a measure of thestability of the targets, and, therefore, has been applied inseveral studies aimed at agricultural monitoring [11]. Thus,from the Alaska Satellite Facility platform, 31 coherenceproducts were obtained for the study area [12].2.4. Optical dataTo compare the phenological stage of the crop, precipitationlevels, and their impacts on the backscatter coefficients,NDVI (Normalized Difference Vegetation Index) [13] imagesof Sentinel-2 satellite were acquired using Google EarthEngine (GEE) [14] platform. In total, 180 images wereacquired for the agricultural year 2020/2021.NDV I =NIR−REDNIR+RED(1)Where NIR is the near infrared band and RED the red band.The VV, VH, coherence, and NDVI data were normalizedon a scale from 0 to 1 as shown in 3, 4 and 5. In addition,to evaluate the practical implications of removing or keepingproducts with precipitation occurrence, using Google EarthEngine [15], a supervised classification was executed. Twodifferent scenarios were tested, the first using all the productsin one season of agricultural year (Sep - Aug), and the secondonly the products without precipitation in the 24 hours beforeimage collection.The classifier Support Vector Machine (SVM) [16] wasused to classify the images into three different classes: nativevegetation, pasture, and double crop. Confusion matriceswere also generated for both scenarios and a table withaccuracy for each scenario and the kappa index.Due to the lack of representative classes by crop (numberof samples greater than 30), it was decided to group the NDVIvalues of all crops to evaluate the impact of precipitation onthe backscatter coefficient and coherence according to NDVIvalues. Then, the normalized NDVI values were subtractedfrom the mean and median values of the VV and VH andcoherence signals (Table 1). Regarding precipitation, valueswere also grouped (considering the day of the satellite passand the previous day) into classes, to compare whether thedifference between NDVI and SAR values were alteredaccording to the variation of precipitation values.3. RESULTSThe behavior of the attributes analyzed mainly about cropdevelopment and precipitation, both backscatter valuesincrease with the development of the crop and then decrease(Figure 3, 4 and 5). The behavior of coherence is the opposite,before the development of the crop it presents values closerto 1, indicating less change in the profile of the soil cover,and from the development of the crop, the value of coherencedecreases, the alteration of the phenological stage of the plantcauses this reduction, until at the end of the crop cycle thecoherence value increases again and reaches values close to 1.Figure 3: NDVI, Coherence, and PrecipitationFigure 4: NDVI, VH Backscatter, and PrecipitationThen, according to the data presented in the Tables 1and 2 with the NDVI and Precipitation intervals, NDVIis below 0.50, the mean and median differences betweenNDVI and backscatter (both VH and VV) are greater, buthttps://proceedings.science/p/164853?lang=pt-br 3089Figure 5: NDVI, VV Backscatter, and Precipitationare reduced as NDVI increases. However, such values donot vary proportionally to precipitation, even when it reachesvalues greater than 20 mm. There is no proportional, oreven expressive, change in any NDVI interval. This resultgoes according to the one proposed by [17] which verified asignificant difference in backscattering in exposed soil areas,but not as significant in vegetated areas.NDVI Average NDVI - VH Median NDVI - VH Average NDVI - VV Median NDVI - VV Precipitation<50 -0.2379 -0.2429 -0.1992 -0.1983 <1<50 -0.4377 -0.4336 -0.4129 -0.4074 1-5<50 -0.4261 -0.4359 -0.4146 -0.4309 5-10<50 -0.5424 -0.5484 -0.5315 -0.5358 10-20<50 -0.3657 -0.3368 -0.3052 -0.2844 20-3050-75 -0.0852 -0.0882 -0.0454 -0.0468 <150-75 -0.1427 -0.1515 -0.1004 -0.0983 1-550-75 -0.1272 -0.1312 -0.0653 -0.0696 5-1050-75 -0.1992 -0.2217 -0.1426 -0.1760 10-2050-75 -0.1578 -0.1593 -0.0885 -0.0962 20-30>75 0.0293 0.0109 0.0694 0.0633 <1>75 0.0589 0.0611 0.0762 0.0739 1-5>75 0.0850 0.0854 0.1395 0.1464 5-10>75 - - - - 10-20>75 0.0050 -0.0097 0.0627 0.0616 20-30Table 1: NDVI difference values and VH, VV bands, groupedby precipitation and NDVI.NDVI Average NDVI - corr Median NDVI - corr Precipitation<50 -0.5391 -0.6384 <1<50 -0.0343 0.0340 1-5<50 -0.3116 -0.3604 5-10<50 -0.0521 -0.0260 10-20<50 -0.0604 -0.0821 20-3050-75 0.2332 0.2599 <150-75 0.3591 0.3750 1-550-75 0.3301 0.3662 5-1050-75 0.3762 0.3898 10-2050-75 0.2198 0.2927 20-30>75 0.5048 0.5216 <1>75 0.5438 0.5646 1-5>75 0.3521 0.3347 5-10>75 - - 10-20>75 0.3766 0.4941 20-30Table 2: NDVI difference values with coherence band, groupedby precipitation and NDVI.Figures 6 and 7 obtained from the classification allowedus to visualize the distribution of training points in the studyarea. In these figures it is possible to see the behavior whenwe removed the images with precipitation in the previous 24hours, that have a more significant presence of the pastureclass.Native Pasture Double CropNative 44 2 0Pasture 2 31 3Double Crop 1 2 87Table 3: Confusion matrix with all crop year dataNative Pasture Double CropNative 38 1 0Pasture 3 15 4Double Crop 0 12 99Table 4: Confusion matrix only with products withoutprecipitation in the last 24 hoursFull No PrecipitationAccuracy 0,93 0,88Kappa 0,88 0,78Table 5: Accuracy and kappa index valuesFigure 6: Classification SVM for the study area and location ofland use and land cover data collected in the Sinop region, MatoGrosso, Brazil, using all available images.Figure 7: Classification SVM for the study area and location ofland use and land cover data collected in the Sinop region, MatoGrosso, Brazil, using only images without previousprecipitation.4. DISCUSSIONSimilarly, about the mean and median differencesbetween NDVI and coherence, there was a progressionof negative differences to positive differences during thecrop development cycle, however, this variation occurredalmost uniformly, regardless of precipitation values. It isnoted that, due to the temporal resolution of Sentinel-1, itwas not expected that the coherence would present clearvariations directly linked to precipitation values, but it couldbe assumed that with high precipitation values, coherencewas unable to detect changes between images, which was notobserved.https://proceedings.science/p/164853?lang=pt-br 3090Therefore, the results demonstrate the non-influence ofprecipitation on backscatter values for areas with crops.Indicating that the precipitation variable may not besignificant as a criterion for removing Sentinel-1 productsfrom a homogeneous dataset, unlike that proposed by [18].With the accuracy and kappa index of the classification(Table ??), in addition to the confusion matrices (Table 3and Table 5), the best case scenario is the one that uses allimages of the period. This remains contrary to that proposedby [18], concerning the use of precipitation as a criterion forthe removal or not of Sentinel 1 products.However, it is worth noting that the spatial resolution ofthe meteorological data, of 200 meters, may have influencedthe results since the precipitation data were the same for theentire study area. This fact may have been crucial so thatprecipitation did not appear to be a criterion for the removalor not of Sentinel-1 products.5. CONCLUSIONSIn this work, we verified the degree of impact of precipitationon the backscatter coefficient of SAR images . In anypolarization and also on coherence products, there was aclear impact on signal values in agricultural crop areas.Moreover, after performing a supervised classificationwith and without the removal of data in the occurrenceof precipitation in the 24 hours before the passage ofSentinel 1, there was no advantage in removing the dateswith precipitation in the 24 hours before the passage ofthe satellite. Also, it was possible to notice that bothaccuracy and kappa index had worse performance when theclassification was made by removing products with possibleinterference of precipitation the previous 24 hours. Thus, atfirst, it is suggested that such products should not be removedfrom the database when precipitation occurs.As further steps we suggest that this analysis be repeatedusing local meteorological stations, aiming to increase theprecision of the results. Also, it suggested that the same studybe repeated using other SAR products, to evaluate possibleimpacts in different wavelengths.6. REFERENCES[1] Nikos Alexandratos and Jelle Bruinsma. World agriculturetowards 2030/2050: the 2012 revision. 2012.[2] Steffen Fritz, Linda See, Juan Carlos Laso Bayas, FrançoisWaldner, Damien Jacques, Inbal Becker-Reshef, AlyssaWhitcraft, Bettina Baruth, Rogerio Bonifacio, Jim Crutchfield,et al. A comparison of global agricultural monitoring systemsand current gaps. Agricultural systems, 168:258–272, 2019.[3] Zhixin Qi, Anthony Gar-On Yeh, Xia Li, and Zheng Lin.A novel algorithm for land use and land cover classificationusing radarsat-2 polarimetric sar data. Remote Sensing ofEnvironment, 118:21–39, 2012.[4] Laura Dingle Robertson, Andrew Davidson, Heather McNairn,Mehdi Hosseini, Scott Mitchell, Diego De Abelleyra,Santiago Verón, and Michael H Cosh. Synthetic apertureradar (sar) image processing for operational space-basedagriculture mapping. 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RemoteSensing, 8(5):406, 2016.https://proceedings.science/p/164853?lang=pt-br 3091Powered by TCPDF (www.tcpdf.org)

Effects of the different precipitation levels on SAR data.

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Effects of the different precipitation levels on SAR data.

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