Rice crop growth monitoring with sentinel 1 SAR data using machine learning models in google earth engine cloud

Abstract

The rainfed rice crop monitoring and yield prediction have been Herculean task with optical remote sensing systems operation under cloud cover. The free-of-cost sentinel 1 based SAR data along with machine learning models in GEE cloud was used for rainfed rice crop monitoring for 214 farm plots on a micro-scale in Hooghly, West Bengal, India. The individual plot rice parcel showed a low median backscattering signature for the SAR data during the land preparation/crop transplanting stages with VH and VV at −17.63 dB and −9.63 dB, respectively; whereas, higher median backscattering was experienced at the peak vegetation stage of VH and VV Polarization with −15.20 dB and −6.34 dB, respectively. The random forest model was found best suited with R2 of 0.87 for total crop biomass estimation. The backscatter values have a sound correlation with Heading NDVI, which validated the suitability of SAR images for crop monitoring under rainfed conditions. Further, crop yield prediction using SAR data and total biomass data through machine learning models showed positive correlation for Random forest, Extreme gradient boosting, and Decision tree models with an Area under receiver operating characteristics curve (AUROC) test accuracy of 0.99. These low-cost, high temporal SAR data based models can be used for near real-time crop monitoring even under overcast conditions in near future.