An Exploration on Satellite Thermal Remote Sensing of Vegetation Water Stress With Two-Stage Trapezoid Model
The two-stage trapezoidal model is an enhanced version of the land surface temperature and fractional vegetation coverage (LST/FVC) space model, which has great potential for applications in soil moisture and vegetation drought monitoring. A significant challenge in monitoring vegetation drought thr...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
IEEE
2025-01-01
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| Series: | IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/11003423/ |
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| Summary: | The two-stage trapezoidal model is an enhanced version of the land surface temperature and fractional vegetation coverage (LST/FVC) space model, which has great potential for applications in soil moisture and vegetation drought monitoring. A significant challenge in monitoring vegetation drought through satellite thermal infrared remote sensing is the separation of vegetation component temperatures. This article separates vegetation component temperatures based on the two-stage trapezoidal model and proposes a simple satellite thermal infrared remote sensing monitoring method, namely, vegetation temperature anomaly index (VTAI). The study assesses the efficacy of VTAI using multiple drought indices, exemplified by two drought events in specific regions of China and Russia in 2010. The accuracy of the vegetation temperature separation method was validated using ground-measured vegetation temperature data from the Heihe River basin. The results show that the two-stage trapezoid-based vegetation temperature separation method significantly outperforms the multiband and multipixel methods based on MODIS data, and outperforms the traditional trapezoid method. The analysis of drought events demonstrated that VTAI is capable of effectively monitoring the timing and spatial extent of drought occurrence. The study improved some challenging-to-obtain parameters in the LST/FVC space dry and wet boundary theory algorithm, which effectively improved the usability of the method and provided some help to the future development of thermal infrared remote sensing drought monitoring. |
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| ISSN: | 1939-1404 2151-1535 |