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° to 45° 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°–45° range (e.g., R ² > 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 τ–ω 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.

Regular monitoring of plant development and soil moisture variations is essential for managing orchard systems and optimizing irrigation. Cosmic Ray Neutron Sensors (CRNS) are increasingly used for reliable, non-invasive soil moisture estimation. However, the potential of CRNS for monitoring plant development remains largely uninvestigated. The objective of this study is to assess the response of thermal (N_th) and epithermal (N_epi) neutron intensities to the seasonal changes in tree structure and water content. In particular, we aim to investigate whether the observed neutron responses can be used as an indicator of plant development in commercial orchard settings. A CRNS was installed at a cherry orchard site in southeastern France and operated continuously for 10 months in 2022. Observations were compared to several proxies for tree canopy characteristics. First, neutron intensity values were compared with monthly plant area index (PAI) estimates derived from images collected with a light detection and ranging (LiDAR) sensor mounted on an unmanned aerial vehicle (UAV). PAI in (m² m⁻²) is defined as the total surface area of all above-ground canopy components, including leaves, stems, and branches per unit horizontal ground surface area. Second, N_th was compared with commonly used vegetation indices derived from multispectral satellite images acquired by PlanetScope and Sentinel-2. The results show a strong correlation between N_th and UAV-derived PAI with R² = 0.86. N_th increased linearly by approximately 4.5% per 1 m² m⁻² increase in PAI. Of the vegetation indices, the Normalized Difference Red Edge (NDRE) index derived from PlanetScope images showed the highest correlation (R² = 0.69) with N_th. The corresponding R² with NDRE from coarser-resolution Sentinel-2 data was lower (R² = 0.51). The correlation between N_th and PAI was higher than that between N_th and SM (R² = 0.61). Results suggest that variations in N_th are potentially valuable for vegetation monitoring, provided the confounding effect of soil moisture can be taken into account.

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.

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.

Cosmic ray neutron sensor (CRNS) has gained popularity in the last decade for its suitability in estimating area-averaged 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 N₀ (N_0,opt) was well correlated with VPD in both orchards (R² = 0.66 for olive and R² = 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,opt correlation approach could be a promising way to account for the bias due to biomass dynamics on the estimation of area-averaged SM.

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.

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 r_Pearson 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 (FWP_min) 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 presents an overview of the Sub-daily Land Atmosphere INTEractions (SLAINTE) mission. SLAINTE comprises a constellation of identical synthetic aperture radars (SAR) with interferometric capability. It aims to bridge a critical observation gap, by providing sub-daily, ≤1 km scale observations related to ecosystem water status, including vegetation water content and surface soil moisture over key regions of scientific, ecological, societal and economic interest. These data will provide unprecedented insight into vegetation water, carbon and health improving our ability to study, understand and model the response of ecosystems to climate change and human impact. This mission concept has been submitted in response to ESA's call for proposals for Earth Explorer 12.

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" [1].

Soil moisture (SM) is an important state variable in land surface models. Here, we investigate the potential of a ground-based global navigation satellite system receiver with two linearly polarized antennas that measure the interference power (IP) of direct and reflected signals in horizontal polarization (H-pol) and vertical polarization (V-pol) to estimate SM. The coefficient of determination between the IP waveforms at H-pol and V-pol ( $\boldsymbol {R}_{ \boldsymbol {v}\mathbf {/} \boldsymbol {h}}^{\mathbf {2}}$ ) was used as a predictor of SM. A coherent specular reflection model was employed to first explore the relationship between $\boldsymbol {R}_{ \boldsymbol {v}\mathbf {/} \boldsymbol {h}}^{\mathbf {2}}$ and SM for different values of soil roughness. That relationship was subsequently applied to estimate SM from $\boldsymbol {R}_{ \boldsymbol {v}\mathbf {/} \boldsymbol {h}}^{\mathbf {2}}$ determined from global positioning system (GPS) signals acquired continuously by a ground-based receiver between May and December 2022 for an area with very smooth bare soil. The results show that the proposed method can estimate the SM of the upper 10-cm layer with high accuracy (with a root-mean-square error (RMSE) of approximately 1.5 vol.%) and demonstrate the potential of the ground-based IP technique as a practical system solution for proximal remote sensing of SM over bare soils .

Data obtained during a ground-based SAR experiment and an associated field campaign have been exploited to study the rate and sources of decorrelation in an agricultural test site in the conditions of observation of a geosynchronous SAR. It was found that the scene is less affected by temporal decorrelation when the primary image is acquired during night time or early morning. Additionally, a periodic oscillation on a sub-daily scale was observed when creating coherence time series with increasing temporal baseline. Two factors which strongly contribute to these oscillations are the daily cycles of soil moisture and evapotranspiration.

ASCAT normalized backscatter (σ₄₀^o) and slope (σ^′) contain valuable information about soil moisture and vegetation. While σ₄₀^o has been assimilated to constrain soil moisture, sometimes together with Leaf Area Index (LAI), this study is the first to assimilate σ^′ directly to constrain vegetation states. Here, we assimilate σ₄₀^o and slope σ^′ into the ISBA-A-gs LSM using the Simplified Extended Kalman Filter (SEKF) using a Deep Neural Network (DNN) as the observation operator. The performances of the data assimilation (DA) and open loop (OL) are evaluated against in-situ soil moisture observations from the International Soil Moisture Network (ISMN), and LAI observations from the Copernicus Global Land Service (CGLS). Given an accurate and physically plausible observation operator, along with well-defined model and observation errors, the data assimilation system should yield improved estimates of the model states. However, results show that the DA performance is neutral compared to the OL in terms of the median unbiased root mean square error (ubRMSE) and Pearson correlation coefficient (ρ) across all validation sites. In addition, an analysis of the residuals and innovations confirms that DA had limited or no impact. This poor performance is perplexing. Furthermore, given the growing interest in the use of machine-learning-based observation operators, it is essential to understand the role that the use of the DNN may be playing in this poor performance. While representativeness errors and error specification play some part, it is demonstrated that the key factor constraining the efficacy of the SEKF is the correct estimation of the Jacobians that control the degree to which the observations update the states in the SEKF. It is argued that the DNN relating model states to satellite observations must have physically-plausible and robust Jacobians for the DNN to be effective in a data assimilation framework.

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.

The presence, duration, and amount of surface canopy water (SCW) is important in microwave remote sensing for agricultural applications. Our current understanding of the effect of SCW on total backscatter and the underlying mechanisms is limited. The aim of this study is to investigate the effect of SCW on backscatter as a function of frequency and polarization, and to understand the underlying mechanisms. For this purpose, the radiative transfer model developed at the Tor Vergata University was used to simulate the total backscatter at L-, C-, and X-band. First, simulations from the standard Tor Vergata model were compared to L-band observations. Then, two additional implementations of the model were developed to account for the effect of SCW and the presence of water on the soil surface on radar backscatter. Representing SCW by the inclusion of additional water in the vegetation leads to an increase in vegetation volume scattering and a reduction in the contribution from double bounce and direct scattering from the ground. This increases total backscatter, particularly at lower frequencies. Results suggest that the difference between backscatter in the presence and absence of SCW can be up to around 2.5 dB in L-band and likely less at higher frequencies. The effect of water on the canopy (SCW) reaches its maximum during the mid and late season as the crop reached its maximum biomass. The influence of dew on the reflectivity of the soil surface resulted in a difference of up to 3.8 dB between backscatter in the presence and absence of SCW. In particular, at low frequencies and low vegetation cover, the presence of water on the soil surface needs to be taken into account to correctly capture the sub-daily dynamics in backscatter. The findings of this study are relevant for current and future SAR missions including Sentinel-1, ROSE-L, NISAR, SAOCOM, ALOS, CosmoSkyMed, TerraSAR-X, TanDEM-X and constellations such as those of ICEYE, and Capella which have dawn/dusk overpasses or multiple overpasses per day.

In this article, our aim is to estimate synthetic aperture radar (SAR) observables, such as backscatter in VV and VH polarizations, as well as the VH/VV ratio, cross ratio, and interferometric coherence in VV, from agricultural fields. In this study, we use the decision support system for agrotechnology transfer (DSSAT) crop-growth simulation model to simulate parcel-level phenological and growth parameters for over 1500 parcels of silage maize in the Netherlands. The crop model was calibrated using field data, including silage maize phenological phases, leaf area index, and above-ground dry biomass (AGB). The simulations incorporate fine-resolution gridded precipitation data and soil parameters to model the interaction between soil-plant-atmosphere and genotype in DSSAT. The crop variables produced by DSSAT are then used as inputs to a support vector regression model. This model is trained to simulate SAR observables in 2017, 2018, and 2019, and its performance is evaluated using independent fields in each of these years. The results show a close fit between modeled and observed SAR C-band observables. The importance of vegetation variables in the estimation of SAR observables is assessed. The AGB showed significant importance in the estimation of backscatter. This study demonstrates the potential value of combining crop-growth simulation models and machine learning to simulate SAR observables. For example, the SVR model developed here could be used as an observation operator in an assimilation context to constrain vegetation and soil water dynamics in a crop-growth model.

Sentinel-1 observes the whole globe every 12 days (6 days when both satellites were operational) and provides a wealth of data relevant to agriculture. Sugarcane cultivators could potentially benefit from these data by using them to assist operational and management practices. However, first, thorough understanding is needed of Sentinel-1 backscatter and its behavior over sugarcane canopies. In this study, we aimed to improve understanding of how Sentinel-1 backscatter responds to sugarcane yield variability and waterlogging. In order to do so we focused on an irrigated sugarcane plantation in Xinavane, Mozambique. In the analysis presented, we assessed different polarizations, their ratio, and benchmarked them against optical indices and passive microwave observations in different seasons. With the help of a large sugarcane yield dataset, we analyzed how backscatter relates to sucrose yield variability in different seasons. We found VV backscatter related to the stalk development, the most important reservoir for sucrose accumulation. In addition, in a season with reported waterlogging, optical and radar observations showed a delay in sugarcane crop development. Further analysis showed the presence of water underneath the canopy caused an increase in all polarizations and the cross ratio (CR). The results imply that Sentinel-1 backscatter contains information on both waterlogging under the canopy as well as sucrose development in the stalk. By isolating and quantifying the impact of waterlogging on backscatter, it will be possible to further quantify sucrose development with backscatter observations and identify waterlogging simultaneously.

Reliable crop monitoring is paramount to achieve the objectives of the Common Agricultural Policy (CAP) and Food and Agriculture Organization. Synthetic Aperture Radar (SAR) provides high-resolution imaging and all-weather data acquisition capabilities for crop monitoring. This study investigates the sensitivity of parcel-level Sentinel-1 interferometric coherence to farming activities (e.g. planting, emergence, harvest and tillage) and weather events. A methodology to detect activities was developed and validated using ground-truth data from four crop types, collected over four years. The proposed approach was able to detect over 60% of all nine different farming activities. The results show that interferometric coherence is a reliable indicator for farming activities that can be considered as events resulting in a clear structural change (e.g. tillage 100%), but less reliable for gradual changes (e.g. Emergence 40%).

In this study we focus on an irrigated sugarcane plantation in southern Mozambique burdened by waterlogging. We show how Sentinel-1 backscatter and Planet NDVI can be used to monitor sugarcane development. Our results demonstrate Sentinel-1 backscatter is able to monitor sucrose development and, in addition, how waterlogging influences the radar signals in different growth stages. Consequently, we show the potential to monitor sugarcane development and waterlogging simultaneously.

Agricultural droughts are extreme events which are often a result of interplays between multiple hydro-meteorological processes. Therefore, assessing drought occurrence, extent, duration and intensity is complex and requires the combined use of multiple variables, such as temperature, rainfall, soil moisture (SM) and vegetation state. The benefit of using information on SM and vegetation state is that they integrate information on precipitation, temperature and evapotranspiration, making them direct indicators of plant available water and vegetation productivity. Microwave remote sensing enables the retrieval of both SM and vegetation information, and satellite-based SM and vegetation products are available operationally and free of charge on a regional or global scale and daily basis. As a result, microwave remote sensing products play an increasingly important role in drought monitoring applications. Here, we provide an overview of recent developments in using microwave remote sensing for large-scale agricultural drought monitoring. We focus on the intricacy of monitoring the complex process of drought development using multiple variables. First, we give a brief introduction on fundamental concepts of microwave remote sensing together with an overview of recent research, development and applications of drought indicators derived from microwave-based satellite SM and vegetation observations. This is followed by a more detailed overview of the current research gaps and challenges in combining microwave-based SM and vegetation measurements with hydro-meteorological data sets. The potential of using microwave remote sensing for drought monitoring is demonstrated through a case study over Senegal using multiple satellite- and model-based data sets on rainfall, SM, vegetation and combinations thereof. The case study demonstrates the added-value of microwave-based SM and vegetation observations for drought monitoring applications. Finally, we provide an outlook on potential developments and opportunities.

Synthetic Aperture Radar (SAR) data handling, processing, and interpretation are barriers preventing a rapid uptake of SAR data by application specialists and non-expert domain users in the field of agricultural monitoring. To improve the accessibility of Sentinel-1 data, we have generated a reduced-volume, multi-year Sentinel-1 SAR database. It includes mean and standard deviation of VV, VH and VH/VV backscatter, pixel counts, geometry, crop type, local incidence angle and azimuth angle at parcel-level. The database uses around 3100 Sentinel-1 images (5 TB) to produce a 12 GB time series database for approximately 770,000 crop parcels over the Netherlands for a period of three years. The database can be queried by Sentinel-1 system parameters (e.g. relative orbit) or user application-specific parameters (e.g. crop type, spatial extent, time period) for parcel level assessment. The database can be used to accelerate the development of new tools, applications and methodologies for agricultural and water related applications, such as parcel-level crop bio-geophysical parameter estimation, inter-annual variability analysis, drought monitoring, grassland monitoring and agricultural management decision-support.