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Isabel Trigo   Dr.  Senior Scientist or Principal Investigator 
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Isabel Trigo published an article in February 2019.
Top co-authors See all
S. I. Seneviratne

204 shared publications

Institute for Atmospheric and Climate Science; ETH Zürich; Zurich Switzerland

Frédéric Chevallier

203 shared publications

Le Laboratoire des Sciences du Climat et de L'Environnement

Florian Pappenberger

189 shared publications

European Centre for Medium-Range Weather Forecasts, Reading, UK

Xubin Zeng

157 shared publications

Department of Hydrology and Atmospheric Sciences, The University of Arizona, Tucson, Arizona

Clément Albergel

146 shared publications

CNRM—Université de Toulouse, Météo-France, CNRS, 31057 Toulouse, France

7
Publications
3
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18
Citations
Publication Record
Distribution of Articles published per year 
(2015 - 2019)
Total number of journals
published in
 
4
 
Publications See all
Article 0 Reads 0 Citations A New Method to Estimate Reference Crop Evapotranspiration from Geostationary Satellite Imagery: Practical Consideration... Henk A. R. De Bruin, Isabel F. Trigo Published: 22 February 2019
Water, doi: 10.3390/w11020382
DOI See at publisher website ABS Show/hide abstract
Reference crop evapotranspiration (ETo) plays a role in irrigation advisory being of crucial importance for water managers dealing with scarce water resources. Following the ETo definition, it can be shown that total solar radiation is the main driver, allowing ETo estimates from satellite observations. As such, the EUMETSAT LSA-SAF operationally provides ETo primarily derived from the European geostationary satellite MSG. ETo estimations following the original FAO report require several meteorological observations gathered over actual well-watered grass. Here we will consider the impact of two effects on ETo using the LSA-SAF and FAO methodologies: (i) local advection, related to the impact of advection of surrounding warm dry air onto the reference non-water stressed surface; and (ii) the so-called surface aridity error, which occurs when calculating ETo according to FAO, but with input data not collected over well-watered grass. The LSA-SAF ETo is not sensitive to any of these effects. However, it is shown that local advection may increase evapotranspiration over a limited field by up to 30%, while ignoring aridity effects leads to a great overestimation. The practical application of satellite estimates of ETo provided by the LSA-SAF are discussed here, and, furthermore, water managers are encouraged to consider its advantages and ways for improvement.
Article 0 Reads 0 Citations Quantifying the Clear-Sky Bias of Satellite Land Surface Temperature Using Microwave-Based Estimates Sofia L. Ermida, Isabel F. Trigo, Carlos C. Dacamara, Carlos... Published: 27 January 2019
Journal of Geophysical Research: Atmospheres, doi: 10.1029/2018jd029354
DOI See at publisher website
Article 1 Read 1 Citation A Physically Constrained Calibration Database for Land Surface Temperature Using Infrared Retrieval Algorithms Isabel Trigo, Virgílio Bento, Carlos Da Camara, João Martins Published: 28 September 2016
Remote Sensing, doi: 10.3390/rs8100808
DOI See at publisher website ABS Show/hide abstract
Land surface temperature (LST) is routinely retrieved from remote sensing instruments using semi-empirical relationships between top of atmosphere (TOA) radiances and LST, using ancillary data such as total column water vapor or emissivity. These algorithms are calibrated using a set of forward radiative transfer simulations that return the TOA radiances given the LST and the thermodynamic profiles. The simulations are done in order to cover a wide range of surface and atmospheric conditions and viewing geometries. This study analyzes calibration strategies while considering some of the most critical factors that need to be taken into account when building a calibration dataset, covering the full dynamic range of relevant variables. A sensitivity analysis of split-windows and single channel algorithms revealed that selecting a set of atmospheric profiles that spans the full range of surface temperatures and total column water vapor combinations that are physically possible seems beneficial for the quality of the regression model. However, the calibration is extremely sensitive to the low-level structure of the atmosphere, indicating that the presence of atmospheric boundary layer features such as temperature inversions or strong vertical gradients of thermodynamic properties may affect LST retrievals in a non-trivial way. This article describes the criteria established in the EUMETSAT Land Surface Analysis—Satellite Application Facility to calibrate its LST algorithms, applied both for current and forthcoming sensors.
PREPRINT 0 Reads 0 Citations A Physically-Constrained Calibration Database for Land Surface Temperature Using Infrared Retrieval Algorithms João P. A. Martins, Isabel F. Trigo, Virgílio A. Bento, Carl... Published: 16 September 2016
EARTH SCIENCES, doi: 10.20944/preprints201608.0073.v2
DOI See at publisher website ABS Show/hide abstract
Land Surface Temperature (LST) is routinely retrieved from remote sensing instruments using semi-empirical relationships between top of atmosphere (TOA) radiances and LST, using ancillary data such as total column water vapor or emissivity. These algorithms are calibrated using a set of forward radiative transfer simulations that return the TOA radiances given the LST and the thermodynamic profiles. The simulations are done in order to cover a wide range of surface and atmospheric conditions and viewing geometries. This work analyses calibration strategies, considering some of the most critical factors that need to be taken into account when building a calibration dataset, covering the full dynamic range of relevant variables. A sensitivity analysis of split-windows and single channel algorithms revealed that selecting a set of atmospheric profiles that spans the full range of surface temperatures and total column water vapor combinations that are physically possible seems beneficial for the quality of the regression model. However, the calibration is extremely sensitive to the low-level structure of the atmosphere indicating that the presence of atmospheric boundary layer features such as temperature inversions or strong vertical gradients of thermodynamic properties may affect LST retrievals in a non-trivial way. This article describes the criteria established in the EUMETSAT Land Surface Analysis – Satellite Application Facility to calibrate its LST algorithms applied both for current and forthcoming sensors.
Article 0 Reads 1 Citation Downscaling Meteosat Land Surface Temperature over a Heterogeneous Landscape Using a Data Assimilation Approach Rihab&nbspmechri, Catherine&nbspottlé, Olivier&nbsppannekouc... Published: 11 July 2016
Remote Sensing, doi: 10.3390/rs8070586
DOI See at publisher website ABS Show/hide abstract
A wide range of environmental applications require the monitoring of land surface temperature (LST) at frequent intervals and fine spatial resolutions, but these conditions are not offered nowadays by the available space sensors. To overcome these shortcomings, LST downscaling methods have been developed to derive higher resolution LST from the available satellite data. This research concerns the application of a data assimilation (DA) downscaling approach, the genetic particle smoother (GPS), to disaggregate Meteosat 8 LST time series (3 km × 5 km) at finer spatial resolutions. The methodology was applied over the Crau-Camargue region in Southeastern France for seven months in 2009. The evaluation of the downscaled LSTs has been performed at a moderate resolution using a set of coincident clear-sky MODIS LST images from Aqua and Terra platforms (1 km × 1 km) and at a higher resolution using Landsat 7 data (60 m × 60 m). The performance of the downscaling has been assessed in terms of reduction of the biases and the root mean square errors (RMSE) compared to prior model-simulated LSTs. The results showed that GPS allows downscaling the Meteosat LST product from 3 × 5 km2 to 1 × 1 km2 scales with a RMSE less than 2.7 K. Finer scale downscaling at Landsat 7 resolution showed larger errors (RMSE around 5 K) explained by land cover errors and inter-calibration issues between sensors. Further methodology improvements are finally suggested.
Article 1 Read 6 Citations Long Term Validation of Land Surface Temperature Retrieved from MSG/SEVIRI with Continuous in-Situ Measurements in Afric... Frank-M. Göttsche, Folke-S. Olesen, Isabel F. Trigo, Annika ... Published: 13 May 2016
Remote Sensing, doi: 10.3390/rs8050410
DOI See at publisher website ABS Show/hide abstract
Since 2005, the Land Surface Analysis Satellite Application Facility (LSA SAF) operationally retrieves Land Surface Temperature (LST) for the Spinning Enhanced Visible and Infrared Imager (SEVIRI) on board Meteosat Second Generation (MSG). The high temporal resolution of the Meteosat satellites and their long term availability since 1977 make their data highly valuable for climate studies. In order to ensure that the LSA SAF LST product continuously meets its target accuracy of 2 °C, it is validated with in-situ measurements from four dedicated LST validation stations. Three stations are located in highly homogenous areas in Africa (semiarid bush, desert, and Kalahari semi-desert) and typically provide thousands of monthly match-ups with LSA SAF LST, which are used to perform seasonally resolved validations. An uncertainty analysis performed for desert station Gobabeb yielded an estimate of total in-situ LST uncertainty of 0.8 ± 0.12 °C. Ignoring rainy seasons, the results for the period 2009–2014 show that LSA SAF LST consistently meets its target accuracy: the highest mean root-mean-square error (RMSE) for LSA SAF LST over the African stations was 1.6 °C while mean absolute bias was 0.1 °C. Nighttime and daytime biases were up to 0.7 °C but had opposite signs: when evaluated together, these partially compensated each other.
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