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The net radiation flux available at the Earth's surface drives evapotranspiration, photosynthesis and other physical and biological processes. The only cost-effective way to capture its spatial and temporal variability at regional and global scales is remote sensing. However, the accuracy of net radiation derived from remote sensing data has been evaluated up to now over a limited number of in situ measurements and ecosystems. This study aims at evaluating estimates and uncertainties on net radiation derived from Landsat-7 images depending on reliability of the input surface variables albedo, emissivity and surface temperature. The later includes the reliability of remote sensing information (spectral reflectances and top of canopy brightness temperature) and shortwave and longwave incoming radiations. Primary information describing the surface is derived from remote sensing observations. Surface albedo is estimated from spectral reflectances using a narrow-to-broadband conversion method. Land surface temperature is retrieved from top of canopy brightness temperature by accounting for land surface emissivity and reflection of atmospheric radiation; and emissivity is estimated using a relationship with a vegetation index and a spectral database of soil and plant canopy properties in the study area. The net radiation uncertainty is assessed using comparison with ground measurements over the Crau–Camargue and lower Rhone valley regions in France. We found Root Mean Square Errors between retrievals and field measurements of 0.25–0.33 (14–19%) for albedo, ~ 1.7 K for surface temperature and ~ 20 W·m − 2 (5%) for net radiation. Results show a substantial underestimation of Landsat-7 albedo (up to 0.024), particularly for estimates retrieved using the middle infrared, which could be due to different sources: the calibration of field sensors, the correction of radiometric signals from Landsat-7 or the differences in spectral bands with the sensors for which the models where originally derived, or the atmospheric corrections. We report a global uncertainty in net radiation of 40–100 W·m − 2 equally distributed over the shortwave and longwave radiation, which varies spatially and temporally depending on the land use and the time of year. In situ measurements of incoming shortwave and longwave radiation contribute the most to uncertainty in net radiation (10–40 W·m − 2 and 20–30 W·m − 2 , respectively), followed by uncertainties in albedo (< 25 W·m − 2 ) and surface temperature (~ 8 W·m − 2 ). For the latter, the main factors were the uncertainties in top of canopy reflectances (< 10 W·m − 2 ) and brightness temperature (5–7 W·m − 2 ). The generalization of these results to other sensors and study regions could be considered, except for the emissivity if prior knowledge on its characterization is not available. [ABSTRACT FROM AUTHOR]

Abstract: Changes in climate and land use affect water resources and agricultural production. It is important to document these changes and to provide prospective scenarios for improving knowledge and tools that will help stakeholders to anticipate their impacts and propose adaptations. The recharge of the aquifer in the Crau plain mainly depends on the irrigation, in excess, of grassland producing high quality hay. The sustainability of this system is challenged by possible decreases in water availability from the Durance River, the main water source for irrigation, and the decrease in irrigated grassland surfaces. We implemented a modelling system combining the STICS crop model, used in a distributed mode, and the MODFLOW aquifer model for analysing the evolution of hay production, aquifer recharge and water level in the aquifer. The modelling system was implemented for several scenarios concerning climate and land use evolutions, as well as water availability for irrigation, in a close future (2025-2035). The main results showed that the level of the aquifer is seriously threatened by a decrease in irrigation level, either because of a reduction of irrigated grassland surfaces, or because of a limitation of water availability for irrigation. Conversely, the hay production (in term of quantitative yield) would be enhanced by the increase in temperature, even in situation of reduction of irrigation. [Copyright &y& Elsevier]

Abstract: Evapotranspiration (ET) is a fundamental variable of the hydrological cycle and its estimation is required for irrigation management, water resources planning and environmental studies. Remote sensing provides spatially distributed cost-effective information for ET maps production at regional scale. We have developed EVASPA too for mapping ET from remote sensing data at spatial and temporal scales relevant to hydrological or agronomica studies. EVASPA includes several algorithms for estimating evapotranspiration and various equations for estimating the required input information (net radiation, ground heat flux, evaporative fraction…), which provides a way to assess uncertainties in the derivation of ET. The tool integrates data from various remote sensing sensors and it can be easily adapted to new sensors. To test the tool, evapotranspiration maps have been produced for the Crau-Camargue pilot site (south-eastern France), where several energy balance stations deployed in contrasted areas provide ground measurements. An overall description of the tool and first results of performance asse sment (comparison to ground data) are presented here. [Copyright &y& Elsevier]

We investigated the use of multispectral thermal imagery to retrieve land surface emissivity and temperature. Conversely to concurrent methods, the temperature emissivity separation (TES) method simply requires single overpass without any ancillary information. This is possible since TES makes use of an empirical relationship that estimates the minimum emissivity ε-min from the emissivity spectral contrast captured over several channels, so-called maximum-minimum difference (MMD). In previous studies, the ε-min - MMD empirical relationship of TES was calibrated and validated for various sensor spectral configurations, where the proposed calibrations involved single or linearly mixed spectra of emissivity at the leaf or soil level. However, cavity effect should be taken into account at the vegetation canopy level, to avoid an underestimation of emissivity, especially for intermediate vegetation conditions between bare soil and full vegetation cover. The current study aimed to evaluate the performances of the TES method when applied to vegetation canopies with cavity effect. We used the SAIL-Thermique model to simulate a library of emissivity spectra for a wide range of soil and plant conditions, and we addressed the spectral configurations of recent and forthcoming sensors. We obtained good results for calibration and validation over the simulated library, except for full cover canopies because of the TES gray body problem. Consistent with previous studies, the calibration/validation results were better with more channels that capture emissivity spectral contrast more efficiently. Our TES calibrations provided larger ε-min values as compared to former studies, especially for intermediate vegetation cover. We explained this trend by the simulated spectral library that involved numerous vegetation canopies with cavity effect, thereby shifting up the ε-min - MMD empirical relationship. Consequently, our TES calibration provided larger (respectively lower) estimates of emissivity (respectively radiometric temperature) that were likely to be more realistic as compared to previous calibrations. Finally, SAIL-Thermique simulations permitted to show that increasing Leaf Area Index induced a displacement of the (ε-min, MMD) pairs along the empirical relationship. This was consistent with the TES underlying assumption, where any change in ε-min induces changes in MMD since ε-max is bounded on [0.98–1]. Further investigations should focus on validating the outcomes of the current study against ground-based measurements, and on assessing TES performances when accounting for instrumental and atmospheric perturbations. [ABSTRACT FROM AUTHOR]

In preparation of the Thermal infraRed Imaging Satellite for High-resolution Natural resource Assessment (TRISHNA) mission, we conducted a thorough analysis of sensitivity for the Temperature-Emissivity Separation (TES) method to the position of the four TRISHNA spectral channels, notably to find an optimal spectral configuration. To that purpose, we designed a fast-computing end-to-end simulator including several components, which we implemented to simulate both pixel-size TRISHNA measurements and land surface temperature (LST) retrievals. Firstly, simulations were conducted over a wide range of realistic scenarii, notably by including vegetation canopy-scale cavity effect. Secondly, the experimental design included the features of second generation Mercury-Cadmium-Telluride (MCT) cooled detectors with lower instrumental noises and finer channels. Thirdly, as opposed to previous studies that used predefined spectral configurations to determine the most suited one, we conducted an optimization of the spectral configuration by crossing, on a pair basis, several positions of the four TIR channels over a range of wavelengths. Fourthly, we quantified the TES sensitivity to atmospheric perturbations, by comparing LST retrievals with and without atmospheric noise. We observed an overall moderate sensitivity of TES LST retrievals to the spectral channel positions, with a maximum RMSE variation of 0.31 K within the atmospheric spectral windows. Furthermore, the TES method was sensitive to three main parameters, namely the instrumental noise, the atmospheric downwelling irradiance, and the transmittance due to ozone and water vapor, with RMSEs larger than 1 K for specific channel locations. Moreover, by considering possible superimposition of two channels, we noted that the TES method could achieve similar performance by considering three or four channels. Eventually, our study enabled us to recommend a new spectral configuration for the TRISHNA TIR instrument, that is more robust to atmospheric perturbations and to uncertainties on channel positions and bandwidths. • We conducted a sensitivity analysis of TES to TRISHNA spectral configuration. • We designed a pair based bi-dimensional approach that crosses channels. • TES is not sensitive to filter position, but is sensitive to instrumental noise. • TES has similar performance with three or four channels. • We propose a new spectral configuration for the TRISHNA TIR instrument. [ABSTRACT FROM AUTHOR]

• A semi-distributed parsimonious rainfall-recharge-discharge model of karst aquifer. • Vulnerability index and soil properties are used to constrain model parameters. • We demonstrate that recharge intensity significantly varies in time and space. • We demonstrate that soil properties have a major influence on karst recharge. Aquifer recharge assessment is a key factor for sustainable groundwater resource management. Although main factors of the spatial and temporal variability of recharge are known, taking them into account in a distributed or semi-distributed model is still a challenging task. This difficulty is increased in karst environments. Indeed, recharge of karst aquifers also depends on the organization of the karst network, which is both highly heterogeneous and difficult to characterize. We developed a reservoir model to simulate the spatial and temporal variability of recharge on karst watersheds. Special attention was paid to the link between model parameters and measurable or qualitative environmental factors of recharge. The spatial variability of soil reservoir capacity was estimated by multifactorial modelling (neural network). Intrinsic vulnerability indices were used to constrain the partitioning between slow and fast flows within the karst aquifer. Comparison of simulated and measured discharge at the outlet was used to calibrate and assess recharge model. The karst hydrosystem of the Fontaine de Vaucluse is renowned for its significant heterogeneity and anisotropy, which has so far limited the application of 2D or 3D modelling. The model developed was successfully applied to this system. Our results showed that the annual recharge is very heterogeneous on the test site. Spatialization of recharge improves discharge modelling as evidenced by increased KGE (from 0.8 to 0.9) and more realistic flows during drought periods. It is therefore essential to spatialize recharge in karst hydrogeological modelling to improve predictive capacity and better understand functioning of the whole hydrosystem. [ABSTRACT FROM AUTHOR]

The assessment of uncertainties in satellite-derived global surface albedo products is a critical aspect for studying the climate, ecosystem change, hydrology or the Earth's radiant energy budget. However, it is challenged by the spatial scaling errors between satellite and field measurements. This study aims at evaluating the forthcoming MODerate Resolution Imaging Spectroradiometer (MODIS) (Collection V006) Bidirectional Reflectance Distribution Function (BRDF)/albedo product MCD43D over a Mediterranean agricultural area. Here, we present the results from the accuracy assessment of the MODIS blue-sky albedo. The analysis is based on collocated comparisons with higher spatial resolution estimates from Formosat-2 that were first evaluated against local in situ measurements. The inter-sensor comparison is achieved by taking into account the effective point spread function (PSF) for MODIS albedo, modeled as Gaussian functions in the North–South and East–West directions. The equivalent PSF is estimated by correlation analysis between MODIS albedo and Formosat-2 convolved albedo. Results show that it is 1.2 to 2.0 times larger in the East–West direction as compared to the North–South direction. We characterized the equivalent PSF by a full width at half maximum size of 1920 m in East–West, 1200 m in North–South. This provided a very good correlation between the products, showing absolute (relative) Root Mean Square Errors from 0.004 to 0.013 (2% to 7%), and almost no bias. By inspecting 1-km plots homogeneous in land cover type, we found poorer performances over rice and marshes (i.e., relative Root Mean Square Error of about 11% and 7%, and accuracy of 0.011 and − 0.008, respectively), and higher accuracy over dry and irrigated pastures, as well as orchards (i.e., relative uncertainty < 3.8% and accuracy < 0.003). The study demonstrates that neglecting the MODIS PSF when comparing the Formosat-2 albedo against the MODIS one induces an additional uncertainty up to 0.02 (10%) in albedo. The consistency between fine and coarse spatial resolution albedo estimates indicates the ability of the daily MCD43D product to reproduce reasonably well the dynamics of albedo. [ABSTRACT FROM AUTHOR]

Thermal infrared satellite observations of the Earth's surface are widely used to retrieve Land Surface Temperature (LST) and monitor LST changes around the world. Since January 2012, the Visible Infrared Imaging Radiometer Suite (VIIRS) onboard the Suomi National Polar-Orbiting Partnership (S-NPP) has provided daily observations of LST with a spatial resolution of 750 m at nadir. Comparison of the standard VIIRS LST product with the equivalent daily standard product from the Moderate Resolution Imaging Spectroradiometer (MODIS) collection-5 and with ground-based measurements over vegetated and inland water surfaces showed good agreement. Analysis indicated the accuracy and precision of the VIIRS product over these cover types was 0.2 K and 2.0 K respectively provided the analyses included appropriate compensation for any spatial heterogeneity in LST within the validation site. However, comparisons between in situ LST and the VIIRS and MODIS LST over arid and semi-arid regions indicate both satellite products significantly underestimate the LST, and the VIIRS algorithm can have large errors in the retrieved LST over areas of high atmospheric water vapor. Errors of up to 4 K were observed over semi-arid and arid areas due to incorrect characterization of emissivity, and differences of up to 15 K were observed over areas with high atmospheric water content between the VIIRS LST and matching MODIS LST. [ABSTRACT FROM AUTHOR]

This paper aimed at estimating albedo and Leaf Area Index (LAI) from FORMOSAT-2 satellite that offers a unique source of high spatial resolution (eight meters) images with a high revisit frequency (one to three days). It mainly consisted of assessing the FORMOSAT-2 spectral and directional configurations that are unusual, with a single off nadir viewing angle over four visible–near infra red wavebands. Images were collected over an agricultural region located in South Eastern France, with a three day frequency from the growing season to post-harvest. Simultaneously, numerous ground based measurements were performed over various crops such as wheat, meadow, rice and maize. Albedo and LAI were estimated using empirical approaches that have been widely used for usual directional and spectral configurations (i.e. multidirectional or single nadir viewing angle over visible–near infrared wavebands). Two methods devoted to albedo estimation were assessed, based on stepwise multiple regression and neural network (NNT). Although both methods gave satisfactory results, the NNT performed better (relative RMSE=3.5% versus 7.3%), especially for low vegetation covers over dark or wet soils that corresponded to albedo values lower than 0.20. Four approaches for LAI estimation were assessed. The first approach based on a stepwise multiple regression over reflectances had the worst performance (relative RMSE=65%), when compared to the equally performing NDVI based heuristic relationship and reflectance based NNT approach (relative RMSE≈34%). The NDVI based neural network approach had the best performance (relative RMSE=27.5%), due to the combination of NDVI efficient normalization properties and NNT flexibility. The high FORMOSAT-2 revisit frequency allowed next replicating the dynamics of albedo and LAI, and detecting to some extents cultural practices like vegetation cuts. It also allowed investigating possible relationships between albedo and LAI. The latter depicted specific trends according to vegetation types, and were very similar when derived from ground based data, remotely sensed observations or radiative transfer simulations. These relationships also depicted large albedo variabilities for low LAI values, which confirmed that estimating one variable from the other would yield poor performances for low vegetation cover with varying soil backgrounds. Finally, this empirical study demonstrated, in the context of exhaustively describing the spatiotemporal variability of surface properties, the potential synergy between 1) ground based web-sensors that continuously monitor specific biophysical variables over few locations, and 2) high spatial resolution satellite with high revisit frequencies. [Copyright &y& Elsevier]

Unlabelled Image • Mediterranean forest in karst environment exploits deep water in vadose zone. • Beech, Silver Fir, and Holm Oak exhibit different strategies in groundwater use. • Importance of including deep water resources in ecohydrological models Karst environments are unusual because their dry, stony and shallow soils seem to be unfavorable to vegetation, and yet they are often covered with forests. How can trees survive in these environments? Where do they find the water that allows them to survive? This study uses midday and predawn water potentials and xylem water isotopes of branches to assess tree water status and the origin of transpired water. Monitoring was conducted during the summers of 2014 and 2015 in two dissimilar plots of Mediterranean forest located in karst environments. The results show that the three monitored tree species (Abies alba Mill, Fagus sylvatica L, and Quercus ilex L.) use deep water resources present in the karst vadose zone (unsaturated zone) more intensively during drier years. Quercus ilex , a species well- adapted to water stress, which grows at the drier site, uses the deep water resource very early in the summer season. Conversely, the two other species exploit the deep water resource only during severe drought. These results open up new perspectives to a better understanding of ecohydrological equilibrium and to improved water balance modeling in karst forest settings. [ABSTRACT FROM AUTHOR]