An Operational Split-Window Algorithm for Retrieving Land Surface Temperature from Geostationary Satellite Data: A Case Study on Himawari-8 AHI Data

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.

Applications of the Advanced Radiative Transfer Modeling System (ARMS) to Characterize the Performance of Fengyun–4A/AGRI

“…The AGRI is very similar to the Advanced Himawari Imager (AHI) onboard the Japanese geostationary meteorological satellites Himawari-8 and -9 [1], and the Advanced Baseline Imager (ABI) onboard the U.S. Geostationary Operational Environmental Satellite (GOES)-R series [2]. These newgeneration geosynchronous imagers are important for numerical data assimilation and weather forecasting [3][4][5][6][7], disaster prevention and mitigation [8,9], as well as atmosphere and surface parameter retrievals [10][11][12][13].…”

A Data Fusion Method for Generating Hourly Seamless Land Surface Temperature from Himawari-8 AHI Data

“…Due to the lack of official AHI LST products, a couple of split-window (SW) algorithms have been developed [64][65][66]. The SW algorithm assumed that the land surface emissivity (LSE) was known in advance, yet accurate LSE was extremely hard to obtain.…”

Retrieving Land Surface Temperature From Chinese FY-3D MERSI-2 Data Using an Operational Split Window Algorithm

“…Several studies have shown that different atmospheric reanalysis products have similar accuracy for LST retrieval [57]- [59]. Furthermore, reanalysis products could provide global atmospheric information with high temporal resolution than satellite retrievals and may be more suitable for LST retrieval [48].…”