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A lack of high‐density rootzone soil moisture (θRZ) observations limits the estimation of continental‐scale, space‐time contiguous θRZ dynamics. We derive a proxy of daily θRZ dynamics — active rootzone degree of saturation (SRZ) — by recursive low‐pass (LP) filtering of surface soil moisture (θS) within a terrestrial water‐energy coupling (WEC) framework. We estimate the LP filter parameters and WEC thresholds for the piecewise‐linear coupling between SRZ and evaporative fraction (EF) at remote sensing and field scale over the Contiguous U.S. We use θS from the Soil Moisture Active‐Passive (SMAP) satellite and 218 in‐situ stations, with EF from the Moderate Resolution Imaging Spectroradiometer. The estimated SRZ compares well against SMAP Level‐4 estimates and in‐situ θRZ, at the corresponding scale. The instantaneous hydrologic state (SRZ) vis‐à‐vis the WEC thresholds is proposed as a rootzone soil moisture stress index (SMSRZ) for near‐real‐time operational agricultural drought monitoring and agrees well with established drought metrics. Plain Language Summary Rootzone soil moisture plays a vital role in agricultural, hydrological, and ecosystem processes. The available spaceborne satellites for monitoring soil moisture can only capture variability in a shallow soil layer at the surface, typically limited to the top 5 cm. Hence, spatiotemporally continuous estimation of rootzone soil moisture dynamics typically relies on soil moisture estimates from land‐surface models, which are subject to errors in the surface meteorological forcing data, process formulations, and model parameters. Some studies suggest that the rootzone soil moisture dynamics can be estimated by filtering the high‐frequency variability in the surface soil moisture. However, such “filters” require observed rootzone data (often unavailable at high spatial density) for calibration. This study uses the relationship between surface soil moisture and evaporative fraction derived using spaceborne observations from the Soil Moisture Active Passive mission and the Moderate Resolution Imaging Spectroradiometer to estimate rootzone soil moisture dynamics for the Contiguous U.S. at 9 km grid resolution. We further demonstrate that this approach can be extended into a near‐real‐time agricultural drought monitor to assess drought impacts on vegetation using surface soil moisture observations. Key Points Terrestrial water‐energy coupling is used to parameterize low‐pass filter to estimate rootzone dynamics from surface soil moisture Rootzone degree of saturation and water‐energy coupling thresholds are estimated using evaporative fraction and surface soil moisture SMAP‐based rootzone degree of saturation can used for operational, near‐real‐time agricultural drought monitoring over Contiguous U.S

Multiangle and multipolarization L-band microwave observations from the Soil Moisture Ocean Salinity (SMOS) mission are assimilated into the Goddard Earth Observing System Model, version 5 (GEOS-5), using a spatially distributed ensemble Kalman filter. A variant of this system is also used for the Soil Moisture Active Passive (SMAP) Level 4 soil moisture product. The assimilation involves a forward simulation of brightness temperatures (Tb) for various incidence angles and polarizations and an inversion of the differences between Tb forecasts and observations into updates to modeled surface and root-zone soil moisture, as well as surface soil temperature. With SMOS Tb assimilation, the unbiased root-mean-square difference between simulations and gridcell-scale in situ measurements in a few U.S. watersheds during the period from 1 July 2010 to 1 July 2014 is 0.034 m³ m−3 for both surface and root-zone soil moisture. A validation against gridcell-scale measurements and point-scale measurements from sparse networks in the United States, Australia, and Europe demonstrates that the assimilation improves both surface and root-zone soil moisture results over the open-loop (no assimilation) estimates in areas with limited vegetation and terrain complexity. At the global scale, the assimilation of SMOS Tb introduces mean absolute increments of 0.004 m³ m−3 to the profile soil moisture content and 0.7K to the surface soil temperature. The updates induce changes to energy fluxes and runoff amounting to about 15% of their respective temporal standard deviation.

Reanalysis soil moisture products are valuable for diverse applications, but their quality assessment is limited due to scale discrepancies when compared to traditional in situ point-scale measurements. The emergence of cosmic ray neutron sensors (CRNSs) with field-scale soil moisture estimates (∼ 250 m radius, up to 0.7 m deep) is more suitable for the product evaluation owing to their larger footprint. In this study, we perform a comprehensive evaluation of eight widely used reanalysis soil moisture products (ERA5-Land, CFSv2, MERRA2, JRA55, GLDAS-Noah, CRA40, GLEAM and SMAP L4 datasets) against 135 CRNS sites from the COSMOS-UK, COSMOS-Europe, COSMOS USA and CosmOz Australia networks. We evaluate the products using six metrics capturing different aspects of soil moisture dynamics. Results show that all reanalysis products generally exhibit good temporal correlation with the measurements, with the median temporal correlation coefficient (R) values spanning 0.69 to 0.79, though large deviations are found at sites with seasonally varying vegetation cover. Poor performance is observed across products for soil moisture anomalies time series, with R values varying from 0.46 to 0.66. The performance of reanalysis products differs greatly across regions, climate, land covers and topographic conditions. In general, all products tend to overestimate data in arid climates and underestimate data in humid regions as well as grassland. Most reanalysis products perform poorly in steep terrain. Relatively low temporal correlation and high bias are detected in some sites from the west of the UK, which might be associated with relatively low bulk density and high soil organic carbon. Overall, ERA5-Land, CRA40, CFSv2, SMAP L4 and GLEAM exhibit superior performance compared to MERRA2, GLDAS-Noah and JRA55. We recommend that ERA5-Land and CFSv2 could be used in humid climates, whereas SMAP L4 and CRA40 perform better in arid regions. SMAP L4 has good performance for cropland, while GLEAM is more effective in shrubland regions. Our findings also provide insights into directions for improvement of soil moisture products for product developers.

Cosmic-ray neutron sensing (CRNS) allows for non-invasive soil moisture estimations at the field scale. The derivation of soil moisture generally relies on secondary cosmic-ray neutrons in the epithermal to fast energy ranges. Most approaches and processing techniques for observed neutron intensities are based on the assumption of homogeneous site conditions or of soil moisture patterns with correlation lengths shorter than the measurement footprint of the neutron detector. However, in view of the non-linear relationship between neutron intensities and soil moisture, it is questionable whether these assumptions are applicable. In this study, we investigated how a non-uniform soil moisture distribution within the footprint impacts the CRNS soil moisture estimation and how the combined use of epithermal and thermal neutrons can be advantageous in this case. Thermal neutrons have lower energies and a substantially smaller measurement footprint around the sensor than epithermal neutrons. Analyses using the URANOS (Ultra RApid Neutron-Only Simulation) Monte Carlo simulations to investigate the measurement footprint dynamics at a study site in northeastern Germany revealed that the thermal footprint mainly covers mineral soils in the near-field to the sensor while the epithermal footprint also covers large areas with organic soils. We found that either combining the observed thermal and epithermal neutron intensities by a rescaling method developed in this study or adjusting all parameters of the transfer function leads to an improved calibration against the reference soil moisture measurements in the near-field compared to the standard approach and using epithermal neutrons alone. We also found that the relationship between thermal and epithermal neutrons provided an indicator for footprint heterogeneity. We, therefore, suggest that the combined use of thermal and epithermal neutrons offers the potential of a spatial disaggregation of the measurement footprint in terms of near- and far-field soil moisture dynamics.

We develop a deep learning–based convolutional-regression model that estimates the volumetric soil moisture content in the top ∼5 cm of soil. Input predictors include Sentinel-1 (active radar) and Sentinel-2 (multispectral imagery), as well as geophysical variables from SoilGrids and modeled soil moisture fields from SMAP and GLDAS. The model was trained and evaluated on data from ∼1000 in situ sensors globally over the period 2015–21 and obtained an average per-sensor correlation of 0.707 and ubRMSE of 0.055 m 3 m −3 , and it can be used to produce a soil moisture map at a nominal 320-m resolution. These results are benchmarked against 14 other soil moisture evaluation research works at different locations, and an ablation study was used to identify important predictors.

Droughts occur naturally, but climate change has generally accelerated the hydrological processes to make them set in quicker and become more intense, with many consequences, not the least of which is increased wildfire risk. There are different types of drought being studied, such as meteorological, agricultural, hydrological, and socioeconomic droughts; however, a lack of unanimous definition complicates drought study. Drought indices are used as proxies to track and quantify droughts; therefore, accurate formulation of robust drought indices is important to investigate drought characteristics under the warming climate. Because different drought indices show different degrees of sensitivity to the same level of continental warming, robustness of drought indices against change in temperature and other variables should be prioritized. A formulation of drought indices without considering the factors that govern the background state may lead to drought artifacts under a warming climate. Consideration of downscaling techniques, availability of climate data, estimation of potential evapotranspiration (PET), baseline period, non-stationary climate information, and anthropogenic forcing can be additional challenges for a reliable drought assessment under climate change. As one formulation of PET based on temperatures can lead to overestimation of future drying, estimation of PET based on the energy budget framework can be a better approach compared to only temperature-based equations. Although the performance of drought indicators can be improved by incorporating reliable soil moisture estimates, a challenge arises due to limited reliable observed data for verification. Moreover, the uncertainties associated with meteorological forcings in hydrological models can lead to unreliable soil moisture estimates under climate change scenarios.

Above-ground cosmic-ray neutron sensing (CRNS) allows for the non-invasive estimation of the field-scale soil moisture content in the upper decimetres of the soil. However, large parts of the deeper vadose zone remain outside of its observational window. Retrieving soil moisture information from these deeper layers requires extrapolation, modelling or other methods, all of which come with methodological challenges. Against this background, we investigate CRNS for downhole soil moisture measurements in deeper layers of the vadose zone. To render calibration with in situ soil moisture measurements unnecessary, we rescaled neutron intensities observed below the terrain surface with intensities measured above a waterbody. An experimental set-up with a CRNS sensor deployed at different depths of up to 10 m below the surface in a groundwater observation well combined with particle transport simulations revealed the response of downhole thermal neutron intensities to changes in the soil moisture content at the depth of the downhole neutron detector as well as in the layers above it. The simulation results suggest that the sensitive measurement radius of several decimetres, which depends on soil moisture and soil bulk density, exceeds that of a standard active neutron probe (which is only about 30 cm). We derived transfer functions to estimate downhole neutron signals from soil moisture information, and we describe approaches for using these transfer functions in an inverse way to derive soil moisture from the observed neutron signals. The in situ neutron and soil moisture observations confirm the applicability of these functions and prove the concept of passive downhole soil moisture estimation, even at larger depths, using cosmic-ray neutron sensing.

Accurate subsurface soil moisture (SM) estimation is critical for vegetation growth, drought monitoring, and climate change mitigation, yet remains a significant challenge. Previous data assimilation (DA) approaches are limited to only surface SM assimilation. In this study, we utilized the proxy subsurface SM estimated via the exponential filter method (ExpF) as another assimilation variable in our 2‐dimensional DA. Meanwhile, the dual updating DA scheme was implemented to simultaneously update model parameters and states. The two DA pathways were incorporated into the proposed framework (DA_1E_D), which enhanced the subsurface SM accuracy, with the effects of 2‐dimensional assimilation being more significant. Under 2‐dimensional DA, the information transfer between layers was more accurately characterized, leading to overall improvements with unbiased root‐mean‐square error (ubRMSE) reductions of 0.015 and 0.005 m3 · m−3, and Kling–Gupta efficiency (KGE) increases of 0.248 and 0.067 for surface and subsurface SM, respectively, across five SM networks. The soil thickness (d2) and hydraulic conductivity exponent (expt2) are the most influential parameters affecting subsurface SM dynamics through model propagation. DA_1E_D also outperformed ExpF in subsurface SM accuracy, particularly in SM networks with weak surface‐subsurface correlation, achieving an average ubRMSE reduction of 0.003 m3 · m−3 and an average KGE increase of 0.202. It was also applied to Soil Moisture Active Passive data at regional scale, demonstrating significant improvements. The model surface‐subsurface SM coupling was adjusted toward the actual coupling after subsurface assimilation and dual updating. This study may provide new insights into the diagnosis and refinements of the model representation of surface‐subsurface processes. Plain Language Summary Soil moisture (SM) is a key variable in the fields of hydrology, ecology, and agriculture, and in characterizing Earth's climate. Particularly, accurate subsurface SM estimation remains challenging for the Earth science community. Here, we used the empirical subsurface SM estimates derived from an exponential filter method (ExpF) as another assimilation variable, in combination with the data assimilation (DA) technique which updates model states and parameters simultaneously, to constrain the model estimates. We found these two pathways lead to significant improvements in subsurface SM estimation. The accuracy of subsurface SM was improved with biases (ubRMSE) significantly reduced by 0.005 m3 · m−3 on average over the modeling grid cells, due to the assimilation of empirical subsurface SM. The effects of parameter updating were relatively smaller but also positive. The proposed DA framework demonstrated superior accuracy to ExpF, with an average ubRMSE reduction of 0.003 m3 · m−3 and an average KGE increase of 0.202. This framework was also applicable to regional scale using satellite products. Moreover, both pathways proved effective in adjusting the physical surface‐subsurface correlation in the land surface model toward actual coupling. Our analysis has important implications for future studies on estimating subsurface SM using land surface models at larger spatiotemporal scales. Key Points A 2‐dimensional data assimilation (DA) framework which accounted for a dual state‐parameter updating scheme was proposed The accuracy of subsurface soil moisture (SM) was improved by assimilating empirical subsurface estimates and dual updating The proposed DA framework can also be applied to Soil Moisture Active Passive data to improve subsurface SM estimation at regional scale

Cosmic ray neutron sensors (CRNSs) are state-of-the-art tools for field-scale soil moisture measurements, yet uncertainties persist due to traditional methods for estimating scaling parameters that lack the capacity to account for site-specific and sensor-specific characteristics. This study introduces a novel, data-driven approach to estimate key scaling parameters (beta, psi, and omega) by directly calculating scaling parameters from measurement data, emphasizing local environmental factors and sensor attributes. The method demonstrates reliability and robustness, with strong correlations between estimated scaling parameters and environmental factors such as cutoff rigidity, latitude, and elevation, as well as consistency with semi-analytical traditional methods, e.g. for beta an R2 of 0.46. The study also reveals systematically higher variability in calibration parameters than previously assumed, underscoring the importance of this new method, of data quality, and of the careful selection of Neutron Monitor Database (NMDB) reference sites. The new method reduces RMSE by up to 25 %, with differences in soil moisture estimates between traditional and data-driven methods reaching 0.04 m3 m−3 and up to 0.12 m3 m−3 under certain conditions. Sensitivity analysis shows that soil moisture estimation is most influenced by scaling parameters in the wet end of the soil moisture spectrum. By improving the accuracy of CRNS data, this approach enhances soil moisture estimation and supports better decisions in agriculture, hydrology, and climate monitoring. Future research should focus on refining these scaling methods and enhancing data quality to further improve CRNS measurement accuracy.

Most studies using cosmic‐ray neutron sensors (CRNS) for soil moisture estimation use high‐energy neutron monitor observations to correct for changes in incoming neutron intensity, but there is interest in over‐water CRNS observations and muon observations for such purposes. This study compares these approaches with a focus on observations from an over‐water pontoon‐based CRNS system. Pontoon and neutron monitor intensity comparisons showed similar responses with the best statistical agreement when neutron monitor observations were from locations of similar cutoff rigidity or when scaling for geomagnetic and elevational effects were applied. Comparison of historic variations in neutron monitor and muon detector intensity, and more recent observations from the pontoon, revealed temporal differences and weaker short‐term responses from the muon detector. Time‐delays in intensity correction for the pontoon and neutron monitors were observed during a Forbush decrease and through cross‐correlation analysis over the comparison period with delays likely a result of longitudinal differences. Pontoon neutron intensity exhibited slightly higher amplitudes over the study period. Some of this was related to periods of irregular water vapour distribution in the atmosphere where current humidity corrections appear insufficient. Application of intensity corrections to soil moisture estimates illustrated the increasing importance of accurate corrections with decreasing cutoff rigidity and increasing elevation. The impact of neutron intensity correction was greatest for wet soil conditions at low cutoff rigidity sites at higher elevations. Over‐water CRNS observations offer a means to correct CRNS observations with the advantages of being locally managed, locally applicable, and directly relevant to CRNS energy spectra.