Practical split-window algorithm for retrieving land surface temperature from Himawari 8 AHI data

Abstract

Land Surface Temperature (LST) is a key parameter for hydrological, meteorological, climatological, and environmental research fields. Accurate regional and global LST products can be obtained from thermal infrared remote sensing data. Himawari-8 is the nextgeneration of Japan geostationary meteorological satellite, which carries a new optical sensor called Advanced Himawari Imager (AHI), with significantly higher temporal and spatial resolutions. AHI has 16 observation bands, with spatial resolutions of 0.5 or 1 km for visible and near-infrared bands and 2 km for infrared bands. AHI can provide full disk images every 10 minutes, and can provide high temporal and spatial resolution LST information for many studies. The bands 14 and 15 of AHI can be used for LST retrieval by using the Split-Window (SW) algorithm. Thus, the objective of this paper is to propose a practical SW algorithm to retrieve LST from AHI data. Land Surface Emissivity (LSE) is one of the essential parameters for SW algorithm. SW algorithm is extremely sensitive to emissivity errors, and the sensitivity is significantly higher for direr atmospheres. A 0.005 error in emissivity will result in a LST error of 1 K or more under drier conditions. The ASTER Global Emissivity Dataset (GED) version 4 was adopted to calculate the LSE in this paper to improve the accuracy of emissivity in barren surfaces. The refined Generalized Split-Window (GSW) algorithm developed for MODIS was adopted to retrieve LST from the brightness temperature of AHI bands 14 and 15. MODTRAN 5.2, TIGR 3 atmospheric profile database, and ASTER spectral library data were used to create a simulation database to obtain the coefficients of the GSW algorithm. The coefficients were determined based on view zenith angle and atmospheric Water Vapor (WV) sub-ranges to improve the accuracy, and the WV was directly calculated using a simple method based on the brightness temperature of AHI bands 14 and 15. Two kinds of emissivity products were used to calculate LSE for AHI bands 14 and 15. The first product is the ASTER GED version 4 monthly product. The second is the MODIS MOD11C3 version 6 monthly emissivity product. The spatial resolution of the two products is 0.05°. Ground measurements collected from three Aerosol Robotic NETwork (AERONET) sites were used to validate the WV result. The root mean square errors (RMSEs) were 1.16 g/cm2, 0.94 g/cm2, and 1.23 g/cm2 for Baotou, Beijing-CAMS, and Hong_Kong_PolyU sites, respectively. The 2079 daytime and 2983 nighttime scenes of AHI images between June 1, 2015 and December 29, 2015 were used for LST retrieval. Ground LST measurements collected from four Heihe Watershed Allied Telemetry Experimental Research (HiWATER) sites and MODIS LST product data of the central points of seven lakes were used to validate the LST. The results show that the proposed algorithm demonstrates a reasonable accuracy, with RMSE less than 3 K, which has a comparable accuracy of current remote sensing LST products, such as MODIS, VIIRS, and FY-3B VIRR LST products. For LSE, ASTER GED v4 provides more realistic values of surface emissivity than MOD11C3 v6 because the emissivities are in accordance with the seasonal variation on NDVI. The MOD11C3 v6 typically provides constant values of emissivity, which was overestimated over the JCHM site. Thus, the LST was underestimated due to the overestimation of the emissivity. A practical SW algorithm for estimating land surface temperature from Himawari 8 AHI data was proposed based on GSW algorithm. ASTER GED v4 product was introduced to estimate the LSE for GSW algorithm. The LST result was evaluated with ground LST measurements collected in four HiWATER sites and the MODIS LST products, with RMSE of less than 3 K. The results also show that ASTER GED v4 product has higher accuracy than MOD11C3 v6 product in our study sites; thus, is more suitable in generating high accuracy LST product.