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.
Main Authors: | , , , , , , , , , , , , , , , , , , , , , |
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2021-04-01
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Subjects: | Leaf Area Index, Remote Sensing, Índice de Superficie Foliar, Teledetección, RADARSAT-2, Sentinel-1, Modelo de Nube de Agua, Aprendizaje Automático, Water Cloud Model, Machine Learning, |
Online Access: | http://hdl.handle.net/20.500.12123/13284 https://www.mdpi.com/2072-4292/13/7/1348 https://doi.org/10.3390/rs13071348 |
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Leaf Area Index Remote Sensing Índice de Superficie Foliar Teledetección RADARSAT-2 Sentinel-1 Modelo de Nube de Agua Aprendizaje Automático Water Cloud Model Machine Learning Leaf Area Index Remote Sensing Índice de Superficie Foliar Teledetección RADARSAT-2 Sentinel-1 Modelo de Nube de Agua Aprendizaje Automático Water Cloud Model Machine Learning |
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Leaf Area Index Remote Sensing Índice de Superficie Foliar Teledetección RADARSAT-2 Sentinel-1 Modelo de Nube de Agua Aprendizaje Automático Water Cloud Model Machine Learning Leaf Area Index Remote Sensing Índice de Superficie Foliar Teledetección RADARSAT-2 Sentinel-1 Modelo de Nube de Agua Aprendizaje Automático Water Cloud Model Machine Learning Hosseini, Mehdi McNairn, Heather Mitchell, Scott Robertson, Laura Dingle Davidson, Andrew Ahmadian, Nima Bhattacharya, Avik Borg, Erik Conrad, Christopher Dabrowska Zielinska, Katarzyna De Abelleyra, Diego Gurdak, Radoslaw Kumar, Vineet Kussul, Nataliia Mandal, Dipankar Rao, Y.S. Saliendra, Nicanor Shelestov, Andrii Spengler, Daniel Veron, Santiago Ramón Homayouni, Saeid Becker Reshef, Inbal A comparison between support vector machine and water Cloud model for estimating crop leaf area index |
description |
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. |
format |
info:ar-repo/semantics/artículo |
topic_facet |
Leaf Area Index Remote Sensing Índice de Superficie Foliar Teledetección RADARSAT-2 Sentinel-1 Modelo de Nube de Agua Aprendizaje Automático Water Cloud Model Machine Learning |
author |
Hosseini, Mehdi McNairn, Heather Mitchell, Scott Robertson, Laura Dingle Davidson, Andrew Ahmadian, Nima Bhattacharya, Avik Borg, Erik Conrad, Christopher Dabrowska Zielinska, Katarzyna De Abelleyra, Diego Gurdak, Radoslaw Kumar, Vineet Kussul, Nataliia Mandal, Dipankar Rao, Y.S. Saliendra, Nicanor Shelestov, Andrii Spengler, Daniel Veron, Santiago Ramón Homayouni, Saeid Becker Reshef, Inbal |
author_facet |
Hosseini, Mehdi McNairn, Heather Mitchell, Scott Robertson, Laura Dingle Davidson, Andrew Ahmadian, Nima Bhattacharya, Avik Borg, Erik Conrad, Christopher Dabrowska Zielinska, Katarzyna De Abelleyra, Diego Gurdak, Radoslaw Kumar, Vineet Kussul, Nataliia Mandal, Dipankar Rao, Y.S. Saliendra, Nicanor Shelestov, Andrii Spengler, Daniel Veron, Santiago Ramón Homayouni, Saeid Becker Reshef, Inbal |
author_sort |
Hosseini, Mehdi |
title |
A comparison between support vector machine and water Cloud model for estimating crop leaf area index |
title_short |
A comparison between support vector machine and water Cloud model for estimating crop leaf area index |
title_full |
A comparison between support vector machine and water Cloud model for estimating crop leaf area index |
title_fullStr |
A comparison between support vector machine and water Cloud model for estimating crop leaf area index |
title_full_unstemmed |
A comparison between support vector machine and water Cloud model for estimating crop leaf area index |
title_sort |
comparison between support vector machine and water cloud model for estimating crop leaf area index |
publisher |
MDPI |
publishDate |
2021-04-01 |
url |
http://hdl.handle.net/20.500.12123/13284 https://www.mdpi.com/2072-4292/13/7/1348 https://doi.org/10.3390/rs13071348 |
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oai:localhost:20.500.12123-132842022-11-02T11:12:11Z A comparison between support vector machine and water Cloud model for estimating crop leaf area index Hosseini, Mehdi McNairn, Heather Mitchell, Scott Robertson, Laura Dingle Davidson, Andrew Ahmadian, Nima Bhattacharya, Avik Borg, Erik Conrad, Christopher Dabrowska Zielinska, Katarzyna De Abelleyra, Diego Gurdak, Radoslaw Kumar, Vineet Kussul, Nataliia Mandal, Dipankar Rao, Y.S. Saliendra, Nicanor Shelestov, Andrii Spengler, Daniel Veron, Santiago Ramón Homayouni, Saeid Becker Reshef, Inbal Leaf Area Index Remote Sensing Índice de Superficie Foliar Teledetección RADARSAT-2 Sentinel-1 Modelo de Nube de Agua Aprendizaje Automático Water Cloud Model Machine Learning 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 2022-11-02T10:53:32Z 2022-11-02T10:53:32Z 2021-04-01 info:ar-repo/semantics/artículo info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion http://hdl.handle.net/20.500.12123/13284 https://www.mdpi.com/2072-4292/13/7/1348 2072-4292 https://doi.org/10.3390/rs13071348 eng info:eu-repo/semantics/openAccess application/pdf MDPI Remote Sensing 13 (7) : 1348. (2021) |