Spatial Variability Of Soil Moisture Research Articles

Spatial Downscaling of ESA CCI Soil Moisture Data Based on Deep Learning with an Attention Mechanism

Soil moisture (SM) is a critical variable affecting ecosystem carbon and water cycles and their feedback to climate change. In this study, we proposed a convolutional neural network (CNN) model embedded with a residual block and attention module, named SMNet, to spatially downscale the European Space Agency (ESA) Climate Change Initiative (CCI) SM product. In the SMNet model, a lightweight Convolutional Block Attention Module (CBAM) dual-attention mechanism was integrated to comprehensively extract the spatial and channel information from the high-resolution input remote sensing products, the reanalysis meteorological dataset, and the topographic data. The model was employed to downscale the ESA CCI SM from its original spatial resolution of 25 km to 1 km in California, USA, in the annual growing season (1 May to 30 September) from 2003 to 2021. The original ESA CCI SM data and in situ SM measurements (0–5 cm depth) from the International Soil Moisture Network were used to validate the model’s performance. The results show that compared with the original ESA CCI SM data, the downscaled SM data have comparable accuracy with a mean correlation (R) and root mean square error (RMSE) of 0.82 and 0.052 m3/m3, respectively. Moreover, the model generates reasonable spatiotemporal SM patterns with higher accuracy in the western region and relatively lower accuracy in the eastern Nevada mountainous area. In situ site validation results in the SCAN, the SNOTEL network, and the USCRN reveal that the R and RMSE are 0.62, 0.63, and 0.77, and 0.077 m3/m3, 0.093 m3/m3, and 0.078 m3/m3, respectively. The results are slightly lower than the validation results from the original ESA CCI SM data. Overall, the validation results suggest that the SMNet downscaling model proposed in this study has satisfactory performance in handling the task of soil moisture downscaling. The downscaled SM model not only preserves a high level of spatial consistency with the original ESA CCI SM model but also offers more intricate spatial variations in SM depending on the spatial resolution of model input data.

Within-field extrapolation away from a soil moisture probe using freely available satellite imagery and weather data

AbstractRecognition of the importance of soil moisture information to the optimisation of water-limited dryland cereal production has led to Australian growers being encouraged to make use of soil moisture sensors. However, irrespective of the merits of different sensing technologies, only a small soil volume is sensed, raising questions as to the utility of such sensors in broadacre cropping, especially given spatial variability in soil water holding capacity. Here, using data collected from contrasting sites in South Australia and Western Australia over two seasons, during which either wheat or barley were grown, we describe a method for extrapolating soil moisture information away from the location of a probe using freely-available NDVI time series and weather data as covariates. Relationships between soil moisture probe data, cumulative NDVI (ΣNDVI), cumulative net precipitation (ΣNP) and seasonal growing degree days (GDD) were significant (P < 0.0001). In turn, these could be used to predict soil moisture status for any location within a field on any date following crop emergence. However, differences in ΣNDVI between different within-field zones did not fully explain differences in the soil moisture from multiple sensors located in these zones, resulting in different calibrations being required for each sensor or zone and a relatively low accuracy of prediction of measured soil moisture (R2adj ~ 0.4–0.7) which may not be sufficient to support targeted agronomic decision-making. The results also suggest that at any location within a field, the range of variation in soil moisture status down the soil profile on any given date will present as greater than the spatial variation in soil moisture across the field on that date. Accordingly, we conclude that, in dryland cereal cropping, the major value in soil moisture sensors arises from an enhanced ability to compare seasons and to relate similarities and differences between seasons as a guide to decision-making.

Analysis of Effects of Spatial Distributed Soil Properties and Soil Moisture Behavior on Hourly Streamflow Estimate through the Integration of SWAT and LSM

This study addresses the challenge of accurately estimating hourly flow and soil moisture by integrating the Soil and Water Assessment Tool (SWAT) with a Land Surface Model (LSM). Our approach enhances SWAT by incorporating spatially distributed soil properties and a physically-based soil moisture process, using the Noah LSM for hourly soil moisture estimation. This integration captures spatial variations in soil moisture and hydraulic properties from remote sensing across the watershed. The parameter sensitivity analysis and the calibration of hourly flow were significantly impacted by the physically-based hourly soil moisture routing and the incorporation of spatially distributed soil properties. Consequently, the modified SWAT model showed improved accuracy in hourly flow simulations for long-term and multiple rainfall events. Validation results showed significant improvements, with Coefficient of Determination (R2) and Nash and Sutcliffe Efficiency (NSE) increasing by 25.95% and 33.3%, respectively, and Percent Bias (PBIAS) decreasing by 85.8%. Notably, the average error for peak flows across eight events decreased by 49%. These findings highlight the importance of initializing soil parameters based on spatial soil moisture distribution and incorporating physical process-based moisture routing to enhance hourly flow simulation accuracy. Future research should focus on validating the physical feasibility of the soil parameter set in the study area with detailed hourly flow and soil moisture data and exploring its applicability in various regions. This study provides valuable insights for the scientific community, water resources, and agricultural decision-makers regarding integrated modeling of soil moisture and hourly flow, which can inform dam operation management, disaster planning, and crop yield improvement.

Sensitivity of the Land–Atmosphere Coupling to Soil Moisture Anomalies during the Warm Season in China and its Surrounding Areas

Significant temporal and spatial variability in soil moisture (SM) is observed during the warm season in China and its surrounding regions. Because of the existence of two different evapotranspiration regimes, i.e., soil moisture-limited and energy-limited, averaging the land–atmosphere (L–A) coupling strength for all soil wetness scenarios may result in the loss of coupling signals. This study examines the daytime-only L–A interactions under different soil moisture conditions, by using two-legged metrics in the warm season from May to September 1981–2020, partitioning the interactions between SM and latent heat flux (SM–LH, the land leg) from the interactions between latent heat flux and the lifting condensation level (LH–LCL, the atmospheric leg). The statistical results reveal large regional differences in warm-season daytime L–A feedback in China and its surrounding areas. As the soil becomes wetter, the positive SM–LH coupling strength increases in arid regions (e.g., northwest China, Hetao, and the Great Indian Desert) and the positive feedback shifts to the negative one in semi-arid/semi-humid regions (northeast and northern China). The negative LH–LCL coupling is most pronounced in wet soil months in arid regions, while the opposite is true for the Tibetan Plateau. In terms of intraseasonal variation, the large variability of SM in north China, the Tibetan Plateau, and India due to the influence of the summer monsoon leads to the sign change in the land segment coupling index, comparing pre- and post-monsoon periods. To further examine the impact of SM anomalies on L–A coupling and to explore evapotranspiration regimes in the North China Plain, four sets of sensitivity experiments with different soil moisture levels over a period of 10 years were conducted. Under relatively dry soil conditions, evapotranspiration is dominated by the soil moisture-limited regime with positive L–A coupling, regardless of external moisture inflow. The critical soil moisture value separating a soil moisture-limited and an energy-limited regime lies between 0.24 m3/m3 and 0.29 m3/m3. Stronger positive feedback under negative soil moisture anomalies may increase the risk of drought in the North China Plain.

Estimation of spatial distribution of soil moisture on steep hillslopes by state-space approach (SSA)

Soil moisture is a critical variable that quantifies precipitation, floods, droughts, irrigation, and other factors with regard to decision-making and risk evaluation. An accurate prediction of soil moisture dynamics is important for soil and environmental management. However, the complex topographic condition and land use in hilly and mountainous areas make it a challenge to monitor and predict soil moisture dynamics in these areas. In this study, the determinants of soil moisture variability were determined by structural equation modeling, and then an attempt was made to estimate the spatial distribution of soil moisture content on steep hillslope using the state-space method. Herein, soil moisture at different depths (0–10, 10–20, and 20–30 cm) was monitored by portable time-domain reflectometer (TDR) along this hillslope (100 m × 180 m). It showed that the spatial variability of soil moisture decreased with increasing soil wetness, primarily in the topsoil (0–10 cm). Soil moisture was correlated with elevation (r = 0.28, 0.50, and 0.28), capillary porosity (r = 0.06, 0.37, and 0.28), soil texture (r for Clay: 0.20, 0.24, and 0.16; r for Sand: −0.25, −0.18, and −0.28), organic carbon (r = −0.31, −0.08, and 0.10) and land use (r = −0.01, 0.28, and 0.24) under different conditions (dry, moderate, and wet). Among these determinants, elevation made direct contributions to soil moisture variation, especially under moderate conditions, while land use made its impacts by altering soil texture. It is encouraging that the state-space approach yielded precise and cost-effective predictions of soil moisture dynamics along this steep hillslope since it gives the minimum root-mean-square error (RMSE) and Akaike information criterion (AIC). Moreover, soil organic carbon (AIC = −4.497, RMSE = 0.104, R2 = 0.899), rock fragment contents (AIC = −4.366, RMSE = 0.111, R2 = 0.878), and elevation (AIC = −3.693, RMSE = 0.156, R2 = 0.629) effectively anticipated the spatial distribution of soil moisture under dry, moderate, and wet conditions, respectively. This study confirms the efficacy of the state-space approach as a valuable tool for soil moisture prediction in areas characterized by complex and spatially heterogeneous conditions.

Temporal covariance of spatial soil moisture variations: A mechanistic error modeling approach

AbstractWhen 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.

The ecohydrological response to soil moisture based on the distributed hydrological assimilation model in the mountain region

AbstractIn the mountain region, there is high spatial variability of soil moisture, which limited soil moisture sites may not express. Data assimilation methods can combine the advantages of model simulation and data observation. This study constructs the distribution of soil moisture based on the distributed hydrological assimilation model and explores the ecohydrological response to soil moisture in the mountain region. The relationship between the coefficient of variation and mean soil moisture shows a hysteresis pattern. The terrain conditions where high soil moisture occurs in the study area are an altitude of about 3500 m, a gentle slope, and a shady slope. The low value of soil moisture is characterized by banded aggregation distribution. There is an initial threshold for the contribution of soil moisture to watershed runoff, and the threshold value in the study area of this paper is 0.28 cm3/cm3. The regions with dense vegetation exhibit higher spatial variability in soil moisture. Vegetation plays a heterogeneous role in the spatial variability of soil moisture when precipitation events occur. The study contributes to a deeper understanding of ecohydrological processes by providing regional‐scale data and uncovering the relationships between soil moisture and various ecohydrological factors.

Investigating multiscale meteorological controls and impact of soil moisture heterogeneity on radiation fog in complex terrain using semi-idealised simulations

Abstract. Coupled surface–atmosphere high-resolution mesoscale simulations were carried out to understand meteorological processes involved in the radiation fog life cycle in a city surrounded by complex terrain. The controls of mesoscale meteorology and microscale soil moisture heterogeneity on fog were investigated using case studies for the city of Ōtautahi / Christchurch, New Zealand. Numerical model simulations from the synoptic to microscale were carried out using the Weather Research and Forecasting (WRF) model and the Parallelised Large-Eddy Simulation Model (PALM). Heterogeneous soil moisture, land use, and topography were included. The spatial heterogeneity of soil moisture was derived using Landsat 8 satellite imagery and ground-based meteorological observations. Nine semi-idealised simulations were carried out under identical meteorological conditions. One contained homogeneous soil moisture of about 0.31 m3 m−3, with two other simulations of halved and doubled soil moisture to demonstrate the range of soil moisture impact. Another contained heterogeneous soil moisture derived from Landsat 8 imagery. For the other five simulations, the soil moisture heterogeneity magnitudes were amplified following the observed spatial distribution to aid our understanding of the impact of soil moisture heterogeneity. Analysis using pseudo-process diagrams and accumulated latent heat flux shows significant spatial heterogeneity of processes involved in the simulated fog. Our results showed that soil moisture heterogeneity did not significantly change the general spatial structure of near-surface fog occurrence, even when the heterogeneity signal was amplified and/or when the soil moisture was halved and doubled. However, compared to homogeneous soil moisture, spatial heterogeneity in soil moisture can lead to changes in fog duration. These changes can be more than 50 min, although they are not directly correlated with spatial variations in soil moisture. The simulations showed that the mesoscale (10 to 200 km) meteorology controls the location of fog occurrence, while soil moisture heterogeneity alters fog duration at the microscale on the order of 100 m to 1 km. Our results highlight the importance of including soil moisture heterogeneity for accurate spatiotemporal fog forecasting.

Comparing quasi-3D soil moisture derived from electromagnetic induction with 1D moisture sensors and correlation to barley yield in variable duplex soil

Farms in Western Australia (WA) are highly variable in soil texture and water retention capacity; therefore, information of soil moisture spatial variability in the field is important for effective crop management. In practice, farmers often rely on point sensors to determine soil moisture in their fields for crop planning. The limitation of point measurements to account for spatial variability highlights the need to develop methods that can assess soil moisture across variable broadacre fields. This information can enable more effective site-specific crop management practices. In this study, we used a mobile non-intrusive electromagnetic induction (EMI) sensor to map soil apparent electrical conductivity (ECa) to predict soil moisture levels at three different depths (0–0.5, 0.5–0.8 and 0.8–1.6 m) across the field and compared it with barley yield production. To convert the depth specific electrical conductivity (EC) from EMI surveys into soil moisture estimates, calibrations between electrical resistivity tomography (ERT) to volumetric moisture were used. These calibrations were developed for the different soil textural classes of the field, with R2 of 0.97–0.99. There was no significant difference between the estimated soil moisture from EMI surveys and the point moisture measurements obtained from soil moisture sensors and soil sample extracts, with Pearson R values of 0.64 for 0–0.5 m depth and 0.73 for 0.5–0.8 m depth. Additionally, correcting the temperature drift relative to the measured soil temperature on EMI surveys by 10% in dry season and 31% in wet season, improved the moisture estimation results. This study successfully demonstrated the effectiveness of spatial soil moisture estimation using EMI sensor in a field characterized by horizontal and vertical soil texture variability. However, no significant correlation was found between the mapped soil moisture or soil texture with barley yield. This may be due to relatively high initial moisture levels resulting from two years of fallow rotation.

Dust Outbreaks across East Iran: Application of Multi-Source Remote Sensing Data (AMSR-E and FengYun3-MWRI) on the Effects of Soil Moisture

One of the most significant hydro-meteorological and agricultural variables is soil moisture, yet measuring it remains a difficult task. Due to the significant spatial fluctuation of soil moisture, it is difficult to quantify it in a particular spot or field across a sizable region. Despite the thermal band's limitations in assessing soil moisture, MODIS and AVHRR, which are inappropriate were utilized in this investigation. The study examined the impact of soil moisture on dust outbreak. Soil moisture in the study domain was monitored using field techniques and the hybrid model. It combined multi-sourced remote sensing data, obtained from AMSER-E and FY-3 satellites. AMSER-E satellite measures the light temperature in five frequencies ranging from 6. 9 to 89 GHz based on data obtained from AMSER-E. Findings revealed areas with a spatial scale of 25 km2 has a 12-hour time step or variability in dust storm, thereby influencing soil moisture content within the zone of study. In addition to introducing acceptable potentials of the passive microwave band for accurate and applied monitoring of the soil moisture, the present results are viewed as a reliable source for studies on drought in time scale. The study shows that Zabol in Sistan has the highest annual average of 80.7 dust storm days. Soil moisture estimates serve a great deal for preparing soil moisture maps and the evaluation of temporal and spatial variations of soil moisture in study region to address issues related to dust storms.
 In order to identify the areas affected by dust storms and understand how dust particles are dispersed in the Sistan region, satellite image processing was employed using MODIS 1 sensor images obtained from the TERRA satellite.

Identifying coal mining subsidence impacts by soil moisture based on optical trapezoid model in Google Earth Engine

AbstractMany coal mining areas overlapped with agricultural land in the world. However, surface subsidence and waterlogging brought on by coal mining inexorably harm the agricultural land. Soil moisture can reflect the variation of mining impact significantly according to the geographic features of the high underground water table, which shows significant spatial gradient variation within the subsidence basin. Previous studies have used site survey and unmanned aerial vehicle (UAV), which are not applicable to large‐scale assessments of the ecological impacts and defining the scale of impacts. Here, we used the Google Earth Engine (GEE) platform and an optical trapezoid model (OPTRAM) to estimate the soil moisture distribution around 50 mining subsidence waterbody samples in eastern China. Based on the spatial variation in soil moisture, the impact boundary of coal mining was determined using buffer analysis and trend fitting. The results showed that: (1) The OPTRAM can well reflect the soil moisture distribution which shows a good correlation between estimated and measured values, with R = 0.83 and RMSE = 0.054. (2) The spatial trend fitting of soil moisture is suitable for evaluating mining impact, and the impact distances are in the range of 96–762 m, 80% of which are located at 128–436 m. We also find that 28% of subsidence waterbody buffer basins were heavily influenced by human activities, and the coal mining impact boundaries cannot be effectively identified. The study provides a new idea to quantify the ecological and agricultural impact boundary of coal mining, which could be applied to other similar regions across the globe.

Soil moisture, stressed vegetation and the spatial structure of soil erosion in a high latitude rangeland

AbstractSoil erosion has been a persistent problem in high‐latitude regions and may worsen as climate change unfolds and encourages increased anthropogenic exploitation. We propose that soil moisture is likely to shape future erosion trends, as moisture stress reduces the capacity of vegetation cover to retard erosive processes. However, the spatial variability of soil moisture in high‐latitude soils—and the ways in which this variability drives the spatial distribution of erosion features—is poorly understood. We addressed this knowledge gap with a study of andosol erosion in southern Iceland. Our study used a combination of high‐resolution (<3 cm) remote sensing data (using normalised difference vegetation index (NDVI) and normalised difference red edge as metrics of plant vitality) and long‐term, in situ measurements of soil moisture to unpick the relationship between moisture stress, vegetation vitality and patchy soil erosion. Mean NDVI increased with distance from eroded areas, varying from ~0.6 in vegetated areas on the margins of erosion patches to ~0.8 in areas >10 m from eroded terrain. We found lower moisture availability close to existing erosion features: mean volumetric soil moisture content varied from 17% (proximal to erosion patch) to 36% (distal to erosion patch). We also found that variability in soil moisture decreased with distance from eroded areas: the coefficient of variation (CV) in soil moisture varied from 0.33 (proximal to erosion patch) to 0.13 (distal to erosion). Our findings indicate that the margins of erosion patches have a stressful soil environment due to exposure to the atmosphere. The vegetation in these locations grows less vigorously, and the exposed soil becomes more vulnerable to erosion, leading to erosion patch expansion and coalescence. If these conditions hold more generally, they may represent a feedback mechanism that facilitates the lateral propagation of soil erosion in high‐latitude regions.

Belowground spectroscopy — Novel spectral approach for estimation of vertical and species-specific distributions of forest soil characteristics and heterotrophic respiration

Spatial variations in soil moisture and characteristics (nutrients and organic components) are important for understanding the heterogeneity of soil heterotrophic respiration (Rh) in forest ecosystems. In this study, "belowground spectroscopy", a new method for estimating soil characteristics and Rh in the field, was developed by measuring the spectral reflectance in the short-wave infrared (SWIR) spectral region. In the cool temperate forest of Hokkaido, 184 training samples were collected from litter, organic, and mineral soil layers on the forest floors of 13 tree species. First, a multiple regression model was developed for Rh estimation using the soil characteristics as substrates. Next, the partial least squares regression (PLSR) model was calibrated to estimate soil characteristics using spectral reflectance observed under various moisture conditions, and then spectroscopy-based Rh estimation model was constructed by combining the two models. In the multiple regression model, soil water content and concentrations of total nitrogen and cellulose were selected as explanatory variables of Rh. The PLSR model for these parameters showed estimation accuracies of 23.1–31.8% of relative root mean square error (rRMSE) in the validation analysis. The combined model showed 68.4% of rRMSE in Rh estimation. Furthermore, this method was applied to field observations using a small spectrometer to estimate vertical and species-specific variations in Rh. The estimated vertical profiles of soil characteristics showed specific variations related to vertical changes in the soil layer. The accumulated value of the estimated vertical Rh was significantly correlated with the Rh measured using a gas analyzer on 12 tree species, except Alnus species. The rRMSE of the modeled Rh compared to that of gas analyzer was 29.8%. These results indicate that belowground spectroscopy calibrated using a dataset under varied moisture conditions is effective for developing new, rapid, and non-destructive monitoring of soil characteristics and Rh in the field.

Combining cosmic-ray neutron sensor and fallout 137Cs to explore the connection of soil water content with soil redistribution in an agroforestry hillslope

To ensure sustainable agricultural management, there is a need not only to quantify soil erosion rates but also to obtain information on the status of soil water content and soil loss under different soil types and land uses. A clear understanding of the temporal dynamics and the soil moisture spatial variability (SMSV) will help to control soil degradation by hydrological processes. This study represents the first attempt connecting cosmic-ray neutron sensors (CRNS) with soil erosion research, a novel approach to explore the complex relationships between soil water content (SWC) and soil redistribution processes using two of the most powerful nuclear techniques, CRNS and fallout 137Cs.Our preliminary results indicate that CRNS captured soil moisture dynamics along the study toposequence and demonstrated the sensitivity of neutron sensors to investigate the effect of parent material on soil water content. The Empirical Orthogonal Function (EOF) analysis of the comprehensive data from seven CRNS surveys revealed that one dominant spatial structure (EOF1) explains 89.2% of SMSV. The soil redistribution rates estimated with 137Cs at the nine locations along the hillslope, together with local factors related to soil properties (SOC, soil depth, hydraulic conductivity) and land use showed significant correlations with EOF. This study provides strong field evidence that soil type significantly affect SMSV, highlighting the key impact on soil erosion and sedimentation rates. Nevertheless, more research is needed to investigate the specific contributions of soil properties to the spatial variability of soil moisture and their subsequent effects on soil redistribution dynamics of interest for soil management.

Spatio-Temporal Heterogeneity of Soil Moisture on Shrub–Grass Hillslope in Karst Region

Influenced by the topography, the spatial variation of soil thickness on karst slopes is very large, and accordingly the spatial variation of soil moisture is also large. Therefore, analyzing the spatial heterogeneity of soil moisture on hillslopes is important for maintaining ecosystem stability. Combining geostatistical methods and GIS technology, the spatial variability and distribution pattern of soil moisture and the influencing factors of spatial variation and surface soil moisture (0–7 cm) on a typical karst shrub–grass hillslope were analyzed. The results showed that the mean soil moisture and coefficient of variation (CV) ranged between 25.7–42.6% and 10.3–20.9%, respectively, showing a moderate variation. The soil moisture presented a moderate or strong spatial autocorrelation in the sampling scale. The occurrence of rainfall events can exert a great influence on reducing the spatial heterogeneity of soil moisture. The spatial distribution pattern of soil moisture showed roughly plaque or stripe distribution. When soil moisture was much lower, the patch space fragmentation of soil moisture was higher. The soil moisture was higher in the low and middle parts of the plot. We can conclude that factors such as topography, vegetation, and weather conditions will exert a significant effect on soil moisture spatial variability. Areas with lower slope and higher vegetation coverage were more conducive to the retention of soil moisture.

Research on Provincial-Level Soil Moisture Prediction Based on Extreme Gradient Boosting Model

As one of the physical quantities concerned in agricultural production, soil moisture can effectively guide field irrigation and evaluate the distribution of water resources for crop growth in various regions. However, the spatial variability of soil moisture is dramatic, and its time series data are highly noisy, nonlinear, and nonstationary, and thus hard to predict accurately. In this study, taking Jiangsu Province in China as an example, the data of 70 meteorological and soil moisture automatic observation stations from 2014 to 2022 were used to establish prediction models of 0–10 cm soil relative humidity (RHs10cm) via the extreme gradient boosting (XGBoost) algorithm. Before constructing the model, according to the measured soil physical characteristics, the soil moisture observation data were divided into three categories: sandy soil, loam soil, and clay soil. Based on the impacts of various factors on the soil water budget balance, 14 predictors were chosen for constructing the model, among which atmospheric and soil factors accounted for 10 and 4, respectively. Considering the differences in soil physical characteristics and the lagged effects of environmental impacts, the best influence times of the predictors for different soil types were determined through correlation analysis to improve the rationality of the model construction. To better evaluate the importance of soil factors, two sets of models (Model_soil&atmo and Model_atmo) were designed by taking soil factors as optional predictors put into the XGBoost model. Meanwhile, the contributions of predictors to the prediction results were analyzed with Shapley additive explanation (SHAP). Six prediction effect indicators, as well as a typical drought process that happened in 2022, were analyzed to evaluate the prediction accuracy. The results show that the time with the highest correlations between environmental predictors and RHs10cm varied but was similar between soil types. Among these predictors, the contribution rates of maximum air temperature (Tamax), cumulative precipitation (Psum), and air relative humidity (RHa) in atmospheric factors, which functioned as a critical factor affecting the variation in soil moisture, are relatively high in both models. In addition, adding soil factors could improve the accuracy of soil moisture prediction. To a certain extent, the XGBoost model performed better when compared with artificial neural networks (ANNs), random forests (RFs), and support vector machines (SVMs). The values of the correlation coefficient (R), root mean square error (RMSE), mean absolute error (MAE), mean absolute relative error (MARE), Nash–Sutcliffe efficiency coefficient (NSE), and accuracy (ACC) of Model_soil&atmo were 0.69, 11.11, 4.87, 0.12, 0.50, and 88%, respectively. This study verified that the XGBoost model is applicable to the prediction of soil moisture at the provincial level, as it could reasonably predict the development processes of the typical drought event.

Research on Cotton Field Irrigation Amount Calculation Based on Electromagnetic Induction Technology

The rapid and efficient acquisition of field-scale farmland soil profile moisture-distribution information is very important for achieving precise irrigation and the adjustment and deployment of irrigation strategies in farmland. EM38-MK2 is a portable, non-invasive device that induces electric currents in soil to generate secondary magnetic fields for the rapid measurement of apparent electrical conductivity in the field. In this study, cotton fields were used as experimental objects to obtain soil apparent conductivity data for three periods, which were combined with soil-moisture content data collected simultaneously from soil samples and measured in the laboratory to construct an apparent soil-profile moisture regression model. A simple kriging interpolation method was used to map the distribution of the irrigation volume in the field, considering only the highest irrigation volume in the field as the maximum water-holding capacity in the field. The results showed that EM38 could accurately detect the spatial variation of soil moisture in the field. The R2 of the linear fit between measured and predicted soil-water content ranged from 0.51 to 0.89; the RMSE ranged from 0.66 to 1.87; and the R2 and RPD of each soil-layer water content model of the single-period model were higher than those of the full-period model. By plotting the distribution of field irrigation, it could be seen that by comparing the predicted field irrigation with the actual irrigation, at least 160 m3 ha−1 of irrigation could be saved in all three periods at an irrigation depth of 40 cm, which is about 30% of the actual irrigation; at an irrigation depth of 60 cm, about 30% and 15% of irrigation could be reduced in July and August, respectively. There are three areas in the study area with high fixed-irrigation volumes located in the northwest corner, near 500 m in the northern half of the study area and 750 m east of the southern half of the study area. The results of this study proved that the use of EM38-MK2 to monitor and evaluate the soil-moisture content of the farmland at different periods can, to a certain extent, guide the irrigation amount needed to achieve efficient and precise irrigation in the field.

Spatiotemporal features of the soil moisture across Northwest China using remote sensing data, reanalysis data, and global hydrological model

Soil moisture is an important factor affecting the change of land surface hydrological processes and the distribution of material and energy exchanges between the land and atmosphere and vegetation’s temporal and spatial distributions, especially in arid and semi-arid regions. This paper focuses on soil moisture features across Northwest China, the core region of the Silk Road Economic Belt. Six soil moisture datasets from the period 1981–2020 were employed, which included ERA5 (the European Centre for Medium-Range Weather Forecasts Atmospheric Reanalysis 5), ESA-CCI (European Space Agency Climate Change Initiative), GLDAS (Global Land Data Assimilation System), MERRA-2 (The Modern-Era Retrospective Analysis for Research and Applications, Version 2), RSSSM (A Remote Sensing-based global 10-day resolution Surface Soil Moisture dataset), and SSM-Feng (Regional multimodal fusion of surface soil moisture data in China). The temporal and spatial variation of the linear trend and abrupt change characteristics at seasonal and annual scale were explored. The results are as follows: 1) ESA-CCI, GLDAS, and MERRA-2 showed a slow increase in annual soil moisture tendency at a rate of less than 0.001 m3/m3/year, while ERA5 and SSM-Feng showed a significant decreasing linear trend at a rate of 1.31 × 10−4 m3/m3/year and 1.01 × 10−4 m3/m3/year (p < 0.05), respectively. 2) In autumn and winter, only GLDAS and MERRA-2 showed significant increasing trends. In the growing season (i.e., from April to October), the soil moisture of ESA-CCI, GLDAS, and MERRA-2 significantly increased at the rates of 3.29 × 10−4 m3/m3/year, 3.30 × 10−4 m3/m3/year, and 6.64 × 10−4 m3/m3/year (p < 0.05), respectively. 3) ERA5 and ESA-CCI have frequent abrupt changes in 1984, 1987, and 2006 for ERA5, 2010–2012 and 2019–2020 for ESA-CCI. 4) In terms of spatial variations, most datasets show that soil moisture has increased across most regions. The ERA5, ESA-CCI, GLDAS, MERRA-2, and SSM-Feng datasets show decreased soil moisture in the Tarim Basin. The conclusions of this study deepen the understanding of temporal and spatial variation in soil moisture in arid areas of Northwest China. Through these conclusions, a certain theoretical basis can be provided for the complex water cycle process in the arid region.

Estimation of surface soil moisture by combining a structural equation model and an artificial neural network (SEM-ANN)

Soil moisture is an important variable of the environment that directly affects hydrological, ecological, and climatic processes. However, owing to the influence of soil type, soil structure, topography, vegetation, and human activities, the distribution of soil water content is spatially heterogeneous. It is difficult to accurately monitor the distribution of soil moisture over large areas. To investigate the direct or indirect influence of various factors on soil moisture and obtain accurate soil moisture inversion results, we used structural equation models (SEMs) to determine the structural relationships between these factors and the degree of their influence on soil moisture. These models were subsequently transformed into the topology of artificial neural networks (ANN). Finally, a structural equation model coupled with an artificial neural network was constructed (SEM-ANN) for soil moisture inversion. The results showed the following: (1) The most important predictor of the spatial variability of soil moisture in the April was the temperature–vegetation dryness index, while land surface temperature was the most important predictor in the August; (2) After the ANN model was improved, the inversion accuracy of surface soil moisture by SEM-ANN model was improved, and the R2 of verification set was increased by 0.01 and 0.02 in April and August, respectively, and the relative analysis error was reduced by 0.5 % and 1.13 %. (3) There were no significant differences in soil moisture distribution trends between the April and August.

Processes underlying the assembly of plant communities: The role of environmental filtering heterogeneity in a competition–dispersal experiment

AbstractQuestionInterspecific competition and seed dispersal are key mechanisms that may influence plant community assembly, but their relative importance remains difficult to disentangle as they can be affected by abiotic conditions (environmental filtering). We experimentally studied the balance between these processes in a Molinia wet meadow plant community in which abiotic constraints are mostly represented by flooding, while agricultural practices such as mowing prevent the dominance of a few competitive species. We hypothesized that (H1) mowing, by decreasing competitive exclusion and (H2) seed addition, by increasing immigration, both enhance plant species diversity, and that (H3) small variations in flooding intensity become secondary to these processes in determining species assembly.LocationFrench National Nature Reserve “Etang de Cousseau”, Nouvelle Aquitaine, France.MethodsIn a three‐year experiment, we added seeds of 17 species characteristic of Molinia wet meadows and managed above‐ground competition through mowing. We measured spatial variations in soil moisture as an indicator of the flooding regime. We recorded species relative cover, species richness and evenness annually, and measured light availability, linked with canopy openness, twice.ResultsFrom the beginning of the experiment, small‐scale variations in soil moisture mainly affected species richness, composition and evenness, and canopy openness. Mowing significantly increased light availability and evenness, and affected the relative cover of several species, but not species richness. The addition of seeds did not influence any of the variables monitored during the experiment.ConclusionOur study helps to disentangle processes underlying community assembly in wet meadows. It demonstrates significant and rapid effects of mowing on canopy opening and on community assembly, while seed addition had only a slight effect, possibly explained by an insufficient decrease in competitive interactions through mowing or unmeasured processes. It also shows that abiotic heterogeneity remains a primary driver of community assembly, even on a small scale. Finally, our results suggest that the respective weight of the different filters (dispersion, environment, competition) is subject to interannual variability or environmental stochasticity.