Rubber is a perennial plant grown to produce natural rubber. It is a raw material for industrial and non-industrial products important to the world economy. The sustainability of natural rubber production is, therefore, critical for smallholder livelihoods and economic development. To maintain price stability, it is important to estimate the yields in advance. Remote sensing technology can effectively provide large-scale spatial data; however, productivity estimates need to be processed from high spatial resolution data generated from satellites with high accuracy and reliability, especially for smallholder livelihood areas where smaller plots contrast with large farms. This study used reflectance data from Sentinel-2 satellite imagery acquired for the 12 months between December 2020 and November 2021. The imagery included 213 plots where data on rubber production in smallholder agriculture were collected. Six vegetation indices (Vis), namely Green Soil Adjusted Vegetation Index (GSAVI), Modified Simple Ratio (MSR), Normalized Burn Ratio (NBR), Normalized Difference Vegetation Index (NDVI), Normalized Green (NR), and Ratio Vegetation Index (RVI) were used to estimate the rubber yield. The study found that the red edge spectral band (band 5) provided the best prediction with R² = 0.79 and RMSE = 29.63 kg/ha, outperforming all other spectral bands and VIs. The MSR index provided the highest coefficient of determination, with R² = 0.62 and RMSE = 39.25 kg/ha. When the red edge reflectance was combined with the best VI, MSR, the prediction model only slightly improved, with a coefficient determination of (R²) of 0.80 and an RMSE of 29.42 kg/ha. The results demonstrated that the Sentinel-2 data are suitable for rubber yield prediction for smallholder farmers. The findings of this study can be used as a guideline to apply in other countries or areas. Future studies will require the use of reflectance and vegetation indices derived from satellite data in combination with meteorological data, as well as the application of complex models, such as machine learning and deep learning.

Drought situations remain a serious problem in the northeast region of Thailand, especially in Buriram province. The official guidelines are not accepted by farmers within the area and are difficult to apply. Therefore, this research aims to find acceptable guidelines for water resource management in order to solve the problem of droughts in Chum Saeng Sub-District, Satuek District, Buriram Province, in which there are three water reservoirs (Nhong Chonlaprathan, Nhong Taban and Nhong Jabok-Takian). In this study, we applied the principles of water balance analysis to evaluate both storage capacity and water demand and we also used community participation to confirm this information. The physical data of the reservoirs considered here comprise water demands, hydrology and monthly rainfall. The evaluation of available water was divided into three parts: the highest rainfall, the lowest rainfall and the average rainfall. It was found that the available water of the three reservoirs would exceed the highest storage capacities of the reservoirs for the highest rainfall and average rainfall cases, whereas when water demands were incorporated, it was found that the available water would exceed the highest storage capacities of the reservoirs only in the highest rainfall case. In addition, the output of the analyses was approved and accepted by the participating communities. It was found that three guidelines were acceptable for including increasing storage capacities: (1) increasing the reserved volumes, (2) expanding the watershed area for collecting rainfall and (3) encouraging the expansion of potential agricultural areas.