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.
Highlights
Maize is a thermophilic crop, but high temperatures have an adverse impact on its growth and development [1,2,3,4,5]
There are three main research methods that have been used to assess the spatiotemporal distribution of the high-temperature risk of crops at the regional scale: (1) The interpolation of temperature data obtained from meteorological stations; (2) the surface temperature inversions using remote sensing data; (3) regression methods based on surface temperature inversion
We focused on the Huang-Huai-Hai summer maize region in China and used MODIS land surface temperature (LST) products to develop a relative high-temperature risk assessment model and hierarchical classification method, based on which, we explored the changes of regional relative high temperatures during 2003–2018
Summary
Maize is a thermophilic crop, but high temperatures have an adverse impact on its growth and development [1,2,3,4,5]. China’s Huang-Huai-Hai summer maize planting area has suffered a large-scale reduction due to the decline in corn pollen viability from high temperatures (>34 ◦C) in the flowering period from 2013 to 2018. The high-temperature risk assessment of crops is a research topic with broad and current interest. There are three main research methods that have been used to assess the spatiotemporal distribution of the high-temperature risk of crops at the regional scale: (1) The interpolation of temperature data obtained from meteorological stations; (2) the surface temperature inversions using remote sensing data; (3) regression methods based on surface temperature inversion
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