Visible Band Index Optimation of Unmanned Aerial Vehicle for Estimating NDVI by Sentinel Imagery on Rice Vegetation

Abstract

Serntinel 2A provide Normalized Difference Vegetation Index to be used as an estimate of soil fertility, plant varieties and productivity. The weakness of satellite data is that the data obtained is often inaccurate due to cloud cover, especially in tropical countries with high rainfall such as Indonesia. The use of unmanned aerial vehicle as an alternative data have limitation as it captured RGB imagery. The research was conducted from July to September 2020 at Pasir Kaliki Village, District of Rawamerta, Karawang Regency, West Java province. The study has discovered that NDVI showed higher number in result of vegetation index compared to NGRDI with correlation coefficient is 0.944625. The regression model resulted as y=4.7722x+0.3845 and MAPE value expresses as 26.74%, where the regression model with Pearson’s correlation coefficient value is 0.877885. A qualitative assessment using statistical data and a spatial assessment using sampled data from the rice vegetation map reveal a high mapping accuracy with the corresponding R2 being as high as 0.7429; however, the mapped rice vegetation accuracy might influenced by other physical factors such as water reflectant, sunlight and the RGB camera limitation itself. Nonetheless, the highest values of NGRDI only reach 0.2 while NDVI can attain at 0.9 at the peak of vegetative phase of rice growth stage. This means that Green Band have limitation in detecting vegetation index. In relation to the different approaches performed, it is noted that the average trend line on both NDVI and NGRDI shown the similarity tendency in all growth stage.