Comparison of Machine Learning Algorithms and Water Cloud Model for Leaf Area Index Estimation Over Corn Fields

Abstract

The Water Cloud Model (WCM) has been widely used for estimation of Leaf Area Index (LAI) from Synthetic Aperture Radar (SAR). In different studies, it was demonstrated that this model performs well if it is calibrated well. However, calibration of this model requires access to both LAI and soil moisture for the calibration points. An alternative, if the soil moisture data are not available, is Machine Learning (ML) algorithms. However, ML methods are highly dependent on the number of calibration points. In this study, 6 different ML algorithms including Neural Network (NN), Support Vector Machine (SVM), Ensemble of Trees (ET), Regression Tree (RT), Radial Basis Function (RBF) and Gaussian Process Model (GPM) are used and compared with the WCM model for estimation of LAI over corn fields. This comparison was done using different numbers of calibration points. The results demonstrated that when a lower number of calibration points are used, WCM outperformed some of the ML algorithms including NN, SVM and ET algorithms. But with more calibration points, all machine learning algorithms outperformed the WCM. The highest accuracies were from the GPM model with a correlation coefficient (R) of 0.93, Root Mean Square (RMSE) of 0.56 m <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> m <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">-2</sup> and Mean Absolute Error (MAE) of 0.38 m <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> m <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">-2</sup> . Theses results were derived using the data collected during the SMAP Validation Experiment 2012 (SMAPVEX12) that was conducted in Manitoba, Canada. Further testing and comparison of the ML algorithms and WCM model using data from other Joint Experiment for Crop Assessment and Monitoring (JECAM) sites are ongoing.