Parallel Computing for Obtaining Regional Scale Rice Growth Conditions Based on WOFOST and Satellite Images

IEEE Access

Bingyu Zhao¹  Jianjun Wu¹  Mengxue Liu¹  Ling Wu²  Meiling Liu²  Xiangnan Liu² 

[1] Faculty of Geographical Science, Beijing Normal University, 100875, China;School of Information Engineering, China University of Geosciences, Beijing, China;

关键词: WOFOST model;    data assimilation;    remote sensing;    parallel algorithm;   

DOI  :  10.1109/ACCESS.2020.3043003

来源: DOAJ

【 摘 要 】

It is very important to obtain continuous regional crop parameters efficiently in the agricultural field. However, remote sensing data can provide spatial-continuous / temporal-disperse crop information while crop growth model can provide temporal-continuous / spatial-disperse crop information. Therefore, the assimilation between crop growth model and remote sensing data is an efficient way for obtaining continuous 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, 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.

【 授权许可】

Unknown