Application of moderate-resolution satellite imagery and the AquaCrop model to forecast transplanted rice yield at the farm scale :|ba case study of a rice seed production community in Saraburi Province, Thailand | |
| Author | Kulapramote Prathumchai |
| Call Number | AIT Diss. no.RS-18-05 |
| Subject(s) | Remote-sensing images--Thailand--Saraburi Aquaculture--Thailand--Saraburi--Remote sensing Aquaculture--Forecasting Leaf area index--Case studies |
| Note | A dissertation submitted in partial fulfillment of the requirements for the degree of Doctor of Philosophy in Remote Sensing and Geographic Information Systems |
| Publisher | Asian Institute of Technology |
| Abstract | Large-scale rice farming is a recent policy of Thailand government by supporting and integrating small farmers. However, achieving these policies requires agricultural tools that can assist farmers in planning and manage the rice plantation. Crop models with remotely sense technologies can be beneficial for farmers and field managers in this regard. In this study, we used the AquaCrop model with moderate resolution (30 m) satellite image to simulate rice yield for small-scale farmers in a community in Nongkhae district of Saraburi province. The research was conducted field surveys on rice characteristics 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. The types of good-fit-line were tested to formulate the relationship of the satellite images and field observation data. HJ-IA1B satellite images were used to calculate the CC value, which was then used to simulate yield. The validated results were applied to approximately 78% of the total sample area of about 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- 1 AIB along with an accurate weather dataset produced a satisfactory outcome when implemented into the validated rice plots, with RMSE = 0.18 t ha+and R2 = 0.88. Although the experiment by input the focusing climate data provide the lower yield prediction, however, for the overall outcome was acceptable. These findings recommend that combining medium-resolution satellite images and crop models are a valuable tool for support people in agriculture section for planning and management including policymakers. |
| Year | 2018 |
| Type | Dissertation |
| School | School of Engineering and Technology (SET) |
| Department | Department of Information and Communications Technologies (DICT) |
| Academic Program/FoS | Remote Sensing (RS) |
| Chairperson(s) | Nagai, Masahiko |
| Examination Committee(s) | Tripathi, Nitin Kumar;Sasaki, Nophea;Kameoka, Takaharu |
| Scholarship Donor(s) | ONTORET Project - AIT Fellowship |
| Degree | Thesis (Ph.D.) - Asian Institute of Technology, 2018 |