A comparison between support vector machine and water Cloud model for estimating crop leaf area index

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 m2m−2 and mean absolute error (MAE) of 0.51 m2m−2 . The WCM produced an R-value of 0.89, with only slightly higher errors (RMSE of 0.75 m2m−2 and MAE of 0.61 m2m−2 ) 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 m2m−2 and MAE of 0.30 m2m−2 ). 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.Fil: Hosseini, Mehdi. Carleton University. Department of Geography and Environmental Studies; Canadá. University of Maryland. Department of Geographical Sciences; Estados UnidosFil: McNairn, Heather. Carleton University. Department of Geography and Environmental Studies; Canada. Agriculture and Agri-Food Canada. Science and Technology Branch; CanadáFil: Mitchell, Scott. Carleton University. Department of Geography and Environmental Studies; Canadá.Fil: Dingle Robertson, Laura. University of Maryland. Department of Geographical Sciences; Estados UnidosFil: Davidson, Andrew. Carleton University. Department of Geography and Environmental Studies; Canadá. University of Maryland. Department of Geographical Sciences; Estados UnidosFil: Ahmadian, Nima. Julius-Maximilians-Universität; AlemaniaFil: Bhattacharya, Avik. Indian Institute of Technology. Centre of Studies in Resources Engineering. Microwave Remote Sensing Lab; IndiaFil: Borg, Erik. German Aerospace Center. Department of National Ground Segment; AlemaniaFil: Conrad, Christopher. University of Halle-Wittenberg. Institute of Geosciences and Geography; AlemaniaFil: Dabrowska-Zielinska, Katarzyna. Institute of Geodesy and Cartography; PoloniaFil: De Abelleyra, Diego. Instituto Nacional de Tecnología Agropecuaria (INTA). Instituto de Clima y Agua; ArgentinaFil: Gurdak, Radoslaw. Institute of Geodesy and Cartography; PoloniaFil: Kumar, Vineet. Indian Institute of Technology. Centre of Studies in Resources Engineering. Microwave Remote Sensing Lab; India. Delft University of Technology. Department of Water Management; Países BajosFil: Kussul, Nataliia. Space Research Institute of National Academy of Sciences of Ukraine and State Space Agency of Ukraine; UcraniaFil: Mandal, Dipankar. Indian Institute of Technology. Centre of Studies in Resources Engineering. Microwave Remote Sensing Lab; IndiaFil: Rao, Y. S. Indian Institute of Technology. Centre of Studies in Resources Engineering. Microwave Remote Sensing Lab; IndiaFil: Saliendra, Nicanor. USDA-ARS Northern Great Plains Research Laboratory; Estados UnidosFil: Shelestov, Andrii. Space Research Institute of National Academy of Sciences of Ukraine and State Space Agency of Ukraine; UcraniaFil: Spengler, Daniel. Deutsches GeoForschungs Zentrum (GFZ). Division of Remote Sensing; AlemaniaFil: Verón, Santiago Ramón. Instituto Nacional de Tecnología Agropecuaria (INTA). Instituto de Clima y Agua; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Universidad de Buenos Aires. Facultad de Agronomía. Departamento de Métodos Cuantitativos y Sistemas de Información; ArgentinaFil: Homayouni, Saeid. Institut National de la Recherche Scientifique (INRS). Center Eau Terre Environnement; CanadáFil: Becker-Reshef, Inbal. University of Maryland, Department of Geographical Sciences; Estados Unido