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A Comparison between Support Vector Machine and Water Cloud Model for Estimating Crop Leaf Area Index

Department of Geography and Environmental Studies, Carleton University, Ottawa, ON K1S 5B6, Canada
Department of Geographical Sciences, University of Maryland, College Park, MD 20742, USA
Science and Technology Branch, Agriculture and Agri-Food Canada, Ottawa, ON K1A 0C6, Canada
Julius-Maximilians-Universität, 97070 Würzburg, Germany
Microwave Remote Sensing Lab, Centre of Studies in Resources Engineering, Indian Institute of Technology Bombay, Mumbai 400 076, India
Department of National Ground Segment, German Aerospace Center (DLR), 17235 Neustrelitz, Germany
Department of Geoecology, Institute of Geosciences and Geography, University of Halle-Wittenberg, Von Seckendorff-Platz 4, 06120 Halle (Saale), Germany
Institute of Geodesy and Cartography, 02-679 Warsaw, Poland
Instituto de Clima y Agua, Instituto Nacional de Tecnología Agropecuaria (INTA), Buenos Aires 1439, Argentina
Department of Water Management, Delft University of Technology, 2628 CN Delft, The Netherlands
Space Research Institute of National Academy of Sciences of Ukraine and State Space Agency of Ukraine, 03680 Kyiv, Ukraine
USDA-ARS Northern Great Plains Research Laboratory, North Dakota, ND 58554, USA
Division of Remote Sensing, Deutsches GeoForschungsZentrum (GFZ), 14473 Helmholtz-Zentrum Potsdam, Germany
Institut National de la Recherche Scientifique (INRS), Center Eau Terre Environnement, Quebec, QC G1K9A9, Canada
Author to whom correspondence should be addressed.
Academic Editors: Emilio Gil and Francisco Javier García-Haro
Remote Sens. 2021, 13(7), 1348;
Received: 1 February 2021 / Revised: 30 March 2021 / Accepted: 30 March 2021 / Published: 1 April 2021
The water cloud model (WCM) can be inverted to estimate leaf area index (LAI) using the intensity of backscatter from synthetic aperture radar (SAR) sensors. Published studies have demonstrated that the WCM can accurately estimate LAI if the model is effectively calibrated. However, calibration of this model requires access to field measures of LAI as well as soil moisture. In contrast, machine learning (ML) algorithms can be trained to estimate LAI from satellite data, even if field moisture measures are not available. In this study, a support vector machine (SVM) was trained to estimate the LAI for corn, soybeans, rice, and wheat crops. These results were compared to LAI estimates from the WCM. To complete this comparison, in situ and satellite data were collected from seven Joint Experiment for Crop Assessment and Monitoring (JECAM) sites located in Argentina, Canada, Germany, India, Poland, Ukraine and the United States of America (U.S.A.). The models used C-Band backscatter intensity for two polarizations (like-polarization (VV) and cross-polarization (VH)) acquired by the RADARSAT-2 and Sentinel-1 SAR satellites. Both the WCM and SVM models performed well in estimating the LAI of corn. For the SVM, the correlation (R) between estimated LAI for corn and LAI measured in situ was reported as 0.93, with a root mean square error (RMSE) of 0.64 m2m2 and mean absolute error (MAE) of 0.51 m2m2. The WCM produced an R-value of 0.89, with only slightly higher errors (RMSE of 0.75 m2m2 and MAE of 0.61 m2m2) when estimating corn LAI. For rice, only the SVM model was tested, given the lack of soil moisture measures for this crop. In this case, both high correlations and low errors were observed in estimating the LAI of rice using SVM (R of 0.96, RMSE of 0.41 m2m2 and MAE of 0.30 m2m2). However, the results demonstrated that when the calibration points were limited (in this case for soybeans), the WCM outperformed the SVM model. This study demonstrates the importance of testing different modeling approaches over diverse agro-ecosystems to increase confidence in model performance. View Full-Text
Keywords: RADARSAT-2; Sentinel-1; leaf area index; water cloud model; machine learning RADARSAT-2; Sentinel-1; leaf area index; water cloud model; machine learning
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MDPI and ACS Style

Hosseini, M.; McNairn, H.; Mitchell, S.; Robertson, L.D.; Davidson, A.; Ahmadian, N.; Bhattacharya, A.; Borg, E.; Conrad, C.; Dabrowska-Zielinska, K.; de Abelleyra, D.; Gurdak, R.; Kumar, V.; Kussul, N.; Mandal, D.; Rao, Y.S.; Saliendra, N.; Shelestov, A.; Spengler, D.; Verón, S.R.; Homayouni, S.; Becker-Reshef, I. A Comparison between Support Vector Machine and Water Cloud Model for Estimating Crop Leaf Area Index. Remote Sens. 2021, 13, 1348.

AMA Style

Hosseini M, McNairn H, Mitchell S, Robertson LD, Davidson A, Ahmadian N, Bhattacharya A, Borg E, Conrad C, Dabrowska-Zielinska K, de Abelleyra D, Gurdak R, Kumar V, Kussul N, Mandal D, Rao YS, Saliendra N, Shelestov A, Spengler D, Verón SR, Homayouni S, Becker-Reshef I. A Comparison between Support Vector Machine and Water Cloud Model for Estimating Crop Leaf Area Index. Remote Sensing. 2021; 13(7):1348.

Chicago/Turabian Style

Hosseini, Mehdi, Heather McNairn, Scott Mitchell, Laura D. Robertson, Andrew Davidson, Nima Ahmadian, Avik Bhattacharya, Erik Borg, Christopher Conrad, Katarzyna Dabrowska-Zielinska, Diego de Abelleyra, Radoslaw Gurdak, Vineet Kumar, Nataliia Kussul, Dipankar Mandal, Y. S. Rao, Nicanor Saliendra, Andrii Shelestov, Daniel Spengler, Santiago R. Verón, Saeid Homayouni, and Inbal Becker-Reshef. 2021. "A Comparison between Support Vector Machine and Water Cloud Model for Estimating Crop Leaf Area Index" Remote Sensing 13, no. 7: 1348.

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