Advances in remote sensing technologies have enabled effective drought monitoring globally, even in data-limited areas. However, the negative impact of drought on crop yields still necessitates stakeholders to make informed decisions according to its severity. This research proposes an algorithm to combine a drought monitoring model, based on rainfall, land surface temperature (LST), and normalized difference vegetation index/leaf area index (NDVI/LAI) satellite products, with a crop simulation model to assess drought impact on rice yields in Thailand. Typical crop simulation models can provide yield information, but the requirement for a complicated set of inputs prohibits their potential due to insufficient data. This work utilizes a rice crop simulation model called the Simulation Model for Use with Remote Sensing (SIMRIW–RS), whose inputs can mostly be satisfied by such satellite products. Based on experimental data collected during the 2018/19 crop seasons, this approach can successfully provide a drought monitoring function as well as effectively estimate the rice yield with mean absolute percentage error (MAPE) around 5%. In addition, we show that SIMRIW–RS can reasonably predict the rice yield when historical weather data is available. In effect, this research contributes a methodology to assess the drought impact on rice yields on a farm to regional scale, relevant to crop insurance and adaptation schemes to mitigate climate change.

Advanced technologies in the agricultural sector have been adopted as global trends in response to the impact of climate change on food sustainability. An ability to monitor and predict crop yields is imperative for effective agronomic decision making and better crop management. This work proposes RiceSAP, a satellite-based AquaCrop processing system for rice whose climatic input is derived from TERRA/MODIS-LST and FY-2/IR-rainfall products to provide crop monitoring and yield prediction services at regional-scale with no need for weather station. The yield prediction accuracy is significantly improved by our proposed recalibration algorithm on the simulated canopy cover (CC) using Sentinel-2 NDVI product. A developed mobile app provides an intuitive interface for collecting farm-scale inputs and providing timely feedbacks to farmers to make informed decisions. We show that RiceSAP could predict yields 2 months before harvest with a mean absolute percentage error (MAPE) of 14.8%, in the experimental field. Further experiments on randomly selected 20 plots with various soil series showed comparable results with an average MAPE of 16.7%. Thus, this work is potentially applicable countrywide; and can be beneficial to all stakeholders in the entire rice supply chain for effective adaptation to climate change.

Climate change presents urgent and complex challenges to agricultural sustainability and food security, particularly in regions reliant on resource-intensive staple crops. Smart agriculture—through the integration of crop modeling, satellite remote sensing, and artificial intelligence (AI)—offers data-driven strategies to enhance productivity, optimize input use, and mitigate greenhouse gas (GHG) emissions. This study introduces Farmdee-Mesook, a mobile-first smart agriculture platform designed specifically for Thai rice farmers. The platform leverages AquaCrop simulation, open-access satellite data, and localized agronomic models to deliver real-time, field-specific recommendations. Usability-focused design and no-cost access facilitate its widespread adoption, particularly among smallholders. Empirical results show that platform users achieved yield increases of up to 37%, reduced agrochemical costs by 59%, and improved water productivity by 44% under alternate wetting and drying (AWD) irrigation schemes. These outcomes underscore the platform’s role as a scalable, cost-effective solution for operationalizing climate-smart agriculture. Farmdee-Mesook demonstrates that digital technologies, when contextually tailored and institutionally supported, can serve as critical enablers of climate adaptation and sustainable agricultural transformation.