Comprehensive evaluation of satellite and model-based soil moisture (SM) products is essential for their further development and application. With the advent of Cosmic Ray Neutron Sensing (CRNS), which has an observation radius of 130-240 m, the spatial representativeness mismatch between these grid-based SM products and ground single-point observations during the evaluation process can be feasibly relieved. In this study, we systematically evaluated 23 gridded SM products, including single-sensor satellite, multi-sensor merged, and model-based products, using 68 CRNS measurement sites across the Europe. Our evaluation revealed that the SMAP-INRAE-BORDEAUX (SMAP-IB) SM retrievals showed the superior consistency with CRNS measurements among all analyzed products, demonstrating both high correlation (R = 0.80) and low unbiased root mean square error (ubRMSE = 0.050 m(3)/m(3)). The CCI/C3S combined active-passive SM products ranked second in performance (R > 0.75, ubRMSE <0.060 m(3)/m(3)). In the bias analysis, 17 products had negative bias (-0.003 m(3)/m(3) to -0.190 m(3)/m(3)) against CRNS measurements, while AMSR2-LPRM at C1 and C2 bands and CCI/C3S at active and passive products had positive bias (0.011 m(3)/m(3) to 0.161 m(3)/m(3)). It was also found that the capabilities of all SM products retrievals degraded in terms of R and ubRMSE with increasing vegetation density, topographic complexity and soil wetness. Most products showed the lowest ubRMSE and highest R values in cropland compared to other land cover types. Our study emphasizes the substantial potential of cosmic field-scale SM observations for the validation of satellite- and model-based SM products, and our findings have the potential to advance algorithm refinement, product improvement, and hydrometeorological applications.

As melting of the firn layer across the Greenland Ice Sheet increases, determining the fate of infiltrating meltwater is crucial for assessing the evolution of the firn and its potential to buffer runoff. While L-band satellite radiometry can estimate the vertically integrated Liquid Water Amount (LWA), using such retrievals in firn studies is challenging because resolving vertical LWA profiles requires complex inversions or physically tuned models. In this study, we developed and tested a decoder consisting of Long Short-Term Memory (LSTM) and Convolutional Neural Network (CNN) layers that assigns a vertical profile to the liquid water measured by L-band radiometry. We trained the decoder on an ensemble of time series generated by the SLF-SNOWPACK model to generate a latent representation of the depth distribution of LWA as determined by model physics. The decoder reproduced the relationship between LWA time series and the modeled liquid mass per cell, maximum depth of infiltration, and duration of wetting with mean errors of 29%, 15%, and 6%, respectively. We then applied the decoder to the L-band retrieved LWA time series for a low and high melt intensity year and assessed time/space trends in the refreezing amount and maximum depth of infiltration. The patterns are consistent with expectations from observations, lending confidence to the predictions. The method thus utilizes observational constraints from L-band time series to create vertical profiles of water and refreezing in firn, which can be incorporated into firn models and serve as validation targets for regional climate models.

The Queen Maud Land (QML) sector of East Antarctica comprises a complex system of grounded ice sheet and fringing ice shelves that regulate ice discharge to the Southern Ocean. Ice-sheet evolution in this region is controlled by interactions between atmospheric forcing, katabatic winds, and bedrock topography, producing strong spatial variability in accumulation, flow, and thermal regimes. While the bordering ice shelves currently act as stabilising buttresses, they are sensitive to oceanic heat intrusions, changing sea-ice conditions, and episodic surface melt. Melt–refreeze processes enhance firn compaction, weaken surface integrity, and may precondition ice shelves for hydrofracture under future warming, despite QML presently exhibiting a positive mass balance trend.We investigate the thermal and structural evolution of snow, firn, and ice in QML using an integrated framework that combines multi-frequency passive microwave observations with physically based modelling. Passive microwave measurements provide complementary sensitivity to near-surface melt processes and deeper firn and ice structure, enabling the detection of both contemporary melt signals and long-term subsurface changes. Lower-frequency observations penetrate deep into the firn and ice column, whereas higher-frequency observations respond to surface temperature, liquid water content, and accumulation variability.Snow, firn, and ice evolution is simulated using the Glacier Energy and Mass Balance (GEMB) model, running on the Ice Sheet and Sea Level System Model (ISSM), providing vertical profiles of temperature, density, and liquid water content driven by meteorological forcing. These profiles are used to forward-model microwave brightness temperatures with the Microwave Emission Model of Layered Snowpacks (MEMLS) across frequencies from 1.4 to 36.5 GHz, accounting for densification and refrozen ice layers. Modelled brightness temperatures are evaluated against satellite observations, providing twice-daily coverage of QML since 2010.We present spatial and temporal patterns of grounded ice-sheet structure, surface and subsurface temperature variability, fresh snow accumulation, and ice shelf melt signatures, together with residuals between observed and modelled brightness temperatures. Our results demonstrate the value of radiometric modelling for constraining firn structure, melt processes, and ice-shelf vulnerability in regions with sparse in situ data. By integrating passive microwave observations with physical firn models, this work supports improved calibration, initialisation, and confidence in projections of mass balance and structural evolution in the Queen Maud Land sector of East Antarctica.

As the Arctic warms, surface melt extends into the Greenland Ice Sheet's accumulation zone, where much of the water infiltrates into the snowpack. This makes monitoring the subsurface water depth and spatial extent important for accurate ice sheet runoff estimations. Subsurface water can be detected using remotely sensed microwave brightness temperatures (TB). We use vertically polarized TB at 1.4 GHz from Soil Moisture and Ocean Salinity satellite (SMOS) and at 6.9, 10.7, and 18.7 GHz from the Advanced Microwave Scanning Radiometers (AMSR-E/2) to estimate the upper depth of liquid water (UDLW) on the ice sheet accumulation area. We build a catalogue of simulated UDLW and TB: realistic UDLW are modeled by the Geological Survey of Denmark and Greenland (GEUS) snow model, forced by the Copernicus Arctic Regional Reanalysis (CARRA), and the corresponding TB are calculated by the Snow Microwave Radiative Transfer (SMRT) model at 19 sites. We train on this catalogue an ensemble of cross-validated Random Forest (RF) models to predict UDLW and its uncertainty from TB at four frequencies. On hold-out modeled data and for water within 5 m of the surface, the RF ensemble achieves a median RMSE of 0.68 m and mean error of -0.09 m. Our retrieval, when applied to observed TB, matches within 2 m UDLW inferred from subsurface temperature profiles down to 4-6 m depth. Performances decrease beyond 5 m depth and for low liquid water amounts. Our retrieval produces daily UDLW maps over the ice sheet's accumulation area during 2010-2023 which reveal the seasonal evolution of UDLW, deliver the first quantitative estimates of subsurface liquid water depth on the ice sheet and offer new insights into meltwater infiltration and storage processes.

This study evaluates the potential of L-band passive microwave data for monitoring soil moisture (SM) in boreal and temperate forests using SMAP and SMOS AM and PM overpasses. SMAP and SMOS Level 3 SM products were first assessed for spring and summer seasons. SMOS showed lower accuracy (r2 = 0.04–0.24, ubRMSE = 0.09–0.13 m3/m3), while SMAP performed better (r2 = 0.18–0.62, ubRMSE = 0.05–0.07 m3/m3) across sites and overpasses. Given the larger number of SMAP TB observations at a fixed incidence angle and greater temporal coverage over the study area, SMAP was selected for SM estimation using ML models. Feature importance analysis identified brightness temperature (TB) as the most influential variable, followed by vegetation water content (VWC), air and soil temperatures, and the microwave polarization difference index (MPDI). Soil and air temperatures were interchangeable during AM overpasses, whereas PM overpasses showed distinct differences, likely due to thermal absorption by dense vegetation. Using optimal features, SM was estimated with CatBoost, Gradient Boosting (GB), Random Forest (RF), and Principal Component Regression (PCR), using stratified shuffle split (SSS) and leave-one-year-out cross-validation (LOYOCV). In SSS, CatBoost achieved slightly higher accuracy than the other ensemble models (AM: r2 = 0.73; PM: R2 = 0.74), while PCR yielded substantially lower accuracy across both overpasses. LOYOCV showed closer rankings among models, with CatBoost ranking highest overall (r2 = 0.58 for AM and 0.54 for PM). Results highlight the feasibility of improved SM estimation in forests using L-band TB, VWC, temperature variables, and MPDI.

Forests are a critical component of the Earth system, accounting for approximately one-third of global photosynthetic activity and carbon storage. They also provide essential habitats for countless species and vital resources for human activities. Low-frequency (L-band; 1–2 GHz) microwave radiometry enables the measurement of forest soil moisture (SM) and L-band vegetation optical depth (L-VOD), offering valuable insights into processes such as tree growth, water infiltration, soil fertility, fuel moisture, carbon stocks, wildfire vulnerability, and biodiversity dynamics. These measurements also support the study of carbon and water fluxes, tree responses to hydrological stress (e.g., drought), and fuel moisture estimation. However, existing algorithms for retrieving SM and L-VOD were primarily developed for low-biomass vegetation types (e.g., grasslands and croplands), differing structurally from forests. This motivates the present review to evaluate the current retrieval approaches, their performance assessment methods, and available validation resources. The review found that systematic uncertainties persist in forest retrievals, despite the demonstrated sensitivity of L-band brightness temperature (TB) to forest SM and L-VOD. Moreover, the focus on non-forest ecosystems has led to a lack of suitable ground truth and reference data for validating forest SM and L-VOD products, and current validation techniques remain underdeveloped. To fully harness the potential of L-band radiometry in forest monitoring, new retrieval algorithms that account for the unique structural and compositional characteristics of forests are required. Additionally, validation efforts must be enhanced both quantitatively and qualitatively—particularly for L-VOD—to improve confidence in these remote sensing products.

Abstract. This paper presents and reviews the modeling of satellite-observed L-band brightness temperature (BT) over land using the two-stream multi-layer microwave emission model (2S-MEM). While the 2S-MEM has been applied in several earlier studies, a comprehensive description tailored to the interpretation of satellite based passive L-band observations has been lacking. Here, we consolidate and clarify the different components of the model formulation in the context of satellite-based L-band radiometry and demonstrate its practical application across various scenarios. Three case studies from different in situ sites and corresponding L-band BT datasets from ESA’s SMOS and NASA’s SMAP satellites are presented. In each case study, BTs are simulated from in situ measurements and compared with corresponding satellite observations. We discuss the challenges of modeling coarse-resolution L-band BTs measured by satellite using in situ point data, and the resulting uncertainties in BT simulations that must be considered in both forward and inverse applications, including geophysical parameter retrievals.

L-band satellite radiometry has emerged as an important tool for monitoring Earth's essential climate variables (ECVs). It relies on spaceborne radiometers operating in a protected band (1.4-1.427 GHz) to measure Earth's surface thermal microwave emission in brightness temperatures. Observations at this band experience minimal atmospheric attenuation and radio-frequency interference (RFI), and can be acquired continuously from day to night, ensuring a short global revisit time. Moreover, L-band radiation can partially penetrate natural materials, allowing for the assessment of subsurface state parameters. These features make L-band radiometry satellites valuable for continuous global climate monitoring, offering unique advantages in tracking specific ECVs that are difficult to measure using other remote sensing techniques. This article reviews recent advances in satellite microwave radiometry at L-band, with a focus on the contributions of key missions-SMOS, Aquarius and SMAP-in retrieving six critical ECVs: 1) surface soil moisture (SM), 2) soil freeze/thaw (FT) status, 3) vegetation aboveground biomass (AGB), 4) sea surface salinity (SSS), 5) sea surface wind (SSW) speed, and 6) sea ice thickness (SIT). It summarizes the rationale behind satellite microwave radiometry and its role in understanding of the spatiotemporal dynamics of these ECVs on a global scale based on more than 16 years of continuous data. Furthermore, it identifies the state-of-the-art status of the discussed ECV products, highlights current challenges, and outlines future directions for the application of microwave radiometry in monitoring ECVs.

The NASA-ISRO Synthetic Aperture Radar (NISAR) mission will advance global soil moisture monitoring through high-resolution L-band observations. However, accurate fine-scale retrieval remains challenging due to the 12-day revisit interval and complex, scale-dependent influences of vegetation and surface roughness on backscatter. This study introduces the SMAP-AVS (Attenuation-Volume scattering-Surface scattering) model, a semi-empirical framework evaluated using ALOS-2 PALSAR observations. Formulated within the Water Cloud Model framework, the methodology moves beyond conventional land-cover-based parameters by adopting pixel-wise parameterization. Leveraging soil moisture temporal stability, the framework assumes strong correlation between coarse-scale (9 km) SMAP L3 products and fine-scale (1 km) variations, enabling separation of vegetation and surface roughness contributions to the SAR signal. The SMAP-AVS framework is resolution-agnostic; while demonstrated at 1 km, it scales to meter-resolution expected from NISAR. Once parameterized, the model operates in snapshot retrieval mode, deriving soil moisture from single SAR acquisitions and NDVI data without further temporal information. Validation across four hydro-climatically diverse U.S. regions (2021-2024) included temporal comparison against International Soil Moisture Network observations and spatial validation using Multi-Radar Multi-Sensor precipitation fields to distinguish physical moisture heterogeneity from artifacts. SMAP-AVS resolves rainfall-driven patterns often missed by coarser products, with spatial correlation coefficients exceeding 0.5 in semi-arid and agricultural regions, demonstrating that the framework captures physically significant hydrological variability and offers a scalable methodology for operational high-resolution soil moisture products.

Accurate retrieval of soil moisture (SM) in densely vegetated regions remains a significant challenge in satellite-based microwave remote sensing because vegetation attenuation and scattering strongly affect the observed brightness temperature (TB). Conventional retrieval algorithms based on the τ-ω model often assume a time-invariant scattering albedo (ω), which may ignore the temporal variability of vegetation scattering and lead to systematic and random errors in retrieved SM. In this study, we propose a new retrieval method, the Hybrid Dual-Channel Algorithm (HDCA), which enables the joint retrieval of SM, vegetation optical depth (VOD), and effective scattering albedo (ω̄) by accounting for first-order vegetation scattering effects. By deriving an analytical relationship between VOD and ω̄ from the microwave polarization difference index (MPDI) and approximating the resulting statistical relationship as a constraint, HDCA constrains the ill-posed multi-parameter inversion from dual-polarized L-band TB observations. Evaluation against representative forest SM measurement sites showed that HDCA substantially improved SM retrieval performance. For example, at the Tambopata site located in the Amazon rainforest, HDCA achieved an R of 0.507, an ubRMSD of 0.060 m3m-3, and a bias of 0.018 m3m-3. At Millbrook and Massachusetts, HDCA showed R of 0.821 and 0.721, ubRMSD of 0.039 and 0.048 m3m-3, bias of 0.008 and 0.025 m3m-3, respectively. Moreover, HDCA SM retrieval over agricultural sites (such as Carman and South Fork from SMAP Core Validation Sites) also showed improved agreement with in situ SM measurements. These results suggest that explicitly accounting for time-varying effective scattering albedo can improve L-band SM retrievals under dense vegetation and provide more reliable SM information for eco-hydrological applications.

In boreal and Arctic regions, where organic soils act as wildfire fuel, NASA's SMAP soil moisture products offer strong potential to improve drought and fuel (organic soil) moisture assessment beyond point-based weather station fire danger models. Since SMAP is not calibrated for organic soils, we evaluated its suitability using a network of fuel moisture stations we established across three SMAP grid cells, as well as fire weather station data across the North American boreal and Arctic. Comparison of SMAP products, brightness temperature, and reflectivity with in situ fuel moisture measurements revealed SMAP products to be dry-biased with low dynamic range (r =-0.03 to 0.40). In contrast, SMAP reflectivity showed good relationships to in situ fuel moisture at 6 cm depth in the Alaska tundra site (r = 0.62), and 10-18 cm depth for the Alberta (r = 0.46) and Ontario (r = 0.62) boreal sites. SMAP soil moisture products were then used to develop a statistical model to predict Drought Code (DC), a weather-based index of fuel availability in the deeper (10-20 cm) organic soil layers. The model, created using hundreds of weather stations across boreal and Arctic regions, explained 63 % of overall deviance (range 28-86 %). Additionally, incorporating SMAP retrievals flagged for dense vegetation increased spatial coverage without compromising model performance. These results indicate that an operational SMAPderived deep organic fuel moisture (e.g. DC) product is feasible if future retrievals account for soil organic content. This would enhance fire danger monitoring and decision support across boreal and Arctic regions.

The Soil Moisture Active Passive mission (SMAP, since 2015) from The National Aeronautics and Space Administration's (NASA) and Soil Moisture and Ocean Salinity mission (SMOS, since 2009) from The European Space Agency's (ESA) measure polarimetric brightness temperature (TB) at L-band (1.4 GHz). They provide estimates of surface soil moisture (SM) and L-band vegetation optical depth (L-VOD) approximately every 2-3 days at the equator, with a spatial resolution of similar to 40 km for a local overpass time of 6 AM/PM. Integrating the AM and PM TB observations from SMAP and SMOS satellite missions can reduce the revisit time to about 1 day over the equator, thus helping to address fast-response hydrologic processes that cannot be addressed with the 2-3 day revisits. This will allow the capture of the SM conditions more often and, hence, capture the rate of decline due to drainage and recharge to groundwater. This occurs early during dry down after storms. The integration of SMOS measurements also works to fill temporal gaps caused by missing data due to SMAP instrument outages. This article details the integration of the SMAP and SMOS observations to achieve a combined SM and L-VOD product. The SMOS TB observations interpolated to 40 degrees incidence angle were first relatively calibrated (RC) to generate SMAP-like SMOS TB (RCTB), making the combined TB records consistent spatially and temporally. The SMAP baseline SM and L-VOD retrieval algorithm was then applied to the RCTB records. We showed that after relative calibration (ARC), the bias between the SMAP and SMOS TBs was reduced from 0.5 to -0.03 K for TB H and from 2.6 to 0.014 K for TB V in the AM cases. For the PM cases, the mean value of differences was reduced from 0.82 to 0.27 K and from 2.88 to 0.19 K for TB H and TB V, respectively. The comparison of the core validation sites (CVS) in situ SM to the retrieved SM from the combined TB record showed an unbiased root-mean-square-difference of 0.039 m3/m3 for both AM and PM cases and the retrieved L-VOD demonstrated consistency with independent biomass and tree height estimates. We also showed an improvement in temporal coverage and that the global mean number of visits to each grid went up from 283 (SMAP only) to 446 (SMAP+SMOS) when both AM and PM overpasses are considered.

Soil moisture (SM) and vegetation optical depth (VOD) are two critical variables in eco-hydrological analysis and for microwave radiative transfer modeling using the $\tau$-$\omega$ model. L-band brightness temperature (TB)-based retrievals of SM and VOD are particularly valuable for characterizing land-atmosphere water and vegetation dynamics; however, spaceborne L-band records are limited prior to 2010. This study demonstrates the feasibility of deriving L-band-like SM from the Advanced Scatterometer (ASCAT2SMAP; A2S). Using the translated SM, we then simulate dual-polarized L-band-like TB ($TB_{v}$ and $TB_{h}$; A2S-TB) and retrieve SM and VOD via a dual-channel algorithm (A2S-DCA). The A2S-based retrievals show overall consistency with Soil Moisture Active Passive (SMAP) products and in situ SM observations. In addition, using an updated $\tau$-$\omega$ parameterization ($\omega _{NN}$ and $h_{NN}$) further reduces SM bias under vegetated conditions (A2S-NNP). These results suggest that combining data-driven translation with physically constrained inversion can extend L-band-like VOD and SM estimates to periods lacking direct L-band observations.

Simultaneously estimating soil moisture (SM) and soil organic matter (OM) from microwave dielectric measurements has substantial value for sustainable agriculture and environmental monitoring, as both crop health and carbon sequestration depend heavily on these soil properties. However, existing dielectric mixing models often treat SM alone, neglecting the influence of OM and introducing discontinuities that complicate dualparameter optimization. Here, we propose a refined optimal estimation (OE) approach that seamlessly incorporates OM into dielectric modeling. Including OM in the dielectric modeling framework not only refines SM estimation but also provides a unique pathway for leveraging soil moisture sensors to assess soil carbon content. By carefully managing SM priors that contain OM-related uncertainties, the ambiguity typically associated with jointly estimating SM and OM is significantly reduced. Field data from the SMAP Validation Experiment 2012 (SMAPVEX12) strongly agree with the simultaneously estimated SM (R = 0.805, RMSE = 0.086 cm3cm-3), as well as OM validated against laboratory-derived organic matter from loss-on-ignition (R = 0.850, RMSE = 0.059 g-1). These improvements have direct implications for practical agricultural water management and ecological stewardship, especially when optimizing irrigation strategies or tracking carbon stocks. By enabling more accurate, spatially explicit, and temporally dynamic estimates of SM and OM, this method broadens the capabilities of remote sensing tools, ultimately aiding both farmers seeking to enhance soil health and environmental managers charged with carbon accounting. To our knowledge, this is the first demonstration of simultaneous SM-OM retrieval from in-situ 50 MHz dielectric measurements using optimal estimation, extending prior OM-aware dielectric modeling beyond singleparameter (SM-only) inversions. Notably, jointly estimating SM and OM improves SM itself relative to SM-only inversion (ubRMSE: 0.097 -> 0.074 m3 m-3), because explicitly accounting for OM variability prevents unmodeled OM from contaminating SM.

The effective permittivity of wet snow and firn links the snow microphysics to its radiometric signature, making it essential for accurately estimating the liquid water amount (LWA) in the snowpack. Here, we compare ten commonly used microwave dielectric mixing models for estimating LWA in wet snow and firn using L-band radiometry. We specifically focus on the percolation zone of the Greenland Ice Sheet (GrIS), where the average volume fraction of liquid water is between 0 % and 6 %. We used L-band brightness temperature (TB) observations from the NASA Soil Moisture Active Passive (SMAP) mission in an inversion-based framework to estimate LWA, applying different dielectric mixing formulations in the forward simulation. We compared the effective permittivities of the mixing models over a range of conditions and evaluated their impact on the LWA retrieval. We also compared the LWA retrievals to the corresponding LWA from two state-of-the-art Surface Energy and Mass Balance (SEMB) models. Both SEMB models were forced with in situ measurements from automatic weather stations (AWS) of the Programme for Monitoring of the Greenland Ice Sheet (PROMICE) and Greenland Climate Network (GC-Net) located in the percolation zone of the GrIS and initialized with relevant in situ profiles of density, stratigraphy, and sub-surface temperature measurements. The results show that the mixing models produce substantially different real and imaginary parts of the dielectric constant, significantly impacting the LWA retrieved from the TB. The correspondence with the SEMB-derived LWA varied by model and site, with correlation coefficients ranging from 0.67 to 0.98 and RMSD values between 5.4 and 23.9 mm. Overall, the power law-based empirical models demonstrated better performance for 2023 melt season. The analysis supports informed selection of dielectric mixing models for improved LWA retrieval accuracy.

This paper presents a novel approach to generating high-resolution, large-scale vegetation optical depth (VOD) maps by integrating ground-based GNSS Transmissometry (GNSS-T) with light detection and ranging (LiDAR) derived vegetation structure data.While global VOD maps from satellite missions such as SMAP and SMOS provide extensive coverage, their spatial resolution remains relatively coarse, and opportunities for validation are somewhat limited. Ground-based GNSS-T systems, which provide relatively accurate and localized measurements, are typically constrained by either low spatial coverage from stationary receivers or limited temporal data from unmanned ground vehicles (UGVs). To address these challenges, we propose an integrated framework that combines the strengths of both stationary and UGV-based GNSS-T systems, enhanced by high-resolution LiDAR data, to upscale localized VOD measurements into comprehensive maps. This method hopes to bridges the gap between stationary and UGV based ground-based observations, demonstrating its effectiveness using data from the SMAP validation experiment (SMAPVEX.22). Our approach provides a valuable contribution to enhancing VOD mapping and supporting advancements in environmental monitoring.

Microwave remote sensing is a widely used and effective method for observing and understanding various land, ocean, and atmospheric processes. Information on the geophysical variables associated with these processes is typically retrieved through inversion of forward models, which describe the remotely sensed observations as functions of the geophysical variables of the scene. Inversion of the forward model is often an ill-posed problem due to the limited information content of microwave measurements and the complexity of the observed scene. The Bayesian approach provides means to incorporate prior information to reduce the ill-posedness of the problem. Considering temporal information is particularly useful in this context, as the temporal characteristics of geophysical variables can be used as prior information to reduce the ill-posedness. Furthermore, considering the temporal domain is natural due to the sequential nature of remote sensing observations. To incorporate such prior information, we introduce a Bayesian inversion of a time series of geophysical variables from a time series of remote sensing observations. The method is formulated in a general form and is therefore applicable to different remote sensing problems. To demonstrate, we applied the method to Soil Moisture and Ocean Salinity (SMOS) L-band brightness temperature measurements to simultaneously retrieve, for the first time, ground permittivity, surface roughness, vegetation optical depth, and scattering albedo over a one-year period at a northern boreal forest site. We compared the retrieved geophysical variables with the ground reference: retrieved and measured ground permittivity agreed with a correlation of 0.91, and retrieved and measured vegetation optical depth agreed with a correlation of 0.68 and the standard deviation of the difference of 0.11. We demonstrated that using the time series method, we could retrieve ground surface roughness and vegetation scattering albedo. Although reference measurements were not available for these variables, the retrieved values were consistent with the ground permittivity and vegetation optical depth. In general, ground surface roughness and vegetation scattering albedo are not retrieved in the nominal case, where data from different satellite overpasses are treated separately. This demonstrates that the time series method enables the retrieval of additional information compared to the usual approach.