Surface Soil Moisture Content Research Articles

Impacts of Climatic Fluctuations and Vegetation Greening on Regional Hydrological Processes: A Case Study in the Xiaoxinganling Mountains–Sanjiang Plain Region, Northeastern China

The Xiaoxinganling Mountains–Sanjiang Plain region represents a crucial ecological security barrier for the Northeast China Plain and serves as a vital region for national grain production. Over the past two decades, the region has undergone numerous ecological restoration projects. Nevertheless, the combined impact of enhanced vegetation greening and global climate change on the regional hydrological cycle remains inadequately understood. This study employed the distributed hydrological model ESSI-3, reanalysis datasets, and multi-source satellite remote sensing data to quantitatively evaluate the influences of climate change and vegetation dynamics on regional hydrological processes. The study period spans from 2000 to 2020, during which there were significant increases in regional precipitation and leaf area index (p < 0.05). The hydrological simulation results exhibited strong agreement with observed river discharge, evapotranspiration, and terrestrial water storage anomalies, thereby affirming the ESSI-3 model’s reliability in hydrological change assessment. By employing both a constant scenario that solely considered climate change and a dynamic scenario that integrated vegetation dynamics, the findings reveal that: (1) Regionally, climate change driven by increased precipitation significantly augmented runoff fluxes (0.4 mm/year) and water storage components (2.57 mm/year), while evapotranspiration trends downward, attributed primarily to reductions in solar radiation and wind speed; (2) Vegetation greening reversed the decreasing trend in evapotranspiration to an increasing trend, thus exerting a negative impact on runoff and water storage. However, long-term simulations demonstrated that regional runoff fluxes (0.38 mm/year) and water storage components (2.21 mm/year) continue to increase, mainly due to precipitation increments surpassing those of evapotranspiration; (3) Spatially, vegetation greening altered the surface soil moisture content trend in the eastern forested areas from an increase to a decrease. These findings suggested that sub-regional ecological restoration initiatives, such as afforestation, significantly influence the hydrological cycle, especially in areas with higher vegetation greening. Nevertheless, persistent increases in precipitation could effectively mitigate the moisture deficits induced by vegetation greening. The study’s outcomes provide a basis for alleviating concerns regarding potential water consumption risks associated with future ecological restoration and extensive vegetation greening projects, thereby offering scientific guidance for sustainable water resource management.

A novel vegetation-water resistant soil moisture index for remotely assessing soil surface moisture content under the low-moderate wheat cover

Soil surface moisture content (SMC) is one of the most critical and fundamental indicators of crop drought because water is vital for vegetation growth. In recent decades, satellite high-spatial-resolution optical multi-spectral remote-sensing sensors have entered a new stage. However, current optical remote sensing SMC indices (SMIs) are sensitive to cropland SMC and vegetation water content (VWC). From dry to saturated, the soil spectra decreased quickly as the SMC increased. For the vegetation spectra, the short-wave infrared (SWIR) bands decreased as the VWC increased. The differences in the responses of near-infrared (NIR) and SWIR bands to water may help distinguish soil moisture from mixed soil-vegetation spectra. This study focuses on (a) analyzing how soil and vegetation reflectance are affected by VWC and SMC, (b) designing a new vegetation-water resistant soil moisture index (VRSMI) based on the differences in the responses of NIR bands and SWIR bands to water, and (c) evaluating the potential of using VRSMI to remotely estimate SMC in low-moderate crop-covered regions. VRSMI uses SWIR1-SWIR2 as the numerator to detect the water absorption features and NIR×(SWIR1)0.5 as the denominator to weaken the vegetation effect. VRSMI and soil-VRSMI (VRSMIs) were tested using laboratory- and field-based spectra datasets. Our study presents the following conclusions: (1) The differences in the responses of NIR and SWIR bands to water help distinguish the effects of soil moisture on mixed soil-vegetation spectra. (2) VRSMIs can provide high-performance SMC estimates under low-moderate vegetation cover (NDVI < 0.55). (3) VRSMIs can be used for cropland SMC evaluation without considering crop VWC (laboratory-based spectra dataset: RMSE = 0.07–0.11, R2 = 0.96–0.98; field-based spectra dataset: RMSE = 0.17–0.18, R2 = 0.63–0.79). Our study indicates that VRSMI and VRSMIs can provide high-performance relative SMC estimates under low-to-moderate vegetation cover.

The Influence of Cropping Systems and Tillage Intensity on Soil CO2 Exchange Rate

In order to control the amount of greenhouse gas emissions from agriculture, it is necessary to select the appropriate crop rotation and tillage intensity depending on the soil type and climatic conditions. However, their implementation in agricultural management methods has been insufficient until now. The main objective of this study was to investigate the changes and dependences in soil net CO2 exchange rate (NCER) and main physico-chemical parameters under different tillage (conventional (CT) and no tillage (NT)) and crop rotation systems. Cropping systems significantly affected the amount of nutrients, but did not affect pH and organic carbon; otherwise, tillage vs. cropping systems had no significant effects on the soil chemical parameters analyzed. The data revealed that in NT treatments, the NCER was 28% higher than in CT. Different crop rotations also revealed a significant effect on NCER from the soil. Carbon dioxide fluxes increased in cropping systems where a higher share of catch crops were included. In NT systems, a comparatively higher soil moisture content was registered. In addition, the rotations with catch crops produced a higher (by 1–3%) soil moisture content. The temperature of the soil surface was not significantly affected by tillage or cropping systems; nevertheless, a trend towards higher soil surface temperatures in CT was determined, which might be affected by enhanced air circulation in the pores. Soil NCER increase correlated negatively with higher soil surface moisture content, while its relationship was positive with soil surface temperature increase. In general, soil surface temperature and moisture were the most significant factors in explaining the fluctuation in NCER from Cambisols in Lithuania under moderate climatic conditions.

Characteristics and Estimation of Dew in the Loess Hilly Region of Northern Shaanxi Province, China

As a non-precipitation water source, dew is important for plant and animal survival and crop production in arid and water-scarce areas. This study assessed the amount of dew in a dry zone in a long-term (2016 to 2022) field observation experiment at the Ansai Experimental Station, a typical loess hilly area in China. Dew primarily occurred in summer and autumn, with a frequency of &gt;50%. The average annual dew amount was 29.20 mm, with an average annual rainfall of 641.8 mm. The average annual dew-to-rain ratio was 4.58%, and the average annual number of dew days was 143.6 d/a. The surface soil moisture content increased by approximately 1.02% with increasing dew amounts. The change in the soil moisture at a 5 cm depth was 0.14% on average and lagged substantially by 1 h. Using the Beysens model, the annual estimated and measured dew amounts in 2022 were 25.27 and 29.84 mm, respectively, and the annual normalized root mean square deviation (NRMSD) was 0.17. Thus, the Beysens model evaluated the dew amount in the study area well at the monthly and annual scales. The quantification of dew resources can provide support for the development, utilization, and management of limited water resources in arid areas, promoting more accurate decision-making for the sustainable development of water resources in the future.

Complement time-series UAV spectral data based on Ambrals kernel-driven model to monitor soil moisture content

ABSTRACT Continuous time-series spectral data are important for inversion of crop or soil information. UAV remote sensing is usually selected under clear and windless weather conditions, but it is not possible to have such weather every day, which results in the UAV not collecting continuous daily spectral information. To explore this issue, we focused on summer maize with four irrigation levels as the research subject. A UAV platform with a multispectral sensor was used to acquire measured spectra of the maize canopy. The solar zenith angle was calculated and substituted into the Ambrals kernel-driven model to obtain simulated spectral data for the maize canopy, and the time-series UAV spectral data were complemented. Then, four vegetation indices (VIs) were established using simulated and measured spectral data, respectively, and the accuracy of the simulated VIs was evaluated. Finally, the simulated and measured VIs were used to monitor and evaluate variations in soil surface moisture content, respectively, and provide irrigation warning. The results demonstrated that Ambrals kernel-driven model can be used to simulate the reflectance of maize canopy collected by UAV. The simulated reflectance can complement time-series UAV spectral data and be used to establish VIs, among which Structure Intensive Pigment Index (SIPI) was established with the highest accuracy (R = 0.729). The VIs established by simulated reflectance can be used to monitor soil surface moisture content, and the monitoring effect is similar to the measured data (R2 = 0.642, RMSE = 0.42). It can evaluate the soil moisture a few days after irrigation and ensure the continuity and timeliness of soil moisture data, so as to improve the crop irrigation system and carry out irrigation warning. These results have certain reference for the supplementation of time-series spectral data and farmland irrigation using UAV multispectral remote sensing.

The Influence of Shallow Groundwater on the Physicochemical Properties of Field Soil, Crop Yield, and Groundwater

The depth of shallow groundwater significantly influences crop growth and yield by altering the physicochemical properties of farmland soil profiles. Concurrently, shallow groundwater is subject to various changes, and it remains unclear how alterations in shallow groundwater depth within field soil impact soil physicochemical properties, crop yields, and the overall dynamics of groundwater transformations. To address these uncertainties, this study utilized a sample plot equipped with a volume lysimeter and implemented four distinct groundwater depths as treatment conditions: G0 (no groundwater depth), G1 (a groundwater depth of 40 cm), G2 (a groundwater depth of 70 cm), G3 (a groundwater depth of 110 cm), and G4 (a groundwater depth of 150 cm). This study was carried out on a weekly basis to monitor fluctuations in ion content in shallow groundwater and soil moisture after the summer maize harvest, and special attention was afforded to non-irrigation conditions. This study also scrutinized the distribution of salt and nutrients in soil profiles and assessed changes in summer maize yield. Very interesting findings were obtained by conducting the study. Firstly, the shallower the groundwater depth, the higher the water and salt content of the soil surface. Small, frequent rainfall events (precipitation ≤ 25 mm) facilitated the effective removal of salt from the soil surface. Despite increased rainfall contributing to salt ion dilution in groundwater, the risk of soil surface salinization increased at the surface level. Secondly, a linear relationship existed between groundwater depth and surface soil moisture and salt content. With every 10 cm increase in groundwater depth, the surface soil moisture and salt content decreased by 0.56% and 0.06 g/kg, respectively. Soil nutrients tended to accumulate in the surface layer, with nutrient content increasing with depth. However, C/N was not notably affected by groundwater depth. Thirdly, Na+ and K+ consistently dominated the soil surface. As soil salinity increased, the prevalence of Cl− and SO42− increased, with the rate of SO42− increase surpassing that of chlorine. HCO3− altered by rainfall served as an indicator of soil alkalization characteristics, while Na+ and K+ in soil, along with Cl− and SO42− derived from groundwater, represented soil salt composition and salinization trends. Ultimately, under the conditions of this study, the most favorable groundwater depth for the growth of summer maize was determined to be 1.1 m. Analyzing the impact of different shallow groundwater depths on the physicochemical properties of farmland soil enhances our understanding of the mechanisms of interaction between groundwater and soil in agricultural ecosystems. This knowledge is instrumental in significantly improving the soil environment, thereby ensuring optimal crop yields.

Evaluating pulse-reserve characteristics of Soil-Plant continuum in India using remote sensing

The pulse-reserve paradigm associated with the plant's increased water uptake following surface soil moisture pulses is not well understood in India. India has a very high spatio-temporal climate and ecological variability. Such ecological and climatological diversity makes the country-scale analysis of the pulse-reserve paradigm challenging. Here, we present the first analysis of this phenomenon using 9 years of space based passive microwave estimates of surface soil moisture and vegetation water content from both X-band and L-band. We observed from both datasets that the pulse-reserve paradigm is widespread in India across seasons and biomes. The seasonal climate in India drives the precipitation characteristics, thus uphold the pulse-reserve paradigm. The estimated Soil Moisture Threshold (SMT), at which the vegetation water content drops, has substantial spatio-temporal variations, unlike other regions worldwide. The comparisons between X-band and L-band products show significant differences in the magnitudes of soil moisture and vegetation optical depths due to inherent properties. However, most of the findings related to the pulse-reserve paradigm, specifically the sensitivity of processes on different factors, remain similar. We found a strong dependence of SMT on seasonal climate, vegetation greenness, and pulse soil moisture. The association of SMT with soil characteristics, such as clay fraction, is weak in India, contrary to global traits. Our analysis provides new insights into the plant water uptake mechanisms in India to improve the parameterization schemes for better representation of the soil–plant continuum structural and functional behavior.

Ensemble of optimised machine learning algorithms for predicting surface soil moisture content at a global scale

Abstract. Accurate information on surface soil moisture (SSM) content at a global scale under different climatic conditions is important for hydrological and climatological applications. Machine-learning-based systematic integration of in situ hydrological measurements, complex environmental and climate data, and satellite observation facilitate the generation of reliable data products to monitor and analyse the exchange of water, energy, and carbon in the Earth system at a proper space–time resolution. This study investigates the estimation of daily SSM using 8 optimised machine learning (ML) algorithms and 10 ensemble models (constructed via model bootstrap aggregating techniques and five-fold cross-validation). The algorithmic implementations were trained and tested using International Soil Moisture Network (ISMN) data collected from 1722 stations distributed across the world. The result showed that the K-neighbours Regressor (KNR) had the lowest root-mean-square error (0.0379 cm3 cm−3) on the “test_random” set (for testing the performance of randomly split data during training), the Random Forest Regressor (RFR) had the lowest RMSE (0.0599 cm3 cm−3) on the “test_temporal” set (for testing the performance on the period that was not used in training), and AdaBoost (AB) had the lowest RMSE (0.0786 cm3 cm−3) on the “test_independent-stations” set (for testing the performance on the stations that were not used in training). Independent evaluation on novel stations across different climate zones was conducted. For the optimised ML algorithms, the median RMSE values were below 0.1 cm3 cm−3. GradientBoosting (GB), Multi-layer Perceptron Regressor (MLPR), Stochastic Gradient Descent Regressor (SGDR), and RFR achieved a median r score of 0.6 in 12, 11, 9, and 9 climate zones, respectively, out of 15 climate zones. The performance of ensemble models improved significantly, with the median RMSE value below 0.075 cm3 cm−3 for all climate zones. All voting regressors achieved r scores of above 0.6 in 13 climate zones; BSh (hot semi-arid climate) and BWh (hot desert climate) were the exceptions because of the sparse distribution of training stations. The metric evaluation showed that ensemble models can improve the performance of single ML algorithms and achieve more stable results. Based on the results computed for three different test sets, the ensemble model with KNR, RFR and Extreme Gradient Boosting (XB) performed the best. Overall, our investigation shows that ensemble machine learning algorithms have a greater capability with respect to predicting SSM compared with the optimised or base ML algorithms; this indicates their huge potential applicability in estimating water cycle budgets, managing irrigation, and predicting crop yields.

Utilization of Remote Sensing Data in the Simulation of the Water and Heat Regime of Land Areas: A Review of Publications

The article presents a revue of the estimates of soil surface moisture, soil water content, and evapotranspiration as elements of water and heat regimes of land surface areas at various spatial scales, made with the use of remote sensing data for Earth in various spectral ranges. In most cases considered in the study, such estimates were obtained with the use of land surface models. A special section is focused on the results of estimating soil surface moisture and water content using satellite data from microwave range, including radar data. Estimates of soil surface moisture content obtained with the use of neural networks are presented. A brief description is given to international hydrological–atmosphere experiments carried out under world research projects aimed to obtain data on the processes of moisture and heat exchange between the land surface and the surface atmosphere layer. Land surface, satellite, and model databases that have been formed using the results of studies in the field under consideration since the mid-1980s are reviewed. Prospects of further studies based on the development of new multispectral instrumentation, the creation of new databases, and the use of a new generation of satellites—global-coverage microsatellites with high-resolution sensors are presented.

HYDRO-METEOROLOGICAL ANALYSIS OF EXTREME WEATHER IN INDONESIA USING COUPLED ATMOSPHERIC-HYDROLOGICAL MODEL

In recent years, hydro-meteorological hazard in the area involving coastlines has been dominated the disaster occurrence in Indonesia. In the last 10 years, the combination of the extreme weather and the environmental capacity degradation result in more flooding vulnerability. In 11-17 January 2021, heavy flood affected South Kalimantan, Indonesia, with causing many casualties. The flood characteristics was similar with the extreme flood in Jakarta in the preceding year in 2020, and more extreme floods occurred in the following year in 2022. Our goal is to analyze the extreme weather from the hydro-meteorological perspective and identify the key factor behind the high impact flood in South Kalimantan. Two factors, sea surface temperature (SST) and soil moisture (SM) content were highlighted and the sensitivity analysis was performed to identify their influence to the flood response at the time of the event. The coupled atmospheric model of Weather Research and Forecasting (WRF) and the hydrological model extension (WRF-Hydro) will be used to reconstruct the flood event.

Estimation of Surface and Subsurface Soil Moisture Using Microwave Remote Sensing: A Typical Analysis

Accurate measurement and monitoring of surface and subsurface soil moisture is essential for understanding hydrological processes, crop growth modeling, crop water requirement, and climate studies. Accurate measurement of the soil moisture content (SMC) in the root zone is essential for precise irrigation authority and plant water stress evaluation. However, the existing passive microwave satellite missions, Soil Moisture and Ocean Salinity (SMOS) and Soil Moisture Active Passive (SMAP), that operate at L-band, can only estimate the top 5 cm of soil moisture. Microwave remote sensing has proven to be a valuable tool for non-invasive soil moisture estimation. This research aims to investigate and develop a methodology for estimating surface and subsurface soil moisture using microwave data from Sentinel-1. The study was conducted to establish the relationship between surface &amp; the backscatter coefficient derived using the Sentinel-1 SAR microwave remote sensing satellite imagery, and relationship between surface and subsurface soil moisture at different depths, in the Godhra region. Two seasons namely summer (Zaid) and monsoon (Kharif) were taken into consideration to build up the relationship between surface soil moisture and co-polarization backscatter coefficient ( For the summer (Zaid) and monsoon (Kharif) seasons, the co-polarization backscatter coefficient ( and surface soil moisture (0-5, cm) were found to have a correlation in terms of R2 as 0.91 and 0.90, respectively. The study explores the relationship between microwave signals and surface soil moisture content (0-5, cm) and then the relationship between surface soil moisture and soil moisture at various depths were also modeled thereby contributing to improved soil moisture estimation techniques and applications. The value of the coefficient of determination (R2) of surface soil moisture (0-5, cm) to subsurface soil moisture at 6-20 cm, 21-40 cm, and 41-60 cm depths were found to be 0.60, 0.51, and 0.46, respectively, in the summer (Zaid) season. The value of the coefficient of determination (R2) of surface soil moisture (0-5, cm) to subsurface soil moisture at 6-20 cm, 21-40 cm, 41-60 cm, 61-80 cm, and 81-100 cm depths were found to be 0.83, 0.61, 0.51, 0.26, and 0.13, respectively. According to the study, it is observed that the relationship between co-polarization backscatter coefficient ( and soil moisture weakens as the depth of soil moisture increases. Overall, the regression models developed between the co-polarization backscatter coefficient ( and surface soil moisture showed very good results, whereas the regression models developed between the surface soil moisture and soil moisture at various depths showed reasonably acceptable results up to the depth of 60 cm. The findings in the present study suggest that Sentinel-1A C-band SAR data can be used to estimate surface soil moisture. It is also shown in this study that the surface soil moisture can be correlated with the subsurface soil moisture up to the depth of 60 cm, satisfactorily using regression equations.

Comparison of Soil Moisture Content Retrieval Models Utilizing Hyperspectral Goniometer Data and Hyperspectral Imagery From an Unmanned Aerial System

AbstractTo understand surface biogeophysical processes, accurately evaluating the geographical and temporal fluctuations of soil moisture is crucial. It is well known that the surface soil moisture content (SMC) affects soil reflectance at all solar spectrum wavelengths. Therefore, future satellite missions, such as the NASA Surface Biology and Geology mission, will be essential for mapping and monitoring global soil moisture changes. Our study compares two widely used moisture retrieval models: the multilayer radiative transfer model of soil reflectance (MARMIT) and the soil water parametric (SWAP)‐Hapke model. We evaluated the SMC retrieval accuracy of these models using unmanned aerial systems (UAS) hyperspectral imagery and goniometer hyperspectral data. Laboratory analysis employed hyperspectral goniometer data of sediment samples from four locations reflecting diverse environments, while field validation used hyperspectral UAS imaging and coordinated ground truth collected in 2018 and 2019 from a barrier island beach at the Virginia Coast Reserve Long‐Term Ecological Research site. The (SWAP)‐Hapke model achieves comparable accuracy to MARMIT using laboratory hyperspectral data but is less accurate when applied to UAS hyperspectral imagery than the MARMIT model. We proposed a modified version of the (SWAP)‐Hapke model, which achieves better results than MARMIT when applied to laboratory spectral measurements; however, MARMIT's performance is still more accurate when applied to UAS imagery. These results are likely due to differences in the models' descriptions of multiply‐scattered light and MARMIT's more detailed description of air‐water interactions.

Near-Surface Soil Moisture Characterization in Mississippi’s Highway Slopes Using Machine Learning Methods and UAV-Captured Infrared and Optical Images

Near-surface soil moisture content variation is a major factor in the frequent shallow slope failures observed on Mississippi’s highway slopes built on expansive clay. Soil moisture content variation is monitored generally through borehole sensors in highway embankments and slopes. This point monitoring method lacks spatial resolution, and the sensors are susceptible to premature failure due to wear and tear. In contrast, Unmanned/Uncrewed Aerial Vehicles (UAVs) have higher spatial and temporal resolutions that enable more efficient monitoring of site conditions, including soil moisture variation. The current study focused on developing two methods to predict soil moisture content (θ) using UAV-captured optical and thermal combined with machine learning and statistical modeling. The first method used Red, Green, and Blue (RGB) color values from UAV-captured optical images to predict θ. Support Vector Machine for Regression (SVR), Extreme Gradient Boosting (XGB), and Multiple Linear Regression (MLR) models were trained and evaluated for predicting θ from RGB values. The XGB model and MLR model outperformed the SVR model in predicting soil moisture content from RGB values. The R2 values for the XGB and MLR models were &gt;0.9 for predicting soil moisture when compared to SVR (R2 = 0.25). The Root Mean Square Error (RMSE) for XGB, SVR, and MLR were 0.009, 0.025, and 0.01, respectively, for the test dataset, affirming that XGB was the best-performing model among the three models evaluated, followed by MLR and SVR. The better-performing XGB and MLR models were further validated by predicting soil moisture using unseen input data, and they provided good prediction results. The second method used Diurnal Land Surface Temperature variation (ΔLST) from UAV-captured Thermal Infrared (TIR) images to predict θ. TIR images of vegetation-covered areas and bare ground areas of the highway embankment side slopes were processed to extract ΔLST amplitudes. The underlying relationship between soil surface thermal inertia and moisture content variation was utilized to develop a predictive model. The resulting single-parameter power curve fit model accurately predicted soil moisture from ΔLST, especially in vegetation-covered areas. The power curve fit model was further validated on previously unseen TIR, and it predicted θ with an accuracy of RMSE = 0.0273, indicating good prediction performance. The study was conducted on a field scale and not in a controlled environment, which aids in the generalizability of the developed predictive models.

The Application of RGB, Multispectral, and Thermal Imagery to Document and Monitor Archaeological Sites in the Arctic: A Case Study from South Greenland

Over the past decades, climate change has accelerated the deterioration of heritage sites and archaeological resources in Arctic and subarctic landscapes. At the same time, increased tourism and growing numbers of site visitors contribute to the degradation and manipulation of archaeological sites. This situation has created an urgent need for new, quick, and non-invasive tools and methodologies that can help cultural heritage managers detect, monitor, and mitigate vulnerable sites. In this context, remote sensing and the applications of UAVs could play an important role. Here, we used a drone equipped with an RGB camera and a single multispectral/thermal camera to test different possible archeological applications at two well-known archaeological sites in the UNESCO World Heritage area of Kujataa in south Greenland. The data collected were used to test the potential of using the cameras for mapping (1) ruins and structures, (2) the impact of human activity, and (3) soil moisture variability. Our results showed that a combination of RGB and digital surface models offers very useful information to identify and map ruins and structures at the study sites. Furthermore, a combination of RGB and NDVI maps seems to be the best method to monitor wear and tear on the vegetation caused by visitors. Finally, we tried to estimate the surface soil moisture content based on temperature rise and the Temperature Vegetation Dryness Index (TVDI), but did not achieve any meaningful connection between TVDI and on-site soil moisture measurements. Ultimately, our results pointed to a limited archaeological applicability of the TVDI method in Arctic contexts.

Effects of Underground Mining on Soil–Vegetation System: A Case Study of Different Subsidence Areas

The influence of coal mining subsidence on the surface ecological environment can be characterized as the evolution of a complex system. Examining the ecological damage caused by coal mining subsidence from the perspective of internal soil–vegetation system relationships is important for the ecological protection and restoration of mining areas. We investigated vegetation coverage, surface soil water and nutrient content, and vadose zone soil moisture in uniform and nonuniform areas of coal mining subsidence. Subsidence reduced vegetation coverage, surface soil moisture and nutrient content, and vadose zone soil moisture while increasing their spatial variability. These effects are more pronounced in areas with nonuniform subsidence. Subsidence also reduced the degree of soil–vegetation system correlation, which was also more pronounced in areas of nonuniform subsidence. Furthermore, a higher degree of soil–vegetation correlation was linked to decreased variability in soil moisture and nutrient content. Areas of nonuniform subsidence were characterized by greater preferential flow during rain infiltration, which contributed to the spatial variability of soil moisture and nutrient content and damage to vegetation growth. Our findings revealed that coal mining subsidence reduces both the quality of environmental factors and the degree of internal correlation between these factors, of which the preferential flow effect is an important underlying mechanism. These findings provide a theoretical basis for ecosystem management and the restoration of land damaged by coal mining subsidence.

Soil Moisture Active Passive (SMAP) Satellite Data and Unsaturated Soil Response

The importance of degree of soil saturation in geotechnical problems resulted in inclusion of unsaturated soil mechanics in various applications for several decades. However, in spite of recent progress in remotely sensed soil moisture measurement, geotechnical community has not yet taken advantage of these advances in analysis of unsaturated soils. NASA launched its Soil Moisture Active Passive (SMAP) satellite in 2015 with the aim of providing surface and root zone soil moisture content over the global land surface at 3-day average intervals. SMAP, as a widely validated and near-real-time database, offers a rich soil moisture database at a global scale that can be used in studies considering unsaturated soil behaviour. A study of the relationship between soil water content and seismic ground response is presented in this paper using SMAP, which includes the tracking of the variations in Earth's surface soil moisture caused by earthquakes.

A method for estimating surface soil moisture from diurnal land surface temperature observations over vegetated regions: A preliminary result over an AmeriFlux site and the REMEDHUS network

Most current optical/thermal-based surface soil moisture (SSM) retrieval methods only utilize the instantaneous measurements made by onboard polar-orbit satellites, and they require soil texture as prior knowledge to obtain the volumetric SSM content. A recent study developed a diurnal land surface temperature (LST)-based SSM retrieval method to directly estimate the volumetric SSM content without the knowledge of soil texture. However, most subsequent studies have only examined the use of this method on bare soils. Therefore, the present study aims to investigate the feasibility of the method over vegetated regions with both experimental data and satellite observations. First, the impacts of vegetation on SSM retrieval were investigated through physics-based Common Land Model simulations and high accuracy with an overall root mean square error (RMSE) of 0.010 m3m−3 was achieved when the underlying surface was covered by moderate fractional vegetation cover from 0 to 0.6 with a given soil texture. Based on the simulated results, the SSM was then estimated using data from an AmeriFlux site dominated by crops during the growing season in 2018; the results were further verified using in-situ measurements, and this showed that the estimated SSM correlated well with in-situ measurements (with an RMSE of 0.078 m3m−3). A further evaluation of the Meteosat Second Generation (MSG)-derived SSM at the pixel scale was made using the proposed method, and the results revealed an overall acceptable accuracy (RMSE of 0.074 m3m−3 and bias of 0.071 m3m−3) against the SMAP/Sentinel-1 SSM product at the same spatial resolution of 3 km.

Evaluation and spatio-temporal analysis of surface energy flux in permafrost regions over the Qinghai-Tibet Plateau and Arctic using CMIP6 models

ABSTRACT The surface energy budget over the Qinghai-Tibet Plateau (QTP) and the Arctic significantly influences the climate system with global consequences. The performances of 30 selected Coupled Model Intercomparison Project Phase 6 (CMIP6) models were evaluated based on six sites in the QTP and Arctic. The simulation results for latent heat flux (LE) were more accurate in the QTP, where the correlation coefficient and root mean square error (RMSE) were 0.9 and 30 W m−2, respectively. The results for sensible heat flux (H) were more accurate in the Arctic, the correlation coefficient and RMSE were 0.8 and 24 W m−2, respectively. Furthermore, the multiple models mean results revealed that the surface energy flux had seasonal variation and regional differences over the QTP and the Arctic. In the QTP, H was the lowest in winter, increased in spring, and reached the maximum in summer. However, the transitional changes in spring and autumn were not apparent in the Arctic, mainly due to seasonal net radiation difference between the two places. LE was affected by precipitation and surface soil moisture content. This work is important for understanding land-atmosphere interactions and useful for improving the accuracy of land surface models simulations.

A geospatial approach in monitoring the variations on surface soil moisture and vegetation water content: a case study of Palakkad District, Kerala, India

A geospatial approach in monitoring the variations on surface soil moisture and vegetation water content: a case study of Palakkad District, Kerala, India

The first global soil moisture and vegetation optical depth product retrieved from fused SMOS and SMAP L-band observations

ESA's Soil Moisture Ocean Salinity (SMOS, since 2009) and NASA's Soil Moisture Active Passive (SMAP, since 2015) are the only two space-borne L-band radiometer missions currently in orbit, which provide key information on global surface soil moisture (SM) and vegetation water content (via the vegetation optical depth, VOD). However, to date very few studies considered merging SMOS and SMAP for both SM and VOD retrievals simultaneously. This study presents the first global long-term and continuous SM and L-band VOD (L-VOD) dataset retrieved after merging the SMOS and SMAP brightness temperature (TB) observations, called the SMOS-SMAP-INRAE-BORDEAUX or SMOSMAP-IB product. We first developed a fitted SMOS TB dataset at a fixed incidence angle of 40°, and next applied a monthly linear rescaling of SMAP TB to SMOS TB for each polarization to produce a merged SMOS/SMAP TB (θ = 40°) dataset. The retrievals were then based on a mono-angular retrieval algorithm sharing a similar forward model with the SMOS-IC and the official SMOS retrieval algorithms. Results showed that the inter-calibration approach we used here could effectively remove the bias between the SMAP TB and fitted SMOS TB, with bias values reduced to 0.01 K (−0.02 K) compared to 3.45 K (1.65 K) for V (H) polarization before inter-calibration. The SMOSMAP-IB SM and L-VOD retrievals based on this new inter-calibrated SMOS/SMAP TB led to metrics that were equally good or better than those of other products (i.e., ESA CCI, SMOS-IC and the official SMAP products). When considering only long duration products, SMOSMAP-IB SM retrievals exhibited (i) the highest overall median R value of 0.72 with in-situ data from ISMN (International Soil Moisture Network) during 2013–2018, followed by SMOS-IC (R = 0.68) and CCI (R = 0.67), and (ii) the same smallest ubRMSD values as CCI (ubRMSD = 0.057 m3/m3vs 0.061 m3/m3 for SMOS-IC). L-VOD retrievals from SMOSMAP-IB were found to have comparable spatial and temporal skills to SMOS-IC. Spatially, they both correlated well with aboveground biomass (R = 0.87), and temporally, they both showed a good representation of the short vegetation NDVI signal and of the forest area loss in the Brazilian Amazon from 2011 to 2019. Developing SMOSMAP-IB is a step forward towards building a time-continuous L-band SM and VOD products in response to the possible failure of one of the SMOS or SMAP sensors in the future.