Estimation of winter wheat crop growth parameters using time series Sentinel-1A SAR data

Abstract

In the present study, Sentinel-1A Synthetic Aperture Radar analysis of time series data at C-band was carried out to estimate the winter wheat crop growth parameters. Five different date images were acquired during January 2015–April 2015 at different growth stages from tillering to ripening in Varanasi district, India. The winter wheat crop parameters, i.e. leaf area index, vegetation water content (VWC), fresh biomass (FB), dry biomass (DB) and plant height (PH) were estimated using random forest regression (RFR), support vector regression (SVR), artificial neural network regression (ANNR) and linear regression (LR) algorithms. The Ground Range Detected products of Interferometric Wide (IW) Swath were used at VV polarization. The three different subplots of 1 m² area were taken for the measurement of crop parameters at every growth stage. In total, 73 samples were taken as the training data-sets and 39 samples were taken as testing data-sets. The highest sensitivity (adj. R²= 0.95579) of backscattering with VWC was found using RFR algorithm, whereas the lowest sensitivity (adj. R²= 0.66201) was found for the PH using LR algorithm. Overall results indicate more accurate estimation of winter wheat parameters by the RFR algorithm followed by SVR, ANNR and LR algorithms.

Keywords:

• Winter wheat
• Sentinel-1A
• RFR
• SVR
• ANNR and LR

Acknowledgement

Authors wish to thank and gratefully acknowledge the ESA for providing freely Sentinel-1A SAR data and SNAP software with S-1TBX for the study.