Assessment of Satellite Rainfall Data Accuracy to In-Situ Rain Gauge Measurements in Riau Islands Province

This study presents a comparative evaluation of two prominent satellite-based precipitation products-Global Satellite Mapping of Precipitation (GSMaP) and Climate Hazards Group InfraRed Precipitation with Station data (CHIRPS)-by validating them using in-situ rain gauge observations from 23 locations across Batam and Bintan Islands, Indonesia, for the period of 2019 to 2023. Statistical metrics including Mean Absolute Error (MAE), Root Mean Square Error (RMSE), Mean Square Error (MSE), Nash-Sutcliffe Efficiency (NSE), Pearson Correlation, and Relative Bias were employed to assess the accuracy of satellite estimates. Spatial analysis was conducted using Inverse Distance Weighting (IDW) interpolation in ArcGIS, while statistical evaluations were performed using Python. Results indicate that CHIRPS generally provides more accurate estimates with lower MAE values (71.43-115.90 mm) compared to GSMaP (79.06-99.12 mm) at most locations. However, GSMaP demonstrates superior correlation with ground observations, particularly at Sri Bintan (r=0.861) and Sungai Enam (r=0.865). Spatial analysis reveals significant regional variations, with CHIRPS showing severe overestimation in eastern Bintan (up to +47.88% bias) while GSMaP exhibits consistent underestimation patterns (-28.31% bias). Both products were processed using Google Earth Engine (GEE) platform for efficient data extraction and analysis. These findings provide crucial insights for selecting appropriate satellite precipitation products for hydrological applications in Indonesian archipelagic regions.