Kalman filter physical retrieval of surface emissivity and temperature from SEVIRI infrared channels: a validation and intercomparison study

Masiello, Guido; Serio, Carmine; Venafra, Sara; Liuzzi, Giuliano; Göttsche, Frank-M.; Trigo, Isabel F.; Watts, P. D.

doi:10.5194/amt-8-2981-2015

Cited by 53 publications

(49 citation statements)

References 38 publications

(51 reference statements)

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“…In order to better observe the changes, in climate for example, there is a need for frequent data acquisition to obtain consistent LST time series [8]. Especially at the regional [9] and global scales, the only way to study the surface temperature is with satellite monitoring [10].…”

Section: Introductionmentioning

confidence: 99%

Online Global Land Surface Temperature Estimation from Landsat

et al. 2017

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This study explores the estimation of land surface temperature (LST) for the globe from Landsat 5, 7 and 8 thermal infrared sensors, using different surface emissivity sources. A single channel algorithm is used for consistency among the estimated LST products, whereas the option of using emissivity from different sources provides flexibility for the algorithm's implementation to any area of interest. The Google Earth Engine (GEE), an advanced earth science data and analysis platform, allows the estimation of LST products for the globe, covering the time period from 1984 to present. To evaluate the method, the estimated LST products were compared against two reference datasets: (a) LST products derived from ASTER (Advanced Spaceborne Thermal Emission and Reflection Radiometer), as higher-level products based on the temperature-emissivity separation approach; (b) Landsat LST data that have been independently produced, using different approaches. An overall RMSE (root mean square error) of 1.52 • C was observed and it was confirmed that the accuracy of the LST product is dependent on the emissivity; different emissivity sources provided different LST accuracies, depending on the surface cover. The LST products, for the full Landsat 5, 7 and 8 archives, are estimated "on-the-fly" and are available on-line via a web application.

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Section: Introductionmentioning

confidence: 99%

Online Global Land Surface Temperature Estimation from Landsat

et al. 2017

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“…Spatial resolution of products is around 3 km but earth is fully scanned every 15 min, and the retrieved surface emissivity and temperature accuracy is of ±0.005 and ±0.2 • C, respectively. nary platform plus ECMWF meteorological analysis (used also here) to directly retrieve surface temperature and the three channels emissivities [8,9]. Spatial resolution of products is around 3 km but earth is fully scanned every 15 min, and the retrieved surface emissivity and temperature accuracy is of ±0.005 and ±0.2 • C, respectively.…”

Section: Introductionmentioning

confidence: 99%

“…MOD11A1 (Collection 5), which uses a grid spatial resolution of 1000 m, is based on the generalized split-window LST algorithm applied to the MODIS thermal multispectral bands [7]. Note that recent works proposed an operational Kalman filter strategy applied to the 3 IR SEVIRI (Spring Enhanced Visible and Infrared Imager) channels from geostationary platform plus ECMWF meteorological analysis (used also here) to directly retrieve surface temperature and the three channels emissivities [8,9]. Spatial resolution of products is around 3 km but earth is fully scanned every 15 min, and the retrieved surface emissivity and temperature accuracy is of ±0.005 and ±0.2 • C, respectively.…”

Section: Introductionmentioning

confidence: 99%

A Software Tool for Atmospheric Correction and Surface Temperature Estimation of Landsat Infrared Thermal Data

et al. 2016

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Land surface temperature (LST) is an important variable involved in the Earth's surface energy and water budgets and a key component in many aspects of environmental research. The Landsat program, jointly carried out by NASA and the USGS, has been recording thermal infrared data for the past 40 years. Nevertheless, LST data products for Landsat remain unavailable. The atmospheric correction (AC) method commonly used for mono-window Landsat thermal data requires detailed information concerning the vertical structure (temperature, pressure) and the composition (water vapor, ozone) of the atmosphere. For a given coordinate, this information is generally obtained through either radio-sounding or atmospheric model simulations and is passed to the radiative transfer model (RTM) to estimate the local atmospheric correction parameters. Although this approach yields accurate LST data, results are relevant only near this given coordinate. To meet the scientific community's demand for high-resolution LST maps, we developed a new software tool dedicated to processing Landsat thermal data. The proposed tool improves on the commonly-used AC algorithm by incorporating spatial variations occurring in the Earth's atmosphere composition. The ERA-Interim dataset (ECMWFmeteorological organization) was used to retrieve vertical atmospheric conditions, which are available at a global scale with a resolution of 0.125 degrees and a temporal resolution of 6 h. A temporal and spatial linear interpolation of meteorological variables was performed to match the acquisition dates and coordinates of the Landsat images. The atmospheric correction parameters were then estimated on the basis of this reconstructed atmospheric grid using the commercial RTMsoftware MODTRAN. The needed surface emissivity was derived from the common vegetation index NDVI, obtained from the red and near-infrared (NIR) bands of the same Landsat image. This permitted an estimation of LST for the entire image without degradation in resolution. The software tool, named LANDARTs, which stands for Landsat automatic retrieval of surface temperatures, is fully automatic and coded in the programming language Python. In the present paper, LANDARTs was used for the local and spatial validation of surface temperature obtained from a Landsat dataset covering two climatically contrasting zones: southwestern France and central Tunisia. Long-term datasets of in situ surface temperature measurements for both locations were compared to corresponding Landsat LST data. This temporal comparison yielded RMSE values ranging from 1.84 • C-2.55 • C. Landsat surface temperature data obtained with LANDARTs were then spatially compared using the ASTER data products of kinetic surface temperatures (AST08) for both geographical zones. This comparison yielded a satisfactory RMSE of about 2.55 • C. Finally, a sensitivity analysis for the effect of spatial validation on the LST correction process showed a variability of up to 2 • C for an entire Landsat image, confirming that the proposed s...

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“…Apart from being a fundamental variable in quantifying elements of the surface energy budget [1], LST has been used to study ocean-atmosphere interactions [2], to track global warming and climate change impacts [3,4], as well as being widely used in studies of vegetation monitoring [5], drought persistence [6] and urban climate assessments [7]. LST also plays a critical role in linking the water and energy cycles through its relationship with surface heat fluxes [8].…”

Section: Introductionmentioning

confidence: 99%

Sensitivity of Landsat 8 Surface Temperature Estimates to Atmospheric Profile Data: A Study Using MODTRAN in Dryland Irrigated Systems

2017

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Abstract:The land surface temperature (LST) represents a critical element in efforts to characterize global surface energy and water fluxes, as well as being an essential climate variable in its own right. Current satellite platforms provide a range of spatial and temporal resolution radiance data from which LST can be determined. One of the most complete records of data comes via the Landsat series of satellites, which provide a continuous sequence that extends back to 1982. However, for much of this time, Landsat thermal data were provided through a single broadband thermal channel, making surface temperature retrieval challenging. To fully exploit the valuable time-series of thermal information that is available from these satellites requires efforts to better describe and understand the accuracy of temperature retrievals. Here, we contribute to these efforts by examining the impact of atmospheric correction on the estimation of LST, using atmospheric profiles derived from a range of in-situ, reanalysis, and satellite data. Radiance data from the thermal infrared (TIR) sensor onboard Landsat 8 was converted to LST by using the MODTRAN version 5.2 radiative transfer model, allowing the production of an LST time series based upon 28 Landsat overpasses. LST retrievals were then evaluated against in-situ thermal measurements collected over an arid zone farmland comprising both bare soil and vegetated surface types. Atmospheric profiles derived from AIRS, MOD07, ECMWF, NCEP, and balloon-based radiosonde data were used to drive the MODTRAN simulations. In addition to examining the direct impact of using various profile data on LST retrievals, randomly distributed errors were introduced into a range of forcing variables to better understand retrieval uncertainty. Results indicated differences in LST of up to 1 K for perturbations in emissivity and profile measurements, with the analysis also highlighting the challenges in modeling aerosol optical depth (AOD) over arid lands and its impact on the TIR bands. Days with high AOD content (AOD > 0.5) in the evaluation study seem to consistently underestimate in-situ LSTs by 1-2 K, suggesting that MODTRAN is unable to accurately simulate the aerosol conditions for the TIR bands. Comparisons between available in-situ and Landsat 8 derived LST illustrate a range of seasonal and land surface dynamics and provide an assessment of retrieval accuracy throughout the nine-month long study period. In terms of the choice of atmospheric profile, when excluding the in-situ data, results show a mean absolute range of between 1.2 K to 1.8 K over bare soil and 3.3 K to 3.8 K over alfalfa for the different meteorological forcing, with the AIRS profile providing the best reproduction over the studied arid land irrigation region.

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Kalman filter physical retrieval of surface emissivity and temperature from SEVIRI infrared channels: a validation and intercomparison study

Cited by 53 publications

References 38 publications

Online Global Land Surface Temperature Estimation from Landsat

Online Global Land Surface Temperature Estimation from Landsat

A Software Tool for Atmospheric Correction and Surface Temperature Estimation of Landsat Infrared Thermal Data

Sensitivity of Landsat 8 Surface Temperature Estimates to Atmospheric Profile Data: A Study Using MODTRAN in Dryland Irrigated Systems

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