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

Hosseini, Mehdi and McNairn, Heather and Mitchell, Scott and Dingle Robertson, Laura and Davidson, Andrew and Ahmadian, Nima and Bhattacharya, Avik and Borg, Erik and Conrad, Christopher and Dabrowska-Zielinska, Katarzyna and de Abelleyra, Diego and Gurdak, Radoslaw and Kumar, Vineet and Kussul, Nataliia and Mandal, Dipankar and Rao, Y. S. and Saliendra, Nicanor and Shelestov, Andrii and Spengler, Daniel and Verón, Santiago R. and Homayouni, Saeid and Becker-Reshef, Inbal (2021) A Comparison between Support Vector Machine and Water Cloud Model for Estimating Crop Leaf Area Index. Remote Sensing, 13 (1348), pp. 1-20. Multidisciplinary Digital Publishing Institute (MDPI). doi: 10.3390/rs13071348. ISSN 2072-4292.

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Official URL: https://www.mdpi.com/2072-4292/13/7/1348

Abstract

Abstract 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

Item URL in elib:https://elib.dlr.de/141898/
Document Type:Article
Title:A Comparison between Support Vector Machine and Water Cloud Model for Estimating Crop Leaf Area Index
Authors:
AuthorsInstitution or Email of AuthorsAuthor's ORCID iD
Hosseini, MehdiDepartment of Geography and Environmental Studies, Carleton University, Ottawa, ON K1S 5B6, Canadahttps://orcid.org/0000-0003-3242-613X
McNairn, HeatherDepartment of Geography and Environmental Studies, Carleton University, Ottawa, ON K1S 5B6, Canadahttps://orcid.org/0000-0003-1006-0018
Mitchell, ScottDepartment of Geography and Environmental Studies, Carleton University, Ottawa, ON K1S 5B6, Canadahttps://orcid.org/0000-0003-4657-0706
Dingle Robertson, LauraScience and Technology Branch, Agriculture and Agri-Food Canada, Ottawa, ON K1A 0C6, Canada;https://orcid.org/0000-0002-9372-1952
Davidson, AndrewDepartment of Geography and Environmental Studies, Carleton University, Ottawa, ON K1S 5B6, Canadahttps://orcid.org/0000-0003-3784-682X
Ahmadian, NimaJulius-Maximilians-Universität, 97070 Würzburg, GermanyUNSPECIFIED
Bhattacharya, AvikMicrowave Remote Sensing Lab, Centre of Studies in Resources Engineering, Indian Institute of Technology Bombay, Mumbai 400 076, India;https://orcid.org/0000-0001-6720-6108
Borg, ErikDepartment of National Ground Segment, German Aerospace Center (DLR), 17235 Neustrelitz, Germany;https://orcid.org/0000-0001-8288-8426
Conrad, ChristopherDepartment of Geoecology, Institute of Geosciences and Geography, University of Halle-Wittenberg, Von Seckendorff-Platz 4, 06120 Halle (Saale), Germany;https://orcid.org/0000-0002-0807-7059
Dabrowska-Zielinska, KatarzynaInstitute of Geodesy and Cartography, 02-679 Warsaw, Poland;UNSPECIFIED
de Abelleyra, DiegoInstituto de Clima y Agua, Instituto Nacional de Tecnología Agropecuaria (INTA), Buenos Aires 1439, ArgentinaUNSPECIFIED
Gurdak, RadoslawInstitute of Geodesy and Cartography, 02-679 Warsaw, Poland;https://orcid.org/0000-0001-8991-7306
Kumar, VineetMicrowave Remote Sensing Lab, Centre of Studies in Resources Engineering, Indian Institute of Technology Bombay, Mumbai 400 076, IndiaUNSPECIFIED
Kussul, NataliiaSpace Research Institute of National Academy of Sciences of Ukraine and State Space Agency of Ukraine, 03680 Kyiv, Ukraine;UNSPECIFIED
Mandal, DipankarMicrowave Remote Sensing Lab, Centre of Studies in Resources Engineering, Indian Institute of Technology Bombay, Mumbai 400 076, Indiahttps://orcid.org/0000-0001-8407-7125
Rao, Y. S.Microwave Remote Sensing Lab, Centre of Studies in Resources Engineering, Indian Institute of Technology Bombay, Mumbai 400 076, IndiaUNSPECIFIED
Saliendra, NicanorUSDA-ARS Northern Great Plains Research Laboratory, North Dakota, ND 58554, USA;UNSPECIFIED
Shelestov, AndriiSpace Research Institute NASU-NSAU, KyivUNSPECIFIED
Spengler, DanielSection 1.4 Remote Sensing, Helmholtz Centre Potsdam - GFZ German Research Centre for Geosciences, Potsdamhttps://orcid.org/0000-0003-2939-8764
Verón, Santiago R.Instituto de Clima y Agua, Instituto Nacional de Tecnología Agropecuaria (INTA), Buenos Aires 1439, ArgentinaUNSPECIFIED
Homayouni, SaeidInstitut National de la Recherche Scientifique (INRS), Center Eau Terre Environnement, Quebec, QC G1K9A9, Canadahttps://orcid.org/0000-0002-0214-5356
Becker-Reshef, InbalDepartment of Geographical Sciences, University of Maryland, College Park, MD 20742, USAUNSPECIFIED
Date:1 April 2021
Journal or Publication Title:Remote Sensing
Refereed publication:Yes
Open Access:Yes
Gold Open Access:Yes
In SCOPUS:Yes
In ISI Web of Science:Yes
Volume:13
DOI :10.3390/rs13071348
Page Range:pp. 1-20
Editors:
EditorsEmailEditor's ORCID iD
Gil, EmilioUniversitat Politècnica de Catalunya | UPC · Department of Agri-Food Engineering and BiotechnologyUNSPECIFIED
Francisco Javier, García-HaroUniversity of Valencia: Burjassot, Valencia, EShttps://orcid.org/0000-0001-5888-0061
Publisher:Multidisciplinary Digital Publishing Institute (MDPI)
Series Name:Remote Sensing
ISSN:2072-4292
Status:Published
Keywords:RADARSAT-2; Sentinel-1; leaf area index; water cloud model; machine learning
HGF - Research field:Aeronautics, Space and Transport
HGF - Program:Space
HGF - Program Themes:Earth Observation
DLR - Research area:Raumfahrt
DLR - Program:R EO - Earth Observation
DLR - Research theme (Project):R - Remote Sensing and Geo Research
Location: Neustrelitz
Institutes and Institutions:German Remote Sensing Data Center > National Ground Segment
Deposited By: Borg, Dr.rer.nat. Erik
Deposited On:29 Apr 2021 12:18
Last Modified:29 Apr 2021 12:18

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