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

Mehdi Hosseini,Scott Mitchell,Y S Rao,Nima Ahmadian,Santiago R Verón,Erik Borg,Christopher Conrad,Andrii Shelestov,Diego De Abelleyra,Daniel Spengler,Vineet Kumar,Andrew Davidson,Laura Dingle Robertson,Avik Bhattacharya,Nataliia Kussul,Dipankar Mandal,Saeid Homayouni,Heather Mcnairn,Nicanor Z Saliendra ,Radosław Gurdak ,K Dąbrowska-Zielińska ,Inbal Becker‐Reshef

doi:10.3390/rs13071348

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.

Highlights

Leaf area index (LAI) is one-half of the total green leaf area per unit ground surface area
These calibrated models are applied to validation sites, and error statistics including correlation coefficient (R), root mean square error (RMSE) and mean absolute error (MAE) are generated to assess the performance of both models
The objective of this study was to evaluate the performance of the semi-empirical water cloud model (WCM) and the machine learning support vector machine (SVM)

Summary

Introduction

Leaf area index (LAI) is one-half of the total green leaf area per unit ground surface area. It is a measure of crop canopy development and is a good indicator of productivity as crops develop through their growing seasons. It is important to monitor how well leaf development is progressing in the first half of the crop growth period, a period of rapid accumulation of leaves. Dense measures of LAI are helpful in determining current conditions and predicted yields. Synthetic aperture radars (SARs) could make a contribution. Implementation of these sensors for LAI monitoring requires extensive testing of models over diverse cropping systems and multiple cropping years

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Conclusion