Variability Of Surface Soil Moisture Research Articles

Estimation of surface soil moisture from Sentinel-1 synthetic aperture radar imagery using machine learning method

Surface soil moisture (SM) is a crucial variable representing the water content in soil at the topmost soil layer. Accurate data of SM are essential for various purposes, including the monitoring of drought, vegetation modeling, weather forecasting, and agriculture management. Over the past decades, microwave remote sensing has been employed to estimate SM at large scales, with the Sentinel-1 satellite mission recently offering Synthetic Aperture Radar (SAR) data and enabling to estimate SM at high spatial resolution. Machine learning (ML) techniques have further enhanced these estimations, with random forest (RF) emerging as a promising method due to its strong capabilities. However, the performance of RF in estimating SM with satellite data has not yet been thoroughly evaluated across large areas. This study presents a new RF-based model for estimating SM over Europe with SAR data from Sentinel-1, along with the climate and terrain data. The predictions from the RF model were compared against the ground measurements from the International Soil Moisture Network (ISMN), Integrated Carbon Observation System (ICOS), and official SMAP/Sentinel-1 SM products. The results demonstrated that the RF model yielded accurate predictions with a Pearson's correlation coefficient (R) of 0.847 outperforming the official SMAP/Sentinel-1 product at spatial resolutions of 1 km and 3 km, which achieved R values of 0.599 and 0.616, respectively. Additionally, the impact of vegetation cover that is represented by using multiple satellite vegetation products on the RF model was investigated. Despite a moderate correlation between backscattering coefficients and vegetation cover, no correlation was found between the RF model's errors and vegetation cover. The results suggest that the RF model and Sentinel-1 SAR data can be used to provide SM estimations at high spatial resolution, characterizing the fine-scale variations in SM, to support various applications such as precision agriculture at the small, localized scales.

Retrieval and evaluation of surface soil moisture from CYGNSS using blended microwave soil moisture products

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

Evaluating the Effects of Precipitation and Evapotranspiration on Soil Moisture Variability Within CMIP5 Using SMAP and ERA5 Data

AbstractThe effects of precipitation (Pr) and evapotranspiration (ET) on surface soil moisture (SSM) play an essential role in the land‐atmosphere system. Here we evaluate multimodel differences of these effects within the Coupled Model Intercomparison Project Phase 5 (CMIP5) compared to Soil Moisture Active Passive (SMAP) products and ECMWF Reanalysis v5 (ERA5) as references in a frequency domain. The variability of SSM, Pr, and ET within three frequency bands (1/7 ∼ 1/30 days−1, 1/30 ∼ 1/90 days−1, and 1/90 ∼ 1/365 days−1) after normalization is quantified using Fourier transform. We analyze the impact of ET and Pr on SSM variability based on a transfer function assuming that these variables form a linear time‐invariant (LTI) system. For the total effects of ET and Pr on SSM variability, the CMIP5 estimations are smaller than the reference data in the two higher frequency bands and are larger than the reference data in the lowest frequency band. Besides, the effects on SSM by Pr and ET are found to be different across the three frequency bands. In each frequency band, the variability of the factor that dominates SSM (i.e., Pr or ET) from CMIP5 is smaller than that from the references. This study identifies the spatiotemporal distribution of differences between CMIP5 models and references (SMAP and ERA5) in simulating ET and Pr effects on SSM within three frequency bands. This study provides insightful information on how soil moisture variability is affected by varying precipitation and evapotranspiration at different time scales within Earth System Models.

Evaluation of soil surface moisture in Dak Nong province, Vietnam by Sentinel-1 radar image

This study focuses on monitoring the variation of surface soil moisture (0-1 cm) and ground soil moisture (0÷1 m) in the soil in Dak Nong province using Sentinel-1 radar images received in December 2017. Active radar signals utilized in this method were sent and received using short wave pulses. The intensity of the received signal is compared to determine the scattering coefficient of the surface. The most common imaging constructs of active shortwave are Synthetic Aperture Radar (SAR), which transmits a series of pulses that pass through a radar antenna. Soil moisture is calculated by measuring electromagnetic radiation in the wavelength range from 0,5÷100 cm based on the contrast between the dielectric properties of water (~80) and soil particles (<4). With increasing humidity, the dielectric constant of the water mixture increases, this change can be detected by radar sensors. The results of moisture estimation by satellite images are checked by the results of field sampling and laboratory analysis. Radar remote sensing technique has demonstrated the ability to estimate soil moisture on the principle of correlating with actual measured data. The return scattering value of the soil consists of two components: the former is the feedback scattering value of the raw soil; while the latter is the feedback scattering value of the vegetation cover. It is recommended that this technology should be applied to territories with complex, difficult-to-access terrains and few meteorological monitoring stations.

A WRF/WRF-Hydro coupling system with an improved structure for rainfall-runoff simulation with mixed runoff generation mechanism

• The structure of WRF-Hydro is improved for runoff generation with mixed mechanisms. • Empirical equations from a conceptual model are transplanted into WRF-Hydro. • Merged rainfall forcing with radar QPE and rain gauges is used for model calibration. • The structure is verified for both one-way and the fully coupled WRF/WRF-Hydro. • Increase of infiltration-excess runoff is found with decrease of saturation-excess. The coupled atmospheric-hydrologic systems help achieve deeper understanding on the interactions between the atmospheric and land-surface processes, improve the spatial and temporal accuracy of hydrologic forecasts and extend the forecast leading time. WRF-Hydro is nowadays a widely used hydrologic module to be coupled with the mesoscale numerical weather model WRF for atmospheric-hydrologic research and applications. The structure of WRF-Hydro is improved and expanded in this study to better adapt to the complicated rainfall-runoff transformation mechanism in the mixed runoff generation regions. The infiltration parameterisation is replaced by an infiltration equation that takes into account the impact of the surface soil moisture variation on the infiltration capacity, and the spatial discretisation of the infiltration capacity from a distribution curve is achieved based on the conception of the topographic index. For the runoff convergence, the river channel leakage loss is introduced into the Muskingum-Cunge (MC) streamflow calculations with variable parameters in time. Efforts are also made to reduce parameter calibration errors in WRF-Hydro. The accuracy of the WRF rainfall forcing is improved by merging weather radar and rain gauge observations using the Kriging with external drift (KED) interpolation tool. The key parameters of WRF-Hydro are then calibrated using the improved rainfall forcing with the merging observations. The performance of the improved WRF-Hydro structure is explored in both one-way and the fully coupled modes with the WRF model. Typical rainstorm events are selected from semi-humid and semi-arid catchments of Northern China as case studies. Results have shown that the improved WRF-Hydro is more effective in simulating floods with high peaks and steep rises-and-falls, but a problem is also shown of receding more quickly for low peaks. The improved model structure has changed the proportion of the mixed runoff generation. In the long term, there is an increase in the infiltration-excess runoff generation and decrease in the saturation-excess amount. Both the one-way and the fully coupled WRF/WRF-Hydro systems have demonstrated the suitability of the improved structure for rainfall-runoff processes dominated by the infiltration-excess runoff generation.

The Coupling of Soil Water and Leaf Phenology Observed by Satellite During Australian Bushfires in 2019–2020

AbstractWater and energy fluxes at the interface between the surface of the earth and the atmosphere depend strongly on soil moisture. Surface soil moisture (SSM) participates in natural phenomena such as evaporation, infiltration and runoff. From 2019 to 2020, bushfires in Australia caused changes in the runoff of surface water, the transpiration of vegetation, and evapotranspiration of the soil, subsequently affecting the soil water cycle. However, the dynamics of surface soil water content during large‐scale bushfires are still not completely understood. Here, we report on patterns of seasonal variation in SSM and vegetation leaf phenology in Australia, identified using L‐band radiometric measurements of soil moisture taken by the soil moisture and ocean salinity mission from 2011 to 2020. This study reveals a decrease in the leaf area index in affected regions of New South Wales, Victoria, Queensland, Western Australia, Australian Capital Territory, Northern Territory, and Tasmania. The observed seasonal variations in SSM are highly synchronous with those of leaf phenology in northern and northeastern Australia. However, the development of the leaf area lags behind that of SSM for up to 90 days in the southeastern subtropical woodlands. The time lag between leaf area development and SSM in the southwest is about 60 days. Bushfires can lead to the decrease of evapotranspiration hence shortening the time lag between SSM and leaf phenology. Our results offer insights into the variations in SSM during bushfires, the relationship between SSM and leaf phenology in Australia, and lay a foundation for improving models of ecological hydrology during bushfires.

Evaluating the Variability of Surface Soil Moisture Simulated Within CMIP5 Using SMAP Data

AbstractThere are significant biases and uncertainties in the simulated soil moisture with land surface models. Here we evaluate multimodel differences in Coupled Model Intercomparison Project Phase 5 (CMIP5) compared to Soil Moisture Active Passive (SMAP) products on different time scales. The variability of surface soil moisture (SSM) within three frequency bands (7–30 days, 30–90 days, and 90–365 days) after normalization is quantified using Fourier transform for the evaluation. Compared to the SMAP observations, the simulated SSM variability within CMIP5 is underestimated in the two higher frequency bands (by 72% and 56%, respectively) and overestimated in the lowest frequency band (by 113%). In addition, these differences concentrate in regions with larger SSM. Finally, these multimodel differences are found to be significantly correlated with mean climate conditions rather than soil texture. This study identifies the spatiotemporal distribution of the model deficiencies within CMIP5 and finds they are systematic in the long‐term simulation on a global scale.

Surface soil moisture variability in a sector of a humid basin characterized by extremely flat relief

AbstractAssessing the degree of spatial organization exhibited by soil moisture plays an important role since hydrological responses of a system are conditioned, to a large degree, by such pattern. This is more significant on plains, because water stays longer on the surface; therefore, it has more time to evaporate or infiltrate and these two processes are basically influenced by soil moisture condition. The aim was to describe surface soil moisture variability by means of a spatial and temporal dimension analysis. Four environments were considered: (A) close to temporary water bodies, poorly drained soils, and humid prairies; (B) extended lowlands, with a cemented horizon in the soil profile, and humid mesophytic meadows; (C) the flat matrix of the landscape, significant proportion of bare soil, and halophytic steppes; and (D) mounds and dunes, deep soils, and mesophytic meadows. Throughout the sampling period, some environments registered high and almost constant values (0.47–0.64 m3/m3); others recorded low and almost invariant values (0.13–0.28 m3/m3), and others showed intermediate values with broader spectrum of variation. In order to further understand surface soil moisture dynamics, multiple linear regression analyses considering soil water storage and water table depth as explanatory variables were applied. In some environments, both variables are significantly involved in the response variable explanation, and in others, soil water storage is the most predictive one. We showed that even though the study area has an extremely flat relief, a soil moisture pattern that underlies the system arises during wet periods and it becomes diffuse in dry ones.

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%.

Assimilation of Ground‐Based GPS Observations of Vertical Displacement into a Land Surface Model to Improve Terrestrial Water Storage Estimates

AbstractGround‐based Global Positioning System (GPS) observations of vertical surface displacement can be used to study terrestrial water storage (TWS) change after properly accounting for nonhydrological loading effects. This study systematically merged ground‐based GPS observations of vertical displacement into a land surface model in order to better estimate TWS. Assimilation was conducted across two snow‐dominated watersheds in the western United States using a one‐dimensional ensemble Kalman filter (EnkF). Modeled estimates were compared against TWS retrievals derived from the Gravity Recovery and Climate Experiment (GRACE) mission and in situ measurements of snow water equivalent (SWE), soil moisture, and runoff. The GPS data assimilation (GPS DA) technique improves prediction skill of TWS anomalies relative to the Open Loop (OL; model run without assimilation) when compared against GRACE TWS retrievals, especially during an extended drought period post‐2011 (e.g., correlation coefficient ROL = 0.46 and RGPSDA = 0.82 in the Great Basin). Furthermore, GPS DA improves SWE estimation with improved R values found over 76% and 69% of all pixels collocated with in situ stations in the Great Basin and Upper Colorado watersheds, respectively. GPS DA estimates of surface soil moisture and runoff, however, exhibit degraded performance relative to the OL due to the limited sensitivity of GPS observations to surface soil moisture variations and the lack of a dynamic surface routing scheme as well as other missing model physics (e.g., river and dam regulation, irrigation‐related water withdrawals, surface water impoundments) that are not included in the Catchment Land Surface Model.

A Semi-Physical Approach for Downscaling Satellite Soil Moisture Data in a Typical Cold Alpine Area, Northwest China

Microwave remote sensing techniques provide a direct measurement of surface soil moisture (SM), with advantages for all-weather observations and solid physics. However, most satellite microwave soil moisture products fail to meet the requirements of land surface studies for high-resolution surface soil moisture data due to their coarse spatial resolutions. Although many approaches have been proposed to downscale the spatial resolution of satellite soil moisture products, most of them have been tested in flat areas where the surface is relatively homogeneous. Thus, those established approaches are often inapplicable for downscaling in cold alpine areas with complex terrain where multiple factors control the variations in surface soil moisture. In this work, we re-inferred and verified the mathematical assumption behind a semi-physical approach for downscaling satellite soil moisture data and extended this approach for cold alpine areas. Instead of directly deriving SM from proxy variables, this approach relies on a relationship between two standardized variables of SM and apparent thermal inertia (ATI), in which the sub grid standard deviation for SM is estimated by a physical hydraulic model taking soil texture data as input. The approach was applied to downscale the soil moisture active passive (SMAP) daily data in a typical cold alpine basin, i.e., the Babao River basin located in the Qilian Mountains of Northwest China. We observed good linearity between the computed ATI and SM observations on most wireless sensor network sites installed in the study basin, which justifies the underlying assumption. The sub grid standard deviations for the SMAP grid estimated through the Mualem-van Genuchten model can broadly represent the real characteristics. The downscaled 1-km resolution results correlated well with the in-situ SM observations, with an average correlation coefficient of 0.74 and a small root mean square error (0.096 cm3/cm3). The downscaled results show more and consistent textural details than the original SMAP data. After removal of biases in the original SMAP data even higher agreements with the observations can be achieved. These results demonstrate the adequacy of the proposed semi-physical approach for downscaling satellite soil moisture data in cold alpine areas, and the resultant fine-resolution data can serve as useful databases for land surface and hydrological studies in those areas.

Evaluation of SMAP, SMOS, and AMSR2 Soil Moisture Products Based on Distributed Ground Observation Network in Cold and Arid Regions of China

Long-term surface soil moisture (SM) data are increasingly needed in water budget and energy balance analysis of watersheds. The performance of nine remotely sensed SM products from Advanced Microwave Scanning Radiometer 2 (AMSR2), Soil Moisture and Ocean Salinity (SMOS), and Soil Moisture Active Passive (SMAP) missions are evaluated based on observations collected from distributed observation networks in the Heihe River Basin (HRB) of China during 2013 to 2017. Results show that the SMAP Level 3 dual channel algorithm SM retrievals reflect the seasonal SM variations well with high temporal correlations of ∼0.7 and high accuracy within 0.04 m3/m3 in terms of unbiased root mean squared error (ubRMSE) over the grassland in the HRB. The SMOS level 3 SM retrievals present increased underestimation and ubRMSE of ∼0.10 m3/m3 as the vegetation increases. The newly published SMOS Institut National de la Recherche Agronomique–Centre d'Etudes Spatiales de la BIOsphere product in version 2 outperforms the SMOS level 3 product with improved temporal correlation coefficient above 0.4 and lower ubRMSE of ∼0.05 m3/m3. AMSR2 Land Parameter Retrieval Algorithm SM products show extremely large overestimation over the vegetated regions in HRB, especially the C-band products. Drastically high underestimation biases are observed in the Japan Aerospace Exploration Agency AMSR2 SM product. Parameter uncertainty analyses indicate that the different parameterization schemes of vegetation optical depth inputs could be one of the main reasons resulting in the systematic overestimation/underestimation biases in the AMSR2/SMOS/SMAP SM retrievals. The findings aim to provide insights into studies on algorithms refinements and data fusions of SM products in HRB.

Retrieving Heterogeneous Surface Soil Moisture at 100 m Across the Globe via Fusion of Remote Sensing and Land Surface Parameters

Successful monitoring of soil moisture dynamics at high spatio-temporal resolutions globally is hampered by the heterogeneity of soil hydraulic properties in space and complex interactions between water and the environmental variables that control it. Current soil moisture monitoring schemes via in situ station networks are sparsely distributed while remote sensing satellite soil moisture maps have a very coarse spatial resolution. In this study, an empirical surface soil moisture (SSM) model was established via fusion of in situ continental and regional scale soil moisture networks, remote sensing data (SMAP and Sentinel-1) and high-resolution land surface parameters (e.g. soil texture, terrain) using a quantile random forest (QRF) algorithm. The model had a spatial resolution of 100 m and performed well under cultivated, herbaceous, forest, and shrub soils (overall R2 = 0.524, RMSE = 0.07 m3 m-3). It has a relatively good transferability at the regional scale among different soil moisture networks (mean RMSE = 0.08–0.10 m3 m-3). The global model was applied to map SSM dynamics at 30–100 m across a field-scale soil moisture network (TERENO-Wüstebach) and an 80-ha cultivated cropland in Wisconsin, USA. Without the use of local training data, the model was able to delineate the variations in SSM at the field scale but contained large bias. With the addition of 10% local training datasets (“spiking”), the bias of the model was significantly reduced. The QRF model was relatively insensitive to the resolution of Sentinel-1 data but was affected by the resolution and accuracy of soil maps. It was concluded that the empirical model has the potential to be applied elsewhere across the globe to map SSM at the regional to field scales for research and applications. Future research is required to improve the performance of the model by incorporating more field-scale soil moisture sensor networks and assimilation with process-based models.

Sub-seasonal variability of surface soil moisture over eastern China

Various surface soil moisture (SM) data from station observations, the Soil Moisture Active Passive (SMAP) mission, three reanalyses (ERA-Interim, CFSR, and NCEP RII), and the Global Land Data Assimilation System (GLDAS) are used to explore the sub-seasonal variations of SM (SSV-SM) over eastern China. Based on the correlation with SM of SMAP, reanalyses, and GLDAS, it is found that the variations of SM observed by Liuhe and Chunan stations can generally represent the SM variations over eastern China. The correlation coefficients between the SMAP and station SM are around 0.7. The SMAP product can well capture the time variation of SM over eastern China. The spectral analysis suggests that periodic variations of SM are mainly and significantly over the 10–30-day period over eastern China in all the data. The significant spectra over the 10–30-day period basically occur during the rainy season over eastern China. For the spatial aspect of SSV-SM, precipitation is the main factor causing the spatial distribution of SSV-SM over eastern China. However, the spectra of the station precipitation are not consistent with those of the station SM, and there is less coherence between the precipitation and SM over the periods during which SM has significant spectra. This indicates that SSV-SM is also affected by other factors.

A Soil Moisture Spatial and Temporal Resolution Improving Algorithm Based on Multi-Source Remote Sensing Data and GRNN Model

Surface soil moisture (SM) plays an essential role in the water and energy balance between the land surface and the atmosphere. Low spatio-temporal resolution, about 25–40 km and 2–3 days, of the commonly used global microwave SM products limits their application at regional scales. In this study, we developed an algorithm to improve the SM spatio-temporal resolution using multi-source remote sensing data and a machine-learning model named the General Regression Neural Network (GRNN). First, six high spatial resolution input variables, including Land Surface Temperature (LST), Normalized Difference Vegetation Index (NDVI), albedo, Digital Elevation Model (DEM), Longitude (Lon) and Latitude (Lat), were selected and gap-filled to obtain high spatio-temporal resolution inputs. Then, the GRNN was trained at a low spatio-temporal resolution to obtain the relationship between SM and input variables. Finally, the trained GRNN was driven by the high spatio-temporal resolution input variables to obtain high spatio-temporal resolution SM. We used the Fengyun-3B (FY-3B) SM over the Tibetan Plateau (TP) to test the algorithm. The results show that the algorithm could successfully improve the spatio-temporal resolution of FY-3B SM from 0.25° and 2–3 days to 0.05° and 1-day over the TP. The improved SM is consistent with the original product in terms of both spatial distribution and temporal variation. The high spatio-temporal resolution SM allows a better understanding of the diurnal and seasonal variations of SM at the regional scale, consequently enhancing ecological and hydrological applications, especially under climate change.

Mechanisms and Early Warning of Drought Disasters: Experimental Drought Meteorology Research over China

Abstract A major experimental drought research project entitled “Mechanisms and Early Warning of Drought Disasters over Northern China” (DroughtEX_China) was launched by the Ministry of Science and Technology of China in 2015. The objective of DroughtEX_China is to investigate drought disaster mechanisms and provide early-warning information via multisource observations and multiscale modeling. Since the implementation of DroughtEX_China, a comprehensive V-shape in situ observation network has been established to integrate different observational experiment systems for different landscapes, including crops in northern China. In this article, we introduce the experimental area, observational network configuration, ground- and air-based observing/testing facilities, implementation scheme, and data management procedures and sharing policy. The preliminary observational and numerical experimental results show that the following are important processes for understanding and modeling drought disasters over arid and semiarid regions: 1) the soil water vapor–heat interactions that affect surface soil moisture variability, 2) the effect of intermittent turbulence on boundary layer energy exchange, 3) the drought–albedo feedback, and 4) the transition from stomatal to nonstomatal control of plant photosynthesis with increasing drought severity. A prototype of a drought monitoring and forecasting system developed from coupled hydroclimate prediction models and an integrated multisource drought information platform is also briefly introduced. DroughtEX_China lasted for four years (i.e., 2015–18) and its implementation now provides regional drought monitoring and forecasting, risk assessment information, and a multisource data-sharing platform for drought adaptation over northern China, contributing to the global drought information system (GDIS).

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.

How to integrate ground-truth and satellite image to estimate surface soil moisture at the field and watershed scales?

Soil moisture is a key environmental variable for developing a coupled hydrological and biogeochemical modeling approach. It is recognized that a relationship does exist between water stress and emission of volatile organic compounds (VOCs) in forested areas, which may have negative effect on human health and ecosystems. Therefore it is necessary to achieve a better understanding of the land phase of the hydrological cycle, namely soil moisture estimation, which modulates surface energy balance and consequently vegetation cover patterns. This work focuses on a new methodological approach to evaluate the spatial variability of surface soil moisture at the field scale using the Bayesian kriging model jointly with TDR measurements and Landsat 8-TM remotely sensed image. The analysis looked for quantifying different deterministic sources of variability, measurement errors and also components not well understood of variability. In particular, the spatial distribution of in situ measurements and digital image data in a scene provided by remote sensing technology is addressed through a geostatistical framework. This technique is explored as alternative to the regression techniques currently used for modeling soil moisture mapping. Tests conducted on an extensively sampled pasture field showed significant improvement, which suggests that the methodological approach could be applied at the watershed scale for validating remotely sensed datasets.

Sub-metre mapping of surface soil moisture in proglacial valleys of the tropical Andes using a multispectral unmanned aerial vehicle

Surface soil moisture is a critical but often neglected component of the hydrologic budget. Within mountain environments, surface soil moisture is highly heterogeneous and challenging to measure. Point measurements are often poorly representative of larger areas, while satellite pixels are generally too coarse in these topographically varied landscapes. In the Cordillera Blanca, Peru, rapid glacier recession is impacting downstream water supply in timing, quantity and quality. Recent research has shown that these proglacial valleys provide vital ecosystem services and store considerable amounts of water within the groundwater systems. However, the spatial and temporal variability of soil water storage is poorly understood. In this tropical Andean setting, we use an unmanned aerial vehicle (UAV) with multispectral (visible, near infrared, thermal infrared) sensors to map sections of two proglacial valleys in the Cordillera Blanca, Peru at sub-metre resolution. We use the Temperature Vegetation Dryness Index (TVDI) (Sandholt et al., 2002), and apply it for the first time to UAV borne imagery to estimate absolute surface soil moisture through calibration with in-scene field measurements. Resulting surface soil moisture maps have 50 cm spatial resolution with an R2 value of 0.55 and 0.76 for the two study sites, Pachacoto and Llanganuco respectively. We analyse the multispectral orthomosaics and soil moisture maps to improve our understanding of the controls on spatial variability in surface soil moisture within the proglacial valleys of the Cordillera Blanca. The maps permit us to identify both important groundwater spring sources and areas where domestic grazing impacts observed soil moisture estimates. The high resolution observations provided by the UAV facilitate a greater understanding of fine scale heterogeneity within these environments.

Spatial and temporal variability of soil moisture in relation with topographic and meteorological factors in south of Ardabil Province, Iran

This research has been tried to evaluate the spatial and temporal variability of surface soil moisture (SM) in a semi-arid and cold region of Ardabil Province in Iran with an area of about 10,000km2. The used SM data is the SMAP Enhanced L2 Radiometer Half-Orbit 9km Soil Moisture, provided by NASA. The study area was subdivided into 120 locations consisting 10 × 12 grids, matching with the pixels of the SMAP images. In order to evaluate the spatial variations of SM, the relation of mean SM with coefficient of variation and standard deviation has been evaluated and, then, the representative location for mean SM of the area has been identified using the index of temporal stability. Moreover, the effect of topographic factors (elevation, slope, and aspect) on spatial variations of SM, and the effect of meteorological factors (rainfall, sunshine hours, temperature, relative humidity, wind speed, and number of dry days) on temporal variations of SM have been investigated. The relation of mean SM with the coefficient of variation and standard deviation represented an exponentially negative and upper convex shape, respectively. The SM content of the representative location had a correlation with the mean SM of the area with the coefficient of determination value of 0.91. Of the three topographic factors, only the slope factor, and of the meteorological factors all of them except the wind speed have showed a significant relationship with SM spatial and temporal variations respectively.