Moisture Monitoring Research Articles

Perspective methods of monitoring soil moisture under irrigation in the near-Ob area in the Novosibirsk region

The aim of the study. Analyzing the known methods and technologies of soil moisture monitoring for irrigation management in the agricultural production areas in the Near-Ob region, Russia. Location and time of the study. Novosibirsk region, Russia, 2023. Methods. Comparative analysis of irrigation regime management technologies described in the literature and currently used in irrigation practice. Results. Methods of quick operational control over soil moisture using as an example a large irrigation system (Verkh-Irmenskaya irrigation system in the Novosibirsk region) were analyzed and compared. The methods were evaluated from the point of view of their applicability in large grain production areas. Of many methods of soil moisture content measurement, the neutron method and the method of soil dielectric constant measurement are described in detail, and their advantages and disadvantages identified. Electrometric methods are proposed for grain production areas. Trends in the development of irrigation technologies are outlined, primarily digitalization and automation, which allow processing large data amounts. Conclusions. At present, irrigation management technologies are mostly represented by experimental developments; the use of artificial intelligence remains mainly the prerogative of research projects. For agricultural production, determination of soil moisture reserves using soil moisture sensors is still the most popular method. From our point of view, electrometric methods are the most promising, as they are mobile, not costly and have high measurement speed.

GNSS-R monitoring of soil moisture dynamics in areas of severe drought: example of Dahra in the Sahelian climatic zone (Senegal)

ABSTRACT With population growth, water will increase in the following decades tremendously. The optimization of water allocation for agriculture requires accurate soil moisture (SM) monitoring. Recent Global Navigation Satellite System Reflectometry (GNSS-R) studies take advantage of continuously emitted navigation signals by the Global Navigation Satellite System (GNSS) constellations to retrieve spatiotemporal soil moisture changes for soil with high clay content. It presents the advantage of sensing a whole surface around a reference GNSS antenna. This article focuses on sandy SM monitoring in the driest condition observed in the study field of Dahra, (Senegal). The area consists of 95% sand and in situ volumetric soil moisture (VSM) range from ~3% to ~5% durinf the dry to the rainy season. Unfortunately, the GNSS signals’ waves penetrated deep into the soil during the dry period and strongly reduced the accuracy of GNSS reflectometry (GNSS-R) surface moisture measurements. However, we obtain VSM estimate at low/medium penetration depth. The correlation reaches 0.9 with VSM error lower than 0.16% for the 5–10-cm-depth probes and achieves excellent temporal monitoring to benefit from the antenna heights directly correlated to spatial resolution. The SM measurement models in our research are potentially valuable tools that contribute to the planning of sustainable agriculture, especially in countries often affected by drought.

Compound minirhizotron device for root phenotype and water content near root zone

The study of phenotype plays a significant role in plant growth and development. In plant phenotype, the growth state and size distribution of roots are crucial to plant growth and development. Exploring the variation of root development and root zone water content is conducive to improving planting technology with efficiency. Whereas, commercial root phenotype monitoring devices on the market are expensive and sophisticated. This study aims to develop a compound minirhizotron device to acquire images of tomato root system and monitor water content near the root zone. The device mainly included core controller module, transmission module, image acquisition module and moisture monitoring module. The device could realize image acquisition of root system at multiple positions and monitoring of water content near root zone under the condition of less damage to root system. Through image processing, parameters such as root length and root diameter could be calculated. Results indicated that tomato roots at the depth of 6–10 cm from the substrate surface were more densely distributed, and root density decreased with increasing substrate depths. And furthermore, total root length showed a gradual downward trend with the downward movement of the substrate layer. In addition, the closer to the vertical downward straight line from the tomato cultivation site, the greater the root diameter within the monitoring range. In the monitoring results of water content near root zone, the closer to the ground, the higher monitored substrate water content. The overall results showed that the compound minirhizotron device could aquire both root system images and water content near root zone stablely and for a long-term, which could improve the efficiency of planting and irrigation technology.

Surface Soil Moisture Retrieval on Qinghai-Tibetan Plateau Using Sentinel-1 Synthetic Aperture Radar Data and Machine Learning Algorithms

Soil moisture is a key factor in the water and heat exchange and energy transformation of the ecological systems and is of critical importance to the accurate obtainment of the soil moisture content for supervising water resources and protecting regional and global eco environments. In this study, we selected the soil moisture monitoring networks of Naqu, Maqu, and Tianjun on the Qinghai–Tibetan Plateau as the research areas, and we established a database of surface microwave scattering with the AIEM (advanced integral equation model) and the mathematical expressions for the backscattering coefficient, soil moisture, and surface roughness of the VV and VH polarizations.We proposed the soil moisture retrieval models of empirical and machine learnings algorithms (backpropagation neural network (BPNN), support vector machine (SVM), K-nearest neighbors (KNN), and random forest (RF)) for the ascending and descending orbits using Sentinel-1 and measurement data, and we also validated the accuracies of the retrieval model in the research areas. According to the results, there is a substantial logarithmic correlation among the backscattering coefficient, soil moisture, and combined roughness. Generally, we can use empirical models to estimate the soil moisture content, with an R² of 0.609, RMSE of 0.08, and MAE of 0.064 for the ascending orbit model and an R² of 0.554, RMSE of 0.086, and MAE of 0.071 for the descending orbit model. The soil moisture contents are underestimated when the volumetric water content is high. The soil moisture retrieval accuracy is improved with machine learning algorithms compared to the empirical model, and the performance of the RF algorithm is superior to those of the other machine learning algorithms. The RF algorithm also achieved satisfactory performances for the Maqu and Tianjun networks. The accuracies of the inversion models for the ascending orbit in the three soil moisture monitoring networks were better than those for the descending orbit.

Construction of Interactive Guidance System and Design of Sharing Platform Under the Background of Agrometeorological Remote Sensing Sensor Network

This subject uses SQL Server database management system as the background database, and designs the meteorological station data table, meteorological element data table, crop data table, and agricultural gas product data table. This paper briefly reviews the development process of agricultural remote sensing and the theoretical basis of its application; Then, the relevant research and applications at home and abroad are expounded from the four fields of crop yield estimation, agricultural resource investigation, agricultural disaster monitoring and precision agricultural management. Finally, it is proposed that agricultural remote sensing should be combined with the ground agricultural observation network technology. The functional modules of the Friendship Platform are analyzed, and then the system implementation is elaborated. The shared platform operates stably, and realizes comprehensive functions such as weather forecast, soil moisture monitoring, meteorological disaster warning and emergency response.

Cold Hardiness of Prunus mume ‘Xiang Ruibai’ and Its Parents Based on Biological Indexes and Physical Parameters

Low temperature is a primary factor limiting the distribution of Prunus mume. In order to produce a variety that has both cold tolerance and the characteristic fragrance of true mume, previous researchers crossbred a strong-tolerance variety apricot mei, P. mume ‘DF’ (‘Dan Fenghou’) and the weak-tolerance variety of true mume, P. mume ‘BY’ (‘Beijing Yudie’). They gained an offspring variety named P. mume ‘XR’ (‘Xiang Ruibai’), but its cold tolerance is unknown at this point. Here, three varieties (XR, BY, and DF) were selected as the materials, and different low-temperature treatments were used, with temperature as the only variable. Conventional biological methods, such as ion leakage rate, different tissues, and plant viability statistics, were used, as well as an innovative use of infrared engineering and moisture monitoring for dynamic observation of the water-to-ice process in tissues. The results were as follows: DF cold tolerance was the highest, followed by XR and then BY. The LT50 of XR was increased by 6 °C after five days of cold priming at 4 °C, which indicated a stronger cold acclimation ability than the parent varieties. The XR variety enhanced the antioxidant capacity by increasing SOD and POD enzyme activities during low temperature treatment, thus enhancing the cold tolerance. The antioxidant enzyme genes PmSOD3, PmPOD2, PmPOD19, and PmPOD22 had important regulatory roles in XR’s cold acclimation process.

Feasibility of irrigation monitoring with cosmic-ray neutron sensors

Abstract. Accurate soil moisture (SM) monitoring is key in irrigation as it can greatly improve water use efficiency. Recently, cosmic-ray neutron sensors (CRNSs) have been recognized as a promising tool in SM monitoring due to their large footprint of several hectares. CRNSs also have great potential for irrigation applications, but few studies have investigated whether irrigation monitoring with CRNSs is feasible, especially for irrigated fields with a size smaller than the CRNS footprint. Therefore, the aim of this study is to use Monte Carlo simulations to investigate the feasibility of monitoring irrigation with CRNSs. This was achieved by simulating irrigation scenarios with different field dimensions (from 0.5 to 8 ha) and SM variations between 0.05 and 0.50 cm3 cm−3. Moreover, the energy-dependent response functions of eight moderators with different high-density polyethylene (HDPE) thickness or additional gadolinium thermal shielding were investigated. It was found that a considerable part of the neutrons that contribute to the CRNS footprint can originate outside an irrigated field, which is a challenge for irrigation monitoring with CRNSs. The use of thin HDPE moderators (e.g. 5 mm) generally resulted in a smaller footprint and thus stronger contributions from the irrigated area. However, a thicker 25 mm HDPE moderator with gadolinium shielding improved SM monitoring in irrigated fields due to a higher sensitivity of neutron counts with changing SM. This moderator and shielding set-up provided the highest chance of detecting irrigation events, especially when the initial SM was relatively low. However, variations in SM outside a 0.5 or 1 ha irrigated field (e.g. due to irrigation of neighbouring fields) can affect the count rate more than SM variations due to irrigation. This suggests the importance of retrieving SM data from the surrounding of a target field to obtain more meaningful information for supporting irrigation management, especially for small irrigated fields.

Seasonality in Moisture Dynamics in the Walls of the rock-cut Churches in Lalibela, Ethiopia: Implications for Weathering

ABSTRACT Moisture plays a key role in rock decay in the built and natural environments. Rock-cut sites are particularly vulnerable to moisture-related weathering as they are carved into rock outcrops and do not have impermeable foundations or roofs to retard the flow of moisture. To characterise the moisture dynamics and its influence on weathering of rock-cut sites, we undertook a moisture monitoring campaign using a non-destructive Microwave Moisture Measurement System (MMMS) at two monolithic rock-cut churches in Lalibela, Ethiopia. The results showed that the walls were more saturated at depth than on the surface during the wet season. This suggests that low surface temperature and higher moisture content at depth will lead to constant-rate drying and accumulation of salts on the surface of the walls during the wet season. In the dry season, there was higher saturation near the surface than at depth (falling-rate drying). High rock surface temperature during the dry season contributes to subsurface drying and accumulation of salts below the surface. This seasonally shifting moisture dynamics will lead to a complex and dynamic damage profile. This study highlights the significant wetting facilitated by a lack of impermeable roofs and foundations at rock-cut structures during rainy periods.

Calibration of the capacitance probe for soil moisture monitoring

To obtain efficient use of water applied in irrigation, it is necessary to understand the dynamics of water in the soil, which is an important factor in agriculture. The aim of this study was to determine models for calibration and to evaluate thelysimetry methodwith undisturbed samples,using a capacitance probe for monitoring the moisture content of a TypicHapluderts soil under laboratory conditions. The predominant soil in the study area is from the TypicHapluderts soil unit. The soil samples were collected with undisturbed samples, in layers of 0 - 10 cm and 10 - 20 cm depth from the surface, using PVC pipes with a diameter of 315 mm and 600 mm height. The determination of the variation in soil moisture was carried out by weight difference (weighing lysimetry method). The measurements with a Frequency Domain Reflectometry (FDR) were made on a daily frequency and 6 repetitions per measurement. Analyzed calibration models (linear, potential and quadratic), was performed the Shapiro-Wilk normality test. The methodology used provided satisfactory results, with multiple repetitions being performed on the same sample. The model that showed greater precision and better fit for the two profiles under study was the quadratic model.

Fully integrated wearable humidity sensor for respiration monitoring.

Respiration monitoring is a promising alternative to medical diagnosis of several diseases. However, current techniques of respiration monitoring often require expensive and cumbersome devices which greatly limit their medical applications. Here, we present a fully integrated wearable device consisting of a flexible LCP-copper interdigital electrode, a sensing layer and a wireless electrochemical analysis system. The developed humidity sensor exhibits a high sensitivity, a good repeatability and a rapid response/recover time. The long-term stability is over 30days at different relative humidity. By integrating the flexible humidity sensor with miniaturized electrochemical analysis system (0.8cm × 1.8cm), response current concerning respiration can be wirelessly transmitted to App-assisted smartphone in real time. Furthermore, the fabricated humidity sensor can realize skin moisture monitoring in a touch-less way. The large-scale production of miniaturized flexible sensor (4mm × 6mm) has significantly contributed to commercial deployment.

The Utilization of Sentinel-1 Soil Moisture Satellite Imagery for Runoff Coefficient Analysis

Soil moisture content can provide information about the relationship between runoff coefficient and water availability to increase agricultural productivity and measure potential erosion rates. Observation of soil moisture content sometimes cannot be directly measured. Therefore, the study was conducted to develop practical soil moisture monitoring techniques using Sentinel-1 satellite images. Moisture content measurements obtained from the Sentinel-1 satellite image are pre-processed through the Sentinel Application Platform (SNAP), which is then processed using Dubois model to get the soil permittivity value from the backscatter value (σ◦). The moisture content was obtained using Topp’s model. The gravimetric methods in situ soil tests were used to validate the soil moisture content estimation obtained from Sentinel-1 image processing. Based on this research, the Sentinel-1 satellite provides a solution for monitoring soil moisture. The relationship between moisture content and runoff coefficient shows a greater the soil moisture value, the higher the runoff coefficient will obtain. This study proves that the developed land and valley condition has a high runoff coefficient. A high runoff coefficient value has susceptibility to high erosion rates. Sites with similar characteristics need to consider the appropriate land cover to ensure the sustainability of land conservation while still benefiting from agricultural activities.

A decadal record of soil moisture space–time variability over a south‐east Australian catchment

AbstractSemi‐arid to temperate south‐east Australian catchments with agricultural landscapes demonstrate unique hydro‐climatic characteristics. Understanding the behaviour of soil moisture over such catchments and the influence of driving factors are crucial for hydrologic, climatic and agricultural applications. However, this is challenging due the complex, non‐linear relationship between these factors and soil moisture, and the lack of long‐term catchment scale data records. To address this, spatial and temporal patterns of soil moisture over two south‐east Australian river catchments (i.e., Krui and Merriwa) and the influence of soil texture, topography, vegetation and rainfall on soil moisture variability were evaluated using a decadal in‐situ dataset. This unique in‐situ soil moisture monitoring network is established over a semi‐arid to temperate catchment representing typical south‐east Australian agricultural landscape and the data record has captured some major climatic events. Time stability of catchment‐scale soil moisture and the potential of predicting catchment mean soil moisture content using one representative station were also examined using a linear regression model. Soil texture was found as the dominating factor driving the spatial variability of soil moisture in the area. The temporal patterns of soil moisture showed a positive agreement with vegetation dynamics and rainfall at topsoil layers (0–5 cm and 0–30 cm). A higher spatial variability of soil moisture was observed during dry catchment conditions compared to wet catchment conditions. The deeper soil layers (30–60 cm and 60–90 cm) showed highly stable soil moisture values, which might be the driving force of the agriculture in the area. A linear regression based prediction model demonstrated a good potential in estimating spatial mean soil moisture content from one representative station. The results are useful in parameterization of soil moisture variability in land surface, climatic and hydrologic models, agricultural applications and in remote sensing of soil moisture.

Working toward a National Coordinated Soil Moisture Monitoring Network: Vision, Progress, and Future Directions

Abstract Soil moisture is a critical land surface variable, impacting the water, energy, and carbon cycles. While in situ soil moisture monitoring networks are still developing, there is no cohesive strategy or framework to coordinate, integrate, or disseminate these diverse data sources in a synergistic way that can improve our ability to understand climate variability at the national, state, and local levels. Thus, a national strategy is needed to guide network deployment, sustainable network operation, data integration and dissemination, and user-focused product development. The National Coordinated Soil Moisture Monitoring Network (NCSMMN) is a federally led, multi-institution effort that aims to address these needs by capitalizing on existing wide-ranging soil moisture monitoring activities, increasing the utility of observational data, and supporting their strategic application to the full range of decision-making needs. The goals of the NCSMMN are to 1) establish a national “network of networks” that effectively demonstrates data integration and operational coordination of diverse in situ networks; 2) build a community of practice around soil moisture measurement, interpretation, and application—a “network of people” that links data providers, researchers, and the public; and 3) support research and development (R&D) on techniques to merge in situ soil moisture data with remotely sensed and modeled hydrologic data to create user-friendly soil moisture maps and associated tools. The overarching mission of the NCSMMN is to provide coordinated high-quality, nationwide soil moisture information for the public good by supporting applications like drought and flood monitoring, water resource management, agricultural and forestry planning, and fire danger ratings.

Using stable water isotopes to understand ecohydrological partitioning under contrasting land uses in a drought‐sensitive rural, lowland catchment

AbstractVegetation plays an essential role in water partitioning, as it strongly influences evapotranspiration, infiltration and water retention. To analyse the influence of vegetation on water partitioning under innovative land management strategies, we used stable water isotopes as natural tracers to monitor precipitation, soil water and groundwater fluxes over the growing season of 2021 (March–October). We selected eight plot sites with four contrasting land covers and soil types in the drought‐sensitive Demnitzer Millcreek Catchment (DMC) in NE Germany. The land use types include forest, grassland, and arable with the latter being subdivided into conventional (e.g., crops) and innovative (e.g., agroforestry) sites. Two weather stations, a flux tower, and in situ soil moisture monitoring complemented our isotopic data with a hydroclimatic context. The year of 2021 had near‐normal precipitation totals compared to the prolonged drought of 2018–20. Soil water storage was highest at the agricultural sites, while lowest at the forest, though this reflected both the influence of soil properties (as forests dominated sand soils while crops loam soils) and the greater evapotranspiration from forests. We also estimated soil water ages and found the greatest isotopic variability and fastest turnover of water in the upper soils of arable sites. The forest soil water had the most limited variability in isotopic composition and tended to be older, revealing lower levels of groundwater recharge. Conventional and innovative cropping sites were similar to each other, likely due to the early tree development stage in agroforestry schemes under the latter. Our investigation revealed the forest sites are potentially most vulnerable to limited water availability in the DMC and land use changes in agricultural land lacked major differences in ecohydrological fluxes over the study year. The study further underlines the need for long‐term observations of recent adaptive land use changes and drought‐sensitive vegetation to improve our understanding and evolve drought resilient land management strategies considering time lags in impacts and non‐stationarity.

Local moisture monitoring of unsaturated triaxial silty sample by a four-probe method based on van der Pauw configuration

Local moisture monitoring of unsaturated triaxial silty sample by a four-probe method based on van der Pauw configuration

Spatial downscaling of satellite soil moisture products based on apparent thermal inertia: Considering the effect of vegetation condition

Many applications such as irrigation management and flood predictions require soil moisture (SM) estimates at fine-resolutions. Soil moisture estimates of current satellite missions are normally at coarse spatial resolutions, such as 25–36 km for Soil Moisture and Ocean Salinity (SMOS) SM data. Many spatial downscaling approaches have been developed to get finer resolution SM. Apparent thermal inertia (ATI), as a reliable approximation of thermal inertia (TI) that could be derived from daily fine-resolution optical/thermal remote sensing (RS) data, has been testified to be highly related to SM. However, the empirical relationship between ATI and SM varies with vegetation cover density. The main purpose of this study is to develop an ATI-based SM downscaling method that is free of vegetation cover effects. The study was conducted in Naqu areas in the central Tibetan Plateau (TP) in southwest China, where spatially intensive in-situ SM measurements were routinely obtained. European Space Agency’s Climate Change Initiative (ESA CCI) soil moisture combined product (version 4.2) was used for this study. The fine-resolution (1 km) ATI data were derived from the Collection 6 MODerate resolution Imaging Spectroradiometer (MODIS) products. A modified quantile-quantile (Q-Q) adjustment method was applied to correct the bias of the CCI SM data before downscaling. A non-linear regression model between bias-corrected CCI SM and fine resolution ATI was built to disaggregate the bias-corrected SM to spatial resolution of 1 km. In this model, Enhanced Vegetation Index (EVI) was introduced to globally and quantitatively adjust the vegetation effect in the relationship between SM and ATI. The bias-corrected CCI SM and the downscaled 1 km resolution SM were evaluated using the in-situ SM measurements obtained from the Multi-scale Soil Moisture and Temperature Monitoring Network on the central TP (CTP-SMTMN) in the study area. The results indicate that the proposed modified Q-Q adjustment method effectively reduced the bias of the original CCI SM data (bias = 0 m3/m3). The downscaled 1 km resolution SM shows fine scale spatial variability of soil moisture and preserves the accuracy (R = 0.552) and temporal variability of the bias-corrected CCI SM. The accuracies of the downscaled SM over areas with different vegetation cover show quite good consistency. The uncertainties of the proposed SM spatial downscaling method were comprehensively analyzed. The proposed ATI-based SM downscaling algorithm is applicable to generating representative and consistent high spatial resolution SM over areas with different vegetation cover density.

IOT and Machine Learning Based Soil Moisture Prediction and Monitoring

IOT and Machine Learning Based Soil Moisture Prediction and Monitoring

Development and Evaluation of a Digital MPF5-Moisture Meter

An important advantage for applying the right amount of water to the fields is provided by the effective irrigation management techniques based on soil moisture monitoring. With a calibrated and exact resolution of 0.03m3/m3 and 36 mV, the developed MPF5 moisture meter monitors soil moisture from a depth of 10 cm to 80 cm using a set of sensors, transducers, capacitors, resistors, and variable micro-switch. The response monitoring system compares the soil moisture to the user-specified target values and generates an alert if the soil moisture falls below the required level for particular crops. There were many similarities between the instrument's output and that of other automated soil moisture meters like the REOTEMP-MM17 moisture meter and the PR2 capacitance moisture meter. The device works very well for both mineral and organic soils to monitor soil moisture for reliable irrigation scheduling

Continuous Twin-Screw wet granulation process with In-Barrel drying and NIR setup for Real-Time Moisture Monitoring

The purpose of this study was to evaluate if wet granule formation and drying could take place in a single operation by utilizing in-barrel drying. The drying kinetics of the formulation were studied in order to select appropriate processing parameters and assess feasibility with short residence times in the extruder. The 18-mm extruder was operated in a 40:1 L:D ratio with 8 zones. The first two zones were used for material feeding and wet granule formation and the remaining zones were used for drying at elevated temperature. The impact of screw configuration as well as screw speed, feed rate, and residence time were all studied to optimize the drying process. Due to limitations of temperature and residence time, vacuum was added to enable sufficient drying. In-line NIR spectroscopy was incorporated into the twin-screw wet granulation (TSWG) process to monitor the moisture content of wet granules in real-time. The set-up was optimized and a predictive model was developed for future experiments.This study demonstrated the success of this technique on a pilot-scale (18-mm) extruder for the first time. Granules were formed and dried to a target loss on drying (LOD) of less than 2 % at moderate temperatures (100 °C – 110 °C) with one single operation. Streamlining wet granulation and drying into one unit operation can have a profound impact on pharmaceutical manufacturing reducing time, footprint, and environmental exposure due to reduced product transfers.

1-km soil moisture retrieval using multi-temporal dual-channel SAR data from Sentinel-1 A/B satellites in a semi-arid watershed

Soil moisture is an important variable in agricultural and hydrological applications. Sentinel-1 (A/B) satellite provides high-resolution (∼10 m) synthetic aperture radar data, and its high revisit capability (6 days at the equator) with multi-track data provides great potential for global soil moisture monitoring. In this study, an algorithm was proposed for soil moisture retrieval using the Multi-Temporal Dual-Channel (MTDC) Sentinel-1 data. The MTDC algorithm first conducted an incidence angle normalization method based on time series data to normalize backscattering coefficients from multiple tracks. Subsequently, using the vegetation water content obtained through a vegetation index climatology, soil roughness and soil unfrozen water content could be estimated for the freezing period (unfrozen water content in the soil is assumed to be stable for January and December). The changes in total backscatter between successive observations were expressed as a function of the two-way vegetation transmissivity multiplied by the difference between soil backscatter, which was directly related to the changes in soil moisture. Considering the retrieved soil roughness as a constant for the entire year, and the average unfrozen water content as the initial soil moisture, the changes in and absolute values of soil moisture for each observation can be estimated by minimizing a cost function between the modeled and observed changes in total backscatter. Retrieval results were validated against the soil moisture network in a semi-arid watershed of Shandian River with an unbiased Root Mean Square Error (ubRMSE) of 0.06 cm3·cm−3, and a bias of −0.04 cm3·cm−3. With the initial soil moisture from in-situ measurements, a higher accuracy was achieved for the MTDC algorithm (ubRMSE = 0.06 cm3·cm−3; bias = −0.01 cm3·cm−3). Further, comparisons with Soil Moisture Active Passive (SMAP) enhanced products further verified the efficacy of the proposed algorithm (ubRMSE = 0.03 cm3·cm−3; bias = −0.004 cm3·cm−3), and demonstrated the feasibility of operational soil moisture products using the Sentinel-1 radar dataset.