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Water shortage is one of the issues people are facing globally nowadays especially in arid and semi-arid regions. In these regions the increase in air temperature with slightly changing rates of precipitation cause frequent droughts. This determines the necessity for the usage of irrigation in agriculture that, in turn, makes it the most water consuming sector of economy.The detection of water stress using different approaches is crucial for irrigation scheduling and precise calculation of the volume of water that covers the gap needed for plants’ normal development.It is known that the Photochemical Reflectance Index (PRI) is highly sensitive to the photosynthetic activity of plants. Especially that can be observed for forests and orchards due to the high heterogeneity of canopy structure. In previous studies it was found that PRI can be used as an indicator for monitoring water stress in plants. However, at present, no full answer is given about the limitations of PRI usability and no clear algorithm is formulated for its use in order to detect water stress of plants.In this regard, we concentrate on studying the response of PRI to the water stress of olive trees rain-fed conditions for the case of semi-arid climate (Tunisia). We performed the analysis of data sets for 2021 and 2022, which are, respectively, a dry year and a year of normal water availability. The data sets included meteorological data, PRI measurements made every five minutes, sap flow measurements, soil moisture content values, and dendrometer measurements.On the first stage of our study we processed PRI data sets in order to find an analytic function that best describes daily dynamics of the index. As the result, a new modeling function is constructed to describe an increase in PRI in the middle of the day when minimal PRI values are reached several hours after sunrise and before sunset. Such PRI behavior was mainly observed in sunny days of the dry year.Further, we looked for the correlations of the characteristics of PRI daily dynamics, particularly minimal of PRI, with the values of sap flow that is a main measurable indicator in the class of transpiration and water stress models (see, e.g., https://doi.org/10.1016/j.agwat.2020.106343 ). In this context, the behavior of PRI was different for 2021 and 2022, which, in our opinion, related with weather conditions. For the dry year of 2021 we found a strong correlation between the minimum of PRI and sap flow in morning hours (R2= 0,68) during the summer season. For the normal year 2022 the same results are not obvious.In the perspective of our study it is planned to compare the results for the rain-fed site with the measurements obtained under irrigated conditions. The final goal of the research we want to achieve is to propose a reliable approach for separation of physically meaningful part of PRI signal from the noises created by the canopy structure. This will lead us to the reliable algorithm of PRI usage for the detection of plants’ water stress condition.

Soil moisture plays an essential role in understanding the soil-vegetation-atmosphere interface and in managing water resources for irrigation. In recent years, the Global Navigation Satellite System Reflectometry (GNSS-R) technique has shown great potential in estimating and monitoring this parameter. In this context, various global operational products are already offered based on data from the CYGNSS satellites. In this study, we propose an analysis of an airborne campaign with measurements from the GLORI instrument at the Urgell agricultural site, in Spain. It is a polarimetric instrument allowing acquisitions in both LHCP (Left Hand Circular Polarized) and RHCP (Right Hand Circular Polarized) polarizations and L1 frequency band.In parallel with three flights carried out on the study site in July 2021, various in situ measurements are carried out on twenty reference plots (soil moisture, Leaf area index, soil roughness). An analysis of the incidence angle effect on the GNSS-R reflectivity measurements is proposed. It illustrates larger effects for RHCP polarization. A normalization of data for one incidence angle is proposed. A sensitivity analysis of GLORI measurements to soil moisture is then discussed. The effect of vegetation cover on the degradation of this sensitivity is highlighted. The LHCP polarization displays a higher sensitivity to soil moisture.An inversion model based on the two-omega approach is calibrated and validated with the in situ data acquired on the reference plots. Reflectivity is simulated as a function of soil moisture and the optical Normalized Difference Vegetation Index (NDVI) index which describes the dynamics of the plant cover. An RMSE close to 0.07m3/m3 is retrieved for soil moisture validation.Soil moisture maps, based on the application of the inversion model, are proposed at a spatial resolution of 100 m for three realized flights. A correlation with precipitation events, as well as the presence or absence of irrigation is clearly observed.

In recent years, GNSS-R data have shown great potential in monitoring and characterizing the states of continental and ocean surfaces. Numerous studies have also demonstrated the potential of GNSS-R polarimetry, showing high precision for the estimation of surface properties such as vegetation, soil moisture, and flooded areas. Very generally, on continental surfaces, the most commonly used observable is reflectivity under the assumption of dominance of the coherent component over the incoherent component. The objective of this study is to analyze GNSS-R data variations as a function of land cover using airborne measurements obtained with the GLObal Navigation Satellite System Reflectometry Instrument (GLORI), which is a polarimetric instrument. GNSS-R measurements were acquired at the agricultural Urgell site in Spain in July 2021. In situ measurements describing the soil and vegetation properties were then obtained simultaneously with flight measurements. For land use mapping, supervised classification is performed based on the Level-2A Sentinel-2 time series for the summer season of the 2021 cloud-free selected and ground-truth observations.The behavior of the observable copolarization (right-right) reflectivity and the cross-polarization (right-left) reflectivity as a function of land use is discussed.The distribution of coherent and incoherent components in the reflected power is estimated for different types of land cover (maize, alfalfa and grass, apple and pear orchards, water and urban constructions and roads).The dependence of our observations on moisture is analyzed by examining the evolution of a (percentage of the incoherent component relative to the total scattering power) over the three flights, since the first flight was conducted in a very dry context, the second flight was conducted after precipitation, and the third flight was conducted after the beginning of the drying process.

In recent decades, climate change has led to a sharp increase in water demand. Particularly in agriculture, this has put a great strain on already scarce water resources, increased the need for irrigation water, and led to overuse of groundwater. Therefore, sustainable management of water resources while maintaining good agricultural yield by monitoring crop water status is necessary for sustainable and rational management of these resources, especially in arid and semi-arid regions. For this purpose, a detailed knowledge of the different processes describing the diurnal water cycle of plants in a large area is essential. However, micrometeorological or physiological experimental measurements and their partitioning are laborious to perform and not very representative of large areas.In this regard, remote sensing is a particularly suitable tool for monitoring agricultural areas because of its global and repeated observation. Several studies have highlighted the sensitivity of radar data to vegetation water content especially over the rainforest with spatial scatterometers that observe differences between morning and evening acquisitions. On the other hand, in situ radar experiments with high temporal frequency have made it possible to analyze radar responses over tropical and boreal forests.This study relates to a similar experiment conducted on an olive orchard located in the semi-arid Mediterranean region of Chichaoua in central Morocco. It allows the acquisition of in situ C-band radar measurements in crop fields, which are acquired continuously, from a tower-based radar system, with a time step of 15 minutes.The temporal evolution of the interferometric coherence r is analyzed on different baselines Dt, ranging from 15 minutes to 30 days, for the main physiological stages of the olive tree. Four different two-month periods, from December 2020 to November 2022, are chosen as the main physiological stages based on field observations.The obtained results of r, especially at 15-min min-steps, show a global behavior similar to that observed in tropical and boreal forests: high values (r ≈1) are observed during the night (weak wind, vegetation resting), then a decrease/increase during the day mainly anti-symetric to the wind cycle. As over boreal and tropical forest, a decrease in r is observed before the wind picks up, with is time coincident with sap flows and ETR variations, traducing its sensitivity to water plant content.Results show that over olive orchard, the r diurnal cycle is less marked than over boreal and tropical forests, due to lower ETR rates and certainly due to a significant soil contribution over this less dense vegetation layer. Furthermore, r values decrease when temporal baselines increase, but values are still meaningful for Dt = 6 days (r = 0.3 compared to 0.6 for Dt = 15 min. for the summer period), available with Sentinel-1 missions.The present study provides particularly interesting results confirming the sensitivity of C-band coherence to vegetation water status, especially in the early morning. Further work needs to be pursued to verify if we are able to detect the water stress of these plants in semi-arid areas such as Chichaoua through coherence.

The basal crop coefficient (Kcb) is the ratio of crop evapotranspiration that primarily corresponds to transpiration. A comparison of different approaches using remote sensing observations for the estimation of Kcb of grapevines is carried out. The study is done over a cv.Tempranillo vineyard located in Catalunya, Spain from March to July 2021.The different approaches tested are the following: 1- a linear relation between NDVI and Kcb(Campos et al., 2010), 2 and 3 - the two different approaches proposed in (Allen and Pereira, 2009), where Kcb is estimated thanks to a density factor. The first version lies on an exponential of Leaf Area Index (LAI), the second version lies on the tree height and the fraction of effective exposed area. 4 and 5- an intercepted photosynthetic radiation (fiPAR) model (Oyarzun et al., 2007) using inferred crop height and width is related to Kcb through the (Lebon et al., 2003) and (Picón-Toro et al., 2012) proposals. 6- The generic tabulated approach proposed by (Allen et al., 1998) is also used to compare to a reference, however it must be remembered that those tabulations are only indicative.The different approaches are compared to the actual Kcb retrieved from a flux tower and the reference evapotranspiration of a nearby weather station. The resulting Kcb are injected into a water budget and daily evapotranspirations are finally compared to actual measurements.The simple linear method did not transfer well on this particular vineyard. The "Allen& Pereira LAI" and the "Oyarzun/Picon" had the best performance with a respective r2, RMSE of 0.57, 0.60 and 0.54 0.62 mm.day-1 on evapotranspiration estimates. However, the former approach through LAI does not seem really operational. The later method only needs to be parameterized with some easy to retrieve descriptions of the plot and plantation. In any case, a drawback of these NDVI based methods is the possible appearance of adventices, or the presence of a inter-row crop. Those must be withdrawn from the NDVI signal of the grapevine.

Irrigated agriculture is the largest consumer of freshwater in the world, particularly in the South Mediterranean region, that already suffers from water shortages. For a rational and sustainable management of water resources, monitoring the water stress status of plants can contribute to an optimal use of irrigation.C-band radar data have shown great potential for monitoring soil and vegetation hydric conditions. Over forests, several studies have observed a diurnal cycle in the backscattering coefficient that can reach up to 1 dB between morning and evening measurements acquired by sun-synchronous satellites. This cycle is assumed to be related to the physiological functioning of trees, in particular to the diurnal cycle of the vegetation water content. A recent study also identified a diurnal cycle in the temporal coherence measured over tropical forests. The authors hypothesized that transpiration was the main factor in the decrease in coherence at dawn, especially since winds are almost zero at that time of day. While the diurnal cycle of radar data is well documented over trees, the behavior of annual crops is yet to be investigated. In this context, the objective of this work is to present a preliminary study of this behavior over wheat by assuming that water movement in the plant could lead to a daily cycle of the interferometric coherence and backscattering coefficient.An experiment funded by LMI TREMA and TOSCA/CNES has been conducted over a winter wheat field in Morocco since January 2020. The experimental setup consists of six C-band antennas installed at the top of a 20 m high tower. It allows the full polarization acquisition of the backscattering coefficient and the interferometric coherence with a 15 minutes time step. The field is also equipped with an eddy covariance and weather stations that allow half-hourly measurements of evapotranspiration and wind speed. In addition to automatic measurements, field campaigns are also carried out to measure soil moisture, surface roughness, vegetation above-ground biomass and cover fraction.The preliminary analysis of in situ radar acquisitions over the 2020 agricultural season reveals the existence of a diurnal cycle of the interferometric coherence whose amplitude increases with the development of vegetation. In particular, a drop in coherence was observed at dawn. This drop is concomitant with the increase in evapotranspiration, which may indicate that it could be due to the sapflow. On the other hand, low coherence values are recorded at the end of the afternoon, which may be related to wind peaks. For the backscattering coefficient, a good agreement is observed between the evolution of its daily average and the evolution of evapotranspiration. These results, which need to be consolidated, demonstrate the existence of important dependencies between the C-band response and the physiological functioning of wheat, which opens insights for the monitoring of crop water status using radar data acquired at sub-daily timescale. This rather highlights the interest of a future geostationary radar mission.

C-band radar remote sensing is a suitable tool for monitoring agricultural areas on a large scale, providing access to information on vegetation such as plant biomass, or on the surface water content of the soil. Recent studies suggest that the water state and the physiological functioning of trees influence radar response leading to marked daily profiles of both radar backscattering coefficient and temporal coherence. The objective of this paper is to make a preliminary comparison between the temporal evolution of Sentinel-1 radar data and in situ radar measurements over a Mediterranean olive orchard located in Morocco. In situ radar data consist in quad polarizations measurements realized from a 20m high tower, every 15 minutes, for the period extending from May 2019 to October 2020.

C-band radar observations are well known to have great potentials for the monitoring of crop hydric conditions. Recent studies suggested that the observed difference of backscattering coefficient ( $\sigma_{o}$ ) between ascending and descending pass over tropical forest could be related to the physiological functioning of the trees. Likewise, the water movement within annual crops could lead to a daily cycle of both $\sigma_{o}$ and temporal coherence ( $\rho$ ). The objective of this paper is to present the preliminary results of an experiment carried out on a winter wheat field in Morocco that was instrumented with six C-band antennas during 2020 growing season. The preliminary results showed strong daily cycles of $\rho$ and $\sigma_{o}$ that are analyzed in relation to wind speed, surface soil moisture and evapotranspiration. This work open insights for the monitoring of the crop hydric status using C-band radar data acquired by Sentinel-1 and by potential future radar geostationary missions.

In the south Mediterranean region already facing water scarcity, up to 80% of available water is used by irrigated agriculture. This work focuses on the analysis of the C-band response of a tree crop with in situ data acquired with a time step of 15 mns in the final objective of developing water stress detection approaches based on radar data. Focus is put on the daily cycle of the radar-backscattering coefficient and of the interferometric coherence. The site is located in the Chichaoua region (Morocco) was equipped in May 2019 with 6 C-band radar antennas installed on a 20 m tower. In parallel, automatic acquisitions at a half hourly time step of latent and sensible heat fluxes, sapflow, soil moisture and temperature profile together with manual measurements of LAI, soil roughness and above ground biomass every 15 days were carried out. The preliminary results show a strong daily cycle of the interferometric coherence with a significant drop of the coherence during daytime. The coherence loss at dawn occurred concurrently with the start of the sapflow while minimum values were observed in the afternoon when wind speed is maximum. A significant daily cycle of the backscattering coefficient is also prominent. The amplitude of the daily cycle decreased from the dormancy period in winter from up to 2dB to less than 1dB in summer when physiologic activity of the trees is at its maximum. These first results open perspectives for the monitoring of the hydric status of crops within the frame of future radar missions in geostationary orbit.

In the Mediterranean basin, olive orchards occupy a large fraction of agricultural lands due to its sustainability to harsh conditions, drought in particular. Since most modeling tools to simulate vegetation functioning are not meant to represent very sparse crops (i.e., rainfed olive trees have a vegetation fraction cover ranging from 2 to 15 %), computing the water needs and the vulnerability to drought of an olive orchard is a challenge. There is indeed a very high contribution of the bare soil signal to the total fluxes, and it is difficult to decipher the contribution of the tree from that of the entire surface. In this context, in an attempt to study the olive tree hydrological functioning at field scale (38 ha), an experimental site was setup and a Soil–Vegetation–Atmosphere (SVAT) model has been applied. To represent the orchard soil–plant–atmosphere interactions, a simulation with default settings was assessed using parameters derived from both the literature and ground measurements. In this default configuration, neither the predicted actual nor the potential transpiration could reach the observed transpiration acquired during the wet season (R2 = 0.67, the Root Mean Square Error (RMSE) = 5.63 mm week−1). We show that the model fails to reproduce the relevant leaf surface that transpires. To address this issue and to improve the estimate of the year-to-year variability of the olive tree transpiration, we propose guidance on how a SVAT model can be modified to more appropriately represent the hydrological functioning of a sparse orchard. Once the tree transpiration is accurately simulated (R2 = 0.93, RMSE = 1.62 mm week−1), we evaluated whether the fully coupled (single patch) or a fully uncoupled (two patch) system better reproduced the total fluxes and their components. Owing to the independent characteristics of the soil columns inherent in the assumption of the 2-patch version, the bare soil column shows a deficiency if the topsoil root extraction is not accounted for. We deduced that we cannot accurately reproduce the soil evaporation in this configuration. This study open perspectives for a better representation of water fluxes over sparse tree crops into both hydrological and SVAT models.

This paper addresses the question of temporal decorrelation at C and L-band in the case of olive tree plantations, considering the very recent tower-based experiments in Morocco, as well as the preliminary results conducted in preparation phases. Based on the expertise gained from the TropiScat-1&2 campaigns, the presented experiments also consist in the acquisition of dense time series using a Vector Network Analyser (VNA) connected to several antennas, herein dedicated to L and C-band data. Likewise, this experiment benefit from auxiliary measurements (meteorological and in-situ data) in order to better understand the processes behind temporal decorrelation, which remains a key issue for future repeat-pass, tandem or geostationary missions. Our first results highlight the importance of the acquisition time during the day, and also rise the challenge on how to disentangle the combined effects of water content variations and wind induced displacements driven by solar convection.

This work deals with crops monitoring in a semi-arid environment, the Mediterranean region, where up to 90% of available water is used for irrigation. In addition to help for yield predictions, temporal monitoring at a regular time basis can help for the optimization of water use. We focused on the daily cycle of the backscattering radar coefficient over two different crop Mediterranean types: olive trees and wheat. With a six-day period between two consecutive acquisitions, the Sentinel-1 mission improves significantly the potential of SAR data for seasonal monitoring of earth surfaces. The available temporal frequency allows for the first time the temporal monitoring of natural surfaces in relation with seasonal changes. However, they are still many issues for better understanding Sentinel-1 temporal signatures and the full potential of these data over crop fields. Indeed, crop fields are characterized by contrasted surface states between bare soils and densely vegetated, with sudden changes due to field works (changing dramatically soil roughness or moisture) or harvests. The MOCTAR experiment consists in the acquisitions of radar fully polarimetric interferometric C-band data acquired continuously at 10 min time step from the top of a tower. The study site is located in the Haouz plain, near the city of Marrakech, in the Chichaoua region, in Morocco. The region is characterized by a semi-arid Mediterranean climate, with an average of 250 mm of yearly precipitation. The region is characterized by two main seasons: wet and dry, extended from October to April and from May to September respectively. Maximum temperatures occur in July-August (average of 27.2 °C) and minimum in January (10.8° C). The study site is composed of two plots of 2.50 ha each, one consisting in olive trees, the other in wheat (Fig. 1). Both are irrigated with drip technique. The study site is documented for more than 10 years, and in situ measurements such as soil moisture, biomass, sapflow sensors (thermal dissipation method) and a micrometric dendrometer are regularly collected.The radar antennas are fixed on a 20 m height tower, in a similar way than the TropiScat experiment They have been installed in May 2019. Four L-band antennas, two emitting and two receiving, one in H and the other in V polarizations, are visible on the bottom row. Above, six antennas operating at C band are mounted on two rows: four on the bottom one (two emitting and two receiving in H and V pol.) and above two receiving antennas in H and V pol. This configuration allows for interferometric fully polarimetric acquisitions also called PolInSAR. The acquisitions are made continuously with a 10 min time step.First results show pronounced daily cycles, with amplitude of about 2 dB. These cycles are likely correlated to diurnal variations of tree water content and sap flow, but need to be further investigated sap flows and dielectric constant measurements made on the trunks. These results will be analyzed by comparison with Sentinel-1 temporal profiles.

Evapotranspiration is one of the most important fluxes of the water budget in semi-arid areas. The estimation of actual crop transpiration is a major issue in those regions due to its remarkable impacts on the precision of irrigation scheduling, crop growth and yield. Rainfed olive trees are adapted to the southern part of the Mediterranean basin even though they are vulnerable to an increased number of drought spells that might occur under current climate change scenarios. This present paper studies both water and energy exchanges over a rainfed olive grove in semi-arid conditions. The hydrological functioning of sparse olive trees is difficult to characterize because of its low LAI. To better understand water exchanges within the Soil–Plant–Atmosphere continuum and better evaluate the evapotranspiration and its components, we combine data arising from eddy covariance, soil water content measurements and the sap flow method. First, we check the consistency of the evapotranspiration partitioning and water balance over three contrasted years: one wet and two dry. Total evapotranspiration (ET) from eddy covariance method compares well with the sum of the evaporation (E) generated from the surface soil moisture measurements and the transpiration derived from the sap flow method. The top meter soil water balance corresponds roughly to ET during the wet year but for the dry years there is an evidence of extraction by roots below the first meter of soil. Inter-annual variations of the transpiration and associated water stress levels are analyzed by the combined use of different types of eco-physiological (sap flow) as well as remotely sensed variables that can be monitored through proxi-detection (albedo, surface temperature, surface soil moisture). The amount and timing of vegetation stress are consistent throughout the various indicators. Consequently, this consistent set of data can be used to constrain a SVAT land-surface model capable of representing the various features of the water and energy budget for this specific land cover.

[Departement_IRSTEA]Territoires [TR1_IRSTEA]SYNERGIE [Axe_IRSTEA]TETIS-ATTOS; International audience; The present study highlights the potential of multi-temporal X-band Synthetic Aperture Radar (SAR) moisture products to be used for the calibration of a model reproducing soil moisture (SM) variations. We propose the MHYSAN model (Modèle de bilan HYdrique des Sols Agricoles Nus) for simulating soil water balance of bare soils. This model was used to simulate surface evaporation fluxes and SM content at daily time scale over a semi-arid, bare agricultural site in Tunisia (North Africa). Two main approaches are considered in this study. Firstly, the MHYSAN model was successfully calibrated for seven sites using continuous thetaprobe measurements at two depths. Then the possibility to extrapolate local SM simulations at distant sites, based on soil texture similarity only, was tested. This extrapolation was assessed using SAR estimates and manual thetaprobe measurements of SM recorded at these distant sites. The results reveal a bias of approximately 0.63% and 3.04%, and an RMSE equal to 6.11% and 4.5%, for the SAR volumetric SM and manual thetaprobe measurements, respectively. In a second approach, the MHYSAN model was calibrated using seven very high resolution SAR (TerraSAR-X) SM outputs ranging over only two months. The simulated SM were validated using continuous thetaprobe measurements during 15 months. Although the SM was measured on only seven different dates for the purposes of calibration, satisfactory results 30 were obtained as a result of the wide range of SM values recorded in these seven images. This led to good overall calibration of the soil parameters, thus demonstrating the considerable potential of Sentinel-1 images for daily soil moisture monitoring using simple models.

International audience; Identifying the irrigated areas is essential for waters managers who are in charge of distributing this resource over a large scale. The monitoring of water soil content and irrigation is a powerful tool for water resource management. The potential of Sentinel-1 (S1) and Sentinel-2 (S2) data for estimating the soil moisture and irrigation is studied over covered surfaces. An inversion algorithm of the Water Cloud Model (WCM) was developed after calibrating and validating the model over the Kairouan plain, a semi-arid region in Tunisia. The aim is to restitute soil moisture over the whole region. The developed algorithm used a synergy between S1, radar data in VV polarization, and NDVI derived from S2 optical data at high spatial resolution. The results showed good accuracy between retrieved and measured soil moisture with a Root Mean Square Error (RMSE) lower than 6 vol.%. Then, the resulting soil moisture maps were used for irrigation mapping. The process used a combination of Support Vector Machine (SVM) and Decision Tree classifications to distinguish between irrigated and non-irrigated agricultural fields. Results from the annual irrigation map show that the overall accuracy on the classification is about 77%.

International audience; Lebanon has experienced serious water scarcity issues recently, despite being one of the wealthiest countries in the Middle East for water resources. A large fraction of the water resources originates from the melting of the seasonal snow on Mount Lebanon. Therefore, continuous and systematic monitoring of the Lebanese snowpack is becoming crucial. The top of Mount Lebanon is punctuated by karstic hollows named sinkholes, which play a key role in the hydrological regime as natural snow reservoirs. However, monitoring these natural snow reservoirs remains challenging using traditional in situ and remote sensing techniques. Here, we present a new system in monitoring the evolution of the snowpack volume in a pilot sinkhole located in Mount Lebanon. The system uses three compact time-lapse cameras and photogrammetric software to reconstruct the elevation of the snow surface within the sinkhole. The approach is validated by standard topographic surveys. The results indicate that the snow height can be retrieved with an accuracy between 20 and 60 cm (residuals standard deviation) and a low bias of 50 cm after co-registration of the digital elevation models. This system can be used to derive the snowpack volume in the sinkhole on a daily basis at low cost.

EvapoTranspiration (ET) is an important component of the water cycle, especially in semi-arid lands. Its quantification is crucial for a sustainable management of scarce water resources. A way to quantify ET is to exploit the available surface temperature data from remote sensing as a signature of the surface energy balance, including the latent heat flux. Remotely sensed energy balance models enable to estimate stress levels and, in turn, the water status of most continental surfaces. The evaporation and transpiration components of ET are also just as important in agricultural water management and ecosystem health monitoring. Single temperatures can be used with dual source energy balance models but rely on specific assumptions on raw levels of plant water stress to get both components out of a single source of information. Additional information from remote sensing data are thus required, either something specifically related to evaporation (such as surface water content) or transpiration (such as PRI or fluorescence). This works evaluates the SPARSE dual source energy balance model ability to compute not only total ET, but also water stress and transpiration/evaporation components. First, the theoretical limits of the ET component retrieval are assessed through a simulation experiment using both retrieval and prescribed modes of SPARSE with the sole surface temperature. A similar work is performed with an additional constraint, the topsoil surface soil moisture level, showing the significant improvement on the retrieval. Then, a flux dataset acquired over rainfed wheat is used to check the robustness of both stress levels and ET retrievals. In particular, retrieval of the evaporation and transpiration components is assessed in both conditions (forcing by the sole temperature or the combination of temperature and soil moisture). In our example, there is no significant difference in the performance of the total ET retrieval, since the evaporation rate retrieved from the sole surface temperature is already fairly close to the one we can reconstruct from observed surface soil moisture time series, but current work is underway to test it over other plots.

We present a unique meteorological and snow observational dataset in Mount Lebanon, a mountainous region with a Mediterranean climate, where snowmelt is an essential water resource. The study region covers the recharge area of three karstic river basins (total area of 1092 km2 and an elevation up to 3088 m). The dataset consists of (1) continuous meteorological and snow height observations, (2) snowpack field measurements, and (3) medium-resolution satellite snow cover data. The continuous meteorological measurements at three automatic weather stations (MZA, 2296 m; LAQ, 1840 m; and CED, 2834 m a.s.l.) include surface air temperature and humidity, precipitation, wind speed and direction, incoming and reflected shortwave irradiance, and snow height, at 30 min intervals for the snow seasons (November–June) between 2011 and 2016 for MZA and between 2014 and 2016 for CED and LAQ. Precipitation data were filtered and corrected for Geonor undercatch. Observations of snow height (HS), snow water equivalent, and snow density were collected at 30 snow courses located at elevations between 1300 and 2900 m a.s.l. during the two snow seasons of 2014–2016 with an average revisit time of 11 days. Daily gap-free snow cover extent (SCA) and snow cover duration (SCD) maps derived from MODIS snow products are provided for the same period (2011–2016). We used the dataset to characterize mean snow height, snow water equivalent (SWE), and density for the first time in Mount Lebanon. Snow seasonal variability was characterized with high HS and SWE variance and a relatively high snow density mean equal to 467 kg m−3. We find that the relationship between snow depth and snow density is specific to the Mediterranean climate. The current model explained 34 % of the variability in the entire dataset (all regions between 1300 and 2900 m a.s.l.) and 62 % for high mountain regions (elevation 2200–2900 m a.s.l.). The dataset is suitable for the investigation of snow dynamics and for the forcing and validation of energy balance models. Therefore, this dataset bears the potential to greatly improve the quantification of snowmelt and mountain hydrometeorological processes in this data-scarce region of the eastern Mediterranean. The DOI for the data is https://doi.org/10.5281/zenodo.583733.

The aim of this study is to analyze the sensitivity of airborne Global Navigation Satellite System Reflectometry (GNSS-R) on soil surface and vegetation cover characteristics in agricultural areas. Airborne polarimetric GNSS-R data were acquired in the context of the GLORI’2015 campaign over two study sites in Southwest France in June and July of 2015. Ground measurements of soil surface parameters (moisture content) and vegetation characteristics (leaf area index (LAI), and vegetation height) were recorded for different types of crops (corn, sunflower, wheat, soybean, vegetable) simultaneously with the airborne GNSS-R measurements. Three GNSS-R observables (apparent reflectivity, the reflected signal-to-noise-ratio (SNR), and the polarimetric ratio (PR)) were found to be well correlated with soil moisture and a major vegetation characteristic (LAI). A tau-omega model was used to explain the dependence of the GNSS-R reflectivity on both the soil moisture and vegetation parameters.

International audience; The GLORI Campaign performed in June-July 2015 to investigate the sensitivity of airborne GNSS-R measurements to land parameters is presented. In this paper data obtained on forest areas are analyzed. Ground truth measurements of tree height, density and diameter at breast height, AGB etc were measured over 100 maritime pine forest plots of various ages. The correlation between forest parameters and GNSS reflectivity in LHCP polarization or polarization ratio (PR) yields to high sensitivity for high elevation angles (70°-90°). Results show that PR illustrates highest potential than the reflectivity in LHCP.

In semi-arid areas, agricultural production is restricted by water availability; hence efficient agricultural water management is a major issue. The design of tools providing regional estimates of evapotranspiration (ET), one of the most relevant water balance fluxes, may help the sustainable management of water resources. Remote sensing provides periodic data about actual vegetation temporal dynamics (through the Normalized Difference Vegetation Index NDVI) and water availability under water stress (through the land surface temperature LST) which are crucial factors controlling ET. In this study, spatially distributed estimates of ET (or its energy equivalent, the latent heat fluxes LE) in the Kairouan plain (Central Tunisia) were computed by applying the Soil Plant Atmosphere and Remote Sensing Evapotraspiration (SPARSE) model fed by low resolution remote sensing data (Terra and Aqua MODIS). The work goal was to assess the operational use of the SPARSE model and the accuracy of the modelled i) sensible heat flux (H) and ii) daily ET over a heterogeneous semi-arid landscape with a complex land cover (i.e. trees, winter cereals, summer vegetables). The SPARSE's layer approach was run to compute instantaneous estimates of H and LE fluxes at the satellite overpass time. The good correspondence (R2 = 0.60 and 0.63 and RMSE = 57.89 W/m-2 and 53.85 W/m-2; for Terra and Aqua, respectively) between instantaneous H estimates and large aperture scintillometer (XLAS)'s H measurements along a pathlength of 4 km over the study area showed that the SPARSE model presents satisfactory accuracy. Results showed that, despite the fairly large scatter, the instantaneous LE can be suitably estimated at large scale (RMSE = 47.20 W/m-2 and 43.20 W/m-2; for Terra and Aqua, respectively and R2 = 0.55 for both satellites). Additionally, water stress was investigated by comparing modelled (SPARSE derived) to observed (XLAS derived) water stress values; we found that most points were located within a 0.2 confidence interval, thus the general tendencies are well reproduced. Even though extrapolation of instantaneous latent heat flux values to daily totals was less obvious, daily ET estimates are deemed acceptable.

International audience; The GLORIE Campaign was performed in June-July 2015 in order to investigate the sensitivity of airborne GNSS-R measurements to land parameters. In this paper we present the first results focusing on agricultural areas. For this purpose ground truth measurements of soil moisture, roughness, plant water content and plant height were measured over 20 agricultural plots of various crops (cereals, vegetables, bare soil). The correlation with GNSS reflectivity in LHCP polarization confirms noticeable sensitivity to soil moisture, and plant related parameters such as NDVI, vegetation water content and plant height.