The Copernicus Imaging Microwave Radiometer (CIMR) is a Copernicus Expansion Mission with an expected launch in 2028+. The satellite will carry a multi-frequency microwave radiometer operating in the L-, C-, X-, Ku-, and Ka-bands. In addition to providing L-band continuity from other missions (e.g., SMOS, SMAP), the CIMR mission brings new soil moisture remote sensing opportunities due to its multi-frequency, multi-resolution, and high temporal revisit characteristics. This study outlines a preliminary version of a soil moisture retrieval approach designed within CIMR preparatory activities. It aims to provide two soil moisture products: the first is based on the inversion of L-band brightness temperature measurements at their native resolution (<60 km). The second product is based on the inversion of enhanced resolution L-band measurements (<15 km) achieved through sharpening the L-band with higher resolution C/X-band measurements. In both cases, the soil moisture retrieval is based on the inversion of the zeroth-order tau-omega radiative transfer model. We present current efforts of algorithm development and performance evaluation based on simulated CIMR L1B data. The results presented here showcase the potential of CIMR to provide soil moisture estimates not only at hydroclimatological scales (<60 km, from L-band) but also at hydrometeorological scales (~10 to 25 km, from L-band, sharpened with C/X bands), meeting the needs of a wide range of science and applications.

Storage of interception water in the canopy (Sc) heavily affects measurements of vegetation optical depth (VOD) from rain, dew and fog, impeding the direct retrieval of tree physiological parameters such as biomass and plant water content. This study presents a time series decomposition of VOD from Global Navigation Satellite System-Transmissometry (GNSS-T) into biomass, plant moisture content (Mg) and Sc. The experiment was conducted at eddy covariance (EC) towers in two temperate forest types in Germany, over the entire vegetation period of 2023 and under fairly wet conditions. Sc-values were 1.5 times (needleleaf) to two times (broadleaf) higher than the average diurnal Mgcycle, allowing partitioning of interception water storage from plant water. Furthermore, we found indications that Scmaxima did not linearly increase with precipitation, suggesting sensitivity of VOD to saturation effects when canopy interception storage reaches a maximum during strong precipitation events. Results indicate the sensitivity of VOD from GNSS-T to canopy wetness. This allows partitioning of canopy water storage from other VOD components and improves the usefulness of VOD as a remote sensing metric for forest canopy water relations. Moreover, it opens pathways to quantify Scand evaporation fluxes independently from EC measurements and field experiments.

Vegetation optical depth (VOD) depends on the water, structure, and biomass of vegetation. Here, we propose a multi-sensor approach to isolate the water component from the VOD and to retrieve gravimetric vegetation moisture (mg) in the western United States. The approach estimates VOD from radar and LiDAR data and minimizes the differences between these estimates and SMAP/AMSR2 VOD observations. This minimization allows to obtain the best fitting value of mgwith help of a dielectric model. Results are consistent both in space (drier vegetation in arid areas) and time (drier vegetation in drier months). The mg estimates are in the same range than in situ mg data, with some underestimation (bias ~ -0.07 kg/kg). Statistical results are reasonable (r ~ 0.45, RMSE ≤0.10 kg/kg), yet the different spatial and temporal representation of in situ and remote measurements have an impact in the direct comparisons. Our results highlight the potential for developing new vegetation moisture datasets based on VOD decomposition.

Vegetation optical depth (VOD) depends on the water, structure, and biomass of vegetation. Here, we propose a multi-sensor approach to isolate the water component from the VOD and to retrieve gravimetric vegetation moisture (mg) in the western United States. The approach estimates VOD from radar and LiDAR data and minimizes the differences between these estimates and SMAP/AMSR2 VOD observations. This minimization allows to obtain the best fitting value of mgwith help of a dielectric model. Results are consistent both in space (drier vegetation in arid areas) and time (drier vegetation in drier months). The mg estimates are in the same range than in situ mg data, with some underestimation (bias ~ -0.07 kg/kg). Statistical results are reasonable (r ~ 0.45, RMSE ≤0.10 kg/kg), yet the different spatial and temporal representation of in situ and remote measurements have an impact in the direct comparisons. Our results highlight the potential for developing new vegetation moisture datasets based on VOD decomposition.

The future Copernicus Imaging Microwave Radiometer (CIMR) mission is planned to be launched in the 2027+ time frame. At its present phase, the first version of each Algorithm Theoretical Basis Document (ATBD) must be defined. CIMR will provide observations at L (1.4 GHz), C (6.9 GHz), X (10.65 GHz), Ku (18.7 GHz) and Ka (36.5 GHz) microwave frequencies. These observations will be relevant to develop high resolution land surface products. Here we present a preliminary study with the aim of exploring the future capabilities that the synergy of CIMR frequencies can provide. Focused on the 0th-order Tau-Omega (τ-ω) model, we analysed the influence of soil roughness (H) and scattering albedo (ω) to retrieve soil moisture (SM) and vegetation optical depth (VOD) at L-band and how these parameters can be potentially estimated from higher frequency bands. We evaluated our results over CONUS, concluding that the soil roughness (H) parameter is affecting VOD and ω mainly in non-forested areas: in those areas, the increase of H produces a decrease in VOD. Our maps of ω revealed dependence with land cover type: generally, the lowest ω values were found in forested areas. Instead, our H map yielded patterns that could be mostly associated with topographic effects. Furthermore, by utilizing a depolarization index, TBdep, we discovered that its values were constrained to nearly zero (indicating minimal soil impact) in areas with vegetation, whereas in bare soils, topography had a significant influence on TBdep. We hypothesize that the use of this index could help in finding relationships among the multi-frequency information from CIMR, allowing us to understand the degree of sensitivity of each band to vegetation and topography.

A joint approach for estimating vertically continuous soil moisture profiles by combining P-band SAR polarimetry with soil hydrological modeling is proposed. The approach compares the decomposed soil scattering component from remotely sensed P-band SAR observations of NASA’s Airborne Microwave Observatory of Subcanopy and Subsurface (AirMOSS) mission with an ensemble of simulated counterparts based on the hydrological model HYDRUS-1D and the soil scattering model multi-layer small perturbation method (SPM). From the best fit between remote sensing and soil modeling, the most probable soil moisture profile can be retrieved. Estimated soil moisture profiles at individual monitoring stations across the U.S. are compared to in situ measurements, as well as the European ReAnalysis (ERA5) land and AirMOSS L4 products. Pearson’s coefficient of determination between estimated and auxiliary products prove the overall feasibility of the proposed method with respective R2of 0.92, 0.95, and 0.87.

This study presents a comparison between satellite-based vegetation optical depth (VOD) from multi-frequency radiometry (X-, C- and L-band), VOD-derived relative water content (RWC) and auxiliary data (e.g., evapotranspiration and soil moisture), which are investigated for their sensitivity to water status of tree canopies under dry and wet conditions for a temperate forest in Thuringia, Central Germany. For this, we estimated RWC directly from VOD normalization assuming no major changes in vegetation biomass or plant structure during the study period (2015-2019).Our results show that RWC seasonalities are aligned for all investigated frequencies showing its maximum in early summer when leaves and twigs of the top and low canopy are particularly wet and photosynthetically active. Investigating drought versus non-drought years, we observed that X-band RWC is the one better capturing drought status by exhibiting low values in the extreme drought year 2018 compared to the wet year 2017 while L-band RWC reflects the ecological memory from the extreme drought conditions in 2018 in year 2019 estimates.

Tracking seasonal dynamics of evapotranspiration (ET) across global biomes and along seasonal time periods using remote sensing is vital for monitoring ecosystem health and indicating early signals of drought. In this study, we assess the potential of adding weather and illumination-independent signals from active and passive microwave remote sensing (SAR backscatter & vegetation optical depth, VOD) to the established set of ET products, like from optical/thermal remote sensing (MODIS, SEVIRI) and reanalysis (ERA-5 land, GLDAS) data.Our study covers a four-year period (2017-2020), including dry (2018 & 2019) and wet (2017) years. The study was conducted over eight ICOS sites across Europe. These sites are predominantly forested with a low biomass dynamic over the observation period.We find that the ET products from in situ Eddy Covariance (EC), MODIS, and GLDAS deviate relatively minor along the seasons (< 1 [mm/day]), but differ between years. Here, the years (2017-2020) indicate a slightly different ET rate between in situ measurements (EC) and derived products (MODIS & GLDAS), which is currently being investigated. The microwave-based indicators (backscatter & VOD) are proxies by their nature and serve as first-order indicators of relative dynamics allowing the identification of seasonal patterns of ET as well as their spatio-temporal anomalies along both dry and wet years.

The retrieval of (almost) daily soil moisture from C-band Sentinel-1 Synthetic Aperture Radar (SAR) records is still influenced by incidence angle effects and vegetation dynamics. In this study we present a method to reduce the effects of both methods on the alpha approximation methods, a time series approach assuming changes in backscattering signals between two consecutive observations are related to a change in soil moisture. By implementing a Fourier series for incidence angle normalization and a linear regression to co-polarized backscatter for a vegetation adaption, the alpha approximation method has been extended to gain high temporal resolution soil moisture time series. The approach was tested in the Rur catchment, Germany and the Apulian Tavoliere, Italy.

A Random Forest (RF) regression-tree method to derive high-resolution (60 m) surface soil moisture maps is proposed in this study. The developed methodology integrates multi-source synergies by incorporating information from the visible, near-infrared until short-wave infrared spectrum (Sentinel-2), reanalysis data (ERA5-Land) and terrain information (SRTM), using exclusively open access data. The analysis focuses on the central part of the Iberian Peninsula and covers a four-year period (2018-2021). The resulting high-resolution soil moisture maps exhibit greater spatial heterogeneity compared to the ESA Climate Change Initiative (CCI) soil moisture, which was used as a reference in the training of the RF model. These maps have been evaluated using in situ soil moisture measurements from the REMEDHUS network, and show good agreement in terms of Pearson's correlation (0.83), and uRMSE (0.028 m3•m-3), demonstrating the method’s significant potential for deriving high-resolution soil moisture information.

Accurate soil carbon data are important for understanding the permafrost response and potential carbon release to future climate change. However, there is a large discrepancy in current soil organic carbon (SOC) estimates in the Arctic, where sparse measurements are unable to capture SOC complexity over the vast and remote region. Polarimetric Synthetic Aperture Radar (SAR) data are sensitive to roughness and moisture conditions of soil and vegetation, and may provide useful information on surface and profile SOC properties ( Yi et al., 2021 , 2022 ). The NASA Arctic Boreal Vulnerability Experiment (ABoVE) airborne campaign acquired an abundance of full-polarimetric P- and L-band SAR data across Alaska and western Canada ( Miller et al., 2019 ), which provides opportunities to test new remote sensing applications. The main objective of this study is to investigate the potential of low-frequency polarimetric SAR data for regional SOC retrieval in the Arctic through data analysis and modeling. We chose the Alaska North Slope as our study area due to more in-situ data available in this area.

In literature, a variety of models, representations and formalisms exist that describe electromagnetic waves and their interaction with media. The choice of model often depends on the required properties of the electromagnetic wave, e.g. the degree of polarization or whether the modeled electromagnetic wave is described coherently (amplitude and phase) or incoherently (only amplitude). In this study, we propose a more unified theoretical framework that can represent all cases of coherent, non-coherent, fully polarized, partially polarized, and non-polarized electromagnetic waves. The novel framework also represents them in such a way that the principles of energy conservation and conservation of polarization states are already manifested in the equations.

The NASA Soil Moisture Active Passive (SMAP) satellite mission aims to produce enhanced resolution surface soil moisture products by combining coincident but multiresolution L-band active and passive microwave measurements. Since the SMAP radar ceased operations early in the mission, Copernicus Sentinel-1 C-band radar observations are used in the combined product. The synergy is built on two basic foundations: first, active and passive signals covary in a known and systematic fashion, and second, measurements are available at multiple resolutions. In this study, we perform numerical simulations and assess global satellite observations to test the first foundation (covariation). Specific focus lies on the role of the vegetation canopy in modulating the active–passive relationship. We use a discrete radiative transfer model to simulate the slope$\beta $and coefficient of determination$R^{2}$of the relationship between active and passive signals, considering three vegetation types for which the model has been extensively assessed in previous experimental studies. We find that a linear relationship between backscatter and emissivity can be established over a range of vegetation conditions. The coupling between active and passive signals decreases with increasing vegetation water content, such that moderate or higher correlations (nonzero slopes) are retained up to 4 kg/m2(6.3 kg/m2) for L-band/L-band and 1.5 kg/m2(2 kg/m2) for the C-band/L-band configuration. We decompose the effects of different soil-vegetation scattering mechanisms, such as double-bounce, and different measurement error levels on the active–passive relationship. Comparisons with satellite data confirm that our simulations capture magnitudes and major trends found across global vegetated land masses.

Soil Moisture and Ocean Salinity mapping by Earth observation satellites has contributed significantly toward a better understanding of the Earth system, such as its hydrosphere or climate. Nevertheless, an increased spatial resolution below 10 km with a radiometric resolution in the range of 2 K–3 K of radiometric data could yield a more complete picture of global hydrological processes and climate change. Operational radiometers, such as SMOS, have already approached prohibitive sizes for spacecraft due to the required large antenna apertures. Therefore, radiometer concepts based on a large number of satellites flying in close proximity (swarms) have been proposed as a possible solution. This article investigates the orbit mechanics of placing a satellite swarm-based motion induced synthetic aperture radiometer (MISAR) in low Earth orbit for Earth observation applications. The aperture synthesis antenna array is formed by a large number of individual antennas on autonomously controlled nanosatellites (deputies) and a correlator antenna in the Y-configuration carried by a chief satellite. The proposed design methodology is based on the optimization of satellite positions within a plane and the subsequent translation of coordinates into initial conditions for general circular orbits (GCOs). This enables a more computationally efficient orbit optimization and ensures the time invariance of the antenna array response. Based on this methodology, simulations have been performed with swarms consisting of up to 96 satellites. Simulations show that the spatial resolution of an aperture synthesis radiometer can be increased to less than 10 km for applications where the requirements on radiometric sensitivity are more relaxed ($\Delta T\sim 3$K).

Incidence angle diversity of space-borne radiometer and radar systems operating at low microwave frequencies needs to be taken into consideration to accurately estimate soil moisture (SM) across spatial scales. In this study, the Single Channel Algorithm (SCA) is first applied to SMOS brightness temperatures at vertical polarization (TBV) to estimateSMat coarse-resolution (25 km) and develop a land cover-specific and incidence angle (32.5°, 42.5° and 52.5°)-adaptive calibration of single scattering albedo (ω) and soil roughness (hs) parameters. These effective parameters are used together with fine-scale multi-angular Sentinel-1 backscatter in a single-pass active-passive downscaling approach to estimateTBVat fine-scale (1 km) for each SMOS incidence angle. TheseTBVare finally inverted to obtain the corresponding high-resolutionSMmaps. Results over the Iberian Peninsula for year 2018 show an increasing trend of ω and a decreasing trend ofhswith SMOS incidence angle, with almost no variability of ω across land cover types. The active-passive covariation parameter is shown to increase with SMOS incidence angle and decrease with Sentinel-1 incidence angle. Coarse and fineTBVmaps from the three SMOS incidence angles show similar distributions (mean differences below 0.38 K). Resulting high-resolutionSMmaps have maximum differences in mean and standard deviation of 0.016 and 0.015 m3/m3, respectively, and compare well within situmeasurements. Our results indicate that model-based microwave approaches to estimateSMcan be adequately adapted to account for the incidence angle diversity of planned missions such as CIMR, ROSE-L and Sentinel-1 next generation.

The attenuation of microwave emissions through the canopy is quantified by the vegetation optical depth (VOD), which is related to the amount of water, the biomass and the structure of vegetation. To provide microwave-derived plant water estimates, one must account for biomass/structure contributions in order to extract the water component from the VOD. This study uses Aquarius scatterometer data to build an L-band global seasonality of vegetation volume fraction (δ), representative of biomass/structure dynamics. The dynamic range of δ is adapted for its application in a gravimetric moisture (Mg) retrieval model. Results show that δ ranging from 0 to 3.35.10-4is needed for modelling physically reasonable Mg values. The global average of δ shows consistent spatial patterns across vegetation distributions, and δ seasonality is coherent with the phenology of the studied vegetation types. These findings enable the separation of information on vegetation water and biomass/structure inherent within VOD.

A method for estimating complex soil permittivity (or moisture) and penetration depth based on SAR decomposition is presented. By combining model- and eigen-based decomposition techniques, SAR observations are separated into single scattering components (from soil & vegetation). The proposed method incorporates a multi-layer rough surface scattering model to simulate the soil scattering contribution. From the decomposed soil scattering component, permittivity and thus penetration depth can be estimated from SAR observations. Results are presented for the AirMOSS campaign within the MOISST site, OK, USA. As first results, a median value of 16.44 + 2.02 for the complex permittivity was estimated from the 19 P-band data takes at the SoilSCAPE in situ station in Canton, OK, USA. Overall, the retrieval results for the real part of the complex permittivity are similar to in situ values at 30 cm soil depth, with differences in respective median values of 3.44. The median of penetration depths at the Canton site is 23.91 cm,

A retrieval methodology for forest water potential from ground-based L-band radiometry is proposed. It contains the estimation of the gravimetric and the relative water content of a forest stand and tests in situ- and model-based functions to transform these estimates into forest water potential. The retrieval is based on vegetation optical depth data from a tower-based experiment of the SMAPVEX 19–21 campaign for the period from April to October 2019 at Harvard Forest, MA, USA. In addition, comparison and validation with in situ measurements on leaf and xylem water potential as well as on leaf wetness and complex permittivity are foreseen to understand limitations and potentials of the proposed approach. As a first result the radiometer-based water potential estimates of the forest stand are concurrent in time and similar in value with their in situ (xylem) counterparts from single trees in the radiometer footprint.

Global and long-term vegetation optical depth (VOD) dataset are very useful to monitor the dynamics of the vegetation features, climate and environmental changes. In this study, the radar-based global ASCAT (Advanced SCATterometer) IB (INRAE-BORDEAUX) VOD was retrieved using a model which was recently calibrated over Africa. In order to assess the performance of IB VOD, the Saatchi biomass and three other VOD datasets (ASCAT V16, AMSR2 LPRM V5 and VODCA LPRM V6) derived from C-band observations were used in the comparison. The preliminary results show that IB VOD has a promising ability to predict biomass$(\mathrm{R}=0.74,\ \text{RMSE} =44.82\ \text{Mg}\ \text{ha}^{-1})$, which is better than V16 VOD$(\mathrm{R}=0.64,\ \text{RMSE} =51.27\ \text{Mg} \text{ha}^{-1})$and VODCA VOD$(\mathrm{R}=0.72,\ \text{RMSE} =47.14\ \text{Mg}\ \text{ha}^{-1})$. Some retrieval issues for IB VOD were found in boreal regions (e.g., Eastern America, Russia). In the future, we will focus on improving our algorithm in those regions, and produce a global and long-term dataset.

The upcoming launch of the L-band Synthetic Aperture Radar (SAR) satellite mission Radar Observing System for Europe L-band SAR (ROSE-L) will enable multi-frequency SAR observations when combined with existing C-band satellite missions (e.g., Sentinel-1). Due to the different penetration depths of the SAR signals, multi-frequency SAR offers great potential for field-scale agricultural monitoring and the estimation of soil and plant parameters. The SARSense campaign, conducted between June and August 2019 at the Selhausen agricultural test site near Jülich, Germany, has yielded a comprehensive dataset that includes both air- and space-borne C- and L-band SAR data, extensive in-situ field measurements of soil and plant parameters as well as unmanned aerial systems (UAS)-based multispectral and thermal infrared measurements and cosmic neutron sensing observations. The study provides both, an insight into the strengths and limitations of the acquired dataset as well as an analysis of the different behaviour of C- and L-band backscattering on changing soil moisture and plant parameters for taproot crops and cereals.

Incidence angle diversity of space-borne L-band radiometers needs to be taken into account for a consistent estimation of surface soil moisture (SM). In this study, the Land Parameter Retrieval Model (LPRM) is applied to SMOS brightness temperatures to calibrate the effective scattering albedo (w) and the soil roughness (h1) parameter against ERA5-land SM. The analysis is carried out for SMOS data at three different incidence angles ($32.5\pm 5^{\circ},\ 42.5\pm 5^{\circ}$and$52.5\pm 5^{\circ}$) focusing in 2016 on the three main land cover types of the Iberian Peninsula according to the Climate Change Initiative (agricultural, forest and grassland). The parameterization shows an increasing trend of w and h1with rise of incidence angle. The SM retrieval have been evaluated with in situ SM measurements of the REMEDHUS network on rainfed crop fields. Both compare well at the three incidence angles, obtaining high correlations (0.81-0.85), an ubRMSE around 0.04 m3m−3and low bias (0-0.015 m3m−3).

The Committee on Earth Observation Satellites (CEOS) Land Product Validation (LPV) subgroup has been established to coordinate the development of standardized validation across the satellite-derived products from different platforms, sensors, and algorithms with reference measurements from the in situ networks. Soil moisture exhibits a high variability in space that challenges the in situ validation. One of the main drivers for this variability is the characteristic heterogeneity in the soil texture. By the machine learning methods using the soil profile measurements and the remotely sensed predictors, spatially continuous maps of basic soil properties such as soil texture and bulk density are available. Those can be used to estimate soil moisture variability within a satellite product grid cell, here exemplarily shown for the Soil Moisture Active Passive (SMAP) 36-km product. The soil moisture standard deviation is described as a function of the mean soil moisture, whereby the approach needs the mean and standard deviation of the hydraulic parameters as input. The resulting global data set helps identifying the number of in situ stations necessary to validate the coarse soil moisture products. For most SMAP grid cells, three to four stations are adequate to estimate the mean soil moisture for validation; however, also regions were identified where 80 stations are necessary.

An active–passive microwave retrieval algorithm for simultaneous determination of soil surface roughness parameters [vertical root-mean-square (RMS) height (${s}$) and horizontal correlation length (${l}$)] is presented for bare soils. The algorithm is based on active–passive microwave covariation, including the improved Integral Equation Method (I2EM), and is tested with global soil moisture active passive (SMAP) observations. The estimated retrieval results for${s}$and${l}$are overall consistent with values in the literature, indicating the validity of the proposed algorithm. Sensitivity analyses showed that the developed roughness retrieval algorithm is independent of permittivity for${\varepsilon }_{s} > 10$[-]. Furthermore, the physical model basis of this approach (I2EM) allows the application of different autocorrelation functions (ACF), such as Gaussian and exponential ACFs. Global roughness retrieval results confirm bare areas in deserts such as Sahara or Gobi. However, the type of ACF used within roughness parameter estimation is important. Retrieval results for the Gaussian ACF describe a rougher surface than retrieval results for the exponential ACF. No correlations were found between roughness results and the amount of precipitation or the soil texture, which could be due to the coarse spatial resolution of the SMAP data. The extension of this approach to vegetated soils is planned as an add-on study.

The estimation of soil permittivity under fully covered grassland is a challenging task that can be approached by either model-based polarimetric decomposition techniques or data-driven machine-learning (ML) methods. In this study, we test the benefits and limitations of those techniques when individually or jointly applied to estimate the permittivity of the top soil (lower than 5-cm depth) from the L-band full-polarimetric SAR data. Training (needed for the ML approach) and reference data for accuracy assessment are based on the soil-permittivity measurements from an in situ sensor network. The applied polarimetric decomposition approaches are unable to estimate high soil-permittivity ranges (permittivity higher than 25-30) under the full-cover grassland leading to an underestimation compared with the in situ values. Purely data-driven ML techniques, here a case-adapted Random Forest (RF) architecture, applied directly to the SAR data achieve similar results as the decomposition approaches, where estimation quality mostly depends on the quality of the training set. The combination of both techniques works best and is able to represent high soil-permittivity ranges. The joint estimation decreases the mean absolute error drastically compared with applying any of the two approaches alone (i.e., from 4.88 and 4.99 of an informed physical model and ML on SAR only to 3.35).

In this article, we present a multipolarimetric estimation approach for two model-based vegetation structure parameters (shape A and orientation distribution ψ of the main canopy elements). The approach is based on a reduced observation set of three incoherent (no phase information) polarimetric backscatter intensities (|SHH|2, |SHV|2, and |SVV|2) combined with a two-parameter (APand ψ) discrete scatterer model of vegetation. The objective is to understand whether this confined set of observations contains enough information to estimate the two vegetation structure parameters from the L-band radar signals. In order to disentangle soil and vegetation scattering influences on these signals and ultimately perform a vegetation only retrieval of vegetation shape A and orientation distribution ψ, we use the subpixel spatial heterogeneity expressed by the covariation of co- and cross-polarized backscatter ΓPP-PQof the neighboring cells and assume it is indicative for the amount of a vegetation-only co-to-cross-polarized backscatter ratio μPP-PQ. The ratio-based retrieval approach enables a relative (no absolute backscatter) estimation of the vegetation structure parameters which is more robust compared to retrievals with absolute terms. The application of the developed algorithm on global L-band Soil Moisture Active Passive (SMAP) radar data acquired from April to July 2015 indicates the potential and limitations of estimating these two parameters when no fully polarimetric data are available. A focus study on six different regions of interest, spanning land cover from barren land to tropical rainforest, shows a steady increase in orientation distribution toward randomly oriented volumes and a continuous decrease in shape arriving at dipoles for tropical vegetation. A comparison with independent data sets of vegetation height and above-ground biomass confirms this consistent and meaningful retrieval of APand ψ. The retrieved shapes and orientation distributions represent the main vegetation elements matching the literature results from model-based decompositions of fully polarimetric L-band data at the SMAP spatial resolution. Based on our findings, APand ψ can be directly applied for parameterizing the vegetation scattering component of model-based polarimetric decompositions. This should facilitate decomposition into ground and vegetation scattering components and improve the retrieval of soil parameters (moisture and roughness) under vegetation.

Vegetation Optical Depth (VOD) plays an important role in monitoring the earth ecosystems. There are many VOD products released based on different satellites and frequencies. But most of the VOD products are derived from passive microwave data, and very few active VOD products have been released to date. This study investigated retrievals of the active microwave VOD product from C-band ASCAT (Advanced SCATterometer) observations using the water cloud model in large areas. To achieve this, the ASCAT backscatter data and ECMWF soil moisture data were used as inputs to retrieve ASCAT VOD over the whole Africa. The correlation between the retrieved VOD product and proxies of vegetation density (Saatchi biomass) were used to evaluate the model performance.

In summer 2019 the SARSense campaign was held in Jülich, Germany, to provide insights into the potentials and specifications of the ESA Copernicus candidate mission ROSE-L (Radar Observation System for Europe). ROSE-L will consist of two satellites that carry a polarimetric L-band SAR. Since the L-band signal can penetrate through many natural materials such as vegetation, dry snow and ice, the mission will provide additional information that cannot be gathered by the Copernicus Sentinel-1 C-band SAR mission. The overall objective of the SARSense 2019 campaign is to analyze the mission design concerning its potential for agricultural monitoring services including target applications such as soil moisture monitoring, irrigation management, crop type discrimination, food security and precision farming. The SARSense in situ measurements of soil moisture, soil temperature, vegetation properties, UAS-based multispectral and thermal mapping, as well as the airborne SAR observations are presented as well as strategies for soil moisture retrieval and first analysis.

A considerable amount of water can be stored in vegetation, especially in regions experiencing large quantities of precipitation (mid-latitudes). In this context, an accurate estimate of the actual water status of the vegetation could lead to an improved understanding of the effect of plant water on the water budget. In this study, we developed and validated a novel approach to retrieve the vegetation volume fraction (δ) (i.e., volume percentage of solid plant material of a canopy in air) and the gravimetric vegetation water content (mg) (i.e., amount of water per wet biomass) for winter wheat. The estimation was based on the attenuation of L-band microwave measurements through vegetation (vegetation optical depth, (τ)-parameter). Ground-based L-band microwave measurements over an entire growing cycle together with in situ measured vegetation characteristics have been used for this purpose. Retrieved δ- and mg-values revealed to be comparable to literature and in situ measurements (i.e., δ was within the range between 0 and 0.01 and the retrieved mghad a mean value of 0.58 (0.55 (in situ) and 0.54 (literature))). Finally, we also tested the sensitivity of the δ- and mg-retrievals to their input-values to investigate their possible mutual dependencies. The analysis showed that already small changes in the input-mgor -δ result in relatively large changes in the retrieved δ or mg.

A method of physical integration of electromagnetic (EM) interaction models is presented here to estimate the backscattering coefficient (BSC) for L-band, brightness temperature (TB) for L- and C-Band and the reflectance for visible (VIS) and near-infrared (NIR) region for dynamic vegetated terrain. The SPIN (Spectrum Invariant Interaction) model is obtained by solving vector radiative transfer (VRT) equations kernel-based and therefore for different wave interaction mechanisms. To demonstrate its application for the microwave region, the measurements during the growing cycle of corn from the Eleventh Microwave, Water, and Energy Balance Experiment (MicroWEX-11) have been used. For the optical part the results are compared with the PROSAIL model. By applying the SPIN model in the radar regime, it could be shown that the modeled backscattering coefficients (BSC) correlate strongly with the vertical polarization measurements (Pearson 0.83, R20.69) and are less correlated with the horizontal measurements (Pearson 0.45, R20.20). In addition, the modeled brightness temperatures (L- and C-band) in both polarization states are also correlated with the MicroWEX-11 measurements (L-band: Pearson 0.755, R20.57; C-band: Pearson 0.73, R20.53). Finally, the optical results are consistent with the results of other standard optical models (Pearson 0.99, R20.98), like PROSAIL.

Soil roughness strongly influences processes like erosion, infiltration, moisture and evaporation of soils as well as growth of agricultural plants. An approach to soil roughness based on active-passive microwave covariation is proposed in order to simultaneously retrieve the vertical RMS height (s) and horizontal correlation length (l) of soil surfaces from simultaneously measured radar and radiometer microwave signatures. The approach is based on a retrieval algorithm for active-passive covariation including the improved Integral Equation Method (I2EM). It is tested with the global active-passive microwave observations of NASA's Soil Moisture Active Passive (SMAP) mission. The developed roughness retrieval algorithm shows independence of permittivity for εs> 10 [-] due to the covariation formalism. Results reveal that s and l can be estimated simultaneously by the proposed approach since surface patterns of nonvegetated areas can be assessed on global scale. In regions with sandy deserts, like the Sahara or the outback in Australia, determined s and l confirm rather smooth to semi-rough surface roughness patterns with most frequent vertical RMS heights smaller 3 cm and corresponding higher horizontal correlation lengths (> 8 cm).

Active microwave-based retrieval of soil moisture in vegetated areas has uncertainties due to the sensitivity of the signal to both soil (dielectric constant and roughness) and vegetation (dielectric constant and structure) properties. A multi-frequency acquisition system would increase the number of observations that may constrain soil and/or vegetation parameter retrievals. In order to realize this constraint, an understanding of microwaves interaction with the surface and vegetation across frequencies is necessary. Different microwave frequencies have varied interactions with the soil-vegetation medium and increasing penetration into the soil and canopy with the decreasing frequency. In this study, we examine the contributions of different scattering mechanisms to coincident observations from two microwave frequencies (L and P) of airborne synthetic aperture radar instruments. We quantify contributions of surface, vegetation volume, and double-bounce scattering components. Results are analyzed and discussed to guide future multi-frequency retrieval algorithm designs.

Active and passive low-frequency microwave measurements from a number of space- and airborne instruments are used to estimate soil moisture. Each of the sensing approaches has distinct advantages and disadvantages. There is increasing interest in combining active and passive measurements in order to realize the advantages and alleviate the disadvantages. In order to combine active and passive measurements, their covariations with respect to soil moisture need to be known. The covariation is dependent on how the active and passive microwaves interact with vegetation canopy and soil surface. In this paper, we introduce a physics-based model for the covariation of active and passive microwaves over soil surfaces with vegetation cover. The analytical form for a covariation function is derived which depends on the scattering and absorption of microwaves by soil and vegetation with different orientations, structures, and water contents. The main finding is that the covariation function β is related to the roughness and vegetation losses in the two measurements. An increase in soil roughness or in vegetation cover leads to less negative values of β, which is pronounced for dense and moist vegetation. Both the soil and vegetation components introduce a polarization dependence of β that is caused by polarization-induced differences in soil scattering and oriented plant structures. The forward modeled covariations are plotted together with statistically derived covariation estimates from two months of global active and passive L-band observations of the Soil Moisture Active Passive mission. The physically modeled and statistically derived estimates of covariation are comparable in magnitude and scale.

Here, we investigate multiple ways of assimilating synthetic aperture radar (SAR) data to L-band Microwave Emission of the Biosphere (L-MEB) model to enhance the model performance over forested areas in the boreal zone. Land C-band satellite SAR backscatter data, X -band interferometric SAR coherence, as well as auxiliary data layers from forest authorities are used as a proxy in calculating the forest transmissivity, instead of traditionally used leaf area index (LAI) parameter. Our earlier experiments have shown, that when particularly ALOS PALSAR (L-band) and multitemporal composite Sentinel-l (C-band) data were applied, an improved agreement was achieved between the measured and simulated brightness temperatures (TBs) over forests. Here, we extend our analysis and examine data acquired by ALOS PALSAR, ESA Sentinel-l, and TanDEM-X mission of DLR, as well as several other auxiliary datasets on forest parameters. Our proposed model based approach indicates the potential of an SAR-based estimation of forest volume transmissivity and represents a viable way of active-passive microwave satellite data fusion and incorporating readily available reference data.

Various approaches exist to model scattering of a vegetation canopy above ground in terms of optical and radar wavelengths. Due to the different scattering properties these two spectral regions are modelled separately for visible/ infrared bands and for microwave regions. The newly developed RadOptics model (RO-M) integrates these two spectral regions semi-physically into one radiative transfer (RT)-based model framework, resting on the law of Beer-Bougert-Lambert. Due to the integrative nature of RO-M, it can calculate/simulate the canopy and soil reflectances for the optical and radar spectrum using a single unified model architecture. By Applying RO-M in radar domain (ROR-M) it is shown that the observed dependence of Backscattering coefficient on Leaf Area Index (LAI), soil moisture content and frequency can be simulated consistently with results in literature. The results of the RO-M within the optical domain (ROO-M) present an equivalent trend of reflectance and band ratio values with LAI compared to studies in literature.

In this study, the τ-ω model framework is used to derive extinction coefficient and canopy penetration depths from multi-frequency SMAP and AMSR2 retrievals of vegetation optical depth together with ICESat LiDAR vegetation heights. The vegetation extinction coefficient serves as an indicator of how strong absorption and scattering processes within the canopy attenuate microwaves at L and C-band. Through inversion of the extinction coefficient, the penetration depth into the canopy can be obtained, which is analyzed on local (Sahel, Illinois) and continental scale (Africa, parts of North America) as well as for a one year time series (04/2015-04/2016). First analyses of the retrieved penetration depth estimates reveal strongest attenuation for densely forested areas, therefore vegetation attenuation should be accounted for when retrieving soil moisture in these areas. For the continents of North America and Africa penetration depths decrease in average with an increase in frequency from L- to C-band. Moreover penetration depth time series were found to match with expected seasonal variations (e.g. vegetation growth period & rainy season) for analyzed local regions.

Vegetation Optical Depth (VOD) at L-band is highly sensitive to the water content and above-ground biomass of vegetation. Hence, it has great potential for monitoring crop phenology and for providing crop yield forecasts. Recently, the Multi-Temporal Dual Channel Algorithm (MT -DCA) has been proposed to retrieve L-band VOD from Soil Moisture Active Passive (SMAP) measurements. In previous research, SMAP VOD has been compared to crop phenology and has been used to derive crop yield estimates. Here, we review and expand these initial research studies. In particular, we quantify the capability of VOD to detect different crop stages, and test different VOD metrics (i.e., maximum, range and integrals of VOD) to provide crop yield estimates in the United States Corn Belt. Results show that VOD captures 50% to 70% of crop changes during growing and maturing phases, and that it explains between 44% (in heterogeneous crop regions) and 74% (in homogenous croplands) of final crop yields.

SMAP project released a new enhanced high-resolution (3km) soil moisture active-passive product. This product is obtained by combining the SMAP radiometer data and the Sentinel-IA and -IB Synthetic Aperture Radar (SAR) data. The approach used for this product draws heavily from the heritage SMAP active-passive algorithm. Modifications in the SMAP active-passive algorithm are done to accommodate the Copernicus Program's Sentinel-IA and -IB multi-angular C-band SAR data. Assessment of the SMAP and Sentinel active-passive algorithm has been conducted and results show feasibility of estimating surface soil moisture at high-resolution in regions with low vegetation density . The beta version of this product is released to public on Nov 1st, 2017. This high resolution (3 km) soil moisture product is useful for agriculture, flood mapping, watershed/rangeland management, and ecological/hydrological applications.

Estimating parameters for global climate models via combined active and passive microwave remote sensing data has been a subject of intensive research in recent years. A variety of retrieval algorithms has been proposed for the estimation of soil moisture, vegetation optical depth and other parameters. A novel attenuation-based retrieval approach is proposed here to globally estimate the gravimetric moisture of vegetation (mg) and retrieve information about the amount of water [kg] per amount of wet vegetation [kg]. The parameter mgis particularly interesting for agro-ecosystems, to assess the status of growing vegetation. The key feature of the proposed approach is that it relies on multi-sensor data from three sensor types (microwave radar, microwave radiometer, and lidar) to solve the physics equations and obtain mg-estimates. The comparability of these estimates to literature values as well as to results of a globally applied, retrieval approach of Grant [4], reveal the potential of the developed method.

In the past the effect of soil roughness was often considered secondary within the determination of soil moisture from remote sensing data. Several studies showed that accurate determination of soil roughness leads to an improved estimation of soil moisture. Two standard parameters in microwave sensing to describe the surface roughness are the standard deviation of the surface height variation s and the surface correlation length l with its corresponding autocorrelation function (ACF). Both parameters (s, l) affect the emissivity measured by radiometers as well as the backscattering observed by radars. In this study, we develop a physics-based approach to retrieve s and l by combining both microwave signals based on active-passive microwave covariation. To test the approach, containing a forward model and a retrieval algorithm, we used active/passive microwave data measured with the ComRAD truck-based SMAP simulator at L-band. Results and validations with corresponding field measurements on ground show that s and l can be estimated when using this approach. The physics-based retrieval algorithm works robustly for two investigated test fields having an RMS-Error of 0.68 cm and 0.69 cm between the microwave-based and field-measured s-values, and of 3.13 cm and 3.04 cm for l-values. Validation of the results reveals that the influence of the ACF, needed within the retrieval, is distinct.

Combined active-passive remote sensing has the potential for capturing the relative advantage of each sensing approach in geophysical retrievals. One cornerstone of combined active-passive microwave sensing is the modeling of the covariation of active and passive signals, which arise from equivalent sensitivities of both sensor types to changes in geophysical properties. In this research contribution, we propose a physics-based active-passive combination of active and passive microwave observations based on Kirchhoff's law of energy conservation. This allows establishing a physics-based forward model as well as a fully data-driven, single-pass retrieval methodology for active-passive microwave covariation. The forward model and the retrieval approach are adaptable to different sensor characteristics (incidence angle, frequency & polarization). The theoretical (forward model) as well as applied (retrieval method) physics-based covariation framework is tested with SMAP (LL) and SMAP/Sentinel-1 (LC) data to reveal potentials and constraints for active-passive microwave sensing. As a result of the conducted study, a linear functional relationship between active and passive microwave observations (e.g. assumed for the SMAP mission) is confirmed, if higher-order scattering can be omitted.

Soil moisture under grassland is assessed at field scale using multiple sensing techniques: in situ soil moisture network measurements (SoilNet), rover-based cosmic ray neutron sensing (CRNS rover) and airborne polarimetric SAR acquisitions (PolSAR) at L-band. The three interdisciplinary techniques acquire on different spatial scales from meters to hectometers. In this study, the methods are blended at the field scale to estimate soil moisture under grassland in a synergistic as well as a stand-alone approach. Data from the TERENO Fendt test site near Weilheim (Germany) were recorded concurrently within the ScaleX campaign on 10thof July, 2015. The multisensor assessment reveals that PolSAR estimates benefit fundamentally from the in situ techniques to effectively remove the vegetation scattering component leading to very accurate permittivity estimates (RMSE <; 1 [-]). The PolSAR analyses verified the full applicability of the low-parameterized vegetation scattering model to sufficiently represent grassland cover. Moreover, the comparison of all moisture products indicates the constraint of PolSAR to assess only the surface moisture at L-band, while the other two techniques are able to assess also soil moisture of deeper layers, reaching down to the root zone.

NASA's Soil Moisture Active-Passive (SMAP) mission aims at disaggregating L-band radiometer (36 km) with L-band radar (1-3 km) observations to obtain an intermediate resolution soil moisture product (1-9 km). Since SMAP's radar stopped operations in July 2015, a substitution with ESA's Sentinel 1 C-band radar is underway. For this purpose, the relationship of L-band radiometer and C-band radar observations needs to be determined, especially considering the frequency dependent influence of vegetation. This study investigates vegetation effects on covariations of backscatter and emissivity, considering the SMAP-Sentinel 1 (C/L-band) and original SMAP (L/L-band) frequency configurations. Covariations are expressed as the linear regression slope β between backscatter and emissivity signatures, which is simulated for corn and coniferous forest stands. Backscatter and emissivity signatures are obtained from Tor Vergata model simulations, whereas random signal disturbances are accounted for by an errors-in-variables model. As a general trend, we find that β tends to zero for increasing VWC, which is mostly explained by a decrease of radar sensitivity to soil moisture. For forest, which shows overall high VWC values (~5-15 kg/m2), we thus find low magnitudes of β in all cases. For corn (~0-7 kg/m2), we find considerable non-zero magnitudes of β for both frequency configurations, whereas the L/L-band case retains high magnitudes of β longer with respect to C/L-band.

L-Band microwave radiometry over land mainly focuses on observations of horizontally (H) and vertically (V) polarized brightness temperatures. However, it has been demonstrated that measurements of the full Stokes vector [1] are sensitive to additional environmental properties, e.g. azimuthal plant row orientation. Furthermore, model simulations show that also a smooth surface with a periodic permittivity pattern can cause azimuthal dependencies of the Stokes parameters. The objective of this paper is to present fully polarimetric Lband measurement results from observations of a striped wood and styrodur target, when rotated 360° in small steps. Measurement results of a striped soil and open water target are also reported. Finally, measurements are compared to model simulations, with very good agreement within the validity range of the model (stripe thickness <; λ/2).

The Radar Vegetation Index (RVI) is widely applied to indicate vegetation cover. The index includes the backscattering intensities of co- and cross-polarization that do not only contain information coming from vegetation scattering at longer wavelength (L-band), but also from the soil underneath. A forward modelling approach using active and passive microwave-derived parameters to obtain the scattering contribution of the soil is pursued. The idea of this research study is a subtraction of the attenuated soil scattering contribution from the measured backscattering intensities, to provide a clean vegetation-based solution, called improved RVI (RVII). For latter analysis, the vegetation volume is forward modeled to calculate vegetation-only RVI-values without any soil scattering contribution. It reveals that, the pre-factor of the standard RVI leads to values up to 1.2, unfavorable for a normalized index running between zero and one. Hence, improvements for the standard RVI equation are proposed here to obtain a better suited value range and for incorporating soil scattering influences and filtering of regions with dominant soil scattering. Moreover, the improved RVI (RVII) is compared with datasets of vegetation and soil parameters (e.g. vegetation water content) for correlation analysis to find the physical parameters contributing to the index.

In this study the framework of the τ - ω model is used to derive vegetation loss coefficients and canopy penetration depths from SMAP multi-temporal retrievals of vegetation optical depth, single scattering albedo and ICESat lidar vegetation heights. The vegetation loss coefficients serve as a global indicator of how strong absorption and scattering processes attenuate L-band microwave radiation. By inverting the vegetation loss coefficients, penetration depths into the canopy can be obtained, which are displayed for the global forest reservoirs. A simple penetration index is formed combining vegetation heights and penetration depth estimates. The distribution and level of this index reveal that for densely forested areas in the tropics the soil signal is attenuated considerably, and this attenuation must be carefully accounted for in soil moisture retrieval algorithms.

SMAP project is working on a new and enhanced high-resolution (3km and 1km) soil moisture product. This product will combine SMAP radiometer data and Sentinel-1A and -1B data, and it will use the heritage SMAP active-passive approach. However, modifications in the SMAP active-passive algorithm are done to accommodate the Sentinel-1A and -1B C-band SAR data. Tests of the SMAP and Sentinel active-passive algorithm has been conducted and results show great promise for the high-resolution soil moisture data. The beta version of this product will be released to public in end of the March, 2017. This high-resolution (1 km and 3 km) soil moisture product will be useful for agriculture, flooding, watershed and rangeland management, and ecological and hydrological applications. Specific examples of interest will be shown from the proposed product for the above mention geophysical applications.

SMAP project is working on a new and enhanced high-resolution (3km and 1km) soil moisture product. This product will combine SMAP radiometer data and Sentinel-1A and -1B data, and it will use the heritage SMAP active-passive approach. However, modifications in the SMAP active-passive algorithm are done to accommodate the Sentinel-1A and -1B C-band SAR data. Tests of the SMAP and Sentinel active-passive algorithm has been conducted and results show great promise for the high-resolution soil moisture data. The beta version of this product will be released to public in end of the March, 2017. This high-resolution (1 km and 3 km) soil moisture product will be useful for agriculture, flooding, watershed and rangeland management, and ecological and hydrological applications. Specific examples of interest will be shown from the proposed product for the above mention geophysical applications.

Vegetation optical depth and scattering albedo are crucial parameters within the widely used τ-ω model for passive microwave remote sensing of vegetation and soil. A multi-sensor data integration approach using ICESat lidar vegetation heights and SMAP radar as well as radiometer data enables a direct retrieval of the two parameters on a physics-derived basis. The crucial step within the retrieval methodology is the calculus of the vegetation scattering coefficient KS, where one exact and three approximated solutions are provided. It is shown that, when using the assumption of a randomly oriented volume, the backscatter measurements of the radar provide a sufficient first order estimate and subsequently lead to effective estimates of vegetation optical depth and scattering albedo acquired with the novel multi-sensor approach.

After failure of the SMAP L-band radar, its substitution by the Sentinel-1A/B C-band instruments for combined active-passive retrieval of soil moisture demands an algorithm update for this dual-frequency (L/C) case. In order to account for the different frequencies and acquisition geometries of the two sensor types, the microwave covariation, being the fundamental building block of the moisture retrieval algorithm, is modeled using a fully physics-based approach. Moreover, a data-driven, single-pass retrieval methodology for dual-frequency microwave covariation is proposed and tested on SMAP and Sentinel-1 data. The retrieval is also physics-based, incidence angle independent and therefore globally applicable without any empirical or statistical calibration.

The NASA Soil Moisture Active Passive (SMAP) mission aims to disaggregate L-band microwave brightness temperatures (~40 km2) with finer resolution radar backscatter (1-3 km2) to obtain an intermediate resolution soil moisture product. The disaggregation is based on a linear functional relationship between backscatter and emissivity microwave observations that is captured by a covariation parameter β. Since SMAP's L-Band radar has stopped operations in July 2015, the substitution of Sentinel 1's C-Band radar for an operational soil moisture product is in preparation. However, while multiple studies have provided understanding of active-passive covariation for the L/L-Band case, little is known about the C/L-Band case. We utilize the Tor Vergata discrete backscatter and emission model to simulate growing wheat and corn stands and calculate the covariation parameter β for the L/L-Band and C/L-Band case. The study aims to provide insights into the strength, temporal dynamics and underlying scattering mechanisms of active-passive covariation for different vegetation types and frequency combinations. Our results indicate that for the C/L-Band case, vegetation cover limitations are generally more severe, and different β-dynamics and underlying scattering mechanisms are observed with respect to the L/L-Band case.

The ESA's SMOS and the NASA's SMAP missions, launched in 2009 and 2015, respectively, are the first two missions having on-board L-band microwave sensors, which are very sensitive to the water content in soils and vegetation. Focusing on the vegetation signal at L-band, we have implemented an inversion approach for SMAP that allows deriving vegetation optical depth (VOD, a microwave parameter related to biomass and plant water content) alongside soil moisture, without reliance on ancillary optical information on vegetation. This work aims at using this new observational data to monitor the phenology of crops in major global agro-ecosystems and enhance present agricultural monitoring and prediction capabilities. Core agricultural regions have been selected worldwide covering major crops (corn, soybean, wheat, rice). The complementarity and synergies between the microwave vegetation signal, sensitive to biomass water-uptake dynamics, and optical indices, sensitive to canopy greenness, are explored. Results reveal the value of L-band VOD as an independent ecological indicator for global terrestrial biosphere studies.1

This letter proposes a novel way to assimilate synthetic aperture radar (SAR) data to L-band Microwave Emission of the Biosphere (L-MEB) model to enhance model performance over forested areas. L- and C-band satellite SAR data are used in order to characterize the forest transmissivity within the emission model, instead of the optical satellite imagery-based leaf area index (LAI) parameter. Examination of several combinations of satellite SAR data as a substitute for LAI within the L-MEB model showed that when ALOS PALSAR (L-band) and multitemporal composite Sentinel-1 (C-band) data are applied, an improved agreement was achieved between the measured and simulated brightness temperatures (TBs) over forests. The root mean squared difference between modeled and measured TBs was reduced from 6.1 to 4.7 K with single PALSAR scene-based transmissivity correction and down to 4.1 K with multitemporal Sentinel-1 composite-based transmissivity correction. Suitability of single Sentinel-1 scenes varied based on seasonal and weather conditions. Overall, this indicates the potential of an SAR-based estimation of forest volume transmissivity and opens a possible way of fruitful active-passive microwave satellite data integration.

Microwave-based retrieval of soil moisture in vegetated areas have uncertainties due the sensitivity of the signal to vegetation structure and dielectric constant. In this study, we propose a framework for developing a joint active L-band and active P-band retrieval algorithm to decrease the retrieval uncertainties. The algorithm focuses on the decomposition of soil, vegetation and dihedral components to compare the observations from the two frequencies.

This study presents a quantitative analysis of vegetation and soil parameters retrieved from observations of an airborne P-band SAR instrument across nine different biomes in North America. These measurements are part of the NASA's AirMOSS mission, and data have been collected between 2012 and 2015. We use a three component decomposition algorithm to separate the contribution of surface and vegetation scattering, and subsequently retrieve surface and vegetation parameters. Applying the retrieval algorithm to data across all the campaign sites, we characterize the dynamics of the parameters across different North American biomes and assess their characteristic range.

The NASA Soil Moisture Active Passive (SMAP) mission aims at producing high-resolution (9 km) global maps of surface soil moisture based on L-band radar and radiometer measurements. In this study, a physically-based retrieval of the active-passive covariation parameter β from one active-passive (single-pass) SMAP acquisition couple is proposed, circumventing empirical time-series regressions. The key to single-pass retrieval of β is the vegetation correction of the backscatter signal. This can be achieved by use of the measured cross-polarized backscatter signal and parameters appropriately describing the structure of the vegetation volume. These parameters can be derived from the observed Γ-parameters of the SMAP baseline algorithm enabling a fully SMAP data-driven, single-pass estimation of the covariation parameter β without any auxiliary information. Moreover, vegetation structural parameters, indicative of preferential vegetation shape and orientation, are retrieved using the observed Γ-parameters.

The NASA Soil Moisture Active Passive (SMAP) mission aims at producing low (36 km) and high-resolution (9 km) global maps of surface soil moisture based on L-band radiometer and radar/radiometer measurements, respectively. In this research study, results of applying a novel retrieval algorithm, the so-called Multi-Temporal Dual Channel Algorithm (MT-DCA) to the first year of SMAP observations are presented. MT-DCA allows retrieving not only soil moisture, but also vegetation optical depth (VOD) and scattering albedo estimates, from passive microwave measurements alone and without reliance of a priori information. At L-band, VOD is proportional to total vegetation water content and albedo accounts for structural changes. The analysis of these parameters at different temporal and spatial scales will reveal the full potential of L-band microwave for global ecology studies.

The applicability of a recently developed compact polarimetric decomposition and inversion algorithm for C-band to estimate soil moisture under growing agricultural vegetation cover is investigated using simulated L-band compact Polarimetric Synthetic Aperture Radar (PolSAR) data. The surface scattering component is separated from the volume scattering component through a model-based compact polarimetric decomposition under the assumption of a randomly oriented vegetation volume and reflection symmetry. The extracted surface scattering component is compared with two physically-based, low frequency surface scattering models such as Extended Bragg (X-Bragg) and Polarimetric Two Scale Model (PTSM). The algorithm is applied on a time series of simulated L-band compact polarimetric E-SAR data from the AgriSAR 2006 campaign over the Görmin test site in Germany. The compact PolSAR derived soil moisture is validated against in situ measurements. Including the entire growing season and three different crop types, the estimated soil moisture values indicate an overall RMSE of 9-12 vol.% and 9-15 vol.% using the X-Bragg and PTSM, respectively.

The objective of the NASA Soil Moisture Active Passive (SMAP) mission is to provide global measurements of soil moisture and freeze/thaw states. SMAP integrates L-band radar and radiometer instruments as a single observation system combining the respective strengths of active and passive remote sensing for enhanced soil moisture mapping. Airborne instruments are a key part of the SMAP validation program. Here, we present an airborne campaign in the Rur catchment, Germany, in which the passive L-band system Polarimetric L-band Multi-beam Radiometer and the active L-band system F-SAR of DLR were flown simultaneously on six dates in 2013. The flights covered the full heterogeneity of the area under investigation, i.e., the main land cover types and all experimental monitoring sites. Here, we used the obtained data sets as a test bed for the analysis of three active-passive fusion techniques: 1) estimation of soil moisture by passive sensor data and subsequent disaggregation by active sensor backscatter data; 2) disaggregation of passive microwave brightness temperature by active microwave backscatter and subsequent inversion to soil moisture; and 3) fusion of two single-source soil moisture products from radar and radiometer. Results indicate that the regression parameters β are dependent on the radar vegetation index. The best performance was obtained by the fusion of radiometer brightness temperatures and radar backscatter, which was able to reach the same accuracy as single-source coarse-scale radiometer soil moisture retrieval but on a higher spatial resolution.

In this paper, the capabilities of hybrid polarimetric synthetic aperture radar are investigated to estimate soil moisture on bare and vegetated agricultural soils. A new methodology based on a compact polarimetric decomposition, together with a surface component inversion, is developed to retrieve surface soil moisture. A model-based compact decomposition technique is applied to obtain the surface scattering component under the assumption of a randomly oriented vegetation volume. After vegetation removal, the surface scattering component is inverted for soil moisture (under vegetation) by comparison with a surface component modeled by two physics-based scattering models: The integral equation method (IEM) and the extended Bragg model (X-Bragg). The developed algorithm, based on a two-layer (random volume over ground) scattering model, is applied on a time series of hybrid polarimetric C-band RISAT-1 right circular transmit linear receive data acquired from April to October 2014 over the Wallerfing test site in Lower Bavaria, Germany. The retrieved soil moisture is validated against in situ frequency-domain reflectometry measurements. Including the entire growing season (all acquired dates) and all crop types, the estimated soil moisture values indicate an overall rmse of 7 vol.% using the X-Bragg model and 10 vol.% using the IEM model. The proposed hybrid polarimetric soil-moisture inversion algorithm works well for bare soils (rmse = 3.1-8.9 vol.%) with inversion rates of around 30-70%. The inversion rate for vegetation-covered soils ranges from 5% to 40%, including all phenological stages of the crops and different soil moisture conditions.

In this study application results are presented derived from multi-parametric SAR observations covering five different thematic domains: forest, agriculture, ocean, urban and cryosphere. In total 21 application products have been selected and described. Their application on different data sets, space- and airborne sensors, was demonstrated and can independently be reproduced by any scientist. The results and algorithms are available soon through Springer.

The NASA Soil Moisture Active Passive (SMAP) mission is designed to produce high-resolution (9 km) global mapping of surface soil moisture based on L-band radar and radiometer measurements. The multi-scale measurements are combined using time-series of active passive microwave data to retrieve the statistical regression parameters (α, β) from successive overpasses. In this study, we introduce a physically-based forward model as well as data-based retrieval of the β-parameter. The forward model stems from analyses of the scattering and loss terms occurring during bare and vegetated soil scattering/emission and allows a physically-based modelling of the β-parameter. This provides possibilities to analyze the different influences of soil roughness as well as vegetation structure and moisture on the β-parameter under different environmental conditions. In addition, a physically-based retrieval of β from one active-passive SMAP acquisition couple is proposed, circumventing lengthy time-series regressions. The key operation enabling a single-pass retrieval of the β-parameter is the vegetation correction of the backscatter signal. This can be achieved by use of the measured cross-polarized backscatter signal together with an appropriate polarimetric vegetation volume model.

The capability of hybrid Polarimetric Synthetic Aperture Radar (PolSAR) to estimate soil moisture (under vegetation) was assessed within the agricultural region of Wallerfing in Lower Bavaria, Germany. A novel methodology based on a hybrid polarimetric decomposition together with a surface component inversion is developed to retrieve surface soil moisture. The model-based, hybrid decomposition technique is used assuming a randomly oriented vegetation volume to obtain the surface scattering component. After vegetation removal, the surface scattering component is inverted for soil moisture by comparison with the surface scattering component, modeled by the Extended Bragg (X-Bragg) model including a depolarization term. The developed algorithm is applied on multi-temporal hybrid polarimetric C-band RISAT-1 data acquired from April to October 2014. The estimated soil moisture values indicate an overall Root Mean Square (RMS) error of 6.2 to 8.5 vol.% accounting for the entire growing season and four different plant types.

This study presents a simplified polarimetric decomposition for soil moisture retrieval under vegetation cover. After removing the volume scattering contribution in the full polarimetric SAR signature, only the surface scattering component is used to retrieve the soil moisture. The simplified algorithm is evaluated on the dense time series of UAVSAR data and detailed ground truth measurements covering the whole crop growth period. The results show that the performance of the soil moisture retrieval depends on both the crop types and the phonological developing stage. The fields covered by soybean obtain better results than other crop type, due to the low crop height and biomass. This study validate the potential of polarimetric decomposition for soil moisture retrieval over vegetated agricultural fields.

L-band fully polarimetric SAR data, acquired within several airborne measurement campaigns from 2006 to 2013 over Germany, are used for soil moisture estimation at high spatial resolution and under distinct agricultural vegetation cover. The fully polarimetric L-band data were acquired by DLR's E-SAR and F-SAR sensors in order to investigate their potential for soil moisture retrieval. A newly developed, iterative, generalized, hybrid decomposition and inversion approach is applied for a flexible soil moisture estimation under a temporally and spatially varying agricultural vegetation. Therefore an iteration-adaptive, generalized scattering model of the vegetation is used. First results of the inverted soil moisture under vegetation cover reveal high inversion rates for the agricultural test sites (>95%). The inverted soil moistures are validated with in situ measurements from simultaneously conducted field campaigns leading to a minimum root mean squarer error (RMSE) of 4.1vol.% for a variety of crop types and in situ moisture conditions (ranging from 5vol.% to 40vol.%).

The objective of the NASA Soil Moisture Active & Passive (SMAP) mission is to provide global measurements of soil moisture and its freeze/thaw state. The SMAP measurement approach is to integrate L-band radar and radiometer as a single observation system combining the respective strengths of active and passive remote sensing for enhanced soil moisture mapping. Airborne instruments will be a key part of the SMAP validation program. Here, we present an airborne campaign in the Rur catchment, Germany, in which the passive L-band system Polarimetric L-band Multi-beam Radiometer (PLMR2) and the active L-band system DLR F-SAR were flown on six dates in 2013. The flights covered the full heterogeneity of the area under investigation, i.e. all types of land cover and experimental monitoring sites. The obtained data sets are used as a test-bed for the analysis of existing and development of new active-passive fusion techniques.

A recently published hybrid decomposition and inversion approach is adapted and enlarged using a generalized scattering model of the vegetation volume for a refined and more flexible soil moisture estimation under a temporally and spatially varying agricultural vegetation cover. Fully polarimetric SAR data of DLR's E-SAR system at L-band are used as observation basis. The results for the AgriSAR campaign, carried out in 2006 within the Peene catchment, reveal a detailed inversion due to the pixel-based procedure and very high inversion rates (>95%) obtaining a gapless inversion along the entire growth cycle in strongly varying agricultural areas. The validation with in situ measurements for a plurality of summer and winter crops states a root mean square error of 4.55vol.%, while a wide moisture range (~2-30vol.%) is covered by the refined soil moisture inversion under vegetation using solely polarimetric SAR techniques.

During the last decade, synthetic aperture radar (SAR) became an indispensable source of information in Earth observation. This has been possible mainly due to the current trend toward higher spatial resolution and novel imaging modes. A major driver for this development has been and still is the airborne SAR technology, which is usually ahead of the capabilities of spaceborne sensors by several years. Today's airborne sensors are capable of delivering high-quality SAR data with decimeter resolution and allow the development of novel approaches in data analysis and information extraction from SAR. In this paper, a review about the abilities and needs of today's very high-resolution airborne SAR sensors is given, based on and summarizing the longtime experience of the German Aerospace Center (DLR) with airborne SAR technology and its applications. A description of the specific requirements of high-resolution airborne data processing is presented, followed by an extensive overview of emerging applications of high-resolution SAR. In many cases, information extraction from high-resolution airborne SAR imagery has achieved a mature level, turning SAR technology more and more into an operational tool. Such abilities, which are today mostly limited to airborne SAR, might become typical in the next generation of spaceborne SAR missions.

The estimation of volumetric soil moisture under low agricultural vegetation from fully polarimetric synthetic aperture radar (SAR) data at L-band using a multi-angular polarimetric decomposition is investigated. Radar polarimetry provides the framework to decompose the backscattered signal into different canonical scattering mechanisms referring to scattering contributions from the underlying soil and the vegetation cover. Multi-angular observation diversity further increases the information space for soil moisture inversion enabling higher inversion rates and a stable inversion performance. The developed approach was applied on the multi-angular L-band data set acquired by German Aerospace Center's ESAR sensor as part of the OPAQUE campaign in 2008. The obtained results are compared against ground measurements collected by the OPAQUE team over a variety of vegetated agricultural fields. The validation of the estimated against ground measured soil moisture results in an root mean square error level of 6-8 vol.% including all test fields with a variety of crop types.

One important secondary objective of the proposed Earth-Explorer Candidate Mission BIOMASS is the retrieval of a digital terrain model (DTM) using satellite-borne P-band SAR data. The interferometric phase acquired in repeat-pass mode may be affected by ionospheric effects leading to corrupted phase estimates and, consequently, to errors in the derived DTM. In contrast, line-of-sight orientation angles induced by azimuth slopes can be estimated using single-pass POLSAR measurements. The orientation angle estimates allow a necessary pre-processing of POLSAR observations and the retrieval of topographic information such as a DTM. In this study, the performance of the circular polarization method for orientation angle estimation is examined over forested areas. To this end, the orientation angles computed from P-band POLSAR data are compared with the results obtained from LIDAR DTMs. In particular, the estimation performance is investigated with respect to the impact of topography and vegetation. POLSAR data at P-band are used that have been acquired by the E-SAR system of DLR over three forested test sites: two boreal forests in Sweden (one over flat terrain located at Remningstorp, the other over terrain with topographic variations at Krycklan) and the tropical forest Mawas in Indonesia over flat terrain.

Soil moisture retrieval under agricultural vegetation is assessed by a hybrid decomposition and inversion algorithm using fully polarimetric SAR data of DLR's E-SAR system at L-band. The results for the AgriSAR and SARTEO campaigns, conducted in 2006 and 2008 within the Peene and the Rur catchment, reveal a very high inversion rate leading to a spatially continuous inversion along the entire growth cycle in agricultural areas. The validation with in situ measurements for a variety of summer and winter crops states a root mean square error of 6.25vol.% and 5.77vol.% respectively, while a wide moisture range (~2-30vol.%) is covered by the soil moisture inversion under vegetation.

The extensive field data base of the TERENO project is utilized to validate inversion results for soil moisture under vegetation within two terrestrial observatories (Central German Lowland Observatory and Lower Rhine Valley Observatory). The recently developed hybrid decomposition and inversion approach is applied to L-band fully polarimetric SAR data acquired within the TERENO campaign of 2011 by DLR's novel high resolution F-SAR sensor in order to investigate its potential. The soil moisture results state a spatially continuous inversion under vegetation with a RMSE between 3.2vol.% and 8.2vol.% for both observatories. Therefore the results are particularly suited for local analyses on the field level in contrast to soil moisture products with low resolution from passive microwave sensors on the catchment level.

The objective of the NASA Soil Moisture Active & Passive (SMAP) mission is to provide global measurements of soil moisture by fused active and passive L-band microwave measurements. With an airborne campaign conducted in the Rur catchment, Germany, in 2012, an active and passive L-band microwave data set is generated. The passive Polarimetric L-band Multi-beam Radiometer (PLMR2) and the active L-band system DLR F-SAR were installed on the DLR Dornier DO 228 aircraft. Despite of the differences between the airborne and the future spaceborne sensors, the data set will serve as a test bed for the analysis of existing and development of enhanced future active/passive data fusion techniques. Moreover, the derivation of (vegetation) parameters for the fusion approaches is planned.

In recent years an increased interest arose in using synthetic aperture radar (SAR) to study and monitor land ice for glaciological applications and climate change research. At long wavelengths, SAR systems can penetrate into glacier ice for several tens to hundreds of meters. This makes them sensitive to near-surface as well as deeper ice volume features. This paper investigates the performance of a volume scattering component, in the course of an electromagnetic (e.m.) model development, to relate Polarimetric SAR (PolSAR) signatures to glacier facie. Indeed, PolSAR ice signatures are still poorly understood, including the importance of scattering from the glacier ice volume and its dependency on frequency and incidence angle. A comparison is performed with airborne Pol-SAR data at L- and P-band collected by DLR's E-SAR system over the Austfonna ice cap in Svalbard, Norway, within the ICESAR campaign.

In this paper a modified three component polarimetric decomposition incorporating multi-angular acquisitions is developed to estimate soil moisture under vegetation cover over agricultural areas. The approach is applied on fully-polarimetric L-band data acquired by DLR's airborne E-SAR sensor in the frame of the OPAQUE campaign conducted in May 2008 in the Weißeritz catchment area, near Dresden, Germany. The results for the estimated soil moisture from the overlapping area of the flight strips demonstrate a significant increase of the inversion rate, if more than one acquisition is used. The inverted soil moisture values are validated against in situ measurements for five test fields with different crop types resulting in an RMSE of approximately 7vol.% for different incidence angle constellations. Finally the results show how topographic effects in the soil moisture retrieval can be compensated by multi-angular constellations.

In this paper, the potential of using polarimetric SAR (PolSAR) acquisitions for the estimation of volumetric soil moisture under agricultural vegetation is investigated. Soil-moisture estimation by means of SAR is a topic that is intensively investigated but yet not solved satisfactorily. The key problem is the presence of vegetation cover which biases soil-moisture estimates. In this paper, we discuss the problem of soil-moisture estimation in the presence of agricultural vegetation by means of L-band PolSAR images. SAR polarimetry allows the decomposition of the scattering signature into canonical scattering components and their quantification. We discuss simple canonical models for surface, dihedral, and vegetation scattering and use them to model and interpret scattering processes. The performance and modifications of the individual scattering components are discussed. The obtained surface and dihedral components are then used to retrieve surface soil moisture. The investigations cover, for the first time, the whole vegetation-growing period for three crop types using SAR data and ground measurements acquired in the frame of the AgriSAR campaign.

In this paper the focus is given to the soil moisture retrieval under a vegetation cover, exemplarily for two crop types winter wheat and rape. For this Pol-SAR data over a whole vegetation period were used acquired in the frame of the AGRISAR campaign in 2006 at L-band. The potentials and limitations over time are investigated by modifications of the model based Freeman decomposition in order to decompose the scattering contributions and to invert the surface and dihedral scattering component for soil moisture estimation. The applied decomposition approaches for agricultural land surfaces are suitable, but exhibit deficiencies in modeling the volume component, even though some improvements due to the modifications could be presented. In the end the soil moisture estimation does not perform constantly well over the whole acquisition period due to the complexity of the crop vegetation.