0000000000433378
AUTHOR
Eduardo Caselles
Estimation of the water table depth of the Calarasi district Island (Romania) at the Danube River using ASTER/DEM data
The water table is the top level of ground water by definition. Therefore surface water is an exposed part of the water table. Airborne measurements, resistivimeters determinations or perforation analyses can be used to determine the water table depth. These methods require, approximately, taking a sample per hectare, which is a very expensive and time-consuming procedure. However, remote sensing constitutes an ideal alternative to determine water table depth, because unlike the existing methodologies, which are very expensive due to equipment and travel expenses, the proposed methodology is cheap and simple. The ASTER GDEM data is available at no charge to users via electronic download and…
Automatic classification-based generation of thermal infrared land surface emissivity maps using AATSR data over Europe
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 fract…
Long-term accuracy assessment of land surface temperatures derived from the Advanced Along-Track Scanning Radiometer
Abstract The accuracy of land surface temperatures (LSTs) derived from the Advanced Along-Track Scanning Radiometer (AATSR) was assessed in a test site in Valencia, Spain from 2002 to 2008. AATSR LSTs were directly compared with concurrent ground measurements over homogeneous, full-vegetated rice fields in the conventional temperature-based (T-based) method. We also applied the new radiance-based (R-based) method over bare soil and water surfaces, where ground LST measurements were not available. In the R-based method, ground LSTs are simulated from AATSR brightness temperatures in the 11 μm band and radiative transfer simulations using surface emissivity data and atmospheric water vapor an…
Application of artificial neural networks and logistic regression to the prediction of forest fire danger in Galicia using MODIS data
Fire danger models are a very useful tool for the prevention and extinction of forest fires. Some inputs of these models, such as vegetation status and temperature, can be obtained from remote sensing images, which offer higher spatial and temporal resolution than direct ground measures. In this paper, we focus on the Galicia region (north-west of Spain), and MODIS (Moderate Resolution Imaging Spectroradiometer) images are used to monitor vegetation status and to obtain land surface temperature as essential inputs in forest fire danger models. In this work, we tested the potential of artificial neural networks and logistic regression to estimate forest fire danger from remote sensing and f…
Automatic Generation of Land Surface Emissivity Maps
The remote sensing measurement of the land surface temperature (LST) from satellites provides an overview of this magnitude on a continuous and regular basis. The study of its evolution in time and space is a critical factor in many scientific fields such as weather forecasting, detection of forest fires, climate change, etc. The main problem of making this measurement from satellite data is the need to correct the effects of the atmosphere and the land surface emissivity (LSE). Nowadays, these corrections are usually made using a split-window algorithm, which has an explicit dependence on land surface emissivity. Therefore, the aim of our work was to define an enhanced vegetation cover met…