Analysis of Paddy Productivity Using NDVI and K-means Clustering in Cibarusah Jaya, Bekasi Regency

Abstract

Information about rice productivity is one of the references for government to maintain food availability. With remote sensing technology, rice productivity can be known faster. This research was conducted using UAV (Unmanned Aerial Vehicle) and Sentinel-2 Satellite. Sentinel-2 NDVI which has a low resolution with high resolution UAV images, both variables have similarity values and regression reaches 0.8. NDVI are grouped into 8 classes using k-means clustering based on the similarity of the waveforms of each data retrieval point. Based on characteristic of k-means classes, field which has earlier planting times and the location closer to the water source, allowing a higher paddy productivity. Further analysis was also carried out to get the best period to estimate paddy productivity using Sentinel-2 imagery. Sentinel-2 was chosen because it has a distance between data as far as 5 days, allowing it to be more accurate. The best time is obtained at 63 DAP (Days After Planting), which is when NDVI reaches its maximum state. The estimation model of rice productivity based on UAV has a high coefficient of determination compared to Sentinel-2 so that the relationship between maximum NDVI UAV and rice productivity is better than Sentinel-2.