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

Authors: HOSSEINI, MEHDI - Carleton University - Canada; MCNAIRN, HEATHER - Aafc Lethrdge Research Center; MITCHELL, SCOTT - Carleton University - Canada; DINGLE-ROBERTSON, LAURA - Aafc Lethrdge Research Center; DAVIDSON, ANDREW - Aafc Lethrdge Research Center; AHMADIAN, NIMA - University Of Wurzburg; BHATTACHARYA, AVIK - Indian Institute Of Technology; BORG, ERIC - German Aerospace Center; CONRAD, CHRISTOPHER - University Of Halle; Saliendra, Nicanor

Submitted to: Remote Sensing Reviews
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 3/30/2021
Publication Date: 4/1/2021
Citation: Hosseini, M., McNairn, H., Mitchell, S., Dingle-Robertson, L., Davidson, A., Ahmadian, N., Bhattacharya, A., Borg, E., Conrad, C., Saliendra, N.Z. 2021. A comparison between support vector machine and the water cloud model for estimating crop leaf area index. Remote Sensing Reviews. 13:1348. https://doi.org/10.3390/rs13071348.
DOI: https://doi.org/10.3390/rs13071348

Interpretive Summary: Leaf Area Index (LAI) is a measure of crop canopy development and is a good indicator of productivity as crops develop through the growing season. It is particularly important to monitor how well leaf development is progressing in the first half of the growing season, a period of rapid leaf accumulation. Temporally frequent measurements of LAI are helpful in determining current conditions and predicted yields using Synthetic Aperture Radars (SARs). However, implementation of these sensors for LAI monitoring requires extensive testing of models over diverse cropping systems and multiple cropping years. This study evaluated the performance of the semi-empirical Water Cloud Model (WCM) and the machine learning Support Vector Machine (SVM) approaches to estimate LAI from C-Band SAR sensors, particularly VV and VH polarizations (radar backscatter from vegetation). To test the robustness of these methods, participants of the Joint Experiment for Crop Assessment and Monitoring (JECAM) project measured soil moisture and LAI in seven countries (Argentina, Canada, Germany, India, Poland, Ukraine and the USA-North Dakota) for four globally important crops (corn, soybeans, wheat and rice). More than 1100 LAI samples were used in this study and were complemented with over 100 satellite images from 2012-2019. Although the results demonstrated promising potential of multi-polarization C-band SARs for crop LAI estimations, the models could be further tested over other regions, years and crops. Through such iterative and collaborative research, robust methods could be produced and applied by agencies to estimate LAI at local and global scales.

Technical 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 well-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 (VV and 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 m^2 m^(-2) and mean absolute error (MAE) of 0.51 m^2 m^(-2) . The WCM produced an R-value of 0.89, with only slightly higher errors (RMSE of 0.75 m^2 m^(-2) and MAE of 0.61 m^2 m^(-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 m^2 m^(-2) and MAE of 0.30 m^2 m^(-2)). However, the results demonstrated that when the calibration points were limited (in this case for soybeans), the WCM outperforms the SVM model. This study demonstrates the importance of testing different modeling approaches over diverse agro-ecosystems to increase confidence in model performance.