Surface Soil Moisture Estimates Research Articles

Yarı kurak bölgelerde sentetik açıklıklı radar (mikrodalga) görüntüleri ile toprak neminin tahmini

Spatial and temporal distribution of soil moisture is a key parameter for agricultural applications at watershed level such as drought monitoring, crop irrigation scheduling, and yield estimations in arid and semi-arid regions. Moreover, radar satellite imagery systems have been used to figure out soil and vegetation distributions spatially and temporally for various regions. Estimation of surface soil moisture using active microwave sensor systems is among useful information for researchers, conservation planners, and decision makers pursuing sustainable use of natural resources. This study was carried out at the soils of selected sixty-four square kilometers test site in Altinova State Farm. It was aimed to determine the relationship between the surface soil moisture and the backscatter values of SAR images (Radarsat-2) obtained four different times. To that end, four SAR images (4 Radarsat-2 images) from Altinova State Farm were used. Surface soil samples were collected simultaneously from 0-20 cm depth at 730 different points with 250 m-intervals, and soil moisture was determined using gravimetric method throughout the study area. In regards to each sampling period, surface soil moisture distribution maps were produced using ordinary kriging method. Considering the soil moisture distribution maps the data obtained in August indicated the most alterations in the surface soil moisture throughout the study area in comparison to the other sampling periods. Therefore, it was revealed that the relationship between backscattering (obtained from Radarsat-2 data in August, 2012) and soil moisture content was better than the other SAR data results (r=0.506, p<0.05).

Surface soil moisture monitoring with near-ground sensors: Performance assessment of a matric potential-based method

This study develops an indirect method for the estimation of surface soil moisture (SM) by means of monitoring in situ soil water potential. Field work was carried out in ten sampling units selected according to coverage, soil, and topographic factors. Relative humidity and soil temperature were hourly measured to assess the soil–water potential through Kelvin's Law for each sampling unit. Water potential was used as input in the soil moisture retention curve for field conditions to assess SM by fitting the Fredlund-Xing model. To verify the performance of our approach, we measured the surface volumetric SM by the gravimetric method for 2,211 soil samples collected in the same survey period. Results show a non-linear relationship between gravimetric SM and the indirect estimation displaying a determination coefficient of 68.4%. The proposed approach is a suitable exploratory alternative to support continuous SM monitoring studies.

An ensemble square root filter for the joint assimilation of surface soil moisture and leaf area index within the Land Data Assimilation System LDAS-Monde: application over the Euro-Mediterranean region

Abstract. This paper introduces an ensemble square root filter (EnSRF) in the context of jointly assimilating observations of surface soil moisture (SSM) and the leaf area index (LAI) in the Land Data Assimilation System LDAS-Monde. By ingesting those satellite-derived products, LDAS-Monde constrains the Interaction between Soil, Biosphere and Atmosphere (ISBA) land surface model (LSM), coupled with the CNRM (Centre National de Recherches Météorologiques) version of the Total Runoff Integrating Pathways (CTRIP) model to improve the reanalysis of land surface variables (LSVs). To evaluate its ability to produce improved LSVs reanalyses, the EnSRF is compared with the simplified extended Kalman filter (SEKF), which has been well studied within the LDAS-Monde framework. The comparison is carried out over the Euro-Mediterranean region at a 0.25∘ spatial resolution between 2008 and 2017. Both data assimilation approaches provide a positive impact on SSM and LAI estimates with respect to the model alone, putting them closer to assimilated observations. The SEKF and the EnSRF have a similar behaviour for LAI showing performance levels that are influenced by the vegetation type. For SSM, EnSRF estimates tend to be closer to observations than SEKF values. The comparison between the two data assimilation approaches is also carried out on unobserved soil moisture in the other layers of soil. Unobserved control variables are updated in the EnSRF through covariances and correlations sampled from the ensemble linking them to observed control variables. In our context, a strong correlation between SSM and soil moisture in deeper soil layers is found, as expected, showing seasonal patterns that vary geographically. Moderate correlation and anti-correlations are also noticed between LAI and soil moisture, varying in space and time. Their absolute value, reaching their maximum in summer and their minimum in winter, tends to be larger for soil moisture in root-zone areas, showing that assimilating LAI can have an influence on soil moisture. Finally an independent evaluation of both assimilation approaches is conducted using satellite estimates of evapotranspiration (ET) and gross primary production (GPP) as well as measures of river discharges from gauging stations. The EnSRF shows a systematic albeit moderate improvement of root mean square differences (RMSDs) and correlations for ET and GPP products, but its main improvement is observed on river discharges with a high positive impact on Nash–Sutcliffe efficiency scores. Compared to the EnSRF, the SEKF displays a more contrasting performance.

An integrated methodology for surface soil moisture estimating using remote sensing data approach

The present study aimed to propose an operational approach for estimating surface soil moisture from Moderate Resolution Imaging Spectroradiometer (MODIS) data by considering diverse environmental variables such as Normalized Difference Vegetation Index (NDVI), land surface temperature (Ts), evapotranspiration, topographic parameters (elevation and aspect) and soil texture (clay, loam and silt). A soil moisture index (SMI) derived from NDVI-Ts space is combined to all other variables, based on stepwise multiple regression, to develop a new SSMC model. Performance of this model was assessed using field-measured data of SSM. Accuracy was performed by the k-fold cross validation method, it showed a R2 (coefficients of determination) of 0.70, RMSE of 1.58% and unRMSE of 0.5%. In addition, the results of the developed model were compared with another soil moisture model SMM proposed in the irrigated perimeter of Tadla (Morocco), and revealed that the established model provided effectiveness results in the study areas.

Estimating surface soil moisture from satellite observations using a generalized regression neural network trained on sparse ground-based measurements in the continental U.S

This study attempted to develop a point-surface collaborative inversion (PSCI) method for the estimation of regional surface soil moisture (SSM) using a generalized regression neural network (GRNN) trained on sparse ground-based measurements. Specifically, GRNN was employed to establish a nonlinear relationship between ground-based measurements from sparse network stations (SNSs) and passive microwave observations from the Soil Moisture Active Passive (SMAP) satellite in the continental U.S. for April 2015 to March 2018. More importantly, the extended triple collocation (ETC) technique was applied to address the scale mismatch issue resulting from the small spatial support of ground-based measurements, whereby individual SNSs’ reliability at the SMAP coarse footprint scale could be determined before fed into GRNN. The 10-fold cross-validation results showed that the GRNN model trained on reliable SNSs obtained a satisfactory performance—the cross-validated R and unbiased RMSE values were 0.88 and 0.050 cm3 cm−3, respectively, which outperformed the back-propagation neural network (BPNN) and the other GRNN model trained on all SNSs. Furthermore, temporal and spatial comparisons between the PSCI-based SSM retrievals and other SSM datasets indicated the former agreed the best with ground measurements both in time and space, suggesting the proposed PSCI method had shown great potential in estimating reliable regional SSM climate records.

An integrated approach to estimate surface soil moisture in agricultural lands

Sentinel-1 and Landsat-8 data were used to retrieve soil moisture from top soil surface (0–5 cm depth) at agricultural land (area under wheat crop). After pre-processing of satellite data and removal of vegetation influence (σ°veg) using Water Cloud Model (WCM), total backscattering coefficient (σ°total) and Normalized Difference Vegetation Index (NDVI) were used to simulate backscattering from soil (σ°soil). Modified Dubois Model (MDM) and Topp's Model were used to retrieve soil moisture using ε. Further, modelled soil moisture was evaluated using in situ soil moisture measurements and a Time Domain Reflectometer during Sentinel-1 overpass (24 January, 25 February and 13 March 2018). Statistical tests showed that an integrated approach has potential to improve soil moisture estimates over the vegetated/cropped area for agricultural and hydrological studies.

Closing the Loop of Satellite Soil Moisture Estimation via Scale Invariance of Hydrologic Simulations

Surface soil moisture plays a crucial role on the terrestrial water, energy, and carbon cycles. Characterizing its variability in space and time is critical to increase our capability to forecast extreme weather events, manage water resources, and optimize agricultural practices. Global estimates of surface soil moisture are provided by satellite sensors, but at coarse spatial resolutions. Here, we show that the resolution of satellite soil moisture products can be increased to scales representative of ground measurements by reproducing the scale invariance properties of soil moisture derived from hydrologic simulations at hyperresolutions of less than 100 m. Specifically, we find that surface soil moisture is scale invariant over regimes extending from a satellite footprint to 100 m. We use this evidence to calibrate a statistical downscaling algorithm that reproduces the scale invariance properties of soil moisture and test the approach against 1-km aircraft remote sensing products and through comparisons of downscaled satellite products to ground observations. We demonstrate that hyperresolution hydrologic models can close the loop of satellite soil moisture downscaling for local applications such as agricultural irrigation, flood event prediction, and drought and fire management.

Version 4 of the SMAP Level‐4 Soil Moisture Algorithm and Data Product

AbstractThe NASA Soil Moisture Active Passive (SMAP) mission Level‐4 Soil Moisture (L.4_SM) product provides global, 3‐hourly, 9‐km resolution estimates of surface (0–5 cm) and root zone (0–100 cm) soil moisture with a mean latency of ~2.5 days. The underlying L4_SM algorithm assimilates SMAP radiometer brightness temperature (Tb) observations into the NASA Catchment land surface model using a spatially distributed ensemble Kalman filter. In Version 4 of the L4_SM modeling system the upward recharge of surface soil moisture from below under nonequilibrium conditions was reduced, resulting in less bias and improved dynamic range of L4_SM surface soil moisture compared to earlier versions. This change and additional technical modifications to the system reduce the mean and standard deviation of the observation‐minus‐forecast Tb residuals and overall soil moisture analysis increments while maintaining the skill of the L4_SM soil moisture estimates versus independent in situ measurements; the average, bias‐adjusted root‐mean‐square error in Version 4 is 0.039 m3/m3 for surface and 0.026 m3/m3 for root zone soil moisture. Moreover, the coverage of assimilated SMAP observations in Version 4 is near global owing to the use of additional satellite Tb records for algorithm calibration. L4_SM soil moisture uncertainty estimates are biased low (by 0.01–0.02 m3/m3) against actual errors (computed versus in situ measurements). L4_SM runoff estimates, an additional product of the L4_SM algorithm, are biased low (by 35 mm/year) against streamflow measurements. Compared to Version 3, bias in Version 4 is reduced by 46% for surface soil moisture uncertainty estimates and by 33% for runoff estimates.

An Improved Soil Moisture Retrieval Algorithm Based on the Land Parameter Retrieval Model for Water–Land Mixed Pixels Using AMSR-E Data

The land parameter retrieval model (LPRM) has been widely used for the retrieval of microwave-based global surface soil moisture (SSM). In the original LPRM algorithm, soil surface effective temperature (SSET) was retrieved from the Ka-band (36.5 GHz) brightness temperature (BT) and then used as input data for the estimation of SSM. In this study, SSET retrieved from the Advanced Microwave Scanning Radiometer of the Earth Observing System Radiometer (AMSR-E) Ka-band BT was validated against MODIS land surface temperature (LST) data, and the results show that when the fraction of water surface (FWS) within an AMSR-E pixel increases in the range from 0–0.01 to 0.15–0.4, the root-mean-square error (RMSE) increases drastically from 1.98 to 11.42 Kelvin (K). When validation was conducted on SSET data retrieved with the K-band (18.7 and 23.8 GHz) BT, the RMSEs were maintained below 2.0 K for FWS ranging from 0 to 0.4. Therefore, in the original LPRM, we substituted the Ka-band BT with the K-band BT to produce more accurate estimations of SSET and hence improved SSM retrievals. Our results show that for FWS ranges of 0–0.01, 0.01–0.05, 0.05–0.15, and 0.15–0.4, the correlation coefficients ( $R$ -values) between SSM retrieved with the improved LPRM and Global Land Data Assimilation System (GLDAS) measurements are 0.78, 0.75, 0.77, and 0.46, respectively, compared with the corresponding $R$ -values of 0.78, 0.65, 0.56, and 0.34 for SSM retrieved with the original LPRM. This demonstrates that against the original LPRM, our improved LPRM can produce more accurate SSM retrievals under water–land mixed pixels.

Synthetic Aperture Radar (SAR) Compact Polarimetry for Soil Moisture Retrieval

Soil moisture is a factor for risk analysis in the agricultural sector, yet access to temporally and spatially detailed data is challenging for much of the world’s agricultural extend. Significant effort has been focused on developing methodologies to estimate soil moisture from microwave satellite sensors. Canada’s RADARSAT Constellation Mission (RCM) is capable of acquiring imagery in a number of modes with a Compact Polarimetry (CP) configuration at different spatial resolutions (1 to 100 m). RCM offers greater polarization diversity, wide swaths and improved temporal frequency (4-day exact revisit time); all important considerations for large area monitoring of agricultural resources. The major goal of this study was to examine whether CP could accurately estimate surface soil moisture over bare fields. A methodology was developed using the calibrated Integral Equation Model (IEM) multi-polarization inversion approach. RADARSAT-2 data was acquired between 2012 and 2017 over a test site in eastern Canada. CP backscatter for two RCM modes (medium resolution 30 m and 50 m (MR30 and MR50)) was simulated using 63 RADARSAT-2 fully polarimetric images. A simple transfer function was developed between RH (right circular-horizontal) and HH (horizontal-horizontal) intensity, as well as RV (right circular-vertical) and VV (vertical-vertical). These HH- and VV-like intensities were then used in the multi-polarization inversion scheme to retrieve soil moisture. CP soil moisture retrievals were validated against soil moisture measurements from a long term in-situ network instrumented with five soil moisture stations. Retrieved and measured soil moisture were well correlated (R &gt; 0.70) with an unbiased root mean square error (ubRMSE) less than 0.06 m3/m3. Overall, the developed method clearly captured the dry down and wetting trends observed through the five years study period. However, results demonstrated that the inversion method introduced a consistent bias (~0.10 m3/m3). Comparison of CP soil moisture estimates to those from the Soil Moisture Active Passive (SMAP) passive microwave satellite confirmed this bias. This study demonstrates the potential of C-band CP data to deliver accurate soil moisture products over wide swaths for regional and national soil moisture monitoring.

Retrieving Surface Soil Moisture over Wheat and Soybean Fields during Growing Season Using Modified Water Cloud Model from Radarsat-2 SAR Data

Surface soil moisture (SSM) retrieval over agricultural fields using synthetic aperture radar (SAR) data is often obstructed by the vegetation effects on the backscattering during the growing season. This paper reports the retrieval of SSM from RADARSAT-2 SAR data that were acquired over wheat and soybean fields throughout the 2015 (April to October) growing season. The developed SSM retrieval algorithm includes a vegetation-effect correction. A method that can adequately represent the scattering behavior of vegetation-covered area was developed by defining the backscattering from vegetation and the underlying soil individually to remove the effect of vegetation on the total SAR backscattering. The Dubois model was employed to describe the backscattering from the underlying soil. A modified Water Cloud Model (MWCM) was used to remove the effect of backscattering that is caused by vegetation canopy. SSM was derived from an inversion scheme while using the dual co-polarizations (HH and VV) from the quad polarization RADARSAT-2 SAR data. Validation against ground measurements showed a high correlation between the measured and estimated SSM (R2 = 0.71, RMSE = 4.43 vol.%, p &lt; 0.01), which suggested an operational potential of RADARSAT-2 SAR data on SSM estimation over wheat and soybean fields during the growing season.

Generation of spatially complete and daily continuous surface soil moisture of high spatial resolution

Surface soil moisture (SSM), as a vital variable for water and heat exchanges between the land surface and the atmosphere, is essential for agricultural production and drought monitoring, and serves as an important boundary condition for atmospheric models. The spatial resolution of soil moisture products from microwave remote sensing is relatively coarse (e.g., ~40 km × 40 km), whereas SSM of higher spatiotemporal resolutions (e.g., 1 km × 1 km and daily continuous) is more useful in water resources management. In this study, first, to improve the spatiotemporal completeness of SSM estimates, we downscaled land surface temperature (LST) output from the China Meteorological Administration Land Data Assimilation System (CLDAS, 0.0625° × 0.0625°) using a data fusion approach and MODIS LST acquired on clear-sky days to generate spatially complete and temporally continuous LST maps across the North China Plain. Second, spatially complete and daily continuous 1 km × 1 km SSM was generated based on random forest models combined with quality LST maps, normalized difference vegetation index (NDVI), surface albedo, precipitation, soil texture, SSM background fields from the European Space Agency Soil Moisture Climate Change Initiative (CCI, 0.25° × 0.25°) and CLDAS land surface model (LSM) SSM output (0.0625° × 0.0625°) to be downscaled, and in situ SSM measurements. Third, the importance of different input variables to the downscaled SSM was quantified. Compared with the original CCI and CLDAS SSM, both the accuracy and spatial resolution of the downscaled SSM were largely improved, in terms of a bias (root mean square error) of −0.001 cm3/cm3 (0.041 cm3/cm3) and a correlation coefficient of 0.72. These results are generally comparable and even better than those in published studies, with our SSM maps featuring spatiotemporal completeness and relatively high spatial resolution. The downscaled SSM maps are valuable for monitoring agricultural drought and optimizing irrigation scheduling, bridging the gaps between microwave-based and LSM-based SSM estimates of coarse spatial resolution and thermal infrared-based LST at a 1 km × 1 km resolution.

Diagnosing Bias in Modeled Soil Moisture/Runoff Coefficient Correlation Using the SMAP Level 4 Soil Moisture Product

AbstractThe physical parameterization of key processes in land surface models (LSMs) remains uncertain, and new techniques are required to evaluate LSMs accuracy over large spatial scales. Given the role of soil moisture in the partitioning of surface water fluxes (between infiltration, runoff, and evapotranspiration), surface soil moisture (SSM) estimates represent an important observational benchmark for such evaluations. Here, we apply SSM estimates from the NASA Soil Moisture Active Passive Level‐4 product (SMAP_L4) to diagnose bias in the correlation between SSM and surface runoff for multiple Noah‐Multiple Physics (Noah‐MP) LSM parameterization cases. Results demonstrate that Noah‐MP surface runoff parameterizations often underestimate the correlation between prestorm SSM and the event‐scale runoff coefficient (RC; defined as the ratio between event‐scale streamflow and precipitation volumes). This bias can be quantified against an observational benchmark calculated using streamflow observations and SMAP_L4 SSM and applied to explain a substantial fraction of the observed basin‐to‐basin (and case‐to‐case) variability in the skill of event‐scale RC estimates from Noah‐MP. Most notably, a low bias in LSM‐predicted SSM/RC correlation squanders RC information contained in prestorm SSM and reduces LSM RC estimation skill. Based on this concept, a novel case selection strategy for ungauged basins is introduced and demonstrated to successfully identify poorly performing Noah‐MP parameterization cases.

Application of Satellite Remote Sensing for Estimation of Dust Emission Probability in the Urmia Lake Basin in Iran

Saline playas in arid and semiarid regions of the world are significant sources of unconsolidated sediments susceptible to aeolian transport. Lake Urmia in Iran, one of the largest saltwater lakes on Earth, has waned to approximately 18% of its original size due to groundwater pumping and surface water diversions. This has led to ecosystem degradation, accelerated desertification and frequent dust storms, causing public health problems. In this paper we introduce a new framework for delineation of dust source zones and estimation of dust occurrence probability based on remotely sensed land surface properties (soil moisture and vegetation cover), soil texture, wind speed, and measured dust frequencies. Observations from the Moderate Resolution Imaging Spectroradiometer (MODIS) for the Urmia Lake basin were utilized to determine the Normalized Difference Vegetation Index (NDVI) and to estimate surface soil moisture with a recently introduced optical trapezoid model. The soil textures extracted from the SoilGrids database and wind speeds obtained from local weather stations together with the estimated surface soil moisture were found to be highly correlated with dust emission probability. When the surface soil moisture is low, wind speed is the major determinant for dust occurrence. With increasing surface soil moisture, the dust occurrence probability decreases. Soil moisture effects are more pronounced at high wind speeds. While at low wind speeds the dust occurrence probabilities for the investigated soil textures loam, clay loam and sandy clay loam are similar, at higher wind speeds the sandy clay loam texture exhibits the highest susceptibility to dust generation.Core Ideas A remote sensing framework for delineation of dust source zones is introduced. Surface soil moisture and soil texture are correlated with dust emission frequency. Dust occurrence probability is highest for low moisture conditions and sandy soils.

Comparative analysis of the accuracy of surface soil moisture estimation from the C- and L-bands

Surface soil moisture (SSM) estimation is of great importance in several areas, such as hydrology, agriculture and risk assessment. C-band SAR (synthetic aperture radar) data have been widely used to estimate SSM, whereas few studies have been performed using L-band SAR due to the low availability of L-band SAR data. In this context, the objective of the present paper is to compare the SSM estimation potentials of the C- (Sentinel-1) and L-bands (PALSAR) for wheat and grassland plots. The inversion approach developed in this study uses neural networks to invert the SAR signal and estimate the SSM. For each radar frequency, the developed neural networks were trained using the following as an input vector: SAR incidence angle, SAR polarization (VV for the C-band and HH for the L-band), and NDVI from optical images. Artificial Neural networks (ANNs) were developed and validated using synthetic and real databases. The results showed that the L-band provided slightly less accurate SSM estimates than the C-band. Moreover, the results showed that the accuracies of the SSM estimates for both frequencies strongly depended on the soil roughness (Hrms) and SSM values. From the synthetic database at SSM values less than 25 vol.%, the ANNs underestimated the SSM for Hrms values less than 1.5 cm and overestimated the SSM for Hrms values greater than 1.5 cm. In addition, the ANNs underestimated the SSM value regardless of the Hrms value when the SSM value was greater than 25 vol.%. An RMSE analysis of the SSM estimates showed that the highest RMSE values were observed for the L-band regardless of the SSM value, and high RMSE values were observed for the C-band only in very wet soil conditions (SSM>25 vol.%). From the real database at NDVI values less than 0.7, the RMSE (root mean square error) of the SSM estimates was 4.6 vol.% for the C-band and 5.3 vol.% for the L-band. Most importantly, the L-band enabled the estimation of the SSM under a well-developed vegetation cover (NDVI > 0.7) with an RMSE of 6.7 vol.%, whereas the C-band SAR signal became completely attenuated for some crops when the NDVI value was greater than 0.7, and thus the estimation of SSM was impossible using the C-band.

An improved surface soil moisture downscaling approach over cloudy areas based on geographically weighted regression

This study proposed a methodological framework for downscaling AMSR-2 surface soil moisture (SSM) products over cloudy areas using MODIS LST/NDVI datasets. The experiment was conducted in a relatively large area of 430,000 km2 in the middle and lower reaches of the Yangtze and Huaihe rivers in China, which is characterized by humid climate and frequent cloudy weather conditions. As MODIS LSTs suffer from serious pixel loss due to cloud interference in this area, an effective LST interpolation method was preliminarily applied to achieve daily LST datasets with quasi-full covers. And rather small RMSEs in the range 1.5 K–3.5 K were obtained when the interpolated LST datasets were validated against a reference LST dataset built from observed relationships between LST and ground-based near-surface air temperatures on clear sky days. A regression equation was then established between AMSR-2 SSM and spatially resampled MODIS datasets using “Geographically Weighted Regression (GWR)” to implement the SSM downscaling process. SSM estimates downscaled by the GWR-based method showed a better performance over those downscaled by the traditional “universal triangle feature (UTF)” based method in view of their “non-biased RMSEs (ubRMSEs)”, correlation coefficients, and mean biases with respect to ground-based soil moisture validation data. Comparisons between SSM estimates from MODIS LST inputs and those from interpolated LST inputs were conducted, and they showed that the SSM estimates downscaled by interpolated LST inputs performed only slightly poorer (with an ubRMSE difference no larger than 0.02 cm3/cm3) than those by MODIS data. Time series analysis further showed that the GWR-based downscaled SSM estimates with reconstructed LST data inputs are in phase with the variation in ground-based soil moisture with the exception of areas of extremely high vegetation cover or low temperatures. The framework proposed in this study thus proved feasible for the derivation of reliable downscaled high spatial resolution SSM estimates, an essential application in mitigating pixel loss under cloudy weather conditions.

Improving surface soil moisture estimates in West Africa through GRACE data assimilation

Recent annual trends of precipitation and terrestrial water storage (TWS) in West Africa have been increasing over the past decade. Despite a significant impact of soil moisture on the TWS in West Africa, there is little research on the recent spatial and temporal behaviors of surface soil moisture (SSM) along with the hydrological trends and variability in West Africa. In this study, we assimilate TWS estimates from the Gravity Recovery and Climate Experiment (GRACE) mission into the Catchment Land Surface Model (CLSM) and evaluate its impacts on SSM simulations for the years, 2002–2017. The evaluation is performed using reference datasets: the African Monsoon Multidisciplinary Analysis (AMMA) in situ soil moisture observations, three currently available microwave satellite SSM observations from the Advanced Scatterometer (ASCAT), the Soil Moisture Ocean Salinity (SMOS), and the Soil Moisture Active Passive (SMAP) satellites and also the triple collocation analysis (TCA). Overall, modeled SSM shows good agreement with the reference datasets in terms of the anomaly SSM correlations. However, both modeled and ASCAT SSM are limited in their representation of the drying rates, as observed by ground observations, SMOS and SMAP estimates. Further, GRACE data assimilation results in improved SSM simulations in the humid regions with large annual TWS variability. This study demonstrates the utility of land data assimilation to inform hydrological conditions in West Africa, where soil moisture monitoring is necessary for water resource and livestock management.

Uncertainty in soil moisture retrievals: An ensemble approach using SMOS L-band microwave data

The uncertainty of surface soil moisture (SM) retrievals from satellite brightness temperature (TB) observations depends primarily on the choice of radiative transfer model (RTM) parameters, prior SM information and TB inputs. This paper studies the sensitivity of several established and experimental SM retrieval products from the Soil Moisture Ocean Salinity (SMOS) mission to these choices at 11 reference sites, located in 7 watersheds across the United States (US). Different RTM parameter sets cause large biases between retrievals. Whereas typical RTM parameter sets are calibrated for SM retrievals, it is shown that a parameter set carefully optimized for TB forward modeling can also be used for retrieving SM. It is also shown that the inclusion of dynamic prior SM estimates in a Bayesian retrieval scheme can strongly improve SM retrievals, regardless of the choice of RTM parameters. The second part of this paper evaluates the ensemble uncertainty metrics for SM retrievals obtained by propagating a wide range of RTM parameters through the RTM, and the relationship with time series metrics obtained by comparing SM retrievals with in situ data. As expected for bounded variables, the total spread in the ensemble SM retrievals is smallest for wet and dry SM values and highest for intermediate SM values. After removal of the strong long-term SM bias associated with the RTM parameter values for individual ensemble members, the remaining anomaly ensemble SM spread shows higher values when SM deviates further from its long-term mean SM. This reveals higher-order biases (e.g. differences in variances) in the retrieval error, which should be considered when characterizing retrieval error. The time-average anomaly ensemble SM spread of 0.037 m3/m3 approximates the actual time series unbiased root-mean-square-difference of 0.042 m3/m3 between ensemble mean retrievals and in situ data across the reference sites.

Large scale operational soil moisture mapping from passive MW radiometry: SMOS product evaluation in Europe & USA

Earth Observation (EO) allows deriving from a range of sensors, often globally, operational estimates of surface soil moisture (SSM) at range of spatiotemporal resolutions. Yet, an evaluation of the accuracy of those products in a variety of environmental conditions has been often limited. In this study, the accuracy of the SMOS SSM global operational product across 2 continents (USA, and Europe) and a range of land use/cover types is investigated. SMOS predictions were compared against near concurrent in-situ SSM measurements from the FLUXNET observational network. In total, 7 experimental sites were used to assess the accuracy of SMOS derived soil moisture for 2 complete years of observations (2010–2011). The accuracy of the SMOS SSM product is investigated in different seasons for the seasonal cycle as well as different continents and land use/cover types. Results showed a generally reasonable agreement between the SMOS product and the in-situ soil moisture measurements in the 0–5 cm soil moisture layer. Root Mean Square Error (RMSE) in most cases was close to 0.1 m3 m−3 (minimum 0.067 m3 m−3). With a few exceptions, Pearson’s correlation coefficient was found up to approx. 55%. Grassland, shrublands and woody savanna land cover types attained a satisfactory agreement between satellite derived and in-situ measurements but needleleaf forests had lower correlation. Better agreement was found for the grassland sites in both continents. Seasonally, summer and autumn underperformed spring and winter. Our study results provide supportive evidence of the potential value of this operational product for meso-scale studies in a range of practical applications, helping to address key challenges present nowadays linked to food and water security.

A Monte Carlo based adaptive Kalman filtering framework for soil moisture data assimilation

The main sources of global soil moisture information are remote sensing observations and land surface model estimates. Data assimilation (DA) aims at optimally combining these data sources through statistical merging. To properly parameterize such merging, one needs to obtain accurate knowledge of model and observation uncertainties, which is the crux of a successful DA system. In this paper, we propose a new Monte Carlo based adaptive Kalman filtering framework (MadKF) that estimates model and observation uncertainties (Q and R) and updates soil moisture forecasts simultaneously. Spatially distributed uncertainties are estimated by applying triple collocation analysis (TCA) to Monte Carlo simulations of the model open-loop, the model analysis, and the observation time series at each grid cell. Error cross-covariances, which are inevitable between these time series, are diagnosed from their ensembles and used to iteratively correct biases they cause in Q and R estimates, and hence, in the Kalman filter gain. The proposed MadKF is tested in a synthetic environment and by assimilating real satellite soil moisture retrievals from the Advanced SCATterometer (ASCAT) into the Antecedent Precipitation Index (API) model forced with daily aggregated satellite precipitation. Synthetic experiments indicate a good convergence of Q and R estimates. Internal DA diagnostics, i.e., the innovation auto-correlation (IAC) and the variance of the normalized innovations, asymptotically converge to their desired values, which indicates that the filter is operating near its optimum and reliably estimates analysis uncertainty. Real-data experiments assimilating ASCAT observations into the API model further indicate that the MadKF is robust against observation error auto-correlations, which typically cause problems in conventional IAC-tuning based adaptive filtering approaches. A performance evaluation over 264 in situ sites within the contiguous United States shows that the MadKF leads to a significant skill gain in surface soil moisture estimation. The MadKF is also potentially applicable when assimilating Level 1 backscatter or brightness temperature observations, or when updating/assimilating other geophysical variables.