Intercalibration of NOAA and Meteosat window channel brightness temperatures

Barroso, Carla; Trigo, Isabel F.; Olesen, F.; DaCamara, Carlos C.; Queluz, Maria Paula

doi:10.1080/01431160500159834

Cited by 14 publications

(8 citation statements)

References 12 publications

(24 reference statements)

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“…It is possible to estimate a wide range of land surface variables, including Land Surface Temperature (LST) data from many different sensors on-board geostationary or polar orbit platforms. The use of various algorithms, often based on very different assumptions, together with the diverse sensor spatial and temporal samplings and viewing perspective, makes it difficult to harmonize the various satellite products available (e.g., Barroso et al, 2005;Pinheiro et al, 2006;Rasmussen et al 2010). This is particularly true in the case of LST, a markedly directional variable that, among other factors, is strongly affected by differences on viewing and illumination geometry.…”

Section: Discussionmentioning

confidence: 99%

Modelling directional effects on remotely sensed land surface temperature

Ermida

DaCamara

Trigo

et al. 2017

Remote Sensing of Environment

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Land Surface Temperature (LST) is a markedly directional variable and its remotely sensed measurement may be strongly affected by viewing and illumination geometries. This study proposes the use of LST products collocated in space and time, but obtained with different viewing angles, to calibrate a simple model capable of characterizing the LST angular variability. The exercise is performed using MODIS (Aqua and Terra) and SEVIRI (Meteosat) LST products, for an area covering Mediterranean Europe and Northern Africa and encompassing the full years of 2011, 2012, 2013 and 2014. The approach relies on a kernel model that is composed by an "emissivity kernel" and a "solar kernel", associated to observation angle anisotropy and to shadowing/sunlit effects on the surface, respectively. The spatial distribution of the kernel coefficients is shown to reflect characteristics of the landscape, both in terms of vegetation cover and topography. Model performance is assessed through several comparison exercises over the 4-year period under analysis. Cross-validation results show that the angular correction by the kernel model leads to a decrease of the root mean square difference between SEVIRI and MODIS daytime (night-time) LST products, from the original uncorrected values of 3.5 K (1.5 K) to 2.3 K (1.3 K). Comparison of both MSG and MODIS LST products against in situ daytime measurements gathered over 2 years at a validation site in Évora (Portugal) reveals that the angular correction leads to a decrease in root mean square error from 4.6 K (2.0 K) to 3.8 K (1.9 K) for MODIS (SEVIRI). The kernel model may be a useful tool to quantify the LST uncertainties associated with viewing and illumination angles. Ermida et al., 2014, Duffour et al., 2015. This effect contributes to enhance the differences among LST satellite products, and therefore increasing the challenge of using multi-sensor and multi-decadal data to provide harmonised LST datasets suitable for long-term climate observations. Accurate estimates of the angular effects on retrieved LST are also crucial when performing in situ and cross-sensor validation exercises (Ermida et al., 2014). Quantification of these effects may also be relevant when using LST for model assessment (e.g. Wang et al. 2014; Trigo et al., 2015) and data assimilation (e.g. English 2008; Ghent et al., 2010).

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Section: Discussionmentioning

confidence: 99%

Modelling directional effects on remotely sensed land surface temperature

Ermida

DaCamara

Trigo

et al. 2017

Remote Sensing of Environment

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“…Land surface temperature (LST), or directional radiometric temperature of the surface, provides the best approximation to the thermodynamic temperature based on a radiance measurements (Norman and Becker 1995). It should be kept in mind, however, that directional effects are important for heterogeneous and non-isothermal surfaces, such as a satellite pixels over land (Barroso et al 2005, Trigo et al 2008a). There, thermodynamic temperature would be better represented by the hemispherical radiometric temperature (Norman and Becker 1995).…”

Section: Products and Algorithmsmentioning

confidence: 99%

The Satellite Application Facility for Land Surface Analysis

Trigo

DaCamara

Viterbo

et al. 2011

International Journal of Remote Sensing

240

221

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Information on land surface properties finds applications in a range of areas related to weather forecasting, environmental research, hazard management and climate monitoring. Remotely sensed observations yield the only means of supplying land surface information with adequate time sampling and a wide spatial coverage. The aim of the Satellite Application Facility for Land Surface Analysis (Land-SAF) is to take full advantage of remotely sensed data to support land, land-atmosphere and biosphere applications, with emphasis on the development and implementation of algorithms that allow operational use of data from European Organization for the Exploitation of Meteorological Satellites (EUMETSAT) sensors. This article provides an overview of the Land-SAF, with brief descriptions of algorithms and validation results. The set of parameters currently estimated and disseminated by the Land-SAF consists of three main groups: (i) the surface radiation budget, including albedo, land surface temperature, and downward short-and longwave fluxes; (ii) the surface water budget (snow cover and evapotranspiration); and (iii) vegetation and wild-fire parameters.

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“…Discrepancies between IR Ts products are generally attributed to differences (1) in the top-of-atmosphere measurements (sensor calibration, spatial resolutions, and spectral channels), (2) in the algorithm and auxiliary data used for atmospheric and surface emissivity correction, (3) in cloudmask, and (4) in angular anisotropy [Barroso et al, 2005;Pinheiro et al, 2006;Trigo et al, 2008;Rasmussen et al, 2010;Guillevic et al, 2013;Ermida et al, 2014].…”

Section: Geo-modis Comparisonmentioning

confidence: 99%

Inversion of AMSR‐E observations for land surface temperature estimation: 2. Global comparison with infrared satellite temperature

et al. 2017

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A comparison of land surface temperature (Ts) derived from the Advanced Microwave Scanning Radiometer–Earth Observing System (AMSR‐E) with infrared Ts is presented. The infrared Ts include clear‐sky estimates from the Moderate Resolution Imaging Spectroradiometer (MODIS), the Spinning Enhanced Visible and Infrared Imager, the Geostationary Operational Environmental Satellite (GOES) Imager, and the Japanese Meteorological Imager. The higher discrepancies between AMSR‐E and MODIS are observed over deserts and snow‐covered areas. The former seems to be associated with Ts underestimation by MODIS, whereas the latter is mostly related to uncertainties in microwave emissivity over snow/ice. Ts differences between AMSR‐E and MODIS are significantly reduced after masking out snow and deserts, with a bias change from 2.6/4.6 K to 3.0/1.4 K for daytime/nighttime and a standard deviation (STD) decrease from 7.3/7.9 K to 5.1/3.9 K. When comparing with all infrared sensors, the STD of the differences between microwave and infrared Ts is generally higher than between IR retrievals. However, the biases between microwave and infrared Ts are, in some cases, of the same order as the ones observed between infrared products. This is the case for GOES, with daytime biases with respect to AMSR‐E and MODIS of 0.45 K and 0.60 K, respectively. While the infrared Ts are clear‐sky estimates, AMSR‐E also provides Ts under cloudy conditions. For frequently cloudy regions, this results in a large increase of available Ts estimates (>250%), making the microwave Ts a very powerful complement of the infrared estimates.

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Intercalibration of NOAA and Meteosat window channel brightness temperatures

Cited by 14 publications

References 12 publications

Modelling directional effects on remotely sensed land surface temperature

Modelling directional effects on remotely sensed land surface temperature

The Satellite Application Facility for Land Surface Analysis

Inversion of AMSR‐E observations for land surface temperature estimation: 2. Global comparison with infrared satellite temperature

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