A COMPARISON of MACHINE-LEARNING REGRESSION ALGORITHMS for the ESTIMATION of LAI USING LANDSAT - 8 SATELLITE DATA

Abstract

The leaf area index (LAI) is one of key variable of crops which plays important role in agriculture, ecology and climate change for global circulation models to compute energy and water fluxes. In the recent research era, the machine-learning algorithms have provided accurate computational approaches for the estimation of crops biophysical parameters using remotely sensed data. The three machine-learning algorithms, random forest regression (RFR), support vector regression (SVR) and artificial neural network regression (ANNR) were used to estimate the LAI for crops in the present study. The three different dates of Landsat-8 satellite images were used during January 2017 - March 2017 at different crops growth conditions in Varanasi district, India. The sampling regions were fully covered by major Rabi season crops like wheat, barley and mustard etc. In total pooled data, 60% samples were taken for the training of the algorithms and rest 40% samples were taken as testing and validation of the machinelearning regressions algorithms. The highest sensitivity of normalized difference vegetation index (NDVI) with LAI was found using RFR algorithms (R2 Combining double low line 0.884, RMSE Combining double low line 0.404) as compared to SVR (R2 Combining double low line 0.847, RMSE Combining double low line 0.478) and ANNR (R2 Combining double low line 0.829, RMSE Combining double low line 0.404). Therefore, RFR algorithms can be used for accurate estimation of LAI for crops using satellite data.