2024, Vol. 7, Special Issue 1

author(s)
Evakordor Jyrwa, Dr. AS Nain and Dr. Jonali Goswami

abstract
For food security, accurate mapping of paddy distribution and yield forecasting are vital for rice, a staple food for billions of people worldwide. Remote sensing techniques have proven important for agricultural system management and monitoring during the past thirty years. This study utilized SAR data from Sentinel-1A sensors to map paddy yields in the Ribhoi district of Meghalaya during the 2022 monsoon season. Employing a univariate spectral statistical model, the research predicted/estimated crop yield by leveraging the correlation between backscatter SAR data (VH and VV polarization) and rice yield from different rice fields. VH+VV backscatter exhibited a positive relationship with yield. The robustness of the relationship between rice yield and VH+VV backscatter, the derived equation was used to estimate rice yield for different rice yield as well as to generate the productivity or yield map of Ribhoi district. The yield estimation model was then validated with the observed yield. The observed yield ranges between 800 to 2000 kg/ha, whereas the estimated yield ranges from 655 to 2000 kg/ha. The yield estimation model demonstrated a reasonable level of accuracy, yielding an RMSE of approximately 262 kg/ha, with an R² of 0.64.