Sled-based side-looking C-band radar profiles were collected around Fairbanks, Alaska, in March 2023 during the NASA SnowEx campaign to improve the conceptual understanding of C-band radar wave interactions with snow in a boreal forest environment. Seven transects with different vegetation and ground conditions were studied. Significant volume scattering from snow was observed in this shallow snowpack, indicating sensitivity at lower snow depths (SDs) which are common in high-latitude snowpacks. Manual removal of the snowpack decreased the backscatter by more than 2 dB in all polarizations, with a larger decrease in the cross-polarization, supporting the potential use of Sentinel-1 to retrieve SD.

To better understand the interactions of satellite C-band radar with the soil-snow-vegetation continuum in a spatio-temporal context and to provide a novel approach for snow depth retrieval from Sentinel-1 observations, a coupled radiative transfer model was developed. This model combines a snow, soil and vegetation radiative transfer model to simulate the Sentinel-1 observations over the Alps, and can be inversed to obtain estimates of snow depth. Performance will be assessed at 1 km spatial resolution for the winter of 2017-2018, across a wide range of elevations, local incidence angles and total accumulated snow, using several performance measures (Pearson correlation and MAE).

Recent research has shown the sensitivity of Sentinel-1 C-band (5.4 GHz) radar data to snow depth. This finding could potentially help fill a long standing gap in remote sensing, but the physical basis behind this sensitivity is not yet sufficiently understood. A field experiment was set-up at two sites in the US Rocky Mountains in Idaho to study the polarimetric radar response, continuously throughout multiple winter seasons. This paper describes the design and properties of the tower-based, fully polarimetric, C-band radar system and presents the first findings. Hourly measurements were made during the winters of 2019–2020 and 2020–2021 at two different sites in Idaho. When studying the time domain responses, the scattering from the snow volume, the ground surface and multiple bounces can be discerned.

Sentinel-1 backscatter observations were assimilated into the Global Land Evaporation Amsterdam Model (GLEAM) using an ensemble Kalman filter. As a forward operator, which is required to simulate backscatter from soil moisture and leaf area index (LAI), we evaluated both the traditional water cloud model (WCM) and the support vector regression (SVR). With SVR, a closer fit between backscatter observations and simulations was achieved. The impact on the correlation between modeled andin situsoil moisture measurements was similar when assimilating the Sentinel data using WCM ($\Delta R = +0.037$) or SVR ($\Delta R = +0.025$).

Theoretical studies have shown that the use of simultaneous mono- and bistatic synthetic aperture radar (SAR) data could be beneficial to agriculture and soil moisture monitoring. This study makes use of extensive ground-truth measurements and synchronous high-resolution fully-polarimetric mono- and bistatic airborne SAR data in L-band to assess and compare the sensitivity of mono- and multistatic systems to maize crop variables, soil moisture, and surface roughness. Its results suggest that bistatic data, even with a very small bistatic angle, provide valuable additional information for maize crop biophysical parameter retrieval. However, this does not appear to be the case for soil moisture retrieval over bare soils.

Seasonal snow is an essential source of water, especially in mountain regions. However, accurate satellite observations of the amount of snow stored in mountains are still lacking. We provide estimates of snow depth at sub-kilometer resolution over the European Alps for 2017–2019 from Sentinel-1 observations. The retrievals are based on a change detection algorithm that includes the masking of wet snow. For dry snow conditions, 300-m Sentinel-1 retrievals have a spatiotemporal correlation of 0.82 and mean absolute error of 0.19m compared with in situ measurements from 743 sites across the Alps. The results show the potential of Sentinel-1 to provide unprecedented snow estimates in regions with complex topography, where satellite observations of snow mass are currently lacking.

The first NASA SnowEx campaign was held in February 2017 over Grand Mesa, Colorado, covering both open and forested areas. The Belspo SNOPOST project aims at using the collected SnowEx data to enhance snow estimates using the Level 1 remote sensing data together with land surface modeling and to document the limitations of snow remote sensing where needed. A preliminary spatiotemporal analysis of in situ and satellite remote sensing data, and modeling estimates of snow will be presented.

With the launch of the Soil Moisture and Ocean Salinity (SMOS) mission in 2009 and the Soil Moisture Active-Passive (SMAP) mission in 2015, a wealth of L-band brightness temperature (Tb) observations has become available. In this letter, SMOS and SMAP Tbs are assimilated separately into the Community Land Model over the Murrumbidgee basin in south-east Australia from April 2015 to August 2017. To overcome the seasonal Tb observation-minus-forecast biases, Tb anomalies from the seasonal climatology are assimilated. The use of climatologies derived from either SMOS or SMAP observations using either 2 years or 7 years of data yields nearly identical results, highlighting the limited sensitivity to the climatology computation and their interchangeability. The temporal correlation between soil moisture data assimilation results and in situ observations is slightly improved for top-layer soil moisture (+0.04) and for root-zone soil moisture (+0.05). The soil moisture anomaly correlation improves moderately for the top-layer soil moisture (+0.15), with a smaller positive impact on the root zone (+0.05).

The objective of this paper is to propose a new semi-empirical radar backscattering model for bare soil surfaces based on the Dubois model. A wide dataset of backscattering coefficients extracted from SAR (synthetic aperture radar) images and in situ soil surface parameter measurements (moisture content and roughness) is used. This dataset contains a wide range of incidence angles (18°-57°) and radar wavelengths (L, C, X), well distributed geographically for regions with different climate conditions (humid, semi-arid and arid sites) and involving many SAR sensors. The proposed model, developed in HH, HV and VV polarizations, uses a formulation of radar signals based on physical principles that validated in numerous studies. The results show that the new model shows a very good performance for different radar wavelength (L, C, X), incidence angles, and polarizations (Root Mean Square Error “RMSE” about 2 dB).

Soil surface roughness determines the backscatter coefficient observed by radar sensors. The objective of this letter was to determine the surface roughness sample size required in synthetic aperture radar applications and to provide some guidelines on roughness characterization in agricultural soils for these applications. With this aim, a data set consisting of ten ENVISAT/ASAR observations acquired coinciding with soil moisture and surface roughness surveys has been processed. The analysis consisted of: 1) assessing the accuracies of roughness parameters s and l depending on the number of 1-m-long profiles measured per field; 2) computing the correlation of field average roughness parameters with backscatter observations; and 3) evaluating the goodness of fit of three widely used backscatter models, i.e., integral equation model (IEM), geometrical optics model (GOM), and Oh model. The results obtained illustrate a different behavior of the two roughness parameters. A minimum of 10-15 profiles can be considered sufficient for an accurate determination of s, while 20 profiles might still be not enough for accurately estimating l. The correlation analysis revealed a clear sensitivity of backscatter to surface roughness. For sample sizes >15 profiles, R values were as high as 0.6 for s and ~0.35 for l, while for smaller sample sizes R values dropped significantly. Similar results were obtained when applying the backscatter models, with enhanced model precision for larger sample sizes. However, IEM and GOM results were poorer than those obtained with the Oh model and more affected by lower sample sizes, probably due to larger uncertainly of l.

Soil surface roughness strongly affects the scattering of microwaves on the soil surface and determines the backscattering coefficient (σ0) observed by radar sensors. Previous studies have shown important scale issues that compromise the measurement and parameterization of roughness especially in agricultural soils. The objective of this paper was to determine the roughness scales involved in the backscattering process over agricultural soils. With this aim, a database of 132 5-m profiles taken on agricultural soils with different tillage conditions was used. These measurements were acquired coinciding with a series of ENVISAT/ASAR observations. Roughness profiles were processed considering three different scaling issues: 1) influence of measurement range; 2) influence of low-frequency roughness components; and 3) influence of high-frequency roughness components. For each of these issues, eight different roughness parameters were computed and the following aspects were evaluated: 1) roughness parameters values; 2) correlation with σ0; and 3) goodness-of-fit of the Oh model. Most parameters had a significant correlation with σ0especially the fractal dimension, the peak frequency, and the initial slope of the autocorrelation function. These parameters had higher correlations than classical parameters such as the standard deviation of surface heights or the correlation length. Very small differences were observed when longer than 1-m profiles were used as well as when small-scale roughness components (100 cm) were disregarded. In conclusion, the medium-frequency roughness components (scale of 5-100 cm) seem to be the most influential scales in the radar backscattering process on agricultural soils.

The objective of this paper is to extend the semi-empirical calibration of the backscattering Integral Equation Model (IEM) initially proposed for SAR data at C- and X-bands to SAR data at L band. A large dataset of radar signal and in situ measurements (soil moisture and surface roughness) over bare soil surfaces were used. A semi-empirical calibration of the IEM was performed at L band in replacing the correlation length derived from field experiments by a fitting parameter. Better agreement was observed between the backscattering coefficient provided by the SAR and that simulated by the calibrated version of the IEM.

SAR (Synthetic Aperture Radar) sensors measure the backscatter (a0) of land covers and SAR images have a number of applications in agricultural soils (soil moisture, crop monitoring, etc.) but the surface roughness of these soils complicates their interpretation and determination of quantitative estimates of useful parameters. The aim of this study is to quantify the spatial variability of different roughness parameters and the sensitivity of a0to them measured at different scales. Ten Envisat/ASAR images acquired between September 2004 and January 2005 on an agricultural area with 10 control plots are analyzed. 132 roughness profiles of 5 m length were measured, and 21 different parameters were calculated. The results show considerable differences in the spatial variability of the parameters and differed depending on the type of parameter in the correlation analysis. This study can be useful to identify roughness parameters and scales that maximize their sensitivity to C-band backscatter.

Despite the continuing efforts to improve existing soil moisture retrieval algorithms, the ability to estimate soil moisture from passive microwave observations is still hampered by problems in accurately modeling the observed microwave signal. This paper focuses on the estimation of effective surface roughness parameters of the L-band Microwave Emission from the Biosphere (L-MEB) model in order to improve soil moisture retrievals from passive microwave observations. Data from the SMAP Validation Experiment 2012 conducted in Canada are used to develop and validate a simple model for the estimation of effective roughness parameters. Results show that the L-MEB roughness parameters can be empirically related to the observed brightness temperatures and the leaf area index of the vegetation. These results indicate that the roughness parameters are compensating for both roughness and vegetation effects. It is also shown, using a leave-one-out cross validation, that the model is able to accurately estimate the roughness parameters necessary for the inversion of the L-MEB model. In order to demonstrate the usefulness of the roughness parameterization, the performance of the model is compared to more traditional roughness formulations. Results indicate that the soil moisture retrieval error can be reduced to 0.054 m3/m3if the roughness formulation proposed in this study is implemented in the soil moisture retrieval algorithm.

Soil moisture retrievals, delivered as a CATDS (Centre Aval de Traitement des Données 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 variable 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 applications, the framework developed could also be used for other purposes where downscaling of coarse-scale observations is required.

The synthetic aperture radar (SAR)-based soil moisture retrieval of agricultural fields is often hampered by vegetation effects on the backscattered signal. The semiempirical water cloud model (WCM) allows for estimating the backscatter of a vegetated surface, accounting for both the contributions of the vegetation and the underlying soil. The latter is often described through the integral equation model (IEM). Unfortunately, the IEM requires an accurate parameterization of the surface roughness which is very difficult to achieve. Therefore, this letter extends the WCM with a bare soil contribution that is based on the IEM, which, however, relies on calibrated or effective roughness parameters. Furthermore, this letter compares a number of vegetation indicators for their use in the WCM. Based on a series of L-band SAR observations, it is shown that effective roughness parameters are a promising tool for soil moisture retrieval under a wheat canopy and that the use of a leaf area index may be recommended above other vegetation indicators, as it leads to the lowest root-mean-square errors of about 5.5 vol%. These results prove the operational potential of L-band SAR data for soil moisture inferred under a wheat canopy throughout the entire crop growth cycle.

Soil roughness plays an essential role in the reflection of the incoming radar signal at the soil surface and is, therefore, highly important in the retrieval of the soil moisture information from the backscattered radar signal. However, soil roughness, generally described by means of the root mean square (rms) height and the correlation length, remains difficult to measure correctly and is, furthermore, found to be highly variable. In order to overcome these difficulties, Verhoest et al. suggested the use of possibility distributions to reflect possible values of roughness parameters for a given roughness state of an agricultural field. These distributions were then further used to retrieve the soil moisture information. Nevertheless, as they estimated the possibility distributions by brute force, without taking into account any interactivity between the roughness parameters, rather wide distributions of retrieved soil moisture content were obtained. This paper first tries to independently estimate the possibility distributions for both roughness parameters on the basis of a synthetically generated roughness data set. Next, the interactivity between the rms height and the correlation length is taken into account through the identification of a joint possibility distribution by means of a possibilistic clustering algorithm. When applied to actual synthetic aperture radar data, the results show that a narrower, i.e., more specific, possibility distribution of the soil moisture content is obtained when the possibilistic retrieval procedure is performed based on the joint possibility distributions.