Soybean EOS Spatiotemporal Characteristics and Their Climate Drivers in Global Major Regions
Currently, analyses related the status of soybeans, a major oil crop, as well as the related climate drivers, are based on on-site data and are generally focused on a particular country or region. This study used remote sensing, meteorological, and statistical data products to analyze spatiotemporal...
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-04-01
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| Series: | Remote Sensing |
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| Online Access: | https://www.mdpi.com/2072-4292/14/8/1867 |
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| author | Zihang Lou Dailiang Peng Xiaoyang Zhang Le Yu Fumin Wang Yuhao Pan Shijun Zheng Jinkang Hu Songlin Yang Yue Chen Shengwei Liu |
| author_facet | Zihang Lou Dailiang Peng Xiaoyang Zhang Le Yu Fumin Wang Yuhao Pan Shijun Zheng Jinkang Hu Songlin Yang Yue Chen Shengwei Liu |
| author_sort | Zihang Lou |
| collection | DOAJ |
| description | Currently, analyses related the status of soybeans, a major oil crop, as well as the related climate drivers, are based on on-site data and are generally focused on a particular country or region. This study used remote sensing, meteorological, and statistical data products to analyze spatiotemporal variations at the end of the growing season (EOS) for soybeans in the world’s major soybean-growing areas. The ridge regression estimation model calculates the average annual temperature, precipitation, and total radiation contributions to phenological changes. A systematic analysis of the spatiotemporal changes in the EOS and the associated climate drivers since the beginning of the 21st century shows the following: (1) in India, soybean EOS is later than in China and the United States. The main soybean-growing areas in the southern hemisphere are concentrated in South America, where two crops are planted yearly. (2) In most of the world’s soybean-growing regions, the rate change of the EOS is ±2 days/year. In the Mississippi River Valley, India, and South America (the first quarter), the soybean EOS is generally occurring earlier, whereas, in northeast China, it is generally occurring later. (3) The relative contributions of different meteorological factors to the soybean EOS vary between soybean-growing areas; there are also differences within the individual areas. This study provides a solid foundation for understanding the spatiotemporal changes in soybean crops in the world’s major soybean-growing areas and spatiotemporal variations in the effects of climate change on soybean EOS. |
| first_indexed | 2024-03-09T13:03:34Z |
| format | Article |
| id | doaj.art-33b76002bd344414814862fc126f1cb3 |
| institution | Directory Open Access Journal |
| issn | 2072-4292 |
| language | English |
| last_indexed | 2024-03-09T13:03:34Z |
| publishDate | 2022-04-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj.art-33b76002bd344414814862fc126f1cb32023-11-30T21:50:53ZengMDPI AGRemote Sensing2072-42922022-04-01148186710.3390/rs14081867Soybean EOS Spatiotemporal Characteristics and Their Climate Drivers in Global Major RegionsZihang Lou0Dailiang Peng1Xiaoyang Zhang2Le Yu3Fumin Wang4Yuhao Pan5Shijun Zheng6Jinkang Hu7Songlin Yang8Yue Chen9Shengwei Liu10Key Laboratory of Digital Earth Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, ChinaKey Laboratory of Digital Earth Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, ChinaGeospatial Sciences Center of Excellence, Department of Geography & Geospatial Sciences, South Dakota State University, Brookings, SD 57007, USADepartment of Earth System Science, Tsinghua University, Beijing 100084, ChinaInstitute of Hydrology and Water Resources, Zhejiang University, Zijingang Campus, Hangzhou 310058, ChinaKey Laboratory of Digital Earth Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, ChinaKey Laboratory of Digital Earth Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, ChinaKey Laboratory of Digital Earth Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, ChinaKey Laboratory of Digital Earth Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, ChinaSchool of Electronic and Information Engineering, National Engineering Research Center for Agro-Ecological Big Data Analysis & Application, Anhui University, Hefei 230093, ChinaSchool of Surveying and Land Information Engineering, Henan Polytechnic University, Jiaozuo 454000, ChinaCurrently, analyses related the status of soybeans, a major oil crop, as well as the related climate drivers, are based on on-site data and are generally focused on a particular country or region. This study used remote sensing, meteorological, and statistical data products to analyze spatiotemporal variations at the end of the growing season (EOS) for soybeans in the world’s major soybean-growing areas. The ridge regression estimation model calculates the average annual temperature, precipitation, and total radiation contributions to phenological changes. A systematic analysis of the spatiotemporal changes in the EOS and the associated climate drivers since the beginning of the 21st century shows the following: (1) in India, soybean EOS is later than in China and the United States. The main soybean-growing areas in the southern hemisphere are concentrated in South America, where two crops are planted yearly. (2) In most of the world’s soybean-growing regions, the rate change of the EOS is ±2 days/year. In the Mississippi River Valley, India, and South America (the first quarter), the soybean EOS is generally occurring earlier, whereas, in northeast China, it is generally occurring later. (3) The relative contributions of different meteorological factors to the soybean EOS vary between soybean-growing areas; there are also differences within the individual areas. This study provides a solid foundation for understanding the spatiotemporal changes in soybean crops in the world’s major soybean-growing areas and spatiotemporal variations in the effects of climate change on soybean EOS.https://www.mdpi.com/2072-4292/14/8/1867global soybean-growing regionssoybean growing season end of season (EOS)spatiotemporal patternsinterannual trendsclimate drivers |
| spellingShingle | Zihang Lou Dailiang Peng Xiaoyang Zhang Le Yu Fumin Wang Yuhao Pan Shijun Zheng Jinkang Hu Songlin Yang Yue Chen Shengwei Liu Soybean EOS Spatiotemporal Characteristics and Their Climate Drivers in Global Major Regions Remote Sensing global soybean-growing regions soybean growing season end of season (EOS) spatiotemporal patterns interannual trends climate drivers |
| title | Soybean EOS Spatiotemporal Characteristics and Their Climate Drivers in Global Major Regions |
| title_full | Soybean EOS Spatiotemporal Characteristics and Their Climate Drivers in Global Major Regions |
| title_fullStr | Soybean EOS Spatiotemporal Characteristics and Their Climate Drivers in Global Major Regions |
| title_full_unstemmed | Soybean EOS Spatiotemporal Characteristics and Their Climate Drivers in Global Major Regions |
| title_short | Soybean EOS Spatiotemporal Characteristics and Their Climate Drivers in Global Major Regions |
| title_sort | soybean eos spatiotemporal characteristics and their climate drivers in global major regions |
| topic | global soybean-growing regions soybean growing season end of season (EOS) spatiotemporal patterns interannual trends climate drivers |
| url | https://www.mdpi.com/2072-4292/14/8/1867 |
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