Parallel Computing for Obtaining Regional Scale Rice Growth Conditions Based on WOFOST and Satellite Images
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. Ther...
| Main Authors: | , , , , , |
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| Format: | Article |
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IEEE
2020-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/9285298/ |
| _version_ | 1818330482692063232 |
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| author | Bingyu Zhao Meiling Liu Jianjun Wu Xiangnan Liu Mengxue Liu Ling Wu |
| author_facet | Bingyu Zhao Meiling Liu Jianjun Wu Xiangnan Liu Mengxue Liu Ling Wu |
| author_sort | Bingyu Zhao |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-12-13T13:04:40Z |
| format | Article |
| id | doaj.art-b41c47436a554c27ae334b0ad67b8c24 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-12-13T13:04:40Z |
| publishDate | 2020-01-01 |
| publisher | IEEE |
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| series | IEEE Access |
| spelling | doaj.art-b41c47436a554c27ae334b0ad67b8c242022-12-21T23:44:51ZengIEEEIEEE Access2169-35362020-01-01822367522368510.1109/ACCESS.2020.30430039285298Parallel Computing for Obtaining Regional Scale Rice Growth Conditions Based on WOFOST and Satellite ImagesBingyu Zhao0https://orcid.org/0000-0002-5530-1869Meiling Liu1Jianjun Wu2Xiangnan Liu3Mengxue Liu4Ling Wu5https://orcid.org/0000-0003-1712-191XFaculty of Geographical Science, Beijing Normal University, 100875, ChinaSchool of Information Engineering, China University of Geosciences, Beijing, ChinaFaculty of Geographical Science, Beijing Normal University, 100875, ChinaSchool of Information Engineering, China University of Geosciences, Beijing, ChinaFaculty of Geographical Science, Beijing Normal University, 100875, ChinaSchool of Information Engineering, China University of Geosciences, Beijing, ChinaIt 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.https://ieeexplore.ieee.org/document/9285298/WOFOST modeldata assimilationremote sensingparallel algorithm |
| spellingShingle | Bingyu Zhao Meiling Liu Jianjun Wu Xiangnan Liu Mengxue Liu Ling Wu Parallel Computing for Obtaining Regional Scale Rice Growth Conditions Based on WOFOST and Satellite Images IEEE Access WOFOST model data assimilation remote sensing parallel algorithm |
| title | Parallel Computing for Obtaining Regional Scale Rice Growth Conditions Based on WOFOST and Satellite Images |
| title_full | Parallel Computing for Obtaining Regional Scale Rice Growth Conditions Based on WOFOST and Satellite Images |
| title_fullStr | Parallel Computing for Obtaining Regional Scale Rice Growth Conditions Based on WOFOST and Satellite Images |
| title_full_unstemmed | Parallel Computing for Obtaining Regional Scale Rice Growth Conditions Based on WOFOST and Satellite Images |
| title_short | Parallel Computing for Obtaining Regional Scale Rice Growth Conditions Based on WOFOST and Satellite Images |
| title_sort | parallel computing for obtaining regional scale rice growth conditions based on wofost and satellite images |
| topic | WOFOST model data assimilation remote sensing parallel algorithm |
| url | https://ieeexplore.ieee.org/document/9285298/ |
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