Long-term accuracy assessment of land surface temperatures derived from the Advanced Along-Track Scanning Radiometer

Coll, César; Valor, E.; Galve, Joan M.; Mira, M.; Bisquert, Mar; García-Santos, Vicente; Caselles, Eduardo; Caselles, V.

doi:10.1016/j.rse.2010.01.027

Cited by 65 publications

(29 citation statements)

References 36 publications

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“…The RMSE of LST is less than 1 K in ideal conditions [22,23]. The AATSR LST data acquisition period is from 20 May 2002 to 8 April 2012, which is available from the AATSR GlobTemperature Level-2 LST product [26] provided by ESA DUE GlobTemperature Project [28].…”

Section: Aatsr Lst Productmentioning

confidence: 99%

Validation and Analysis of Long-Term AATSR Land Surface Temperature Product in the Heihe River Basin, China

et al. 2017

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Abstract:The Advanced Along-Track Scanning Radiometer (AATSR) land surface temperature (LST) product has a long-term time series of data from 20 May 2002 to 8 April 2012 and is a crucial dataset for global change studies. Accuracy and uncertainty assessment of satellite derived LST is important for its use in studying land-surface-atmosphere interactions. However, the validation of AATSR-derived LST products is scarce in China, especially in arid and semi-arid areas. In this study, we evaluated the accuracy of the AATSR LST product using ground-based measurements from 2007 to 2011 in the Heihe River Basin (HRB), China. The AATSR-derived LST results over Yingke site are closer to ground measurements than those over A'rou site for both daytime and nighttime temperatures. For nighttime, the averaged bias, STD, RMSE and R 2 over both sites are 0.67 K, 3.03 K, 3.13 K and 0.93 K, respectively. Based on the accuracy assessment, we analyzed the AATSR-derived annual LST variations both in the HRB region and the two validation sites for the period of 2003 to 2011. The results at the A'rou site show an obvious increasing trend for daytime from 2003 to 2011. For the whole HRB region, the warming trend is clearly shown in the downstream of HRB.

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Section: Aatsr Lst Productmentioning

confidence: 99%

Validation and Analysis of Long-Term AATSR Land Surface Temperature Product in the Heihe River Basin, China

et al. 2017

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“…Land-surface temperature (LST) is a key parameter of the physics of land-surface processes at the regional and global scales, and it includes combined effects of all surface-atmosphere interactions and energy fluxes [1][2][3][4][5][6][7]. Due to the strong heterogeneity of land surface characteristics such as vegetation, soil, water, and topography [8,9], LST changes rapidly over both spatial and temporal scales [10][11][12].…”

Section: Introductionmentioning

confidence: 99%

Evaluation of ASTER-Like Daily Land Surface Temperature by Fusing ASTER and MODIS Data during the HiWATER-MUSOEXE

Yang

Weng

et al. 2016

Remote Sensing

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Land surface temperature (LST) is an important parameter that is highly responsive to surface energy fluxes and has become valuable to many disciplines. However, it is difficult to acquire satellite LSTs with both high spatial and temporal resolutions due to tradeoffs between them. Thus, various algorithms/models have been developed to enhance the spatial or the temporal resolution of thermal infrared (TIR) data or LST, but rarely both. The Spatial and Temporal Adaptive Reflectance Fusion Model (STARFM) is the widely-used data fusion algorithm for Landsat and MODIS imagery to produce Landsat-like surface reflectance. In order to extend the STARFM application over heterogeneous areas, an enhanced STARFM (ESTARFM) approach was proposed by introducing a conversion coefficient and the spectral unmixing theory. The aim of this study is to conduct a comprehensive evaluation of the ESTARFM algorithm for generating ASTER-like daily LST by three approaches: simulated data, ground measurements and remote sensing products, respectively. The datasets of LST ground measurements, MODIS, and ASTER images were collected in an arid region of Northwest China during the first thematic HiWATER-Multi-Scale Observation Experiment on Evapotranspiration (MUSOEXE) over heterogeneous land surfaces in 2012 from May to September. Firstly, the results of the simulation test indicated that ESTARFM could accurately predict background with temperature variations, even coordinating with small ground objects and linear ground objects. Secondly, four temporal ASTER and MODIS data fusion LSTs (i.e., predicted ASTER-like LST products) were highly consistent with ASTER LST products. Here, the four correlation coefficients were greater than 0.92, root mean square error (RMSE) reached about 2 K and mean absolute error (MAE) ranged from 1.32 K to 1.73 K. Finally, the results of the ground measurement validation indicated that the overall accuracy was high (R 2 = 0.92, RMSE = 0.77 K), and the ESTARFM algorithm is a highly recommended method to assemble time series images at ASTER spatial resolution and MODIS temporal resolution due to LST estimation error less than 1 K. However, the ESTARFM method is also limited in predicting LST changes that have not been recorded in MODIS and/or ASTER pixels.

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“…The LST validation sites must be homogeneous from the point scale to several kilometers [67,68]. The spatial variability of the validation stations has large effects on the validation results.…”

Section: Effects Of Spatial Scale On the Lst And Lse Evaluationmentioning

confidence: 99%

Estimating Land Surface Temperature from Feng Yun-3C/MERSI Data Using a New Land Surface Emissivity Scheme

2017

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Land surface temperature (LST) is a key parameter for a wide number of applications, including hydrology, meteorology and surface energy balance. In this study, we first proposed a new land surface emissivity (LSE) scheme, including a lookup table-based method to determine the vegetated surface emissivity and an empirical method to derive the bare soil emissivity from the Global LAnd Surface Satellite (GLASS) broadband emissivity (BBE) product. Then, the Modern Era Retrospective-Analysis for Research and Applications (MERRA) reanalysis data and the Feng Yun-3C/Medium Resolution Spectral Imager (FY-3C/MERSI) precipitable water vapor product were used to correct the atmospheric effects. After resolving the land surface emissivity and atmospheric effects, the LST was derived in a straightforward manner from the FY-3C/MERSI data by the radiative transfer equation algorithm and the generalized single-channel algorithm. The mean difference between the derived LSE and field-measured LSE over seven stations is approximately 0.002. Validation of the LST retrieved with the LSE determined by the new scheme can achieve an acceptable accuracy. The absolute biases are less than 1 K and the STDs (RMSEs) are less than 1.95 K (2.2 K) for both the 1000 m and 250 m spatial resolutions. The LST accuracy is superior to that retrieved with the LSE determined by the commonly used Normalized Difference Vegetation Index (NDVI) threshold method. Thus, the new emissivity scheme can be used to improve the accuracy of the LSE and further the LST for sensors with broad spectral ranges such as FY-3C/MERSI.

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Long-term accuracy assessment of land surface temperatures derived from the Advanced Along-Track Scanning Radiometer

Cited by 65 publications

References 36 publications

Validation and Analysis of Long-Term AATSR Land Surface Temperature Product in the Heihe River Basin, China

Validation and Analysis of Long-Term AATSR Land Surface Temperature Product in the Heihe River Basin, China

Evaluation of ASTER-Like Daily Land Surface Temperature by Fusing ASTER and MODIS Data during the HiWATER-MUSOEXE

Estimating Land Surface Temperature from Feng Yun-3C/MERSI Data Using a New Land Surface Emissivity Scheme

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