Evaluation of the VIIRS and MODIS LST products in an arid area of Northwest China

Self Cite

This study analyzed the scaling problem of land surface temperature (LST) data retrieved with the Temperature Emissivity Separation (TES) algorithm. We compiled a remotely sensed dataset that included Thermal Airborne Hyperspectral Imager (TASI) and satellite-based Advanced Spaceborne Thermal Emission Reflection (ASTER) data, which were acquired simultaneously. This dataset provided the range of spatial heterogeneities of land surface necessary for the study, which was quantified by the dispersion variance. The LST scaling problem was studied by comparing the remotely sensed LST products in two ways. First, the LST products calculated in the distributed method and the lumped method were compared. Second, the airborne and satellite-based LST products derived from the TES algorithm were compared. Four upscaling methods of LST were used in the process. A scaling correction methodology was developed based on the comparisons. The results showed that the scaling effect could be as large as 0.8 K when the spatial resolution of the TASI LST data was coarse. The scaling effect increases quickly with the spatial resolution until it reaches the characteristic scale of the landscape and is positively correlated with the spatial heterogeneity. The first two upscaling methods denoted as Methods 1-2 can upscale the LST more effectively when compared with the other two scaling methods (Methods 3-4). The scaling effect for the ASTER data is not notable. The comparison between the TASI and 6490ASTER data showed that they were highly consistent, with a root mean square error (RMSE) of approximately 0.88 K, when the pixels were relatively homogeneous. When the spatial heterogeneity was significant, the RMSE was as large as 2.68 K. The scaling correction methodology provided resolution-invariant results with scaling effects of less than 0.5 K.

Section: Hiwater Experimentsmentioning

confidence: 99%

Section: Study Regionsmentioning

confidence: 99%

Analysis of the Land Surface Temperature Scaling Problem: A Case Study of Airborne and Satellite Data over the Heihe Basin

Liu

et al. 2015

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“…Previous studies [37,39] indicated that the split-window algorithms generally underestimate the LST over very warm arid regions, especially barren surfaces. The barren surface emissivity values used in the split-window algorithm are constants and only depend on surface type.…”

Section: Introductionmentioning

confidence: 96%

“…The aims of this paper are to use the refined GSW algorithm [21] to estimate LST from FY-3B/VIRR data and to evaluate the results with long-term ground-measured LST for four barren surface sites collected during the Heihe Watershed Allied Telemetry Experimental Research (HiWATER) field campaign [37,38]. Previous studies [37,39] indicated that the split-window algorithms generally underestimate the LST over very warm arid regions, especially barren surfaces.…”

Section: Introductionmentioning

confidence: 99%

Evaluation of Land Surface Temperature Retrieval from FY-3B/VIRR Data in an Arid Area of Northwestern China

Jiang

Liu

et al. 2015

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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 OPEN ACCESSRemote Sens. 2015, 7 7081 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.

“…In the context of remote sensing applications, LST inversion can currently be performed at multiple spatial and temporal scales [3][4][5][6], and the accuracy of the satellite-based LST product is close to or less than 1.0 K [7,8]. The importance of the temperature distribution in heterogeneous and non-isothermal pixels has been increasingly recognized for the full characterization of the surface temperature state.…”

Section: Introductionmentioning

confidence: 99%

A Robust Inversion Algorithm for Surface Leaf and Soil Temperatures Using the Vegetation Clumping Index

Bian

Cao

et al. 2017

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Abstract:The inversion of land surface component temperatures is an essential source of information for mapping heat fluxes and the angular normalization of thermal infrared (TIR) observations. Leaf and soil temperatures can be retrieved using multiple-view-angle TIR observations. In a satellite-scale pixel, the clumping effect of vegetation is usually present, but it is not completely considered during the inversion process. Therefore, we introduced a simple inversion procedure that uses gap frequency with a clumping index (GCI) for leaf and soil temperatures over both crop and forest canopies. Simulated datasets corresponding to turbid vegetation, regularly planted crops and randomly distributed forest were generated using a radiosity model and were used to test the proposed inversion algorithm. The results indicated that the GCI algorithm performed well for both crop and forest canopies, with root mean squared errors of less than 1.0 • C against simulated values. The proposed inversion algorithm was also validated using measured datasets over orchard, maize and wheat canopies. Similar results were achieved, demonstrating that using the clumping index can improve inversion results. In all evaluations, we recommend using the GCI algorithm as a foundation for future satellite-based applications due to its straightforward form and robust performance for both crop and forest canopies using the vegetation clumping index.