The Soil Moisture Active Passive mission (SMAP, since 2015) from The National Aeronautics and Space Administration's (NASA) and Soil Moisture and Ocean Salinity mission (SMOS, since 2009) from The European Space Agency's (ESA) measure polarimetric brightness temperature (TB) at L-band (1.4 GHz). They provide estimates of surface soil moisture (SM) and L-band vegetation optical depth (L-VOD) approximately every 2-3 days at the equator, with a spatial resolution of similar to 40 km for a local overpass time of 6 AM/PM. Integrating the AM and PM TB observations from SMAP and SMOS satellite missions can reduce the revisit time to about 1 day over the equator, thus helping to address fast-response hydrologic processes that cannot be addressed with the 2-3 day revisits. This will allow the capture of the SM conditions more often and, hence, capture the rate of decline due to drainage and recharge to groundwater. This occurs early during dry down after storms. The integration of SMOS measurements also works to fill temporal gaps caused by missing data due to SMAP instrument outages. This article details the integration of the SMAP and SMOS observations to achieve a combined SM and L-VOD product. The SMOS TB observations interpolated to 40 degrees incidence angle were first relatively calibrated (RC) to generate SMAP-like SMOS TB (RCTB), making the combined TB records consistent spatially and temporally. The SMAP baseline SM and L-VOD retrieval algorithm was then applied to the RCTB records. We showed that after relative calibration (ARC), the bias between the SMAP and SMOS TBs was reduced from 0.5 to -0.03 K for TB H and from 2.6 to 0.014 K for TB V in the AM cases. For the PM cases, the mean value of differences was reduced from 0.82 to 0.27 K and from 2.88 to 0.19 K for TB H and TB V, respectively. The comparison of the core validation sites (CVS) in situ SM to the retrieved SM from the combined TB record showed an unbiased root-mean-square-difference of 0.039 m3/m3 for both AM and PM cases and the retrieved L-VOD demonstrated consistency with independent biomass and tree height estimates. We also showed an improvement in temporal coverage and that the global mean number of visits to each grid went up from 283 (SMAP only) to 446 (SMAP+SMOS) when both AM and PM overpasses are considered.

The Walnut Gulch Experimental Watershed (WGEW) Long-Term Agroecosystem Research (LTAR) network common experiment addresses the aspirational practice of brush management (BM) to reverse the prevailing condition of woody plant encroachment (WPE) and increase perennial native grass production. Across the western United States, the decision to implement BM includes consideration of management objectives, cost, and the expected impact on a diverse suite of ecosystem services. Maintaining or restoring grass cover will help meet the LTAR sustainable production, economic, and social goals, and averting degradation will meet environmental goals. This common experiment, focused on hydrologic and erosion impacts of BM, aims to inform land management decisions on three major plant communities in the Southwestern United States: creosote bush (Larrea tridentata), mesquite (Prosopis velutina), and pinyon-juniper (PJ, Pinus and Juniperus spp.). On the WGEW, applying tebuthiuron pellets to creosote bush increased grass cover and reduced runoff and erosion. The 2016 BM experiment on the Santa Rita Experimental Range applied a commonly used liquid herbicide cocktail but achieved only 7% mortality on mesquite, probably because of the timing of the aerial application. Experiments manipulating rainfall amount and intensity on plots receiving fire, chemical, or mechanical BM treatments on PJ communities aim to improve process representation in simulation models. The deliverables of these BM experiments will be to (i) improve the performance of runoff and erosion models, (ii) enhance our ability to identify areas most at risk from reduced hydrologic function and soil erosion after shrub proliferation, and (iii) better predict how landscapes will respond to BM interventions. Ranchers, land management agencies, and watershed conservation organizations will benefit from training and availability of improved tools to focus treatments on areas where greatest net benefits might be realized.

The European Space Agency (ESA) launched the Soil Moisture and Ocean Salinity (SMOS) mission in 2009; currently, multiple global soil moisture (SM) products are based on the measurements of its L-band (1.4 GHz) radiometer. We compared four SMOS products with each other: Level 2, Level 3, IC (INRA-CESBIO), and near real-time products. The comparisons focused on core validation sites (CVS), whose spatial representativeness errors allow the estimation of the SM product performance for bias-insensitive metrics [unbiased root-mean-square error (ubRMSE) and correlation ( $R$ ), and anomaly $R$ ] with negligible uncertainty and for bias-sensitive metrics [mean difference (MD) and root-mean-square difference (RMSD)] with acceptable uncertainty. When the products were compared with CVS independently, the results showed that the ubRMSE, $R$ , and anomaly $R$ of the IC product were better than those of the other products, while the MD was larger. However, the differences between the performances were smaller when the products were assessed using only the data points when each product had a valid retrieval. This indicates that the algorithms have similar performance and that data screening and quality flagging of the retrievals markedly affects the performance. The NASA Soil Moisture Active Passive (SMAP) mission produces a similar SM product as SMOS using an L-band radiometer. The closeness of the ubRMSE, $R$ , and the anomaly $R$ performance of the IC product and the SMAP product (0.039 versus 0.041 $\text{m}<^>{3}/\text{m}<^>{3}$ , 0.80 versus 0.81, and 0.75 versus 0.75) demonstrate that the SMOS and SMAP radiometers can achieve similar SM sensitivity.

Much of the western United States is covered by rangelands used for grazing and wildlife. Woody plant cover is increasing in areas historically covered by grasslands and can cause numerous problems, including losses in wildlife habitat, forage for grazing, and overall losses in soil health. Land managers and conservationists are working to control these increases in woody plants, but need tools to help determine target areas to focus efforts and resources where they are most needed. In this work, we present RaBET (Rangeland Brush Estimation Tool), which uses transparent, well-understood methodologies with remotely sensed data to map woody canopy cover across large areas of rangelands. We demonstrate that our process produced more accurate results than two currently available tools based on advanced machine learning techniques. We compare two methods of map validation: traditional field methods of plant canopy measurements; and aircraft-based photography, which decreases the amount of time and resources needed. RaBET is a remote sensing-based application for obtaining repeatable, accurate measures of woody cover to aid land managers and conservationists in the control of woody plants on rangelands.

Across the American Southwest water development has played a critical role in managing rangelands. Earthen berms have been constructed throughout US rangelands to manage runoff and reduce erosion. The berms altered runoff patterns to increase soil moisture with positive local vegetative response. However, altered runoff patterns can be considered a disturbance that affects broader scale vegetation patterns. We hypothesized that the hydrologic impacts of earthen berms in semiarid rangelands will be reflected in contrasting upslope and downslope vegetation patterns. A supervised classification of grass, shrubs, and bare soil was performed using orthographic imagery taken in June 2016 to quantify the effects of 181 earthen berms in the uplands and floodplain of the Altar Valley in Southern Arizona, US. Intact berms blocked runoff, creating downslope runoff “shadows” within which the precipitation is the dominant water input. We documented more bare soil downslope of intact berms in comparison with upslope. Grass and shrub cover affected by berms were related to soil texture. Grass and shrub cover were not different above and below intact berms on fine textured soils, but on sites with coarser textured soil, grass cover was lower downslope of berms. Where breaches occurred on coarser textured soils, the up and downslope differences in grass cover diminished. This study points to the role of conservation structures in adding additional complexity to already heterogeneous landscapes by creating patchwork assemblages of vegetation and bare soil proximal to earthen runoff and erosion control berms.

A thermal hydraulic disaggregation of soil moisture (THySM) algorithm was implemented to downscale NASA's soil moisture active passive (SMAP) enhanced soil moisture (SM) product to 1 km over the continental United States (CONUS). This algorithm was developed by combining thermal inertia theory with a soil hydraulic-based approach that considers fine-scale SM spatial distribution driven by both heat fluxes and hydraulic conductivity in soils. Relative soil wetness values were estimated using land surface temperature and normalized difference vegetation index for the thermal inertia model and using soil properties for the hydraulic model. The relative soil wetness values at 1 km from both models were then combined by using weighting functions whereby the spatial distribution of SM was governed more by thermal fluxes during times of strong heat transport and infiltration during moisture abundant soil conditions. THySM values were evaluated using in situ SM measurements from SMAP Core Validation Sites (CVS), the US Department of Agriculture Soil Climate Analysis Network, and the National Oceanic and Atmospheric Administration Climate Reference Network over CONUS. THySM shows higher accuracy than the SMAP / Sentinel-1 (SPL2SMAP_S) 1 km SM product when compared to in situ measurements. The accuracy of THySM is 0.048 m3/m3 based on unbiased root mean square error (ubRMSE), outperforming SPL2SMAP_S by 0.01–0.02 m3/m3. The ubRMSE of THySM 1 km SM over the SMAP grassland/rangeland-dominated CVS sites is better than 0.04 m3/m3, which meets the SMAP mission SM accuracy requirement applied at 9 and 36 km.

•The Long-Term Agroecosystem Research Network launched the LTAR Agricultural Performance Indicator Framework to evaluate how agricultural innovations perform relative to sustainable intensification goals in five domains: Environment, Productivity, Economic, Human Condition, and Social.•Here we describe our progress and plans for measuring the performance of agricultural innovations on rangelands.•We present a method for measuring outcomes of management innovations against site-specific benchmarks, which can be applied in grazinglands worldwide.•LTAR typically studies management on fine scales (ecological site, ranch); how to measure effects on broad scales (landscape, community) remains a persistent question.•LTAR’s Agricultural Performance Indicator Framework will evolve with stakeholder engagement.

This article extends the application of an existing change-detection-based, time-series soil moisture retrieval algorithm to nonconcurrent active and passive measurements from WindSat/AMSR2 and the Soil Moisture Active Passive radar, which was active from late April until mid-July of 2015. A time series of L-band radar backscatter observations was used to populate an underdetermined matrix equation whose optimal solution was derived via a bounded linear least squares estimator, and whose bounds were derived from a time series of radiometer-derived soil moisture estimates (taken by either WindSat or AMSR2). Surface soil moisture estimates are compared with in-situ measurement probes, which were treated as ground truth. Error statistics and time-series results for the validation sites are presented here and conclusions derived therefrom. The overall RMSE and unbiased RMSE for the retrieval algorithm, taken across all reference pixels considered in the study, were 0.070 $\mathbf{m}^{3}/\mathbf{m}^{3}$ and 0.067 $\mathbf{m}^{3}/\mathbf{m}^{3}$, respectively, when using WindSat to constrain the algorithm. When using AMSR2 to constrain the algorithm, the RMSE and unbiased RMSE were 0.093 $\mathbf{m}^{3}/\mathbf{m}^{3}$ and 0.090 $\mathbf{m}^{3}/\mathbf{m}^{3}$, respectively.

Mapping the spatial distribution of woody vegetation is important for monitoring, managing, and studying woody encroachment in grasslands. However, in semi-arid regions, remotely sensed discrimination of tree species is difficult primarily due to the tree similarities, small and sparse canopy cover, but may also be due to overlapping woody canopies as well as seasonal leaf retention (deciduous versus evergreen) characteristics. Similar studies in different biomes have achieved low accuracies using coarse spatial resolution image data. The objective of this study was to investigate the use of multi-temporal, airborne hyperspectral imagery and light detection and ranging (LiDAR) derived data for tree species classification in a semi-arid desert region. This study produces highly accurate classifications by combining multi-temporal fine spatial resolution hyperspectral and LiDAR data (~1 m) through a reproducible scripting and machine learning approach that can be applied to larger areas and similar datasets. Combining multi-temporal vegetation indices and canopy height models led to an overall accuracy of 95.28% and kappa of 94.17%. Five woody species were discriminated resulting in producer accuracies ranging from 86.12% to 98.38%. The influence of fusing spectral and structural information in a random forest classifier for tree identification is evident. Additionally, a multi-temporal dataset slightly increases classification accuracies over a single data collection. Our results show a promising methodology for tree species classification in a semi-arid region using multi-temporal hyperspectral and LiDAR remote sensing data.

In August 2020, soil moisture active passive (SMAP) released a new version of its soil moisture and vegetation optical depth (VOD) retrieval products. In this article, we review the methodology followed by the SMAP regularized dual-channel retrieval algorithm. We show that the new implementation generates SM retrievals that not only satisfy the SMAP accuracy requirements, but also show a performance comparable to the single-channel algorithm that uses the V polarized brightness temperature. Due to a lack of in situ measurements we cannot evaluate the accuracy of the VOD. In this article, we show analyses with the intention of providing an understanding of the VOD product. We compare the VOD results with those from SMOS. We also study the relation of the SMAP VOD with two vegetation parameters: tree height and biomass.

DATA REPORT article Front. Sustain. Food Syst., 15 February 2021 | https://doi.org/10.3389/fsufs.2020.612785

The U.S. Department of Agriculture's Agricultural Research Service (USDA-ARS) maintains seven in situ soil moisture networks throughout the continental United States, some since 2002. These networks are crucial for understanding the spatial and temporal extent of droughts in their historical context, parameterization of hydrologic models, and local agricultural decision support. However, the estimates from these networks are dependent upon their ability to provide reliable soil moisture information at a large scale. It is also not known how many network stations are sufficient to monitor watershed scale dynamics. Therefore, the objectives of this research were to: (1) determine how temporally stable these networks are, including the relationships between various sensors on a year-to-year and seasonal basis, and (2) attempt to determine how many sensors are required, within a network, to approximate the full network average. Using data from seven in situ, it is concluded that approximately 12 soil moisture sensors are sufficient in most environments, presuming their locations are distributed to capture the hydrologic heterogeneity of the watershed. It is possible to install a temporary network containing a suitable number of sensors for an appropriate length of time, glean stable relationships between locations, and retain these insights moving forward with fewer sensor resources.

A soil moisture (SM) disaggregation algorithm based on thermal inertia (TI) theory was implemented to downscale the soil moisture active passive (SMAP) enhanced product (SPL2SMP$\_$E) from 9 to 1 km over the continental United States. The algorithm applies land surface temperature and normalized difference vegetation index from moderate resolution imaging spectroradiometer (MODIS) at higher spatial resolution to estimate relative soil wetness within a coarse SMAP grid-this MODIS-derived relative wetness is then used to produce the downscaled SMAP SM. Results from the algorithm were evaluated in terms of their spatio-temporal coverage and accuracy using in situ measurements from SMAP core validation sites (CVS), the U.S. Department of Agriculture Soil Climate Analysis Network (SCAN), and the National Oceanic and Atmospheric Administration Climate Reference Network (CRN). Results were also compared with the baseline SPL2SMP$\_$E and the SMAP/Sentinel-1 (SPL2SMAP$\_$S) 1 km product. Overall, the unbiased root-mean-square error (ubRMSE) of the disaggregated SM at the CVS using the TI approach is approximately 0.04 $\text{m}^3/\text{m}^3$, which is the SMAP mission requirement for the baseline products. The TI approach outperforms the SMAP/Sentinel SL2SMAP$\_$S 1 km product by approximately 0.02 $\text{m}^3/\text{m}^3$. Over the agriculture/crop areas from SCAN and CRN sparse network stations, the TI approach exhibits better ubRMSE compared to SPL2SMP$\_$E and SPL2SMAP$\_$S by about 0.01 and 0.02 $\text{m}^3/\text{m}^3$, indicating its advantage in these areas. However, a drawback of this approach is that there are data gaps due to cloud cover as optical sensors cannot have a clear view of the land surface.

Over the past four decades, satellite systems and land surface models have been used to estimate global-scale surface soil moisture (SSM). However, in areas such as densely vegetated and irrigated regions, obtaining accurate SSM remains challenging. Before using satellite and model-based SSM estimates over these areas, we should understand the accuracy and error characteristics of various SSM products. Thus, this study aimed to compare the error characteristics of global-scale SSM over vegetated and irrigated areas as obtained from active and passive satellites and model-based data: Advanced Scatterometer (ASCAT), Soil Moisture and Ocean Salinity (SMOS), Advanced Microwave Scanning Radiometer 2 (AMSR2), Soil Moisture Active Passive (SMAP), European Centre for Medium-Range Weather Forecasts Reanalysis 5 (ERA5), and Global Land Data Assimilation System (GLDAS). We employed triple collocation analysis (TCA) and caluclated conventional error metrics from in-situ SSM measurements. We also considered all possible triplets from 6 different products and showed the viability of considering the standard deviation of TCA-based numbers in producing robust results. Over forested areas, it was expected that model-based SSM data might provide more accurate SSM estimates than satellites due to the intrinsic limitations of microwave-based systems. Alternately, over irrigated regions, observation-based SSM data were expected to be more accurate than model-based products because land surface models (LSMs) cannot capture irrigation signals caused by human activities. Contrary to these expectations, satellite-based SSM estimates from ASCAT, SMAP, and SMOS showed fewer errors than ERA5 and GLDAS SSM products over vegetated conditions. Furthermore, over irrigated areas, ASCAT, SMOS, and SMAP outperformed other SSM products; however, model-based data from ERA5 and GLDAS outperformed AMSR2. Our results emphasize that, over irrgated areas, considering satellite-based SSM data as alternatives to model-based SSM data sometimes produces misleading results; and considering model-based data as alternatives to satellite-based SSM data in forested areas can also sometimes be misleading. In addition, we discovered that no products showed much degradation in TCA-based errors under different vegetated conditions, while different irrigation conditions impacted both satellite and model-based SSM data sets. The present research demonstrates that limitations in satellite and modeled SSM data can be overcome in many areas through the synergistic use of satellite and model-based SSM products, excluding areas where satellite-based data are masked out. In fact, when four satellite and model data sets are used selectively, the probability of obtaining SSM with stronger signal than noise can be close to 100%.

In order to validate its soil moisture products, the NASA Soil Moisture Active Passive (SMAP) mission utilizes sites with permanent networks of in situ soil moisture sensors maintained by independent calibration and validation partners in a variety of ecosystems around the world. Measurements from each core validation site (CVS) are combined in a weighted average to produce an estimate of soil moisture at a 33-km scale that represents the SMAP's radiometer-based retrievals. Since upscaled estimates produced in this manner are dependent on the weighting scheme applied, an independent method of quantifying their biases is needed. Here, we present one such method that uses soil moisture measurements taken from a dense, but temporary, network of soil moisture sensors deployed at each CVS to train a random forests regression expressing soil moisture in terms of a set of spatial variables. The regression then serves as an independent source of upscaled estimates against which permanent network upscaled estimates can be compared in order to calculate bias statistics. This method, which offers a systematic and unified approach to estimate bias across a variety of validation sites, was applied to estimate biases at four CVSs. The results showed that the magnitude of the uncertainty in the permanent network upscaling bias can sometimes exceed 80% of the upper limit on SMAP's entire allowable unbiased root-mean-square error (ubRMSE). Such large CVS bias uncertainties could make it more difficult to assess biases in soil moisture estimates from SMAP.

O’Neill, P., S. Chan, R. Bindlish, M. Chaubell, A. Colliander, F. Chen, S. Dunbar, T. Jackson, J. Peng, M. Cosh, T. Bongiovanni, J. Walker, X. Wu, A. Berg, H. McNairn, M. Thibeault, J. MartínezFernández, Á. González-Zamora, E. Lopez-Baeza, K. Jensen, M. Seyfried, D. Bosch, P. Starks, C. Holifield Collins, J. Prueger, Z. Su, R. van der Velde, J. Asanuma, M. Palecki, E. Small, M. Zreda, J. Calvet, W. Crow, Y. Kerr, S. Yueh, and D. Entekhabi, August 31, 2020. Calibration and Validation for the L2/3_SM_P Version 7 and L2/3_SM_P_E Version 4 Data Products, SMAP Project, JPL D-56297, Jet Propulsion Laboratory, Pasadena, CA.

The FengYun-3 (FY-3) series satellite is the second generation of Chinese polar-orbiting meteorological satellite missions. The FY-3D satellite was launched on November 2017 and has been providing valuable data for meteorological applications, including brightness temperature (T-B) data from the MicroWave Radiation Imager (MWRI). In this study, we developed a global soil moisture retrieval algorithm, based on the radiative transfer equation (RTE) for using the FY-3D MWRI T-B to continue the soil moisture record from FY-3 satellites. We adopted a new empirical model to compute vegetation water content (VWC) based on the leaf area index (LAI) and canopy height (H) for vegetation effects correction. The Q(p) model, which addresses the soil surface roughness effects using dual-polarization information, is then used for soil moisture retrieval. Validation of the FY-3D soil moisture was conducted with the in-situ data and the validation results showed encouraging accuracy over a variety of landcovers, with bias and unbiased root-mean-squared difference (ubRMSE) at or below the level of 0.06 m(3) . m(-3). Monthly averaged soil moisture products generated from FY-3D could represent the seasonal changes in soil moisture and show reasonable spatial distribution of soil moisture at a global scale.

Soil moisture retrieval is particularly challenging during and immediately after precipitation events because of the transient movement of water in the shallow subsurface. Conventional L-band microwave radiometer-based soil moisture products use algorithms that assume a static state and a constant vertical soil moisture distribution. This study assessed the retrieval performance of a SMAP radiometer-based soil moisture product during and immediately after rain events. The removal of the rain event samples systematically improved the unbiased root-mean-square error (ubRMSE) from 0.037 (all measurements) to 0.028 m(3) m(-3) (transitory measurements screened out), while the magnitude of the bias became larger (from -0.005 to -0.014 m(3) m(-3)); RMSE improved from 0.047 to 0.042 m(3) m(-3), and the Pearson correlation saw a minor positive change from 0.813 to 0.824. The results indicate that removing samples during the transitional period causes the comparison to improve, but also suggests that the true bias may be larger than the one estimated using all the samples. Furthermore, the results revealed that the effect was stronger for areas with high clay content. An assessment of the performance of the product during the rain events (overpass within 3 h from the start of the rain) showed that the ubRMSE degraded from the benchmarked 0.036 m(3) m(-3) (during no rain events at all) to 0.043 m(3) m(-3) (during rain). The results also showed that the bias became wetter, which is expected because SMAP sensed the water on the surface before propagating to the in situ sensors. SMAP maintains its soil moisture sensitivity even during rain events and screening of rain events may not be necessary to ensure sufficient soil moisture retrieval quality.

In the past decade, a variety of algorithms have been introduced to downscale passive microwave soil moisture observations. Some exploit the soil moisture information from optical/thermal sensing of land surface temperature (LST) and vegetation dynamics while others use active microwave (radar) observations. In this study, downscaled soil moisture data at 9or 1-km resolution from several algorithms are intercompared against in situ soil moisture measurements to determine their reliability in an operational system. The finescale satellite data used here for downscaling the coarse-scale SMAP data are observations of LST from the Geostationary Operational Environmental Satellite (GOES) and vegetation index (VI) from the NASA Moderate Resolution Imaging Spectroradiometer (MODIS) for the warm seasons in 2015 and 2016. Three recently developed downscaling algorithms are evaluated and compared: a simple regression algorithm based on 9-km thermal inertial data, a data mining approach called regression tree based on 9and 1-km LST and VI, and the NASA SMAP enhanced 9-km soil moisture product algorithm. Seven sets of in situ soil moisture data from intensive networks were used for validation, including 1) the CREST-SMART network in Millbrook, NewYork; 2)WalnutGulchWatershed inArizona; 3) LittleWashitaWatershed inOklahoma; 4) Fort Cobb Reservoir Experimental Watersheds in Oklahoma; 5) Little River Watershed in Georgia; 6) the Tibetan Plateau network inChina, and 7) theOzNet inAustralia. Soilmoisturemeasurements of the in situ networkswere upscaled to the corresponding SMAP reference pixels at 9 km and used to assess the accuracy of downscaled products at a 9-km scale. Results revealed that the downscaled 9-km soil moisture products generally outperform the 36-km product for most in situ datasets. The linear regression algorithm using the thermal sensing based evaporative stress index (ESI) had the best agreement with the in situ measurements from networks in the contiguous United States according to the site-by-site comparison. In addition, the inertial thermal linear regression method demonstrated the lowest unbiased RMSEwhen comparing to thematched-up in situ datasets as well. In general, this method is promising for operational generation of fine-resolution soil moisture data product.

Climatology (mean seasonal cycle) errors often cause large differences between soil moisture products. However, relatively little work has been done to objectively evaluate soil moisture mean seasonal cycle (SMC) information acquired from different sources. This study evaluates surface (0-10 cm) SMCs obtained from four land surface models (LSMs), two C/X-band and two L-band remote-sensing (RS) products using 5 dense networks and 75 sparse in-situ soil moisture measurement sites located within the contiguous United States. Results show that relative to older C/X-band products derived from the Advanced Microwave Scanning Radiometer for EOS (AMSR-E), newer L-band products derived from the Soil Moisture Ocean and Salinity (SMOS) mission provide more accurate SMC estimates (with an average of 35% root-mean-square-error reduction). In fact, the latest SMOS INRA-CESBIO (SMOS-IC) product provides SMC intra-seasonal variability and dynamic range information that is 3-34% and 2-37% more accurate, respectively, than all four LSM-based SMCs examined here. Hence, SMC validation against SMOS-IC SMC results may improve LSMs' ability to accurately capture SMC characteristics, and the common strategy of scaling remote sensing SMC information to match LSM SMC estimates is likely sub-optimal for assimilating L-band soil moisture retrievals. Although the SMOS-IC product has made significant progresses towards retrieving absolute soil moisture values, a temporally constant dry bias is found in SMOS-IC surface SMCs over all land cover types. Addressing this bias should be a priority for future generations of SMOS retrieval algorithms.