## 1 Introduction  
Rice farming is one of the most critical activities in the global agriculture sector, producing a staple food for the majority of the world's increasing population, which is expected to exceed 9 billion by 2050 and will require approximately 60% more food. Accurate and up-to-date assessments of the spatial distribution of rice cultivation areas are a vital requirement for all stakeholders including policy makers, rice farmers and consumers. Timely assessment with high precision is also crucial for water resource management, market price control and handling of humanitarian food crises [1; 2; 3]. To maintain stable rice production, Thailand has recently introduced community-based policies to create regional agricultural plantations. The concept of these policies is to encourage aggregation of small rice fields (farmer-based) into one large (community-based) field. There are three major requirements of this policy: expert field managers, an action plan and site-specific technology [4; 5]. In Thailand, where rice farming is commonly practiced in a water-shortage environment, development of management strategies is essential for improving crop production. Under limited water resource conditions, crop models are useful tools for supporting decision-making regarding effective field management. However, such models are complicated and require a large number of parameters. Models also require advanced skills from end-users for model calibration and operation [6; 7]. For example, the DASST [8] and APSIM models require many parameters [6; 9; 10] and the CropSyst, WOFOST and SPAC models are relatively complicated for end-users. These difficulties prevent the development and extensive application of these models [6]. Traditionally, crop monitoring and yield estimation are based on ground surveys and reports but this approach is expensive and time-consuming [11; 12; 13]. As technologies have developed, crop modeling has become increasingly important for providing the necessary information to farmers. Forecasting crop yield is essential for decision-making regarding soil and plant management, crop selection, marketing, storage and transport [14]. Specifically, forecasting the crop yield is vital for assisting in agricultural planning and decision-making policies. For example, small-scale farmers require information on crop yield to allow them to estimate costs and potential profits before investing their time and money in crop cultivation [15]. Farm-scale yield data have become a primary factor in agriculture policy and crop insurance ratings. Nevertheless, researchers often have trouble analyzing these programs because existing farm-scale yield data are sparse, not necessarily broadly representative and might suffer from selection bias [16]. Crop growth models are useful for estimating growth, development and yield but they need high-quality spatially-distributed input data to derive reliable estimates [17]. Comparison of the simulated rice yield of small-holder farmers and their different agricultural behaviors would provide necessary information for implementing Thailand's community-based policy. Agricultural monitoring and management have dramatically benefited from the increased availability of a wide variety of remotely-sensed satellite imagery, ground-sensed data (e.g., weather station networks) and crop models, delivering a wealth of actionable data to stakeholders allowing the streamlining and improvement of agricultural practices [18]. The AquaCrop model is another decision-support tool used for modeling and devising strategies for the efficient management of crop-water productivity [19]. This model is also capable of predicting crop productivity, water requirements and water-use efficiency under water-limiting conditions [20]. Moreover, AquaCrop estimated the biological yield of rice more accurately compared to the Crop Environment Resource Synthesis-Rice and ORYZA2000 models [21].