## 1 Introduction
Other previous studies have used the AquaCrop model to simulate crop growth and yield in different environments [22], ranging from potato [23], soybean [24], maize [9; 21; 22; 25; 26], wheat [6; 20; 27; 28; 29] to rice [30; 31; 32]. Other studies, however, suggest that AquaCrop be further tested in different locations in order to increase the accuracy of crop production prediction in various conditions [19]. In addition to crop models, remote sensing (RS) is another appropriate tool used to assess crop growth and production at various scales [33; 34]. RS data with coarse and middle resolution are widely used in rice cultivation research [35]. Recently, several RS methods have been developed to map rice areas in different parts of the world, such as optical RS-based mapping, microwave RS-based mapping and the integration of the two methods [7]. Using different techniques, optical images from such satellites as the Advanced Very High-Resolution Radiometer (AVHRR) with 1.1 km spatial resolution, the Moderate Resolution Imaging Spectroradiometer (MODIS) with 250, 500, 1000 m spatial resolution and the Landsat-Vegetation data set (30 m spatial resolution) have been used to estimate cropping area of rice and crop yield [20; 36; 37; 38; 39; 40]. Although these techniques demonstrate advantages for rice monitoring at regional to global scales because of their wide coverage and relatively long data archiving periods, the low resolution of satellite data are not suitable for rice crop mapping in fields that are relatively small in size, irregular in shape and sometimes fragmented by well-builtroads and dense water networks, or mostly mixed with other land cover types. These small-scale rice fields are the critical limitation of RS methods because the area of some rice fields is smaller than the spatial resolution of these satellite platforms. As a result, the mixed-pixel problem is prominent, resulting in creating uncertainty in the discrimination of the spectral signatures of rice from other land cover type [40; 41]. Few studies have attempted to estimate the rice yield using high-resolution RS data (such as Quickbird; 0.65 m, WorldView; 0.31 m and IKONOS; NIR 3.2 m, PAN 0.82 m) in the past two decades [36] but their approach has encountered problems with swath width and high cost [42; 43]. Using Landsat imagery has also encountered a problem with obtaining the cloud-free images due to the temporal resolution, as the revisit frequency is 16 days [7; 34]. As a result, it makes it impossible to obtain phenology information during the relevant crop period. Previous studies suggest the combination use of satellite data with an acceptable spatial resolution and a more frequent revisit cycle [40] but again, it would result in high costs. Despite shortfalls, a recent study found that imagery from the Chinese HJ-1A/B satellite (30 m spatial resolution and 4 days revisiting) has been successfully applied in China to rice-field mapping along with other crops such as rice, maize, sunflower, and wheat [40; 44; 45; 46]. Here in this study, we integrated moderate-resolution optical images from HJ1A/B (30 m) with the AquaCrop model to simulate the yield of transplanted rice on small farms in a Rice Seed Production Community as a pilot case study area to support agricultural planning policies in Thailand. In addition to using Aquacrop model, our study attempted to employ the advantages of HJ1A/B with an adequate spatial resolution for small farm (not less than 900 m\({}^{2}\) or 1 pixel in the image) and high temporal observation.