Through the water-uptake at their roots, and the transpiration at their leaves, vegetation plays a key role in the movement of water from the soil to the atmosphere. To improve our understanding of processes dictating the uptake and transpiration of water by vegetation at large scales, dynamics of vegetation water content are an important source of information. Active microwave instruments have been used for decades to estimate vegetation water content, owing to their high sensitivity to water at earth’s surface. Recent developments in the retrieval of normalised C-band backscatter and the associated backscatter-incidence angle relation from the ASCAT scatterometer onboard the series of Metop satellites have enabled the use of the dynamic backscatter-incidence angle relation for monitoring of vegetation water content from sub-seasonal to multi-year time scale. The result is a global data record of daily estimates of the backscatter-incidence angle relation spanning 2007 to 2025. Additionaly, measurements from the scatterometer onboard the ERS and the future Metop-SG B satellite series, respectfully preceding and succeeding ASCAT, can be included to create a record spanning multiple decades. In this contribution, the spatial and temporal variation of the ASCAT backscatter-incidence angle relation are linked to dynamics of vegetation water content and biomass in different settings to demonstrate the potential of the dynamic C-band backscatter-incidence angle relation for monitoring of vegetation water dynamics.

Irrigation is a significant component of the water cycle in agricultural regions, but information about how much and where irrigation is applied is scarce and uncertain. Remote sensing-based products have been developed to retrieve irrigation across scales, but they are limited by spatial uncertainties and confounding factors, particularly in highly fragmented agricultural landscapes. This paper presents a novel method to estimate irrigation at the field scale by integrating Sentinel-1 C-band Synthetic Aperture Radar (SAR) backscatter data and Sentinel-2 NDVI data within a modeling framework. The proposed approach involves jointly calibrating the Water Cloud Model (WCM) and a two-layer Soil Water Balance (SWB) model to simultaneously simulate backscatter, irrigation, surface and root-zone soil moisture, and other water balance components. The model is driven by reanalysis meteorological data, and selected parameters in the WCM and SWB are calibrated exclusively using Sentinel-1 backscatter data, thus making the framework independent of in situ ground measurements. The model is tested on 38 fields within an irrigation district in the Po Valley, Italy, during 2018, for which irrigation data are available as reference. It is shown that the capability to estimate irrigation events depends on the treatment of backscatter and the choice of the calibration period. Average time series correlations of 0.43 to 0.95 are obtained for daily to bi-weekly irrigation estimates, respectively, with an associated bias amounting to 0.15 mm/day and 2.25 mm/15 days, and around 25 mm for annual estimates.

The potential of dense Sentinel-2 time series to serve as a basis for operational crop monitoring systems is hindered by cloud cover, especially at high latitudes. Sentinel-1 data can overcome this limitation, but similar to optical data, are prone to saturation, i.e. when changes in vegetation biomass are not reflected in the remote sensing signal. Time-integration is a strategy commonly used in optical remote sensing to mitigate saturation effects. In this pilot study, we tested whether this approach can also improve the relationship between Sentinel-1 backscatter and maize traits, using Sentinel-1 A and B backscatter data. Our test site consisted of a forage maize experimental field in Sweden. Evaluated plant traits included the number of leaves, the phenological stage, the leaf area index, the dry matter yield and the dry matter content. Linear and logistic models were adjusted between time-integrated values of the backscattering coefficient ([Formula: see text], [Formula: see text], [Formula: see text] and [Formula: see text]) and field-measured traits. Our results indicate a good agreement between Sentinel-1 time-integrated signal and maize traits, with [Formula: see text] of 0.97, 0.93, 0.94, 0.95 and 0.86 for phenological stage, leaf number, leaf area index, dry matter yield and dry matter content, respectively, and systematically outperformed models built with non-cumulative [Formula: see text] values. Our findings also indicate that the time-integrated models perform equally well with data acquired from a single Sentinel-1 satellite. These results, if confirmed for a wider range of geographical extent and management conditions, could pave the way for a remote sensing-based, weather-independent and saturation-insensitive decision support tool.

The aim of this presentation is to highlight the confluence of developments in plant physiology, biogeosciences and microwave remote sensing and a potential route to a global perspective on plant hydraulics. The context is the continued development of a satellite mission concept based on a Low Earth Orbit (LEO) constellation of Synthetic Aperture Radars (SAR) that would provide sub-daily observations including vegetation water content and vegetation wet/dry state (Steele-Dunne et al., 2023, Matar et al., 2023). A recurring challenge in mission concept development has been the scarcity sub-daily microwave data, specifically radar data. These are critical to consolidate measurement and observation requirements, and to demonstrate the science case.Here, we will highlight research activities centred on our installation of a network of GNSS transmissivity (GNSS-T) sensors at existing forest monitoring sites across Europe. GNSS-T is an emerging measurement technique that provide crucial insight into sub-daily changes in the vegetation as a dielectric medium. Because GNSS-T is relatively inexpensive, it enables data collection across a wide range of biomes, complementing sparser tower-based sensors and providing critical observations to support mission development. We will outline how we are using GNSS-T observations with radiative transfer modeling to consolidate observation and measurement requirements. We will illustrate how we using GNSS-T observations to investigate the link between microwave observations and biogeophysical variables at the heart of plant water relations and the surface water and energy balances. We will also discuss how the exploitation of GNSS-T for these purposes is not trivial, highlighting some of the theoretical considerations we have encountered and our attempts to handle them. Finally, we will put our activities in the wider context of developments in plant physiology and biogeosciences to discuss opportunities to bring these fields closer together. This is essential to reach the global perspective needed to address urgent scientific and societal challenges.

Abstract A recent study (Shan et al., 2024, https://doi.org/10.1016/j.rse.2024.114167) showed that using a Deep‐Neural‐Network (DNN)‐based observation operator in land data assimilation (DA) does not guarantee improvements in surface soil moisture (WG2). Here, we conduct a synthetic experiment to explain this outcome by testing whether DNN‐based operators reproduce physically consistent Jacobians (sensitivities) required by DA. The ISBA‐A‐gs land surface model is perturbed to generate “synthetic true” WG2 and leaf area index (LAI), and a Water Cloud Model (WCM) is used to generate synthetic backscatter. Two DNNs are trained taking simulated states from the open loop run as input and the synthetic backscatter as output. The first is trained on WG2 and LAI while the second uses LAI and soil moisture from multiple layers, that is including redundant inputs. The synthetic observations are then assimilated using the two DNNs and the WCM as observation operators. Results suggest that the assimilation using a DNN‐based observation operator improves the estimates of WG2. However, DNN Jacobians are no longer physically plausible when the model simulations used as training inputs contain errors relative to the “true” data, or when redundant input variables are included. This confirms that a strong predictive performance does not automatically reflect accurate representation of underlying physical sensitivities which prove problematic in subsequent DA. This study has important implications for deep‐learning‐based DA and Earth system models (ESM): DA or ESM trained on reanalysis or simulations may learn inaccurate Jacobians if input contains errors and redundant variables. This potential limitation could remain hidden when evaluation focuses primarily on predictive performance.

The derivation of geophysical parameters from passive microwave observations over land has always been challenging. Soil conditions, land cover, and the atmosphere affect the measurements to varying degrees, and it is difficult to isolate these individual contributions. In this study, we assess whether multiangle observations provide additional information that can strengthen existing retrieval algorithms. Between October and November 2024, a series of airborne flights carrying the advanced microwave precipitation radiometer (AMPR) were conducted over the United States. Three land-based flights with multiangle observations from 0 degrees to 45 degrees and dual-polarized measurements at 10.7, 19.35, and 37.1 GHz were analyzed. The data showed a strong linear relationship between the microwave polarization ratio and the incidence angles within the 25 degrees-45 degrees range (e.g., $R<^>{2} \gt 0.9$ for 71.2% of all flight scans analyzed at 10.7 GHz). The linear model for the polarization ratio showed a similar performance in terms of root-mean-square error (RMSE) to simulations based on a $\tau $ - $\omega $ radiative transfer model and commonly used assumptions. The observed linearity was further evaluated with satellite observations from the AMSR2. This evaluation confirmed the observed linearity across all three frequencies. The slope of the relationship between the polarization ratio and the incidence angle was calculated for each multiangle flight scan, which was sensitive to both soil moisture (SM) and vegetation. This new parameter, which was derived from multiple observations, appeared to be consistent in time and space, revealing similar patterns along flight lines acquired at different times. The slope was used as input in regression models (RMs) to derive SM. A model solely based on 10.7-GHz data revealed a strong correlation ( $R<^>{2} = 0.81$ ) with Level-3 SM from the SM active passive (SMAP) mission, demonstrating the potential of multiangle retrievals with established SM products.

Cosmic ray neutron sensor (CRNS) has gained popularity in the last decade for its suitability in estimating areaaveraged soil moisture (SM). The presence of fresh biomass influences the CRNS signal due to its water content, introducing bias to soil moisture estimation. Calibration and correction methods have been developed to account for this bias, but they usually require laborious sampling. Here, a novel approach is tested to assess the impact of biomass water equivalent (BWE) on CRNS soil moisture estimation. It was conducted in two contrasting environments from 15/11/21-1/02/23 for an olive orchard in Saudi Arabia, and from 15/02/22-30/03/23 for a cherry orchard in France. Water-uptake rates were monitored using sap flow sensors, as well as actual evapotranspiration (AET) and in-situ SM within the CRNS footprint. Concurrent environmental variables were also measured with a research-grade weather stations. It was found that when vapor pressure deficit (VPD) > 1.8kPa, CRNS-derived SM (CRNS-SM) closely matched in-situ SM measurements, which indicates minimal influence from BWE. Conversely, when VPD is lower than 1.8kPa, CRNS-SM overestimates the in-situ moisture. An optimization approach was used to find a temporally-varying value of N0 parameter that minimizes the difference between soil moisture estimated with CRNS and in-situ sensors. Furthermore, the results showed that the relative change in the optimized value of N-0 (N-0,N-opt) was well correlated with VPD in both orchards (R-2 = 0.66 for olive and R-2 = 0.74 for cherry orchards), indicating a strong correlation between these variables. These findings suggest that integrating VPD and CRNS observations, and using the VPD-N-0,N-opt correlation approach could be a promising way to account for the bias due to biomass dynamics on the estimation of area-averaged SM.

Biomass water equivalent represents the cumulative vegetation water content and biological hydrogen contained within plant tissue and can provide valuable insights into ecosystem-scale water dynamics. Several studies conducted in crop and forest fields have reported that variation in biomass water equivalent can decrease the accuracy of soil moisture estimates when using the Cosmic-Ray Neutron Sensor (CRNS): a novel approach for real-time soil moisture monitoring. Indeed, with an increasing biomass water equivalent, more neutrons at epithermal and thermal energy levels are held by the hydrogen atoms in the vegetation, leading to an overestimation of soil moisture. In this study, we explore the impact of such variations in biomass water equivalent on the estimated soil moisture from CRNS sensors installed in two distinct orchard plantations. The first plantation is an olive orchard located in northern Saudi Arabia (desert climate), while the second is a cherry orchard located in southeastern France (Mediterranean climate). Utilizing a site-specific calibration value (N0), soil moisture was derived from neutron counts, and compared to reference in-situ soil moisture. The estimated and reference soil moisture difference was analyzed as a function of biomass water equivalent variations. As biomass water equivalent measurements were not possible to obtain at equivalent CRNS acquisition rates, the vapor pressure deficit (VPD), a meteorological variable, in plants, it is used to describe the difference in water vapor pressure between the inside of a leaf and the surrounding air, was used as a proxy. An optimization procedure was performed to update N0 (N0.opt) in such a way that the difference between estimated and reference soil moisture is minimized. Variations in N0,opt are subsequently correlated with VPD to confirm the link between neutron count variations and seasonal changes in biomass water equivalent. Results showed that without considering the effects of biomass water equivalent on neutron counts, the estimated soil moisture overestimates the reference soil moisture when the VPD is low (no stress conditions) and matches the reference soil moisture when the VPD is high (water stress conditions). Moreover, the results showed that the change in N0 (1 - N0.opt / N0) correlated well with VPD (R2 = 0.7). An improved understanding of the potential effects of biomass water equivalent on CRNS signals is required for understanding water dynamics in trees and providing insights for optimizing irrigation.

Soil moisture (SM) plays a central role in water cycle dynamics and land-atmosphere interactions, acting across local and regional scales. Few studies have explored the use of the ground-based global navigation satellite system reflectometry (GNSS-R) interference pattern technique (IPT) for SM estimation. In these studies, SM was estimated from the GPS elevation angle where lower reflectivity occurs (notch), which is difficult to determine in real GNSS-R interference power (IP) acquisitions. This study introduces the use of IP amplitude at vertical polarization (V-pol), readily extracted from the IP oscillations, as an alternative for SM estimation beneath vegetation cover. An empirical model was developed for estimating SM in irrigated grassland using a GNSS-R receiver with a linearly polarized antenna. The experiment, conducted between June 6 and August 8, 2022, covered the grassland's growth phase and preharvesting and postharvesting. The study incorporated normalized difference water index (NDWI) from the Sentinel-2 satellite to account for vegetation's impact on IP amplitude. Results indicated that the IP amplitude at V-pol accurately estimates SM (RMSE =0.04 m3/m3). Moreover, the results show that the vegetation layer mainly attenuates the IP amplitude with a nonsignificant scattered contribution to the IP, allowing for the simplification of the empirical model by ignoring the scattered contribution of vegetation. The simplified empirical model can be numerically resolved to estimate the NDWI if the SM is known. In summary, this study highlights the effectiveness of the ground-based IPT for close-range sensing of SM and a biomass proxy, such as NDWI.

The relation between microwave backscatter and incidence angle estimated from observations of the Advanced Scatterometer (ASCAT) onboard the Metop satellites contains valuable information on the dynamics of vegetation water content and structure. The relation between backscatter and incidence angle (parameterized using so-called slope and curvature parameter) has been related to vegetation water dynamics in studies on the North American Grasslands and the Cerrado Savannah. The current approach to estimate time series of the slope and curvature parameters involves a kernel smoother, weighing observations according to their temporal distance to the day of interest. While this approach provides a robust representation of backscatter-incidence angle relation over longer time scales, it does not accurately capture the timing of short-term changes. To further improve the correspondence between backscatter-incidence angle relation and vegetation water dynamics, the timing of short-term changes should be preserved in the estimation of slope and curvature. This would allow slope and curvature to be reconciled with independent estimates of biogeophysical variables, and allow us to isolate high-frequency variations due to, for example, intercepted precipitation or soil moisture. Here, an alternative method is introduced to estimate the ASCAT backscatter-incidence angle relation using temporally constrained least squares. While the proposed method yields similar performance to the kernel smoother in aggregated statistics, this method retains the timing of short-term changes.

Plants are subject to stress conditions at multiple time-scales, from minutes and hours (e.g., radiation stress) to years or decades (e.g., prolonged drought). The processes controlling how plants respond to such stressors are also time-scale dependent, from rapid physiologic and structural responses such as stomatal regulation or leaf movement, to slow responses such as pigment changes or adjustments of growth and allocation. How these different processes evolve and interact under diverse stressors influences tree health and long-term functioning and, depending on plants ability to recover, might lead to tree health decline and mortality. Observations of tree stress from space typically rely on reflectance indices, which are associated with changes or declines in leaf pigment content, leaf area, and/or fractional of vegetation cover. These changes are driven by slow or delayed reactions to environmental stress (leaf discoloration, defoliation, reduced growth, mortality and compositional changes). Microwave measurements, on the contrary, allow to more directly track vegetation water content, but they are typically available at coarse spatiotemporal scales. Signs of plant health decline or onset of mortality trajectories can, thus, take a long time to detect based on currently available remote-sensing information, limiting our ability for early detection of stress hotspots (e.g., stands at risk of drought-induced mortality). Here, we aim to explore the potential to use sub-daily microwave observations for early detection of plant stress, in the context of SLAINTE, a mission idea recently submitted in response to ESA’s 12th call for Earth Explorers. To do this, we analyze sapflow measurements covering over a decade in an evergreen broadleaf forest at the Puéchabon study site (FRA-Pue, southern France) to evaluate how sub-daily information of vegetation water fluxes might be used to identify onset and development of plant stress. We define a set of sub-daily metrics (timing of peak sapflow, sensitivity to meteorological drivers, hysteretic behaviour) and evaluate how these vary within the growing season, across years and during extreme events for multiple trees. These derived metrics could, in principle, be derived from sub-daily satellite-based observations, facilitating therefore timely assessments of plant health declines.

Changes in sub-daily vegetation water content capture the pulse of the Earth's ecosystems. They reflect the interplay between plant function, evaporation, and soil moisture, and underpin land-atmosphere exchange of water and carbon from leaf to global scales. Current and planned microwave missions provide a snapshot every few days. These are adequate to observe inter- and intra-annual variations of above ground biomass (AGB), the slow response in water status over weeks and months, and to map (a-posteriori) biomass loss due to deforestation or mortality. However, they are not sufficient to capture the sub-daily, or even daily, dynamics needed to study ecosystem health. The SLAINTE (Irish for health) mission aims to fill this critical observation gap at sub-daily scales enabling us to “zoom in” on the fast dynamics associated with water status. Sub-daily observations of VWC are needed to study the vegetation response to the daily cycle in vapour pressure deficit (VPD), the impact of stomatal regulation, and the rate at which vegetation is able to recharge VWC lost during the day. They reveal how ecosystems respond to biotic and abiotic stress (e.g. changing temperature and vapour pressure deficit, soil moisture, insects, disease) and disturbances (e.g. drought, fire). Observing these processes is critical to understand the resilience of terrestrial ecosystems and their water resources in the face of increasing climate variability and extremes, and pressures from human land and water use. The availability of sub-daily SAR data would also fill a critical gap in Earth system knowledge where observations of rapid changes in SSM are essential. For example, they would allow us to observe short-lived wetting/drydown events associated with irrigation, triggering and evolution of flash floods and shallow landslides and the development of hazardous storms. SLAINTE comprises a small constellation of monostatic L-band Synthetic Aperture Radars (SAR) that will provide sub-daily, ≤1 km scale observations related to ecosystem water status. It has been developed as one of ESA’s New Earth Observation Mission Ideas and was recently submitted in response to ESA’s call for the 12th Earth Explorer. Here, we will provide an overview of the SLAINTE mission idea, our ambitions, and an overview of preliminary science studies. We hope to stimulate discussion with the wider EGU community on how the provision of routine, sub-daily (In)SAR observations could be exploited to address the scientific challenges across the geosciences.

Advances in Earth observation capabilities mean that there is now a multitude of spatially resolved data sets available that can support the quantification of water and carbon pools and fluxes at the land surface. However, such quantification ideally requires efficient synergistic exploitation of those data, which in turn requires carbon and water land-surface models with the capability to simultaneously assimilate several such data streams. The present article discusses the requirements for such a model and presents one such model based on the combination of the existing Data Assimilation Linked Ecosystem Carbon (DALEC) land vegetation carbon cycle model with the Biosphere Energy-Transfer HYdrology (BETHY) land-surface and terrestrial vegetation scheme. The resulting D&B model, made available as a community model, is presented together with a comprehensive evaluation for two selected study sites of widely varying climate. We then demonstrate the concept of land-surface modelling aided by data streams that are available from satellite remote sensing. Here we present D&B with four observation operators that translate model-derived variables into measurements available from such data streams, namely fraction of photosynthetically active radiation (FAPAR), solar-induced chlorophyll fluorescence (SIF), vegetation optical depth (VOD) at microwave frequencies and near-surface soil moisture (also available from microwave measurements). As a first step, we evaluate the combined model system using local observations and finally discuss the potential of the system presented for multi-stream data assimilation in the context of Earth observation systems.

Vegetation is a key part of the water and carbon cycle and the interaction between Earth's surface and atmosphere. Understanding water dynamics within vegetation is crucial for improving models that represent vegetation processes. Previous studies have investigated exploiting ASCAT scatterometer data from the METOP satellites to evaluate dynamics in vegetation water content. ASCAT has been operational since 2007 and captures microwave backscatter from multiple angles, revealing the relation between backscatter and the incidence angle. This relation reflects the relative contributions of volume and surface scattering—the former affected by water on and within vegetation, and the latter influenced by water in the top soil layer. Currently, a weighted regression using ASCAT observations from 42 days is used to estimate the parameters representing this relation: the slope and curvature, or the first and second order derivative of a second order Taylor approximation, respectively. This estimation method is implemented in the Soil Water Retrieval Retrieval Package developed by TU Wien. Adverse artefacts of this estimation method are the aggregation of observations corresponding to varying states of the earth surface, e.g. before and after a forest fire. Here, we present results from a study to improve the estimation method for ASCAT's slope and curvature parameters, tailored to quantification of vegetation processes. Goals include: representing parameters at briefer temporal scales, reducing the impact of interception, and restricting temporal aggregation around instantaneous events of change such as storms. In addition to analysing real ASCAT observations, synthetic ASCAT observations are simulated using a radiative transfer model, enabling a thorough comparison of estimated slope against simulated ground truth values. Preliminary results show that simulated ASCAT slope time series represent the dynamics of real ASCAT slope, indicating that synthetic observations can be used to quantify improvement of the slope estimation method.

Vegetation water content varies in response to the shifting balance between transpiration loss and water supply through the soil--plant--atmosphere continuum. These variations are coupled to carbon dynamics by stomatal regulation of gas exchange, linking transpiration and photosynthesis, and through rootzone soil moisture, determined in part by the allocation and turnover of carbon to roots. Microwave sensors have been demonstrated to be sensitive to variations in vegetation water content and related measures of plant hydraulic status, such as plant water potential (PWP). We use synthetic experiments representative of a European deciduous forest to explore whether time series observations of PWP can constrain an intermediate complexity terrestrial ecosystem model (DALEC) with fully coupled carbon and water balances using a Bayesian model-data fusion framework (CARDAMOM). To generate a synthetic truth, we calibrated DALEC using detailed site-specific inventory data from the Hainich ICOS site (DE-Hai), spanning 2006-2011, from which we generated a synthetic time series of average daily mean PWP. The Hainich forest is a temperate forest dominated by beech and established on clay-rich soil. We used the calibrated model as the basis for a series of synthetic data assimilation experiments under conditions of reduced data availability to represent information typically available from satellites and/or global products (e.g. Leaf Area Index, aboveground biomass, soil characteristics) to assess the potential to constrain C cycle dynamics using information on time varying PWP. We compared the diagnostics to a baseline experiment with no assimilated PWP information. Assimilation of PWP reduced the bias in estimates of GPP and ET relative to the synthetic “truth”, with a small reduction in the width of the 90% confidence range, compared to the baseline experiment. PWP observations provided more notable constraints on model parameters that were connected to plant hydraulics and water supply, including root dynamics. The emergent constraint on root dynamics is significant, because below-ground processes are inherently challenging to observe remotely. Assimilating PWP also constrained within-ensemble covariance between certain parameter pairings, and between fluxes, particularly pairings linked to the water balance, and between the water balance and productivity, highlighting the potential for enhanced constraint through the addition of complementary information. Once the signal noise exceeded 0.20 MPa, there was very limited information transfer into either the model parameters retrieved during the inversion, or the resultant fluxes. Our synthetic experiments demonstrate the potential for satellite estimates of PWP (e.g. through microwave VOD) to provide constraints on carbon-water coupling, that these constraints extend to both fast processes (GPP, ET), and slower processes (root dynamics), and that such observations would be highly complementary to C-cycle information from other EO data streams.

Monitoring the water status of forests is paramount for assessing vegetation health, particularly in the context of increasing duration and intensity of droughts. In this study, a methodology was developed for estimating forest water potential at the canopy scale from ground-based L-band radiometry. The study uses radiometer data from a tower-based experiment of the SMAPVEX 19-21 campaign from April to October 2019 at Harvard Forest, MA, USA. The gravimetric and the relative water content of the forest stand was retrieved from radiometer-based vegetation optical depth. A model-based methodology was adapted and assessed to transform the relative water content estimates into values of forest water potential. A comparison and validation of the retrieved forest water potential was conducted with in situ measurements of leaf and xylem water potential to understand the limitations and potentials of the proposed approach for diurnal, weekly and monthly time scales. The radiometer-based water potential estimates of the forest stand were found to be consistent in time with rPearson correlations up to 0.6 and similar in value, down to RMSE = 0.14 [MPa], compared to their in situ measurements from individual trees in the radiometer footprint, showing encouraging retrieval capabilities. However, a major challenge was the bias between the radiometer-based estimates and the in situ measurements over longer times (weeks & months). Here, an approach using either air temperature or soil moisture to update the minimum water potential of the forest stand (FWPmin) was developed to adjust the mismatch. These results showcase the potential of microwave radiometry for continuous monitoring of plant water status at different spatial and temporal scales, which has long been awaited by forest ecologists and tree physiologists.

This paper introduces a SAR mission concept uniquely designed for sub-daily interferometric-compatible revisits, essential for the timely monitoring of ecosystem water status in regions of significant scientific, ecological, societal, and economic value. The key concept is based on the strategic deployment of a constellation of several small satellites in short-revisit low-Earth orbits, equipped with low complexity SAR payloads to enhance efficiency and minimize overall costs. In particular, L-band SARs with decametric spatial resolution and sub-daily revisit will be considered. The paper provides an overview of the mission requirements, technical concept, scientific relevance, and acquisition potential. The analysis is based on a study conducted in the frame of an ESA Earth Explorer 12 mission proposal titled "SLAINTE".

A dataset of sub-daily C-band data, acquired with a ground-based synthetic aperture radar, has been used to study soil and vegetation dynamics during a complete growing season in a controlled agricultural test site. The data have been exploited to analyse the rate and sources of decorrelation in the scene, as well as the consequences of the observation conditions of a sub-daily satellite (with either low, medium or geosynchronous orbit): short revisit times, availability of multiple acquisitions during a single day, and shallow observations at some incidence angles. Repeat-pass coherence is found to be less affected by temporal decorrelation when the primary image is acquired during nighttime or the last hours predawn. Regarding the incidence angle, VV has increased sensitivity to certain phenological stages as the incidence angle increases. Additionally, a periodic oscillation on a sub-daily scale is observed when creating coherence time series with increasing temporal baseline. Factors which strongly contribute to these oscillations are the daily cycles of temperature, soil moisture and vegetation water dynamics.