Abstract
An operational split-window (SW) algorithm was developed to retrieve high-temporal-resolution land surface temperature (LST) from global geostationary (GEO) satellite data. First, the MODTRAN 5.2 and SeeBor V5.0 atmospheric profiles were used to establish a simulation database to derive the SW algorithm coefficients for GEO satellites. Then, the dynamic land surface emissivities (LSEs) in the two SW bands were estimated using the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) Global Emissivity Dataset (GED), fractional vegetation cover (FVC), and snow cover products. Here, the proposed SW algorithm was applied to Himawari-8 Advanced Himawari Imager (AHI) observations. LST estimates were retrieved in January, April, July, and October 2016, and three validation methods were used to evaluate the LST retrievals, including the temperature-based (T-based) method, radiance-based (R-based) method, and intercomparison method. The in situ night-time observations from two Heihe Watershed Allied Telemetry Experimental Research (HiWATER) sites and four Terrestrial Ecosystem Research Network (TERN) OzFlux sites were used in the T-based validation, where a mean bias of −0.70 K and a mean root-mean-square error (RMSE) of 2.29 K were achieved. In the R-based validation, the biases were 0.14 and −0.13 K and RMSEs were 0.83 and 0.86 K for the daytime and nighttime, respectively, over four forest sites, four desert sites, and two inland water sites. Additionally, the AHI LST estimates were compared with the Collection 6 MYD11_L2 and MYD21_L2 LST products over southeastern China and the Australian continent, and the results indicated that the AHI LST was more consistent with the MYD21 LST and was generally higher than the MYD11 LST. The pronounced discrepancy between the AHI and MYD11 LST could be mainly caused by the differences in the emissivities used. We conclude that the developed SW algorithm is of high accuracy and shows promise in producing LST data with global coverage using observations from a constellation of GEO satellites.
Highlights
During the surface energy balance processes, land surface temperature (LST) is one of the most critical parameters, governing the energy exchange in the surface-atmosphere continuum [1,2]
Larger biases and root-mean-square error (RMSE) were obtained in April and July, and smaller biases and RMSEs were obtained in January and October, with the largest bias (−2.44 K) and RMSE (3.45 K) values obtained in July for the Si Dao Qiao (SDQ) site
The ASTER Global Emissivity Dataset (GED) product was introduced to estimate the emissivity for the SW algorithm, and the accuracy of the proposed SW algorithm was evaluated using the Advanced Himawari Imager (AHI) as a case study
Summary
During the surface energy balance processes, land surface temperature (LST) is one of the most critical parameters, governing the energy exchange in the surface-atmosphere continuum [1,2]. The Copernicus Global Land service plan (http://land.copernicus.eu/global/) is similar to the Globtemperature project, which intends to merge the Satellite Application Facility on Land Surface Analysis (LSA SAF) LST product retrieved from MSG SEVIRI with the LST retrieved from other GEO satellites to generate global LST products [21]. This project mainly covers the United States, Europe, Africa, and Australia, and lacks coverage in Asia (especially China and India)
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