EGU21-10688
https://doi.org/10.5194/egusphere-egu21-10688
EGU General Assembly 2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.

An efficient and accurate method for obtaining regional scale rice growth conditions based on WOFOST model and satellite images

Bingyu Zhao1, Meiling Liu2, Jiianjun Wu1,3,4, Xiangnan Liu2, Mengxue Liu1,5,6, and Ling Wu2
Bingyu Zhao et al.
  • 1Faculty of Geographical Science, Beijign Normal University, Beijing, China (bingyuzhao@mail.bnu.edu.cn)
  • 2School of Information Engineering, China University of Geosciences, Beijing, Beijing, China (liuml@cugb.edu.cn)
  • 3State Key Laboratory of Remote Sensing Science, Beijing Normal University, Beijing, China (jjwu@bnu.edu.cn)
  • 4Beijing Key Laboratory for Remote Sensing of Environment and Digital Cites, Beijing Normal University, Beijing, China (jjwu@bnu.edu.cn)
  • 5State Key Laboratory of Earth Surface Processes and Resource Ecology, Beijing Normal University, Beijing, China (jjwu@bnu.edu.cn)
  • 6College of Resources Science and Technology, Beijing Normal University, Beijing, China (mengxueliu@mail.bnu.edu.cn)

It is very important to obtain regional crop growth conditions efficiently and accurately in the agricultural field. The data assimilation between crop growth model and remote sensing data is a widely used method for obtaining vegetation growth information. This study aims to present a parallel method based on graphic processing unit (GPU) to improve the efficiency of the assimilation between RS data and crop growth model to estimate rice growth parameters. Remote sensing data, Landsat and HJ-1 images were collected and the World Food Studies (WOFOST) crop growth model which has a strong flexibility was employed. To acquire continuous regional crop parameters in temporal-spatial scale, particle swarm optimization (PSO) data assimilation method was used to combine remote sensing images and WOFOST and this process is accompanied by a parallel method based on the Compute Unified Device Architecture (CUDA) platform of NVIDIA GPU. With these methods, we obtained daily rice growth parameters of Zhuzhou City, Hunan, China and compared the efficiency and precision of parallel method and non-parallel method. Results showed that the parallel program has a remarkable speedup (reaching 240 times) compared with the non-parallel program with a similar accuracy. This study indicated that the parallel implementation based on GPU was successful in improving the efficiency of the assimilation between RS data and the WOFOST model and was conducive to obtaining regional crop growth conditions efficiently and accurately.

How to cite: Zhao, B., Liu, M., Wu, J., Liu, X., Liu, M., and Wu, L.: An efficient and accurate method for obtaining regional scale rice growth conditions based on WOFOST model and satellite images, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-10688, https://doi.org/10.5194/egusphere-egu21-10688, 2021.

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