Modelling potential vegetation index for paddy field mapping in Malaysia using satellite imagery

— Agriculture is one of the most important sectors to humanity that provides many materials such as food, fibers, fuel and raw materials. Nowadays, this sector needs to consider many aspects like, the expanding of human population that will never end and must be fulfilled in the context of environmental sustainability and climate change, while maintaining the viability of agricultural activities to ensure both livelihoods and subsistence. Therefore, this study proposes the application of remote sensing which can advance agricultural practices by delivering valuable information for managing paddy rice plantations. It focuses on essential pre-processing steps, including atmospheric correction and noise reduction. It concentrated on the fundamental pre-processing stages, such as atmospheric correction and noise reduction. To minimize the quantity of the image data and enable it to concentrate more on the area of interest, the image also must go through a subset process. Processing would become smoother after the subset or smaller image than it would have been before the image was filtered to provide data with less noise. Subsequently, the image underwent the crucial band math step for this investigation. The proposed study showed that the reference image has a kappa coefficient of 0.8764 and an overall accuracy of 90.6250% when compared to the NDVI image. The reference image's kappa coefficient is 0.9502 and its overall accuracy is 96.2500% when compared to the kNDVI. The overall accuracy and kappa coefficient for the band ratio combo image are 95.2350% and 0.9112, respectively. With the use of an aspect ratio-based vegetation index and a quick study of the paddy plantation, it is feasible to derive valuable information from the optimal ratio vegetation index for paddy plants. Furthermore, this research identified the best possible ratio for the vegetation index to represent the paddy plants compared to other plants.