Occupying over 12% of the global cropland area, rice is the predominant crop in manyregions of the world. Southeast Asia alone accounts for 31% of the world’s rice harvesting area,making this region vital for the food security of the growing global population. Current literaturein the field indicates that there are several factors impacting rice productivity, however there aregaps pertaining to country-specific studies, namely the impact of climate change and challengesregarding effective monitoring. Therefore, this study focuses on four research questions, they are:(1) the climate parameters influencing rice productivity in Thailand; (2) the correlation betweenrice biophysical variables and growth rate as a determinant to overall rice yield; (3) the potentialof satellite sensors for rice yield; and (4) the development of a regression model for rice yieldestimations.For the first question, climate data (measured by two rainfall parameters and six crucialtemperature parameters) and rice yield data, which were collected at the provincial levelbetween the years 1981-2015, are used to determine the impacts of the climate on riceproductivity in Thailand. The result indicates a significance increasing/decreasing trend in themean minimum temperature, mean maximum temperature, and cumulative rainfall. The studyfurther investigates the importance of geographical variation by adopting spatial autocorrelation(Moran’s I index). The result reveals that in 1992 there was a significant shift in cumulative rainfalland the average temperature.Furthermore, field experiments were conducted on rice crops in Thailand during the wetseason of 2017 to explore the correlation between rice biophysical variables and growth rate. Thetemporality of rice biophysical variables is demonstrated by separating rice variety and irrigationsystem. The leaf area index (LAI) peaks in the flowering stage and LAI development can be slightlydifferent depending on the rice variety and irrigation system. The correlation between yield andother rice biophysical elements on a specific variety (RD41) is highly correlated to rice age, stemdensity, height, chlorophyll contents, and wet and dry biomass. The correlation between yield,and wet and dry biomass during the harvesting stage was the strongest.To develop a rice yield prediction model, data collected from the time series of twodifferent satellite sensors: Sentinel-2 (optical) and Sentinel-1 (Synthetic Aperture Radar, or SAR)were utilised. The vegetation indices (NDVI and EVI) and backscatter coefficient (sigma nought;σ0) usefully tracked rice phenology. The study furthers develop a linear regression model for riceyield estimations based on different sensors and yields from in-situ measurements via CropCutting Experiments (CCEs). The accuracy of the results is compared to official rice yields.The correlation between vegetation indices, backscatter coefficient, and rice yield variablesis investigated in different growth stages and irrigation systems. Based on the simple regressionmodel for the optical sensors, the developed yield estimation model is correlated with NDVI in thepanicle stage (r = 0.37 and SEE = 0.70 tonnes/ha). While SAR (σ0) is significant in the ascendingVV/VH ratio during the harvesting stage (r = 0.54 and SEE = 0.68 tonnes/ha). The findings suggestremotely sensed data can be a good predictor for rice yield during the booting and mature stages