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

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dc.contributor.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
Verón, Santiago R.
Homayouni, Saeid
Becker-Reshef, Inbal
dc.date.accepted
2021-03-30
dc.date.accessioned
2025-02-25T22:18:39Z
dc.date.available
2025-02-25T22:18:39Z
dc.date.issued
2021-04-01
dc.date.submitted
2021-02-01
dc.description.abstract - en
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.
dc.identifier.citation
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., … Becker-Reshef, I. (2021). A comparison between support vector machine and water cloud model for estimating crop leaf area index. Remote Sensing, 13(7), 1348. https://doi.org/10.3390/rs13071348
dc.identifier.doi
https://doi.org/10.3390/rs13071348
dc.identifier.issn
2072-4292
dc.identifier.uri
https://open-science.canada.ca/handle/123456789/3468
dc.language.iso
en
dc.publisher - en
MDPI AG
dc.rights - en
Creative Commons Attribution 4.0 International (CC BY 4.0)
dc.rights - fr
Creative Commons Attribution 4.0 International (CC BY 4.0)
dc.rights.uri - en
https://creativecommons.org/licenses/by/4.0/
dc.rights.uri - fr
https://creativecommons.org/licenses/by/4.0/deed.fr
dc.subject - en
Agriculture
Crops
Agricultural technology
Radar
dc.subject - fr
Agriculture
Cultures
Technologie agricole
Radar
dc.subject.en - en
Agriculture
Crops
Agricultural technology
Radar
dc.subject.fr - fr
Agriculture
Cultures
Technologie agricole
Radar
dc.title - en
A comparison between support vector machine and water cloud model for estimating crop leaf area index
dc.type - en
Article
dc.type - fr
Article
local.acceptedmanuscript.articlenum
1348
local.article.journalissue
7
local.article.journaltitle - en
Remote Sensing
local.article.journalvolume
13
local.peerreview - en
Yes
local.peerreview - fr
Oui
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