Surface Soil Moisture Changes Research Articles

Nonlinear effects of surface soil moisture changes on vegetation greenness over the Tibetan plateau

As an essential link between terrestrial and climatic ecosystems, vegetation has been altered by the soil hydrological environment associated with frozen soil thaw. However, it is not clear whether fluctuating soil moisture (SM) within the frozen soil zone alters the hydrologic environment to alleviate water stress in plants further, and there are scant previous studies at large scale on whether there is a threshold for SM on vegetation greening. This study integrated SM monitoring data at 125 stations from existing studies, then quantified the advantages of six remote sensing/reanalysis SM products: QTP-DNN-Sm, Global Land Data Assimilation System (GLDAS-Noah), European Centre for Medium-Range Weather Forecasts atmospheric reanalysis (ERA5-Land), European Space Agency Climate Change Initiative (ESA CCI), Global-SM, and QTP-SM. Moreover, we assessed the influence of single and multiple regional environmental elements (temperature, precipitation, land surface temperature (LST), normalized difference vegetation index (NDVI), and snow) on SM, as well as identified four trends of SM and vegetation growth for 51.26% of the Tibetan Plateau (TP). The results are as follows: 1) The overall performance of QTP-DNN-Sm products was slightly better than that of Global-SM, GLDAS-Noah, QTP-SM, ESA CCI, and ERA5-Land, with higher median Pearson correlation coefficient (R) value (0.685, 0.686, 0.699, 0.704, 0.300, and 0.582 for QTP-DNN-Sm, Global-SM, GLDAS-Noah, QTP-SM, ESA CCI, and ERA5-Land, respectively) and lowest median unbiased Root Mean Square Error (ubRMSE) (0.061, 0.064, 0.068, 0.064, 0.042, 0.076, and 0.047 m3/m3 for QTP-DNN-Sm, Global-SM, GLDAS-Noah, QTP-SM, ESA CCI, ERA5-Land, and ESA CCI, respectively). 2) NDVI in the frozen soil zone was the best variable to explain SM based on the GeoDetector-based factor detection, and interaction detection results indicated that the interaction between NDVI and temperature was gradually emerging to explain SM from permafrost zones to seasonally frozen ground zones. 3) The nonlinear relationship function between SM and NDVI showed that vegetation growth in 47.76% of the area (mainly distributed in the Changjiang River, Yarlung Zangbo River, and Yellow River basins) was more influenced by phenology. Thresholds existed in 3.49% of TP, where the cumulative effect of SM affects vegetation growth. In 0.65% of the regions, vegetation growth experienced eco-physiological processes of positive relief of water stress and physical processes of negative damage. The ease with which SM altered vegetation growth trends was consistent with the degradation degree of frozen soil type. Although the percentage of regions where the thresholds exist is relatively small, the positive/negative effects of the complex localized intersection between SM and vegetation in these regions could threaten the balance and stability of fragile alpine ecosystems sustained by permafrost.

Drying–Wetting Changes of Surface Soil Moisture and the Influencing Factors in Permafrost Regions of the Qinghai-Tibet Plateau, China

Soil moisture (SM), an important variable in water conversion between the atmosphere and terrestrial ecosystems, plays a crucial role in ecological processes and the evolution of terrestrial ecosystems. Analyzing and exploring SM’s processes and influencing factors in different permafrost regions of the Qinghai-Tibet Plateau (QTP) can better serve the regional ecological security, disaster warning, water management, etc. However, the changes and future trends of SM on the QTP in recent decades are uncertain, and the main factors affecting SM are not fully understood. The study used SM observations, the Global Land Evapotranspiration Amsterdam Model (GLEAM) SM products, meteorological and vegetation data, Mann–Kendall test, Theil–Sen estimation, Ensemble Empirical Mode Decomposition (EEMD), and correlation methods to analyze and explore the characteristics and influencing factors of SM change in different permafrost regions of the QTP. The results show that: (1) At the pixel scale, GLEAM SM products can better reflect SM changes in the QTP in the warm season. The seasonal permafrost region is closer to the real SM than the permanent region, with a median correlation coefficient (R) of 0.738, median bias of 0.043 m3 m−3, and median unbiased root mean square errors (ubRMSE) of 0.031 m3 m−3. (2) The average SM in the QTP warm season increased at a rate of 0.573 × 10−3 m3 m−3 yr−1 over the recent 40 years, and the trend accelerated from 2005–2020. In 64.31% of the region, the soil was significantly wetted, mainly distributed in the permafrost region, which showed that the wetting rate in the dry region was faster than in the wet region. However, the wetting trend does not have a long-term continuity and has a pattern of “wetting–drying-wetting” on interannual and decadal levels, especially in the seasonal permafrost region. (3) More than 65% of the SM wetting trend on the QTP is caused by temperature, precipitation, and vegetation. However, there is apparent spatial heterogeneity in the different permafrost regions and vegetation cover conditions, and the three factors have a more substantial explanatory power for SM changes in the seasonal permafrost region. With the global climate change, the synergistic SM–Climate–Vegetation effect on the QTP tends to be more evident in the seasonal permafrost region.

A Method to Estimate Surface Soil Moisture and Map the Irrigated Cropland Area Using Sentinel-1 and Sentinel-2 Data

Considering variations in surface soil moisture (SSM) is essential in improving crop yield and irrigation scheduling. Today, most remotely sensed soil moisture products have difficulties in resolving irrigation signals at the plot scale. This study aims to use Sentinel-1 radar backscatter and Sentinel-2 multispectral imagery to estimate SSM at high spatial (10 m) and temporal resolution (at least 5 days) over an agricultural domain. Three supervised machine learning algorithms, multilayer perceptron (MLP), a convolutional neural network (CNN), and linear regression models, were trained to estimate changes in SSM based on the variation in surface reflectance and backscatter over five different crops. Results showed that CNN is the best algorithm as it understands spatial relations and better represents two-dimensional images. Estimated values for SSM were in agreement with in-situ measurements regardless of the crop type, with RMSE=0.0292 (cm3/cm3) and R2=0.92 for the Sentinel-2 derived SSM and RMSE=0.0317 (cm3/cm3) and R2=0.84 for the Sentinel-1 soil moisture data. Moreover, a time series of estimated SSM based on Sentinel-1 (SSM-S1), Sentinel-2 (SSM-S2), and SSM derived from SMAP-Sentinel1 was compared. The developed SSM data showed a significantly higher mean SSM state over irrigated agriculture relative to the rainfed cropland area during the irrigation season. The multiple comparisons (fisher LSD) were tested and found that these two groups are different (pvalue=0.035 in 95% confidence interval). Therefore, by employing the maximum likelihood classification on the SSM data, we managed to map the irrigated agriculture. The overall accuracy of this unsupervised classification is 77%, with a kappa coefficient of 65%.

Simultaneously estimating surface soil moisture and roughness of bare soils by combining optical and radar data

Both radar and optical signals are sensitive to the change of surface soil moisture (SSM) and surface roughness properties (such as root mean squared height- RMSH), and the accuracy of retrieved SSM from single radar and optical remote sensing data is influenced by the spatiotemporal change of surface roughness. Here, we attempt to explore a method to simultaneously estimate SSM and RMSH of bare soil by combining optical and radar data, so as to weaken the effect of surface roughness on SSM inversion results. To achieve this goal, two satellite synchronous ground experiments were carried out, collecting 88 sampling plots each with an area of 50 m × 50 m. Radar backscattering coefficient and spectral reflectance are uniformly corrected to a fixed observation direction and solar incident direction respectively, which can eliminate the difference of satellite signal resulted from various sun-satellite geometry. Combining radar backscattering and optical reflectance model, Sentinel-1 and Sentinel-2 data are used to simultaneously retrieve SSM and RMSH of bared soils, and some conclusions are given as below: 1) a strong correlation is observed for (radar and optical) satellite signals and soil surface parameters (SSM and RMSH); 2) a higher accuracy was obtained by the combined use of optical and radar data, indicated by the decreased root mean squared error of retrieved SSM (~0.045 cm3/cm3) and RMSH (~0.8 cm); 3) the further improvement of retrieved SSM and RMSH was achieved by introducing their initial values, revealing that the prior knowledge of soil properties is also beneficial to improve the retrieval accuracy. This study proposed an framework for simultaneous estimation of SSM and RMSH by combining optical and radar data, and its feasibility is verified by experimental data.

Mapping of tank silt application using Sentinel-2 images over the Berambadi catchment (India)

Mapping soil properties is becoming more and more challenging due to the increase in anthropogenic modification of the landscape, calling for new methods to identify these changes. A striking example of anthropogenic modifications of soil properties is the widespread practice in South India of applying large quantities of silt from dry river dams (or “tanks”) to agricultural fields. Whereas several studies have demonstrated the interest of tank silt for soil fertility, no assessment of the actual extent of this age-old traditional practice exists. Over South-Indian pedological context, this practice is characterized by an application of black-colored tank silt to red-colored soils such as Ferralsols. The objective of this work was to evaluate the usefulness of Sentinel-2 images for mapping tank silt applications, hypothesizing that observed changes in soil surface color can be a proxy for tank silt application. We used data collected in a cultivated watershed in South India including 217 soil surface samples characterized in terms of Munsell color. We used two Sentinel-2 images acquired on February and April 2017. The surface soil color over each Sentinel-2 image was classified into two soil types (“Black” and “Red” soils). A change of soil color from “Red” in February 2017 to “Black” in April 2017 was attributed to tank silt application. Soil color changes were analyzed accounting for possible surface soil moisture changes. The proposed methodology was based on a well-balanced Calibration data created from the initial imbalanced Calibration dataset thanks to the Synthetic Minority Over-sampling Technique (SMOTE) methodology, coupled to the Cost-Sensitive Classification And Regression Trees (Cost-Sensitive CART) algorithm. To estimate the uncertainties of i) the two-class classification at each date and ii) the change of soil color from “Red” to “Black”, a bootstrap procedure was used providing fifty two-class classifications for each Sentinel-2 image. The results showed that 1) the CART method allowed to classify the “Red” and “Black” soil with correct overall accuracy from both Sentinel-2 images, 2) the tank silt application was identified over 202 fields and 3) the soil color changes were not related to a surface soil moisture change between both dates. With the actual availability of the Sentinel-2 and the past availability of the LANDSAT satellite imageries, this study may open a way toward a simple and accurate method for delivering tank silt application mapping and so to study and possibly quantify retroactively this farmer practice.

The scenario-based variations and causes of future surface soil moisture across China in the twenty-first century

Surface soil moisture (SSM) is a key factor for water and heat exchanges between land surface and the atmosphere. It is also important to water resources, agriculture, and ecosystems. In the backdrop of global warming, SSM variations and potential causes are not well-known at regional scales. Based on soil moisture (SM) data from GLDAS-Noah and 16 global climate models (GCMs) selected from 25 GCMs in CMIP5, we analyzed spatial distribution and temporal changes of SSM in China and quantified fractional contributions of four meteorological factors to the SSM variations. The selected models have the same direction of historic trends in SSM during 1981–2005 as those in the GLDAS SSM data which were also further used to calibrate the trends simulated by the 16 GCMs. Based on the calibration results for the 16 GCMs, future SSMs for nine regions were analyzed in mainland China under four Intergovernmental Panel on Climate Change emission scenarios. No significant changes were identified in SSM across most regions of mainland China under RCP2.6 scenario. However, there is a general wetting tendency in the arid regions and drying tendency across the humid regions under all the scenarios except RCP2.6. In general, the higher the global temperature raises, the more grids with significant increase or significant decrease in SSM. These findings contradicted prevailing view that wet regions get wetter and dry regions get drier. Attribution analysis indicates that precipitation acts as the major driver for SSM variations and contributes up to 43.4% of SSM variations across China. These results provide new insights into future SSM response to climate warming and a scientific basis to mitigation and adaptation works related to SSM in the future.

Historical Changes in Surface Soil Moisture Over the Contiguous United States: An Assessment of CMIP6

AbstractThe newly released simulations from the Sixth Phase of the Coupled Model Intercomparison Project (CMIP6) provide an opportunity to re‐examine long‐term soil moisture trends. In this study, we assessed the historical simulation (1850–2014) of surface soil moisture from 18 general circulation models that are part of CMIP6 and compared them with model simulations from CMIP5 and NLDAS‐2, and in situ observations over CONUS. Our results show that for CONUS as a whole, there is no significant long‐term trend in soil moisture, but significant drying trends exist over medium‐term, short‐term, and near‐present periods based on the CMIP6. Our comparison of CMIP6 to NLDAS‐2 (1980–2014) and in situ observations (2004–2014) demonstrates that it more accurately captures soil moisture trends in CONUS than CMIP5. CMIP6 shows a significant wetting trend in the Northwest, Midwest, Northeast, and Southeast. There is greater agreement amongst the individual models in CMIP6 than CMIP5, but intermodel variability still exists.

The Application of CYGNSS Data for Soil Moisture and Inundation Mapping in Australia

Cyclone global navigation satellite system (CYGNSS) has provided a valuable opportunity for high spatiotemporal monitoring of land surface reflectivity over the past few years. CYGNSS with a constellation of eight microsatellites is able to constantly observe the “scattered” global positioning system signals from the land. In this study, we validate the CYGNSS land reflectivity data in Australia for mapping the spatial extent of the inundated area and for determining temporal changes in surface soil moisture. CYGNSS level 1 data acquired for the period of 2017–2020 is assessed against various measurements, including satellite and ground-based measurements. Empirical mode decomposition is used to better analyze the CYGNSS time series and their relationship with the independent measurements. Furthermore, the mission's ability to capture surface reflectivity changes in response to extreme climatic events is analyzed. The results show that high spatial and temporal resolution CYGNSS data can largely represent the top layer ( $\sim$ 5 cm) soil moisture spatial and temporal variations close to soil moisture active passive. CYGNSS surface reflectivity results are also found to be sensitive to surface water changes and able to depict inundated land surface.

Monitoring of wheat crops using the backscattering coefficient and the interferometric coherence derived from Sentinel-1 in semi-arid areas

Radar data at C-band has shown great potential for the monitoring of soil and canopy hydric conditions of wheat crops. In this study, the C-band Sentinel-1 time series including the backscattering coefficients σ0 at VV and VH polarization, the polarization ratio (PR) and the interferometric coherence ρ are first analyzed with the support of experimental data gathered on three plots of irrigated winter wheat located in the Haouz plain in the center of Morocco covering five growing seasons. The results showed that ρ and PR are tightly related to the canopy development. ρ is also sensitive to soil preparation. By contrast, σ0 was found to be widely linked to changes in surface soil moisture (SSM) during the first growth stages when Leaf Area Index remains moderate (<1.5 m2/m2). In addition, drastic changes in the crop geometry associated to heading had a strong impact on the C-band σ0, in particular for VH polarization. The coupled water cloud and Oh models (WCM) were then calibrated and validated on the study sites. The comparison between the predicted and observed σ0 yielded a root mean square error (RMSE) values ranging from 1.50 dB to 2.02 dB for VV and between 1.74 dB to 2.52 dB for VH with significant differences occurring in the second part of the season after heading. Finally, new approaches based on the inversion of the WCM for SSM retrieval over wheat fields were proposed using Sentinel-1 radar data only. To this objective, the dry above-ground biomass (AGB) and the vegetation water content (VWC) were retrieved from the interferometric coherence and the PR. The relationships were then used as the vegetation descriptor in the WCM. The best retrieval results were obtained using the relationship between ρVV and the AGB (R and RMSE of 0.82, 0.05 m3/m3 respectively and no bias). The new retrieval approaches were then applied to a large database covering a rainfed field in Morocco and 18 plots of rainfed and irrigated wheat of the Kairouan plain (Tunisia) and compared to other classical techniques of SSM retrieval including simple linear relationships between SSM and σ0. The method based on the WCM and the ρVV-AGB relationships also provided with slightly better results than the others on the validation database (r = 0.75, RMSE = 0.06 m3/m3 and bias = 0.01 m3/m3 over the 18 plots of Tunisia) but the simple linear relationships performed also reasonably well (r = 0.62, RMSE = 0.07, bias = −0.01 in Tunisia for instance). This study opens perspectives for high resolution soil moisture mapping from Sentinel-1 data over south Mediterranean wheat crops and in fine, for irrigation scheduling and retrieval through the assimilation of these new products in an evapotranspiration model.

Study on Stability of Surface Soil Moisture and Other Meteorological Variables Within Time Intervals of SMOS and SMAP

The different orbit design and launching conditions of Soil Moisture and Ocean Salinity (SMOS, ESA) and Soil Moisture Active Passive (SMAP, NASA) result in different passing time over any point on the ground. The time lag between the two satellites is thought to be one of the reasons to induce uncertainties in soil moisture data comparison and validation. This letter calculates the temporal difference between SMOS and SMAP at first; it is found that their mismatch mainly concentrates within a period of 30–90 min. During such time lag, the change in surface soil moisture (5 cm) and other meteorological variables is analyzed on the basis of the U.S. Climate Reference Network (USCRN) high-frequency (5-min) field observations and Murrumbidgee Soil Moisture Monitoring Network (MSMMN) in situ measurements (20-min). This letter found that in most cases, air temperature, wind, and relative humidity present a moderate change of about 10%–20%, while solar radiation shows very strong variation from tens to hundreds (%). Soil moisture and soil temperature are always stable, the value of soil moisture at the two time points when SMOS and SMAP pass overhead are almost the same, and the averaged minimum and maximum fluctuations of soil moisture are only 0.004/0.003 and 0.007/0.01 $\text{m}^{3}/\text{m}^{3}$ , respectively, which are far less than the nominal accuracy of satellites (0.04 $\text{m}^{3}/\text{m}^{3})$ and probably unrecognizable. Soil moisture experiences a natural fading of very small magnitude during the time intervals of satellites, the temporal mismatch may not induce external uncertainties in soil moisture data comparison and validation, and it is safe to conclude that the impact is negligible.

Potential for the Detection of Irrigation Events on Maize Plots Using Sentinel-1 Soil Moisture Products

Although the real timing and flow rates used for crop irrigation are controlled at the scale of individual plots by the irrigator, they are not generally known by the farm upper management. This information is nevertheless essential, not only to compute the water balance of irrigated plots and to schedule irrigation, but also for the management of water resources at regional scales. The aim of the present study was to detect irrigation timing using time series of surface soil moisture (SSM) derived from Sentinel-1 radar observations. The method consisted of assessing the direction of change of surface soil moisture (SSM) between observations and a water balance model, and to use thresholds to be calibrated. The performance of the approach was assessed on the F-score quantifying the accuracy of the irrigation event detections and ranging from 0 (none of the irrigation timing is correct) to 100 (perfect irrigation detection). The study focused on five irrigated and one rainfed plot of maize in South-West France, where the approach was tested using in situ measurements and surface soil moisture (SSM) maps derived from Sentinel-1 radar data. The use of in situ data showed that (1) irrigation timing was detected with a good accuracy (F-score in the range (80–83) for all plots) and (2) the optimal revisit time between two SSM observations was 2–4 days. The higher uncertainties of microwave SSM products, especially when the crop is well developed (normalized difference of vegetation index (NDVI) &gt; 0.7), degraded the score (F-score = 69), but various possibilities of improvement were discussed. This paper opens perspectives for the irrigation detection at the plot scale over large areas and thus for the improvement of irrigation water management.

Long-term changes in surface soil moisture based on CCI SM in Yunnan Province, Southwestern China

Soil moisture (SM) plays an important role in regional runoff variations, energy dynamics, and vegetation productivity. It is also widely used to detecting agricultural drought. Recently, a severe drought occurred in Yunnan Province in southwestern China, so long-term changes in surface SM were of concern. However, there were only 18 SM stations in Yunnan Province, and the longest observation was less than five years; thus, long-term changes in Climate Change Initiative (CCI) SM were investigated. CCI SM and in situ SM had similar trends of temporal variations and a systematic difference. The revised CCI SM was built by a linear regression model based on the relationship between in situ SM at each station and the corresponding grid of CCI SM. The revised CCI SM showed an abnormally low value during the severe drought in 2009, indicating that the monthly revised CCI SM could detect drought in Yunnan Province. SM drought easily occurred during the dry seasons, and the frequency of drought seems to decrease, but the severity of drought increased in the 21st century. The seasonal SM struggled to reflect drought correctly and accurately. CCI SM needs validation before use due to its variable quality. In summary, CCI SM provides a better tool than in situ SM to investigate long-term changes in SM and detect drought, and can be used for climate modeling.

Contributions of climate change and groundwater extraction to soil moisture trends

Abstract. Climate change affects water availability for soil, and groundwater extraction influences water redistribution by altering water demand, both of which significantly affect soil moisture. Quantifying their relative contribution to the changes in soil moisture will further our understanding of the mechanisms underlying the global water cycle. In this study, two groups of simulations were conducted with and without groundwater (GW) extraction (estimated based on local water supply and demand) from 1979 to 2010 using the Chinese Academy of Sciences land surface model, CAS-LSM, with four global meteorological forcing datasets (GSWP3, PRINCETON, CRU-NCEP, and WFDEI). To investigate the contribution of climate change and GW extraction, a trajectory-based method was used. Comparing the simulated results with the in situ dataset of the International Soil Moisture Network (ISMN) and the satellite-based soil moisture product of the European Space Agency's Climate Change Initiative (ESA-CCI) indicated that the CAS-LSM reasonably reproduced the distribution of soil moisture and matched the temporal changes well. Globally, our results suggested a significant decreasing trend in surface soil moisture (0–10 cm, 0.98×10-4 mm3 mm−3 yr−1) over the 32-year period tested. The drying trends were mainly observed in arid regions such as the tropical desert regions in North Africa and the Arabian Peninsula, while the wetting trends were primarily in tropical forested areas in South America and northeastern Asia. Climate change contributed 101.2 % and 90.7 % to global drying and wetting trends of surface soil moisture, respectively, while GW extraction accounted for −1.2 % and 9.3 %, respectively. In deep soil, GW extraction contributed 1.37 % and −3.21 % to the drying and wetting trends, respectively. The weak influence of GW extraction may be because this activity occurs in limited areas. GW extraction contributed more than 35 % to the change in surface soil moisture in wetting areas where GW overexploitation occurs. GW is mainly extracted for irrigation to alleviate soil water stress in semiarid regions that receive limited precipitation, thereby slowing the drying trend and accelerating the wetting trend of surface soil. However, GW exploitation weakens the hydraulic connection between the soil and aquifer, leading to deeper soils drying up. Overall, climate change dominated the soil moisture trends, but the effect of GW extraction cannot be ignored.

Global Surface Soil Moisture Dynamics in 1979–2016 Observed from ESA CCI SM Dataset

Soil moisture (SM) is an important variable for the terrestrial surface system, as its changes greatly affect the global water and energy cycle. The description and understanding of spatiotemporal changes in global soil moisture require long time-series observation. Taking advantage of the European Space Agency (ESA) Climate Change Initiative (CCI) combined SM dataset, this study aims at identifying the non-linear trends of global SM dynamics and their variations at multiple time scales. The distribution of global surface SM changes in 1979–2016 was identified by a non-linear methodology based on a stepwise regression at the annual and seasonal scales. On the annual scale, significant changes have taken place in about one third of the lands, in which nonlinear trends account for 48.13%. At the seasonal scale, the phenomenon that “wet season get wetter, and dry season get dryer” is found this study via hemispherical SM trend analysis at seasonal scale. And, the changes in seasonal SM are more pronounced (change rate at seasonal scales is about 5 times higher than that at annual scale) and the areas seeing significant changes cover a larger surface. Seasonal SM fluctuations distributed in southwestern China, central North America and southern Africa, are concealed at the annual scale. Overall, non-linear trend analysis at multiple time scale has revealed more complex dynamics for these long time series of SM.

Consistency and Discrepancy of Global Surface Soil Moisture Changes From Multiple Model‐Based Data Sets Against Satellite Observations

AbstractA large population of global soil moisture data sets generated by a variety of models is compared with the latest satellite‐based Essential Climate Variable (ECV) soil moisture product in a common framework. The model‐based surface soil moisture data sets include Global Land Data Assimilation System (GLDAS), reanalysis products, Coupled Model Intercomparison Project Phase 5 Global Climate Models (GCMs), and Inter‐Sectoral Impact Model Intercomparison Project (including observation‐driven outputs ISI‐MIP_OBS and GCM‐driven outputs ISI‐MIP_GCM). We evaluate the model‐based surface soil moisture against ECV with focuses on spatial patterns, temporal correlations, long‐term trends, and relationships with precipitation and Normalized Difference Vegetation Index. The results indicate that all data sets reach a good agreement on the spatial patterns of surface soil moisture, which are also consistent with that of precipitation. However, data sets produced by different techniques have considerable discrepancies in the absolute values of surface soil moisture. Specifically, GCMs tend to underestimate the absolute values of surface soil moisture relative to ECV. In comparisons that remove the influence of absolute values (e.g., unbiased root‐mean‐square error), all model‐based data sets show comparable performances against ECV. GLDAS, reanalysis, and ISI‐MIP_OBS data sets show significant positive temporal correlations with ECV. Model‐based data sets and ECV consistently indicate widespread drying trends during 1980–2005, but the regional trends vary in different data sets. Compared to ECV, GLDAS and reanalysis data sets exhibit more intensive drying trends, while Coupled Model Intercomparison Project Phase 5 and ISI‐MIP_GCM tend to underestimate the drying. In most of the regions, the wetting/drying trends are consistent with the increases/decreases in precipitation and Normalized Difference Vegetation Index.

Dominant Features of Global Surface Soil Moisture Variability Observed by the SMOS Satellite

Soil moisture observations are expected to play an important role in monitoring global climate trends. However, measuring soil moisture is challenging because of its high spatial and temporal variability. Point-scale in-situ measurements are scarce and, excluding model-based estimates, remote sensing remains the only practical way to observe soil moisture at a global scale. The ESA-led Soil Moisture and Ocean Salinity (SMOS) mission, launched in 2009, measures the Earth’s surface natural emissivity at L-band and provides highly accurate soil moisture information with a 3-day revisiting time. Using the first six full annual cycles of SMOS measurements (June 2010–June 2016), this study investigates the temporal variability of global surface soil moisture. The soil moisture time series are decomposed into a linear trend, interannual, seasonal, and high-frequency residual (i.e., subseasonal) components. The relative distribution of soil moisture variance among its temporal components is first illustrated at selected target sites representative of terrestrial biomes with distinct vegetation type and seasonality. A comparison with GLDAS-Noah and ERA5 modeled soil moisture at these sites shows general agreement in terms of temporal phase except in areas with limited temporal coverage in winter season due to snow. A comparison with ground-based estimates at one of the sites shows good agreement of both temporal phase and absolute magnitude. A global assessment of the dominant features and spatial distribution of soil moisture variability is then provided. Results show that, despite still being a relatively short data set, SMOS data provides coherent and reliable variability patterns at both seasonal and interannual scales. Subseasonal components are characterized as white noise. The observed linear trends, based upon one strong El Niño event in 2016, are consistent with the known El Niño Southern Oscillation (ENSO) teleconnections. This work provides new insight into recent changes in surface soil moisture and can help further our understanding of the terrestrial branch of the water cycle and of global patterns of climate anomalies. Also, it is an important support to multi-decadal soil moisture observational data records, hydrological studies and land data assimilation projects using remotely sensed observations.

A comparison of two methods for estimating surface soil moisture based on the triangle model using optical/thermal infrared remote sensing over the source area of the Yellow River

ABSTRACTAs an important surface parameter, surface soil moisture (SSM) plays a significant role in water resources management, crop growth, land degradation, and vegetation coverage as well as global climate change studies. In particular, the triangle model based on the spatial relationship between the land surface temperature (LST) and vegetation index from optical/thermal infrared remote sensing data has been widely used for the estimation of SSM. In the present study, Moderate Resolution Imaging Spectroradiometer (MODIS) remote sensing data, including the MOD11A2 LST and MOD13A2 normalised differential vegetation index (NDVI) from July to October, 2008 to 2010, over the source area of the Yellow River (SAYR) in the alpine vegetation region of the Tibetan Plateau, are selected to construct the LST/NDVI triangle space. Two methods based on the triangle space, namely, the temperature vegetation drought index (TVDI) method and second order polynomial method, is used to estimate the SSM at the regional scale. Finally, an analysis between the estimated SSM and ground-measured SSM is carried out to explore not only the quality of these two methods in triangle space but also the applicability of these two methods in the alpine vegetation region of the Tibetan Plateau. In addition, the spatial distribution of SSM in the study area is investigated. The results of the validation of the estimated SSM results using ground-measured data show that the accuracy of the second order polynomial method is significantly higher than that of the TVDI method. The TVDI method root mean square error (RMSE) is 0.072 m3 m−3, coefficient of determination R2 is 0.461, and bias is −0.022 m3 m−3, while the second order polynomial method RMSE is 0.075 m3 m−3, R2 is 0.519, and bias is 0.007 m3 m−3. Moreover, the SSM decreases from west to the east; this distribution can be obtained with the estimated results by using either of the two methods. In addition, the SSM estimations by using the two methods are in good correlation with the Climate Change Initiative (CCI) soil moisture product. The present study indicates that the second order polynomial method can simulate the change in SSM in the triangle space more realistically and effectively and that the second order polynomial method is more suitable for the estimation of SSM over the alpine vegetation region of the Tibetan Plateau.

Spatial clusters and temporal trends of seasonal surface soil moisture across China in responses to regional climate and land cover changes

AbstractInvestigating the spatiotemporal changes of regional surface soil moisture in responses to climate and land cover changes is vital for understanding the underlying mechanisms of hydrological processes. While previous studies mainly attributed the causes of soil moisture changes to climatic factors, few took land cover into consideration. We analyzed the seasonal‐differentiated effects of climate and land cover changes on surface soil moisture of China's 80 drainage basins using the Essential Climate Variable Soil Moisture product spanning the 1979–2010 period. The low–low spatial clusters of annual (Jan–Dec), warm‐season (Apr–Sept) and cool‐season (Oct–Dec and Jan–Mar) surface soil moisture have spread from Northwest China to northeast and even Central China during the past three decades. In cool seasons, significant decreasing trends of surface soil moisture in most drainage basins of Northwest, Northeast, Central, and South China were detected. But in warm seasons, the surface soil moisture in Central and South China showed significant increasing trends. Precipitation, potential evapotranspiration and the ratio of the two are three driving forces for warm‐season surface soil moisture changes in Northeast and Northwest China. These climatic factors are main contributors to the declining trends of cool‐season surface soil moisture in Central and South China. Land cover changes showed to be the major factor driving the significant decreasing trends of cool‐season surface soil moisture in Northwest, Central, and South China. Due to vegetation's self‐shading effect, the large‐scale reforestation and vegetation growth are believed as main causes of the increasing trends of warm‐season surface soil moisture in Central and South China.

Responses of soil moisture to climate change based on projections by the end of the 21st century under the high emission scenario in the ‘Huang–Huai–Hai Plain’ region of China

Based on the future potential projections in the late 21st century under the Representative Concentration Pathway (RCP) 8.5 scenario, the maximal responses of surface soil moisture (SSM) to an extreme climate change are exhibited in the ‘Huang–Huai–Hai Plain’ (‘3H’) region of China. The Common Land Model (CoLM) and the approach of conditional nonlinear optimal perturbation related to parameters (CNOP-P) are employed to explore the above issue. Three climate change scenarios, associated with temperature change, precipitation change and changes in both temperature and precipitation, are provided by applying the CNOP-P approach and denoted as the CNOP-P-type temperature change scenario, the CNOP-P-type precipitation change scenario and the CNOP-P-type climate change scenario, respectively. For the CNOP-P-type scenarios, the changes in both climatology and climate variability relative to the reference climate condition are included. To explore the different responses of SSM to different types of climate scenarios, the hypothesized climate change scenarios are examined as well, in which only the change in climatology is considered. Numerical results have suggested that the CNOP-P-type scenario induces greater SSM responses in terms of variation magnitudes than the hypothesized climate change does, especially in the semi-arid regions north of 35°N. For the two types of climate change scenarios, the differences about precipitation and soil ice changes result in the difference about SSM changes in the northern region, though the difference about evapotranspiration variations helps to narrow the difference about SSM. The above results imply that climate variability is important to SSM in the semi-arid region.

Comparative study of soil moisture estimations from SMOS satellite mission, GLDAS database, and cosmic-ray neutrons measurements at COSMOS station in Eastern Poland

The paper presents a detailed comparative study of three surface soil moisture datasets retrieved from Cosmic-Ray Soil Moisture Observing System (COSMOS) in situ neutron measurement, CATDS Soil Moisture Ocean Salinity (SMOS) satellite microwave observations, and modelled data of the Global Land Data Assimilation System (GLDAS). Subsurface datasets were also calculated from the in situ and satellite measurements by using an exponential filter and were compared with the GLDAS estimates. For these comparisons, the Triple Collocation (TC) method, Nash–Sutcliffe Efficiency (NSE) coefficients, trend charts, and scatterplots were used.The main goal of this work was to verify the concordance of cosmic-ray neutron measurements with low-resolution soil moisture data from the CATDS SMOS and GLDAS products. The second objective was to determine the possibility of obtaining comparable subsurface soil moisture products from the aforementioned soil moisture data source.The obtained results show that data from the COSMOS sensor, which assesses soil moisture in an area 600m in diameter, agree reasonably well with the CATDS SMOS and GLDAS data having spatial resolution of about 25km. These conclusions suggest that COSMOS Derlo measurements can be particularly useful for validation of low-resolution satellite soil moisture observations as well as modelled values.The results obtained by using the TC method also revealed satisfactory agreement among all studied surface soil moisture data.However, the performed analysis shows some preponderance of SMOS and COSMOS data over GLDAS products. Although GLDAS data show a noticeable smoothing effect, COSMOS and CATDS SMOS more effectively reveal temporal soil moisture changes. Thus, for some applications, the use of CATDS SMOS estimates rather than GLDAS products may be more appropriate.Results retrieved by using an exponential filter are significant and encouraging. In particular, subsurface soil moisture values calculated from CATDS SMOS show stronger correlation with COSMOS Derlo data than those of GLDAS. Furthermore, COSMOS Derlo data also respond more intensively to surface soil moisture changes.