Comparing different profiles to characterize the atmosphere for three MODIS TIR bands

Pérez-Planells, Lluís; García-Santos, Vicente; Caselles, Vicente

doi:10.1016/j.atmosres.2015.04.001

Cited by 21 publications

(16 citation statements)

References 13 publications

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“…The accuracy of NCEP/FNL in Europe was similar to the research of Pérez-Planells et al [18] in Spain. According to the validation of MODIS TIR bands by Pérez-Planells et al [18] ⁄ for atmospheric downward radiance.…”

Section: Comparison With Previous Studiessupporting

confidence: 68%

“…Moreover, ERA-Interim is recommended for use with the atmospheric correction of thermal infrared data in high water vapor content conditions. The accuracy of NCEP/FNL in Europe was similar to the research of Pérez-Planells et al [18] in Spain. According to the validation of MODIS TIR bands by Pérez-Planells et al [18] using global radiosonde profiles, the biases (RMSEs) of atmospheric transmittances for NCEP/FNL were between −0.019 (±0.02) and 0.007 (±0.04).…”

Section: Comparison With Previous Studiessupporting

confidence: 68%

“…According to the validation of MODIS TIR bands by Pérez-Planells et al [18] ⁄ for atmospheric downward radiance. NCEP/FNL showed a large bias when the water vapor content was greater than 5 g/cm 2 , which is consistent with the research of Barsi et al [19] showing that NCEP/FNL is expected to be less accurate in wetter conditions.…”

Section: Comparison With Previous Studiesmentioning

confidence: 99%

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Evaluating Eight Global Reanalysis Products for Atmospheric Correction of Thermal Infrared Sensor—Application to Landsat 8 TIRS10 Data

Meng

Cheng

2018

Remote Sensing

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Global reanalysis products have been widely used for correcting the atmospheric effects of thermal infrared data, but their performances have not been comprehensively evaluated. In this paper, we evaluate eight global reanalysis products (NCEP/FNL; NCEP/DOE Reanalysis2; MERRA-3; MERRA-6; MERRA2-3; MERRA2-6; JRA-55; and ERA-Interim) commonly used in the atmospheric correction of Landsat 8 TIRS10 data by referencing global radiosonde observations collected from 163 stations. The atmospheric parameters (atmospheric transmittance, upward radiance, and downward radiance) simulated with MERRA-6 and ERA-Interim were accurate than those simulated with other reanalysis products for different water vapor contents and surface elevations. When global reanalysis products were applied to retrieve land surface temperature (LST) from simulated Landsat 8 TIRS10 data, ERA-Interim and MERRA-6 were accurate than other reanalysis products. The overall LST biases and RMSEs between the retrieved LSTs and LSTs that were used to generate the top-of-atmosphere radiances were less than 0.2 K and 1.09 K, respectively. When eight reanalysis products were used to estimate LSTs from thirty-two Landsat 8 TIRS10 images covering the Heihe River basin in China, the various reanalysis products showed similar validation accuracies for LSTs with low water vapor contents. The biases ranged from 0.07 K to 0.24 K, and the STDs (RMSEs) ranged from 1.93 K (1.93 K) to 2.02 K (2.04 K). Considering the above evaluation results, MERRA-6 and ERA-Interim are recommended for thermal infrared data atmospheric corrections.

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Section: Comparison With Previous Studiessupporting

confidence: 68%

Section: Comparison With Previous Studiessupporting

confidence: 68%

Section: Comparison With Previous Studiesmentioning

confidence: 99%

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Evaluating Eight Global Reanalysis Products for Atmospheric Correction of Thermal Infrared Sensor—Application to Landsat 8 TIRS10 Data

Meng

Cheng

2018

Remote Sensing

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“…This simulated profile is interpolated spatially and temporally to the selected site and it is representative of the atmosphere in a 1 • spatial resolution. This atmospheric profile was demonstrated to be the best option to correct the atmospheric effect compared with other profiles like that offered by the MOD07 product [34].…”

Section: Landsat 7 Etm+ Land-surface Temperaturesmentioning

confidence: 99%

Landsat and Local Land Surface Temperatures in a Heterogeneous Terrain Compared to MODIS Values

et al. 2016

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Land Surface Temperature (LST) as provided by remote sensing onboard satellites is a key parameter for a number of applications in Earth System studies, such as numerical modelling or regional estimation of surface energy and water fluxes. In the case of Moderate Resolution Imaging Spectroradiometer (MODIS) onboard Terra or Aqua, pixels have resolutions near 1 km 2 , LST values being an average of the real subpixel variability of LST, which can be significant for heterogeneous terrain. Here, we use Landsat 7 LST decametre-scale fields to evaluate the temporal and spatial variability at the kilometre scale and compare the resulting average values to those provided by MODIS for the same observation time, for the very heterogeneous Campus of the University of the Balearic Islands (Mallorca, Western Mediterranean), with an area of about 1 km 2 , for a period between 2014 and 2016. Variations of LST between 10 and 20 K are often found at the sub-kilometre scale. In addition, MODIS values are compared to the ground truth for one point in the Campus, as obtained from a four-component net radiometer, and a bias of 3.2 K was found in addition to a Root Mean Square Error (RMSE) of 4.2 K. An indication of a more elaborated local measurement strategy in the Campus is given, using an array of radiometers distributed in the area.

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“…These differences could be due to the empirical expression to retrieve the minimum emissivity from MMD at the AST05 product algorithm, which is not fitted to low contrast surfaces [12], as well as to the atmospheric correction applied at the product AST05, since NCEP atmospheric profiles are less accurate than profiles obtained by in situ radiosoundings [44,45]. As an example, Pérez-Planells et al in [45] showed that using interpolated NCEP profiles instead of radiosoundings yielded errors of ±0.6 K for sites close to sea level, which would be equivalent to ±0.01 in terms of emissivity. Moreover, previous studies pointed out that ASTER product atmospheric correction loses accuracy in warm and humid areas close to sea level [23,24].…”

Section: Lse and Lst Evaluationmentioning

confidence: 99%

Comparison and Evaluation of the TES and ANEM Algorithms for Land Surface Temperature and Emissivity Separation over the Area of Valencia, Spain

et al. 2017

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Land Surface temperature (LST) is a key magnitude for numerous studies, especially for climatology and assessment of energy fluxes between surface and atmosphere. Retrieval of accurate LST requires a good characterization of surface emissivity. Both quantities are coupled in a single radiance measurement; for this reason, for N spectral bands available in a remote sensor, there will always be N + 1 unknowns. To solve the indeterminacy, temperature-emissivity separation methods have been proposed, among which the Temperature Emissivity Separation (TES) algorithm is one of the most widely used. The Adjusted Normalized Emissivity Method (ANEM) was proposed as a modification of the Normalized Emissivity Method (NEM) algorithm by adjusting the initial emissivity guess using an estimation provided by the Vegetation Cover Method (VCM). In this work, both methods were applied to a set of five ASTER scenes over the area of Valencia, Spain, which were recalibrated and atmospherically corrected using local radiosoundings and ground measurements. These scenes were compared to the ASTER temperature and emissivity standard products (AST08 and AST05, respectively). The comparison to reference measurements showed a better agreement of ANEM LST in low spectral contrast surfaces, with biases of +0.4 K, +0.8 K for TES and +1.4 K for the AST08 product in a rice crop site. For sea surface temperature, bias was −0.1 K for ANEM, +0.3 K for TES and +1.3 K for the AST08 product. The larger differences of the AST08 product could be ascribed mainly to the atmospheric correction based on NCEP profiles in contrast to the local correction used in TES and ANEM and to a lesser extent the Maximum-Minimum Difference (MMD) empirical relationship used by TES. In terms of emissivity, ANEM obtained biases up to ±0.007 (positive over vegetation and negative over water), while TES biases were up to −0.015. The AST05 product showed differences up to −0.050, although for high contrast areas, such as sand surfaces, it showed better accuracy than both TES and ANEM. A comparison between TES and ANEM on four different classes within the scene showed a systematic difference between both algorithms, which was more pronounced for low spectral contrast surfaces. Therefore, ANEM improves the accuracy at low spectral contrast surfaces, while obtaining similar results to TES at higher spectral contrast surfaces, such as urban areas. The combination of both methods could provide a procedure benefiting from the strengths shown by each of them.

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Comparing different profiles to characterize the atmosphere for three MODIS TIR bands

Cited by 21 publications

References 13 publications

Evaluating Eight Global Reanalysis Products for Atmospheric Correction of Thermal Infrared Sensor—Application to Landsat 8 TIRS10 Data

Evaluating Eight Global Reanalysis Products for Atmospheric Correction of Thermal Infrared Sensor—Application to Landsat 8 TIRS10 Data

Landsat and Local Land Surface Temperatures in a Heterogeneous Terrain Compared to MODIS Values

Comparison and Evaluation of the TES and ANEM Algorithms for Land Surface Temperature and Emissivity Separation over the Area of Valencia, Spain

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