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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.

Drought prediction is crucial especially where the rainfall regime is irregular and agriculture is mainly based on irrigated crops, such as in Mediterranean countries. In this work, the main objective is to develop an EO-based agricultural drought monitoring system (ADMOS) for operative irrigation networks management at different spatial and temporal scales. Different levels of drought are identified based on an integrated indicator combining anomalies of rainfall, soil moisture, land surface temperature and vegetation indices, allowing to consider the different droughts types and their timing looking on the end-user’s perspective. Multiple remote sensing data, which differ on sensing techniques, spatial and temporal resolutions and electromagnetic frequencies, are used for each anomaly computation. The analyses have been performed over two Irrigation Consortia in Italy (the Chiese and Capitanata ones), which differ for climate, irrigation volumes and techniques, and crop types. The obtained results show a negative correlation between cumulated ADMOS and the irrigation volumes in the Capitanata area, while in the Chiese Consortium a zero correlation is obtained with an almost constant amount of irrigation volumes provided to the crops every year independently from the drought condition. In both areas, crop yields seem to be almost uncorrelated to the drought index, as production is highly sustained by irrigation. Moreover, discrepancies on the anomalies sign is observed, especially when soil moisture is considered. The results also clearly show that asynchronies may exist especially between soil moisture anomalies and vegetation or land surface temperature anomalies.

The Madeira is one of the major tributaries of the Amazon River and is characterized by a large alluvial floodplain throughout the stream continuum. This study aims to better assess the hydrological functioning of the Madeira Basin over its alluvial floodplains at both local and global scales. We used the semi-distributed hydrological Soil and Water Assessment Tool (SWAT) model to simulate water discharge at a daily time step and water resources for each hydrological compartment. A new hydraulic module for water routing was implemented in the SWAT model considering the floodplain either as a simple reservoir or as a continuum where the water can flow along with the floodplain network. Both water surface estimated by L-band passive microwaves (SWAF data) and digital elevation model—shuttle radar topography mission (DEM–SRTM data) were used to delineate the floodplain, as inputs for the model. On the global scale, the amount of water stored in the Madeira floodplain is between 810 ± 230 km3 per year when the floodplains are delimited with SWAF and 1,300 ± 350 km3 per year with the DEM floodplain delineation between 2008 and 2018. Spatial altimetry (Jason 2-3) data were also applied to alluvial areas to validate the water height dynamic in floodplains at a local scale. Results show that more than 60% of the alluvial validation points display a correlation above 0.40 ± 0.02 regardless of the floodplain delineation. This study permits us to better characterize the spatio-temporal storage dynamics of the Madeira floodplains at both local and global scales, and it underlines the importance of a precise floodplain delineation, before computing biogeochemical fluxes and sediment yield.

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.