Monitoring and Risk Assessment of High-Temperature Heat Damage for Summer Maize Based on Remote Sensing Data

Yang, Lei; Song, Jinling; Han, Lijuan; Zhu, Xiao Bo

doi:10.1109/igarss39084.2020.9323336

Cited by 2 publications

(1 citation statement)

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“…By overlapping the temperature data measured by the ground weather station with two land surface temperature products, each weather station could extract five temperature observations, which were the temperature data obtained by one weather station and four MODIS land surface temperature products. First, we divided all weather stations from 2009 to 2018 into training datasets and test datasets in a 4:1 ratio and ensured that there were observation data for modeling every year to improve the generalization ability of the model [51]. Then, the temperature data obtained by weather station observations were used as the dependent variable, and the ground temperature values obtained from the four daily observations of the two MODIS surface temperature products were used as the independent variables to establish the regression model.…”

Section: Construction Of Temperature Time-series Datamentioning

confidence: 99%

Monitoring the Impact of Heat Damage on Summer Maize on the Huanghuaihai Plain, China

Yang

Song

et al. 2023

Remote Sensing

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As an important food crop, summer maize is widely planted all over the world. Monitoring its growth and output is of great significance for world food security. With the trend of global warming and deterioration, the frequency of high temperature and heat damage affecting summer corn has been increasing in the past ten years. Therefore, there is an increasing demand for monitoring the high temperature and heat damage of summer maize. At present, there are nearly a hundred indices or methods for research on high temperature and heat damage. However, research based on the vegetation index cannot fully describe the damage caused by high-temperature thermal damage, and there is an obvious asynchrony effect. Research based on hyperspectral remote sensing has many inconveniences in data acquisition and complex physical model construction. Therefore, this study uses remote sensing data, including MODIS surface reflection data, MODIS land surface temperature products, as well as ground observation data and statistical data, combined with multiple remote sensing indices and land surface temperature, to construct a remote sensing index, LSHDI (land surface heat damage index). The LSHDI first searches for a location with the worst vegetation growth conditions in the three-dimensional feature space based on the LST (land surface temperature), the normalized difference vegetation index (NDVI), and the land surface water index (LSWI). Then, it calculates the distance between each point and this location to measure the degree of vegetation affected by high temperature and heat damage. Finally, because there is no reliable disaster verification dataset that has been published at present, this study uses soil moisture as a reference to explain the performance and stability of the LSHDI. The results showed that their coefficient of determination was above 0.5 and reached a significance level of 0.01. The LSHDI can well-reflect the high temperature and heat damage of land surface vegetation and can provide important data support and references for agricultural management departments.

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Section: Construction Of Temperature Time-series Datamentioning

confidence: 99%