Estimating rice crop intensity (RCI) using spatial analysis with multi-source satellite sensor data

Monitoring crops, particularly rice paddy crops, is a vital responsibility for evaluating the performance of the agriculture sector to improve the nation's food security and counteract the adverse effects of climate change. Satellite data monitoring is becoming more prevalent compared to labor-intensive field surveys today. However, the application of multitemporal analysis on several satellite sensors, such as Landsat-8, Landsat-9, and Sentinel-2, has seen very little research on it, especially on the rice intensity index (RCI) estimation. Moreover, the data availability using multi-source satellites was significantly valuable for creating a time series of NDVI values in 16-day periods up to $72.6\pm 30.9{\%}$. Based on the integration of three years' worth of multitemporal NDVI calculation from Landsat-8, Landsat-9, and Sentinel-2, this study has an acceptable accuracy level of 71.9% overall in Pandeglang Regency, Banten Province, Indonesia. Based on spatial analysis, the primary RCI index in Pandelang Recency is twice a year for 49,955 ha or 97% of the total rice area. The other RCI is once a year (740 ha) and three times a year (808 ha). This study suggested a novel and straightforward way of identifying and estimating the rice intensity using spatial analysis to identify which region has a minimum performance once in a short period.