Soil Moisture Fields Research Articles

Enhancing field soil moisture content monitoring using laboratory-based soil spectral measurements and radiative transfer models

Enhancing field soil moisture content monitoring using laboratory-based soil spectral measurements and radiative transfer models

Accurate Characterization of Soil Moisture in Wheat Fields with an Improved Drought Index from Unmanned Aerial Vehicle Observations

Soil moisture content is a crucial indicator for understanding the water requirements of crops. The effective monitoring of soil moisture content can provide support for irrigation decision-making and agricultural water management. Traditional ground-based measurement methods are time-consuming and labor-intensive, and point-scale monitoring cannot effectively represent the heterogeneity of soil moisture in the field. Unmanned aerial vehicle (UAV) remote sensing technology offers an efficient and convenient way to monitor soil moisture content in large fields, but airborne multispectral data are prone to spectral saturation effects, which can further affect the accuracy of monitoring soil moisture content. Therefore, we aim to construct effective drought indices for the accurate characterization of soil moisture content in winter wheat fields by utilizing unmanned aerial vehicles (UAVs) equipped with LiDAR, thermal infrared, and multispectral sensors. Initially, we estimated wheat plant height using airborne LiDAR sensors and improved traditional spectral indices in a structured manner based on crop height. Subsequently, we constructed the normalized land surface temperature–structured normalized difference vegetation index (NLST-SNDVI) space by combining the SNDVI with land surface temperature and calculated the improved Temperature–Vegetation Drought Index (iTVDI). The results are summarized as follows: (1) the structured spectral indices exhibit better resistance to spectral saturation, making the NLST-SNDVI space closer to expectations than the NLST-NDVI space, with higher fitting accuracy for wet and dry edges; (2) the iTVDI calculated based on the NLST-SNDVI space can effectively characterize soil moisture content, showing a significant correlation with measured surface soil moisture content; (3) the global Moran’s I calculated based on iTVDI deviations ranges between 0.18 and 0.30, all reaching significant levels, indicating that iTVDI has good spatial applicability. In conclusion, this study proved the effectiveness of the drought index based on a structured vegetation index, and the results can provide support for crop moisture monitoring and irrigation decision-making in the field.

Effects of soil moisture and an AC-electric field on the phytoremediation of the Cd-contaminated soil

ABSTRACT Phytoremediation enhanced by electric field has been considered a green and low-cost technology for remediating heavy metal-contaminated soils. Soil moisture is a main environmental factor that affects Cd availability in the soil. However, the effects of soil moisture and AC-electric field on the remediation efficiency of willow (Salix spp.) and S. Alfredii interplanted together remain unclear. In the present study, we designed four treatments (60% soil field capacity, 60% soil field capacity + 0.5 V·cm−1 AC, 100% soil field capacity, 100% soil field capacity + 0.5 V·cm−1 AC) to explore the impacts of soil moisture and AC-electric field on soil Cd availability and Cd accumulation in plants. The results showed that the application of an AC-electric field significantly increased soil Cd availability by 20.9% and 10.8% under both 60% and 100% soil field capacity, respectively. Both high water with and without AC-electric field treatments reduced the proportion of acid-extractable and reducible Cd of soil but increased the proportion of residual Cd. Compared with the control, an AC-electric field with 60% soil field capacity significantly enhanced the biomass of S. Alfredii shoots by 31.2% and increased Cd accumulation in willow leaves and S. Alfredii shoots by 14.6% and 32.3%, respectively. In addition, the biomass production of willow was significantly enhanced but the uptake of Cd by willow was dramatically decreased under an AC-electric field with high water treatment. Therefore, these results suggest that the AC-electric field combined with 60% soil field capacity may be a more promising remediation technique to clean up the Cd-contaminated soil.

Estimating the soil water retention curve by the HYPROP-WP4C system, HYPROP-based PCNN-PTF and inverse modeling using HYDRUS-1D

Due to the difficulty of direct field measurement, soil hydraulic properties are often obtained in laboratory settings using small undisturbed soil samples or estimated indirectly through pedotransfer functions (PTFs). The pseudo-continuous pedotransfer function (PCNN-PTF) is a neural network-based approach for estimating soil hydraulic properties. The main objective of this study was to use field soil moisture and tension data to assess soil water retention curves obtained from the HYPROP-WP4C system and the HYPROP-based PCNN-PTF. The in-situ soil water retention data were simultaneously acquired using Acclima TDT and MeterGroup MPS-6 sensors (every 30 min, May to September 2020) from 11 hybrid bermudagrass plots under different irrigation treatments in Riverside, California. We utilized extended evaporation and dewpoint methods using HYPROP-WP4C (Meter Group Inc., USA) devices to obtain lab-measured SWRCs. In addition, SWRCs were estimated from Rosetta (Schaap et al., 2001) and by inverse modeling in HYDRUS-1D utilizing in-situ moisture retention data. Although the hysteresis impacted field data, overall, there was a good agreement between the in-situ and lab water retention data for most samples, especially within the pF range of 2–3.5. The PCNN-PTF outperformed Rosetta in estimating laboratory (RMSE=0.034 cm3 cm−3 vs 0.063 cm3 cm−3) and in-situ soil moisture data (RMSE=0.048 cm3 cm−3 vs 0.082 cm3 cm−3). Inverse modeling of in-situ data also performed well in estimating the SWRC (RMSE=0.043 cm3 cm−3); however, further attention is required in dry and saturated soil conditions. We developed a simple, free, and easy-to-access tool called PC-PTF for estimating the SWRC using the PCNN-PTF model evaluated in this study. The PC-PTF can be accessed from the Verdi Water Management Group website: http://www.ucrwater.com/software-and-tools.html.

Monitoring soil moisture in cotton fields with synthetic aperture radar and optical data in arid and semi-arid regions

Monitoring soil moisture in cotton fields with synthetic aperture radar and optical data in arid and semi-arid regions

Time domain reflectometry coil probes for measuring thin surface layer soil moisture: Field tests over 21 years (2002–2022) under highly variable climate conditions in Mongolia

A coil probe (CP) for time-domain reflectometry (TDR) with a sensor length of 40 mm (CP40) was developed for long-term field soil moisture measurements in a thin surface soil layer (0–3 cm depth). In laboratory, soil moisture measurements of CP40 were nearly identical to those of a traditional two-rod type TDR (2RTDR) probe (rod length: 15 cm, diameter: 0.3 cm, spacing: 3 cm). The CP40 measurement accuracy was between 0.01 m3/m3 and 0.03 m3/m3. For long-term field soil moisture measurements, five CP40 units were installed in the highly wetted soil at the Sanzai site (SS), which is in the permafrost area of the Taiga, and dry soil at the Mandalgobi site (MGS) in the semi-arid area of Mongolia. Four units accurately measured soil moisture at both sites over six years (2002–2007and 2008–2009). Three units succeeded in conducting precisely continuous soil moisture measurements between 2008 and 2022 at the MGS. Two units successfully measured soil moisture over 21 years at both the sites. The representativeness of the CP40 soil moisture measurements in highly wetted soils was low because of heavy rainfall and soil heterogeneity. The accuracy of CP40 soil moisture measurements in highly wetted soils was slightly lower than that of the traditional 2RTDR probe. However, the accuracy of the CP40 soil-moisture measurements in the dry soil was comparable to that of the traditional 2RTDR probe. CP40 units are durable and their field soil moisture measurements demonstrate stable and precise performance (bias, RMSE), even under severe environmental conditions.

Impact of rootzone construction and irrigation methods on soil moisture in sports fields under greenhouse conditions

Impact of rootzone construction and irrigation methods on soil moisture in sports fields under greenhouse conditions

Combing transfer learning with the OPtical TRApezoid Model (OPTRAM) to diagnosis small-scale field soil moisture from hyperspectral data

Accurate, timely, and continuous soil moisture information is helpful for crop stress diagnosis and irrigation management decision. OPtical TRApezoid Model (OPTRAM) based on optical satellite data has been proven to be an effective method for assessing soil moisture status. However, the applicability of OPTRAM to small-scale field soil moisture assessment remains to be explored. In this study, we propose a strategy for the genetically parameterized OPTRAM and evaluate its applicability on Unmanned Aerial Vehicle (UAV) high-resolution hyspectral data. The results showed that: (1) When OPTRAM was used to genetically parameterized with PROSAIL generated dataset, 46 characteristic narrowband bands (|R|= 0.52–0.78) were determined in the spectral region of near infrared (NIR) (750–850 nm) and SWIR (1060–1080 and 1450–1500 nm); (2) By fine-tuned soil moisture estimation model using transfer learning strategy, the reliable soil moisture estimation was achieved in three crops (R2=0.57–0.64; RMSE=0.008–0.022 m3m−3);(3) Compared to soil moisture estimation model using a single spectral region (NIR or SWIR), the DSWC model that combine NIR and SWIR was more effective for tracking soil moisture; (4) The scale effect was observed when the fine-tuned soil moisture estimation model was applied on the high-resolution UAV images. The model performance was stable in pixel size of 1–7 cm and began to drop at pixel size of 11 cm. The above results advance the application of OPTRAM on small farmland soil moisture assessment and demonstrate the application potential of OPTRAM on narrow-band hyperspectral data. This study provides a new candidate for the use of hyperspectral data to estimate soil moisture, and scientific support for precision agriculture and irrigation scheduling.

Within‐field soil moisture variability and time‐invariant spatial structures of agricultural fields in the US Midwest

AbstractUnderstanding 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.

Calibration of Hybrid-Maize Model for Simulation of Soil Moisture and Yield in Production Corn Fields

Model calibration is essential for acceptable model performance and applications. The Hybrid-Maize model, developed at the University of Nebraska-Lincoln, is a process-based crop simulation model that simulates maize growth as a function of crop and field management and environmental conditions. In this study, we calibrated and validated the Hybrid-Maize model using soil moisture and yield data from eight commercial production fields in two years. We used a new method for the calibration and multi-parameter optimization (MPO) based on kriging with modified criteria for selecting the parameter combinations. The soil moisture-related parameter combination (SM-PC3) improved simulations of soil water dynamics, but improvement in model performance is still required. The grain yield-related parameter combination significantly improved the yield simulation. We concluded that the calibrated model is good enough for irrigation water management at the field scale. Future studies should focus on improving the model performance in simulating total soil water (TSW) dynamics at different soil depths by including more soil water processes in a more dynamic manner.

Urban irrigation reduces moist heat stress in Beijing, China

AbstractAlthough urban irrigation can modulate local hydrothermal conditions and mitigate urban heat island effects, its impact on moist heat stress (MHS) is poorly understood. Employing the Weather Research and Forecasting Single-Layer Urban Canopy Model (WRF-SLUCM), we evaluated the effect of urban irrigation on the MHS in Beijing, China, and found that the updated initial soil moisture (SM) field improved the simulation of temperature, relative humidity, and wind speed. Besides, urban irrigation reduced urban and rural MHS, and particularly reduced afternoon and evening MHS by up to 1.2 °C but increased morning MHS by up to 0.4 °C. In addition, the effect of different irrigation times on MHS showed that irrigation at 02 and 20 h increased urban and rural MHS, with the best cooling effect at 00 and 13 h, which reduced the MHS by up to 2.65 °C in urban areas and 0.71 °C in rural areas. The findings highlighted mechanistically the effect of urban irrigation on MHS and shed light on how to mitigate urban heat island effects on urban sustainable development.

Identifying the Spatial Heterogeneity and Driving Factors of Satellite-Based and Hydrologically Modeled Profile Soil Moisture

Profile soil moisture (PSM), the soil water content in the whole soil layer, directly controls the major processes related to biological interaction, vegetation growth, and runoff generation. Its spatial heterogeneity, which refers to the uneven distribution and complexity in space, influences refined spatial management and decision-making in ecological, agricultural, and hydrological systems. Satellite instruments and hydrological models are two important sources of spatial information on PSM, but there is still a gap in understanding their potential mechanisms that affect spatial heterogeneity. This study is designed to identify the spatial heterogeneity and the driving factors of two PSM datasets; one is preprocessed from a satellite product (European Space Agency Climate Change Initiative, ESA CCI), and the other is simulated from a distributed hydrological model (the DEM-based distributed rainfall-runoff model, DDRM). Three catchments with different climate conditions were chosen as the study area. By considering the scale dependence of spatial heterogeneity, the profile saturation degree (PSD) datasets from different sources (shown as ESA CCI PSD and DDRM PSD, respectively) during 2017 that are matched in terms of spatial scale and physical properties were acquired first based on the calibration data from 2014–2016, and then the spatial heterogeneity of the PSD from different sources was identified by using spatial statistical analysis and the semi-variogram method, followed by the geographic detector method, to investigate the driving factors. The results indicate that (1) ESA CCI and DDRM PSD are similar for seasonal changes and are overall consistent and locally different in terms of the spatial variations in catchment with different climate conditions; (2) based on spatial statistical analysis, the spatial heterogeneity of PSD reduces after spatial rescaling; at the same spatial scale, DDRM PSD shows higher spatial heterogeneity than ESA CCI PSD, and the low-flow period shows higher spatial heterogeneity than the high-flow period; (3) based on the semi-variogram method, both ESA CCI and DDRM PSD show strong spatial heterogeneity in most cases, in which the proportion of C/(C0 + C) is higher than 0.75, and the spatial data in the low-flow period mostly show larger spatial heterogeneity, in which the proportion is higher than 0.9; the spatial heterogeneity of PSD is higher in the semi-arid catchment; (4) the first three driving factors of the spatial heterogeneity of both ESA CCI and DDRM PSD are DEM, precipitation, and soil type in most cases, contributing more than 50% to spatial heterogeneity; (5) precipitation contributes most to ESA CCI PSD in the low-flow period, and there is no obvious high contribution of precipitation to DDRM PSD. The research provides insights into the spatial heterogeneity of PSM, which helps develop refined modeling and spatial management strategies for soil moisture in ecological, agricultural, and hydrological fields.

Testing a Soil Moisture Sensor Based Irrigation Controller in both Laboratory and Field Conditions for Fargo Silty Clay Soils

Highlights A soil matric potential sensor-based irrigation system was tested in lab and field experiments. Soil matric potential and soil moisture levels for each threshold were determined for Fargo silty clay soil through lab experiment. Thresholds 5, 6, 7, and 8 at -35, -45, -55, and -70 kPa were tested through the field experiment for watermelon production. Abstract. Irrigation scheduling plays a pivotal role in water management. Conventional irrigation methods often struggle to maintain consistent moisture across fields due to uniform water application irrespective of field conditions and soil moisture levels. Automatic soil moisture sensors offer a solution by tailoring irrigation based on field conditions. These systems trigger irrigation when soil is too dry or skip it when moisture levels are sufficient. Hence, setting the right threshold value is key for sensor-based irrigation. This study involved laboratory testing of an irrigation controller, and determining soil matric potential and volumetric water content at various thresholds (1 to 9) for Fargo Silty Clay soil. Subsequently, a field experiment assessed its performance for a drip irrigation system at different thresholds (5, 6, 7, and 8) for watermelon cultivation, both with and without clear plastic mulch, in a randomized layout. Thresholds 5, 6, 7, and 8 (-35, -45, -55, and -70 kPa), along with no irrigation, were considered with one irrigation event daily. Results from laboratory and field tests underscore the controller’s efficacy in managing drip irrigation timing and duration. Notably, threshold 7 (-55 kPa) combined with clear plastic mulch emerged as the most optimal treatment, yielding the highest watermelon production of 129.6 tons/ha. Moreover, this approach delivered premium produce quality, but utilizing only 142.2 mm of irrigation water. This study emphasizes the role of precise irrigation scheduling. By integrating smart technology and thoughtful threshold setting, agricultural practices can be elevated, yielding abundant crops while conserving water and promoting sustainability. Keywords: Controller, Drip irrigation, Mulch, Sensor, Watermelon.

Funções de pedotransferência para estimativa da condutividade hidráulica e umidade do solo no bioma Cerrado

ABSTRACT The Cerrado biome is a strategic region for Brazilian agriculture, and obtaining physical hydraulic properties is fundamental to understanding the dynamics of soil water and its impact on productivity. However, the lack and difficulty of obtaining such properties opens an opportunity to use pedotransfer functions (PTFs). In the context, the objective of the present study was to develop PTFs using multiple linear regression to estimate hydraulic conductivity of the saturated soil (Ks) and soil moisture at tensions of 0, 6, 10, 33, 100, and 1,500 kPa for the Cerrado biome. For this, two different predictor datasets were used. Dataset 1 consists of sand, silt, clay, bulk density, particle density, total porosity, microporosity, and macroporosity, and dataset 2 consists of the variables in dataset 1 plus soil moisture at field capacity (FC) and soil moisture at the permanent wilting point (PMP). Dataset 2 presented the best performance compared to Dataset 1 for estimating Ks and soil moisture, highlighting the importance of moisture in FC and PWP as predictors in the development of PTFs. However, the PTFs developed for Ks presented a low predictive capacity in all predictor datasets, unlike the soil moistures that presented high precision, with R² greater than 0.8 and errors close to zero.

Wavelet analysis for quantifying NavIC-IR multipath signal frequency with the aim of determining soil moisture in agricultural fields

ABSTRACTThis paper explores the potential of Global Navigation Satellite Systems (GNSS) multipath signals for soil moisture retrieval, specifically focusing on Navigation with Indian Constellations (NavIC). Utilizing GNSS-Interferometric Reflectometry (IR) techniques, wavelet analysis is employed to extract the low-frequency signal sensitive to soil moisture from the reflected multipath signals. The reconstructed low-frequency multipath signal is then subjected to Lomb-Scargle Periodogram (LSP) analysis to determine multipath frequency. A comprehensive sensitivity analysis reveals the dependency of multipath frequency on field soil moisture, particularly in the presence of vegetation. The sensitivity varies across different crop growth stages, categorized by crop height. The Pearson correlation coefficient (r) for crop height less than one wavelength was 0.81 and for greater than one wavelength it was 0.67. The sensitivity of multipath frequency for soil moisture was 0.48 rad s−1 cm3 cm−3 and 0.47 rad s−1 cm3 cm−3 for crop height less than one wavelength and crop height greater than one wavelength respectively.This research underscores the potential of GNSS-IR techniques, incorporating wavelet analysis and LSP, for soil moisture sensitivity, especially in areas with varying vegetation and crop growth stages.

Surface soil moisture estimation in bare agricultural soil using modified Dubois model for Sentinel-1 C-band SAR data

Surface soil moisture has vital role in water energy balance, climate change and agriculture mainly for crop water requirements and irrigation scheduling. Microwave remote sensing with its unique characteristics of high penetration and sensitivity towards dielectric constant, has enabled the researchers to explore various techniques for soil moisture estimation. With the launch of Sentinel-1 (A&B) Synthetic Aperture Radar (SAR) satellites, the hindrance in accessing high spatial and temporal resolution data is eliminated. The current study focuses on surface soil moisture estimation for bare agricultural fields in the semi-arid region. Field soil moisture up to 5 cm depth using HydraGo Probe sensor and surface roughness synchronizing with satellite pass dates were collected from total 102 locations spanning four dates. Volumetric and sensor-based soil moisture are well correlated with R2 = 0.85. The Modified Dubois Model (MDM) was applied to obtain the relative permittivity of the soil for the backscattering coefficient (σ◦) for VV polarization, which is used as one of the inputs in universal Topp’s model for soil moisture calculation. Model derived soil moisture is well correlated with ground-based soil moisture for the entire range of the soil moisture (0.02-0.18 m3m-3) with R2 = 0.85 and RMSE=0.005. The entire soil moisture was categorized in three soil moisture ranges to evaluate the sensitivity. The highest correlation was observed for 0.06-0.1 m3m-3 with R2 = 0.73 and RMSE=0.003 followed by 0.015-0.6 m3m-3 with R2 = 0.81 and RMSE=0.001 and 0.11-0.18 m3m-3 with R2 = 0.48 and RMSE=0.019 which is significantly low. Performance accuracy of MDM is encouraging for bare soil moisture estimation for even the lower range of surface soil moisture.

Soil Hydrological Properties’ Response to Long-Term Grazing on a Desert Steppe in Inner Mongolia

Soil hydrological properties play an important role in maintaining ecosystem functions. It is critical to understand how those properties respond to human disturbance especially in semi-arid areas. In the present study, we investigated the effects of different long-term grazing intensities (no grazing, light grazing, moderate grazing, and heavy grazing) on eight parameters that related to soil hydrological properties in different soil depths based on a grazing platform that was established in 2004 on a desert steppe in Inner Mongolia. The relationships among different parameters and between hydrological and chemical properties were also analyzed. The results show that grazing intensity, soil depth, and their interaction all have significant effects on soil moisture content, saturation capacity, field capacity, and bulk density. At different soil depths (0–10, 10–20, and 20–30 cm), soil bulk density was negatively correlated with saturation capacity, capillary capacity, and non-capillary porosity but positively correlated with field capacity. Furthermore, we found that field capacity and soil moisture content were positively correlated but non-capillary soil porosity was negatively correlated with most soil nutrients. Our results indicate that overgrazing has detrimental effects on soil hydrological properties which may further negatively affect soil nutrient content. Light grazing may be an optimal grazing intensity on this semi-arid steppe with respect to soil hydrological properties.

Using a Reanalysis-Driven Land Surface Model for Initialization of a Numerical Weather Prediction System

Abstract Realistic initialization of the land surface is important to produce accurate NWP forecasts. Therefore, making use of available observations is essential when estimating the surface state. In this work, sequential land surface data assimilation of soil variables is replaced with an offline cycling method. To obtain the best possible initial state for the lower boundary of the NWP system, the land surface model is rerun between forecasts with an analyzed atmospheric forcing. We found a relative reduction of 2-m temperature root-mean-square errors and mean errors of 6% and 12%, respectively, and 4.5% and 11% for 2-m specific humidity. During a convective event, the system was able to produce useful (fractions skill score greater than the uniform forecast) forecasts [above 30 mm (12 h)−1] down to a 100-km length scale where the reference failed to do so below 200 km. The different precipitation forcing caused differences in soil moisture fields that persisted for several weeks and consequently impacted the surface fluxes of heat and moisture and the forecasts of screen level parameters. The experiments also indicate diurnal- and weather-dependent variations of the forecast errors that give valuable insight on the role of initial land surface conditions and the land–atmosphere interactions in southern Scandinavia.

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

Abstract We develop a deep learning–based convolutional-regression model that estimates the volumetric soil moisture content in the top ∼5 cm of soil. Input predictors include Sentinel-1 (active radar) and Sentinel-2 (multispectral imagery), as well as geophysical variables from SoilGrids and modeled soil moisture fields from SMAP and GLDAS. The model was trained and evaluated on data from ∼1000 in situ sensors globally over the period 2015–21 and obtained an average per-sensor correlation of 0.707 and ubRMSE of 0.055 m3 m−3, and it can be used to produce a soil moisture map at a nominal 320-m resolution. These results are benchmarked against 14 other soil moisture evaluation research works at different locations, and an ablation study was used to identify important predictors. Significance Statement Soil moisture is a key variable in various agriculture and water management systems. Accurate and high-resolution estimates of soil moisture have multiple downstream benefits such as reduced water wastage by better understanding and managing the consumption of water, utilizing smarter irrigation methods and effective canal water management. We develop a deep learning–based model that estimates the volumetric soil moisture content in the top ∼5 cm of soil at a nominal 320-m resolution. Our results demonstrate that machine learning is a useful tool for fusing different modalities with ease, while producing high-resolution models that are not location specific. Future work could explore the possibility of using temporal input sources to further improve model performance.

Analysis of Hotspots and Trends in Soil Moisture Research since the 21st Century

Soil moisture is a key factor in ecosystems that profoundly affects carbon, nitrogen, and water cycles on land surfaces, vegetation growth, and climate change. Consequently, numerous scholars have researched and authored scientific literature on soil moisture and related topics. Using the Web of Science database, we conducted a bibliometric analysis of 60,581 papers published in the field of soil moisture between 2000 and 2022. The findings revealed the following trends. (1) The number of publications on soil moisture has consistently increased in the 21st century at an increasing rate. For instance, although the annual increase was only 94 publications in 2005, it surged to 321 publications in 2020. (2) The United States (US), China, and developed European countries emerged as primary research institutions and authors. The US occupies a leading position in soil moisture research, boasting the highest number of publications and total citations in the field, whereas China ranks second in both publications and total citations. (3) Regarding international collaboration, the US has established close partnerships with numerous international research institutions. However, China’s international cooperation in this field requires improvement. (4) The Journal of Hydrology holds the top position in terms of both the total number of published articles and citations. Research on water resources ranked first in terms of its H-index. (5) Keyword analysis highlighted several current research hotspots, including the coupled covariance effect of soil moisture and land surface environmental factors in the context of climate change, soil moisture utilization rate, crop yield, influence mechanism of soil moisture on soil ecosystem structure, and development of high-precision soil moisture data products. In conclusion, this study provides a systematic review of the research hotspots and trends in soil moisture studies in the 21st century. The objective is to offer a comprehensive reference to aid in understanding the evolutionary patterns of soil moisture research in multiple dimensions.