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The understanding and prediction of the variability of the hydrological state of watersheds across the planet is growing, as water is a fundamental human need and therefore important for science and society. In the last 20 years, significant advances have been made toward hydrological modeling of large river basins, and also continental to global-scale land areas. Remote sensing has been widely used in hydrology because, in addition to being a clear advantage in regions with a poor monitoring network, it has proven to be suitable for use in global and continental hydrological applications.The estimation of river discharge is of paramount importance, as it is considered an aggregator of all water cycle processes in the basin. On the one hand, estimates of discharge solely from space remain limited because they are not the primary focus of current satellite missions. On the other hand, simulations of hydraulic variables have been performed with large-scale hydrologic and hydrodynamic models, but the accuracy of their estimates can be improved with recent techniques such as data assimilation (DA). DA techniques have been developed to use remotely sensed datasets to obtain the best estimate of the current state of a system by optimally combining observations and large-scale hydrological models. Recent studies have also demonstrated the advantages of assimilating several types of datasets at the same time, which can help to further constrain the model state variables to be more physically representative.Thus, the main objective of this research is to develop a proof-of-concept for estimating hydraulic variables such as discharge and water level by assimilating multiple remotely sensed datasets into a large-scale hydrologic and hydrodynamic model. Experiences on the assimilation of different mission datasets into a large-scale hydrological model are discussed, including radar altimetry-derived water level from JASON, ENVISAT and Sentinel missions, terrestrial water storage from GRACE mission, flooded area extent from SWAMPS database and soil moisture from the SMOS mission.To develop our proof-of-concept, the Amazon as the study area. We used the hydrologic-hydrodynamic MGB model and the Local Ensemble Kalman Filter as the DA method as it has been commonly used in hydrologic models. Different localization and multivariable assimilation techniques were implemented to improve the effectiveness of the DA.The results indicate that the multi-mission assimilation approach is able to smooth/average the improvement of the state variables of the model, such as discharge and water level anomaly, compared to the experiment of assimilating the mission datasets individually. This proof-of-concept allows us to spatialize the improvement of the dynamics of hydrological-hydrodynamic variables based on large-scale hydrologic modeling and DA from global remote sensing sources only, without requiring in-situ data. As our proof-of-concept is based on datasets globally available and a hydrologic-hydrodynamic model that can be applied almost everywhere, it is fully replicable in any region of the world and represents a great potential for regional to continental studies.

Agriculture is the largest consumer of water worldwide, accounting for about 70% of the global freshwater withdrawals. Thus, crop water use efficiency and impacts of water stress on crop water consumption are the key concerns for agricultural water management.Present study investigates the variability of evapotranspiration (ET) and crop water use efficiency by integrating very high spatial resolution (1 – 4 m) thermal infrared (TIR) data from airborne measurements and visible to near infrared data from Planet satellite with a numerical water-energy balance model and a diagnostic surface energy balance model. The analysis is done for an intensive agriculture area in central Italy near the city of Modena, where several fruit trees fields are present along with fresh vegetables. An intensive airborne campaign was organized in the summer of 2022 for three consecutive days in July. A hyperspectral TIR camera (Telops Hyper-Cam LW) has been operated at a spectral resolution of 8 cm-1, resulting in 64 wavebands, and covering a wavelength range of 850 cm-1 to 1350 cm-1 (7,39 µm – 11,8 µm). During the 3 days of flight acquisitions, three overpasses per day are planned: 9:00, 12:30 and 16:00 h, respectively and two areas were intensively surveyed at both 4 and 1 m spatial resolution. Planet data at 3.7 m spatial resolution were used to derive different vegetation indices, such as vegetation fraction coverage, NDVI and leaf area index. During airborne overpasses ground data of spectral reflectance, vegetation variables, LST and soil water content (SM) were collected in different fields. In addition, two different pear trees fields were monitored with an eddy covariance station and soil moisture profile measurements, respectively.To investigate the diurnal and spatial patterns of evapotranspiration, soil moisture variability and crop water use efficiency, we used two numerical models: the surface energy balance model STIC based on Penman-Monteith and Shuttleworth-Wallace (Mallick et al., 2018) and the water-energy balance model FEST-EWB which computes continuously in time and is distributed in space soil moisture and evapotranspiration fluxes solving for a land surface temperature that closes the energy–water balance equations (Corbari et al., 2011).Differences and similarities in ET estimates have been analysed from the two models for different soil moisture conditions and crop types, considering crop water use efficiency and water stress, and have been compared to eddy covariance measurements for accuracy evaluation considering both instantaneous and daily data. The assimilation of instantaneous estimates of ET into the water-energy balance model allowed to directly derived soil moisture maps at high spatial resolution which have been found in agreement with ground SM measurements.

Building climate data records of soil moisture (SM) requires computing long time series by merging retrievals from sensors on-board different satellites, which implies to perform a bias correction or rescaling on the original time series. Due to their long time span and high temporal frequency, model data could be used as a common reference for the rescaling. However, avoiding model dependence in observational climate data records is needed for some applications. In this article, the possibility of using as reference remote sensing data from one of the $L$-band sensors specifically designed to measure SM is discussed. Advanced Microwave Scanning Radiometer 2 SM time series were rescaled by matching their cumulative distribution functions (CDFs) to those of Soil Moisture and Ocean Salinity (SMOS), Soil Moisture Active Passive (SMAP), and Global Land Data Assimilation System (GLDAS) NOAH model time series. The CDF computation was investigated as a function of the time series length, finding significant differences from four to nine years. Replacing temporal by spatial variance does not allow us to compute better CDFs from short time series. The rescaled time series show a high correlation ($R>0.8$) to the original ones and a low bias with respect to the reference ($< $0.03 m $^{3}\cdot$ m$^{-3}$). The time series rescaled using several SMOS or SMAP datasets were also evaluated against in situ measurements and show performances similar to or slightly better than those rescaled using the model GLDAS. The impact of random errors and gaps of the observational data into the rescaling was evaluated. These results show that it is actually possible to use $L$-band data as reference to rescale time series from other sensors to build long time series of SM.

Soil carbon storage is a well identified climate change mitigation solution. The extensive in-situ monitoring of the soil carbon storage in cropland for agricultural policy and offset carbon markets is prohibitive, especially at intra-field scale. For this reason, comprehensive Monitoring, Reporting and Verification (MRV) of soil carbon and its explanatory variables at large scale needs to rely on remote sensing and modelling tools that provide the spatio-temporal dynamics of the carbon budget and it’s components at high resolution with associated uncertainties. In this paper, we present AgriCarbon-EO v1.0: an end-to-end processing chain that enables the estimation of carbon budget components of major crops and cover crops at intra-field resolution (10 m) and large scale (over 110×110 km) by assimilating remote sensing data in physically-based radiative transfert and agronomic models. The data assimilation in AgriCarbon-EO is based on a novel Bayesian approach that combines Normalised Importance Sampling (NIS) and Look-Up Table (LUT) generation. This approach propagates the 10 m uncertainties across the processing chain from the reflectances to the output variables. The chain considers as input a land cover map, multi-spectral reflectance maps from the Sentinel-2 and Landsat-8 satellites, and daily weather forcing. The PROSAIL radiative transfer model is inversed in a first step to obtain Green Leaf Area Index (GLAI). The GLAI time series are then assimilated into the SAFYE-CO2 crop model taking into consideration their uncertainty. The chain is applied over winter wheat in the south-west of France during the cropping seasons 2017 and 2019. We compare the results against the net ecosystem exchange measured at the FR-AUR ICOS site (RMSE = 1.69 - 2.4 gC m−2 , R2 = 0.88 - 0.88), biomass (RMSE = 250 g m−2 , R2 = 0.9), and combine harvester yield maps. We quantify the difference between pixel and field and pixel scale simulations of biomass (bias = -47 g m−2 , -39 % variability), and the impact of the number of remote sensing acquisitions on the outputs (-66 % of mean uncertainty of biomass). Finally, we conduct a coherency analysis at regional scale to test the consistency of the observed patterns with soil texture, altitude and exposition variability. Results show higher biomass for higher clay soils and earlier emergence and senescence for south western exposition.

The agricultural sector is the biggest and least efficient water user, accounting for around 80% of total water use in South Europe, which will be further impacted by climate change in the incoming years. Precision agriculture tools are then needed to increase water use efficiency.Here, the proposed system couples together remotely sensed land surface temperature (LST), leaf area index (LAI) and ground soil moisture data (SM) with a pixel wise crop-water-energy balances model, for improving irrigation management. The SAFY (Simple Algorithm for Yield) crop model has been fully coupled with the energy water balance FEST-EWB model, exchanging in a double direction the LAI evolution in time from SAFY, which is used by FEST-EWB for evapotranspiration computation, while FEST-EWB provides soil moisture (SM) and LST to SAFY model for constraining crop growth.A data assimilation framework, based on the Ensemble Kalman filter approach, is implemented to reduce the requirements for parameters calibration, either for soil assimilating satellite LST and for crop growth using LAI. This framework allows overcoming the issues related to crop exposure to shocks due extreme events non-reproducible by the model alone, as well as nutrient lack, crops hybrids or precise amount of irrigation water.The FEST-EWB-SAFY model has been applied in two Irrigation Consortia in the North and South of Italy which differ for climate and agricultural practices, using data from Sentinel2, Landsat 7 and 8 satellites. The model has then been validated in specific fields where ground measurements of evapotranspiration, soil moisture and crop yields are available.Overall, the results suggested that the under-calibrated model estimates of LST, LAI, SM and yield are enhanced through the assimilation of satellite data, suggesting the potential for improving irrigation management at both field and Irrigation Consortium scales.

The flow conditions (e.g., river network) for rivers and open channels are often forced into hydrogeological models that use a constant horizontal grid resolution without correction for grid mismatching. As a result, the flow velocity will be significantly underestimated if the width of rivers is substantially narrower than the grid size of these models. Furthermore, the exchange between the river and the subsurface is overestimated, resulting in an erroneously large vertical exchange.In response to this challenge, in this work, the subscale channel flow is approximated in the kinematic wave equation by a scaled roughness coefficient. A relationship between grid cell size and river width is used for this purpose, which follows a simplified modification of the Manning-Strickler equation. In addition, the exchange between the subsurface and the river, as well as the rate of ex- and in-filtration, are scaled across river beds based on grid resolution. As a result, even though the grid size is relatively large, the exchange rates are corrected across river beds. The effectiveness of the scaling of river parametrization is validated against groundwater gauges and remote sensing-based surface soil moisture in a fully coupled subsurface-land surface ParFlow-CLM at a spatial resolution of 0.055° (~6 km) over the Upper Rhine Basin. The validity of the results is examined through an innovative application of the First Order Reliability Method (FORM) for the time period 2012-2014. Results indicate that the scaling approach improves the estimates of soil moisture, particularly in the summer and autumn seasons when cross-validated with independent CCI-SM observations. This improvement is achieved (SM RMSE reduction from 0.03 to 0.005) due to the effective impacts of the scaling river parametrization on SM estimation. FORM results show that the accuracy of ParFlow-CLM soil moisture simulations by using scaling approach is more than 95, 89, 85 and 92 percent for Autumn, Winter, April and Summer, respectively. The scaling river parametrization also shows overall improvements in groundwater level estimation, particularly over the central and northern regions where the groundwater level is shallow.

The Amazon River basin harbors some of the world's largest wetland complexes, which are of major importance for biodiversity, the water cycle and climate, and human activities. Accurate estimates of inundation extent and its variations across spatial and temporal scales are therefore fundamental to understand and manage the basin's resources. More than fifty inundation estimates have been generated for this region, yet major differences exist among the datasets, and a comprehensive assessment of them is lacking. Here we present an intercomparison of 29 inundation datasets for the Amazon basin, based on remote sensing only, hydrological modeling, or multi-source datasets, with 18 covering the lowland Amazon basin (elevation 1000 km2) is 323,700 km2. The highest spatial agreement is observed for floodplains dominated by open water such as along the lower Amazon River, whereas intermediate agreement is found along major vegetated floodplains fringing larger rivers (e.g., Amazon mainstem floodplain). Especially large disagreements exist among estimates for interfluvial wetlands (Llanos de Moxos, Pacaya-Samiria, Negro, Roraima), where inundation tends to be shallower and more variable in time. Our data intercomparison helps identify the current major knowledge gaps regarding inundation mapping in the Amazon and their implications for multiple applications. In the context of forthcoming hydrology-oriented satellite missions, we make recommendations for future developments of inundation estimates in the Amazon and present a WebGIS application (https://amazon-inundation.herokuapp.com/) we developed to provide user-friendly visualization and data acquisition of current Amazon inundation datasets. Ministry of Education (MOE) Nanyang Technological University The work was part of the SABERES project financed by the BNPParibas Foundation as part of its “Climate & Biodiversity Initiative” program 2019. A.S.F. was supported by CNPq (Conselho Nacional de Desenvolvimento Científico e Tecnologico, ´ Brazil) [grant number 141161/2017-5]. F.P., J.F.F., M.P.B. and F.A. received support from CNES (SWOT-ST project SWOT for SOUTH AMERICA, ID: 6018-4500066497). F.P. and M.P.B. also received support from CNES (SWOT-ST project SWOT Wetlands Hydrology Monitoring). F.P. is supported by the IRD Groupement De Recherche International (GDRI) SCaHyLab. J.M.M. received support from NASA IDS grant NNX17AK49G and the US National Science Foundation (Division of Environmental Biology, grant 1753856). E.P. acknowledges Nanyang Technological University (SUG-NAP EP3/19) and Ministry of Education of Singapore (AcRF Tier1 RT 06/19 and AcRF Tier2 RT 11/ 21). A.F.R. acknowledges the Research Foundation of Sao ˜ Paulo (FAPESP, grant #2019/24049-5). S.W. has been supported by the French AMANECER-MOPGA project funded by ANR and IRD (ref. ANR18-MPGA-0008). M.C. received funding from NASA IDS grant NNX17AK49G. The SWAF dataset development was financed by the CATDS and the SWOT-AVAL programs by CNES.

The Soil Moisture and Ocean Salinity (SMOS) satellite is performing systematic L-band observations since 2009, allowing a large number of science and operational applications. Several recent studies have shown the need of the continuity of L-band observations, in particular with an increased angular resolution. In this contribution, two instrumental concepts are presented to reach native resolutions of 5–10 km. In addition, using airborne data, it is also shown that the accuracy of downscaling coarser resolution L-band data to 5–10 km using a high resolution auxiliary data set, is significantly lower than that of native high resolution observations.

Inland water storage is a key reservoir in the continental water cycle but the scientific knowledge about its spatio-temporal dynamic is still poor, especially over tropical areas. By coupling the Soil and Water Assessment Tool (SWAT) model and the Soil WAter Fraction (SWAF) data to increase the inundation delineation precision, inland water storage in the Madeira Basin located in Southern Amazon Basin was computed from 1998 to 2018 each day. During this period, the maximum of water storage was reached in March every year and varied from $3.01\times 10^{11}\ \mathrm{m}^{3}$ to $1.22\times 10^{11}\ \mathrm{m}^{3}$ . The Madeira Basin and each floodplain section have a peculiar temporal hydrologic response. The methodology presented in this paper can be extended to the entire Amazon Basin and other large-scale watersheds.

The last decade has witnessed a series of extreme droughts across the globe. The impacts of these droughts have been devastating for the ecosystem and human activities. In this paper we present the assessment of the drought events in the last decade from the remote sensing-based root zone soil moisture anomalies. The root zone soil moisture is obtained from the SMOS surface soil moisture. And the drought index is defined as the monthly anomaly of the root zone soil moisture. Our results show the distribution of droughts over the last decade in various regions across the globe.

Urban expansion, driven by population and economic growth, has been a major contributor to increased levels of energy consumption across the globe. Beirut, Lebanon’s capital, is no exception in facing a surge in its demand for electricity as it expands. However, with frequent power outages, underpinning a largely problematic power sector, Beirut’s demand for electricity is becoming a real hurdle that impedes the city’s economic growth and development. This paper introduces a near-city-scale building energy model, BEirut Energy Model BEEM, which estimates the building stock’s electricity consumption in two different districts in Beirut. The methodology uses rule-based expert data for an archetypal classification of the buildings based on their functions and periods of construction with their corresponding attributes including the number of floors, number of apartments, and bimonthly electricity consumption to generate a 3D model for 3630 buildings coupled to the hourly weather conditions and topographic map, which is then simulated in EnergyPlus. The predicted consumption of 2311 buildings is then calibrated with actual available metered data, to adapt the model to Beirut’s occupancy and users’ behaviors. Calibrated results are mapped to reveal the spatiotemporal distribution of energy peak demands which provide insights for future interventions. An analysis of the spatial distribution of electricity use demonstrates a spatial clustering that underlies urban energy demand which can be used for smart grid zoning.

Achieving the United Nations Sustainable Development Goal 2 that addresses food security and sustainable agriculture requires the promotion of readily transferable and scalable agronomical solutions. The combination of high-resolution remote sensing data, field information, and physical models is identified as a robust way of answering this requirement. Here, we present the AgriCarbon-EO tool, a decision support system that provides the yield, biomass, water and carbon budget components of agricultural fields at a 10m resolution and at a regional scale. The tool assimilates high resolution optical remote sensing data from Copernicus Sentinel-2 satellites into a radiative transfer model and a crop model. First, the application of a spatial Bayesian retrieval approach to the PROSAIL radiative transfer model provides Leaf Area Index (LAI) with its associated uncertainty. Second, LAI is assimilated into the SAFYE-CO2 crop model using a temporal Bayesian retrieval that enables the calculation of the yield, biomass, carbon and water budgets components with their associated uncertainties. In addition to remote sensing data, input datasets of crop types, weather and soil data are used to constrain the system. The concise weather data is provided from local weather stations or weather forecasts and is used to force the crop model (SAFYE-CO2) dynamics. The soil data are used in two folds. First to better parametrize the soil emissions in the radiative model retrievals and second to parametrise the water infiltration in the soil module of the crop model. The AgriCarbon-EO tool has been optimized to enable the computation of the yield, carbon, and water budget at high spatial resolution (10m) and large scale (100km2). The model is applied over the South-West of France covered by 3 Sentinel-2 tiles for major crops (wheat, maize, sunflower). The outputs are validated over experimental plots for biomass, yield, soil moisture, and CO2 fluxes located all in the South-West of France. The experimental sites include the FR-AUR and FR-LAM ICOS sites and 22 cropland fields (biomass sampling). The validation exercise is done for the 2017-2018 and 2019-2020 cultural years. We show the added value of the use of high resolution in driving the crop model to take into account the impact of complex processes that are embedded in the LAI signal like vegetation water stress, disease, and agricultural practices. We show that the system is capable of providing the yield, carbon, and water budget of major crops accurately. At the regional scale, we give global estimates of the carbon budget, water needs, and yields per crop type. We present the impact of intra-plot heterogeneity in the estimation of yield and the annual carbon and water budget showing the added value for high-resolution intra-plot modeling.

Increasing soil carbon stocks has been identified as a major climate change mitigation solution. As a consequence, an objective of 4/1000 yearly increment in soil carbon stocks has been proposed at the COP21. Sustainable agriculture provides several solutions to meet this objective and among those solutions, the implementation of cover crops has been identified as most efficient. Currently, a comprehensive modeling tool that takes into account the major bio-geophysical processes with associated uncertainties, while assimilating frequent high-resolution observations at large scale could allow accounting for the effect of cover crops on the carbon budget in a realistic way. In this study, we quantify the components of the carbon budget at high resolution and we analyse the effect of cover crops. Computations are based on the newly developed AgriCarbon-EO tool which assimilates full resolution (10-20m) Sentinel-2 optical data into a radiative transfer model (PROSAIL), and a crop model (SAFYE-CO2). The assimilation scheme is based on a Bayesian approach which provides the retrieved biogeophysical variables with their associated uncertainties. Uncertainties are essential when determining the carbon stocks. For instance, the future European Common Agricultural Practice (CAP) may take into consideration the uncertainty of the determination of the soil carbon stocks changes in the evaluation of the subsidies. The main inputs of the computations are weather data, soil texture maps, crop maps and surface reflectances. The Sentinel-2 Leaf Area Index (LAI) are obtained from those Sentinel-2 surface reflectance by inverting the PROSAIL model. These are then assimilated into the SAFY_CO2 model to determine the carbon budget components. To validate our approach, we implemented the AgriCarbon-EO tool over a set of plots in south-western France over which we dispose of biomass measurements for cover crops in wheat/cover crop/maize rotations for 2017-2018 and 2019-2020 agricultural seasons. Also, the CO2 fluxes are validated against eddy covariance flux measurements in the same context. Our study shows that the cover crops allow on average 250gC/m² of organic carbon with a high spatial heterogeneity. This has important implications regarding the dynamic of carbon storage in agronomic soils and demonstrates the importance of high-resolution agronomic modeling.

The conflicting use of water is becoming more and more evident, also in regions that are traditionally rich in water. With the world’s population projected to increase to 8.5 billion by 2030, the simultaneous growth in income will imply a substantial increase in demand for both water and food. Climate change impacts will further stress the water availability enhancing also its conflictual use. The agricultural sector is the biggest and least efficient water user, accounts for around 24% of total water use in Europe, peaking at 80% in the southern regions.This paper shows the implementation of a system for real-time operative irrigation water management at high spatial and temporal able to monitor the crop water needs reducing the irrigation losses and increasing the water use efficiency, according to different agronomic practices supporting different level of water users from irrigation consortia to single farmers. The system couples together satellite (land surface temperature LST and vegetation information) and ground data, with pixel wise hydrological crop soil water energy balance model. In particular, the SAFY (Simple Algorithm for Yield) crop model has been coupled with the pixel wise energy water balance FEST-EWB model, which assimilate satellite LST for its soil parameters calibration. The essence of this coupled modelling is that the SAFY provides the leaf area index (LAI) evolution in time used by the FEST-EWB for evapotranspiration computation while FEST-EWB model provides soil moisture (SM) to SAFY model for computing crop grow for assigned water content.The FEST-EWB-SAFY has been firstly calibrated in specific fields of Chiese (maize crop) and Capitanata (tomatoes) where ground measurements of evapotranspiration, soil moisture and crop yields are available, as well as LAI from Sentinel2-Landsat 7 and 8 data. The FEST-EWB-SAFY model has then been validated also on several fields of the RICA farms database in the two Italian consortia, where the economic data are available plus the crop yield. Finally, the modelled maps of LAI have then been validated over the whole Consortium area (Chiese and Capitanata) against satellite data of LAI from Landsat 7 and 8, and Sentinel-2.Optimized irrigation volumes are assessed based on a soil moisture thresholds criterion, allowing to reduce the passages over the field capacity threshold reducing the percolation flux with a saving of irrigation volume without affecting evapotranspiration and so that the crop production. The implemented strategy has shown a significative irrigation water saving, also in this area where a traditional careful use of water is assessed.The activity is part of the European project RET-SIF (www.retsif.polimi.it).

The Soil Moisture and Ocean Salinity (SMOS) satellite, launched in 2009 by ESA, has provided, for the first time, systematic passive L-band (1.4 GHz) measurements from space with a spatial resolution of ~ 40 km. SMOS data are an essential component of the ESA Climate Change Initiative (CCI) for ocean salinity and soil moisture and they are used by the CCI biomass. A specific SMOS neural network soil moisture product is assimilated operationally at the European Centre for Medium Range Weather Forecasts (ECMWF). L-band surface SM measurements have also been used to estimate root zone soil moisture, to derive drought indices, to enable food security monitoring and to improve satellite precipitation estimates. SMOS data have also been used to detect frozen soils, thin ice-sheets over the ocean and ice melting in Antarctica and Greenland.Different studies on scientific and operational applications of L-band radiometry have shown the need of the continuity of L-band observations with an increased resolution with respect to the current generation of sensors. Resolutions from 1 km to 10 km would be a breakthrough for many applications over ocean, land and ice. One approach to obtain those resolutions could be downscaling coarse resolution data using an auxiliary dataset with higher resolution. However, using airborne data, we will show that the accuracy of the data downscaled to 1 km decreases significantly when the initial native resolution is 40 km with respect to downscaling from initial resolutions of 5-10 km. We will present two instrumental concepts to reach native resolutions of 5-10 km.The SMOS-HR mission project, completed the Phase 0 study at the French Centre National d’Etudes Spatiales (CNES) with contributions from Airbus Defence & Space and CESBIO. The goal was to ensure the continuity of L-band measurements while increasing the spatial resolution to ~10 km, which requires a typical antenna size of ~18 meters. Taking into account the difficulty of deploying a real aperture of this size in space and the successful alternative approach used by SMOS, SMOS-HR will perform aperture synthesis using an array of 230 small antennas distributed in a cross with four 12 m arms. During the Phase A study (ongoing at CNES) a mission concept with a central carrier surrounded by a swarm of nanosatellites will also be studied.

The Middle East and North Africa (MENA) region is facing the challenge of lingering droughts. Precipitation and soil moisture are two Essential Climate Variables (ECVs) that are relevant for drought monitoring. We assessed the discrepancies in drought monitoring using remote sensing data from the Tropical Rainfall Measuring Mission (TRMM) and European Space Agency Climate Change Initiative (ESA-CCI) and European Centre for Medium-Range Weather Forecasts (ECMWF) reanalysis data (ERA5). A standardized index approach is applied to the four datasets. The indices are spatially and temporally consistent except for the ESA-CCI soil moisture (SM) dataset. The indices depict drought events over the North-West Africa region and show that the TRMM standardized precipitation index (SPI), ERA5 SPI and standardized soil moisture index (SMI) detect drought events in the Near East. A binary classification analysis showed that the indices can accurately and precisely identify drought events across the MENA region except for North East Africa. The indices show that the MENA region was recently under severe to extreme drought conditions, which are driving the exploitation of available water resources in an unsustainable manner. A focus on the Haouz Plain, Morocco, and Aleppo, Syria, shows the critical situation, while the conditions over Al Jazirah, Sudan, are less critical.

Physical models simulating the radiative budget (RB) and remote sensing (RS) observation of three-dimensional (3D) landscapes are critical to better understand human and natural components of the Earth system and further develop RS technology. DART is one of the most comprehensive 3D models of Earth-atmosphere optical radiative transfer (RT), from ultraviolet (UV) to thermal infrared (TIR). It simulates the optical signal of proximal, aerial and satellite imaging spectrometers and laser scanners, the 3D RB and solar induced chlorophyll fluorescence (SIF) signal, for any urban or natural landscape and any experimental or instrument configuration. It is freely available for research and teaching activities (https://dart.omp.eu). Here, five recent advances are presented. 1) Atmosphere RT. 2) RT in non repetitive topography. 3) Monte Carlo modelling for fast RS image simulation of large landscapes. 4) SIF modelling for vegetation simulated as facets and turbid cells. 5) RS image inversion for mapping the optical properties of urban material and the urban radiative budget.

Remote sensing (RS) dedicated to the study of land surfaces benefits from more and more advanced sensors. However, the interpretation of RS data is often is often inaccurate due to the complexity of the observed land surfaces. Therefore, RS models, in particular physical models, that simulate RS observations of the three-dimensional (3D) landscapes are critical to correctly interpret RS data. DART is one of the most comprehensive 3D models of Earthatmosphere optical radiative transfer (RT), from ultraviolet (UV) to thermal infrared (TIR). It simulates the optical signal of proximal, aerial and satellite imaging spectrometers and laser scanners, the 3D RB and solar-induced chlorophyll fluorescence (SIF) signal, for any urban or natural landscape and any experimental or instrument configuration. It is freely available for research and teaching activities (dart.omp.eu). After illustrating three significant sources of inaccuracy in RS interpretation, five recent DART advances are presented: RT in the atmosphere and topography, fast RS image simulation of large landscapes, SIF modelling, and satellite image inversion.

International audience; Recent years have seen the development of 1-D and 2-D regional-scale hydrological-hydrodynamic models, which differ greatly from reach-scale applications in terms of subgrid assumptions, parameterization, and applied resolution. Although 1-D and 2-D comparisons have already been performed at reach and local scales, model differences at regional scale are poorly understood. Moreover, there is a need to improve the coupling between hydrological and hydrodynamic models. It is addressed here by applying the MGB model at 1-D and 2-D dimensions for the whole ~700,000 km2 Negro basin (Amazon), which presents different wetland types. Long-term continuous simulations are performed and validated with multisatellite observations of hydraulic variables. Results showed that both approaches are similarly able to estimate discharges and water levels along main rivers, especially considering parameter uncertainties, but differ in terms of flood extent and volume and water levels in complex wetlands. In these latter, the diffuse flow and drainage patterns were more realistically represented by the 2-D scheme, as well as wetland connectivity across the basin. The 2-D model led to higher drainage basinwide, while the 1-D model was more sensitive to hydrodynamic parameters for discharge and flood extent and had a similar sensitivity for water levels. Finally, tests on the coupling between hydrologic and hydrodynamic processes suggested that their representation in an online way is less important for tropical wetlands than model dimensionality, which largely impacts water transfer and repartition.

International audience; Research to understand the nitrogen cycle has been thriving. The production of reactive nitrogen by humans exceeds the removal capacity through denitrification of any natural ecosystem. The surplus of reactive nitrogen is also a significant pollutant that can shift biological diversity and distribution, promotes eutrophication in aquatic ecosystems, and affects human health. Denitrification is the microbial respiration in anoxic conditions and is the main process that removes definitively nitrates from the ecosystem by returning of reactive nitrogen (Nr) to the atmosphere as N 2 and N 2 O emissions. This process occurs in the oceans, aquatic ecosystems and temporary flooded terrestrial ecosystems. Wetlands ecosystems are rich in organic matter and they have regular anoxic soil conditions ideal for denitrification to occur. In the current paper, we provide a meta-analysis that aims at exploring how research around global nitrogen, de-nitrification and wetlands had evolved in the last fifty years. Back in the time, wetland ecosystems were seen as non-exploitable elements of the landscape, and now they are being integrated as providers of ecosystem services. A significant improvement of molecular biology techniques and genetic extraction have made the denitrification process fully understood allowing constructed wetlands to be more efficient and popular. Yet, large uncertainties remain concerning the dynamic quantification of the global denitrification capacity of natural wetland ecosystems. The contribution of the current investigation is to provide a way forward for reducing these uncertainties by the integration of satellite-based Earth Observation (EO) technology with parsimonious physical based models.

Downscaling of L-Band microwave using Sentinel-3 land surface temperatureA large number of agricultural and water management applications require sub-kilometric frequent revisit surface Soil Moisture (SM) observations. L-band passive radiometer acquisitions are especially suited for soil moisture retrieval since they are less susceptible to attenuation by vegetation than active methods and are less sensitive to surface roughness than C or X – bands. However, while providing a 3 days global coverage for ascending and descending orbits with the currently available missions (SMOS/SMAP) the spatial resolution of the space-borne L-band radiometers is of ~40 km. Downscaling technics have been extensively used to increase the resolution of the SM products by combining data from optical (Merlin et al. 2012) and SAR sensors (Tomer et al. 2015). Here, we use land surface temperature data from the Sentinel-3 sensors to disaggregate the SMOS SM product into the DISPATCH algorithm. DISPATCH is based on the link between the evaporative efficiency and the SM (Merlin et al. 2010). The exercices is applied over Italy and compared to in-situ SM observations and model outputs over two sites in Northern and southrn Italy (Chiese and Capitanata). The algorithm is run using MODIS and the Sentinel-3 data for a comparative results. The potential of the combined use of Sentienl-3/MODIS and SMOS/SMAP is also investigate. The current study extends the application of an existing algorithm to new operational data from the Copernicus programe while accessing the advantages and ceavates.

Numerous surface soil moisture (SSM) products are available from remote sensing, ranging different spatial and temporal resolutions. Varying techniques are employed to retrieve SSM and different spatial scales highlight different distributions. Notwithstanding this variety between the available data, all of them should be coherent with the recorded rainfall and irrigation.In this work we have crossed recorded precipitations with a number of SSM products deriving from remote sensing: Soil Moisture Ocean Salinity (SMOS) mission, Soil Moisture Active Passive (SMAP) mission, European Space Agency Climate Change Initiative (ESA-CCI) products, Copernicus Global Land Operations product, a Neural Network SSM retrieval algorithm and AMSR-2 data.All the dataset products have been compared with recorded precipitation from on-ground stations over two agricultural sites in Italy: one in the north, near Lake Garda (Chiese Irrigation Consortium) and the other in the south-east in the Apulia region (Capitanata Irrigation Consortium).In both cases, a first SSM-rain comparison through well-established indexes (Pearson and Spearman correlations) has not yielded encouraging results.Then, a methodology has been developed to determine whether the variation of SSM is consistent with the presence/absence of precipitation. An Agreement Index (AI) has been derived as a way to measure the coherency between SSM and precipitation. Any time a measure of SSM is available, a positive or negative value for the AI is recorded, according to the rainfall registered since the previous measurement. During the irrigation season (March through September), the presence of this artificial input of water into the system is also taken into account. For every year, the proportion between “coherent” SSM-rainfall pairings (positive AIs) and “non-coherent” pairings (negative AIs) has been computed.This method is applied to all SSM products in the dataset, and results are compared. When aggregating the results for all the pixels within the irrigation consortia, all seem to align to a similar proportion between “coherent” and “non-coherent” SSM-rainfall pairings, notwithstanding the wide variety of data types, spatial resolutions and retrieval methods. However, even if the overall performances of the products are similar, each shows different spatial distributions, as each product is influenced differently by the physical features of the different areas.

The Middle East-North Africa (MENA) region has a prevailing hot and dry climate. The increasing demographic pressure brings an increased demand on water resources. In this study, we analyzed the drought dynamics using precipitation and soil moisture remote sensing data over the MENA region in the last two decades. Two types of remotely sensed micro-wave datasets (i.e. TRMM 3B43 and ESA-CCI SM) were used, to depict meteorological and agricultural droughts. Our results showed that the Standardized Precipitation Index (SPI) [1] from TRMM showed a good correlation with SPI from in-situ rainfall data and predicted successfully historical droughts. The standardized index shows lesser extreme agricultural droughts than meteorological drought for the same period. The region faced the biggest number of extreme meteorological droughts in 2015 and the biggest number of extreme agricultural droughts in 2013. These outputs help local and regional water management authorities as well as policy makers in combating water scarcity in the region. Further studies will look into optical remote sensing.

The derivation of the radiative budget of urban environments has gained a high interest in the recent years as an essential part of the global energy budget of big cities. Urban 3D (three-dimensional) heterogeneity hampers its assessment with in-situ measurements, which stresses the interest of Earth Observation (EO) satellites. Improvements in remote-sensing technology and 3D urban databases open the way to new deterministic approaches. This paper presents a new method that derives maps of urban shortwave exitance, albedo and radiative budget $$Q_{SW}$$* from EO satellite images (e.g. Sentinel 2, Landsat-8), using the discrete anisotropic radiative transfer model. In a preliminary step, it derives maps of urban material optical properties from an EO satellite image, at the spatial resolution of this EO image. The method is applied to the city of Basel, Switzerland, in the frame of the European Community H2020 URBANFLUXES (www.urbanfluxes.eu) project which aims at improving our knowledge on anthropogenic heat fluxes in European cities. Results are very encouraging. Indeed, urban $$Q_{SW}$$* derived from 234 EO satellite images, ranging from 200 to 800 W/m2 through the year, is very close to in-situ measured $$Q_{SW}$$*: ≈15 W/m2 temporal root mean square error (i.e. 2.7% mean relative difference) relative to measurements.

Two satellites are currently monitoring surface soil moisture (SM) using L-band observations: SMOS (Soil Moisture and Ocean Salinity), a joint ESA (European Space Agency), CNES (Centre national d’études spatiales), and CDTI (the Spanish government agency with responsibility for space) satellite launched on November 2, 2009 and SMAP (Soil Moisture Active Passive), a National Aeronautics and Space Administration (NASA) satellite successfully launched in January 2015. In this study, we used a multilinear regression approach to retrieve SM from SMAP data to create a global dataset of SM, which is consistent with SM data retrieved from SMOS. This was achieved by calibrating coefficients of the regression model using the CATDS (Centre Aval de Traitement des Données) SMOS Level 3 SM and the horizontally and vertically polarized brightness temperatures (TB) at 40° incidence angle, over the 2013 – 2014 period. Next, this model was applied to SMAP L3 TB data from Apr 2015 to Jul 2016. The retrieved SM from SMAP (referred to here as SMAP_Reg) was compared to: (i) the operational SMAP L3 SM (SMAP_SCA), retrieved using the baseline Single Channel retrieval Algorithm (SCA); and (ii) the operational SMOSL3 SM, derived from the multiangular inversion of the L-MEB model (L-MEB algorithm) (SMOSL3). This inter-comparison was made against in situ soil moisture measurements from more than 400 sites spread over the globe, which are used here as a reference soil moisture dataset. The in situ observations were obtained from the International Soil Moisture Network (ISMN; https://ismn.geo.tuwien.ac.at/) in North of America (PBO_H2O, SCAN, SNOTEL, iRON, and USCRN), in Australia (Oznet), Africa (DAHRA), and in Europe (REMEDHUS, SMOSMANIA, FMI, and RSMN). The agreement was analyzed in terms of four classical statistical criteria: Root Mean Squared Error (RMSE), Bias, Unbiased RMSE (UnbRMSE), and correlation coefficient (R). Results of the comparison of these various products with in situ observations show that the performance of both SMAP products i.e. SMAP_SCA and SMAP_Reg is similar and marginally better to that of the SMOSL3 product particularly over the PBO_H2O, SCAN, and USCRN sites. However, SMOSL3 SM was closer to the in situ observations over the DAHRA and Oznet sites. We found that the correlation between all three datasets and in situ measurements is best (R > 0.80) over the Oznet sites and worst (R = 0.58) over the SNOTEL sites for SMAP_SCA and over the DAHRA and SMOSMANIA sites (R= 0.51 and R= 0.45 for SMAP_Reg and SMOSL3, respectively). The Bias values showed that all products are generally dry, except over RSMN, DAHRA, and Oznet (and FMI for SMAP_SCA). Finally, our analysis provided interesting insights that can be useful to improve the consistency between SMAP and SMOS datasets.

The European Space Agency (ESA) Soil Moisture and Ocean Salinity (SMOS) satellite has provided, for the first time, systematic passive L-band (1.4 GHz) measurements from space. This new data set, with a spatial resolution of ~40 km, has allowed a number of outstanding results over land (soil moisture, vegetation properties, frozen soils, …), ocean (salinity, meso-scale phenomena, river plumes, high winds, …) and cryosphere. SMOS, together with the NASA missions SMAP and Aquarius, have demonstrated the interest of the continuity of L-band observations. However, higher spatial resolution (1–10 km) is needed for applications related to water resources management and food security, for instance. Over the ocean as well as in coastal areas, higher resolution will bring the possibility to study in detail meso-scale processes and salinity (and density) variations closer to the coast. Over ice, higher spatial resolution will allow to monitor melting events in the coastal regions of Antarctica, for instance. In order to ensure the continuity of Earth observations in the L-band, while improving the resolution of the current generation of radiometers, new mission concepts are needed. We present the SMOS-HR (High-Resolution) project, which is currently in Phase 0 at CNES (Centre National d’Etudes Spatiales).

Neural networks were used to estimate the ability of different sets of predictors to capture the variability of above ground biomass (AGB). SMOS brightness temperatures (TBs) for only two incidence angles capture 85% of the AGB variance. Adding soil moisture or L-band vegetation optical depth (L-VOD) increase the ability to capture the AGB variance to 90 % and 92 %, respectively. With respect to using only TBs, L-VOD improves the AGB estimation in regions of low vegetation.

International audience; Within the framework of the efforts of the European Space Agency (ESA) to develop the most consistent and complete record of surface soil moisture (SSM), this study investigated a statistical approach to retrieve a global and long-term SSM dataset from space-borne observations. More specifically, this study investigated the ability of physically based statistical regressions to retrieve SSM from two passive microwave remote sensing observations: the Advanced Microwave Scanning Radiometer (AMSR-E; 2003–Sept. 2011) and the Soil Moisture and Ocean Salinity (SMOS) satellite. Regression coefficients were calibrated using AMSR-E horizontal and vertical brightness temperature (TB) observations and SMOS level 3 SSM (SMOSL3; as a training dataset). This calibration process was carried out over the June 2010–Sept. 2011 period, over which both SMOS and AMSR-E observations coincide. Based on these calibrated coefficients, a global SSM product (referred here to as AMSR-reg) was computed from the AMSR-E TB observations during the 2003–2011 period. The regression quality was assessed by evaluating the AMSR-reg SSM product against the SMOSL3 SSM product over the period of calibration, in terms of correlation (R) and Root Mean Square Error (RMSE). A good agreement (mean global R = 0.60 and mean global RMSE = 0.057 m3/m3), was obtained between the AMSR-reg and SMOSL3 SSM products particularly over Australia, central USA, central Asia, and the Sahel. In a second step, the AMSR-reg SSM retrievals and commonly used AMSR-E SSM retrievals derived from the Land Parameter Retrieval Model (AMSR-LPRM), were evaluated against two kinds of SSM references (i) the global MERRA-Land SSM simulations and (ii) in situ measurements over 2003–2009. The results demonstrated that both AMSR-reg and AMSR-LPRM (better when considering global simulations) successfully captured the temporal dynamics of the references used having comparable correlation values. AMSR-reg was more consistent with MERRA-land than AMSR-LPRM in terms of unbiased RMSE (ubRMSE) with a global average of ubRMSE of 0.055 m3/m3 for AMSR-reg and 0.084 m3/m3 for AMSR-LPRM. In conclusion, the statistical regression, which is tested here for the first time using long-term spaceborne TB datasets, appears to be a promising approach for retrieving SSM from passive microwave remote sensing TB observations.

International audience; The Soil Moisture and Ocean Salinity satellite (SMOS) was launched in November 2009 and started delivering data in January 2010. The commissioning phase ended in May 2010. Subsequently, the satellite has been in operation for over six years while the retrieval algorithms from Level 1 (L1) to Level 2 (L2) underwent significant evolutions as knowledge improved. Moreover, other approaches for retrieval at L2 over land were investigated while Level 3 (L3) and Level 4 (L4) were initiated. In this paper, these improvements were assessed by inter-comparisons of the current L2 (V620) against the previous version (V551) and new products (using neural networks referred to as SMOS-NN) and L3 (referred to as SMOS-L3). In addition, a global evaluation of different SMOS soil moisture (SM) products (SMOS-L2, SMOS-L3, and SMOS-NN) was performed comparing products with those of model simulations and other satellites. Finally, all products were evaluated against in situ measurements of soil moisture (SM). To achieve such a goal a set of metrics to evaluate different satellite products are suggested.The study demonstrated that the V620 shows a significant improvement (including those at L1 improving L2) with respect to the earlier version V551. Results also show that neural network based approaches can often yield excellent results over areas where other products are poor. Finally, global comparison indicates that SMOS behaves very well when compared to other sensors/approaches and gives consistent results over all surfaces from very dry (African Sahel, Arizona), to wet (tropical rain forests). RFI (Radio Frequency Interference) is still an issue even though detection has been greatly improved through the significant reduction of RFI sources in several areas of the world. When compared to other satellite products, the analysis shows that SMOS achieves its expected goals and is globally consistent over different eco climate regions from low to high latitudes and throughout the seasons.

The main objectives of this study were to provide a proxy “validation” of the Soil Moisture and Ocean Salinity (SMOS) mission's vegetation optical depth product ( τ SMOS ) on a global scale, to give a first indication of the potential of τ SMOS to capture large-scale vegetation dynamics, and to contribute towards investigations into the possible use of optical vegetation indices (VI's) for the estimation of τ . The analyses were performed by comparing the spatial and temporal behaviour of τ SMOS relative to four MODIS-based VI's, with that of the vegetation optical depth from a similar sensor, AMSR-E ( τ AMSR-E ). 16-day and annual average values of the passive microwave optical depth ( τ ) for the year 2010 were obtained from SMOS (1.4 GHz) and AMSR-E (6.9 GHz) observations. The VI's chosen for this study were the Normalized Difference Vegetation Index (NDVI), Enhanced Vegetation Index (EVI), Leaf Area Index (LAI) and Normalized Difference Water Index (NDWI). The highest global-scale, annual correlation was found between τ SMOS and τ AMSR-E from ascending orbits (Spearman's R = 0.80). On global, annual scales, τ SMOS showed higher correlations with τ AMSR-E than with the VI's, while τ AMSR-E was more highly correlated with VI's than with τ SMOS . Timeseries of both τ and the VI's were made per landcover class, for the northern hemisphere, tropics and southern hemisphere. Although the large-scale spatial and spatio-temporal behaviour of τ SMOS and τ AMSR-E is generally similar, the results highlight some notable differences in observing vegetation with optical vs . passive microwave sensors, and certain crucial differences between the two passive microwave sensors themselves. Overall, the results found in this study give a good first confidence in the SMOS L3 τ product and its potential use in vegetation studies. These results provide an essential general reference for future (global-scale) vegetation monitoring with passive microwaves, for the future inclusion of τ SMOS in long-term, multi-sensor datasets, and for passive microwave algorithm development.

Models that simulate the radiative budget (RB) and remote sensing (RS) observation of landscapes with physical approaches and consideration of the three-dimensional (3-D) architecture of Earth surfaces are increasingly needed to better understand the life-essential cycles and processes of our planet and to further develop RS technology. DART (Discrete Anisotropic Radiative Transfer) is one of the most comprehensive physically based 3-D models of Earth-atmosphere optical radiative transfer (RT), from ultraviolet to thermal infrared. It simulates the optical 3-D RB and signal of proximal, aerial and satellite imaging spectrometers and laser scanners, for any urban and/ or natural landscapes and for any experimental and instrumental configurations. It is freely available for research and teaching activities. Here, an application is presented after a summary of its theory and recent advances: inversion of Sentinel 2 images for simulating time series of urban radiative budget ‘Q* sw ’ maps through the determination of maps of urban surface material. Results are very encouraging: satellite and in-situ Q* sw are very close (RMSE ≈ 15W/m2; i.e., 2.7% mean relative difference).

International audience; After almost 9 years in orbit L-band satellite radiometry has demonstrated its impacts and values for a wide range of science and applications. However, so as to cover specific applications use of other sensors can prove very valuable. In particular use of altimetry, optical and thermal infrared measurements can offer new avenues. Many of them were tested with existing satellites at the time of SMOS launch, but with the Copernicus' Sentinel-3 mission's data now available, new applications and operational products can be envisioned.

The European Space Agency's (ESA) SMOS mission, in orbit since more than 8 years, carries a passive microwave interferometric radiometer measuring in L-Band and provides accurate global observations of emitted radiation originating from the Earth's surfaces since the atmosphere is almost transparent in this spectral range. In addition, over land the effect of vegetation on the measurements is smaller than for shorter wavelengths. The scientific objectives of the SMOS mission directly respond to the need for global observations of soil moisture and ocean salinity, two key variables used in predictive hydrological, oceanographic and atmospheric models. SMOS observations also provide information on the characterisation of ice and snow covered surfaces and the sea ice effect on ocean-atmosphere heat fluxes and dynamics, which affects large-scale processes of the Earth's climate system.

The vegetation optical depth (VOD) measured at microwave frequencies is related to the vegetation water content and provides information complementary to visible/infra-red vegetation indices. This study is devoted to the characterisation of a new VOD data set obtained from SMOS (Soil Moisture and Ocean Salinity) satellite observations at L-band (1.4 GHz). Three different SMOS L-band VOD (L-VOD) data sets (SMOS Level 2, Level 3 and SMOS-IC) were compared with data sets on tree height, visible/infra-red indexes (NDVI, EVI), cumulated precipitation, and above ground biomass (AGB) for the African continent. For all relationships, SMOS-IC showed the lowest dispersion and highest correlation. Overall, we found a strong (R > 0.85) correlation with no clear sign of saturation between L-VOD and four AGB data sets. The relationship linking L-VOD to tree height (R = 0.87) and Baccini's AGB (R = 0.94) was strong and linear. The relationships between L-VOD and three other AGB data sets were linear per land cover class, but with a changing slope depending on the land cover type. For low vegetation classes, the annual mean of L-VOD spans a range from 0 to 0.7 and it is linearly correlation with the amount of the average annual precipitations. SMOS L-VOD showed a higher sensitivity to AGB as compared to NDVI and K/X/C-VOD (VOD measured, respectively, at 19, 10.7, and 6.9 GHz). The results showed that although the spatial resolution of L-VOD is coarse (~ 40 km), the high temporal frequency and sensitivity to AGB makes SMOS L-VOD a very promising index for large scale monitoring of the vegetation status, in particular biomass.

Soil moisture retrievals, delivered as a CATDS (Centre Aval de Traitement des Donnees SMOS) Level-3 product of the Soil Moisture and Ocean Salinity (SMOS) mission, form an important information source, particularly for updating land surface models. However, the coarse resolution of the SMOS product requires additional treatment if it is to be used in applications at higher resolutions. Furthermore, the remotely sensed soil moisture often does not reflect the climatology of the soil moisture predictions, and the bias between model predictions and observations needs to be removed. In this paper, a statistical framework is presented that allows for the downscaling of the coarse-scale SMOS soil moisture product to a finer resolution. This framework describes the interscale relationship between SMOS observations and model-predicted soil moisture values, in this case, using the va riable infiltration capacity (VIC) model, using a copula. Through conditioning, the copula to a SMOS observation, a probability distribution function is obtained that reflects the expected distribution function of VIC soil moisture for the given SMOS observation. This distribution function is then used in a cumulative distribution function matching procedure to obtain an unbiased fine-scale soil moisture map that can be assimilated into VIC. The methodology is applied to SMOS observations over the Upper Mississippi River basin. Although the focus in this paper is on data assimilation apcations, the framework developed could also be used for other purposes where downscaling of coarse-scale observations is required.

An algorithm using in situ measurements for training a neural network (NN) to retrieve soil moisture (SM) from SMOS observations is discussed. The in situ data are measurements of the SM content in the 0–5 cm depth layer from the SCAN, SNOTEL and USCRN networks. It is shown that this approach can be used to retrieve SM at continental scale in North America. The NN retrieval (NN inSitu ) is evaluated against in situ data not used during the training phase and against maps of the SMOS level 3 SM product and ECMWF SM models. NN inSitu SM values are closer to ECMWF values for wet areas. A method to use NNs as a tool to classify in situ sites representative of the remote sensing observations scale is briefly discussed.

International audience; In this study, we evaluated a new SMOS (Soil Moisture and Ocean Salinity) soil moisture (SM) product, developed by the collaboration of INRA (Institut National de la Recherche Agronomique) and CESBIO (Centre d'Etudes Spatiales de la BIOsphere), against the operational SMOS level 3 SM product (SMOSL3). This new product (hereinafter referred to as SMOS-INRA-CESBIO, i.e. SMOSIC in short) differs from SMOSL3 three ways: (i) the SMOSIC algorithm considers the pixel as homogeneous and does not take into account the heterogeneity of the pixel; (ii) uses a new calibration of the effective scattering albedo and soil roughness parameters; (iii) no time correlation is applied on the optical depth. The evaluation was done over North America using the (European Center for Medium range Weather Forecasting) ECMWF SM simulation as a reference, using data for 2011. A better performance of the SMOSIC SM product with respect to ECMWF was found: (i) SMOSIC had higher correlation coefficients (temporal dynamics) and lower unbiased RMSD (absolute values) values with ECMWF over most of the study area and (ii) the spatial patterns of the SMOSIC temporal mean SM maps were in a better agreement with ECMWF.

We introduced an approach to simulate and separate atmospheric contribution in ground-based thermal-infrared (TIR) camera measurements. Different from the traditional approach which uses the look-up table built from 1-D radiative transfer model (RTM), this approach directly simulates 3-D ray propagations and interactions in the heterogeneous urban environment by using the Discrete Anisotropic Radiative Transfer (DART) model. The atmospheric turbid cells that occupy every part of the urban scene are created using the vertical constituent distribution and the optical property profiles in the existing databases or from the actual meteorological measurements. The two components of atmospheric effects on the TIR at-sensor radiance are attenuated transmission and path thermal emission. Taking both into account, the at-surface radiance corresponding to the signal emitted only from the urban surface can be derived.

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 minute intervals for the snow seasons (November–June) between 2011 and 2016 for MZA and 2014–2016 for CED and LAQ. Precipitation data was 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 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 data set 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: 10.5281/zenodo.291405.

The Soil Moisture and Ocean Salinity (SMOS) mission provides multi-angular, dual-polarised brightness temperatures at 1.4 GHz, from which global soil moisture and vegetation optical depth (tau) products are retrieved. This paper presents a study of SMOS' tau product in 2010 and 2011 for crop zones of the USA. Retrieved tau values for 504 crop nodes were compared to optical/IR vegetation indices from the MODES (Moderate Resolution Imaging Spectroradiometer) satellite sensor, including the Normalised Difference Vegetation Index (NDVI), Enhanced Vegetation Index (EVE), Leaf Area Index (LAI), and a Normalised Difference Water Index (NOW!) product. tau values were observed to increase during the growing season and decrease during senescence in these areas, as did MODIS vegetation indices. SMOS' tau values generally peaked later than MODES LAI values, with an estimated time difference of about 19 days. A linear regression between tau and the MODIS products was carried out for each node and values of the determination coefficient, R-2, slope, b' and intercept, b '' were found. The average R-2 value varied from 0.32 to 035 for the different vegetation indices. The linear regression between LAI and tau produced an average slope of b' = 0.06, and an average intercept of b '' = 0.14. The effects of crop fraction and dominant crop type were investigated and crop fraction was found to have a low effect on R-2 values. R-2 values appeared to be lower for wheat and hay and higher for corn. b' and b '' values had higher standard deviations for wheat but were generally close to the mean values for corn, soybean and hay. (C) 2013 Published by Elsevier Inc. (Less)

In this paper we present a study on the use of remote sensing data combined to the 3D modeling of radiative transfer (RT) and energy balance in urban canopies in the aim to improve our knowledge on anthropogenic heat fluxes in several European cities (London, Basel, Heraklion, and Toulouse). The approach is based on the forcing by the use of LandSAT8 data of a coupled radiative transfer model DART (Direct Anisotropic Radiative Transfer) (www.cesbio.upstlse.fr/dart) with an energy balance module. LandSAT8 visible remote sensing data is used to better parametrize the albedo of the urban canopy and thermal remote sensing data is used to enhance the anthropogenic component in the coupled model. This work is conducted in the frame of the H2020 project URBANFLUXES, which aim is to improve the efficiency of remote-sensing data usage for the determination of the anthropogenic heat fluxes in urban canopies [5].

The need of better accuracy for analyzing remote sensing (RS) data of complex Earth surfaces explains the increasing need of models that simulate RS data with physical approaches. Similarly, the study of Earth surfaces functioning requires physical models that simulate the 3D radiative budget (RB) of these surfaces. DART (Discrete Anisotropic Radiative Transfer is one of the most comprehensive physically based 3D models that model the Earth-atmosphere radiation interaction from visible to thermal infrared wavelengths. It simulates optical signals at the entrance of terrain / airborne / satellite imaging radiometers and laser scanners, as well as the 3D RB, of urban / natural landscapes for any experimental and instrumental configurations. Its licenses are free for research and teaching activities. Here, we present its major recent advances.

The recently launched H2020 project URBANFLUXES (URBan ANthrpogenic heat FLUX from Earth observation Satellites) investigates the potential of EO to retrieve anthropogenic heat flux, as a key component in the Urban Energy Budget (UEB). URBANFLUXES advances existing Earth Observation (EO) based methods for estimating spatial patterns of turbulent sensible and latent heat fluxes, as well as urban heat storage flux at city scale and local scale. Independent methods and models are engaged to evaluate the derived products and statistical analyses provide uncertainty measures. Optical, thermal and SAR data are exploited to improve the accuracy of the UEB components spatial distribution calculation. Synergistic use of different types and of various resolution EO data allows estimates in local and city scale. Ultimate goal of the URBANFLUXES is to develop a highly automated method for estimating UEB components to use with Copernicus Sentinel data, enabling its integration into applications and operational services.

Monitoring near-surface soil freeze/thaw patterns is becoming essential under the context of global changes as it is more sensitive to climatic fluctuation compared with subsurface thermal characteristics and its evolution could be an early warning of changes in near-surface permafrost. It requires continuous long term and stable data record for understanding hydrological, ecological and biogeochemical responses of permafrost to global climate change. AMSR2 (Advanced Microwave Scanning Radiometer 2) is designed as a successor of AMSR-E (Advanced Microwave Scanning Radiometer – Earth Observing System) to ensure continuity of such observation. In this study, a linear regression is used to inter-calibrate the AMSR-E and AMSR2 brightness temperatures. Then discriminant function algorithm is adopted to produce a long term freeze/thaw data record. It is compared with in situ air temperature measurements from both the temporal and spatial aspects. The results show that the accuracy is consistent between AMSR-E and AMSR2 with a value above 85 %, according to the result of spatial distribution accuracy. Analysis is conducted with this data record to explore the spatial distribution of frost days, its changing trend and the frost probability of each pixel on a specific date. The mean annual frost days of high northern latitude (HNL, > 45° N) zone is 214.2 &pm; 69.5 days and the trend of frost days indicates that the frost period is decreasing at a rate of −0.0065 day/month in 27 % of the domain which is defined by significance level of the F-test, and most of which are concentrated in the high latitude area specifically over the Northeast of Canada, Central and Eastern Russia and most part of Eastern Europe. The significant changes in frost days mostly occur in regions of discontinuous permafrost and transient permafrost. The spatial distribution of the frost days and its trend variations are found to be consistent with the minimum temperature anomalies trend. It indicates that the global warming is not constant at different regions over the globe. Further analysis over the Qinghai-Tibetan Plateau where discontinuous permafrost, island permafrost, seasonally frozen ground exist demonstrated that the frost period is shortening slightly over the past decade, and the last frost date is advanced over more than half of the region. It is considered to be a remarkable indication for permafrost degradation in this area.