Thailand has recently introduced agricultural policies to promote large-scale rice farming through supporting and integrating small-scale farmers. However, achieving these policies requires agricultural tools that can assist farmers in rice farming planning and management. Crop models, along with remote sensing technologies, can be useful for farmers and field managers in this regard. In this study, we used the AquaCrop model along with moderate-resolution satellite images (30 m) to simulate the rice yield for small-scale farmers. We conducted field surveys on rice characteristics in order to calibrate the crop model parameters. Data on rice crop, leaf area index (LAI), canopy cover (CC) and agricultural practices were used to calibrate the model. In addition, the optimal rice constant value for conversion of CC was investigated. HJ-1A/B satellite images were used to calculate the CC value, which was then used to simulate yield. The validated results were applied to 126 sample pixels within transplanted rice fields, which were extracted from satellite imagery of activated rice plots using equivalent transplanting methods to the study area. The rice yield simulated using the AquaCrop model and assimilated with the results of HJ-1A/B, produced a satisfactory outcome when implemented into the validated rice plots, with RMSE = 0.18 t ha −1 and R 2 = 0.88. These results suggest that integration of moderate-resolution satellite imagery and this crop model are useful tools for assisting rice farmers and field managers in their planning and management.

Data sharing is essential for government agencies during disaster management as it requires high collaborative efforts among various organizations. Recently, social media have been increasingly used during the disasters for disseminating and receiving information to and from the public. By using social media for communications, the government can receive real-time data from the public and from organizations. The challenge lies in how to combine social media with government data, which is gathered from multiple sources, in multiple formats using multiple terminologies. This paper focuses on how to manage, integrate, and verify data acquired from multiple sources. The proposed model was designed by using frame-based data collection and ontology-based data integration, combined with the effective use of dynamic data from social media, with the aim of improving the disaster assistance.