Global Atmospheric Profiles from Reanalysis Information (GAPRI): a new database for earth surface temperature retrieval

Mattar, Cristián; Durán‐Alarcón, Claudio; Jiménez-Muñoz, Juan C.; Santamaría-Artigas, Andrés; Olivera-Guerra, Luis; Sobrino, José A.

doi:10.1080/01431161.2015.1054965

Cited by 31 publications

(18 citation statements)

References 34 publications

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“…In previous studies [17,21], several Thermodynamic Initial Guess Retrieval (TIGR) data tank versions (TIGR 61 , TIGR 1761 and TIGR 2311 [22][23][24]) and STanDard atmospheres included in MODTRAN code (STD 66 ) were used. However, a recently developed atmospheric profile database, the Global Atmospheric Profiles from Reanalysis Information (GAPRI [18]), that yielded optimal results when deriving atmospheric data for the LST retrieval algorithm [8], was used. The GAPRI database consists of 4714 atmospheric profiles selected over land (GAPRI 4714 ) and covers tropical, mid-latitude, subarctic, and arctic atmospheric conditions (Figure 1).…”

Section: Lst Algorithm Coefficients Fit and Evaluation Using Simulatementioning

confidence: 99%

“…The single-channel method developed by [8] is only water vapor (w) dependent, which minimizes the input data required and provides an operational methodology to retrieve surface temperature from the Landsat-8 TIRS band. This methodology was designed to obtain surface temperatures using the Global Atmospheric Profiles from Reanalysis Information (GAPRI) radiosonde database [18] that includes 4714 atmospheric profiles and is representative of w conditions at a global scale. Nonetheless, due to the fact that this method was minimized to only one atmospheric parameter, w, an error in this source could increase the error in surface temperature retrieval, especially when atmospheric water vapor content increases.…”

Section: Introductionmentioning

confidence: 99%

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An Improved Single-Channel Method to Retrieve Land Surface Temperature from the Landsat-8 Thermal Band

et al. 2018

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Land surface temperature (LST) is one of the sources of input data for modeling land surface processes. The Landsat satellite series is the only operational mission with more than 30 years of archived thermal infrared imagery from which we can retrieve LST. Unfortunately, stray light artifacts were observed in Landsat-8 TIRS data, mostly affecting Band 11, currently making the split-window technique impractical for retrieving surface temperature without requiring atmospheric data. In this study, a single-channel methodology to retrieve surface temperature from Landsat TM and ETM+ was improved to retrieve LST from Landsat-8 TIRS Band 10 using near-surface air temperature (T a) and integrated atmospheric column water vapor (w) as input data. This improved methodology was parameterized and successfully evaluated with simulated data from a global and robust radiosonde database and validated with in situ data from four flux tower sites under different types of vegetation and snow cover in 44 Landsat-8 scenes. Evaluation results using simulated data showed that the inclusion of T a together with w within a single-channel scheme improves LST retrieval, yielding lower errors and less bias than models based only on w. The new proposed LST retrieval model, developed with both w and T a , yielded overall errors on the order of 1 K and a bias of −0.5 K validated against in situ data, providing a better performance than other models parameterized using w and T a or only w models that yielded higher error and bias.

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Section: Lst Algorithm Coefficients Fit and Evaluation Using Simulatementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

An Improved Single-Channel Method to Retrieve Land Surface Temperature from the Landsat-8 Thermal Band

et al. 2018

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“…where, ε is the surface emissitivity, (γ, δ) are parameters depending on the radiance and brightness temperature of the Landsat thermal band and ϕ 1 , ϕ 2 , ϕ 3 are atmospheric variables function of the atmospheric water vapor content (ω) and derived from radiative transfer simulations using the GAPRI database [54]. The ω variable is obtained from the MODIS product MOD05.…”

Section: Landsatmentioning

confidence: 99%

Stepwise Disaggregation of SMAP Soil Moisture at 100 m Resolution Using Landsat-7/8 Data and a Varying Intermediate Resolution

et al. 2019

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Global soil moisture (SM) products are currently available from passive microwave sensors at typically 40 km spatial resolution. Although recent efforts have been made to produce 1 km resolution data from the disaggregation of coarse scale observations, the targeted resolution of available SM data is still far from the requirements of fine-scale hydrological and agricultural studies. To fill the gap, a new disaggregation scheme of Soil Moisture Active and Passive (SMAP) data is proposed at 100 m resolution by using the disaggregation based on physical and theoretical scale change (DISPATCH) algorithm. The main objectives of this paper is (i) to implement DISPATCH algorithm at 100 m resolution using SMAP SM and Landsat land surface temperature and vegetation index data and (ii) to investigate the usefulness of an intermediate spatial resolution (ISR) between the SMAP 36 km resolution and the targeted 100 m resolution. The sequential disaggregation approach from 36 km to ISR (ranging from 1 km to 30 km) and from ISR to 100 m resolution is evaluated over 22 irrigated field crops in central Morocco using in-situ SM measurements collected from January to May 2016. The lowest root mean square difference (RMSD) between the 100 m resolution disaggregated and in-situ SM is obtained when the ISR is around 10 km. Therefore, the two-step disaggregation is more efficient than the direct disaggregation from SMAP to 100 m resolution. Moreover, we propose a moving average window algorithm to increase the accuracy in the 100 m resolution SM as well as to reduce the low-resolution boxy artifacts on disaggregated images. The correlation coefficient between 100 m resolution disaggregated and in situ SM ranges between 0.5–0.9 for four out of the six extensive sampling dates. This methodology relies solely on remote sensing data and can be easily implemented to monitor SM at a high spatial resolution over irrigated regions.

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“…According to the method used in Mattar et al [51], TWV can be calculated from the reanalysis profiles. Figure 6a shows the TWV biases between reanalysis profiles and radiosondes.…”

Section: Intercomparison Of Water Vapor Contentmentioning

confidence: 99%

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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Global Atmospheric Profiles from Reanalysis Information (GAPRI): a new database for earth surface temperature retrieval

Cited by 31 publications

References 34 publications

An Improved Single-Channel Method to Retrieve Land Surface Temperature from the Landsat-8 Thermal Band

An Improved Single-Channel Method to Retrieve Land Surface Temperature from the Landsat-8 Thermal Band

Stepwise Disaggregation of SMAP Soil Moisture at 100 m Resolution Using Landsat-7/8 Data and a Varying Intermediate Resolution

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

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