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A High-Temperature Risk Assessment Model for Maize Based on MODIS LST

Author

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  • Xinlei Hu

    (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)

  • Zuliang Zhao

    (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)

  • Lin Zhang

    (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
    Key Laboratory of Remote Sensing for Agri-Hazards, Ministry of Agriculture and Rural Affairs, Beijing 100083, China)

  • Shaoming Li

    (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)

  • Xiaodong Zhang

    (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

Currently, high-temperature risk assessments of crops at the regional scale are usually conducted by comparing the observed air temperature at ground stations or via the remote sensing inversion of canopy temperature (such as MODIS (moderate-resolution imaging spectroradiometer) land surface temperature (LST)) with the threshold temperature of the crop. Since this threshold is based on the absolute temperature value, it is difficult to account for changes in environmental conditions and crop canopy information between different regions and different years in the evaluation model. In this study, MODIS LST products were used to establish an evaluation model (spatiotemporal deviation mean (STDM)) and a classification method to determine maize-growing areas at risk of high temperatures at the regional scale. The study area was the Huang-Huai-Hai River plain of China where maize is grown and high temperatures occur frequently. The spatiotemporal distribution of the high-temperature risk of summer maize was determined in the study area from 2003 to 2018. The results demonstrate the applicability of the model at the regional scale. The distribution of high-temperature risk in the Huang-Huai-Hai region was consistent with the actual temperature measurements. The temperatures in the northwestern, southwestern, and southern parts were relatively high and the area was classified as a stable zone. Shijiazhuang, Jiaozuo, Weinan, Xi’an, and Xianyang city were located in a zone of increasing high temperatures. The regions with a stable high-temperature risk were Xiangfan, Yuncheng, and Luoyang city. Areas of decreasing high temperatures were Handan, Xingtai, Bozhou, Fuyang, Nanyang, Linfen, and Pingdingshan city. Areas that need to focus on preventing high-temperature risks include Luoyang, Yuncheng, Xianyang, Weinan, and Xi’an city. This study provides a new method for the detailed evaluation of regional high-temperature risk and data support.

Suggested Citation

  • Xinlei Hu & Zuliang Zhao & Lin Zhang & Zhe Liu & Shaoming Li & Xiaodong Zhang, 2019. "A High-Temperature Risk Assessment Model for Maize Based on MODIS LST," Sustainability, MDPI, vol. 11(23), pages 1-15, November.
    • Handle: RePEc:gam:jsusta:v:11:y:2019:i:23:p:6601-:d:289915
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    References listed on IDEAS

    as
    1. Lin Zhang & Zhe Liu & Diyou Liu & Quan Xiong & Ning Yang & Tianwei Ren & Chao Zhang & Xiaodong Zhang & Shaoming Li, 2019. "Crop Mapping Based on Historical Samples and New Training Samples Generation in Heilongjiang Province, China," Sustainability, MDPI, vol. 11(18), pages 1-17, September.
    2. Ethan E. Butler & Peter Huybers, 2013. "Adaptation of US maize to temperature variations," Nature Climate Change, Nature, vol. 3(1), pages 68-72, January.
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    1. Vipin Kumar Oad & Xiaohua Dong & Muhammad Arfan & Vicky Kumar & Muhammad Salman Mohsin & Syed Saad & Haishen Lü & Muhammad Imran Azam & Muhammad Tayyab, 2020. "Identification of Shift in Sowing and Harvesting Dates of Rice Crop ( L. Oryza sativa ) through Remote Sensing Techniques: A Case Study of Larkana District," Sustainability, MDPI, vol. 12(9), pages 1-15, April.
    2. Shuai Han & Buchun Liu & Chunxiang Shi & Yuan Liu & Meijuan Qiu & Shuai Sun, 2020. "Evaluation of CLDAS and GLDAS Datasets for Near-Surface Air Temperature over Major Land Areas of China," Sustainability, MDPI, vol. 12(10), pages 1-19, May.

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