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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.