Rice is the primary source of nutrition food of more than half of the world’s population, and it is hugely important in the global economic growth, food security, water use, and climate change. The need for satellite systems to monitor rice crops and assist in rice crop management is gaining in popularity. The European Space Agency’s (ESA) launched Sentinel-2 A + B twin platform’s which enhanced the temporal, spatial, and spectral resolution, opening the way for their widely use in crop monitoring. Aside from the technical features of the Sentinel-2 A and B constellation, the easily accessible type of information they generate as well as the appropriate support software have been significant improvements for rice crop monitoring. In this study, the spectral reflectance has been analysed to find how far their potential in determining rice growth phases. The highest spectrum in reflectance was observed in the near infrared (NIR) region (842 nm). Because of the structure of mesophyll cells tissues and the inner backscatter of air spaces, moisture content, and air–water abstraction layers within the leaves, the reflectance in the NIR region seems to be much larger than in the visible band. The multi-temporal vegetation index namely Normalized Difference Vegetation Index (NDVI), Soil Adjusted Vegetation Index (SAVI), and Normalized Difference Moisture Index (NDMI) have derived from ten Sentinel-2 images cover the entire rice season. These indices have been tested to determine the rice growth phases over the rice season. The spatial distribution of each tested indices is displayed in the map output. The maps are then analysed and compared to determine the potential of each index in determining rice growth phases. It was discovered in this study that there was a quadratic correlation between all of the tested indices and rice age. The Normalized Difference Vegetation Index (NDVI) is the most accurate vegetation index for estimating rice growth phases, followed by SAVI and NDMI.
Organic farming is a technique, which involves cultivation of plants in natural ways. In Malaysia, because the organic food industry is still small-scale, over 60% of organic foods are still imported and it is not possible for the availability and output of organic goods to meet the local demand and need. This study was intended to utilize the GIS and AHP technique to identify suitable areas for organic farming in Sabak Bernam, Malaysia. The study was conducted based on the objectives where criteria for organic farming were identified through background research. Pair-wise comparison (PWCM) method using questionnaires answered by experts were used to determine the weights for each of the parameters used. For this study, seven (7) criteria were considered. The criteria were then weighted according to importance and those weighted criteria were combined to produce a suitability map. A site suitability model was built using the Modelbuilder tool in Arcmap. The model used the AHP and Weighted Overlay basis which provides promising result for the analysis of finding suitable sites for organic farming. Results obtained showed that the majority of land within the Sabak Bernam district is suitable to carry out organic cultivation where the land is far from road networks, contains high organic matter content, gentle slopes with flat aspects low elevation and less than 10 meters from drainages. While land deemed not suitable involves land in dense urban area. This simultaneously means that the land is too close to road networks where underground contamination is a major possibility.
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