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A P-band SAR moisture estimation method is introduced for complex soil permittivity and penetration depth estimation using fully polarimetric P-band SAR signals. This method combines eigen- and model-based decomposition techniques for separation of the total backscattering signal into three scattering components (soil, dihedral, and volume). The incorporation of a soil scattering model allows for the first time the estimation of complex soil permittivity and permittivity-based penetration depth. The proposed method needs no prior assumptions on land cover characteristics and is applicable to a variety of vegetation types. The technique is demonstrated for airborne P-band SAR measurements acquired during the AirMOSS campaign (2012–2015). The estimated complex permittivity agrees well with climate and soil conditions at different monitoring sites. Based on frequency and permittivity, P-band penetration depths vary from 5 cm to 35 cm. This value range is in accordance with previous studies in the literature. Comparison of the results is challenging due to the sparsity of vertical soil in situ sampling. It was found that the disagreement between in situ measurements and SAR-based estimates originates from the discrepancy between the in situ measuring depth of the top-soil layer (0–5 cm) and the median penetration depth of the P-band waves (24.5–27 cm). [ABSTRACT FROM AUTHOR]

The vegetation optical depth (VOD) variable contains information on plant water content and biomass. It can be estimated alongside soil moisture from currently operating satellite radiometer missions, such as SMOS (ESA) and SMAP (NASA). The estimation of water fluxes, such as plant water uptake (PWU) and transpiration rate (TR), from these earth system parameters (VOD, soil moisture) requires assessing water potential gradients and flow resistances in the soil, the vegetation and the atmosphere. Yet water flux estimation remains an elusive challenge especially on a global scale. In this concept study, we conduct a field-scale experiment to test mechanistic models for the estimation of seasonal water fluxes (PWU and TR) of a winter wheat stand using measurements of soil moisture, VOD, and relative air humidity (RH) in a controlled environment. We utilize microwave L-band observations from a tower-based radiometer to estimate VOD of a wheat stand during the 2017 growing season at the Selhausen test site in Germany. From VOD, we first extract the gravimetric moisture of vegetation and then determine the relative water content (RWC) and vegetation water potential (VWP) of the wheat field. Although the relative water content could be directly estimated from VOD, our results indicate this may be challenging for the phenological phases, when rapid biomass and plant structure development take place within the wheat canopy. We estimate water uptake from the soil to the wheat plants from the difference between the soil and vegetation potentials divided by the flow resistance from soil into wheat plants. The TR from the wheat plants into the atmosphere was obtained from the difference between the vegetation and atmosphere water potentials divided by the flow resistances from plants to the atmosphere. For this, the required soil matric potential (SMP), the vapor pressure deficit (VPD), and the flow resistances were obtained from on-site observations of soil, plant, and atmosphere together with simple mechanistic models. This pathfinder study shows that the L-band microwave radiation contains valuable information on vegetation water status that enables the estimation of water dynamics (up to fluxes) from the soil via wheat plants into the atmosphere, when combined with additional information of soil and atmosphere water content. Still, assumptions have to be made when estimating the vegetation water potential from relative water content as well as the water flow resistances between soil, wheat plants, and atmosphere. Moreover, direct validation of water flux estimates for the assessment of their absolute accuracy could not be performed due to a lack of in situ PWU and TR measurements. Nonetheless, our estimates of water status, potentials, and fluxes show the expected temporal dynamics, known from the literature, and intercompare reasonably well in absolute terms with independent TR estimates of the NASA ECOSTRESS mission, which relies on a Priestly–Taylor type of retrieval model. Our findings support that passive microwave remote-sensing techniques qualify for the estimation of vegetation water dynamics next to traditionally measured stand-scale or plot-scale techniques. They might shed light on future capabilities of monitoring water dynamics in the soil–plant–atmosphere system including wide-area, remote-sensing-based earth observation data. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

The vegetation optical depth (VOD) parameter contains information on plant water content and biomass, and can be estimated alongside soil moisture from currently operating satellite radiometer missions, such as SMOS (ESA) and SMAP (NASA). The estimation of water fluxes, such as plant water uptake (PWC) and transpiration rate (TR), from these Earth system parameters (VOD, soil moisture) requires assessing potential (suction tension) gradients of water and flow resistances in the soil, the vegetation and the atmosphere, yet it remains an elusive challenge especially on global scale. Here, we used a field-scale experiment to test mechanistic models for the estimation of seasonal water fluxes (PWC and TR) of a winter wheat stand including measurements of soil moisture, VOD, and relative air humidity (RH) under a controlled environment. We utilized microwave L-band observations from a tower-based radiometer to estimate VOD of a wheat stand during the 2017 growing season at the Selhausen laboratory in Germany. From VOD, we first extracted the gravimetric moisture of vegetation and then determined subsequently the relative water content (RWC) and the vegetation water potential (VWP) of the wheat field. Although the relative water content could directly be estimated from VOD, our results indicate this may be problematic for the phenological phases, when rapid biomass and plant structure development take place in the wheat canopy. The water uptake from the soil to the wheat plants was estimated from the difference between the soil and vegetation potentials divided by flow resistance from soil into wheat plants. The transpiration rate from the wheat plants into the atmosphere was obtained from the difference between the vegetation and atmosphere potentials divided by flow resistances from plants to the atmosphere. For this, the required soil matric potential (SMP), the vapor pressure deficit and the flow resistances were obtained from on-site observations of soil, plant and atmosphere and simple mechanistic models. This pathfinder study shows that the L-band microwave radiation contains valuable information on vegetation water status that enables the estimation of water dynamics (up to fluxes) from the soil via wheat plants into the atmosphere, when combined with additional information of soil and atmosphere water content. Still, assumptions when estimating the vegetation water potential from relative water content as well as when estimating the water flow resistances between soil, wheat plants and atmosphere had to be made. Moreover, validation of water flux estimates for assessing their absolute accuracy could not be performed due to a lack of in situ PWC and TR measurements. Nonetheless, our estimates of water status, potentials and fluxes show the expected temporal dynamics and intercompare reasonably well in absolute terms, providing confidence in further developing the proposed approach. Our findings support that passive microwave remote sensing techniques allow for the estimation of vegetation water dynamics next to traditionally measured stand-scale or plot-scale techniques. This might shed light on the potential capabilities of monitoring water dynamics in the soil-plant-atmosphere system using wide-area, remote sensing-based Earth observation data. [ABSTRACT FROM AUTHOR]

With the upcoming L-band Synthetic Aperture Radar (SAR) satellite mission Radar Observing System for Europe L-band SAR (ROSE-L) and its integration into existing C-band satellite missions such as Sentinel-1, multi-frequency SAR observations with high temporal and spatial resolution will become available. The SARSense campaign was conducted between June and August 2019 to investigate the potential for estimating soil and plant parameters at the agricultural test site in Selhausen (Germany). It included C- and L-band air- and space-borne observations accompanied by extensive in situ soil and plant sampling as well as unmanned aerial system (UAS) based multispectral and thermal infrared measurements. In this regard, we introduce a new publicly available SAR data set and present the first analysis of C- and L-band co- and cross-polarized backscattering signals regarding their sensitivity to soil and plant parameters. Results indicate that a multi-frequency approach is relevant to disentangle soil and plant contributions to the SAR signal and to identify specific scattering mechanisms associated with the characteristics of different crop type, especially for root crops and cereals. [ABSTRACT FROM AUTHOR]