Assessment of surface water dynamics through satellite mapping with Google Earth Engine and Sentinel-2 data in Manipur, India

Abstract

Accurate surface water mapping is crucial for watershed planning and safeguarding regional water resources. The study aimed to extract extent of seasonal surface water, focusing on selected districts of Manipur, northeast India from 2016 to 2021, utilized Sentinel-2 data in the Google Earth Engine (GEE) platform. Employing multiple indices and the Random Forest classifier, the methodology addressed challenges such as cloud and shadow interference, particularly in high-altitude regions. Results revealed Bishnupur with the maximum surface water extent (124 km2) and Tengnoupal with the minimum (0.24 km2) during the study period. A notable 6% gain in Bishnupur surface water was observed from pre- to post-monsoon in 2016, while changes in other districts were negligible. Conversely, a maximum loss of 7% occurred in Bishnupur during pre-monsoon from 2016 to 2021. Overall, post-monsoon expansion exceeded that of pre-monsoon in all districts. Discrepancies were evident in both seasons in 2021. The applied techniques proved reliable and innovative, ensuring accurate surface water extent mapping. The GEE platform facilitated enhanced access to satellite data, significantly expediting processing through machine learning algorithms. The findings of this study have the potential to inform surface water planning and management, offering valuable insights for efficient resource utilization.