Search

To minimize uncertainty related to soil processes in extreme events, we need accurate soil hydraulic properties across the entire range of soil water content. However, conventional methods are time-consuming and limited to specific ranges. To estimate soil hydraulic properties throughout the entire range, we conducted inverse modeling using upward infiltration experiments, where a shortwave infrared imaging camera was used to obtain high-resolution soil moisture data in space and time. Because the commonly used van Genuchten–Mualemmodel is unsuitable for describing soil hydraulic properties for dry conditions, we tested an alternative model, the Peters-Durner-Iden model, which considers both capillary and film water. The inverse modeling successfully estimated soil hydraulic properties for sandy loam and loam soils, and we demonstrated that the Peters-Durner-Iden model captured soil moisture dynamics better than the van Genuchten–Mualemmodel for dry conditions. However, both models could not adequately describe the soil moisture data for the other soils. The direct observation of the water flow via shortwave infrared images clarified that the reduced success was because of violating the one-dimensional flow assumption for coarse-textured soils and the micro-heterogeneity in soil hydraulic properties for soils with fine silt and clay materials. • Soil hydraulic properties were estimated from shortwave infrared images. • Peters-Durner-Iden model captured soil moisture dynamics for dry conditions. • The framework was most suitable for sandy loam and loam soils. • Micro-heterogeneity of soils prevented the success of the framework. [ABSTRACT FROM AUTHOR]

Measurements of the soil water characteristic (SWC) and unsaturated hydraulic conductivity [K(h)] curves, which are at the core of modeling flow and transport processes in porous media, are laborious and prone to experimental errors. To overcome some of the current experimental limitations, we examined the potential feasibility of shortwave infrared (SWIR) imaging of water imbibition into dry soil during a controlled laboratory experiment in conjunction with inverse numerical modeling to determine the wetting SWC and the K(h) function. To generate time series of high‐resolution surface moisture maps, the imaged surface reflectance was converted to surface soil moisture via a recently developed physical radiative transfer model. The moisture time series were then used to parameterize the HYDRUS 2D/3D numerical code for forward simulations. The optimization was performed with simulated annealing in MATLAB that was linked to HYDRUS 2D/3D to automate the inversion process. The obtained SWC wetting curves were subsequently compared with Tempe cell and Dewpoint PotentiaMeter measurements. Although further research and refinements of the proposed method are needed, the results of this exploratory study obtained for a broad range of soil textures are promising and demonstrate the potential feasibility of the proposed approach for rapid estimation of soil hydraulic properties. [ABSTRACT FROM AUTHOR]

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 $\tau $ – $\omega $ models and proposes a new inversion approach for simultaneous retrievals of SM, VOD, and vegetation-scattering albedo ($\omega $) 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 $\omega $ 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 $\tau $ – $\omega $ model under noisy observations and remains homoscedastic for SM and $\omega $ , 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 $\omega $ 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. [ABSTRACT FROM AUTHOR]

Optical remote sensing of earth surface processes commonly relies on the red, green, blue (RGB), near-infrared (NIR) and shortwave-infrared (SWIR) electromagnetic bands. Most of the optical sensors mounted on unmanned aerial vehicles and satellites provide the RGB and NIR bands, but only a few offer SWIR output. The Red-NIR reflectance space has been widely applied for remote sensing of various land surface variables including soil moisture. The linear relationship between the Red-NIR reflectance of bare soil is established as the base and then moisture isolines are assumed perpendicular to the soil line. In this study, we show that this assumption is not consistent with the actual Red-NIR space geometry, which in many cases introduces soil moisture estimation errors. To overcome this limitation, we propose a new mathematical transformation to the original Red-NIR space followed by newly defined soil moisture isolines that are more consistent with the actual observations. This new Transformed Red-NIR (TRN) model is compared with the Conventional Red-NIR (CRN) model using data from a sugarcane field located in southwestern Iran. Twelve Land Remote-Sensing Satellite (Landsat)-8 images were acquired during the sugarcane growth season. For validation of the remotely sensed data, ground reference soil moisture was determined at 22 locations at five different depths via core sampling and oven-drying. Our results indicate that the TRN model significantly improves the accuracy of remotely sensed soil moisture. [ABSTRACT FROM AUTHOR]

Considerable advancement in spatiotemporal resolution of remote sensing and ground-based measurements has enabled refinement of parameters used in land surface models for simulating surface water fluxes. However, land surface modeling capabilities are still inadequate for accurate representation of subsurface properties and processes, which continue to limit the accuracy of land surface model simulation. Our objective in this study was to examine the performance of the variously parameterized Noah land surface model with multiphysics option (Noah-MP) in simulating evapotranspiration (ET) and soil moisture dynamics in stony soils using verification from eddy covariance ET and in situ soil moisture data during the growing season of year 2015, obtained from the Lower Sheep subcatchment within the Reynolds Creek Experimental Watershed in southwestern Idaho. We evaluated the performance of Noah-MP considering four different scenarios with 1) a one-layer soil profile with Noah-MP default soil hydraulic parameters and three more fivelayer soil profiles using 2) Noah-MP default soil hydraulic parameters; 3) soil hydraulic parameters derived from a pedotransfer function using field observations; and 4) hydraulic parameters from scenario 3, which also accounted for stone content in each layer. Each modeling experiment was forced with the same set of initial conditions, atmospheric input, and vegetation parameters. Our results indicate that enhanced representation of soil profile properties and stone content information noticeably improve the Noah-MP land surface model simulation of soil moisture content and evapotranspiration. [ABSTRACT FROM AUTHOR]

In-depth knowledge about the global patterns and dynamics of land surface net water flux (NWF) is essential for quantification of depletion and recharge of groundwater resources. Net water flux cannot be directly measured, and its estimates as a residual of individual surface flux components often suffer from mass conservation errors due to accumulated systematic biases of individual fluxes. Here, for the first time, we provide direct estimates of global NWF based on near-surface satellite soil moisture retrievals from the Soil Moisture Ocean Salinity (SMOS) and Soil Moisture Active Passive (SMAP) satellites. We apply a recently developed analytical model derived via inversion of the linearized Richards' equation. The model is parsimonious, yet yields unbiased estimates of long-term cumulative NWF that is generally well correlated with the terrestrial water storage anomaly from the Gravity Recovery and Climate Experiment (GRACE) satellite. In addition, in conjunction with precipitation and evapotranspiration retrievals, the resultant NWF estimates provide a new means for retrieving global infiltration and runoff from satellite observations. However, the efficacy of the proposed approach over densely vegetated regions is questionable, due to the uncertainty of the satellite soil moisture retrievals and the lack of explicit parameterization of transpiration by deeply rooted plants in the proposed model. Future research is needed to advance this modeling paradigm to explicitly account for plant transpiration. [ABSTRACT FROM AUTHOR]

• A new laboratory method for estimating soil water retention is introduced. • The method only requires soil water content-dependent SWIR reflectance data. • Validation for 21 vastly different soils indicates a good performance. The soil water retention curve (SWRC) is an essential soil property that relates soil water content and matric potential. It plays a crucial role in soil water dynamics and the understanding of various hydrological phenomena at the land surface, including infiltration, runoff, evaporation, and energy exchange processes. In recent years, proximal sensing methods have shown great potential for retrieving this challenging-to-measure property from spectral reflectance. However, a physically-based approach is still lacking as current methods rely on empirical data-driven algorithms. Here we propose a novel physics-based laboratory method that, for the first time, enables direct estimation of the entire SWRC from saturated to dry using soil water content/reflectance data pairs within the shortwave infrared domain. The main hypothesis underlying the new method is that soil optical properties not only vary with soil water content but also with the pore scale distribution of capillary and adsorbed soil water. For evaluation, retrieved soil water retention curves of 21 soils that vastly differ in physical and hydraulic properties were compared to direct measurements. The results suggest that the new method is a rapid and efficient alternative to established laboratory measurement methods. [ABSTRACT FROM AUTHOR]

Soil moisture (SM) is a key hydrologic state variable that is of significant importance for numerous Earth and environmental science applications that directly impact the global environment and human society. Potential applications include, but are not limited to, forecasting of weather and climate variability; prediction and monitoring of drought conditions; management and allocation of water resources; agricultural plant production and alleviation of famine; prevention of natural disasters such as wild fires, landslides, floods, and dust storms; or monitoring of ecosystem response to climate change. Because of the importance and wide‐ranging applicability of highly variable spatial and temporal SM information that links the water, energy, and carbon cycles, significant efforts and resources have been devoted in recent years to advance SM measurement and monitoring capabilities from the point to the global scales. This review encompasses recent advances and the state‐of‐the‐art of ground, proximal, and novel SM remote sensing techniques at various spatial and temporal scales and identifies critical future research needs and directions to further advance and optimize technology, analysis and retrieval methods, and the application of SM information to improve the understanding of critical zone moisture dynamics. Despite the impressive progress over the last decade, there are still many opportunities and needs to, for example, improve SM retrieval from remotely sensed optical, thermal, and microwave data and opportunities for novel applications of SM information for water resources management, sustainable environmental development, and food security. Key Points: Recent soil moisture measurement and monitoring techniques and estimation models from the point to the global scales and their limitations are presentedThe importance and application of soil moisture information for various Earth and environmental sciences disciplines such as forecasting weather and climate variability, modeling hydrological processes, and predicting and monitoring extreme events and their impacts on the environment and human society are presented [ABSTRACT FROM AUTHOR]

Highlights • An analytical model for estimation of land surface net water flux is introduced. • Near-surface soil moisture is the only input variable. • The model can be parameterized without the need for calibration with measured data. Abstract The accurate determination of land surface water fluxes at various spatiotemporal scales remains a challenge in hydrological sciences. It is intuitive that land surface net water flux (i.e., infiltration minus evapotranspiration) directly affects near-surface soil moisture. However, there exists no hydrological model suitable to calculate net water flux based on measured near-surface soil moisture data. This is a consequence of the mathematical structure of existing models that use 'boundary conditions' to determine 'internal conditions', whereas what is needed is a model amenable to use near-surface soil moisture data (an internal condition) to determine the surface water flux (a boundary condition). To pursue the idea of utilizing remotely-sensed or in situ (i.e., sensor networks) near surface soil moisture data for estimation of net water flux, we derived an analytical model via inversion of Warrick's 1975 analytical solution to the linearized Richards' equation for arbitrary time-varying surface flux boundary conditions. The applicability of the new analytical solution was evaluated based on actual water flux observations as well as HYDRUS-1D simulations for four vastly different sites in Arizona, California, Idaho, and Indiana. Our results demonstrate that the proposed model reasonably captures net water flux variations in natural settings, including layered and vegetated soils, especially at larger time scales (e.g., monthly). While the model works for a wide range of climatic conditions, the prediction accuracy is somewhat lower for extreme dry or wet conditions. A major advantage of the new model is that it does not require calibration, which provides an unprecedented opportunity for large scale estimation of land surface net water flux using remotely sensed near-surface soil moisture observations. [ABSTRACT FROM AUTHOR]

The OPtical TRApezoid Model (OPTRAM) has recently been proposed for estimation of soil moisture using only optical remote sensing data. The model relies on a physical linear relationship between the soil moisture content and shortwave infrared transformed reflectance (STR) and can be parameterized universally (i.e., a single calibration for a given area) based on the pixel distribution within the STR-Normalized Difference Vegetation Index (NDVI) trapezoidal space. The main motivation for this study was to evaluate how the universal parameterization of OPTRAM works for long periods of time (e.g., several decades). This is especially relevant for uncovering the soil moisture and agricultural drought history in response to climate change in different regions. In this study, MODIS satellite observations from 2001 to 2017 were acquired and used for the analysis. Cosmic-ray neutron (CRN) soil moisture data, collected with the COsmic-ray Soil Moisture Observing System (COSMOS) at five different sites in the U.S. covering diverse climates, soil types, and land covers, were applied for evaluation of the MODIS-OPTRAM-based soil moisture estimates. The OPTRAM soil moisture estimates were further compared to the Soil Moisture Active and Passive (SMAP) (L-band), the Soil Moisture Ocean Salinity (SMOS) (L-band), and the Advanced AScatterometer (ASCAT) (C-band) soil moisture retrievals. OPTRAM soil moisture data were also analyzed for potential monitoring of agricultural drought through comparison of the OPTRAM-based Soil Water Deficit Index (OPTRAM-SWDI) with the widely-applied Crop Moisture Index (CMI). Evaluation results indicate that OPTRAM-based soil moisture estimates provide overall unbiased RMSE and R between 0.050 and 0.085 cm 3  cm −3 and 0.10 to 0.70, respectively, for all investigated sites. The performance of OPTRAM is comparable with the ASCAT retrievals, but slightly less accurate than SMAP and SMOS. OPTRAM and the three microvave satellites captured CRN soil moisture temporal dynamics very well for all five investigated sites. A close agreement was observed between the OPTRAM-SWDI and CMI drought indices for most selected sites. In conclusion, OPTRAM can estimate temporal soil moisture dynamics with reasonable accuracy for a range of climatic conditions (semi-arid to humid), soil types, and land covers, and can potentially be applied for agricultural drought monitoring. [ABSTRACT FROM AUTHOR]

A century of research focused primarily on agricultural soils has largely ignored stony soils, which dominate some forests and are poorly understood in terms of the stone influence on soil hydraulic properties. Motivated by this knowledge gap, we quantified the influence of soil-containing stone fragments on bulk soil hydraulic properties by determining the water retention curve (WRC) of soil, stone and stone-soil mixtures with varied volumetric stone content. The measured WRC for seven different stone types based on their composition showed maximum and minimum saturated water contents of 0.55 m 3 m −3 in pumice and 0.025 m 3 m −3 in fine sandstone, respectively. The stony soil water retention function was measured using the simplified evaporation method. Contrasting scenarios were studied considering a broad range of stone inclusions; ( i ) negligibly porous, ( ii ) significantly porous but less porous than the background soil, ( iii ) more porous than the background soil. An averaging scheme to describe the WRC of stony soil was proposed based on the individual WRC of the background and stone inclusion which was in good agreement with the experimental data. The HYDRUS-3D model was also employed to simulate the evaporation experiment used for the WRC measurements. The model simulations supported the basic assumptions of the proposed averaging scheme. [ABSTRACT FROM AUTHOR]

The “trapezoid” or “triangle” model constitutes the most popular approach to remote sensing (RS) of surface soil moisture based on coupled thermal (i.e., land surface temperature) and optical RS observations. The model, hereinafter referred to as Thermal-Optical TRAapezoid Model (TOTRAM), is based on interpretation of the pixel distribution within the land surface temperature - vegetation index ( LST - VI ) space. TOTRAM suffers from two inherent limitations. It is not applicable to satellites that do not provide thermal data (e.g., Sentinel-2) and it requires parameterization for each individual observation date. To overcome these restrictions we propose a novel OPtical TRApezoid Model (OPTRAM), which is based on the linear physical relationship between soil moisture and shortwave infrared transformed reflectance ( STR ) and is parameterized based on the pixel distribution within the STR - VI space. The OPTRAM-based surface soil moisture estimates derived from Sentinel-2 and Landsat-8 observations for the Walnut Gulch and Little Washita watersheds were compared with ground truth soil moisture data. Results indicate that the prediction accuracies of OPTRAM and TOTRAM are comparable, with OPTRAM only requiring observations in the optical electromagnetic frequency domain. The volumetric moisture content estimation errors of both models were below 0.04 cm 3 cm − 3 with local calibration and about 0.04–0.05 cm 3 cm − 3 without calibration. We also demonstrate that OPTRAM only requires a single universal parameterization for a given location, which is a significant advancement that opens a new avenue for remote sensing of soil moisture. [ABSTRACT FROM AUTHOR]

P-band radar remote sensing applied during the Airborne Microwave Observatory of Subcanopy and Subsurface (AirMOSS) mission has shown great potential for estimation of root zone soil moisture. When retrieving the soil moisture profile (SMP) from P-band radar observations, a mathematical function describing the vertical moisture distribution is required. Because only a limited number of observations are available, the number of free parameters of the mathematical model must not exceed the number of observed data. For this reason, an empirical quadratic function (second order polynomial) is currently applied in the AirMOSS inversion algorithm to retrieve the SMP. The three free parameters of the polynomial are retrieved for each AirMOSS pixel using three backscatter observations (i.e., one frequency at three polarizations of Horizontal-Horizontal, Vertical-Vertical and Horizontal-Vertical). In this paper, a more realistic, physically-based SMP model containing three free parameters is derived, based on a solution to Richards' equation for unsaturated flow in soils. Evaluation of the new SMP model based on both numerical simulations and measured data revealed that it exhibits greater flexibility for fitting measured and simulated SMPs than the currently applied polynomial. It is also demonstrated that the new SMP model can be reduced to a second order polynomial at the expense of fitting accuracy. [ABSTRACT FROM AUTHOR]

Satellite remote sensing technology provides a promising means for near real-time monitoring of crop water status and requirements in agricultural and hydrological applications. Estimation of actual evapotranspiration (ET a) often requires thermal information; however, not every satellite is equipped with a thermal sensor, which limits the estimation of ET a. To address this limitation, here we propose a satellite-based ET a estimation model, OPTRAM-ET, based on the optical trapezoid model (OPTRAM) estimates of soil moisture and a vegetation index (VI). We applied the OPTRAM-ET model to Sentinel-2 and Landsat-8 satellite data and evaluated the model for ET a estimates using 16 eddy covariance flux towers in the United States and Germany with different landcover types, including agriculture, orchard, permanent wetland, and foothill forests. Next, OPTRAM-ET was compared with the conventional land surface temperature (LST)-VI model. The proposed OPTRAM-ET model showed promising performance over all the studied landcover types. In addition, OPTRAM-ET showed comparable performance to the conventional LST-VI model. However, since the OPTRAM-ET model does not need thermal data, it benefits from higher spatial and temporal resolution data provided by ever-increasing drone- and satellite-based optical sensors to predict crop water status and demand. Unlike the LST-VI model, which needs to be calibrated for each satellite image, a temporally-invariant region-specific calibration is possible in the OPTRAM-ET model. Therefore, OPTRAM-ET is substantially less computationally demanding than the LST-VI model. • OPTRAM-ET is a novel method for remote sensing of ET a. • OPTRAM-ET relies on optical reflectance data and does not require thermal data. • Its estimates agree well with eddy covariance data of ET a. • Only a one-time calibration of OPTRAM-ET is required for each region. • A new linkage between ET a and SWIR-derived soil moisture was established. [ABSTRACT FROM AUTHOR]

• A new model links soil water retention and soil spectral reflectance. • Validation for 21 vastly different soils confirms good performance of the model. • Results imply potential for soil water retention retrieval from reflectance data. Soil hydraulic properties, including soil water retention, control various terrestrial hydrological processes such as infiltration, runoff, and evapotranspiration. Measuring and monitoring these properties at large scales is challenging. Therefore, over the past decade, remote sensing has been investigated as a promising tool for large-scale mapping of soil hydraulic properties. Nonetheless, a solid physical relationship between soil hydraulic and spectral properties is still lacking. To close this knowledge gap, this study introduces a novel physical-empirical model that to our best knowledge for the first time connects the soil water retention curve (SWRC) to soil spectral reflectance. The new model has been developed based on the hypothesis that the capillary and adsorbed water components of the SWRC exhibit vastly different optical properties due to their distinct distribution within the soil porous system. The model was validated using soil water retention and reflectance measurements of 21 soils, vastly differing in physical and hydraulic properties. The model provides not only a new and accurate soil moisture-reflectance functional relationship, but also a potential means for retrieval of the soil water retention curve from spectral reflectance in the shortwave infrared domain. [ABSTRACT FROM AUTHOR]

• We introduce a new analytical relationship between soil moisture and water flux. • Flux can be calculated from single-depth moisture data along the unsaturated zone. • The new solution is based on Richards equation with arbitrary hydraulic functions. • This solution allows shallow or deep groundwater table at the bottom boundary. Finding a relationship between soil moisture and soil water flux at a single soil depth has been of particular interest in recent years. Such a relationship, however, is challenging to derive due to a high degree of nonlinearity of the soil water flow governing equation, known as Richards equation. This paper presents a new algebraic soil moisture-flux relationship based on an approximate analytical solution of Richards equation with arbitrary soil hydraulic functions. This solution accounts for the groundwater contributions to soil moisture variations along the unsaturated zone. The new solution was evaluated using numerical solutions of Richards equation via the HYDRUS-1D model. Despite its simplicity, the new solution could reproduce HYDRUS-1D simulations for a homogeneous soil profile with coefficient of determination (R2) higher than 0.9 in most cases. The new solution offers a potential approach to modeling groundwater recharge in existing groundwater models. In particular, this model can potentially provide a more realistic recharge estimate compared to the kinematic-wave approximation of Richards equation, that neglects upward flows through the vadose zone. Future research is needed to account for soil layering and root water uptake in the soil moisture-flux relationship. [ABSTRACT FROM AUTHOR]

Technological advances in satellite remote sensing have offered a variety of techniques for estimating surface soil water content as a key variable in numerous environmental studies. Optical methods are particularly valuable for remote sensing of soil moisture since reflected solar radiation is the strongest passive signal available to satellites and thus observations at optical wavelengths are capable of providing high spatial resolution data. Since remote sensors do not measure soil water content directly, mathematical algorithms that describe the connection between the measured signal and surface water content must be derived. Here, we present a physically-based soil moisture retrieval model in the solar domain (350–2500 nm) that is based on the Kubelka–Munk two-flux radiative transfer theory. The model is designed to describe diffuse reflectance from a uniform, optically thick, absorbing and scattering medium. The theory suggests a linear relationship between a transformed reflectance and soil water content in the short wave infrared bands (e.g. band 7 of Landsat and MODIS satellites) providing an easy-to-use algorithm in these bands. Accuracy of this model was tested and preliminarily verified using laboratory-measured spectral reflectance data of different soils. Further studies on potentials and challenges of this model for large-scale application using optical satellites data remain a topic of ongoing research. [ABSTRACT FROM AUTHOR]

Monitoring of surface soil moisture (SM) through microwave radiometry typically relies on the inversion of a radiative transfer model. Conventional inversion algorithms require proper calibration of surface roughness and radiometric parameters of the overlying canopy, including the scattering albedo and optical depth. However, uncertainty in global characterization of these parameters is one of the main sources of error in satellite SM retrievals. To cope with this uncertainty, this paper presents a new algorithm, called "temporal polarization ratio algorithm" (TPRA), that enables retrieval of SM independent of surface roughness and vegetation parameters. This approach uses the temporal differences of polarized emissivity observations assuming that the surface roughness and vegetation parameters are invariant over a window of time. Unlike the classical dual channel algorithms (DCA), TPRA is not only free of surface calibration but also robust to systematical errors arising from surface soil temperature errors. One caveat is that the algorithm is unable to retrieve SM when surface emissivity does not change appreciably in time. The performance of TPRA is evaluated through several controlled numerical experiments and validated using the Soil Moisture Active Passive (SMAP) satellite retrievals as well as in-situ SM measurements over Australia. The results show improved SM retrievals compared with the DCA and SMAP official products as long as the surface emissivity changes sufficiently over the retrieval time period. • A new algorithm is designed for the retrieval of soil moisture at L-band. • Soil moisture retrievals are independent of surface roughness. • Soil moisture retrievals are independent of vegetation parameters. • Soil moisture retrievals are robust to systematical errors in soil temperature. [ABSTRACT FROM AUTHOR]

• We present an analytical model to map surface soil moisture using GRACE data. • The new model autonomously translates GRACE into relative surface soil moisture. • The new products provide useful information to improve microwave retrievals. Passive microwave radiometry from space through missions such as the Soil Moisture and Ocean Salinity (SMOS) and Soil Moisture Active Passive (SMAP) satellites is the most reliable means for mapping global surface soil moisture (SSM). Nonetheless, microwave SSM retrievals are uncertain over densely vegetated surfaces or areas with high radio frequency interference. This paper presents a new observationally driven approach to remote sensing of global SSM based on the terrestrial water storage anomaly (TWSA) data acquired from the Gravity Recovery and Climate Experiment (GRACE) satellite. This approach rests on a physically based, yet parsimonious, model based on the Richards' equation and the assumption that the TWSA temporal rate of change (dS/dt) approximates the land surface net water flux (NWF) as the surface boundary condition. The GRACE-based SSM is found to be in a reasonable agreement with in-situ data and highly correlated with the SMAP and SMOS retrievals, especially over wet regions where the assumption of NWF ≈ dS/dt holds valid. The GRACE retrievals contain new SSM information relative to the microwave satellite data and provide a potential solution to improve the microwave data over densely vegetated surfaces or areas with high radio frequency interference. [ABSTRACT FROM AUTHOR]

A new algorithm called combined constrained multi-channel algorithm (C-CMCA) is presented for simultaneous retrieval of soil moisture (SM) and vegetation optical depth (VOD) in L-band with improved resolution. Unlike widely used algorithms, the new approach optimally fuses multiple sources of surface temperatures into the inversion process and confines the retrievals to their feasible climatological range rather than the mean and paves the way to account for the slow changes of VOD through a Sobolev-norm regularization. Through controlled numerical experiments that assume a random error in the surface temperatures, it is shown that the algorithm can decrease the root mean squared error (RMSE) by 78% and 81% when compared with the unconstrained version and 54% and 7% when a single source of surface temperature is used in retrievals of SM and VOD, respectively. The use of the Sobolev-norm regularization decreases the RMSE by more than 25% at the expense of a negligible bias. Implementation of the algorithm, using data from the NASA's Soil Moisture Active Passive (SMAP) satellite in 2016, demonstrates that the monthly RMSE of SM retrievals improves by more than 6% when compared with the SMAP enhanced products considering the ground measurements from the International SM Network (ISMN) as a reference while the monthly RMSE of VOD decreases by more than 62% when compared with the VOD climatology used in the SMAP SCA products. Analysis of the results demonstrates that, without increasing the native resolution of radiometric observations, the information content of the a priori constraints cannot only improve resolutions of the retrievals but also make them robust to the background water contamination in the vicinity of coastal zones and over lowland floodplains. For example, over Florida peninsula, the annual SM RMSE and bias are reduced by more than 65%. • A priori climatological constraints reduce uncertainty of soil moisture retrievals. • Assimilating multiple soil temperatures into inversion leads to reduced uncertainty. • Sobolev-norm regularization improves retrievals of VOD in L-band. • Retrieval of soil moisture beyond SMAP native resolution is feasible. • C-CMCA soil moisture retrievals are robust to background water contamination. [ABSTRACT FROM AUTHOR]

Quantifying evapotranspiration (ET) in rainfed cropping systems can be challenging due to complicated interactions among site-specific soil, plant, and management factors. In Northeast China, ET and soil water status in maize fields often display strong spatial and temporal variations due to the changes in tillage practice, planting pattern, and maize plant density. Previous studies have shown that near-surface soil water content (θ) observations at multiple scales provide the potential to estimate surface soil water fluxes. In this study, we introduced a new method to estimate daily field ET by using a soil water flux model mainly based on the time-series of θ at a depth of 2.5 cm. The new method required a calibration of soil water diffusivity with maximum net water flux in the near-surface soil layer, which was related to precipitation redistribution below the canopy. Finally, the new method was evaluated using observed ET values over a 2-year period in a maize field, where independent measurements of soil water evaporation (E) and transpiration (T) were made with heat-pulse sensors and sap-flow gauges, respectively. Field observations showed that E dominated water loss during the seedling stage (16% of total ET). As the canopy was fully developed, E sharply decreased to a value of 0.4 mm d−1, and T accounted for about 89% of ET since the silking stage. The new method to estimate ET performed well in drying periods, while it tended to underestimate ET in wet periods with substantial infiltration into the surface layer. On rain-free days, the ET values estimated with the new method matched well with the measured E + T values, with R2 and RMSE values of 0.85 and 1.93 mm d−1. Therefore, the new approach provides an effective way to quantify maize ET. • A new method is proposed to estimate evapotranspiration (ET) in a maize field. • New method requires measurements on meteorological, plant and soil parameters. • ET of maize field was partitioned using heat pulse and sap flow techniques. • New method gives accurate daily ET compared against independent measurements. [ABSTRACT FROM AUTHOR]

Near-surface soil conditions (i.e., moisture and temperature) moderate mass and energy exchange at the soil-atmosphere interface. While remote sensing offers an effective means for mapping near-surface moisture content across large areas, in situ measurements, targeting those specific remotely sensed soil depths, are poorly understood and high-resolution near-surface measurement capabilities are lacking. Time domain reflectometry (TDR) is a well-established, accurate measurement method for soil dielectric permittivity and moisture content. A TDR array was designed to provide centimeter-resolution measurements of near-surface soil moisture. The array consists of nine stainless steel TDR rods spaced 1 cm apart, acting as waveguide pairs to form eight two-rod TDR probes in series. A critical aspect of the design was matching the spacing of the coaxial cable-TDR rod transition to avoid unwanted reflections in the waveforms. The accuracy of the TDR array permittivity measurement (±1 permittivity unit) was similar to that of conventional TDR as verified in dielectric liquids. Electric field numerical simulations showed minimal influence of adjacent rods during a given rod-pair measurement. The evaporation rate determined by the TDR array compared well with mass balance data in a laboratory test. Near-surface soil moisture profile dynamics were monitored at centimeter-depth resolution using the TDR array in a field experiment where volumetric moisture content estimates (0-8 cm) were within 2% of conventional three-rod TDR probes averaging across 0 to 8 cm and from 1- to 3-cm depths. [ABSTRACT FROM AUTHOR]

A novel method for scaling water flow and solute transport during soil-water redistribution was developed. The scaled results are invariant for a broad range of soil textures and initial conditions. The scaling method gives insight regarding transport processes and provides an approximate solution of the highly non-linear governing equations. A recently developed method for scaling Richards' equation during soil-water redistribution was extended here to derive invariant solutions for solute transport for a range of soils and initial conditions. Any arbitrary model for hydraulic properties can be used in Richards' equation. A transport model including both terms of convection and diffusion/dispersion is considered where the soil-solute reactions described by a linear sorption isotherm are considered. To evaluate the proposed method, Hydrus-1D simulations were used to solve the water flow and solute transport equations for various soil textures and initial conditions. The resulting soil water content and solute concentrations profiles were scaled using the proposed method. The scaled results were nearly invariant for loam to clay soil textures and for a variety of initial conditions. The invariance of the scaled results implies a robust approach to scaling water flow and solute transport during the redistribution process and readily produces approximate solutions of the highly nonlinear governing equations. [ABSTRACT FROM AUTHOR]

Recently, large efforts have been made to develop surface soil moisture (SSM) products based on observations from passive microwave L-band satellites at the global scale. Despite vast previous efforts to assess satellite SSM products based on in situ data, the performance of L-band SSM products remains still little known over the tropical areas including the rainforests. To close this knowledge gap, a comprehensive evaluation of five L-band SSM products from the Soil Moisture and Ocean Salinity (SMOS) and Soil Moisture Active Passive (SMAP) satellites, including SMOS-IC, SMAP SCA-V, DCA, and MTDCA, as well as the recently developed SMAP INRAE-BORDEAUX (SMAP-IB) was conducted in tropical areas from 2015 to 2020. The investigation was implemented by using in situ observations, and expanded by the Triple Collocation Analysis (TCA) and double instrumental variable (IVd) methods. The results revealed that all the five L-band SSM products show a good capacity to estimate SSM in most moderately vegetated areas of the tropical areas while they exhibit some uncertainties in dense vegetation (e.g., rainforests). The SSM climatology of the five L-band products in rainforests agreed generally well with that of in situ measurements especially for SMOS-IC (R = 0.75), followed by SMAP-IB (R = 0.72). Notably, the newly developed SMAP-IB shows satisfactory performance in the tropics with the highest R of 0.73 and the smallest unbiased root mean square difference (ubRMSD) of 0.041 m3/m3 from the study based on in situ SM data, and R of >0.81 from the combined TCA/IVd method in most vegetation conditions. SMAP-DCA SSM demonstrates comparable performance to SMAP-SCA-V (R = 0.66) from the in situ based analysis. This study is expected to deepen our understanding on the skill of L-band SSM products in tropical areas and further promote possible upgrades in algorithms and applications in these regions. • Five L-band SSM products were evaluated over the tropics. • The newly developed SMAP-IB showed satisfying performances. • The combined TCA/IVd and in situ analyses led to consistent results. • L-band SSM shows acceptable climatology results in rainforests. [ABSTRACT FROM AUTHOR]

NASA's Soil Moisture Active Passive (SMAP) satellite mission has been providing high-quality global estimates of soil moisture (SM) and vegetation optical depth (VOD) using L-band radiometry since 2015. To date, a variety of retrieval algorithms as well as surface roughness and scattering albedo have been developed. However, a comprehensive evaluation of different algorithms with the new surface parameters across diverse biomes, climates, and terrain slopes is lacking. To narrow down this knowledge gap, here we examine the performance of various existing algorithms, including V-pol Single Channel Algorithms (SCA-V), H-pol Single Channel Algorithms (SCA H), classic DCA, extended DCA (E -DCA), regularized DCA (RDCA), land parameter retrieval model (LPRM), multi-temporal DCA (MT-DCA), constrained multi-channel algorithm (CMCA), and spatially constrained multi-channel algorithm (S-CMCA). The SM estimates are evaluated against in-situ measurements from the International Soil Moisture Network (ISMN) while VOD estimates are compared with the two-band enhanced vegetation index (EVI2), tree height, and aboveground biomass. This study has led to several important findings: (1) The overall bias, root mean square error (RMSE), and unbiased RMSE (ubRMSE) of SM estimates from different algorithms generally increase with vegetation density while their temporal correlations with in-situ measurements decrease as the terrain slope increases. (2) The divergence between different SM estimates is relatively larger over forested areas than non-forested areas. (3) In terms of temporal correlation with in-situ measurements, the SCA-V and RDCA outperform other algorithms over most land cover types and climates. (4) SCA-H typically underestimates SM more compared to other algorithms across sparsely vegetated areas and most climates. (5) The ubRMSE values demonstrate that all algorithms have close performance when EVI2 is less than 0.3; however, the performance of classic DCA decays notably when EVI2 exceeds 0.3. (6) VOD retrievals from RDCA exhibit improved spatial correlations with EVI2, tree height, and aboveground biomass across the globe compared to other algorithms. Overall, RDCA exhibits a good compromise between the high performance of SM and VOD. • Performances of SM and VOD from nine algorithms are evaluated. • The overall bias, RMSE, ubRMSE of SM estimates increase with vegetation density. • The divergence between SM estimates is larger over forested areas than non-forested areas. • RDCA exhibits a good compromise between the high performance of SM and VOD. [ABSTRACT FROM AUTHOR]