Soil Moisture Monitoring Network Research Articles

QLB-NET: A Dense Soil Moisture and Freeze–Thaw Monitoring Network in the Qinghai Lake Basin on the Qinghai–Tibetan Plateau

Abstract Soil moisture (SM) and soil freeze–thaw (FT) are two relatively active surface parameters that are significant to the sustainable development of the water–land–air–plant–human nexus. Over time, regional or global SM and FT datasets with different spatial resolutions have been developed. In response to the requirements of multiscale product validation and multisource uncertainty tracking, a soil moisture and soil temperature (ST) monitoring network in the Qinghai Lake Basin (QLB-NET) was established in September 2019. The QLB-NET is characterized by densely distributed in situ sites (82 sites) measuring SM and ST at 5-, 10-, and 30-cm depths, with 60 sites in a large-scale network covering an area of 36 km × 40 km and 22 sites evenly distributed across two small-scale 1 km × 1 km networks. Quantitative analyses of the in situ measurements show that the QLB-NET can provide stable and reliable ground truth for SM and FT over coarse grid scales, e.g., 36 km × 36 km, 25 km × 25 km, and 0.25° × 0.25°. When statistics are correspondingly performed over 50 out of 54, 25 out of 29, and 25 out of 28 sites, the results are described as follows: 1) the standard deviation of the mean SM varies between 0.0127 and 0.0196 m3 m−3, with the corresponding difference between the upper and lower quartiles being less than 0.02 m3 m−3; 2) the ground freeze–thaw state can be correctly identified with high probabilities ranging from 85.3% to 100% on two freeze–thaw transitional dates. The QLB-NET observed datasets are distributed online and will be continuously updated through cooperation with the National Tibetan Plateau Data Center (http://data.tpdc.ac.cn), facilitating product validation and uncertainty tracking, spatiotemporal analysis of SM change and FT transition, optimization of the SM and FT retrieving algorithms and scaling methods, and development of the mountainous microwave radiative transfer model.

Seasonal dynamics and spatial patterns of soil moisture in a loess catchment

Abstract. The spatial and seasonal patterns in soil moisture and the processes controlling them in semi-arid landscapes are not well understood. Loess landscapes minimize any confounding effects of variation in soil characteristics and are thus ideal for studying topographic influences on soil moisture in drylands. In this study, volumetric soil moisture was monitored monthly for 5.5 years at 20 cm intervals between the surface and 5 m depth at 89 sites across a small (0.43 km2) catchment on the Chinese Loess Plateau. The median soil moisture was computed for each month and depth for each monitoring site as a measure of the typical soil moisture conditions. Seasonal changes in soil moisture were mainly concentrated in the shallow (0–100 cm) soil, with a clear seasonal separation between wet conditions in October–March and dry conditions in May–July, even though precipitation is highest in July–August. Soil moisture was higher on the northwest-facing slopes due to increased drying from solar radiation on the southeast-facing slopes. This effect of slope aspect was greater between October and March, when the zenith angle of the sun was lower and the aspect-dependent difference in solar radiation reaching the surface was larger. The wetter, northwest-facing slopes were also characterized by larger annual soil moisture storage changes. Soil texture was nearly uniform across both slopes, and soil moisture was not correlated with the topographic wetness index, suggesting that variations in evapotranspiration dominated the spatial pattern of soil moisture in shallow soils under both wet and dry conditions. Water balance calculations indicate that over 90 % of the annual precipitation was seasonally cycled in the soil between 0 and 300 cm, suggesting that only a minor fraction infiltrates to groundwater and becomes streamflow. Our findings may be broadly applicable to loess regions with monsoonal climates and may have practical implications for catchment-scale hydrologic modeling and the design of soil moisture monitoring networks.

Impact of soil textures on agricultural drought evolution and field capacity estimation in humid regions

Soil moisture is an important indicator for monitoring agricultural drought, and is closely related to soil texture. However, the role that soil textures play in agricultural drought evolution in humid climates is not well understood. In the study, we systematically assessed the feasibility of the Soil Water Deficit Index (SWDI) for agricultural drought monitoring in humid regions, examined the impact of soil texture and groundwater depth on agricultural drought evolution, and estimated the field holding capacity for different soil textures in humid areas based on a large-scale regional soil moisture monitoring network. The results show that: (1) The SWDI has reasonably good performance in detecting severe and extreme agricultural drought, but it tends to overestimate the drought intensity during non-severe/extreme drought periods in humid regions, particularly for clay soils. (2) Sand soils are more prone to agricultural drought than clay soils, which are more resistant to severe and extreme agricultural drought with high air temperatures and low precipitation. (3) During drought aggravation, clay soil showed a delay of about 0–18 weeks from the lowest SWDI to the maximum groundwater depth, while the delay was only 0–5 weeks for non-clay soil textures. (4) The 95th percentile is proposed as the best estimator of field holding capacity for clay and loam, while Min(maxgs) is the best estimator for sand, loamy sand, and sandy loam. Overall, these findings provide insights into the role of soil texture and groundwater depth in agricultural drought evolution in humid regions, and offer practical guidance for field holding capacity estimation for different soil textures.

Remote sensing and climate services improve irrigation water management at farm scale in Western-Central India

The enormous progress in weather and extended range predictions for the Indian monsoon over the last decade has not been translated to operationalized irrigation water management tools despite many agricultural advisories from operational agencies. The limited implementation is mainly due to the resolution mismatches of forecasts and decision-needs and a lack of soil moisture monitoring networks. Sustained soil moisture monitoring suffers from the high cost to farmers in installing distributed sensors. Here we develop an irrigation water management tool for the farmers at farm scale, which starts with utilizing and merging a few available soil moisture sensors and L-band satellite observations of surface soil moisture using machine learning. Such derived soil moisture field is used as the initial condition with the multi-ensemble future rainfall for the following few weeks given the weather/extended range forecasts in a farm-scale ecohydrological model. This ecohydrological model is integrated with Monte-Carlo simulations within a stochastic optimization framework to minimize water use while not allowing the soil moisture to drop below a threshold level with a certain probability. The optimization results in water arrangement decisions 2 weeks in advance and water application decisions 1–7 days in advance. We also estimate the water storage capacity needed at farm scale for effective water utilization. We find that 20–45 % and 17–35 % water savings were achievable for Kharif and Rabi seasons, respectively, without losing any yield when applied to grape farms of Nashik, Maharashtra, India. The proposed framework is co-developed with the farmers and can be used in any region for any crops, since it is generalized and easy to transfer. This is an extension of our earlier work to an end-to-end system using satellite data for soil moisture.

Examining the variation of soil moisture from cosmic-ray neutron probes footprint: experimental results from a COSMOS-UK site

Utilising cosmic-ray neutron probes is a relatively new approach in obtaining larger area soil moisture and various operational monitoring networks have been established worldwide utilising this technology to measure operationally this parameter. One such network located in the United Kingdom (UK) is the Cosmic-ray Soil Moisture Observing System, so-called COSMOS-UK, established in 2013. The present study aims at investigating the true footprint and the variations within the footprint detectable area at the COSMOS-UK sites using as a case study one such site located in Riseholme, UK. At the selected experimental site extensive fieldwork was conducted in July 2017 that allowed examining the agreement among the soil moisture data retrieved by the Time Domain Transmissometer (TDT) sensors and the corresponding estimates from the COSMOS-UK network station probe. The COSMOS-UK site footprint was compared using GPS-aided information from ground instrumentation, assisted by drone imagery acquisition and the implementation of geospatial interpolation methods in a Geographical Information System (GIS) environment. Altogether, this information was used for assessing the soil moisture footprint extent from the COSMOS-UK site. The COSMOS-UK station footprint was representative for an area shorter in size than the alleged footprint of 600 m diameter, as generally proposed in various relevant investigations. The COSMOS network slightly overestimated soil moisture content measured by the TDT sensor probes installed in the area. Our study findings although concern specifically the studied experimental site contribute towards efforts aiming at assessing the COSMOS-UK soil moisture measurement footprint showcasing the added value of geospatial analysis in this direction.

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.

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.

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.

ISSUES OF OPTIMAL SOIL MOISTURE CONTROL BASED ON DUAL-BAND SENSORS

The water content in the soil is spatially heterogeneous, which depends on climatic factors, land use, topography and properties of the soil itself. Currently, radio frequency soil moisture meters are the most common. The technical documentation of these devices indicates that the measurement error in them can reach 1 %. However, as the results of known experimental measurements show, such a result is achieved only after calibration with respect to a specific type of soil. Calculations show that in the absence of such calibration, the error can grow up to 15 %. In this regard, spectral methods of measuring soil moisture are more advantageous. The wellknown results of the conducted studies show that in this method the main interfering factor is the content of organic substances in the soil and taking into account only the phosphorus content in the soil makes it possible to achieve a measurement error of 6.5 %. This circumstance emphasizes the prospects of exploring additional ways to increase the efficiency of spectral methods for measuring the moisture content in the soil. The question of the optimal construction of a soil moisture monitoring network based on contact dualband optical soil moisture sensors operating in the NIR and SWIR ranges is formulated and solved. It is shown that the use of the usual averaging of the measurement results obtained on two ranges of the sensor when measuring with a constant frequency can lead to an inaccurate result due to differences in the dynamic ranges of optical signals at the detector inputs caused by the non-identity of the humidification conditions of the sub-field. The possibility of choosing an adaptive measurement mode is shown when sub-stages with a large dynamic range of humidity changes should be measured with a higher frequency, proportional to the specified dynamic range, which is equivalent to the implementation of an adaptive measurement mode.

The Patitapu Soil Moisture Network (PTSMN) dataset and its deployment in New Zealand’s hill country

A significant portion of New Zealand’s beef and sheep pastoral farming systems are located in the East Coast hill country of the North Island. In these landscapes, one of the main drivers of pasture growth is the highly variable soil water content. Decision making about fertiliser applications, stock and feed budgeting are guided by pasture growth models which rely on soil water information. However, due to the difficulties related to conventional soil moisture data collection, soil water dynamics are still poorly understood in hill country. To capture spatial and temporal soil moisture variability on diverse terrain positions, a soil moisture monitoring network was established at the field scale. The Patitapu Soil Moisture Network (PTSMN) is composed of twenty multi-sensor probes, collecting data from four consecutive depths at 0.1 m intervals. The paper describes the PTSMN deployment and the strategic selection of sensor locations through an extensive spatial analysis and discusses the challenges associated with the network deployment in an operating hill country farm. Additionally, we demonstrate the non-normal nature of soil moisture distribution and that Year 1 and Year 2 showed similarly shaped density curves in the 0.2 − 0.4 m soil depths indicating temporal stability. Similar magnitudes of temporal stability suggested the PTMSN is a temporally stable network although stability changed with depth. Results indicated that soil moisture variability decreased with depth and that higher mean soil moisture content was associated with low variability. Descending and ascending transition stages, i.e., drying, and rewetting periods resulted in the highest spatial variability. As PTSMN is a member of the International Soil Moisture Network, the calibrated, quality checked 2-year dataset has been made accessible online. The presented soil moisture dataset is expected to contribute to the research of near-surface and root-zone soil water dynamics and to assist with the establishment of future networks.

Preferential flow patterns in forested hillslopes of east Tibetan Plateau revealed by dye tracing and soil moisture network

AbstractPreferential flow (PF) through soil and regolith results in a rapid vertical and lateral water movement within the profile. This study focused on quantifying PF in contrasting forested hillslopes of the Hailuo Valley, located on China's east Tibetan Plateau. Quantifying PF in this region is challenging since the underlying matrix is complex, with shallow soils, thick underlying saprolite, and the combined effect of moraines and weathered residual material. We developed new methods that integrated dye tracing with a high‐frequency soil moisture monitoring network to characterise PF pathways and quantity PF frequency on contrasting forested hillslopes (coniferous and broadleaved). Dye tracing experiments showed that soil‐root interfaces in the upper soil layer (10–50 cm) and soil‐rock interfaces in the deeper soil layer (50–100 cm) were the primary PF pathways and that large rocks strongly influence the percolation depth of water. The high‐frequency soil moisture network revealed that the mean PF frequency was 67.8% and 71.7% in the coniferous and broadleaved forests, respectively. The frequency of PF in the deeper soil layer increased from upslope to the downslope locations on both forested hillslopes, highlighting the tendency for PF to occur downslope. In addition to matrix conditions (e.g., stony saprolite soil), the total amount and maximum intensity of precipitation (as throughfall in this study) were identified as factors that control PF occurrence. In contrast, the initial soil water conditions (at 10, 50 and 100 cm depth) were insignificant in predicting PF occurrence. Results of this field‐based study highlight that PF is a ubiquitous and critical subsurface flow mechanism that regulates rainfall‐runoff relationships in forests of the Tibetan Plateau, underscoring the need to consider PF in hillslope hydrological modelling.Highlights Combined dye tracing and soil moisture monitoring network were applied to explore PF on forested hillslopes. Root networks and rock fragments constituted the main PF paths. PF frequency increased from upslope to downslope in two forest types. Initial soil moisture conditions showed no controlling effect on PF occurrence.

Calibration and validation of soil water reflectometers

AbstractIn this study, we tested soil water reflectometers represented by the CS65x series soil water reflectometers. The objectives were to (a) quantify the sensing volume of the CS65x sensors, (b) evaluate common univariate and multivariate calibration models, and (c) assess the implications of using uncalibrated sensors. The support volume was determined by analyzing the response of the bulk dielectric permittivity to a changing amount of surrounding media in the radial and axial directions of the sensor. Calibration models were tested using a dataset of ∼300 soil samples spanning nine soil textural classes. A time series of rootzone soil water storage recorded in two contrasting soils and two intensive (n ∼300) spatial surveys of near‐surface soil moisture were used to quantify the implications of using uncalibrated sensors. The electromagnetic signal of CS655 was concentrated within a ∼3.5‐cm radius around the center of the sensor rods. Univariate and multivariate calibration equations reduced the mean absolute error (MAE) in soil moisture observations by ∼40% compared with factory default equations. Uncalibrated sensors did not change the pattern of soil moisture temporal dynamics but resulted in a strong bias in soil water storage in a silty clay loam soil and altered the soil moisture spatial patterns obtained in moderate to wet field conditions in a silty clay soil. The CS655 sensor with the Kargas and Soulis calibration equation (MAE = 0.026 cm3 cm−3) appears to be an acceptable candidate for deployment in soil moisture monitoring networks seeking to avoid site‐specific sensor calibrations.

Downscaling of soil moisture products using deep learning: Comparison and analysis on Tibetan Plateau

Soil moisture (SM) is a very important physical parameter in land surface processes. The low-resolution microwave products are extremely useful for large-scale studies, but are limited in their ability to resolve landscape-level variability. In this study, to overcome this limitation, we adopted three different deep learning based downscaling methods and performed a comparative analysis. Firstly, a downscaling method based on a deep belief network (DBN) model with a fully connected structure was employed to fit the complex nonlinear relationship between soil moisture and the auxiliary surface parameters. Then, to improve this simple point-to-point relationship, a neighborhood constraint based improved DBN model was developed to consider the effect of local spatial correlation among the surface factors on soil moisture. Finally, to maintain the integrity of the spatial structure in the process of model construction, a residual network (ResNet) model consisting of several residual dense blocks (RDBs) was introduced to use convolutions to obtain more complete and powerful spatial feature maps. The Tibetan Plateau was selected as the study area because of its importance to the global water cycle. The four soil moisture monitoring networks in the research area were used to verify the effectiveness of the proposed methods in the downscaling of soil moisture products, and four quantitative indicators were used to evaluate the effectiveness of the methods. From the verified results, it is shown that the three deep learning methods can maintain a higher correlation and better reduce the bias, on the whole, and the ResNet model, in particular, shows a higher stability. From the perspective of the spatial patterns and details, when compared to back-propagation neural network (BPNN) and random forest (RF) models, the three deep learning methods can provide more feasible spatial patterns and details, as well as reducing the uncertainty in the central regions of the Tibetan Plateau. The ResNet model shows a superior ability to enhance the texture details with lower uncertainty, due to the fact that it can meet the requirements of fitting the soil moisture to complex surfaces. Furthermore, the results of the ResNet model can also effectively capture the temporal changes of the microwave soil moisture product well.

Development and validation of an operational multi-layered model for estimation of soil moisture at point-scale in South Africa

Data discontinuity is the major challenge that hinders the integrity of datasets from the sparse soil moisture monitoring networks in developing countries. In this study, a simplified, multi-layered soil water balance model to estimate daily soil moisture at point-scale from standard weather data and minimal physical soil properties was developed. The model requires values for soil water retentivity properties such as wilting point, field capacity and saturation of each soil layer. It also requires measurements or estimates of reference evapotranspiration (ETo ) in addition to rainfall as climate inputs. The developed model was evaluated using point-scale in-situ soil moisture measurements acquired over a minimum of two years from three well-calibrated stations representing different soil types and climatic conditions in South Africa. The results indicate that the proposed model was capable of estimating total soil moisture content at all three sites, with coefficient of determination (r 2) values greater than 0.84, index of agreement (d) values greater than 0.95 and root mean square error (RMSE) values less than 7.30 mm. The findings of this study suggest that the proposed model can be reliably used for daily estimation of soil moisture at point-scale using climate data and minimal soil physical properties, to fill in gaps, and to extend datasets in locations facing data-discontinuity.

Developing a strategy for the national coordinated soil moisture monitoring network

AbstractSoil moisture is a critical land surface variable, affecting a wide variety of climatological, agricultural, and hydrological processes. Determining the current soil moisture status is possible via a variety of methods, including in situ monitoring, remote sensing, and numerical modeling. Although all of these approaches are rapidly evolving, there is no cohesive strategy or framework to integrate these diverse information sources to develop and disseminate coordinated national soil moisture products that will improve our ability to understand climate variability. The National Coordinated Soil Moisture Monitoring Network initiative has developed a national strategy for network coordination with NOAA's National Integrated Drought Information System. The strategy is currently in review within NOAA, and work is underway to implement the initial milestones of the strategy. This update reviews the goals and steps being taken to establish this national‐scale coordination for soil moisture monitoring in the United States.

Improving soil moisture prediction of a high-resolution land surface model by parameterising pedotransfer functions through assimilation of SMAP satellite data

Abstract. Pedotransfer functions are used to relate gridded databases of soil texture information to the soil hydraulic and thermal parameters of land surface models. The parameters within these pedotransfer functions are uncertain and calibrated through analyses of point soil samples. How these calibrations relate to the soil parameters at the spatial scale of modern land surface models is unclear because gridded databases of soil texture represent an area average. We present a novel approach for calibrating such pedotransfer functions to improve land surface model soil moisture prediction by using observations from the Soil Moisture Active Passive (SMAP) satellite mission within a data assimilation framework. Unlike traditional calibration procedures, data assimilation always takes into account the relative uncertainties given to both model and observed estimates to find a maximum likelihood estimate. After performing the calibration procedure, we find improved estimates of soil moisture and heat flux for the Joint UK Land Environment Simulator (JULES) land surface model (run at a 1 km resolution) when compared to estimates from a cosmic-ray soil moisture monitoring network (COSMOS-UK) and three flux tower sites. The spatial resolution of the COSMOS probes is much more representative of the 1 km model grid than traditional point-based soil moisture sensors. For 11 cosmic-ray neutron soil moisture probes located across the modelled domain, we find an average 22 % reduction in root mean squared error, a 16 % reduction in unbiased root mean squared error and a 16 % increase in correlation after using data assimilation techniques to retrieve new pedotransfer function parameters.

Next-Generation Soil Moisture Sensor Web: High-Density In Situ Observation Over NB-IoT

Soil moisture is an essential variable both in environmental monitoring research and application. With the requirement of high-precision soil moisture data, it is highly necessary to construct in-situ soil moisture sensors Web in high density, in which the economics, complexity, and low-power consumption of sensor Webs should be essentially considered. However, most current existing soil moisture monitoring networks have limitations due to high power consumption, complex architecture, and expensive equipment. These issues are not conducive to high-density soil moisture observation. In view of the existing problems in the current soil moisture in-situ sites, an effective resolution for high-density soil moisture observation is needed. For the first time, we are bringing Narrow-Band Internet of Things (NB-IoT), a low-power Internet-of-Things technology, into geospatial sensor Web for soil moisture observation. Moreover, we built a high-density in-situ soil moisture sensor Web via NB-IoT and compared it with ZigBee at the Baoxie experimental zone in Wuhan, China, there about 1 km <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> , and we acquired data for up to 12 months. We analyze the acquisition record of battery status, signals, and soil moisture. We analyzed the battery life, the impact of the signal on the battery performance, the signal quality, and data acquisition clearly and intuitively. This unprecedented study and application, we discovered and concluded that the low-power sensor Web of the NB-IoT communication protocol could be suitable for high-density soil moisture observation.

Single-Pass Soil Moisture Retrieval Using GNSS-R at L1 and L5 Bands: Results from Airborne Experiment

Global Navigation Satellite System—Reflectometry (GNSS-R) has already proven its potential for retrieving a number of geophysical parameters, including soil moisture. However, single-pass GNSS-R soil moisture retrieval is still a challenge. This study presents a comparison of two different data sets acquired with the Microwave Interferometer Reflectometer (MIR), an airborne-based dual-band (L1/E1 and L5/E5a), multiconstellation (GPS and Galileo) GNSS-R instrument with two 19-element antenna arrays with four electronically steered beams each. The instrument was flown twice over the OzNet soil moisture monitoring network in southern New South Wales (Australia): the first flight was performed after a long period without rain, and the second one just after a rain event. In this work, the impact of surface roughness and vegetation attenuation in the reflectivity of the GNSS-R signal is assessed at both L1 and L5 bands. The work analyzes the reflectivity at different integration times, and finally, an artificial neural network is used to retrieve soil moisture from the reflectivity values. The algorithm is trained and compared to a 20-m resolution downscaled soil moisture estimate derived from SMOS soil moisture, Sentinel-2 normalized difference vegetation index (NDVI) data, and ECMWF Land Surface Temperature.

A soil moisture monitoring network to assess controls on runoff generation during atmospheric river events

AbstractSoil moisture is a key modifier of runoff generation from rainfall excess, including during extreme precipitation events associated with Atmospheric Rivers (ARs). This paper presents a new, publicly available dataset from a soil moisture monitoring network in Northern California's Russian River Basin, designed to assess soil moisture controls on runoff generation under AR conditions. The observations consist of 2‐min volumetric soil moisture at 19 sites and 6 depths (5, 10, 15, 20, 50, and 100 cm), starting in summer 2017. The goals of this monitoring network are to aid the development of research applications and situational awareness tools for Forecast‐Informed Reservoir Operations at Lake Mendocino. We present short analyses of these data to demonstrate their capability to characterize soil moisture responses to precipitation across sites and depths, including time series analysis, correlation analysis, and identification of soil saturation thresholds that induce runoff. Our results show strong inter‐site Pearson's correlations (&gt;0.8) at the seasonal timescale. Correlations are strong (&gt;0.8) during events with high antecedent soil moisture and during drydown periods, and weak (&lt;0.5) otherwise. High event runoff ratios are observed when antecedent soil moisture thresholds are exceeded, and when antecedent runoff is high. Although local heterogeneity in soil moisture can limit the utility of point source data in some hydrologic model applications, our analyses indicate three ways in which soil moisture data are valuable for model design: (1) sensors installed at 6 depths per location enable us to identify the soil depth below which evapotranspiration and saturation dynamics change, and therefore choose model soil layer depths, (2) time series analysis indicates the role of soil moisture processes in controlling runoff ratio during precipitation, which hydrologic models should replicate, and (3) spatial correlation analysis of the soil moisture fluctuations helps identify when and where distributed hydrologic modelling may be beneficial.