Land surface temperature retrieval from MSG1-SEVIRI data

Sobrino, José A.; Romaguera, M.

doi:10.1016/j.rse.2004.06.009

Cited by 135 publications

(82 citation statements)

References 11 publications

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“…These results suggest that the retrieval capabilities of the CSW algorithm could benefit from further improvements prior to using the algorithm in various applications, including regeneration of coefficients based on the critical impacting factors and investigation of the co-linearity between MODIS and COMS LST products. Additionally, the collection of more in situ observations of LST for different land covers and weather conditions could greatly help to improve the future assessments of the quantitative accuracy of the COMS LST data, because the MODIS LST data is not ground truth data [16,28,40,55].…”

Section: Discussionmentioning

confidence: 99%

“…The surface emissivity for the infrared radiation channel IR1 was set to vary from 0.9478 to 0.9968 in progressive increments of 0.0049, and the emissivity difference between IR1 and IR2 channels was set to vary from −0.012 to 0.012 in progressive increments of 0.004. The conditions used in this study are similar to Sobrino and Romaguera [40] and Tang et al [32]. As a result, the number of total simulations amounted to 359 (profiles and SZAs) × 12 (surface lapse rates) × 11 (emissivity values) × 7 (emissivity differences) = 331,716.…”

Section: Development Of Lst Retrieval Algorithmmentioning

confidence: 91%

“…In this paper, MODTRAN4 [36] has been used, because it represents the state-of-the-art in realistic computing of absorption and scattering in the terrestrial atmosphere at high spectral resolution (1 cm ) over the various infrared spectral ranges, providing accurate simulations of atmospheric radiative transfer [37][38][39]. Furthermore, the model was based on the angular dependence of the detected radiance, similar to what has been done in previous studies [2,31,32,40]. We assumed that the sky was completely clear and that the main factors affecting the accuracy of the retrieved LST data using the SW method were atmospheric profiles of temperature and water vapor, surface emissivity, SZA and lapse rate at the surface layer.…”

Section: Development Of Lst Retrieval Algorithmmentioning

confidence: 99%

“…The spectral response functions of COMS IR1 and IR2 are slightly different from those of the Spinning Enhanced Visible and Infra-Red Imager (SEVIRI) instrument onboard the Meteosat Second Generation 2 (MSG2) satellite. In this study, we developed an SW-type LST retrieval algorithm, because the accuracy and efficiency of SW-type LST algorithms are well known [2,3,16,31,32,40]. Various types of linearized SW algorithms have been developed based on the differential absorption in two adjacent split-window channels (10-12.5 μm) linearizing the radiative transfer equation (e.g., [1][2][3]6,10]).…”

Section: Development Of Lst Retrieval Algorithmmentioning

confidence: 99%

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Evaluation of Land Surface Temperature Operationally Retrieved from Korean Geostationary Satellite (COMS) Data

Cho

Suh

2013

Remote Sensing

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Abstract:We evaluated the precision of land surface temperature (LST) operationally retrieved from the Korean multipurpose geostationary satellite, Communication, Ocean and Meteorological Satellite (COMS). The split-window (SW)-type retrieval algorithm was developed through radiative transfer model simulations under various atmospheric profiles, satellite zenith angles, surface emissivity values and surface lapse rate conditions using Moderate Resolution Atmospheric Transmission version 4 (MODTRAN4). The estimation capabilities of the COMS SW (CSW) LST algorithm were evaluated for various impacting factors, and the retrieval accuracy of COMS LST data was evaluated with collocated Moderate Resolution Imaging Spectroradiometer (MODIS) LST data. The surface emissivity values for two SW channels were generated using a vegetation cover method. The CSW algorithm estimated the LST distribution reasonably well (averaged bias = 0.00 K, Root Mean Square Error (RMSE) = 1.41 K, correlation coefficient = 0.99); however, the estimation capabilities of the CSW algorithm were significantly impacted by large brightness temperature differences and surface lapse rates. The CSW algorithm reproduced spatiotemporal variations of LST comparing well to MODIS LST data, irrespective of what month or time of day the data were collected from. The one-year evaluation results with MODIS LST data showed that the annual mean bias, RMSE and correlation coefficient for the CSW algorithm were −1.009 K, 2.613 K and 0.988, respectively.

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Section: Discussionmentioning

confidence: 99%

Section: Development Of Lst Retrieval Algorithmmentioning

confidence: 91%

Section: Development Of Lst Retrieval Algorithmmentioning

confidence: 99%

Section: Development Of Lst Retrieval Algorithmmentioning

confidence: 99%

See 2 more Smart Citations

Evaluation of Land Surface Temperature Operationally Retrieved from Korean Geostationary Satellite (COMS) Data

Cho

Suh

2013

Remote Sensing

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“…While Landsat-8 images are already freely distributed through the U.S. Geological Survey (USGS), to the best of our knowledge, no SWA for surface temperature retrieval from TIRS has been published. Although several SWAs have been developed for use with other sensors [14], [15], [16], [17], some adaptations are required in order to implement them for the TIRS spectral bands. Therefore, the objective of this letter is to develop a SWA, adapted for use with Landsat-8 TIRS data, along with its accuracy assessment.…”

Section: Landsat-8 Imagerymentioning

confidence: 99%

Feasibility Study of Landsat-8 Imagery for Retrieving Sea Surface Temperature (Case Study Persian Gulf)

Bayat¹,

Hasanlou²

2016

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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ABSTRACT:Sea surface temperature (SST) is one of the critical parameters in marine meteorology and oceanography. The SST datasets are incorporated as conditions for ocean and atmosphere models. The SST needs to be investigated for various scientific phenomenon such as salinity, potential fishing zone, sea level rise, upwelling, eddies, cyclone predictions. On the other hands, high spatial resolution SST maps can illustrate eddies and sea surface currents. Also, near real time producing of SST map is suitable for weather forecasting and fishery applications. Therefore satellite remote sensing with wide coverage of data acquisition capability can use as real time tools for producing SST dataset. Satellite sensor such as AVHRR, MODIS and SeaWIFS are capable of extracting brightness values at different thermal spectral bands. These brightness temperatures are the sole input for the SST retrieval algorithms. Recently, Landsat-8 successfully launched and accessible with two instruments on-board: (1) the Operational Land Imager (OLI) with nine spectral bands in the visual, near infrared, and the shortwave infrared spectral regions; and (2) the Thermal Infrared Sensor (TIRS) with two spectral bands in the long wavelength infrared. The two TIRS bands were selected to enable the atmospheric correction of the thermal data using a split window algorithm (SWA). The TIRS instrument is one of the major payloads aboard this satellite which can observe the sea surface by using the split-window thermal infrared channels (CH10: 10.6 μm to 11.2 μm; CH11: 11.5 μm to 12.5 μm) at a resolution of 30 m. The TIRS sensors have three main advantages comparing with other previous sensors. First, the TIRS has two thermal bands in the atmospheric window that provide a new SST retrieval opportunity using the widely used split-window (SW) algorithm rather than the single channel method. Second, the spectral filters of TIRS two bands present narrower bandwidth than that of the thermal band on board on previous Landsat sensors. Third, TIRS is one of the best space born and high spatial resolution with 30 m. in this regards, Landsat-8 can use the Split-Window (SW) algorithm for retrieving SST dataset. Although several SWs have been developed to use with other sensors, some adaptations are required in order to implement them for the TIRS spectral bands. Therefore, the objective of this paper is to develop a SW, adapted for use with Landsat-8 TIRS data, along with its accuracy assessment. In this research, that has been done for modelling SST using thermal Landsat 8-imagery of the Persian Gulf. Therefore, by incorporating contemporary in situ data and SST map estimated from other sensors like MODIS, we examine our proposed method with coefficient of determination (R 2 ) and root mean square error (RMSE) on check point to model SST retrieval for Landsat-8 imagery. Extracted results for implementing different SW's clearly shows superiority of utilized method by R 2 =0.95 and RMSE=0.24.

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Mapping Surface Heat Fluxes by Assimilating SMAP Soil Moisture and GOES Land Surface Temperature Data

Steele‐Dunne

Farhadi

et al. 2017

Water Resources Research

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Surface heat fluxes play a crucial role in the surface energy and water balance. In situ measurements are costly and difficult, and large‐scale flux mapping is hindered by surface heterogeneity. Previous studies have demonstrated that surface heat fluxes can be estimated by assimilating land surface temperature (LST) and soil moisture to determine two key parameters: a neutral bulk heat transfer coefficient (CHN) and an evaporative fraction (EF). Here a methodology is proposed to estimate surface heat fluxes by assimilating Soil Moisture Active Passive (SMAP) soil moisture data and Geostationary Operational Environmental Satellite (GOES) LST data into a dual‐source (DS) model using a hybrid particle assimilation strategy. SMAP soil moisture data are assimilated using a particle filter (PF), and GOES LST data are assimilated using an adaptive particle batch smoother (APBS) to account for the large gap in the spatial and temporal resolution. The methodology is implemented in an area in the U.S. Southern Great Plains. Assessment against in situ observations suggests that soil moisture and LST estimates are in better agreement with observations after assimilation. The RMSD for 30 min (daytime) flux estimates is reduced by 6.3% (8.7%) and 31.6% (37%) for H and LE on average. Comparison against a LST‐only and a soil moisture‐only assimilation case suggests that despite the coarse resolution, assimilating SMAP soil moisture data is not only beneficial but also crucial for successful and robust flux estimation, particularly when the uncertainties in the model estimates are large.

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Land surface temperature retrieval from MSG1-SEVIRI data

Cited by 135 publications

References 11 publications

Evaluation of Land Surface Temperature Operationally Retrieved from Korean Geostationary Satellite (COMS) Data

Evaluation of Land Surface Temperature Operationally Retrieved from Korean Geostationary Satellite (COMS) Data

Feasibility Study of Landsat-8 Imagery for Retrieving Sea Surface Temperature (Case Study Persian Gulf)

Mapping Surface Heat Fluxes by Assimilating SMAP Soil Moisture and GOES Land Surface Temperature Data

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