Vicente Caselles

Single band atmospheric correction tool for thermal infrared data: application to Landsat 7 ETM+

Atmospheric correction of Thermal Infrared (TIR) remote sensing data is a key process in order to obtain accurate land surface temperatures (LST). Single band atmospheric correction methods are used for sensors provided with a single TIR band. Which employs a radiative transfer model using atmospheric profiles over the study area as inputs to estimate the atmospheric transmittances and emitted radiances. Currently, TIR data from Landsat 5-TM, Landsat 7-ETM+ and Landsat 8-TIRS can be atmospherically corrected using the on-line Atmospheric Correction Parameter Calculator (ACPC, http://atmcorr.gsfc.nasa.gov). For specific geographical coordinates and observation time, the ACPC provides the atmospheric transmittance, and both upwelling and downwelling radiances, which are calculated from MODTRAN4 radiative transfer simulations with NCEP atmospheric profiles as inputs. Since the ACPC provides the atmospheric parameters for a single location, it does not account for their eventual variability within the full Landsat scene. The new Single Band Atmospheric Correction (SBAC) tool provides the geolocated atmospheric parameters for every pixel taking into account their altitude. SBAC defines a three-dimensional grid with 1°×1° latitude/longitude spatial resolution, corresponding to the location of NCEP profiles, and 13 altitudes from sea level to 5000 meters. These profiles are entered in MODTRAN5 to calculate the atmospheric parameters corresponding to a given pixel are obtained by weighted spatial interpolation in the horizontal dimensions and linear interpolation in the vertical dimension. In order to compare both SBAC and ACPC tools, we have compared with ground measurements the Landsat-7/ETM+ LST obtained using both tools over the Valencia ground validation site.

Discrepancies between eddy covariance and lysimeter measurements in the assessment of energy balance modeling in vineyards

Remote sensing-based models are a potential technique when evapotranspiration (ET) estimates are needed on a regional scale. These remote sensing methods are typically validated and calibrated using in situ measurements. Eddy covariance (EC) and lysimetry are two of the most prevalent techniques for measuring ET. Some discrepancies arise between these two techniques consequence of the measurement footprint or the spatial variability in atmospheric and surface conditions. An experiment was carried out in the growing season of 2015 in a ~4 ha row-crop vineyard in a semi-arid advective location in Central Spain, encouraged by the necessity to assess the feasibility of EC measurements in this area and under these conditions. A 9-m2 monolithic weighting lysimeter was available. An EC system was deployed together with a net radiometer and a set of soil heat flux plates. Data of the different terms of the energy balance equation were stored every 15 min, and then averaged at an hourly and daily scales. In this work we focus on the comparison between ET measurements from the two methods, EC and lysimetry. The imbalance in the surface energy budget was first analyzed. A lack of closure around 20% was observed. After forcing the closure, discrepancies between EC and lysimeter measurements still remained. Average estimation errors of ±0.09 mm h-1 and ±0.5 mm d-1 were obtained at hourly and daily scales, respectively, whereas a deviation of only 2% was observed in the accumulated ET for the entire experiment. These results support the use of adjusted EC technique to monitor accurate ET in vineyards.

Understanding the Effects of Fires on Surface Evapotranspiration Patterns Using Satellite Remote Sensing in Combination with an Energy Balance Model

Forest fires are highly destructive for nature, affecting the landscape, the natural cicle of the vegetation, and the structure and functioning of ecosystems. Beyond that, they also provoke changes in the local and regional meteorology, and particularly in the surface energy flux patterns. In a fire-affected area, changes in the ecosystem structure and species composition modify the evapotranspiration (LE) and the rest of the terms involved in the energy balance equation. Besides, these changes in the local energy balance may persist for decades (Randerson et al., 2006). There is an increasing concern among the scientific community about the effect of forest fires on climate change at this point (Randerson et al. 2006). In this work we focus on the study of the changes in the energy flux patterns after a forest fire, with particular emphasis on the evapotranspiration, which effect on the global system should be further analyzed by the radiative forcing models. The physical characterization of the hydrological processes plays a very important role in the framework of the activities for the management of hydrological resources. Particularly, the soil-vegetation-atmosphere energy exchanges are the basis of an appropriate hydrological balance, and thus, of an appropriate planning of the hydrological resources. The fusion of physical models for estimating the hydrological balance, and particularly the evapotranspiration, with technological advances for the characterization of hydrological, hydro-geological, and atmospheric issues, is of great utility. Although there are several surface-based methods that can accurately measure surface heat fluxes at point locations, it is not feasible to use a network of these systems to create spatially distributed flux maps because of the high variability of real landscapes. As stated by Scott et al. (2000), micrometeorological approaches can only realistically provide measurements representative of a particular type of vegetation cover when there is a reasonably extensive, uniform area of that vegetation immediately upwind of the instruments. The use of remote sensing techniques supplies the frequent lack of ground-measured variables and parameters required to apply the local models at a regional scale. Modelling evapotranspiration is very sensitive to the surface features and conditions. For this reason, a regional model must