Accurate field-scale estimation and mapping of root-zone soil moisture is critical for precision irrigation management and optimizing crop yield, especially in Florida's sandy agroecosystems where low water-holding capacity and high nutrient leaching increase irrigation challenges. While microwave satellites provide soil moisture at large scale, their coarse resolution (km scale) and surface (0-5 cm) estimates limit their application for within-field irrigation decisions. In this study, we developed and evaluated a field-scale mapping framework that integrates a Utility Terrain Vehicle (UTV)-mounted dual-polarized L-band (1.4 GHz) radiometer with i) the tau-omega radiative transfer model, and ii) a hybrid (tau-omega-XGBoost) approach that integrates tau-omega outputs with extreme gradient boosting to estimate and map soil moisture at 10, 20, 30, and 40 cm depths. The framework combines temporal brightness temperature observations with ancillary variables (e.g., vegetation water content, effective soil temperature) to parameterize tau-omega model at the field-scale. The resulting estimates were then assimilated into extreme gradient boosting (XGBoost) as additional predictors and physical constraints to improve retrieval accuracy. Results indicated that tau-omega and hybrid approaches produced mean RMSE of similar to 0.02-0.04 cm(3) cm(-3), with best performance at upper depths and vertical polarization outperforming horizontal polarization. Allowing spatial variability in surface roughness improved tau-omega model parameterization and retrieval accuracy. The hybrid model slightly outperformed the tau-omega model, especially at deeper depths (mean unbiased RMSE of 0.020 cm(3) cm(-3)). Overall, the proposed framework not only provides a mesoscale bridge between point sensors and satellite pixels for field-scale mapping of root-zone soil moisture to support irrigation management in sandy agroecosystems, but it can also benefit airborne- and satellite-based soil moisture retrievals.

Accurate spatial quantification of soil organic carbon (SOC) stocks and their associated uncertainties is vital for climate mitigation and sustainable grazing management. However, regional SOC mapping remains challenging due to complex soil–landscape interactions, dynamic environmental processes, and limited field observations. This study advances regional SOC mapping by developing a parsimonious, uncertainty-aware digital soil mapping framework to estimate topsoil (0–20 cm) SOC stocks across Florida’s grazing lands at 30 m resolution. Grounded in the STEP–AWBH soil-forming framework, the approach explicitly operationalizes time-invariant and dynamic controls through spatiotemporal feature construction. SOC stocks were modeled using Quantile Regression Forests to generate point estimates and 90 % prediction intervals, with model performance evaluated using spatial cross-validation. A baseline model trained with contemporary samples achieved modest results (R2 = 0.45; RMSE = 8.92 t ha−1), whereas cross-temporal spiking with legacy grazing land samples improved both point prediction accuracy (R2 = 0.57; RMSE = 7.88 t ha−1) and uncertainty estimation, yielding better-calibrated prediction intervals and a lower continuous ranked probability score. In contrast, unconstrained spiking across heterogeneous land uses degraded model performance, underscoring the importance of land-use compatibility and target-domain relevance when integrating legacy soil data. SHAP-based interpretation revealed that SOC variability was associated with pedological properties, including available water capacity and soil order, and temporal signatures of hydroclimate, soil moisture dynamics, and vegetation phenology related to persistence, variability, and long-term trends. Total topsoil SOC storage for Florida’s grazing lands was estimated at 33.95 Tg, with a mean stock of 19.12 t ha−1. The resulting high-resolution SOC stock and uncertainty maps are hosted on an open-access interactive GIS platform, providing a robust baseline for monitoring and managing soil carbon in subtropical grazing systems.

Florida’s sandy soils have low nitrogen (N) retention and are prone to leaching. Corn (Zea mays L.) grown under these conditions with fixed N fertilizer rates makes recommendations unreliable, highlighting the need for sensor-based tools to predict N status in the middle of the season. This study evaluated whether combining canopy optical reflectance-based indices with leaf chlorophyll proximal sensing improves the prediction of corn grain yield (CGY). A three-year irrigated field experiment (2022–2024) evaluated six N rates (0–392 kg N ha⁻¹ in 78.5 kg increments) and an additional seventh rate of 471 kg N ha⁻¹ in 2024 using a randomized complete block design with four replications. Proximal sensing measurements of NDVI, NDRE, and SPAD were collected at 20, 34, 41, and 62 days after planting (DAP), corresponding to growth stages V4 to V14. Six models [multiple linear regression (MLR), partial least squares regression (PLSR), random forest (RF), extreme gradient boosting (XGBoost), least absolute shrinkage and selection operator (LASSO), and ridge regression] were compared using a 5 × 5 nested cross-validation framework to ensure unbiased model comparisons. Model performance was comparable, with test R² values of 0.60–0.74 and RMSE of 2552–3157 kg ha⁻¹. PLSR, Lasso, and MLR achieved the highest and most stable predictive accuracy (R² = 0.73–0.74), whereas RF and XGBoost exhibited greater variability across validation folds. These results demonstrate the potential of proximal sensing-based prediction of CGY for improving in-season N management in sandy agroecosystems.

Groundwater is a vital resource that supports drinking water supplies, agriculture, and ecosystems worldwide, but it is increasingly threatened by overexploitation, contamination, and urbanization. Florida is particularly vulnerable due to its shallow Surficial Aquifer System (SAS), karst geology, widespread septic systems, agricultural activities, and climate-related hazards. Groundwater vulnerability maps are important tools for identifying high-risk areas and guiding protection strategies. While deep learning models such as Convolutional Neural Networks (CNNs) and U-Net have improved mapping accuracy by capturing complex spatial patterns, their limited interpretability can reduce stakeholder confidence and practical adoption.This study presents an interpretable groundwater vulnerability mapping framework that combines CNN and U-Net models with the SHapley Additive exPlanations (SHAP) method. The analysis was conducted across Florida using seven DRASTIC parameters: depth to water, net recharge, aquifer media, soil media, topography, vadose zone impact, and hydraulic conductivity. Both models showed strong predictive performance, with AUROC values of 88.52% for CNN and 89.16% for U-Net. SHAP analysis identified depth to water and topography as the most influential factors, with shallow groundwater conditions substantially increasing vulnerability. The results demonstrate the value of explainable AI for transparent groundwater vulnerability assessment and aquifer protection planning.

Accurate soil moisture (SM) information at sub-kilometer resolution is essential for hydrologic modeling, weather forecasting, precision irrigation, and drought monitoring. Satellite missions such as Soil Moisture Active Passive (SMAP) have transformed global SM monitoring, but their coarse resolution, shallow sensing depth, and multi-day revisit cycle limit local-scale applications. Retrieval accuracy further declines over dense vegetation and complex terrain. This study presents a deep learning downscaling framework that integrates convolutional neural networks and long short-term memory networks (CNN-LSTM) to produce daily SM maps at 100 m resolution for surface (5 cm) and root-zone (20 cm) depths. The model was trained with seven years of SM data from similar to 650 stations across the U.S., using dynamic predictors from SMAP and MODIS (i.e., brightness temperature, roughness coefficient, surface reflectance in red, near-infrared, and shortwave infrared) and static features predictors from elevation, land cover, and Soil Landscapes of the U.S. (SOLUS100) databases. Compared to SMAP level-3 SM, the CNN-LSTM improved surface SM accuracy, with median correlation coefficient (R) increasing from 0.61 to 0.82, unbiased root mean square error (ubRMSE) decreasing from 0.06 to 0.05 cm(3) cm(-3), and Kling-Gupta Efficiency (KGE) increasing from 0.30 to 0.61. Root-zone SM achieved R similar to 0.72, ubRMSE similar to 0.05 cm(3) cm(-3) and KGE similar to 0.53. The model also outperformed SMAP in forested and mountainous areas, capturing SMAP sub-pixel SM variability across the three well-instrumented watersheds and Florida. In Florida, where no training data was used, performance decreased but was still better than SMAP. These findings indicate that the CNN-LSTM framework bridges the spatial resolution gap in SM products and support small scale agricultural and hydrological applications.

Accurate monitoring of soil profile water content is essential for agronomic and environmental applications. Time domain reflectometry (TDR) sensors estimate volumetric water content from bulk soil dielectric permittivity. The SoilVUE10 is a high-frequency TDR sensor designed to measure volumetric water content at multiple depths. However, its performance under varying soil types, irrigation systems, and installation methods is not well documented. We evaluated the performance of the 50-cm-long SoilVUE10 sensor at depths of 5, 10, 20, 30, 40, and 50 cm by comparison with gravimetric measurements and Acclima TDR (315H and 310S) estimates in two field experiments encompassing different soil types, irrigation systems (subsurface drip and flood irrigation), and sensor installation methods. Results showed that the performance of the SoilVUE10 in soils with high clay content (>22%) was strongly affected by installation method. In fine-textured soils (sandy clay and sandy clay loam), the manufacturer-recommended installation method under drip irrigation led to large underestimations, likely due to the formation of a thin compacted soil layer around the sensor waveguides, which impeded proper hydraulic contact between the soil and sensor. In soils with low clay content (sandy loam), the SoilVUE10, when installed using the manufacturer-recommended method under flood irrigation, showed slightly better agreement with gravimetric data (root mean square error [RMSE] 0.038 cm(3) cm(-3)) than the Acclima TDR (RMSE = 0.044 cm(3) cm(-3)) across all depths. Installation by auguring a larger borehole and backfilling with parent soil improved sensor-soil hydraulic contact and reduced the RMSE by 27% in soils with low clay content.

Widely used models for the soil water characteristic (SWC), like the van Genuchten model, are primarily based on the assumption that soil pores resemble bundles of cylindrical capillary tubes. While such models effectively describe the wet part of a SWC, they often fail to accurately represent the dry part where water retention is primarily governed by surface adsorptive forces rather than capillary forces. To address this limitation, many have developed alternative models that incorporate additional parameters or mechanisms to better characterize the dry end. Here we propose a novel first-order continuous mathematical expression that modifies the van Genuchten model without adding any additional fitting parameters, covering the entire SWC range from full saturation to oven dry conditions. The new Improved van Genuchten (IvG) function maintains the simplicity of the classical model while significantly improving its ability to represent the dry end of the water content spectrum. We evaluated the new expression using water retention data for a wide range of soil textures from sand to clay. We further integrated the new function with the Mualem hydraulic conductivity model to numerically calculate the unsaturated hydraulic conductivity, yielding reasonable estimates across diverse soil types.

Accurate real-time information about soil moisture (SM) at a large scale is essential for improving hydrological modeling, managing water resources, and monitoring extreme weather events. This study presents a framework using convolutional long short-term memory (ConvLSTM) network to produce short- (1, 3, and 7 days ahead) and mid-term (14 and 30 days ahead) forecasts of SM at surface (0–10 cm) and subsurface (10–40 and 40–100 cm) soil layers across the contiguous U.S. The model was trained with five-year period (2018–2022) datasets including Soil Moisture Active Passive (SMAP) level 3 ancillary covariables, North American Land Data Assimilation System phase 2 (NLDAS-2) SM product, shortwave infrared reflectance from Moderate Resolution Imaging Spectroradiometer (MODIS), and terrain features (e.g., elevation, slope, curvature), as well as soil texture and bulk density maps from the Soil Landscape of the United States (SOLUS100) database. To develop and evaluate the model, the dataset was divided into three subsets: training (January 2018–January 2021), validation (2021), and testing (2022). The outputs were validated with observed in situ data from the Soil Climate Analysis Network (SCAN) and the United States Climate Reference Network (USCRN) soil moisture networks. The results indicated that the accuracy of SM forecasts decreased with increasing lead time, particularly in the surface (0–10 cm) and subsurface (10–40 cm) layers, where strong fluctuations driven by rainfall variability and evapotranspiration fluxes introduced greater uncertainty. Across all soil layers and lead times, the model achieved a median unbiased root mean square error (ubRMSE) of 0.04 cm3 cm−3 with a Pearson correlation coefficient of 0.61. Further, the performance of the model was evaluated with respect to both land cover and soil texture databases. Forecast accuracy was highest in coarse-textured soils, followed by medium- and fine-textured soils, likely because the greater penetration depth of microwave observations improves SM retrieval in sandy soils. Among land cover types, performance was strongest in grasslands and savannas and weakest in dense forests and shrublands, where dense vegetation attenuates the microwave signal and reduces SM estimation accuracy. These results demonstrate that the ConvLSTM framework provides skillful short- and mid-term forecasts of surface and subsurface soil moisture, offering valuable support for large-scale drought and flood monitoring.

The vadose zone—the variably saturated, near‐surface environment that is critical for ecosystem services such as food and water provisioning, climate regulation, and infrastructure support—faces increasing pressures from both anthropogenic and natural factors, including changing climatic conditions. A more comprehensive understanding of vadose zone processes and interactions is imperative to effectively address these challenges and safeguard water and soil resources. This review outlines selected key issues, knowledge gaps, and research opportunities across six thematic sections. Each section presents a problem statement, a summary of recent innovations, and a compilation of emerging challenges and study opportunities. The selected topics include scaling and modeling of vadose zone properties and processes, soil moisture monitoring initiatives, surface energy balance, interplay between preferential water flow paths and biogeochemical processes, interactions between fires and vadose zone dynamics, and emerging contaminants and their fate in the vadose zone. This overview is intended to serve as a compendium of vadose zone science that encompasses both insights gained from prior research and anticipated needs for the coming years.

Digital Soil Mapping (DSM) enhances the delivery of soil information but typically requires costly and extensive field data to develop accurate soil prediction models. The Reference Area (RA) approach can reduce soil sampling intensity; however, its subjective delineation may compromise model accuracy when predicting soil properties. In this study, we introduce the autoRA algorithm, an innovative automated soil sampling design method that utilizes Gower’s Dissimilarity Index to delineate RAs automatically. This approach preserves environmental variability while retaining accuracy compared to an exhaustive predictive model (EPM) based on extensive sampling of the entire area of interest. Our objective was to evaluate the sensitivity and efficiency of autoRA by varying target areas (10–50% of the total area) and block size spatial resolutions (5–150 pixels) in regions of Florida, USA, and Rio de Janeiro, Brazil. We modeled a hypothetical soil property derived from a combination of commonly used DSM covariates and user inputs into autoRA. Model performance was assessed using R², root mean square error (RMSE), and Bias, aggregated into a Euclidean Distance (ED) metric. Among all configurations, the optimal RA selection – characterized by the lowest ED – was achieved with a target area of 50% and a block size of 10 pixels, closely matching the accuracy of the EPM. For example, in Rio de Janeiro, the EPM produced an ED of 0.17, while the best RA configuration yielded an ED of 0.15. In Florida, the EPM had an ED of 0.35 compared to 0.38 for the optimal RA. Additionally, the 50%-RA with a block size of 10 significantly reduced total costs by approximately 61% in Rio (from US$258,491 to US$100,611) and 63% in Florida (from US$289,690 to US$106,296). Overall, autoRA systematically identifies cost-effective sampling configurations and reduces the investigation area while maintaining model accuracy. By automating RA delineation, autoRA mitigates the subjectivity inherent in traditional methods, thereby supporting more reproducible, strategic, and efficient DSM workflows.

The reference area (RA) approach has been frequently used in soil surveying and mapping projects, since it allows for reduced costs. However, a crucial point in using this approach is the choice or delineation of an RA, which can compromise the accuracy of prediction models. In this study, an innovative algorithm that delineates RA (autoRA—automatic reference areas) is presented, and its efficiency is evaluated in Sátiro Dias, Bahia, Brazil. autoRA integrates multiple environmental covariates (e.g., geomorphology, geology, digital elevation models, temperature, precipitation, etc.) using the Gower’s Dissimilarity Index to capture landscape variability more comprehensively. One hundred and two soil profiles were collected under a specialist’s manual delineation to establish baseline mapping soil taxonomy. We tested autoRA coverages ranging from 10% to 50%, comparing them to RA manual delineation and a conventional “Total Area” (TA) approach. Environmental heterogeneity was insufficiently sampled at lower coverages (autoRA at 10–20%), resulting in poor classification accuracy (0.11–0.14). In contrast, larger coverages significantly improved performance: 30% yielded an accuracy of 0.85, while 40% and 50% reached 0.96. Notably, 40% struck the best balance between high accuracy (kappa = 0.65) and minimal redundancy, outperforming RA manual delineation (accuracy = 0.75) and closely matching the best TA outcomes. These findings underscore the advantage of applying an automated, diversity-driven strategy like autoRA before field campaigns, ensuring the representative sampling of critical environmental gradients to improve DSM workflows.

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.

In-depth knowledge about soil moisture dynamics is crucial for irrigation management in precision agriculture. This study evaluates the feasibility of high spatial resolution near-infrared remote sensing with unmanned aerial systems for soil moisture estimation to provide decision support for precision irrigation management. A new trapezoid model based on near-infrared transformed reflectance (NTR) and the normalized difference vegetation index (NDVI) is introduced and used for estimation and mapping of root zone soil moisture and plant extractable water. The performance of the proposed approach was evaluated via comparison with ground soil moisture measurements with advanced time domain reflectometry sensors. We found the estimates based on the NTR−NDVI trapezoid model to be highly correlated with the ground soil moisture measurements. We believe that the presented approach shows great potential for farm-scale precision irrigation management but acknowledge that more research for different cropping systems, soil textures, and climatic conditions is needed to make the presented approach viable for the application by crop producers.

Soil moisture (SM) is of paramount importance for society and the global environment. Accurate SM information is at the core of a plethora of applications that include forecasting of weather and climate variability, projection and monitoring of drought conditions, precise agricultural irrigation management, conservation of water resources, monitoring of ecosystem response to climate change, and impact mitigation of natural disasters such as wildfires, landslides, floods, or dust storms, whose occurrence is intimately connected to the moisture status of the land surface. Variations in SM can have substantial impacts on ecosystem health, agricultural productivity, and forestry. In this chapter we present state-of-the-art and novel technologies and sensors for proximal sensing of SM together with the underlying concepts and theories.

The land surface temperature (LST) governs the radiative energy budget of the Earth's surface and thus is one of the main input variables for land-surface models aimed at the estimation of soil moisture and evapotranspiration, monitoring of drought conditions and crop development, mitigation of urban heat islands, quantifying soil, vegetation, and whole ecosystem response to climate change, simulating hydrological processes, and forecasting of extreme climate events (i.e., drought, wildfires, and flooding). Because the LST affects a wide range of physical, chemical, and biological soil processes from the local to the global scales, and because of its significant spatiotemporal variations, advanced methods for measurement of the LST are of essence. Proximal sensing techniques based on thermal infrared imaging or passive microwaves are powerful means for LST determination. In this Chapter we present state-of-the-art proximal LST sensing techniques, discuss the underlying theories and algorithms, and provide examples for the application of LST information.

Osmotic adjustment (OA) is a major component of drought resistance in crops. The genetic basis of OA in wheat and other crops remains largely unknown. In this study, 248 field-grown durum wheat elite accessions grown under well-watered conditions, underwent a progressively severe drought treatment started at heading. Leaf samples were collected at heading and 17 days later. The following traits were considered: flowering time (FT), leaf relative water content (RWC), osmotic potential (ψs), OA, chlorophyll content (SPAD), and leaf rolling (LR). The high variability (3.89-fold) in OA among drought-stressed accessions resulted in high repeatability of the trait (h2 = 72.3%). Notably, a high positive correlation (r = 0.78) between OA and RWC was found under severe drought conditions. A genome-wide association study (GWAS) revealed 15 significant QTLs (Quantitative Trait Loci) for OA (global R2 = 63.6%), as well as eight major QTL hotspots/clusters on chromosome arms 1BL, 2BL, 4AL, 5AL, 6AL, 6BL, and 7BS, where a higher OA capacity was positively associated with RWC and/or SPAD, and negatively with LR, indicating a beneficial effect of OA on the water status of the plant. The comparative analysis with the results of 15 previous field trials conducted under varying water regimes showed concurrent effects of five OA QTL cluster hotspots on normalized difference vegetation index (NDVI), thousand-kernel weight (TKW), and/or grain yield (GY). Gene content analysis of the cluster regions revealed the presence of several candidate genes, including bidirectional sugar transporter SWEET, rhomboid-like protein, and S-adenosyl-L-methionine-dependent methyltransferases superfamily protein, as well as DREB1. Our results support OA as a valuable proxy for marker-assisted selection (MAS) aimed at enhancing drought resistance in wheat.

Evapotranspiration is a key component of the hydrologic cycle. Accurate short-, medium-, and long-term forecasts of actual evapotranspiration (ETa) are crucial not only for quantifying the impacts of climate change on the water and energy balance, but also for real-time estimation of crop water demand and irrigation water allocation in agriculture. Despite considerable advances in satellite remote sensing technology and the availability of long ground-measured and remotely sensed ETa timeseries, real-time ETa forecasts are deficient. Applying a state-of-the-art deep learning (DL) approach, Long Short-Term Memory (LSTM) models were employed to nowcast (realtime) and forecast (ahead of time) ETa based on (1) major meteorological and ground-measured (i.e., soil moisture) input variables and (2) long ETa time-series from the Moderate Resolution Imaging Spectmradiometer (MODIS) onboard of the NASA Aqua satellite. The conventional LSTM and convolutional LSTM (ConvLSTM) DL models were evaluated for seven distinct climatic zones across the contiguous United States. The employed LSTM and ConvLSTM models were trained and evaluated with data from the National Climate Assessment-Land Data Assimilation System (NCA-LDAS) and with MODIS/Aqua Net Evapotranspiration MYD16A2 product data. The obtained results indicate that when major atmospheric and soil moisture input variables are used for the conventional LSTM models, they yield accurate daily ETa forecasts for short (1, 3, and 7 days) and medium (30 days) time scales, with normalized root mean squared errors (NRMSE) and Nash-Sutcliffe efficiencies (NSE) of less than 10% and greater than 0.77, respectively. At the watershed scale, the univariate ConvLSTM models yielded accurate weekly spatiotemporal ETa forecasts (mean NRMSE less than 6.4% and NSE greater than 0.66) with higher computational efficiency for various climatic conditions. The employed models enable precise forecasts of both the current and future states of ETa, which is crucial for understanding the impact of climate change on rapidly depleting water resources.

Root zone soil moisture (RZSM) estimation and monitoring based on high spatial resolution remote sensing information such as obtained with an Unmanned Aerial System (UAS) is of significant interest for field-scale precision irrigation management, particularly in water-limited regions of the world. To date, there is no accurate and widely accepted model that relies on UAS optical surface reflectance observations for RZSM estimation at high spatial resolution. This study is aimed at the development of a new approach for RZSM estimation based on the fusion of high spatial resolution optical reflectance UAS observations with physical and hydraulic soil information integrated into Automated Machine Learning (AutoML). The H2O AutoML platform includes a number of advanced machine learning algorithms that efficiently perform feature selection and automatically identify complex relationships between inputs and outputs. Twelve models combining UAS optical observations with various soil properties were developed in a hierarchical manner and fed into AutoML to estimate surface, near-surface, and root zone soil moisture. The addition of independently measured surface and near-surface soil moisture information to the hierarchical models to improve RZSM estimation was investigated. The accuracy of soil moisture estimates was evaluated based on a comparison with Time Domain Reflectometry (TDR) sensors that were deployed to monitor surface, near-surface and root zone soil moisture dynamics. The obtained results indicate that the consideration of physical and hydraulic soil properties together with UAS optical observations improves soil moisture estimation, especially for the root zone with a RMSE of about 0.04 cm3 cm-3. Accurate RZSM estimates were obtained when measured surface and near-surface soil moisture data was added to the hierarchical models, yielding RMSE values below 0.02 cm3 cm-3 and R and NSE values above 0.90. The generated high spatial resolution RZSM maps clearly capture the spatial variability of soil moisture at the field scale. The presented framework can aid farm scale precision irrigation management via improving the crop water use efficiency and reducing the risk of groundwater contamination.

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.