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Predicting and managing the impacts of flash droughts is difficult owing to their rapid onset and intensification. Flash drought monitoring often relies on assessing changes in root-zone soil moisture. However, the lack of widespread soil moisture measurements means that flash drought assessments often use process-based model data like that from the North American Land Data Assimilation System (NLDAS). Such reliance opens flash drought assessment to model biases, particularly from vegetation processes. Here, we examine the influence of vegetation on NLDAS-simulated flash drought characteristics by comparing two experiments covering 1981–2017: open loop (OL), which uses NLDAS surface meteorological forcing to drive a land surface model using prognostic vegetation, and data assimilation (DA), which instead assimilates near-real-time satellite-derived leaf area index (LAI) into the land surface model. The OL simulation consistently underestimates LAI across the United States, causing relatively high soil moisture values. Both experiments produce similar geographic patterns of flash droughts, but OL produces shorter duration events and regional trends in flash drought occurrence that are sometimes opposite to those in DA. Across the Midwest and Southern United States, flash droughts are 4 weeks (about 70%) longer on average in DA than OL. Moreover, across much of the Great Plains, flash drought occurrence has trended upward according to the DA experiment, opposite to the trend in OL. This sensitivity of flash drought to the representation of vegetation suggests that representing plants with greater fidelity could aid in monitoring flash droughts and improve the prediction of flash drought transitions to more persistent and damaging long-term droughts. Significance Statement: Flash droughts are a subset of droughts with rapid onset and intensification leading to devastating losses to crops. Rapid soil moisture decline is one way to detect flash droughts. Because there is a lack of widespread observational data, we often rely on model outputs of soil moisture. Here, we explore how the representation of vegetation within land surface models influences the U.S. flash drought characteristics covering 1981–2017. We show that the misrepresentation of vegetation status propagates soil moisture biases into flash drought monitoring, impacting our understanding of the onset, magnitude, duration, and trends in flash droughts. Our results suggest that the assimilation of near-real-time vegetation into land surface models could improve the detection, monitoring, and prediction of flash droughts. [ABSTRACT FROM AUTHOR]

Summary Soil moisture shapes ecological patterns and processes, but it is difficult to continuously measure soil moisture variability across the landscape. To overcome these limitations, soil moisture is often bioindicated using community‐weighted means of the Ellenberg indicator values of vascular plant species. However, the ecology and distribution of plant species reflect soil water supply as well as atmospheric water demand. Therefore, we hypothesized that Ellenberg moisture values can also reflect atmospheric water demand expressed as a vapour pressure deficit (VPD). To test this hypothesis, we disentangled the relationships among soil water content, atmospheric vapour pressure deficit, and Ellenberg moisture values in the understory plant communities of temperate broadleaved forests in central Europe. Ellenberg moisture values reflected atmospheric VPD rather than soil water content consistently across local, landscape, and regional spatial scales, regardless of vegetation plot size, depth as well as method of soil moisture measurement. Using in situ microclimate measurements, we discovered that forest plant indicator values for moisture reflect an atmospheric VPD rather than soil water content. Many ecological patterns and processes correlated with Ellenberg moisture values and previously attributed to soil water supply are thus more likely driven by atmospheric water demand. [ABSTRACT FROM AUTHOR]

In this study, the emphasis is on assessing how satellite-derived vegetation indices respond to drought stress characterized by meteorological observations. This study aimed to understand the dynamics of grassland vegetation and assess the impact of drought in the Wielkopolskie (PL41) and Podlaskie (PL84) regions of Poland. Spatial and temporal characteristics of grassland dynamics regarding drought occurrences from 2020 to 2023 were examined. Pearson correlation coefficients with standard errors were used to analyze vegetation indices, including NDVI, NDII, NDWI, and NDDI, in response to drought, characterized by the meteorological parameter the Hydrothermal Coefficient of Selyaninov (HTC), along with ground-based soil moisture measurements (SM). Among the vegetation indices studied, NDDI showed the strongest correlations with HTC at r = −0.75, R2 = 0.56, RMSE = 1.58, and SM at r = −0.82, R2 = 0.67, and RMSE = 16.33. The results indicated drought severity in 2023 within grassland fields in Wielkopolskie. Spatial–temporal analysis of NDDI revealed that approximately 50% of fields were at risk of drought during the initial decades of the growing season in 2023. Drought conditions intensified, notably in western Poland, while grasslands in northeastern Poland showed resilience to drought. These findings provide valuable insights for individual farmers through web and mobile applications, assisting in the development of strategies to mitigate the adverse effects of drought on grasslands and thereby reduce associated losses. [ABSTRACT FROM AUTHOR]

The forests of central Germany (Saxony, Saxony-Anhalt, and Thuringia) are vital components of the local ecosystems, the economy, and recreation. However, in recent years, these forests have faced significant challenges due to prolonged climate-change-induced droughts, causing water shortages, tree stress, and pest outbreaks. One of the key components of the forests' vitality and productivity is the availability of soil moisture. Given the anticipated increase in the frequency and severity of drought events, there is a growing demand for accurate and real-time soil moisture information. This underscores the need for development of an appropriate monitoring tool to make forest management strategies more effective. The article introduces an operational high-resolution soil moisture monitoring framework for the forests in central Germany. The key components of this system include the advanced LWF-BROOK90 1D water balance model, a large database of the National Federal Forest Inventory, high-resolution forest soil maps, real-time climate data from the German Meteorological Service, and a web information platform for the presentation of daily updated results. This system informs the public and empowers forest managers and other decision-makers to take targeted, local measures for sustainable forest management, aiding in both drought mitigation and long-term forest health in the face of climate change. The validation of the system using soil moisture measurements from 51 stations with various sensor depths (up to 100 cm) showed an overall good agreement (0.76 median Pearson correlation), which was found to be higher for deciduous rather than coniferous forests. Finally, the framework is discussed against the background of the main limitations of existing monitoring systems and how operational soil moisture measurements contribute to better interpretation of simulations. [ABSTRACT FROM AUTHOR]

Measuring the soil water content (SWC) is a fundamental component of the sustainable management of water resources, soil preservation, and high irrigation efficiency. Non-destructive SWC measurements using soil moisture sensors (SMSs) enables timely irrigation and reduces overirrigation and water stress. Within this context, the performance of four commercial single-point soil moisture sensors (Watermark and tensiometer (Irrometer Company, Inc., Riverside, CA, USA), SM150 (Delta-T Devices, Cambridge, UK)), FieldScout TDR300 (Spectrum Technologies, Aurora, IL, USA) and one soil profile PR2 probe (Delta-T Devices, Cambridge, UK) were tested under anthropogenic eutric cambisol with a silty clay loamy texture (20, 30, and 40 cm) to evaluate accuracy and sensitivity to changes in the SWC in an irrigated apple orchard. The Watermark and tensiometer were additionally tested in the laboratory to convert soil water tension (kPa) to the volumetric soil water content (%vol.). In general, all tested SMSs responded to changes in the SWC, with sensor-to-sensor differences. The Watermark and tensiometer underestimated the SWC, while the TDR overestimated the SWC. The SM150 and PR2 showed high accuracy, i.e., SM150—RMSE-2.24 (20 cm), 2.18 (30 cm) and 2.34 (40 cm), MSE—5.02 (20 cm), 2.93 (30 cm) and 1.89 (40 cm), and PR2—RMSE-1.8 (20 cm), 1.3 (30 cm) and 1.55 (40 cm), MSE-3.23 (20 cm), 1.7 (30 cm) and 2.39 (40 cm) at all observed soil depths. [ABSTRACT FROM AUTHOR]

Agricultural irrigation is a significant contributor to freshwater consumption. However, the current irrigation systems used in the field are not efficient. They rely mainly on soil moisture sensors and the experience of growers but do not account for future soil moisture loss. Predicting soil moisture loss is challenging because it is influenced by numerous factors, including soil texture, weather conditions, and plant characteristics. This article proposes a solution to improve irrigation efficiency, which is called DRLIC (deep reinforcement learning for irrigation control). DRLIC is a sophisticated irrigation system that uses deep reinforcement learning (DRL) to optimize its performance. The system employs a neural network, known as the DRL control agent, which learns an optimal control policy that considers both the current soil moisture measurement and the future soil moisture loss. We introduce an irrigation reward function that enables our control agent to learn from previous experiences. However, there may be instances in which the output of our DRL control agent is unsafe, such as irrigating too much or too little. To avoid damaging the health of the plants, we implement a safety mechanism that employs a soil moisture predictor to estimate the performance of each action. If the predicted outcome is deemed unsafe, we perform a relatively conservative action instead. To demonstrate the real-world application of our approach, we develop an irrigation system that comprises sprinklers, sensing and control nodes, and a wireless network. We evaluate the performance of DRLIC by deploying it in a testbed consisting of six almond trees. During a 15-day in-field experiment, we compare the water consumption of DRLIC with a widely used irrigation scheme. Our results indicate that DRLIC outperforms the traditional irrigation method by achieving water savings of up to 9.52%. [ABSTRACT FROM AUTHOR]

Global food production relying on irrigated agriculture accounts for >70% of the global freshwater withdrawal. A thorough understanding of soil–water characteristics (SWCs) and critical soil–water values in the soil and subsoil is important for effective management of irrigated water. A critical soil–water "window" for plants is generally taken as the plant‐available water window without considering diffusion‐dominated soil aeration as a co‐requisite. This study examined SWC curves in vadose soil profiles (up to 1.5‐m depth) in eight pasture soils. The soil moisture measurements were made over matric potentials ranging from −1 to −1500 kPa using tension table and pressure plate apparatus. The van Genuchten model was used to parameterize the measured SWC curve, while the Millington‐Quirk model was used to derive soil–gas diffusivity from measured soil physical properties. We defined critical soil–water windows considering the threshold values for both plant‐available water and soil–gas diffusivity to ensure water and aeration corequisites for plant growth. The results clearly distinguished depth‐dependent regimes of gravitational, plant‐available, and unavailable water in selected profiles and their responses to soil structural changes across the depth. In some of the observed soil profiles, only 30%–60% of the plant‐available water window was able to be utilized by plants because the remainder existed under soil conditions where soil aeration was inadequate for plant growth, emphasizing the importance of considering both the plant's water and aeration requirements during irrigation scheduling. Further, the infiltration profiles in two selected soils under simulated irrigation highlighted the importance of a priori knowledge of the soil structure in deeper soil layers for scheduling irrigation. Core Ideas: Depthwise soil–water characteristics were measured in eight pasture sites.Density effects of soil moisture retention were observed in selected profiles.Both plant‐available water and soil aeration requirements are used to define a critical soil–water window.Simulated irrigation was used to investigate infiltration dynamics in selected profiles. [ABSTRACT FROM AUTHOR]

Understanding soil moisture variability and estimating high‐resolution soil moisture at subfield to field scales is critical for agricultural research and applications. However, systematic investigation of subfield scale soil moisture variability over cropland is still lacking from both measurement and satellite remote sensing. In this study, we aim to investigate (1) the characteristics of within‐field soil moisture distribution over typical cropland in the US Midwest and (2) the capabilities of satellite remote sensing in capturing the spatiotemporal variabilities of soil moisture at subfield scale. Specifically, we conducted soil moisture field experiments in three typical commercial agricultural fields (∼85 acres per field) in central Illinois, representing typical commercial farmlands in the US Midwest, and compared the soil moisture measurements with satellite remote sensing data from optical and active microwave sensors. In each field, dense soil moisture samples (spaced at 50–60 m) were obtained for two dry down events in May and July 2021, and multiple long‐term soil moisture stations were installed. We found prominent time‐invariant spatial structures of soil moisture at within‐field scales both during the dry down period and over longer time scales, and the stability is minimally affected by plant water use during the growing season. Comparing the field campaign measurements with satellite remote sensing data, we found that surface reflectance of shortwave infrared bands, such as SWIR1 (1610 nm) from Sentinel‐2, can capture relative surface soil moisture patterns at within‐field scales, but their relationships with soil moisture are field specific. These findings and the improved understanding of within‐field soil moisture dynamics could potentially help future research on high‐resolution soil moisture estimation with multi‐source remote sensing data. Core Ideas: High‐density soil moisture sampling was conducted in agricultural fields.Time‐invariant spatial structures of within‐field soil moisture were evaluated.Strong but field‐specific relationships between soil moisture and optical remote sensing were found.A pathway for remote sensing based high‐resolution soil moisture estimation was proposed. [ABSTRACT FROM AUTHOR]

Ground‐mounted photovoltaic sites are often treated as impervious surfaces in stormwater permits. This ignores the pervious soils beneath and between solar arrays and leads to an overestimation of runoff. Our objective was to improve solar farm stormwater hydrology models by explicitly considering the disconnected impervious nature of solar design and site characteristics. Experimental sites established on utility scale solar farms in Colorado, Georgia, Minnesota, New York, and Oregon had perennial vegetative plantings with mean precipitation ranging from 40.6 to 124.5 cm, and soil texture ranging from loamy sand to clay. Soil moisture measurements were collected beneath arrays, under drip edges, and in the vegetated area between arrays at each site. Hydrus‐3D models for soil moisture and stormwater hydrology were developed that accounted for precipitation falling on solar panels, drip edge redistribution of rainfall, infiltration, and runoff in the pervious areas between solar arrays and beneath panels. Drip edge runoff averaged 3‐ to 10‐times incident precipitation at the New York and Minnesota sites, respectively. Root mean square error values between measured sub‐hourly soil moisture and predicted moisture for large measured single storm events averaged 0.029 across all five sites. Predicted runoff depths were strongly affected by precipitation depth, soil texture, soil profile depth, and soil bulk density. Runoff depths across the five experimental sites averaged 13%, 25%, and 45% of the 2‐, 10‐, and 100‐year design storm depths, clearly showing that these solar farms do not behave like impervious surfaces, but rather as disconnected impervious surfaces with substantial infiltration of runoff in the vegetated areas between and beneath solar arrays. Core Ideas: Hydrus‐3D accurately estimated soil moisture and infiltration beneath and between solar arrays.Solar panel drip edge runoff averaged from 3X to 10X precipitation depth falling on panels.Design storm depth, soil texture and depth, and soil bulk density had a large impact on solar farm runoff.Solar farm runoff on coarse‐textured, deep soils was much less than runoff from fine‐textured, shallow soils.Solar farm hydrology is accurately represented with a disconnected impervious modeling framework. [ABSTRACT FROM AUTHOR]

This article illustrates the development of SolarFertigation (SF), an IoT (Internet of Things) solution for precision agriculture. Contrary to similar systems on the market, SolarFertigation can monitor and optimize fertigation autonomously, based on the analysis of data collected through the cloud. The system is made up of two main components: the central unit, which enables the precise deployment and distribution of water and fertilizers in different areas of the agricultural field, and the sensor node, which oversees collecting environmental and soil data. This article delves into the evolution of the system, focusing on structural and architectural changes to develop an infrastructure suitable for implementing a predictive model based on artificial intelligence and big data. Aspects concerning both the sensor node, such as energy management, accuracy of solar radiation readings, and qualitative soil moisture measurements, as well as implementations to the hydraulic system and the control and monitoring system of the central unit, are explored. This article provides an overview of the results obtained from solar radiation and soil moisture measurements. In addition, the results of an experimental campaign, in which 300 salad plants were grown using the SolarFertigation system in a photovoltaic field, are presented. This study demonstrated the effectiveness and applicability of the system under real-world conditions and highlighted its potential in optimizing resources and increasing agricultural productivity, especially in agrivoltaic settings. [ABSTRACT FROM AUTHOR]

Permafrost warming has been observed all around the Arctic, however, variations in temperature trends and their drivers remain poorly understood. We present a comprehensive analysis of climatic changes spanning 25 years (1998–2023) at Bayelva (78.92094°N, 11.83333°E) on Spitzbergen, Svalbard. The quality controlled hourly data set includes air temperature, radiation fluxes, snow depth, rainfall, active layer temperature and moisture, and, since 2009, permafrost temperature. Our Bayesian trend analysis reveals an annual air temperature increase of 0.9 ± 0.5°C/decade and strongest warming in September and October. We observed a significant shortening of the snow cover by −14 ± 8 days/decade, coupled with reduced winter snow depth. The active layer simultaneously warmed by 0.6 ± 0.7°C/decade at the top and 0.8 ± 0.5°C/decade at the bottom. While the soil surface got drier, in particular during summer, soil moisture below increased in accordance with the longer unfrozen period and higher winter temperatures. The thawed period prolonged by 10–15 days/decade at different depths. In contrast to earlier top‐soil warming, we observed stable temperatures since 2010 and only little permafrost warming (0.14 ± 0.13°C/decade). This is likely due to recently stable winter air temperature and continuously decreasing winter snow depth. This recent development highlights a complex interplay among climate and soil variables. Our distinctive long‐term data set underscores (a) the changes in seasonal warming patterns, (b) the influential role of snow cover decline, and (c) that air temperature alone is not a sufficient indicator of change in permafrost environments, thereby highlighting the importance of investigating a wider range of parameters, such as soil moisture and snow characteristics. Plain Language Summary: Permafrost is warming across the Arctic, but it is not yet well understood why temperature trends vary and what affects them the most. Our detailed study investigates 25 years (1998–2023) of data at the Bayelva permafrost observatory on Svalbard. We analyzed a quality‐controlled data set, including hourly measurements of air temperature, radiation, snow depth, rainfall, permafrost temperature, and active layer conditions. We looked beyond annual averages, examining changes in each month. The air warmed strongly by 0.9°C/decade and even stronger in September and October. Continuous snow cover shortened by −14 days/decade and winter snow depth decreased. Simultaneously, the active layer warmed by 0.6°C/decade at the top and 0.8°C/decade at the bottom. While the surface dried in summer, deeper soil layers became moister due to a longer unfrozen period and higher temperatures in winter. The thawed period extended by 10–15 days/decade, with slightly stronger changes toward later freezing in autumn. We found that soil warming stopped in recent years and attributed this effect to the lower winter snow depth since 2010. Therefore, if we want to know how permafrost warms or cools in the future, we need to consider additional measurements such as soil moisture and snow properties. Key Points: Snow cover thinning and winter air temperature variability are the most important drivers of trends in permafrost temperatureWhile mean annual air temperature continues to increase, the top soil at Bayelva, Svalbard, stopped warming since 2010The fully snow‐covered season shortened by −14 days per decade since 1998, which led to longer unfrozen conditions in the active layer [ABSTRACT FROM AUTHOR]

The present study was designed to provide a model for surface soil moisture numerical estimation. This assessment is done based on the direct ground measurement of soil moisture in 5 cm (SM5) and 10 cm (SM10) depths using machine learning models. To do this, various meteorological variables (16 variables) were used as model inputs. The data were evaluated on a daily scale during 2017–2020. Of these data, 75% of days were randomly considered as train and 25% as test. The components relevant to air and soil temperature, relative air humidity, evaporation, and vapor pressure are the most important factors that affect daily soil moisture. A mixture of these variables is used as model input. For this purpose, two machine learning models, including a multilayer perceptron (MLP) neural network and an adaptive neuro-fuzzy inference system (ANFIS) were used. Three agriculture meteoritical stations located in three different climates were assessed: (1) Gharakhil Station (semi-humid and moderate), Zarghan Station (semi-arid and cold), and Zahak Station (extra-arid and moderate). According to the comparison between estimates and measurements, both models had a relatively desired performance in Gharakhil and Zarghan (57% < R2 < 66% for SM5 and 45% < R2 < 58% for SM10). However, the performances were weak and almost unacceptable in the extra-arid Zahak climate (14% < R2 < 17% for SM5 and 18% < R2 < 22% for SM10). According to the relative root mean square error (RRMSE) and Nash–Sutcliffe value of stations, the models in humid climates, performed better than those in arid and extra-arid climates. The best RRMSE value was obtained by ANFIS in Gharakhil Stations (0.193 for SM5 and 0.178 for SM10), while the weakest RRMSE value was obtained in Zahak Station, which equaled 0.887 (via MLP) and 0.767 (via ANFIS) for SM5 and SM10, respectively. The applied models were not superior to each other; however, the ANFIS model was slightly superior to MLP in most cases. [ABSTRACT FROM AUTHOR]

In recent decades, there has been increasing interest in studying the variability in soil water properties and, specifically, the spatiotemporal variability in the soil water content. This is motivated by the notable theoretical and applied research interests in soil moisture dynamics and their implications for many natural processes. This study aimed to study whether there are variations in the spatial pattern of the temporal stability of soil moisture over time and to analyze the possible influences of certain hydroclimatic (soil water content, precipitation, and evapotranspiration) and soil factors (texture, bulk density, and organic matter content) on these variations. This study was conducted within the Soil Moisture Measurement Stations Network of the University of Salamanca (REMEDHUS, Spain) under Mediterranean conditions, with daily surface moisture data (0-5 cm depth) obtained from 20 stations for the 2006-2023 period. The results showed differences between the average pattern obtained with the 18-year data series and that obtained with the data series for each year. In more than half of the years studied, the representative station differed from that derived from the average pattern. The mean annual precipitation and summer precipitation characteristics seem to be the main factors influencing the variability in the spatial pattern of the temporal stability of soil moisture. [ABSTRACT FROM AUTHOR]

Overground rock is a prominent feature of rocky desertification landscape in karst farmland; however, people often pay attention to their adverse effects, leaving their positive effects on ecohydrological processes and plant growth as rarely studied and utilized. In this study, the effects of overground rock film mulching (ORFM) on soil water flow behavior, soil water content and temporal and spatial heterogeneity were investigated through a dye tracer test and soil moisture measurement. Moreover, the effects of this technology on the root characteristics of crops (maize and broad bean) were analyzed. The results showed that ORFM treatment significantly increased soil water content and its spatio-temporal heterogeneity by preventing preferential flow at the rock–soil interface. It suggested that this practice can provide a more favorable soil moisture environment for crop growth, which was confirmed by the differences in root characteristics of crops (maize and broad bean) under different treatments in this study. It was found that ORFM treatment reduced the root radial extent of crops but increased the root biomass and root bifurcation rate, which are widely considered to be key factors in improving the efficiency of fine root absorption. Therefore, we believe that ORFM has great potential to improve the effective use of soil water and agricultural water management in karst areas, which is essential for sustainable agricultural development in the region. [ABSTRACT FROM AUTHOR]

When estimating field‐scale average soil moisture from sensors measuring at fixed positions, spatial variability in soil moisture leads to "measurement errors" of the spatial mean, which may persist over time due to persistent soil moisture patterns resulting in autocorrelated measurement errors. The uncertainty of parameters that are derived from such measurements may be underestimated when they are assumed to be independent. Temporal autocorrelation models assume stationary random errors, but such error models are not necessarily applicable to soil moisture measurements. As an alternative, we propose a mechanistic error model that is based on the spatial variability of the water retention curve and assumes a uniform water potential. We tested whether spatial soil moisture variability and its temporal covariance could be predicted based on (1) mean soil moisture, (2) water retention variability, and (3) (co)variances of the van Genuchten parameters using a first‐order expansion of the retention curve. The proposed models were tested in a numerical and a field experiment. For the field experiment, in situ sensor measurements and water retention curves were obtained in a field plot. Both experiments showed that water retention variability under a uniform water potential is a good predictor for spatial soil moisture variability, and that soil moisture errors are strongly correlated in time and neglecting them would be an incorrect assumption. The temporal error covariance could be predicted as a function of the mean moisture contents at two observation times. Further research is required to assess the impact of these temporal correlations on soil moisture predictions. Core Ideas: Soil moisture is spatially variable, and spatial variations are correlated in time.Due to spatial variability, the estimated average soil moisture from a limited number of measurements is uncertain.The error of the spatial mean estimated from a limited number of sensors at fixed positions is correlated in time.A mechanistic error modeling approach is presented based on the spatial variability of the water retention curve.Such a model can predict measurement variability and error covariance in time as a function of mean soil moisture. [ABSTRACT FROM AUTHOR]

Soil moisture plays a major role in agricultural yield, hydrological, and environmental monitoring studies. Soil-moisture's accurate estimation is important for successfully improving crop yield efficiently using agricultural resources and effectively managing water resources. The current article aims to list different approaches to estimating soil moisture such as using microwave, radar, and sensor-based methods of electronic and communication engineering and also IoT-based methods from computer engineering fields to find the journey of use of state-of-the-art technology methods for measuring soil moisture. Out of the mentioned methods, the Sensor & IoT-based method is playing a vital role and is currently being used in advanced countries. Therefore this research is focused on experimenting the water flow control and measurement of soil moisture using sensors on micro-controller-based prototypes. Experiments showed that it is feasible to estimate the requirement of water more accurately than traditional methods. Thus more water savings can be benefited. [ABSTRACT FROM AUTHOR]

Satellite ocean color and sea surface temperature (SST) observations from 2012 to 2021 and sea surface salinity (SSS) measurements from the Soil Moisture Active Passive (SMAP) mission from 2015 to 2021 are used to characterize and quantify the seasonal and interannual variability in the physical, optical, and biological sea surface features in the Costa Rica Thermal Dome (CRTD) region. High-resolution climatology and the seasonal variability in SST, SSS, and ocean color properties are produced. Chlorophyll-a (Chl-a) concentration, SST, and SSS show these three properties are linked with similar spatial patterns and seasonal variations, i.e., elevated Chl-a concentrations, match the depressed SST and increased SSS and vice versa. This reflects that the physical driving force is the same for these three ocean properties and implies that nutrient supply associated with the physical processes is the major driver for the seasonal biological variability. The interannual changes in Chl-a, SST, and SSS also show that these three ocean properties are consistent among themselves. The positive (negative) Chl-a anomaly generally occurs with negative (positive) SST anomaly and enhanced (reduced) SSS. The in situ measurements evidently show that the subsurface ocean dynamics in the upper 100 m controls the sea surface variability for Chl-a, SST, and SSS. We report that no significant enhancement of Chl-a is observed in the CRTD region during the central Pacific (CP)-type 2020–2021 La Niña event, while Chl-a changes are significant in the other three of four ENSO events between 2012 and 2021. Furthermore, the difference in Chl-a variability driven by the CP-type ENSO and eastern Pacific (EP)-type ENSO is further discussed. [ABSTRACT FROM AUTHOR]

One of the alternative methods for managing irrigation water is deficit irrigation, particularly alternate furrow irrigation (AFI). This deficit in irrigation is affected by uncontrolled rainfall. In line with this, rainfall uncertainty causes a variation between the measured actual crop evapotranspiration and the theoretical crop evapotranspiration. Let us imagine that rain falls during the deficit irrigation research, and the soil moisture under the deficit experiment is then raised to the soil field capacity. It is incorrect to report the result as a deficit. Thus, there is a research gap on the effect of rainfall uncertainty on the quantity of theoretical and actual crop evapotranspiration under deficit irrigation. This study was carried out at the Arba Minch University demonstration site on onion crops. Using CROPWAT 8.0 software, the reference evapotranspiration (ETo) was calculated using the Penman-Monteith formula. The crop coefficient and ETo were used to calculate the theoretical crop evapotranspiration. In contrast, actual crop evapotranspiration was calculated using soil moisture measurements before and after each irrigation event after applying theoretical crop evapotranspiration. As a result, there is a significant difference between the calculated theoretical crop evapotranspiration and actual crop evapotranspiration from a deficit study. Thus, the calculated seasonal theoretical crop evapotranspiration was 201.72 mm. On the other hand, the actual crop evapotranspiration was 275.82 mm. This revealed that the actual crop evapotranspiration was greater than the calculated theoretical crop evapotranspiration by 36.7%. Uncontrolled rainfall was identified as the output's cause. This has an evident effect on the deficit in experimental research. Hence, conducting the deficit experiment in a greenhouse is more reasonable. In addition, it is possible to assess actual crop evapotranspiration based on daily soil moisture measurements and report the deficit level based on the measured amount. [ABSTRACT FROM AUTHOR]

This article discusses the use of qualitative methods to study soil moisture and stream states in a subcatchment in northern Sweden. The study area consists of a 20 ha area for soil moisture observations and a 9 ha area for stream state observations. The qualitative methods used provide valuable information on soil moisture patterns and factors influencing variation. The article also highlights the importance of monitoring temporary streams and the drivers of flow in these streams. The study used a classification system to classify stream states and found that a large portion of the network was active during the snowmelt period but dried out during drier periods. The article also mentions the usefulness of qualitative approaches in citizen science projects and the availability of the study's data. The document is a list of references for the research article, providing additional studies related to hydrology, soil moisture, stream networks, and catchment studies. These studies contribute to the understanding of soil moisture and streamflow dynamics in different environments and their implications for water resource management. [Extracted from the article]

Soil moisture is a key preconditioning factor influencing hillslope stability and the initiation of landslides. Direct measurements of soil moisture on a large scale are logistically complicated, expensive, and therefore sparse, resulting in large data gaps. In this study, we calibrate a numerical land surface model to improve our representation of post-monsoon soil drying in landslide-prone Nepal. We use a parameter perturbation experiment to identify optimal parameter sets at three field monitoring sites and evaluate the performance of those optimal parameter sets at each location. This process enables the calibration of key soil hydraulic parameters, in particular a higher hydraulic conductivity and a lower saturation moisture content relative to the default parameter setting. Runs with the calibrated model parameters provide a substantially more accurate (50 % or greater reduction in root mean squared error) soil moisture record than those with the default model parameters, even when calibrated from sites as much as 250 km apart. This process enables meaningful calculation of post-monsoon soil moisture decay at locations with no in situ monitoring, so as to inform a key component of landslide susceptibility mapping in Nepal and other regions where field measurements of soil moisture are limited. [ABSTRACT FROM AUTHOR]

The study investigates the relationship between variations of topographic wetness indices, resampled digital elevation model (DEM) resolutions and soil moisture in Croatia's Pannonian region during all seasons. It delves into the spatial distribution of soil moisture, vital for various sectors including agriculture, forestry, ecology, and military. Utilizing topographic wetness index (TWI), the research focuses on resampling high-resolution DEM into coarser resolutions and flow-routing methods concerning in-situ soil moisture measurements. It encompasses the Kaznica River catchment area, characterized by diverse topography and soil types, including fine-grained automorphic and hydromorphic clays. The study extensively conducts field measurements, evaluating soil moisture at various depths (1, 15, 30, and 45cm) across all seasons, cross-referenced with meteorological data. Through an examination of six runoff algorithms and six spatial resolutions, it concludes that the optimal TWI resolution for correlation with soil moisture is 150m at a 30cm soil depth. However, it stresses the necessity of calibrations for precise soil depth, cell resolutions and season. The shallowest soil depth has the lowest correlation coefficients in all periods, while the highest coefficients were achieved in period with the highest soil moisture values. Recognizing the complexity of factors influencing soil moisture, it recommends the integration of additional data sources like remote sensing, other geomorphological indexes, and detailed land cover analysis to enhance the accuracy of predictions. [ABSTRACT FROM AUTHOR]

The accurate measurement of soil moisture content emerges as a critical parameter within the ambit of agricultural irrigation management, wherein the precise prediction of this variable plays an instrumental role in enhancing the efficiency and conservation of agricultural water resources. This study introduces an innovative, cutting-edge hybrid model that ingeniously integrates Gated Recirculation Unit (GRU) and Transformer technologies, meticulously crafted to amplify the precision and reliability of soil moisture content forecasts. Leveraging meteorological and soil moisture datasets amassed from eight monitoring stations in Hebei Province, China, over the period from 2011 to 2018, this investigation thoroughly assesses the model's efficacy against a diverse array of input variables and forecast durations. This assessment is concurrently contrasted with a range of conventional machine learning and deep learning frameworks. The results demonstrate that (1) the GRU–Transformer model exhibits remarkable superiority across various aspects, particularly in short-term projections (1- to 2-day latency). The model's mean square error (MSE) for a 1-day forecast is notably low at 5.22%, reducing further to a significant 2.71%, while the mean coefficient of determination (R2) reaches a high of 89.92%. Despite a gradual increase in predictive error over extended forecast periods, the model consistently maintains robust performance. Moreover, the model shows exceptional versatility in managing different soil depths, notably excelling in predicting moisture levels at greater depths, thereby surpassing its performance in shallower soils. (2) The model's predictive error inversely correlates with the reduction in parameters. Remarkably, with a streamlined set of just six soil moisture content parameters, the model predicts an average MSE of 0.59% and an R2 of 98.86% for a three-day forecast, highlighting its resilience to varied parameter configurations. (3) In juxtaposition with prevalent models such as Support Vector Regression (SVR), K-Nearest Neighbors (KNN), Gradient Boosting Decision Tree (GBDT), XGBoost, Random Forest, and deep learning models like Deep Neural Network (DNN), Convolutional Neural Network (CNN), and standalone GRU-branch and Transformer-branch models, the GRU–Transformer framework demonstrates a significant advantage in predicting soil moisture content with enhanced precision for a five-day forecast. This underscores its exceptional capacity to navigate the intricacies of soil moisture data. This research not only provides a potent decision-support tool for agricultural irrigation planning but also makes a substantial contribution to the field of water resource conservation and optimization in agriculture, while concurrently imparting novel insights into the application of deep learning techniques in the spheres of agricultural and environmental sciences. [ABSTRACT FROM AUTHOR]

This work proposes a complementary concentric open-ring microwave resonator sensor for accurate sensing of the electrical properties of soil and its moisture content. The device operation for microwave S-band at 3.1 GHz allows lower phase-dependent measurements for the accurate estimation of the dielectric constant. The device is fabricated on an RO48350B substrate of $ {\varepsilon _r} = 3.48 $ ε r = 3.48 and the corresponding results were found to close match the electromagnetic simulation data. Silt soil and red soil were tested and analysed up to 21% percentage of the moisture level. Transmission coefficient ( $ \mathrm{\Gamma } $ Γ )-based numerical technique was used to extract the dielectric constant of different soil samples. The proposed sensor design is also tested for on-field measurements of soil and moisture level from different soil surface depth has been extracted. [ABSTRACT FROM AUTHOR]

Near-surface soil hydraulic properties (SHPs) are fundamental for describing and predicting water and energy exchange processes, particularly at the soil–atmosphere interface, and regulating evapotranspiration, infiltration, and runoff in different ecosystems. In this study, a new method was proposed to estimate near-surface SHPs by combining sensor-based soil infiltrability measurements with inverse modeling using HYDRUS-2D. The infiltration rate (IR) was estimated by combining the linear source inflow method with image processing, and Ks was estimated from the near-surface steady-state IR (NSIRM). The SWRC parameters described by the van Genuchten model were estimated using the inverse modeling method of HYDRUS-2D for the fitting of sensor-measured infiltration data. Subsequently, the parameters of the van Genuchten model, including α , n , and l , were inversely estimated. Three undisturbed soils, including two stand humus samples from cork oak (Quercus suber L.) and oleander (Pinus tabuliformis L.) stands and one sandy loam from a farmland, were sampled near the soil surface to validate the proposed method. The estimated Ks was evaluated by the constant head method (CHM). The estimated parameters of the SWRC were validated by those determined through the simultaneous measurement of the soil moisture content and water potential using sensor techniques. The results showed that the Ks estimated from the NSIRM for each soil sample were 23.40 ± 1.21, 23.86 ± 1.83, and 22.99 ± 2.26 mm h−1, respectively. In comparison, the Ks determined by the CHM were 24.41 ± 1.53, 24.26 ± 0.37, and 23.81 ± 0.10 mm/h, respectively. The relative errors of the proposed method were 4.14%, 1.64%, and 3.42%, respectively. For the SWRC estimation, the normalized root mean square errors (NRMSEs) between the measurements and the estimates for each soil sample were 0.1724, 0.1454, and 0.0606, respectively. Based on this, the AWC was obtained, and Ku was deduced from the estimated Ks and SWRC parameters for each soil sample. In general, the proposed method successfully estimates near-surface SHPs, simplifies the measurement device, and provides a new perspective for the in situ determination of near-surface SHPs under field conditions in the near future. [ABSTRACT FROM AUTHOR]

The permittivity of soil-rock composite media is crucial for interpreting data originating from geophysical surveys such as ground-penetrating radar (GPR) imaging. At present, relevant theoretical research on the correlation analysis between the electrical and physical parameters of soil-rock composite media has not yet resulted in a complete reference system. This makes it difficult to characterize soil-rock composite media using the electromagnetic characteristic parameters of rock and soil media. In this study, the indoor electromagnetic testing method was used to extract electrical parameters such as the relative permittivity of soil-rock composite, and the influence of water content, density, soil-rock ratio, and other factors on the electrical parameters of soil-rock composite medium was analyzed. Furthermore, an artificial neural network prediction model was established, and the accuracy of the model was evaluated. The results showed that the relative permittivity tends to decrease with an increase in saturation when the moisture content of the mixture is high. The rock has a lower permittivity value than the surrounding soil particles. When the rock content increases, the average permittivity and conductive permittivity of the composite medium decreases. The main factors affecting the electrical properties of soil-rock composite media (in order of their influence, from most to least) are water content, compaction degree, and soil-rock ratio. These results provide a reliable reference for field geophysical exploration. [ABSTRACT FROM AUTHOR]

Land surface models (LSMs) are an important tool for advancing our knowledge of the Earth system. LSMs are constantly improved to represent the various terrestrial processes in more detail. High-quality data, freely available from various observation networks, are being used to improve the prediction of terrestrial states and fluxes of water and energy. To optimize LSMs with observations, data assimilation methods and tools have been developed in the past decades. We apply the coupled Community Land Model version 5 (CLM5) and Parallel Data Assimilation Framework (PDAF) system (CLM5-PDAF) for 13 forest field sites throughout Europe covering different climate zones. The goal of this study is to assimilate in situ soil moisture measurements into CLM5 to improve the modeled evapotranspiration fluxes. The modeled fluxes will be evaluated using the predicted evapotranspiration fluxes with eddy covariance (EC) systems. Most of the sites use point-scale measurements from sensors placed in the ground; however, for three of the forest sites we use soil water content data from cosmic-ray neutron sensors, which have a measurement scale closer to the typical land surface model grid scale and EC footprint. Our results show that while data assimilation reduced the root-mean-square error for soil water content on average by 56 % to 64 %, the root-mean-square error for the evapotranspiration estimation is increased by 4 %. This finding indicates that only improving the soil water content (SWC) estimation of state-of-the-art LSMs such as CLM5 is not sufficient to improve evapotranspiration estimates for forest sites. To improve evapotranspiration estimates, it is also necessary to consider the representation of leaf area index (LAI) in magnitude and timing, as well as uncertainties in water uptake by roots and vegetation parameters. [ABSTRACT FROM AUTHOR]

Tropical volcanic islands are biodiversity hotspots where the Critical Zone (CZ) still remains poorly studied. In such steep topographic environments associated with extreme climatic events (cyclones), deployment and maintenance of monitoring equipment is highly challenging. While a few Critical Zone Observatories (CZOS) are located in tropical volcanic regions, none of them includes a Tropical Montane Cloud Forest (TMCF) at the watershed scale. We present here the dataset of the first observatory from the French network of critical zone observatories (OZCAR) located in an insular tropical and volcanic context, integrating a 'Tropical Montane Cloud Forest': The ERORUN‐STAFOR observatory. This collaborative observatory is located in the northern part of La Réunion island (Indian Ocean) within the 45.0 km2 watershed of Rivière des Pluies (i.e., Rainfall river) which hosts the TMCF of Plaines des Fougères, one of the best preserved natural habitats in La Réunion Island. Since 2014, the ERORUN‐STAFOR monitoring in collaboration with local partners collected a multidisciplinary dataset with a constant improvement of the instrumentation over time. At the watershed scale and in its vicinity, the ERORUN‐STAFOR observatory includes 10 measurement stations covering the upstream, midstream and downstream part of the watershed. The stations record a total of 48 different variables through continuous (sensors) or periodic (sampling) monitoring. The dataset consists of continuous time series variables related to (i) meteorology, including precipitation, air temperature, relative humidity, wind speed and direction, net radiation, atmospheric pressure, cloud water flux, irradiance, leaf wetness and soil temperature, (ii) hydrology, including water level and temperature, discharge and electrical conductivity (EC) of stream, (iii) hydrogeology, including (ground)water level, water temperature and EC in two piezometers and one horizontally drilled groundwater gallery completed by soil moisture measurements under the canopy. The dataset is completed by periodic time series variables related to (iv) hydrogeochemistry, including field parameters and water analysis results. The periodic sampling survey provides chemical and isotopic compositions of rainfall, groundwater, and stream water at different locations of this watershed. The ERORUN‐STAFOR monitoring dataset extends from 2014 to 2022 with an acquisition frequency from 10 min to hourly for the sensor variables and from weekly to monthly frequency for the sampling. Despite the frequent maintenance of the monitoring sites, several data gaps exist due to the remote location of some sites and instrument destruction by cyclones. Preliminary results show that the Rivière des Pluies watershed is characterized by high annual precipitation (>3000 mm y−1) and a fast hydrologic response to precipitation (≈2 h basin lag time). The long‐term evolution of the deep groundwater recharge is mainly driven by the occurrence of cyclone events with a seasonal groundwater response. The water chemical results support existing hydrogeological conceptual models suggesting a deep infiltration of the upstream infiltrated rainfall. The TMCF of Plaine des Fougères shows a high water storage capacity (>2000% for the Bryophytes) that makes this one a significant input of water to groundwater recharge which still needs to be quantified. This observatory is a unique research site in an insular volcanic tropical environment offering three windows of observation for the study of critical zone processes through upstream‐midstream‐downstream measurements sites. This high‐resolution dataset is valuable to assess the response of volcanic tropical watersheds and aquifers at both event and long‐term scales (i.e., global change). It will also provide insights in the hydrogeological conceptual model of volcanic islands, including the significant role of the TMCFs in the recharge processes as well as the watershed hydrosedimentary responses to extreme climatic events and their respective evolution under changing climatic conditions. All data sets are available at https://doi.org/10.5281/zenodo.7983138. [ABSTRACT FROM AUTHOR]

Soil moisture is the significant hydrologic factor deals with energy balance between the land and the atmosphere. Since the observation of soil moisture at point scale is infrequent and expensive, remote sensing determines the distribution of soil moisture in large scale. In this study, remote sensing techniques have been used to calculate the soil moisture index in the lower Bhavani river basin, Tamil Nadu, India. Landsat 8 satellite data were used for deriving soil moisture in reference with land surface temperature (LST) and normalized difference vegetative index (NDVI). The derived soil moisture was compared to the in situ soil moisture measurements, which were taken at 83 sites at the field level. When a linear regression model was fit between in situ observations and derived soil moisture, a high coefficient of determination (R2) value of 0.83 was found which can be efficiently used for the moisture estimation across greater areas. Since the land-use patterns influences the LST and soil moisture, the variations of these parameters in each land-use classes were studied using independent t test and found that LST demonstrated statistical non-significance (p > 0.05) for each of the studied groups, indicating that each land-use classes temperature were similar, whereas soil moisture in water bodies versus fallow land (p = 0.019), built-ups versus water bodies (p = 0.023), forest versus fallow land (p = 0.018), vegetation versus built-ups (p = 0.028), and built-ups versus forest (p = 0.011) has statistical significance value, which indicates that soil moisture between these compared classes was not similar. [ABSTRACT FROM AUTHOR]

Soil moisture measurements at the field scale are highly beneficial for different hydrological applications including the validation of space-borne soil moisture products, landscape water budgeting or multi-criteria calibration of rainfall-runoff models from field to catchment scale. Many of these applications require information on soil water dynamics in deeper soil layers. Cosmic-ray neutron sensing (CRNS) allows for non-invasive monitoring of field-scale soil moisture across several hectares around the instrument but only for the first few tens of centimeters of the soil. Simple depth-extrapolation approaches often used in remote sensing applications may be used to estimate soil moisture in deeper layers based on the near-surface soil moisture information. However, most approaches require a site-specific calibration using depth-profiles of in-situ soil moisture data, which are often not available. The physically-based soil moisture analytical relationship SMAR is usually also calibrated to sensor data, but could be applied without calibration if all its parameters were known. However, in particular its water loss parameter is difficult to estimate. In this paper, we introduce and test a simple modification of the SMAR model to estimate the water loss in the second layer based on soil physical parameters and the surface soil moisture time series. We apply the model at a forest site with sandy soils with and without calibration. Comparing the model results against in-situ reference measurements down to depths of 450 cm shows that the SMAR models both with and without modification do not capture the observed soil moisture dynamics well. The performance of the SMAR models nevertheless meets a previously used benchmark RMSE of ≤ 0.06 cm3 cm−3 in both, calibrated and uncalibrated scenarios. Only with effective parameters in a non-physical range, a better model performance could be achieved. Different transfer functions to derive surface soil moisture from CRNS do not translate into markedly different results of the depth-extrapolated soil moisture time series simulated with SMAR. However, a more accurate estimation of the sensitive measurement depth of the CRNS improved the soil moisture estimates in the second layer. Despite the fact that the soil moisture dynamics are not well represented at our study site using physically reasonable parameters, the modified SMAR model may provide valuable first estimates of soil moisture in a deeper soil layer derived from surface measurements based on stationary and roving CRNS as well as remote sensing products where in-situ data for calibration are not available. [ABSTRACT FROM AUTHOR]

Cosmic-ray neutron sensing (CRNS) has emerged as a reliable method for soil moisture and snow estimation. However, the applicability of this method beyond research has been limited due to, among others, the use of relatively large and expensive sensors. This paper presents the tests conducted on a new scintillator-based sensor especially designed to jointly measure neutron counts, muons and total gamma rays. The neutron signal is first compared against two conventional gas-tube-based CRNS sensors at two locations. The estimated soil moisture is further assessed at four agricultural sites, based on gravimetric soil moisture collected within the sensor footprint. Muon fluxes are compared to the incoming neutron variability measured at a neutron monitoring station and total gammas counts are compared to the signal detected by a gamma ray spectrometer. The results show that the neutron dynamic detected by the new scintillator-based CRNS sensor is well in agreement with conventional CRNS sensors. The derived soil moisture also agreed well with the gravimetric soil moisture measurements. The muons and the total gamma rays simultaneously detected by the sensor show promising features to account for the incoming variability and for discriminating irrigation and precipitation events, respectively. Further experiments and analyses should be conducted, however, to better understand the accuracy and the added value of these additional data for soil moisture estimation. Overall, the new scintillator design shows to be a valid and compact alternative to conventional CRNS sensors for non-invasive soil moisture monitoring and to open the path to a wide range of applications. [ABSTRACT FROM AUTHOR]

The Global Navigation Satellite System-Reflectometry (GNSS-R) can estimate land surface soil moisture (SSM) as a viable and promising approach. However, it has some large uncertainty in retrieving SSM. In this study, the SSM is retrieved from different Cyclone GNSS (CYGNSS) SSM retrieval models formed with different SSM reference data products, including two blended microwave SSM products from the European Space Agency's Climate Change Initiative (CCI) and the National Oceanic and Atmospheric Administration's Soil Moisture Operational Product System (SMOPS), and a single microwave sensor-derived Soil Moisture Active Passive (SMAP) Level-3 product. The performance of the developed retrieval models, characterized by spatial resolutions of 36 km × 36 km and 0.25° × 0.25°, is evaluated using K-fold cross-validation. Furthermore, the accuracy of these models is validated against ground measurements acquired from Chinese automated soil moisture observation network. In order to alleviate the impact of spatial mismatching between the predicted gridded SSM and the point-scale in-situ measurements, a cumulative distribution function (CDF) rescaling strategy is applied. The results indicated that all models are effective at capturing spatial variations in SSM, and the SMOPS-based model achieves the highest correlation coefficient (0.930) and the lowest root mean square error (RMSD, 0.028 cm3/cm3), followed by the CCI-based model (0.906 and 0.042 cm3/cm3). The SMAP-based model performs poorly in the comparison. The suboptimal performance of models evaluated with Chinese automated soil moisture measurements is largely attributed to the insufficient calibration of the original reference data in the region. [ABSTRACT FROM AUTHOR]

This synthetic study explores the value of near‐surface soil moisture and soil temperature measurements for the estimation of soil moisture and soil temperature profiles, soil hydraulic and thermal parameters, and latent heat and sensible heat fluxes using data assimilation (ensemble Kalman filter) in combination with unsaturated zone flow modeling (HYDRUS‐1D), for 12 United States Department of Agriculture soil textures in a homogeneous and bare soil scenario. The soil moisture profile is estimated with a root mean square error (RMSE) of 0.04 cm3/cm3 for univariate soil temperature assimilation and 0.01 cm3/cm3 for univariate soil moisture assimilation. Soil temperature assimilation performs better for soils with higher clay content compared to soils with higher sand content. The latent and sensible heat fluxes are estimated with smaller RMSE for univariate soil temperature assimilation compared to univariate soil moisture assimilation for 8 out of 12 soil types. As the climate condition changes from hot semi‐arid to sub‐humid climate, the soil moisture assimilation performs better for high permeable soil but worse for low permeable soil. In summary, the findings suggest that for most soil texture classes, assimilating soil temperature in vadose zone models is skillful to improve latent heat flux, soil moisture profile, and soil hydraulic parameters. Joint assimilation with soil moisture can further enhance the accuracy of the model outputs for all range of soil texture and climate conditions. Core Ideas: Soil moisture can be estimated with good accuracy using soil temperature assimilation.Soil temperature data assimilation is superior for estimating soil hydraulic parameters in clayey soils.Heat flux is better estimated by soil temperature assimilation than soil moisture assimilation.Assimilating both soil moisture and soil temperature improved estimation of state, parameter and heat fluxes.Soil moisture assimilation performs better for high (low) permeable soil in humid (arid) climate. [ABSTRACT FROM AUTHOR]

This paper presents a comparison between the Sentinel-1 (S1)/Sentinel-2 (S2)-derived soil moisture products at plot scale (S2MP) and in situ soil moisture measurements at a 10 cm depth for several winter and summer crops. Specifically, the paper discusses the consistency between the in situ soil moisture measurements, usually performed at a 10 cm soil depth, and the variable S1 C-band penetration depth in soil due to soil humidity conditions, vegetation development and S1 acquisition configuration. The aim is to provide end users with the strength and limitations of S1-derived soil moisture, mainly the S2MP soil moisture product, for their further applications. Both the estimated and measured soil moisture (SM) were evaluated over three testing fields in a Mediterranean climatic context, with crop cycles including wheat, tomato, cover crops and soybeans. The main results showed that the comparison between the S2MP-estimated SM based on S1 backscattering (at ~5 cm depth) with a 10 cm in situ SM is not always relevant during the crop cycle. In dry conditions, the S1 SM significantly underestimated the 10 cm SM measurements with an underestimation that could reach around 20 vol.% in some extremely dry conditions. This high underestimation was mainly due to the difference between the topsoil SM captured by the S1 sensor and the 10 cm in depth SM. Moderately wet conditions due to rainfall or irrigation showed less of a difference between the S1-estimated SM and the 10 cm in situ SM and varying between −10 and −5 vol.% due to the homogeneity of the SM at different soil depths. For extremely wet conditions, the S1 SM started to underestimate the SM values with an underestimation that can reach an order of −10 vol.%. A comparison of the S1-estimated SM as a function of the vegetation development showed that, for the studied crop types, the S1 SM estimates are only valid for low and moderate vegetation cover with a Normalized Difference Vegetation Index (NDVI) of less than 0.7. For dense vegetation cover (NDVI > 0.7), overestimations of the SM (average bias of about 4 vol.%) are mainly observed for developed tomato and soybean crops due to fruits' emergence, whereas an extreme underestimation (average bias reaching −15.5 vol.%) is found for developed wheat cover due to the vertical structure of the wheat kernels. The results also suggest that the optimal SM estimations by S1 could be mainly obtained at low radar incidence angles (incidence angle less than 35°). [ABSTRACT FROM AUTHOR]

The problem of estimating soil moisture by remote (satellite) methods remains topical. To do this, regression models based on the correlation between radar data and ground-based measurements of soil moisture are constructed. Ground-based measurements were taken at two stations in Germany (Falkenberg and Gevenich), which are a part of the International Soil Moisture Network (ISMN). Sentinel-1 satellite data are used as radar data. Multiple regressions with a determination coefficient up to 0.91 are constructed. It is proposed to use in regressions not only radar, but also meteorological data, which makes it possible to increase the coefficient of determination and reduce the standard error of regression. For the possible spread of regressions obtained for one territory to another, two criteria are proposed: proximity of the values of the Selyaninov hydrothermal coefficient (HTC) and similarity of the soil texture. According to these conditions, two stations in Ryazan oblast and in Kalmykia were chosen. Their archival information on soil moisture is contained in the ISMN database up to 1998. Each of the selected stations satisfies only one of the chosen criteria. [ABSTRACT FROM AUTHOR]

Surface soil moisture (SSM) is one of the factors affecting plant growth. Methods involving direct soil moisture measurement in the field or requiring laboratory tests are commonly used. These methods, however, are laborious and time-consuming and often give only point-by-point results. In contrast, SSM can vary across a field due to uneven precipitation, soil variability, etc. An alternative is using satellite data, for example, optical data from Sentinel-2 (S2). The main objective of this study was to assess the accuracy of SSM determination based on S2 data versus standard measurement techniques in three different agricultural areas (with irrigation and drainage systems). In the field, we measured SSM manually using non-destructive techniques. Based on S2 data, we estimated SSM using the optical trapezoid model (OPTRAM) and calculated eighteen vegetation indices. Using the OPTRAM model gave a high SSM estimating accuracy (R2 = 0.67, RMSE = 0.06). The use of soil porosity in the OPTRAM model significantly improved the results. Among the vegetation indices, at the NDVI ≤ 0.2, the highest value of R2 was obtained for the STR to OPTRAM index, while at the NDVI > 0.2, the shadow index had the highest R2 comparable with OPTRAM. [ABSTRACT FROM AUTHOR]

In this paper, a device for measuring soil moisture at multiple depths was designed and implemented. Measuring soil moisture data allows you to set the optimal moisture environment for crop growth and helps increase production and yield through healthy growth of crops. In addition, the importance of measuring accurate soil moisture data is being emphasized to interpret and prevent natural disasters such as droughts and floods caused by climate change. However, it is difficult to measure moisture changes at the depth of crop roots by measuring moisture on the soil surface. Therefore, in this paper, a soil moisture measurement device was implemented by integrating capacitive sensors and environmental sensors. The device's features include variable measurement of moisture at a depth of approximately 10 to 60 cm rather than the soil surface, and real-time wireless transmission using Lora. When water was supplied to river sand in 10mL units as an experimental method, the measurement range of the implemented sensor was 0~70mL, and it was confirmed that the sensor responded sensitively to changes in moisture in the soil. [ABSTRACT FROM AUTHOR]

Root-zone soil moisture (RZSM) plays a key role in the hydrologic cycle and regulates water–heat exchange. Although site observations can provide soil profile moisture measurements, they have a restricted representation. Satellites can determine soil moisture on a large scale, yet the depth of detection is limited. RZSM can be estimated on a large scale using the soil moisture analytical relationship (SMAR) and surface soil moisture (SSM). However, the applicability of the SMAR to different deep-root zones and covariate sources is unclear. This paper investigates the applicability of the SMAR in the Shandian River Basin, upstream of the Luan River in China, by combining site and regional soil moisture, soil properties, and meteorological data. In particular, we first compared the estimation results of the SMAR at different depths (10–20 cm; 10–50 cm) and using covariates from different sources (dataset, SMAR-P1; literature, SMAR-P2) at the site in order to generate SMAR calibration parameters. The parameters were then regionalized based on multiple linear regression by combining the SMAR-P1, SMAR-P2, and SMAR calibration parameters in the 10–50 cm root zone. Finally, the Shandian River RZSM was estimated using regional surface soil moisture and the aforementioned regionalized parameters. At the site scale, diffusion coefficient b obtained in the 10–20 cm root zone at the same depth as the surface layer exceeded the upper limit of the SMAR by one. This is not fit an environment within the site context, and thus the SMAR is not applicable at this particular depth. The opposite is observed for the 10–50 cm root zone. In addition, SMAR-P1 (RMSE = 0.02) outperformed SMAR-P2 (RMSE = 0.04) in the estimation of the RZSM at 10–50 cm. Parameter regionalization analysis revealed the failure of SMAR-P2 to pass the significance test (p > 0.05) for building a multivariate linear model, while SMAR-P1 successfully passed the significance test (p < 0.05) and finished the parameter regionalization process. The median RMSE and median R2adj of the regional RZSM results were determined as 0.12 and 0.3, respectively. The regional RZSM agrees with the spatial trend of the Shandian River. This study examines the suitability of the SMAR model in varying deep-root zones and with diverse covariate sources. The results provide a crucial basis for future utilization of the SMAR. [ABSTRACT FROM AUTHOR]

Hydrologic modeling has been a useful approach for analyzing water partitioning in catchment systems. It will play an essential role in studying the responses of watersheds under projected climate changes. Numerous studies have shown it is critical to include subsurface heterogeneity in the hydrologic modeling to correctly simulate various water fluxes and processes in the hydrologic system. In this study, we test the idea of incorporating geophysics‐obtained subsurface critical zone (CZ) structures in the hydrologic modeling of a mountainous headwater catchment. The CZ structure is extracted from a three‐dimensional seismic velocity model developed from a series of two‐dimensional velocity sections inverted from seismic travel time measurements. Comparing different subsurface models shows that geophysics‐informed hydrologic modeling better fits the field observations, including streamflow discharge and soil moisture measurements. The results also show that this new hydrologic modeling approach could quantify many key hydrologic fluxes in the catchment, including streamflow, deep infiltration, and subsurface water storage. Estimations of these fluxes from numerical simulations generally have low uncertainties and are consistent with estimations from other methods. In particular, it is straightforward to calculate many hydraulic fluxes or states that may not be measured directly in the field or separated from field observations. Examples include quickflow/subsurface lateral flow, soil/rock moisture, and deep infiltration. Thus, this study provides a useful approach for studying the hydraulic fluxes and processes in the deep subsurface (e.g., weathered bedrock), which needs to be better represented in many earth system models. Plain Language Summary: The precipitation received in a watershed is usually partitioned into streamflow, plant water use, soil/rock moisture, and groundwater. While many of these components can be directly measured in the field or estimated from hydrometeorological measurements, the moisture stored in the subsurface (soil and rock layers) is difficult to quantify and measure. Using the computer to simulate the water flow and storage in a watershed provides a useful tool for quantifying water partitioning in a catchment. Previous research highlighted the importance of including both above‐ground and below‐ground variations in computational modeling. Compared to above‐ground heterogeneity, it is much more challenging to characterize below‐ground heterogeneity because we cannot see the variations in the subsurface. In this study, we propose to determine the subsurface heterogeneity/variations with geophysical imaging (more specifically, the seismic refraction method), and the geophysics‐obtained subsurface model is then incorporated into the computational simulation of a watershed or catchment. A small mountainous headwater catchment in Idaho is used to demonstrate this method, and the results show that the new modeling well reproduces the field‐measured streamflow of the catchment. This new modeling also gives reliable estimates for some properties that are difficult to determine using traditional methods. With the changing climate, the proposed new method will help better understand how a catchment will respond to the projected weather, such as extreme snowfalls or severe droughts. Key Points: The 3D velocity model of a mountain catchment was created based on the results of a series of 2D seismic refraction testsThe 3D subsurface critical zone structure was extracted from the velocity model and incorporated into hydrologic modelingGeophysics‐informed hydrologic modeling provides valuable additional information on the hydrological partitioning in the catchment [ABSTRACT FROM AUTHOR]

In this study, we investigate how seasonal streamflow and soil moisture patterns have responded to variability in vegetation phenology in humid, temperate forested watersheds without significant seasonal snowmelt over the last four decades. We characterize spring streamflow peaks using 50th percentiles of cumulative daily precipitation, streamflow, and soil moisture measurements, and investigate interactions with remotely sensed, greenup anomalies. After removing a dominant precipitation control, 1‐day earlier greenup is usually associated with about 1‐day early spring flow peak at four low‐elevation deciduous catchments using both sequential and multiple linear regressions. This indicates that the strong dependency of seasonal flow regimes on precipitation is mediated by vegetation seasonality, especially by greenup variability. In contrast, we find less significant correlations of the greenup anomalies on flow percentiles from two paired evergreen and two high‐elevation deciduous catchments. At a plot scale, similar correlations were found only at an upslope topographic position, where precipitation also showed tighter coupling with moisture seasonal patterns than downslope. Our study suggests that rainfall‐runoff and rainfall‐soil moisture relations have been closely mediated by vegetation seasonality in deciduous forests, especially by greenup anomalies, but patterned along topoclimate and hillslope gradients. This study emphasizes that it is important to understand phenological responses to ongoing climate change (in both long‐term and interannual variability) for prediction of seasonal flow regimes especially in deciduous forested catchments. Key Points: Topoclimate modulates coupling of precipitation and greenup with seasonal runoff dynamicsDeciduous catchments show tighter coupling between greenup and seasonal runoff dynamics than paired evergreen onesHillslope modulates coupling of precipitation and greenup with seasonal soil moisture dynamics [ABSTRACT FROM AUTHOR]

A new method for estimating cumulative evapotranspiration using superconducting gravimeter data is presented. Evapotranspiration is computed from the classical water balance equation using precipitation data, estimated runoff, and water storage assessed from temporal variations of gravity. The gravity-based methodology is applied to a study site located at the Argentinian-German Geodetic Observatory (AGGO) in the flatland area of Buenos Aires Province (Argentina), which houses the only superconducting gravimeter in South America. For the period from April 2017 to May 2018, the cumulative evapotranspiration is estimated at 710.7 ± 25.7 mm. Evapotranspiration values are successfully compared with estimates provided by three methods, based on the water balance equation using soil moisture data, the numerical modelling of water flow in the unsaturated zone and the MODIS product (MOD16A2). The analysis of evapotranspiration also allows us to identify a period of soil water stress which is validated by soil moisture measurements. [ABSTRACT FROM AUTHOR]

Remotely sensed soil moisture observations provide an opportunity to monitor hydrological conditions from droughts to floods. The European Space Agency's (ESA) Climate Change Initiative has released both Combined and Passive datasets, which include multiple satellites' measurements of soil moisture conditions since the 1980s. In this study, both volumetric soil moisture and soil moisture standardized anomalies from the U.S. Climate Reference Network (USCRN) were compared with ESA's Combined and Passive datasets. Results from this study indicate the importance of using standardized anomalies over volumetric soil moisture conditions as satellite datasets were unable to capture the frequency of conditions observed at the extreme ends of the volumetric distribution. Overall, the Combined dataset had slightly lower measures of soil moisture anomaly errors for all regions; although these differences were not statistically significant. Both satellite datasets were able to detect the evolution from worsening to amelioration of the 2012 drought across the central United States and 2019 flood over the upper Missouri River basin. While the ESA datasets were not able to detect the magnitude of the extremes, the ESA standardized datasets were able to detect the interannual variability of extreme wet and dry day counts for most climate regions. These results suggest that remotely sensed standardized soil moisture can be included in hydrological monitoring systems and combined with in situ measures to detect the magnitude of extreme conditions. Significance Statement: This study examines how well soil moisture extremes, wet or dry, can be detected from space using one of the lengthiest remotely sensed soil moisture datasets. Comparisons with high-quality station data from the U.S. Climate Reference Network revealed the satellite datasets could capture the frequency of extreme conditions important for climate monitoring, but often missed the absolute magnitudes of the extremes. Future research should focus on how to combine satellite and station data to improve the detection of extreme values important for monitoring. [ABSTRACT FROM AUTHOR]

Accurate, timely, and accessible meteorological and soil moisture measurements are essential for a number of applications including weather forecasting, agricultural decision-making, and flood and drought prediction. Such data are becoming increasingly available globally, but the large number of networks and various data reporting formats often make utilization of such data difficult. The TexMesonet is a "network of networks" developed within the state of Texas to collect, process, and make public data collected from more than 1700 monitoring stations throughout the state. This paper describes the TexMesonet, with special attention paid to monitoring sites installed and managed by the Texas Water Development Board. It also provides a case study exemplifying how these data may be used and gives recommendations for future data applications. [ABSTRACT FROM AUTHOR]

For effective measurement and collection of soil moisture information an electronic system has been designed and developed. Since soil water holding capacity varies depending on the soil structure so a sensor probe is designed with a sensing system for efficient detection of moisture in the soil. The capacitive sensor probe is designed to detect a change of capacitance response from 15 to 1200pF. According to this change of capacitance response, a series RLC network is designed to find out an optimum frequency for efficient sensing of moisture. The designed sensor probe is calibrated for three different types of soil (silt/sandy/clay) using Thermogravimetric (TG) Analysis. To process the sensing data, DataLogger (DL) algorithm has been developed and programmed in an ARM controller for monitoring and storing of data on server using IoT (Internet of Things). For the designed sensing circuit, an optimum frequency of 218 kHz is found out from the resonance curve. At this frequency, the response of electronic interfacing circuit is observed in terms of capacitance and voltage. Data obtained from linear analysis (R square: ~ 0.99, Pearson's r: ~ 0.99) shows that the designed sensor system responds appreciably to the moisture change. [ABSTRACT FROM AUTHOR]