Mapping active paddy rice area over monsoon asia using time-series Sentinel – 2 images in Google earth engine; a case study over lower gangetic plain
Arabinda Maiti, Prasenjit Acharya, Srikanta Sannigrahi, Qi Zhang, Somnath Bar, Suman Chakraborti, Bijoy Krishna Gayen, Gunadhar Barik, Surajit Ghosh, Milap Punia
Abstract
We proposed a modification of the existing approach for mapping active paddy rice fields in monsoon-dominated areas. In the existing PPPM approach, LSWI higher than EVI at the transplantation stage enables the identification of rice fields. However, it fails to recognize the fields submerged later due to monsoon floods. In the proposed approach (IPPPM), the submerged fields, at the maximum greenness time, were excluded for better estimation. Sentinel–2A/2B time-series images were used for the year 2018 to map paddy rice over the Lower Gangetic Plain (LGP) using Google earth engine (GEE). The overall accuracy (OA) obtained from IPPPM was 85%. Further comparison with the statistical data reveals the IPPPM underestimates (slope (β1) = 0.77) the total reported paddy rice area, though R2 remains close to 0.9. The findings provide a basis for near real-time mapping of active paddy rice areas for addressing the issues of production and food security.