Remote Sensing (Apr 2022)

Soybean EOS Spatiotemporal Characteristics and Their Climate Drivers in Global Major Regions

  • Zihang Lou,
  • Dailiang Peng,
  • Xiaoyang Zhang,
  • Le Yu,
  • Fumin Wang,
  • Yuhao Pan,
  • Shijun Zheng,
  • Jinkang Hu,
  • Songlin Yang,
  • Yue Chen,
  • Shengwei Liu

DOI
https://doi.org/10.3390/rs14081867
Journal volume & issue
Vol. 14, no. 8
p. 1867

Abstract

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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.

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