Retrieval of Rugged Mountainous Areas Land Surface Temperature From High-Spatial-Resolution Thermal Infrared Remote Sensing Data

He, Zhi-Wei; Tang, Bo-Hui

doi:10.1109/tgrs.2023.3316624

Cited by 4 publications

(1 citation statement)

References 64 publications

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“…LST holds significant utility across a spectrum of domains, spanning from the monitoring of mountainous ecosystems, the analysis of crop growth patterns, the assessment of hydrological cycles, the surveillance and management of forests, environmental conservation and monitoring, and the estimation of evapotranspiration. Over the years, multiple algorithms have been developed to infer LST from satellite Thermal Infrared (TIR) data, typically within the 8-14 µm range [5]. These algorithms include the Single Channel (SC) algorithm [6,7], Split Window (SW) algorithm [8][9][10][11][12][13], Temperature Emissivity Separation (TES) algorithm [14,15], Day-Night (DN) algorithm [16], and Dual-Angle (DA) algorithm [17].…”

Section: Introductionmentioning

confidence: 99%

Retrieval of Plateau Lake Water Surface Temperature from UAV Thermal Infrared Data

Sima,

Tang,

et al. 2024

Atmosphere

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The lake water surface temperature (LWST) is a critical parameter influencing lake ecosystem dynamics and addressing challenges posed by climate change. Traditional point measurement techniques exhibit limitations in providing comprehensive LWST data. However, the emergence of satellite remote sensing and unmanned aerial vehicle (UAV) Thermal Infrared (TIR) technology has opened new possibilities. This study presents an approach for retrieving plateau lake LWST (p-LWST) from UAV TIR data. The UAV TIR dataset, obtained from the DJI Zenmuse H20T sensor, was stitched together to form an image of brightness temperature (BT). Atmospheric parameters for atmospheric correction were acquired by combining the UAV dataset with the ERA5 reanalysis data and MODTRAN5.2. Lake Water Surface Emissivity (LWSE) spectral curves were derived using 102 hand-portable FT-IR spectrometer (102F) measurements, along with the sensor’s spectral response function, to obtain the corresponding LWSE. Using estimated atmospheric parameters, LWSE, and UAV BT, the un-calibrated LWST was calculated through the TIR radiative transfer model. To validate the LWST retrieval accuracy, the FLIR Infrared Thermal Imager T610 and the Fluke 51-II contact thermometer were utilized to estimate on-point LWST. This on-point data was employed for cross-calibration and verification. In the study area, the p-LWST method retrieved LWST ranging from 288 K to 295 K over Erhai Lake in the plateau region, with a final retrieval accuracy of 0.89 K. Results demonstrate that the proposed p-LWST method is effective for LWST retrieval, offering technical and theoretical support for monitoring climate change in plateau lakes.

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