Spatial variability in paddy intensity and pattern of groundwater trend in part of Lower Gangetic Plain

Evaluating the intensity of paddy cultivation and tracking the evolving patterns of groundwater levels is essential for a comprehensive assessment of the impact of paddy farming on water resources across diverse geographic regions. Due to the notable water-intensive nature of paddy cultivation, it is imperative to take into account the condition of groundwater resources. In regions such as the cloud-prone Lower Gangetic Plain, characterized by small landholdings, traditional coarse-resolution optical remote sensing methods are inadequate in delivering sufficient information. Therefore, a comprehensive framework that integrates advanced machine learning techniques with multi-temporal SAR datasets becomes essential for assessing the spatio-temporal variations in paddy intensity. In this investigation, Sentinel SAR images of 2022 were employed to evaluate the effectiveness of a machine learning model for mapping paddy rice at a 10-m scale. The analysis of multi-temporal Sentinel-1A data revealed a trajectory of rice growth phases that aligns with the crop calendar of the study area. Notably, the characteristics of σ VH (backscattering coefficient) demonstrated a strong correlation with the growth stages of rice throughout the Aus, Aman, and Boro seasons. This study highlighted the superiority of the machine learning-based Random Forest (RF) model, which achieved an overall accuracy of ≥ 90% in identifying areas under paddy cultivation and discerning their seasonal distribution in cloud-prone regions. Regarding groundwater variability, an assessment was conducted using Central Groundwater Board (CGWB) data spanning from 2002 to 2022. The results indicated that 70% of the paddy-cultivated area in the study region exhibited a decreasing trend in groundwater levels. This integrated assessment provides a holistic understanding of the interplay between paddy cultivation intensity and its impact on groundwater resources, serving as a valuable resource for sustainable land and water management.