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

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 m2 m−2 and mean absolute error (MAE) of 0.51 m2 m−2. The WCM produced an R-value of 0.89, with only slightly higher errors (RMSE of 0.75 m2 m−2 and MAE of 0.61 m2 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 m2 m−2 and MAE of 0.30 m2 m−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.

LAI estimation through remotely sensed NDVI following hail defoliation in maize (Zea mays L.) using Sentinel-2 and UAV imagery

“…Recent studies (e.g., Hosseini et al, 2021 ) obtained even better results than ours when estimating LAI in maize by using water cloud or support vector machine models. However, the authors of those studies did not test their methods under damaged maize conditions, and they also warned that the proposed methodology still needed some implementation exploration, e.g., calibrations were conducted for three LAI intervals, and additional data other than LAI might be needed.…”

Biophysical Impact of Sunflower Crop Rotation on Agricultural Fields

“…In addition to VIs, biophysical variables could be extracted from satellite data, including the leaf area index (LAI) and the fraction of absorbed photosynthetically active radiation (FAPAR). LAI is extracted from satellite data using complex biophysical models and better conveys the biophysical characteristics of the plant [12]. This index describes the amount of biomass on the earth's surface and its condition, and it makes it possible to qualitatively assess the crop yield and deliver land productivity maps [13].…”

Efecto del espaciamiento en la estimación indirecta del índice de área foliar en plantaciones dendroenergéticas de Eucalipto

“…Por su parte, Weiss et al (2004) y Hosseini et al (2021) mencionan que la FDH presenta ventajas como una menor dependencia a la angulación solar y a sombras para la estimación, una menor dependencia a la nubosidad y una facilidad de postedición de fotografías para poder afinar el cálculo del IAF. Sin embargo, es importante destacar que se debe considerar el efecto de variables como el viento, que genera sesgos importantes en la estimación del IAF (Xiao et al, 2006).…”