J. Timmermans

Assessing Groundwater Storage Changes Using Remote Sensing–Based Evapotranspiration and Precipitation at a Large Semiarid Basin Scale

A method is presented that uses remote sensing (RS)-based evapotranspiration (ET) and precipitation estimates with improved accuracies under semiarid conditions to quantify a spatially distributed water balance, for analyzing groundwater storage changes due to supplementary water uses. The method is tested for the semiarid Konya basin (Turkey), one of the largest endorheic basins in the world. Based on the spatially distributed water balance estimation, the mean irrigation for croplands was 308 mm yr−1, which corresponds to a total reduction of 2270 million cubic meters per year (106 m3 yr−1, or MCM yr−1) in the groundwater storage during the study period 2005–09. The storage change estimated as the residual of the spatially distributed water balance was confirmed by the volume change calculated from groundwater table observations. To obtain an improved precipitation distribution, the monthly Tropical Rainfall Measuring Mission (TRMM) rainfall product was assessed. After a bias removal, TRMM data were combined with the snow water equivalent estimated by a multivariate analysis using snow gauge observations, the Moderate Resolution Imaging Spectroradiometer (MODIS) snow cover product, and the digital elevation model. With respect to the distribution of ET, the standard SEBS and the soil moisture integrated SEBS-SM models were compared; SEBS-SM proved to better reflect the water-limited evapotranspiration regime of semiarid regions. The RS-based distributed water balance calculation as presented in this study can be applied in other large basins, especially in semiarid and arid regions. It is capable of estimating spatially distributed water balances and storage changes, which otherwise, by ground-based point measurements, would not be feasible

Auto-correcting for atmospheric effects in thermal hyperspectral measurements

Abstract Correct estimation of soil and vegetation thermal emissivities is of huge importance in remote sensing studies. It has been shown that the emissivity of leaves retrieved from field observations show subtle spectral features that are related to leaf water content. However, such field measurements provide additional challenges before leaf water content can be successfully obtained, specifically atmospheric correction. The aim of this research was to investigate how information within hyperspectral thermal observations can be used to auto-correct the atmospheric influence. Hyperspectral thermal measurements were taken over a large variety of soil and vegetation types (including vineyard and barley) during ESA’s REFLEX campaign in 2012 using a MIDAC FTIR radiometer. Using MODTRAN simulations, a simple quadratic model was constructed that emulates the atmosphere radiative transfer between the target and the sensor. Afterwards, this model was used to estimate the concentrations of H20 (g) and CO2 (g) while simultaneously correcting for these gas absorptions. Finally, a temperature-emissivity separation was applied to estimate the emissivities of the different land surface components. The uncertainty of the approach was evaluated by comparing the retrieved gas concentrations against parallel measurements of a LICOR 7500. It was found that most measurements of gas concentrations were successfully retrieved, with uncertainties lower than 25%. However, absolute correction of the absorption features proved more difficult and resulted in overestimations of the correction-terms. This was mainly due to overlapping of spectral features with the observations in the simulations that proved troublesome.