## 5 Conclusions  
In this study, we presented a new approach for rice yield estimation at the farm level by integration of moderate-resolution optical satellite imagery and a crop model. The AquaCrop model was proposed as one of the best models for rice yield prediction as it requires a lesser number of input parameters in comparison to other crop models. The RS data provided useful crop information for parameter calibration and canopy cover (CC) calculation. One benefit of this approach is the reduction in cost of field experiments/observations because the moderate-resolution imagery provides enough quality for crop information on small rice farms. The results show that the performance of the calibrated AquaCrop model was satisfactory compared to observed crop parameters from the small paddy areas, adequately simulating rice yield in the rainy season for individual farms with different planting dates, planting methods and rice varieties. Although small numbers of cultivar-specific crop parameters were applied for calibration, the simulated yield results were good. The outcome set of the cultivar-specific parameters which were calibrated in this research could be applicable for initial model calibration in other paddy areas. Although the range of the constant value can be scaled down for model calibration before application, it would be necessary to adjust the user-specific parameters based on different regions and conditions. Furthermore, we found that CC calculation using surface reflectance NDVI from HJ-1A/B images, HJ1A/B can deliver useful information for the yield forecasting model and achieved satisfactory results in this case study of rice grown using the transplant method. We conclude that assimilation of moderate-resolution HJ1A/B imagery can be efficiently used with the AquaCrop model to extract crop information that would otherwise be difficult and costly to collect by field observations. This method can, therefore, be suggested to farmers or field managers for use as part of community-based policy and other agriculture planning projects. We further suggest  
Figure 8: The relationship between simulation yield and observed yield; error bar represents the standard deviation of field measurement (Average of each group). that this model can be implemented in other paddy areas. To increase even more accuracy of our study, future research should focus on the application of rice crop cycles to the dry season in continuous crop-cycle years, crop parameters (LAI, CC, agricultural practices, etc.) for more cultivar rice varieties and the number of test plots/area in order to improve the simulated yield accuracy and expand applicable areas. Concerning a different kind of the method of the rice plantation would be challenged in the future study. With regards to the pre-processing of HJ1A/B satellite data, we also suggest that the importance of spatial autocorrelation should be taken into account for improved classification accuracy in future studies. The future study could evaluate the optimize of various vegetation index for LAI/CC extraction that would be developed and integrated with radar imagery. Also, cloud eliminate techniques should improve the results on multi-aspect. In addition, to increase the potential of yield forecasting and implementation, long-term meteorological data should be sufficiently developed in rice growing areas and countries for reliable estimates of rice production. [PERSON] would like to thank Dr. [PERSON] for principal funding and suggestions throughout this research. The National Electronics and Computer Technology (NECTEC), the Rice Seed Production Community and the farmers of Huaykhamin are acknowledged for providing support, information and permission for the field survey. Likewise, we acknowledge the China Centre for Resources Satellite Data and Application (CRESDA) and the CSRS Center of Kasetsart University for free usage of the HJ1A/B images.