Validation of Land Surface Temperature products derived from the Visible Infrared Imaging Radiometer Suite (VIIRS) using ground-based and heritage satellite measurements

Guillevic, Pierre; Biard, J.; Hulley, Glynn; Privette, Jeffrey L.; Hook, Simon J.; Olioso, Albert; Göttsche, Frank-M.; Radocinski, R. G.; Román, M. O.; Yu, Yunyue; Csiszar, Ivan

doi:10.1016/j.rse.2014.08.013

Cited by 127 publications

(104 citation statements)

References 63 publications

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“…However, this is not an absolute validation unless one of the satellite products has been independently validated [21]. As the VIIRS is expected to replace MODIS in the future, the cross comparison to the MODIS LST will provide the evaluation of the VIIRS LST retrieval performance with respect to characterization of the differences, i.e., spatial pattern, systematic error budget, which may reflect the algorithm difference, limitations and error sources.…”

Section: Cross Satellite Comparison Methodsmentioning

confidence: 99%

“…The pyranometer is usually mounted on a 10 meter high tower in each SURFRAD site, facing downward to measure the surface upwelling radiation. The spatial representativeness is about 70 m × 70 m [21]. Observations from SURFRAD stations have been widely used for evaluating satellite-based estimates of surface radiation, for validating hydrology, weather prediction, climate models and satellite LST products from ASTER, GOES and MODIS [1,15,[30][31][32][33].…”

Section: Surfrad Ground Observationsmentioning

confidence: 99%

“…Some researchers have evaluated the VIIRS LST product during its beta maturity stage [7,21,22]. In this study, we present evaluations of the VIIRS LST data since its provisional stage from April 2014 when a newly calibrated algorithm coefficients set was implemented.…”

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confidence: 99%

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Quality Assessment of S-NPP VIIRS Land Surface Temperature Product

Liu

et al. 2015

Remote Sensing

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Abstract:The VIIRS Land Surface Temperature (LST) Environmental Data Record (EDR) has reached validated (V1 stage) maturity in December 2014. This study compares VIIRS v1 LST with the ground in situ observations and with heritage LST product from MODIS Aqua and AATSR. Comparisons against U.S. SURFRAD ground observations indicate a similar accuracy among VIIRS, MODIS and AATSR LST, in which VIIRS LST presents an overall accuracy of −0.41 K and precision of 2.35 K. The result over arid regions in Africa suggests that VIIRS and MODIS underestimate the LST about 1.57 K and 2.97 K, respectively. The cross comparison indicates an overall close LST estimation between VIIRS and MODIS. In addition, a statistical method is used to quantify the VIIRS LST retrieval uncertainty taking into account the uncertainty from the surface type input. Some issues have been found as follows: (1) Cloud contamination, particularly the cloud detection error over a snow/ice surface, shows significant impacts on LST validation; (2) Performance of the VIIRS LST algorithm is strongly dependent on a correct classification of the surface type; (3) The VIIRS LST quality can be degraded when significant brightness temperature OPEN ACCESS Remote Sens. 2015, 7 12216 difference between the two split window channels is observed; (4) Surface type dependent algorithm exhibits deficiency in correcting the large emissivity variations within a surface type.

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Section: Cross Satellite Comparison Methodsmentioning

confidence: 99%

Section: Surfrad Ground Observationsmentioning

confidence: 99%

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Quality Assessment of S-NPP VIIRS Land Surface Temperature Product

Liu

et al. 2015

Remote Sensing

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“…However, a previous study indicated that assigning a fixed emissivity value to the barren soil may lead to an overestimation of the spectral emissivity values over very warm arid regions and generally results in an underestimation of the LST over these regions. Using maps of physically retrieved dynamic surface emissivities (like those from ASTER-TES, MODIS MOD11B1 or MOD21 products) as algorithm inputs may improve the inversion accuracy of the GSW algorithm [37,39] for barren surfaces. Hence, the ASTER emissivity dataset is used to calculate the band emissivities of the four evaluation sites.…”

Section: Calculations Of Lsementioning

confidence: 99%

“…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: 97%

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

Jiang

Liu

et al. 2015

Remote Sensing

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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.

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Analysis of the long‐term high‐resolution infrared radiation sounder land surface temperature against ground measurements during 1980–2009 in the Poyang Lake basin, China

Wang

Shen

Pan

et al. 2018

Intl Journal of Climatology

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Land surface temperature (LST) plays an essential role in the energy exchanges between land and atmosphere. It reflects the changes of available terrestrial energy in the context of global warming, and thus can serve as an indicator of regional climate change. The recently developed HIRS (High‐resolution infrared radiation sounder onboard NOAA satellites) LST product is among the longest LST records. We have analysed the HIRS LST against ground measurements from 66 stations in the Poyang Lake basin, China, considering the temporal variations at daily and interannual time scales during 1980–2009. The error metrics of correlation coefficient, Bias and unbiased Root Mean Square Deviation (ubRMSD) are used, together with the long‐term linear trends. The two data sets have considerable agreement with respect to daily variations. Their correlation coefficients are higher than 0.95 for all stations; and those for daily anomalies range from 0.73 to 0.85 across the basin. The Bias and the ubRMSD have indicated that the HIRS data have generally underestimated the LST across the basin, mainly because of its large underestimation of the occurrences of temperature higher than 35 °C. Nevertheless, the HIRS LST has largely overestimated the occurrences of summer days with temperature ranging 20–30 °C. The Bias and the ubRMSD appear to be smaller in the cropped plains and much larger in the afforested mountain areas. As for the interannual variations, the HIRS LST shows highest correlations with ground measurement in spring; they are lowest in summer, possibly due to the interference of monsoonal precipitation. Both the HIRS LST and ground measurements show significant increasing trends with similar patterns in spring and winter. However, the long‐term trends are significantly overestimated across the basin in summer, but underestimated in autumn in the HIRS LST data.

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Validation of Land Surface Temperature products derived from the Visible Infrared Imaging Radiometer Suite (VIIRS) using ground-based and heritage satellite measurements

Cited by 127 publications

References 63 publications

Quality Assessment of S-NPP VIIRS Land Surface Temperature Product

Quality Assessment of S-NPP VIIRS Land Surface Temperature Product

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

Analysis of the long‐term high‐resolution infrared radiation sounder land surface temperature against ground measurements during 1980–2009 in the Poyang Lake basin, China

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