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.

Soil moisture drydown plays an important role in many hydrometeorological processes such as regulating surface energy budget, evapotranspiration, and infiltration. In this study, we analyzed the spatial and temporal characteristics of global soil moisture drydown using the daily-scale long-term satellite soil moisture product NNSM. We find that the time-series of τSand τLremained stable over the years. The spatial distribution of global τSand τLshows an anti-spatial correlation pattern, implying that strong land-atmosphere interaction in the short and long term occurs in different regions. τS. of NNSM is closer to the observation measurement than SMAP. The results show that NNSM can provide a long-term global reference for global soil moisture memory characterization, and for improving land surface models.

Soil moisture and vegetation optical depth (VOD; related to vegetation water content) retrieved from SMAP and SMOS satellites are widely used for a range of hydrosphere and biosphere applications. However, while soil moisture has been globally well-validated, VOD validation has been sparse. Furthermore, simultaneously retrieval of these parameters results in uncertainties both individually in soil moisture and VOD retrievals as well as in compensation between the parameters. Here, we show global locations where soil moisture and VOD retrievals will have lower uncertainty, based on complementary brightness temperature information content and signal-to-noise ratio metrics. In these same locations, we show that error still propagates more into VOD. However, using VOD regularization algorithms, this error is greatly reduced, especially at sub-weekly timescales where algorithmic error can be most apparent. Despite these regularization approaches that reduce errors, there are yet vast differences in available global regularized retrievals originating from different algorithmic choices.

Microwave vegetation optical depth (VOD) and soil moisture (SM) can be simultaneously retrieved based on L-band radiometry with polarization information. VOD is indicative of the vegetation water content (VWC) because it captures the extinction of land surface emission. If the connectivity of VOD to VWC is robust, the pair of VWC-SM observations can be viable bases for understanding soil-plant-atmosphere water relations, providing new perspectives on ecosystem science. Simultaneous SM-VOD retrievals are feasible by inverting the τ–ω model with two independent datasets in dual channel algorithms. However, given correlated satellite vertical and horizontal brightness temperatures (TBvand TBh), an ill-posed inverse problem arises where TB errors result in high uncertainties of retrievals. In this study, we apply the Degrees-of-Information (DoI) metric and propose a Signal-to-Noise Ratio (SNR) metric to assess the “retrievability” of VOD given the SMAP TBv-TBhlinear dependence. The application of these metrics allows determining where the VOD retrievals are robust and reliable. This is a necessary step in supporting applications of VOD in ecology and hydrology. Results show that regions with mainly non-woody vegetation have the best potential for VOD retrievals, though regularization is necessary. We then assess VOD time variations from two regularization products that reduce the impact of under-determined inversions: the L3-DCA and the MTDCA, which constrain VOD time dynamics with and without using a priori VOD climatology, respectively. Though they both reduce noise, especially in the VOD retrievals, they result in differences in VOD seasonal amplitude and coupling to SM at high frequencies as we outline here.

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 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.

Understanding and reducing the uncertainties in the inversion of the first-order radiative transfer models at the L-band are important for the improved spaceborne retrievals of soil moisture (SM) and vegetation optical depth (VOD) over dense canopy. This article quantifies and compares the sensitivity of dual-channel inversion of the two-stream (2S) and τ-ω models and proposes a new inversion approach for simultaneous retrievals of SM, VOD, and vegetation-scattering albedo (ω) from a single satellite overpass. In particular, the inversion algorithm incorporates the information of the nearby spatial observations, assuming that the values of VOD and ω remain locally invariant, and constrains its solutions to high-resolution a priori physical/climatological knowledge of the retrieval variables. The results demonstrate that the uncertainty in the inversion of 2S model is slightly higher than the τ-ω model under noisy observations and remains homoscedastic for SM and ω, while grows heteroscedastically for higher VOD values due to the shape of the cost function. The results are validated using the SMAP data, the dense Mesonet SM network, the in situ measurements from the International SM Network (ISMN), and the derived VOD from the Moderate Resolution Imaging Spectroradiometer (MODIS)-normalized difference vegetation index (NDVI) over the state of Oklahoma in the United States. It is shown that the new approach can recover simultaneously high-resolution features of SM, VOD, and ω only from a single Soil Moisture Active Passive (SMAP) overpass, where the unbiased root-mean-squared error (ubRMSE) of SM and VOD is reduced by 30% and 70%, respectively, when compared with an unconstrained time-windowed inversion approach.

Remote sensing of geophysical parameters often requires parameter estimation from mutually dependent measurements, reducing retrieval robustness when the number of retrieved parameters equals the number of unknowns. The actual number of parameters that can be retrieved from the total information (e.g., degrees of information (DOI)) is reviewed here in the context of current L-band (1.4 GHz) satellite measurements for retrieving soil and vegetation water content. A limitation of DOI metric is noted where measurements at the noise floor (i.e., in vegetated regions) tend to spuriously decrease estimated mutual information. Thus, the signal-to-noise ratio must be considered together with DOI. A proposed retrieval technique that overcomes the mutually-dependent information, called the multi-temporal dual-channel algorithm, is reviewed and its microwave vegetation parameter retrievals are discussed.

The multi-temporal dual-channel algorithm (MT-DCA) is reviewed in its approach in simultaneously estimating soil moisture and vegetation optical depth (VOD) from SMAP brightness temperature (TB) measurements. At a single incidence angle, the two polarized TB measurements from SMAP at a given location do not provide two degrees of information (DOI). Therefore, this approach assumes a constant VOD between SMAP overpasses to increase DOI and stabilize the soil moisture-VOD estimation. Retrieved time-mean VOD covaries spatially with vegetation biomass, but its temporal dynamics have yet to be validated with in-situ measurements. Recent SMAP VOD applications in plant ecology and crop monitoring are discussed. Ultimately, these studies suggest insightful vegetation information is present in both weekly and seasonal SMAP VOD variations. Further study and ground monitoring campaigns will continue to reveal the extent of geophysical information in the VOD signal.

Current satellite soil moisture retrieval algorithms require estimation techniques or a priori information about microwave vegetation properties, specifically the vegetation optical depth and single scattering albedo. Most approaches assume a constant single scattering albedo (ω), a function of canopy architecture and orientation, despite few investigations of this property. Here, dynamic ω is retrieved over cropland and natural landscape pixels with Soil Moisture Active Passive (SMAP) brightness temperature measurements within the multi-temporal dual-channel algorithm using a moving window retrieval approach. A longer moving window length (number of overpasses with ω constant) is recommended to ensure adequate degrees of information for retrieval and to prevent spurious, rapid changes in ω. It was determined that the mean and standard deviation of both soil moisture and vegetation optical depth are reduced with increased ω. ω also interestingly decreased during the vegetation growth phase suggesting it may be an effective parameter absorbing other physics not accounted for in the zeroth-order radiative transfer equation.

SMAP and SMOS missions estimate soil moisture using a zeroth-order radiative transfer model, the τ-ω model. Its simplifying assumption of a weakly scattering vegetation medium is insufficient in the presence of woody biomass, greater than 30% of the land surface. Here, a simplified first-order radiative transfer equation for use in retrieval algorithms is proposed. The inclusion of first-order scattering increases sensitivity to soil moisture especially for wet surfaces. The recently developed multi-temporal dual channel algorithm (MT-DCA) is implemented over Africa using both the τ-ω model and the proposed first-order equation with SMAP 36 km gridded brightness temperature measurements as inputs. The algorithm finds large changes in soil moisture mean and standard deviation in areas with woody vegetation and little change elsewhere. This implies that inclusion of first-order scattering can significantly change mean soil moisture retrievals and increase their temporal variability in regions with woody biomass.