Soil Moisture Estimation Research Articles

Contactless estimation of soil moisture using leaky Rayleigh waves and a fully convolutional network

AbstractSoil moisture is a key factor that influences various aspects of ecosystem functioning. Measuring soil moisture without installing any objects in the soil is desirable because it allows for accurate characterizations of soil moisture while minimizing impacts on soil structure and ecology. In this study, we explored the potential of leaky Rayleigh waves as a proxy to contactlessly estimate soil moisture. We developed an ultrasonic system containing a transducer, receivers, and acoustic barrier. The specimens of sand, silt, and clay were utilized. Experiments were conducted over 4 months. We used a widely used soil‐embedded moisture sensor to compare and develop relationships between leaky Rayleigh waves and soil moisture. Our results showed that as soil moisture increased, the velocity and amplitude of leaky Rayleigh waves decreased because water molecules attracted to the soils led to their attenuation. However, their magnitudes were not considerable except for very dry soils. To overcome these limited relations to estimate soil moisture from leaky Rayleigh waves, we constructed authentic images based on the observed leaky Rayleigh waves and used them as inputs for a fully convolutional network. We found that the combination of the ultrasonic system and deep learning approach developed in this study were suitable for estimating soil moisture without soil disturbances (RMSE = 0.01 m3 m−3). This study suggests that leaky Rayleigh waves have the potential to serve as a reliable proxy for determining soil moisture without the need for physical contact.

Retrieval of Surface Soil Moisture over Wheat Fields during Growing Season Using C-Band Polarimetric SAR Data

Accurate estimation and regular monitoring of soil moisture is very important for many agricultural, hydrological, or climatological applications. Our objective was to evaluate potential contributions of polarimetry to soil moisture estimation during crop growing cycles using RADARSAT-2 C-band images. The research focused on wheat field data collected during Soil Moisture Active Passive Validation Experiment (SMAPVEX12) conducted in 2012 in Manitoba (Canada). A sensitivity analysis was performed to select the most relevant non-polarimetric and polarimetric variables extracted from RADARSAT-2, and statistical models were developed to estimate soil moisture. In fine, three models were developed and validated: a non-polarimetric model based on cross-polarized backscattering coefficient σHV0; a polarimetric mixed model using six polarimetric and non-polarimetric retained variables after the sensitivity analysis; and a simplified polarimetric mixed model considering only the phase difference (ϕHH−VV) and the co-polarized backscattering coefficient σHH0. The validation reveals significant positive contributions of polarimetry. It shows that the non-polarimetric model has a much larger error (RMSE = 0.098 m3/m3) and explains only 19% of observed soil moisture variation compared to the polarimetric mixed model, which has an error of 0.087 m3/m3, with an explained variance of 44%. The simplified model has the lowest error (0.074 m3/m3) and explains 53.5% of soil moisture variation.

A new variant of the optical trapezoid model (OPTRAM) for remote sensing of soil moisture and water bodies

Over the past few years, the Optical Trapezoid Model (OPTRAM) has been widely used as a means for high-resolution mapping of surface soil moisture using optical satellite data. In this paper, we propose a new variant of OPTRAM that can map not only soil moisture, but also water bodies such as lakes and rivers. The proposed variant was tested using laboratory experimental data as well as Landsat-8 reflectance observations. Results showed the new OPTRAM variant has greater skill than the original variant in separating land and water pixels. In addition, the new variant showed less sensitivity to the model parameters, and hence, is less user dependent. To quantitatively examine the user-dependency of the model, we analyzed OPTRAM soil moisture based on Landsat-8 satellite images in California, where we varied the model parameters in a plausible range. The correlations of the resulting maps in terms of R2 between two largely different sets of parameters were found in the range of 0.47-0.52 for the original variant and 0.67-0.76 for the new variant. Because some OPTRAM parameters can be quite uncertain, particularly in wet regions, the reduced sensitivity promises more consistent soil moisture estimates across the range of parameter choices.

Sensitivity of surface soil moisture retrieval to satellite-derived vegetation descriptors over wheat fields in the Kairouan plain

ABSTRACT Soil moisture estimation is a key component in hydrological processes and irrigation amounts' estimation. The synergetic use of optical and radar data has been proven to retrieve the surface soil moisture at a field scale using the Water Cloud Model (WCM). In this work, we evaluate the impact of staellite-derived vegetation descriptors to estimate the surface soil moisture. Therefore, we used the Sentinel-1 data to test the polarization ratio ( σ V H 0 / σ V V 0 ) and the normalized polarization ratio (IN) and the frequently used optical Normalized Difference vegetation Index (NDVI) as vegetation descriptors. Synchronous with Sentinel-1 acquisitions, in situ soil moisture were collected over wheat fields in the Kairouan plain in the center of Tunisia. To avoid the bare soil roughness effect and the radar signal saturation in dense vegetation context, we considered the data where the NDVI values vary between 0.25 and 0.7. The soil moisture inversion using the WCM and NDVI as a vegetation descriptor was characterized by an RMSE value of 5.6 vol.%. A relatively close performance was obtained using IN and ( σ V H 0 / σ V V 0 ) with RMSE under 7. 5 vol.%. The results revealed the consistency of the radar-derived data in describing the vegetation for the retrieval of soil moisture.

IMERG Precipitation Improves the SMAP Level-4 Soil Moisture Product

Abstract The NASA Soil Moisture Active Passive (SMAP) mission Level-4 Soil Moisture (L4_SM) product provides global, 9-km resolution, 3-hourly surface and root-zone soil moisture from April 2015 to the present with a mean latency of 2.5 days from the time of observation. The L4_SM algorithm assimilates SMAP L-band (1.4 GHz) brightness temperature (Tb) observations into the NASA Catchment land surface model as the model is driven with observation-based precipitation. This paper describes and evaluates the use of satellite- and gauge-based precipitation from the NASA Integrated Multi-satellitE Retrievals for the Global Precipitation Measurement (IMERG) products in the L4_SM algorithm beginning with L4_SM Version 6. Specifically, IMERG is used in two ways: (i) The L4_SM precipitation reference climatology is primarily based on IMERG-Final (Version 06B) data, replacing the Global Precipitation Climatology Project Version 2.2 data used in previous L4_SM versions, and (ii) the precipitation forcing outside of North America and the high latitudes is corrected to match the daily totals from IMERG, replacing the gauge-only, daily product or uncorrected weather analysis precipitation used there in earlier L4_SM versions. The use of IMERG precipitation improves the anomaly time series correlation coefficient of L4_SM surface soil moisture (versus independent satellite estimates) by 0.03 in the global average and by up to ∼0.3 in parts of South America, Africa, Australia, and East Asia, where the quality of the gauge-only precipitation product used in earlier L4_SM versions is poor. The improvements also reduce the time series standard deviation of the Tb observation-minus-forecast residuals from 5.5 K in L4_SM Version 5 to 5.1 K in Version 6. Significance Statement Soil moisture links the land surface water, energy, and carbon cycles. NASA Soil Moisture Active Passive (SMAP) satellite observations and observation-based precipitation data are merged into a numerical model of land surface water and energy balance processes to generate the global, 9-km resolution, 3-hourly Level-4 Soil Moisture (L4_SM) data product. The product is available with ∼2.5-day latency to support Earth science research and applications, such as flood prediction and drought monitoring. We show that a recent L4_SM algorithm update using satellite- and gauge-based precipitation inputs from the NASA Integrated Multi-satellitE Retrievals for the Global Precipitation Measurement (IMERG) products improves the temporal variations in the estimated soil moisture, particularly in otherwise poorly instrumented regions in South America, Africa, Australia, and East Asia.

A Deep Learning Data Fusion Model Using Sentinel-1/2, SoilGrids, SMAP, and GLDAS for Soil Moisture Retrieval

Abstract 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 m3 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. Significance Statement Soil moisture is a key variable in various agriculture and water management systems. Accurate and high-resolution estimates of soil moisture have multiple downstream benefits such as reduced water wastage by better understanding and managing the consumption of water, utilizing smarter irrigation methods and effective canal water management. We develop a deep learning–based model that estimates the volumetric soil moisture content in the top ∼5 cm of soil at a nominal 320-m resolution. Our results demonstrate that machine learning is a useful tool for fusing different modalities with ease, while producing high-resolution models that are not location specific. Future work could explore the possibility of using temporal input sources to further improve model performance.

Improvement and application on the estimation model of windbreak and sand fixation function based on remote sensing soil moisture factor

Soil moisture factor is one of the important parameters in the study of wind and sand fixation functions of ecosystems. Traditional methods often use potential evaporation, rainfall, and irrigation observed by meteorological stations to estimate soil moisture, which has significant limitations in terms of spatial continuity and data availability. Based on the development of remote sensing technology in soil moisture detection, we selected four remote sen-sing indicators for soil moisture (MODIS evapotranspiration ratio method, SMAP soil moisture ratio method, visible shortwave infrared drought index method, and remote sensing humidity index method) to improve the estimation of soil moisture factor in the modified wind erosion equation model (RWEQ), and used the improved algorithm to analyze the spatiotemporal variations and driving factors of wind prevention and sand fixation services in the northwest region of Liaoning Province from 2001 to 2021. The results showed that the MODIS evapotranspiration ratio method had the highest correlation with traditional meteorological methods in calculating soil moisture. The formula obtained by fitting the two could be used to improve the calculation of soil moisture factor in the RWEQ model. From 2001 to 2021, the wind prevention and sand fixation capacity in the northwest region of Liaoning Province showed strong spatial distribution characteristics in the northern and eastern regions, while weak in the central and western regions. According to Mann-Kendall trend testing, 72.7% of the regions in northwest Liaoning Province were showing an upward trend in their ability to prevent wind and fix sand. The application of geographic detector models for driving factor analysis showed that the change in wind and sand fixation capacity was a process of multiple factors interacting with each other, greatly influenced by soil type, annual wind speed, and economic development level. Moreover, the interaction between various driving factors had a higher impact on wind and sand fixation than that of single factors. The results could improve the RWEQ model estimation and provide technical support for the long-term analysis of ecological function formation mechanisms and driving forces in the northwest region of Liaoning.

Optimizing Soil Moisture Retrieval: Utilizing Compact Polarimetric Features with Advanced Machine Learning Techniques

Soil moisture plays a crucial role in various environmental processes and is essential for agricultural management, hydrological modeling, and climate studies. Synthetic Aperture Radar (SAR) remote sensing presents significant potential for estimating soil moisture due to its ability to operate in all weather conditions and provide day-and-night imaging capabilities. Among the SAR configurations, the Compact Polarimetric (CP) mode has gained increasing interest as it relaxes system constraints, improves coverage, and enhances target information compared to conventional dual polarimetric SAR systems. This paper introduces a novel approach for soil moisture retrieval utilizing machine learning algorithms and CP SAR features. The CP SAR features are derived from a series of RADARSAT Constellation Mission (RCM) CP SAR imagery acquired over Canadian experimental sites equipped with Real-Time In Situ Soil Monitoring for Agriculture (RISMA) stations. This study employs a diverse dataset of compact polarimetric SAR features and corresponding ground truth soil moisture measurements for training and validation purposes. The results of our study achieved a Root Mean Square Error (RMSE) of 6.88% with a coefficient of determination R2 equal to 0.60, which corresponds to a correlation R between true and predicted soil moisture values of 0.75, using optimized Ensemble Learning Regression (ELR) with a decision-tree-based model. These results improved, yielding an RMSE of 5.67% and an R2 equal to 0.73 (R = 0.85), using an optimized Gaussian Process Regression (GPR) model.

A double two-sources energy-water balance model for improving evapotranspiration estimates and irrigation management in fruit trees fields

Improving the use of water in irrigated agriculture is meaningful in a period of increasing water scarcity conditions. A more accurate estimate of evapotranspiration (ET) and its components thus becomes fundamental to better quantify the irrigation water volumes. Many existing models, based on different remote sensing data, provide daily estimates of latent heat flux (LE) from correlation between net radiation and instantaneous estimates of LE, computed as residual component of the energy balance equation or from a correlation of land surface temperature (LST) with vegetation indices., However, they mainly lack of solutions which are continuous in time (e.g. hourly), independent from satellite LST availability and a simultaneous estimation of soil moisture. Addressing this gap, a double two-sources energy-water balance model (FEST‐2×2‐EWB) is developed based on the decoupling of both water and energy fluxes between the bare soil or grass interrow and the trees rows. This novel parameterization approach enables the differentiation of water uptake from the root zone, which varies between tall trees and grass, considering the dynamics of soil moisture (SM) in the superficial and deep layers. Additionally, it provides partitioned values for transpiration and evaporation. The new model has been evaluated in two irrigated trees fields in the North of Italy, a walnut trees field from 2019 to 2021 and a pear trees field, for the year 2022. Results of the study showcased a root-mean-square-error (RMSE) of about 55 W m− 2 and a bias of about 40 W m− 2 for hourly latent and sensible heat fluxes when compared to the eddy covariance stations located in the fields, while a RMSE of 2 °C (bias of 1.5 °C) for LST and of 0.04 for SM. The FEST‐2×2‐EWB model significantly enhances the accuracy of ET simulation in fruits trees areas in respect to the original one source and one-layer version of the same model. Finally, the application of an irrigation optimization strategy with this new model, allowed to demonstrate its potentiality in water saving (about 90 mm in a year) in respect to farmers applied irrigation, and with a difference of about 60 mm between using the double two-sources model and single source one.

A Study on C-band Synthetic Aperture Radar Soil Moisture Estimation Based on Machine Learning Using Soil Physics, Topography, and Hydrological Information

In this study, we applied machine learning to estimate soil moisture levels in South Korea by harnessing data from the Sentinel-1 C-band synthetic aperture radar (SAR). Our approach incorporated not only the relationship between backscattering coefficients and soil moisture but also diverse physical characteristics. This encompassed topographic information, soil physics data, and antecedent precipitation which is a hydrological factor influencing the initial condition of soil moisture. We applied a variety of machine-learning techniques and conducted a comprehensive analysis to compare the performance of each model.

State estimation for one‐dimensional agro‐hydrological processes with model mismatch

AbstractThe importance of accurate soil moisture data for the development of modern closed‐loop irrigation systems cannot be overstated. Due to the diversity of soil, it is difficult to obtain an accurate model for the agro‐hydrological system. In this study, soil moisture estimation in one‐dimensional (1D) agro‐hydrological systems with model mismatch is the focus. To address the problem of model mismatch, a nonlinear state‐space model derived from the Richards equation is utilized, along with additive unknown inputs. The determination of the number of sensors required is achieved through sensitivity analysis and the orthogonalization projection method. To estimate states and unknown inputs in real‐time, a recursive expectation maximization (EM) algorithm derived from the conventional EM algorithm is employed. During the E‐step, the extended Kalman filter (EKF) is used to compute states and covariance in the recursive Q‐function, while in the M‐step, unknown inputs are updated by locally maximizing the recursive Q‐function. The estimation performance is evaluated using comprehensive simulations. Through this method, accurate soil moisture estimation can be obtained, even in the presence of model mismatch.

Research on Soil Moisture Estimation of Multiple-Track-GNSS Dual-Frequency Combination Observations Considering the Detection and Correction of Phase Outliers.

Soil moisture (SM), as one of the crucial environmental factors, has traditionally been estimated using global navigation satellite system interferometric reflectometry (GNSS-IR) microwave remote sensing technology. This approach relies on the signal-to-noise ratio (SNR) reflection component, and its accuracy hinges on the successful separation of the reflection component from the direct component. In contrast, the presence of carrier phase and pseudorange multipath errors enables soil moisture retrieval without the requirement for separating the direct component of the signal. To acquire high-quality combined multipath errors and diversify GNSS-IR data sources, this study establishes the dual-frequency pseudorange combination (DFPC) and dual-frequency carrier phase combination (L4) that exclude geometrical factors, ionospheric delay, and tropospheric delay. Simultaneously, we propose two methods for estimating soil moisture: the DFPC method and the L4 method. Initially, the equal-weight least squares method is employed to calculate the initial delay phase. Subsequently, anomalous delay phases are detected and corrected through a combination of the minimum covariance determinant robust estimation (MCD) and the moving average filter (MAF). Finally, we utilize the multivariate linear regression (MLR) and extreme learning machine (ELM) to construct multi-satellite linear regression models (MSLRs) and multi-satellite nonlinear regression models (MSNRs) for soil moisture prediction, and compare the accuracy of each model. To validate the feasibility of these methods, data from site P031 of the Plate Boundary Observatory (PBO) H2O project are utilized. Experimental results demonstrate that combining MCD and MAF can effectively detect and correct outliers, yielding single-satellite delay phase sequences with a high quality. This improvement contributes to varying degrees of enhanced correlation between the single-satellite delay phase and soil moisture. When fusing the corrected delay phases from multiple satellite orbits using the DFPC method for soil moisture estimation, the correlations between the true soil moisture values and the predicted values obtained through MLR and ELM reach 0.81 and 0.88, respectively, while the correlations of the L4 method can reach 0.84 and 0.90, respectively. These findings indicate a substantial achievement in high-precision soil moisture estimation within a small satellite-elevation angle range.

Global estimates of daily evapotranspiration using SMAP surface and root-zone soil moisture

The interplay between soil moisture and evapotranspiration modulates the water available to sustain soil evaporation and influences canopy stomatal conductance controls on vegetation transpiration. Modeling this behavior remains challenging. Indeed, satellite remote sensing based Penman-Monteith (PM) ET models tend not to directly consider soil moisture constraints on evaporation and transpiration due to a lack of consistent soil moisture data. To address this issue, we modified a PM model to include satellite enhanced surface and root zone soil moisture from the National Aeronautics and Space Administration (NASA) Soil Moisture Active Passive (SMAP) mission. The resulting model was used to produce global 9-km daily ET estimates, including contributing fluxes from soil evaporation, transpiration and evaporation of canopy-intercepted precipitation. The global PM ET estimates were assessed using in situ sap flow and AmeriFlux measurements, upscaled FLUXCOM latent heat flux, as well as against other independent global ET data products (GLEAM, GLDAS, SSEBop, and LandFlux-EVAL). The modelled transpiration showed similar seasonal variation and positive correlation to in situ sap flow measurements available from several forest sites (R2 = 0.85; p < 0.01). When compared against AmeriFlux data, the PM ET estimates showed favorable agreement with annual ET measurements extracted from 34 diverse sites (R2 = 0.58; p < 0.01; RMSE = 227 mm yr−1). The PM 8-day ET results also reflected a similar hemispheric seasonality as the global FLUXCOM record (R2 = 0.94–0.98; p < 0.01), while comparisons against other global ET products showed moderate mean differences of between 49 and 107 mm yr−1 (11–25%) over the global domain. Our PM ET estimates varied up to 52% in response to SMAP surface soil moisture dynamics, displaying stronger surface (0–5 cm depth) than root zone (0–100 cm) soil moisture sensitivity. While PM ET sensitivity to soil moisture was greater in arid climate regions, it was also significant in humid climate zones, with analysis indicating that the inclusion of soil moisture predominantly acts as a sustaining influence on ET, especially in moisture limited drylands. PM ET sensitivity to temperature was stronger in humid forest regions relative to other climate and land cover regimes. Overall, the model results clarify the influence of soil moisture heterogeneity on the global ET pattern as informed by satellite-based estimates of surface and root zone soil moisture. Potential enhancements to the spatial and vertical resolution of soil moisture inputs are expected to enable further ET improvements through more realistic model representation of soil and plant available water.

Estimation of root zone soil moisture at point scale based on soil water measurements from cosmic-ray neutron sensing in a karst catchment

The cosmic-ray neutron sensing (CRNS) is an emerging method for continuously monitoring soil water content (SWC) at an intermediate scale. However, when multiple hydrologic units are present within its footprint, the potential application of CRNS in water resources management is restricted. Here we propose a new strategy to predict point-scale SWC in root zone established on CRNS-based soil moisture and improved relative difference method. A total of 768 days of soil moisture data were collected by CRNS at the intermediate scale and EC-TM sensors at the point scale in a karst catchment. The original and improved mean relative difference methods predicted point-scale SWCs within and without the effective measuring depth, respectively. The mean effective measuring depth was 13.16 cm, ranging from 10.13 to 19.23 cm. Both land use type and soil structure played essential roles in regulating point scale SWC in the soil profile. Point-scale SWC in root zone can be predicted accurately (P < 0.001) based on SWC data derived from the CRNS system. The prediction accuracy of point scale SWC can be improved by increasing the averaging time of the soil moisture values. Our results demonstrated that the proposed strategy was reliable for CRNS to predict SWC beyond the effective measurement depth. This study provides a good perspective for effectively managing of water resources in areas with complex hydrological processes.

Soil Moisture Estimation by Radar Data Based on Multiple Regression

Soil Moisture Estimation by Radar Data Based on Multiple Regression

Soil Moisture Estimation by Assimilating In‐Situ and SMAP Surface Soil Moisture Using Unscented Weighted Ensemble Kalman Filter

AbstractHighly accurate soil moisture information is necessary to understand land surface processes. However, observational techniques do not produce adequately accurate spatial‐temporal continuous regional soil moisture data. The data assimilation method can be used to improve the soil moisture estimations by merging multi‐source observed data, but its performance is affected by error covariance and the quality of assimilated data. We designed eight numerical experiments to analyze how to improve the filter performance through soil moisture assimilation using the unscented weighted ensemble Kalman filter (UWEnKF) and 1‐D Richards equation at Maqu and Erlinghu (ELH) observational sites in the source region of Yellow River (SRYR), China. The experimental results show that the filter performance improves as the quality of assimilated data increases in the soil moisture assimilation experiment when assimilating in‐situ surface soil moisture (SSM) observations, SMAP SSM data and downscaled SMAP SSM data. In other aspects, filter performance is readily affected by model and observation error covariances in soil moisture assimilation. If the SMAP SSM data are taken to be perfect (i.e., small bias), UWEnKF performs differently between different sites because of the underestimation or overestimation of SMAP SSM and model simulations compared to the in‐situ observations. Additionally, different soil moisture assimilation results can be obtained with different initial values at the beginning of the assimilation period. Overall, filter performance can be improved primarily by improving the quality of assimilated data (e.g., downscaling the remote sensing data), and by creating a reasonable and effective method for determining error covariance.

Improving the Estimation of Soil Moisture in Semi-Arid Regions Using Data from Different Remote Sensing Techniques

Satellite-derived soil moisture fields received attention due to their large spatial coverage and spatial resolution that suits many applications. The sensors used vary from passive (e.g., LANDSAT-8) to active (e.g., SENTINEL-1) with varying accuracy problems. Passive sensing can only determine relative indices between pixels within a vegetation class and not the real value of moisture. Active sensing suffers from the sensitivity of its detecting behaviour to the level of moisture (anomalous backscatter). The above problems impose limitations on the application without frequent ground-based calibration. The paper investigates possible models to improve the estimation of soil moisture using the powers of the two sensors. In addition, a Hydrologic Surface Moisture indicator (HSM) is included as a third source of information. The paper tests modeling combinations of the three soil moisture predictors (Landsat-8, Sentinel-1, and HSM). The models are validated using in-situ measurements. The results showed that Landsat-8 data can be rescaled using HSM to provide the actual soil moisture in the soil. On the other side, it is possible to remove the anomaly from the Sentinel-1 backscatter using either Landsat-8 or HSM. The elimination of the above problems explained a significant portion of the differences between the two sensors.

Apparent Hysteresis in Archie’s Law: Implications of Changing Pore Fluid Conductivity for Electrical Resistivity Monitoring of Infiltration

The electrical resistivity of the vadose zone is highly dependent on soil moisture content (VWC), making ERT a potential tool for non-intrusive infiltration monitoring. There’s a long history of using Archie’s law to convert resistivity values to VWC, often under the assumption that the pore-fluid conductivity is constant. Analysis of two years of soil conductivity and VWC data from sensors buried in a bioswale in Philadelphia, Pennsylvania showed that the characteristic curve of VWC versus resistivity exhibited hysteresis. The same value of resistivity was associated with different VWC during imbibition and drying. Accurate VWC could be estimated using Archie’s law only after incorporating changes in pore-fluid conductivity. Additionally, pore-fluid conductivity varied seasonally, with the highest values recorded during winter from road salt runoff. Archie exponents differed for sensors within 25 cm of each other despite both the uniform nature of the bioswale soil and fits to Archie’s Law for individual sensors, indicating considerable heterogeneity. This pattern was confirmed at two additional sites. Assuming the conductivity of the pore-fluid remains constant during infiltration not only produces erroneous soil moisture estimates, it can even reverse the apparent soil moisture trend. We conclude that to use ERT to monitor infiltration it is essential to independently record changes in pore-fluid conductivity, and that multiple sensors are required because changes in pore-fluid conductivity and Archie parameters can vary on a sub-meter scale for even apparently homogeneous sites. Additionally, long-term changes in Archie parameters, notably m, are possible, and may indicate long-term changes in soil fabric.

Evaluation of soil moisture estimation techniques based on Sentinel-1 observations over wheat fields

Soil moisture (SM) is a key variable in agriculture and its monitoring is essential. SM determines the amount of water available to plants, having a direct impact on the development of crops, on the forecasting of crop yields and on the surveillance of food security. Microwave remote sensing offers a great potential for estimating SM because it is sensitive to the dielectric characteristics of observed surface that depend on surface soil moisture. The objective of this study is the evaluation of three change detection methodologies for SM estimation over wheat at the agricultural field scale based on Sentinel-1 time series: Short Term Change Detection (STCD), TU Wien Change Detection (TUWCD) and Multitemporal Bayesian Change Detection (MTBCD). Different methodological alternatives were proposed for the implementation of these techniques at the agricultural field scale. Soil moisture measurements from eight experimental wheat fields were used for validating the methodologies. All available Sentinel-1 acquisitions were processed and the eventual benefit of correcting for vegetation effects in backscatter time series was evaluated. The results were rather variable, with some experimental fields achieving successful performance metrics (ubRMSE ∼ 0.05 m3/m3) and some others rather poor ones (ubRMSE > 0.12 m3/m3). Evaluating median performance metrics, it was observed that both TUWCD and MTBCD methods obtained better results when run with vegetation corrected backscatter time series (ubRMSE ∼0.07 m3/m3) whereas STCD produced similar results with and without vegetation correction (ubRMSE ∼0.08 m3/m3). The soil moisture content had an influence on the accuracy of the different methodologies, with higher errors observed for drier conditions and rain-fed fields, in comparison to wetter conditions and irrigated fields. Taking into account the spatial scale of this case study, results were considered promising for the future application of these techniques in irrigation management.

Estimation of Surface and Subsurface Soil Moisture Using Microwave Remote Sensing: A Typical Analysis

Accurate measurement and monitoring of surface and subsurface soil moisture is essential for understanding hydrological processes, crop growth modeling, crop water requirement, and climate studies. Accurate measurement of the soil moisture content (SMC) in the root zone is essential for precise irrigation authority and plant water stress evaluation. However, the existing passive microwave satellite missions, Soil Moisture and Ocean Salinity (SMOS) and Soil Moisture Active Passive (SMAP), that operate at L-band, can only estimate the top 5 cm of soil moisture. Microwave remote sensing has proven to be a valuable tool for non-invasive soil moisture estimation. This research aims to investigate and develop a methodology for estimating surface and subsurface soil moisture using microwave data from Sentinel-1. The study was conducted to establish the relationship between surface &amp; the backscatter coefficient derived using the Sentinel-1 SAR microwave remote sensing satellite imagery, and relationship between surface and subsurface soil moisture at different depths, in the Godhra region. Two seasons namely summer (Zaid) and monsoon (Kharif) were taken into consideration to build up the relationship between surface soil moisture and co-polarization backscatter coefficient ( For the summer (Zaid) and monsoon (Kharif) seasons, the co-polarization backscatter coefficient ( and surface soil moisture (0-5, cm) were found to have a correlation in terms of R2 as 0.91 and 0.90, respectively. The study explores the relationship between microwave signals and surface soil moisture content (0-5, cm) and then the relationship between surface soil moisture and soil moisture at various depths were also modeled thereby contributing to improved soil moisture estimation techniques and applications. The value of the coefficient of determination (R2) of surface soil moisture (0-5, cm) to subsurface soil moisture at 6-20 cm, 21-40 cm, and 41-60 cm depths were found to be 0.60, 0.51, and 0.46, respectively, in the summer (Zaid) season. The value of the coefficient of determination (R2) of surface soil moisture (0-5, cm) to subsurface soil moisture at 6-20 cm, 21-40 cm, 41-60 cm, 61-80 cm, and 81-100 cm depths were found to be 0.83, 0.61, 0.51, 0.26, and 0.13, respectively. According to the study, it is observed that the relationship between co-polarization backscatter coefficient ( and soil moisture weakens as the depth of soil moisture increases. Overall, the regression models developed between the co-polarization backscatter coefficient ( and surface soil moisture showed very good results, whereas the regression models developed between the surface soil moisture and soil moisture at various depths showed reasonably acceptable results up to the depth of 60 cm. The findings in the present study suggest that Sentinel-1A C-band SAR data can be used to estimate surface soil moisture. It is also shown in this study that the surface soil moisture can be correlated with the subsurface soil moisture up to the depth of 60 cm, satisfactorily using regression equations.