An algorithm for retrieving land surface temperatures using VIIRS data in combination with multi-sensors.

Area of Science:

• Earth Science
• Remote Sensing
• Atmospheric Science

Background:

• Retrieving land surface temperature (LST) from satellite data is crucial for environmental monitoring.
• Visible Infrared Imager Radiometer Suite (VIIRS) data lacks a dedicated water vapor channel, complicating transmittance calculations.
• Existing methods often struggle with accuracy due to limitations in water vapor content estimation.

Purpose of the Study:

• To develop and validate a practical algorithm for retrieving LST from VIIRS data in mid-latitude regions.
• To address the challenge of absent water vapor channels in VIIRS by utilizing data from Moderate Resolution Imaging Spectroradiometer (MODIS).
• To assess the accuracy and reliability of the proposed LST retrieval algorithm.

Main Methods:

• Proposed a practical algorithm to retrieve LST from VIIRS data.
• Integrated Moderate Resolution Imaging Spectroradiometer (MODIS) data to obtain water vapor content, compensating for VIIRS's limitations.
• Analyzed estimation errors of water vapor content and land surface emissivity.

Main Results:

• The algorithm demonstrated a mean retrieval error of 0.634 K with water vapor errors within ±0.5 K.
• Land surface emissivity errors within -0.005 to +0.005 resulted in a mean retrieval error less than 1.0 K.
• Validation showed an average LST retrieval error of 0.734 K across twenty-three land types and high accuracy compared to ground station data.

Conclusions:

• The proposed algorithm effectively retrieves LST from VIIRS data by integrating MODIS data for water vapor content.
• The method shows high accuracy, with errors comparable to or better than existing products.
• Exploiting multi-sensor advantages significantly enhances the accuracy of land surface temperature retrieval.