Sensible and latent heat flux from radiometric surface temperatures at the regional scale: methodology and validation

The CarboEurope Regional Experiment Strategy (CERES) was designed to develop and test a range of methodologies to assess regional surface energy and mass exchange of a large study area in the south-western part of France. This paper describes a methodology to estimate sensible and latent heat fluxes on the basis of net radiation, surface radiometric temperature measurements and information obtained from available products derived from the Meteosat Second Generation (MSG) geostationary meteorological satellite, weather stations and ground-based eddy covariance towers. It is based on a simplified bulk formulation of sensible heat flux that considers the degree of coupling between the vegetation and the atmosphere and estimates latent heat as the residual term of net radiation. Estimates of regional energy fluxes obtained in this way are validated at the regional scale by means of a comparison with direct flux measurements made by airborne eddy-covariance. The results show an overall good matching between airborne fluxes and estimates of sensible and latent heat flux obtained from radiometric surface temperatures that holds for different weather conditions and different land use types. The overall applicability of the proposed methodology to regional studies is discusse

The Physics and Mass Assembly of distant galaxies with the E-ELT

One of the main science goal of the future European Extremely Large Telescope will be to understand the mass assembly process in galaxies as a function of cosmic time. To this aim, a multi-object, AO-assisted integral field spectrograph will be required to map the physical and chemical properties of very distant galaxies. In this paper, we examine the ability of such an instrument to obtain spatially resolved spectroscopy of a large sample of massive (0.1<Mstellar<5e11Mo) galaxies at 2<z<6, selected from future large area optical-near IR surveys. We produced a set of about one thousand numerical simulations of 3D observations using reasonable assumptions about the site, telescope, and instrument, and about the physics of distant galaxies. These data-cubes were analysed as real data to produce realistic kinematic measurements of very distant galaxies. We then studied how sensible the scientific goals are to the observational (i.e., site-, telescope-, and instrument-related) and physical (i.e., galaxy-related) parameters. We specifically investigated the impact of AO performance on the science goal. We did not identify any breaking points with respect to the parameters (e.g., the telescope diameter), with the exception of the telescope thermal background, which strongly limits the performance in the highest (z>5) redshift bin. We find that a survey of Ngal galaxies that fulfil the range of science goals can be achieved with a ~90 nights program on the E-ELT, provided a multiplex capability M Ngal/8.Comment: 21 pages, 13 figures, 7 tables. Accepted for publication in MNRA

The Photodetector Array Camera and Spectrometer (PACS) on the Herschel Space Observatory

The Photodetector Array Camera and Spectrometer (PACS) is one of the three science instruments on ESA's far infrared and submillimetre observatory. It employs two Ge:Ga photoconductor arrays (stressed and unstressed) with 16x25 pixels, each, and two filled silicon bolometer arrays with 16x32 and 32x64 pixels, respectively, to perform integral-field spectroscopy and imaging photometry in the 60-210\mu\ m wavelength regime. In photometry mode, it simultaneously images two bands, 60-85\mu\ m or 85-125\mu\m and 125-210\mu\ m, over a field of view of ~1.75'x3.5', with close to Nyquist beam sampling in each band. In spectroscopy mode, it images a field of 47"x47", resolved into 5x5 pixels, with an instantaneous spectral coverage of ~1500km/s and a spectral resolution of ~175km/s. We summarise the design of the instrument, describe observing modes, calibration, and data analysis methods, and present our current assessment of the in-orbit performance of the instrument based on the Performance Verification tests. PACS is fully operational, and the achieved performance is close to or better than the pre-launch predictions

Mapping surface features of an Alpine glacier through multispectral and thermal drone surveys

Glacier surfaces are highly heterogeneous mixtures of ice, snow, light-absorbing impurities and debris material. The spatial and temporal variability of these components affects ice surface characteristics and strongly influences glacier energy and mass balance. Remote sensing offers a unique opportunity to characterize glacier optical and thermal properties, enabling a better understanding of different processes occurring at the glacial surface. In this study, we evaluate the potential of optical and thermal data collected from field and drone platforms to map the abundances of predominant glacier surfaces (i.e., snow, clean ice, melting ice, dark ice, cryoconite, dusty snow and debris cover) on the Zebrù glacier in the Italian Alps. The drone surveys were conducted on the ablation zone of the glacier on 29 and 30 July 2020, corresponding to the middle of the ablation season. We identified very high heterogeneity of surface types dominated by melting ice (30% of the investigated area), dark ice (24%), clean ice (19%) and debris cover (17%). The surface temperature of debris cover was inversely related to debris-cover thickness. This relation is influenced by the petrology of debris cover, suggesting the importance of lithology when considering the role of debris over glaciers. Multispectral and thermal drone surveys can thus provide accurate high-resolution maps of different snow and ice types and their temperature, which are critical elements to better understand the glacier’s energy budget and melt rates

Automatic classification-based generation of thermal infrared land surface emissivity maps using AATSR data over Europe

This is the author’s version of a work that was accepted for publication in Remote Sensing of Environment. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Remote Sensing of Environment, 124, 321-333.DOI :10.1016/j.rse.2012.05.024.The remote sensing measurement of land surface temperature from satellites provides a monitoring of this magnitude on a continuous and regular basis, which is a critical factor in many research fields such as weather forecasting, detection of forest fires or climate change studies, for instance. The main problem of measuring temperature from space is the need to correct for the effects of the atmosphere and the surface emissivity. In this work an automatic procedure based on the Vegetation Cover Method, combined with the GLOBCOVER land surface type classification, is proposed. The algorithm combines this land cover classification with remote sensing information on the vegetation cover fraction to obtain land surface emissivity maps for AATSR split-window bands. The emissivity estimates have been compared with ground measurements in two validation cases in the area of rice fields of Valencia, Spain, and they have also been compared to the classification-based emissivity product provided by MODIS (MOD11_L2). The results show that the error in emissivity of the proposed methodology is of the order of ±0.01 for most of the land surface classes considered, which will contribute to improve the operational land surface temperature measurements provided by the AATSR instrument. © 2012 Elsevier Inc. All rights reserved.This work was funded by the Generalitat Valenciana (project PRO-METEO/2009/086, and contract of Eduardo Caselles) and the Spanish Ministerio de Ciencia e Innovacion (projects CGL2007-64666/CLI, CGL2010-17577/CLI and CGL2007-29819-E, co-financed with FEDER funds). AATSR data were provided by European Space Agency (ESA) under Cat-1 project 3466. We also thank ESA and the ESA GLOBCOVER Project, led by MEDIAS-France, for the GLOBCOVER classification data. The comments and suggestions of three anonymous reviewers that improved the paper are also acknowledged.Caselles, E.; Valor, E.; Abad Cerdá, FJ.; Caselles, V. (2012). Automatic classification-based generation of thermal infrared land surface emissivity maps using AATSR data over Europe. Remote Sensing of Environment. 124:321-333. https://doi.org/10.1016/j.rse.2012.05.024S32133312

Satellite remote sensing for ice sheet research

Potential research applications of satellite data over the terrestrial ice sheets of Greenland and Antarctica are assessed and actions required to ensure acquisition of relevant data and appropriate processing to a form suitable for research purposes are recommended. Relevant data include high-resolution visible and SAR imagery, infrared, passive-microwave and scatterometer measurements, and surface topography information from laser and radar altimeters