Abstract
This paper uses the refined Generalized Split-Window (GSW) algorithm to derive the land surface temperature (LST) from the data acquired by the Visible and Infrared Radiometer on FengYun 3B (FY-3B/VIRR). The coefficients in the GSW algorithm corresponding to a series of overlapping ranges for the mean emissivity, the atmospheric Water Vapor Content (WVC), and the LST are derived using a statistical regression method from the numerical values simulated with an accurate atmospheric radiative transfer model MODTRAN 4 over a wide range of atmospheric and surface conditions. The GSW algorithm is applied to retrieve LST from FY-3B/VIRR data in an arid area in northwestern China. Three emissivity databases are used to evaluate the accuracy of different emissivity databases for LST retrieval, including the ASTER Global Emissivity Database (ASTER_GED) at a 1-km spatial resolution (AG1km), an average of twelve ASTER emissivity data in the 2012 summer and emissivity spectra extracted from spectral libraries. The LSTs retrieved from the three emissivity databases are evaluated with ground-measured LST at four barren surface sites from June 2012 to December 2013 collected during the HiWATER field campaign. The results indicate that using emissivity extracted from ASTER_GED can achieve the highest accuracy with an average bias of 1.26 and −0.04 K and an average root mean square error (RMSE) of 2.69 and 1.38 K for the four sites during daytime and nighttime, respectively. This result indicates that ASTER_GED is a useful emissivity database for generating global LST products from different thermal infrared data and that using FY-3B/VIRR data can produce reliable LST products for other research areas.
Highlights
As one of the key parameters in land-surface process physics and a result of surface-atmosphere interactions at local and global scales, land surface temperature (LST) is important to climatology, meteorology, hydrology, ecology, and a wide range of interdisciplinary research areas [1,2,3,4,5,6,7,8,9,10]
The single-channel, multi-channel, and multi-angle methods belong to the first category (i.e., land surface emissivity (LSE) is known and used as a priori information in these algorithms), while the stepwise retrieval method, simultaneous retrieval of LSEs and LST with known atmospheric information, and simultaneous retrieval with unknown atmospheric information belong to the second category
FengYun 3B (FY-3B)/VIRR LST obtained using the emissivity derived from AG1km, ASTER_2012 and spectral library are referred to as TAG1km, T2012 and TSpec, respectively
Summary
As one of the key parameters in land-surface process physics and a result of surface-atmosphere interactions at local and global scales, land surface temperature (LST) is important to climatology, meteorology, hydrology, ecology, and a wide range of interdisciplinary research areas [1,2,3,4,5,6,7,8,9,10]. Satellite remote sensing is the only possible way for measuring LST at a high spatial resolution and temporal frequency [11] due to the strong spatial heterogeneity in land surface characteristics, such as vegetation, topography and soil physical properties [11,12,13]. According to [14], satellite-derived LST products with an accuracy of 0.3 K for ocean and according to whether the land surface emissivity (LSE) was used as a priori information.
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