New land surface temperature retrieval algorithm for heavy aerosol loading during nighttime from Gaofen-5 satellite...

Area of Science:

• Earth Science
• Remote Sensing
• Atmospheric Science

Background:

• Land surface temperature (LST) is crucial for understanding land-atmosphere interactions, water cycles, and energy budgets.
• The Chinese Gaofen-5 (GF5) satellite provides high-resolution thermal infrared data essential for Earth observation.
• Accurate LST retrieval is challenged by atmospheric factors like aerosols, particularly during nighttime.

Purpose of the Study:

• To identify the optimal spectral channel combination for GF5 data to minimize aerosol effects on LST retrieval.
• To propose a novel four-channel LST retrieval method for GF5 infrared data, specifically for nighttime conditions with heavy dust aerosols.
• To analyze the error propagation of various factors affecting the proposed LST retrieval method.

Main Methods:

• Simulated data were used to evaluate different spectral channel combinations for LST retrieval.
• A new four-channel LST retrieval algorithm was developed and applied to GF5 nighttime infrared data.
• Error analysis was conducted, considering noise equivalent temperature difference (NEΔT), land surface emissivity (LSE) uncertainty, water vapor content (WVC) uncertainty, and aerosol optical depth (AOD) uncertainty.

Main Results:

• The spectral channel combination of channels 7, 8, 9, and 10 (CC1) demonstrated superior performance over channels 7, 8, 11, and 12 (CC2) in reducing aerosol effects.
• CC1 achieved root mean square errors (RMSEs) of 0.28 K (AOD=0.1) and 1.94 K (AOD=1.0), while CC2 showed RMSEs of 0.28 K (AOD=0.1) and 2.54 K (AOD=1.0).
• Error analysis indicated that LST errors were within acceptable ranges for CC1 and CC2, with LSE uncertainty and NEΔT being significant error sources.

Conclusions:

• The selected channel combination (CC1) is optimal for nighttime LST retrieval from GF5 data under dusty conditions.
• The proposed four-channel LST retrieval method offers improved accuracy and robustness against aerosol interference.
• The findings contribute to more reliable LST estimations from satellite remote sensing, vital for environmental monitoring and climate studies.