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Evaluation of Drought Monitoring Effect of Winter Wheat in Henan Province of China Based on Multi-Source Data

Author

Listed:
  • Yuan Li

    (College of Land Science and Technology, China Agricultural University, Beijing 100083, China)

  • Yi Dong

    (College of Land Science and Technology, China Agricultural University, Beijing 100083, China)

  • Dongqin Yin

    (College of Land Science and Technology, China Agricultural University, Beijing 100083, China
    Key Laboratory of Remote Sensing for Agri-Hazards, Ministry of Agriculture and Rural Affairs, Beijing 100083, China)

  • Diyou Liu

    (College of Land Science and Technology, China Agricultural University, Beijing 100083, China)

  • Pengxin Wang

    (Key Laboratory of Remote Sensing for Agri-Hazards, Ministry of Agriculture and Rural Affairs, Beijing 100083, China
    College of Information and Electrical Engineering, China Agricultural University, Beijing 100083, China)

  • Jianxi Huang

    (College of Land Science and Technology, China Agricultural University, Beijing 100083, China
    Key Laboratory of Remote Sensing for Agri-Hazards, Ministry of Agriculture and Rural Affairs, Beijing 100083, China)

  • Zhe Liu

    (College of Land Science and Technology, China Agricultural University, Beijing 100083, China)

  • Hongshuo Wang

    (College of Land Science and Technology, China Agricultural University, Beijing 100083, China
    Key Laboratory of Remote Sensing for Agri-Hazards, Ministry of Agriculture and Rural Affairs, Beijing 100083, China)

Abstract

Monitoring agricultural drought is important to food security and the sustainable development of human society. In order to improve the accuracy of soil moisture and winter wheat yield estimation, drought monitoring effects of optical drought index data, meteorological drought data, and passive microwave soil moisture data were explored during individual and whole growth periods of winter wheat in 2003–2011, taking Henan Province of China as the research area. The model of drought indices and relative meteorological yield of winter wheat in individual and whole growth periods was constructed based on multiple linear regression. Results showed a higher correlation between Moderate-Resolution Imaging Spectroradiometer (MODIS) drought indices and 10 cm relative soil moisture (RSM10) than 20 cm (RSM20) and 50 cm (RSM50). In the whole growth period, the correlation coefficient (R) between vegetation supply water index (VSWI) and RSM10 had the highest correlation (R = −0.206), while in individual growth periods, the vegetation temperature condition index (VTCI) was superior to the vegetation health index (VHI) and VSWI. Among the meteorological drought indices, the 10-day, 20-day, and 30-day standard precipitation evapotranspiration indices (SPEI1, SPEI2, and SPEI3) were all most relevant to RSM10 during individual and whole growth periods. RSM50 and SPEI3 had a higher correlation, indicating that deep soil moisture was more related to drought on a long time scale. The relationship between Advanced Microwave Scanning Radiometer for EOS soil moisture (AMSR-E SM) and VTCI was stable and significantly positive in individual and whole growth periods, which was better compared to VHI and VSWI. Compared with the drought indices and the relative meteorological yield in the city, VHI had the best monitoring effect during individual and whole growth periods. Results also showed that drought occurring at the jointing–heading stage can reduce winter wheat yield, while a certain degree of drought occurring at the heading–milk ripening stage can increase the yield. In the whole growth period, the combination of SPEI1, SPEI2, and VHI had the best performance, with a coefficient of determination (R 2 ) of 0.282 with the combination of drought indices as the independent variables and relative meteorological yield as the dependent variable. In the individual growth period, the model in the later growth period of winter wheat performed well, especially in the returning green–jointing stage (R 2 = 0.212). Results show that the combination of multiple linear drought indices in the whole growth period and the model in the returning green–jointing period could improve the accuracy of winter wheat yield estimation. This study is helpful for effective agricultural drought monitoring of winter wheat in Henan Province.

Suggested Citation

  • Yuan Li & Yi Dong & Dongqin Yin & Diyou Liu & Pengxin Wang & Jianxi Huang & Zhe Liu & Hongshuo Wang, 2020. "Evaluation of Drought Monitoring Effect of Winter Wheat in Henan Province of China Based on Multi-Source Data," Sustainability, MDPI, vol. 12(7), pages 1-19, April.
    • Handle: RePEc:gam:jsusta:v:12:y:2020:i:7:p:2801-:d:340225
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    References listed on IDEAS

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    1. Zuo, Depeng & Cai, Siyang & Xu, Zongxue & Peng, Dingzhi & Kan, Guangyuan & Sun, Wenchao & Pang, Bo & Yang, Hong, 2019. "Assessment of meteorological and agricultural droughts using in-situ observations and remote sensing data," Agricultural Water Management, Elsevier, vol. 222(C), pages 125-138.
    2. Aiguo Dai, 2013. "Increasing drought under global warming in observations and models," Nature Climate Change, Nature, vol. 3(1), pages 52-58, January.
    3. Aiguo Dai, 2013. "Erratum: Increasing drought under global warming in observations and models," Nature Climate Change, Nature, vol. 3(2), pages 171-171, February.
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