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

Autor: Nataliia Kussul, Heather McNairn, Santiago R. Verón, Scott Mitchell, Dipankar Mandal, Avik Bhattacharya, Vineet Kumar, Katarzyna Dabrowska-Zielinska, Andrii Shelestov, Nima Ahmadian, Radoslaw Gurdak, Laura Dingle Robertson, Daniel Spengler, Erik Borg, Y. S. Rao, Inbal Becker-Reshef, Christopher Conrad, Diego de Abelleyra, Andrew A. Davidson, Saeid Homayouni, Nicanor Z. Saliendra, Mehdi Hosseini
Přispěvatelé: Laboratoire des sciences de l'ingénieur, de l'informatique et de l'imagerie (ICube), École Nationale du Génie de l'Eau et de l'Environnement de Strasbourg (ENGEES)-Université de Strasbourg (UNISTRA)-Institut National des Sciences Appliquées - Strasbourg (INSA Strasbourg), Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Institut National de Recherche en Informatique et en Automatique (Inria)-Les Hôpitaux Universitaires de Strasbourg (HUS)-Centre National de la Recherche Scientifique (CNRS)-Matériaux et Nanosciences Grand-Est (MNGE), Université de Strasbourg (UNISTRA)-Université de Haute-Alsace (UHA) Mulhouse - Colmar (Université de Haute-Alsace (UHA))-Institut National de la Santé et de la Recherche Médicale (INSERM)-Institut de Chimie du CNRS (INC)-Centre National de la Recherche Scientifique (CNRS)-Université de Strasbourg (UNISTRA)-Université de Haute-Alsace (UHA) Mulhouse - Colmar (Université de Haute-Alsace (UHA))-Institut National de la Santé et de la Recherche Médicale (INSERM)-Institut de Chimie du CNRS (INC)-Centre National de la Recherche Scientifique (CNRS)-Réseau nanophotonique et optique, Université de Strasbourg (UNISTRA)-Université de Haute-Alsace (UHA) Mulhouse - Colmar (Université de Haute-Alsace (UHA))-Centre National de la Recherche Scientifique (CNRS)-Université de Strasbourg (UNISTRA)-Centre National de la Recherche Scientifique (CNRS), Gil, Emilio, Francisco Javier, García-Haro
Jazyk: angličtina
Rok vydání: 2021
Předmět:
Zdroj: Remote Sensing, 13(7)
Remote Sensing 13 (7) : 1348. (2021)
INTA Digital (INTA)
Instituto Nacional de Tecnología Agropecuaria
instacron:INTA
Remote Sensing
Remote Sensing, 2021, 13 (7), ⟨10.3390/rs13071348⟩
Remote Sensing, Vol 13, Iss 1348, p 1348 (2021)
Remote Sensing; Volume 13; Issue 7; Pages: 1348
ISSN: 2072-4292
DOI: 10.3390/rs13071348⟩
Popis: 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 Unidos Fil: McNairn, Heather. Carleton University. Department of Geography and Environmental Studies; Canada. Agriculture and Agri-Food Canada. Science and Technology Branch; Canadá Fil: Mitchell, Scott. Carleton University. Department of Geography and Environmental Studies; Canadá. Fil: Dingle Robertson, Laura. University of Maryland. Department of Geographical Sciences; Estados Unidos Fil: Davidson, Andrew. Carleton University. Department of Geography and Environmental Studies; Canadá. University of Maryland. Department of Geographical Sciences; Estados Unidos Fil: Ahmadian, Nima. Julius-Maximilians-Universität; Alemania Fil: Bhattacharya, Avik. Indian Institute of Technology. Centre of Studies in Resources Engineering. Microwave Remote Sensing Lab; India Fil: Borg, Erik. German Aerospace Center. Department of National Ground Segment; Alemania Fil: Conrad, Christopher. University of Halle-Wittenberg. Institute of Geosciences and Geography; Alemania Fil: Dabrowska-Zielinska, Katarzyna. Institute of Geodesy and Cartography; Polonia Fil: De Abelleyra, Diego. Instituto Nacional de Tecnología Agropecuaria (INTA). Instituto de Clima y Agua; Argentina Fil: Gurdak, Radoslaw. Institute of Geodesy and Cartography; Polonia Fil: 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 Bajos Fil: Kussul, Nataliia. Space Research Institute of National Academy of Sciences of Ukraine and State Space Agency of Ukraine; Ucrania Fil: Mandal, Dipankar. Indian Institute of Technology. Centre of Studies in Resources Engineering. Microwave Remote Sensing Lab; India Fil: Rao, Y. S. Indian Institute of Technology. Centre of Studies in Resources Engineering. Microwave Remote Sensing Lab; India Fil: Saliendra, Nicanor. USDA-ARS Northern Great Plains Research Laboratory; Estados Unidos Fil: Shelestov, Andrii. Space Research Institute of National Academy of Sciences of Ukraine and State Space Agency of Ukraine; Ucrania Fil: Spengler, Daniel. Deutsches GeoForschungs Zentrum (GFZ). Division of Remote Sensing; Alemania Fil: 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; Argentina Fil: Homayouni, Saeid. Institut National de la Recherche Scientifique (INRS). Center Eau Terre Environnement; Canadá Fil: Becker-Reshef, Inbal. University of Maryland, Department of Geographical Sciences; Estados Unidos
Databáze: OpenAIRE
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