This research focuses on the ability of remote sensing vegetation index acquired from the Landsat-8 operational land imager (OLI)/thematic infrared sensor (TIRS) and Satellite Pour l’Observation de la Terre (SPOT-6 and 7) HRV, HRVIR, HRG system as effective indicator to measure rice growing condition and predicting yield rate. The normalized difference vegetation indices (NDVI), leaf area indices (LAI) and perpendicular vegetation indices (PVI) that embrace supervised and unsupervised application is used to examine dynamics in rice-growing regions, harvest prediction modelling, estimating production yields, determining differences in plant biomass and the assessment of ecosystem services in rice-growing areas. The result shows multivariate regression model correlated nicely with two vegetation indices to produce significant value or classification accuracy of R2 = 0.3813 (38%) for NDVI, while PVI with significant value of R2 = 0.3102 (31%). In a normal growing period, the amount of vegetation biomass is at NDVI 0.4550/PVI 0,2227 in the month of December, January, February and March, while, active vegetation at NDVI 0.8225/PVI 0.2882 in the month of May, June, July, August, September and October. Due to the extensively wide range at a rate of NDVI 0.7115-0.8225/PVI 0.2882-0.2960, the areas are favourable for the cultivation of rice. Furthermore, the research is interested in getting the overall value of a vegetation condition for the study areas, therefore, it is clear that a simple mean of the existing data will suffice, since the existing data has high estimate of vegetation across the landscape.
Key Words: Empirical, yields determinant, Remote sensing, Vegetation condition indices, Vegetation health indices