Grazinglands Research Laboratory

The NASA Soil Moisture Active Passive (SMAP) mission has utilized a set of core validation sites as the primary methodology in assessing the soil moisture retrieval algorithm performance. Those sites provide wellcalibrated in situ soil moisture measurements within SMAP product grid pixels for diverse conditions and locations. The estimation of the average soil moisture within the SMAP product grid pixels based on in situ measurements is more reliable when location specific calibration of the sensors has been performed and there is adequate replication over the spatial domain, with an up-scaling function based on analysis using independent estimates of the soil moisture distribution. SMAP fulfilled these requirements through a collaborative Cal/Val Partner program. This paper presents the results from 34 candidate core validation sites for the first eleven months of the SMAP mission. As a result of the screening of the sites prior to the availability of SMAP data, out of the 34

Surface soil moisture retrieval algorithms based on passive microwave observations, developed and verified at high spatial resolution, were evaluated in a regional scale experiment. Using previous investigations as a base, the Southern Great Plains Hydrology Experiment (SGP97) was designed and conducted to extend the algorithm to coarser resolutions, larger regions with more diverse conditions, and longer time periods. The L-band electronically scanned thinned array radiometer (ESTAR) was used for daily mapping of surface soil moisture over an area greater than 10000 km/sup 2/ for a one month period. Results show that the soil moisture retrieval algorithm performed the same as in previous investigations, demonstrating consistency of both the retrieval and the instrument. Error levels were on the order of 3% for area Integrated averages of sites used for validation. This result showed that for the coarser resolution used that the theory and techniques employed in the algorithm apply at this scale. Spatial patterns observed in the Little Washita Watershed in previous investigations were also observed. These results showed that soil texture dominated the spatial pattern at this scale. However, the regional soil moisture patterns were a reflection of the spatially variable rainfall and soil texture patterns were not as obvious.

The National Aeronautics and Space Administration (NASA) Soil Moisture Active Passive (SMAP) satellite mission was launched on January 31, 2015. The observatory was developed to provide global mapping of high-resolution soil moisture and freeze-thaw state every two to three days using an L-band (active) radar and an L-band (passive) radiometer. After an irrecoverable hardware failure of the radar on July 7, 2015, the radiometer-only soil moisture product became the only operational soil moisture product for SMAP. The product provides soil moisture estimates posted on a 36 km Earth-fixed grid produced using brightness temperature observations from descending passes. Within months after the commissioning of the SMAP radiometer, the product was assessed to have attained preliminary (beta) science quality, and data were released to the public for evaluation in September 2015. The product is available from the NASA Distributed Active Archive Center at the National Snow and Ice Data Center. This paper provides a summary of the Level 2 Passive Soil Moisture Product (L2_SM_P) and its validation against in situ ground measurements collected from different data sources. Initial in situ comparisons conducted between March 31, 2015 and October 26, 2015, at a limited number of core validation sites (CVSs) and several hundred sparse network points, indicate that the V-pol Single Channel Algorithm (SCA-V) currently delivers the best performance among algorithms considered for L2_SM_P, based on several metrics. The accuracy of the soil moisture retrievals averaged over the CVSs was 0.038 m <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</sup> /m <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</sup> unbiased root-mean-square difference (ubRMSD), which approaches the SMAP mission requirement of 0.040 m <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</sup> /m <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</sup> .

Validation is an important and particularly challenging task for remote sensing of soil moisture. A key issue in the validation of soil moisture products is the disparity in spatial scales between satellite and in situ observations. Conventional measurements of soil moisture are made at a point, whereas satellite sensors provide an integrated area/volume value for a much larger spatial extent. In this paper, four soil moisture networks were developed and used as part of the Advanced Microwave Scanning Radiometer-Earth Observing System (AMSR-E) validation program. Each network is located in a different climatic region of the U.S., and provides estimates of the average soil moisture over highly instrumented experimental watersheds and surrounding areas that approximate the size of the AMSR-E footprint. Soil moisture measurements have been made at these validation sites on a continuous basis since 2002, which provided a seven-year period of record for this analysis. The National Aeronautics and Space Administration (NASA) and Japan Aerospace Exploration Agency (JAXA) standard soil moisture products were compared to the network observations, along with two alternative soil moisture products developed using the single-channel algorithm (SCA) and the land parameter retrieval model (LPRM). The metric used for validation is the root-mean-square error (rmse) of the soil moisture estimate as compared to the in situ data. The mission requirement for accuracy defined by the space agencies is 0.06 m <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</sup> /m <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</sup> . The statistical results indicate that each algorithm performs differently at each site. Neither the NASA nor the JAXA standard products provide reliable estimates for all the conditions represented by the four watershed sites. The JAXA algorithm performs better than the NASA algorithm under light-vegetation conditions, but the NASA algorithm is more reliable for moderate vegetation. However, both algorithms have a moderate to large bias in all cases. The SCA had the lowest overall rmse with a small bias. The LPRM had a very large overestimation bias and retrieval errors. When site-specific corrections were applied, all algorithms had approximately the same error level and correlation. These results clearly show that there is much room for improvement in the algorithms currently in use by JAXA and NASA. They also illustrate the potential pitfalls in using the products without a careful evaluation.

Abstract Biochar is a widely known soil amendment. Here we synthesize the available information on influence of biochar application on different soil properties and crop productivity using meta-analysis. Global data on influence of biochar applications on different soil physical, chemical, microbial properties, and crop productivity were extracted from literature and statistically analyzed. Based on selection criteria, 59 studies from the literature published between 2012 and 2021 were selected for the meta-analysis. Correlations were developed between effect size of biochar application on different soil properties and crop productivity. Application of biochar increased soil pH, cation exchange capacity, and organic carbon by 46%, 20%, and 27%, respectively, with greater effects in coarse and fine-textured soils. Effects on chemical properties were variable among biochar prepared from different feedstocks. Among physical properties, biochar application reduced bulk densities by 29% and increased porosity by 59%. Biochar prepared at higher pyrolytic temperatures (&gt; 500 ℃) improved bulk density and porosity to greater extents (31% and 66%, respectively). Biochar prepared at lower pyrolytic temperatures (&lt; 500 ℃) had a greater effect on microbial diversity (both bacterial and fungal), with more diverse bacterial populations in medium and coarse textured soils, while fungal diversity increased in fine textured soils. Biochar applications increased crop productivity only in fine and coarse textured soil. The effect size of biochar application on crop productivity was correlated with responses to physical properties of soils. The meta-analysis highlighted the need to conduct long-term field experiments to provide better explanations for changes in biochar properties as it undergoes aging, its longer-term effects on soil properties, and timing of re-application of different biochars.

Growth in animals is defined as accretion of protein, fat and bone. Although growth typically is measured as the change in live weight, nutrient retention is estimated more precisely by measuring empty body weight and composition, whereas production economics are measured ideally through carcass weights and quality. As a percentage of live weight gain, carcass weight gain usually is a much higher percentage during the feedlot phase than during the growing phase of production because dressing percentage (ratio of carcass:live weight) increases with maturation and is greater with concentrate than with roughage diets. At a given fraction of mature body size (maximum body protein mass), body fat percentage seems to be a constant. Mature size may be altered genetically and nutritionally. Protein accretion declines to zero when cattle reach their mature body size (approximately 36% fat in empty body weight in modern cattle) even though mature animals can continue to accrete fat. Although fat accretion can be reduced by limiting the supply of net energy, rate of fat accretion by finishing steers given ad libitum access to high-concentrate diets seems to reach a plateau at approximately 550 g daily. Protein mass, in contrast, increases in proportion to empty body weight. The protein:fat ratio of the carcass can be increased through increasing mature size, by administering hormones or hormonal modifiers, by limiting energy intake during the growing period or finishing period, or by slaughtering cattle at an earlier stage of maturity. Energetically, efficiency of accretion of fat is approximately 1.7 times that of protein. But because more water is stored with deposited protein than with deposited fat, lean tissue gain is four times as efficient as accretion of fat tissue. Conversion of protein to fat is very inefficient, suggesting that excess protein is utilized inefficiently.

Fifty-one cyclic beef cows were mated with fertile bulls. At 36 h after the start of oestrus, cows were assigned to receive sesame oil (controls) or progesterone (100 mg) on Days 1, 2, 3 and 4 of pregnancy. Peripheral plasma concentration of progesterone was measured until slaughter on Days 5 or 14. Cows were randomly assigned to be slaughtered on Days 5 or 14 or remain intact and palpated per rectum on Day 40 to verify pregnancy. Uteri on Days 5 and 14 were flushed for recovery of luminal protein and conceptus tissue. Conceptus and endometrial tissues were cultured with [3H]leucine and submitted to two-dimensional-PAGE and fluorography. Administration of progesterone increased peripheral plasma progesterone concentration on Day 2-5. Conceptuses recovered from progesterone-treated cows on Day 14 were advanced in development compared to conceptuses from control cows. Conceptuses recovered from progesterone-treated cows were viable as polypeptides associated with maintenance of pregnancy in cattle were synthesized and released at an earlier time and pregnancy was maintained beyond Day 40. Early progesterone stimulation altered the synthesis and release of polypeptides from endometrial explant cultures on Day 5. Results indicate a role of progesterone in the maternal regulation of conceptus growth and development in early pregnancy of cattle.

Estimation of soil moisture at large scale has been performed using several satellite-based passive microwave sensors and a variety of retrieval methods over the past two decades. The most recent source of soil moisture is the European Space Agency Soil Moisture and Ocean Salinity (SMOS) mission. A thorough validation must be conducted to insure product quality that will, in turn, support the widespread utilization of the data. This is especially important since SMOS utilizes a new sensor technology and is the first passive L-band system in routine operation. In this paper, we contribute to the validation of SMOS using a set of four in situ soil moisture networks located in the U.S. These ground-based observations are combined with retrievals based on another satellite sensor, the Advanced Microwave Scanning Radiometer (AMSR-E). The watershed sites are highly reliable and address scaling with replicate sampling. Results of the validation analysis indicate that the SMOS soil moisture estimates are approaching the level of performance anticipated, based on comparisons with the in situ data and AMSR-E retrievals. The overall root-mean-square error of the SMOS soil moisture estimates is 0.043 m <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</sup> /m <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</sup> for the watershed networks (ascending). There are bias issues at some sites that need to be addressed, as well as some outlier responses. Additional statistical metrics were also considered. Analyses indicated that active or recent rainfall can contribute to interpretation problems when assessing algorithm performance, which is related to the contributing depth of the satellite sensor. Using a precipitation flag can improve the performance. An investigation of the vegetation optical depth (tau) retrievals provided by the SMOS algorithm indicated that, for the watershed sites, these are not a reliable source of information about the vegetation canopy. The SMOS algorithms will continue to be refined as feedback from validation is evaluated, and it is expected that the SMOS estimates will improve.

Abstract The Soil Moisture Active Passive (SMAP) mission Level-4 Surface and Root-Zone Soil Moisture (L4_SM) data product is generated by assimilating SMAP L-band brightness temperature observations into the NASA Catchment land surface model. The L4_SM product is available from 31 March 2015 to present (within 3 days from real time) and provides 3-hourly, global, 9-km resolution estimates of surface (0–5 cm) and root-zone (0–100 cm) soil moisture and land surface conditions. This study presents an overview of the L4_SM algorithm, validation approach, and product assessment versus in situ measurements. Core validation sites provide spatially averaged surface (root zone) soil moisture measurements for 43 (17) “reference pixels” at 9- and 36-km gridcell scales located in 17 (7) distinct watersheds. Sparse networks provide point-scale measurements of surface (root zone) soil moisture at 406 (311) locations. Core validation site results indicate that the L4_SM product meets its soil moisture accuracy requirement, specified as an unbiased RMSE (ubRMSE, or standard deviation of the error) of 0.04 m3 m−3 or better. The ubRMSE for L4_SM surface (root zone) soil moisture is 0.038 m3 m−3 (0.030 m3 m−3) at the 9-km scale and 0.035 m3 m−3 (0.026 m3 m−3) at the 36-km scale. The L4_SM estimates improve (significantly at the 5% level for surface soil moisture) over model-only estimates, which do not benefit from the assimilation of SMAP brightness temperature observations and have a 9-km surface (root zone) ubRMSE of 0.042 m3 m−3 (0.032 m3 m−3). Time series correlations exhibit similar relative performance. The sparse network results corroborate these findings over a greater variety of climate and land cover conditions.

The Food and Agriculture Organization of the United Nations estimates that 843 million people worldwide are hungry and a greater number suffer from nutrient deficiencies. Approximately one billion people have inadequate protein intake. The challenge of preventing hunger and malnutrition will become even greater as the global population grows from the current 7.2 billion people to 9.6 billion by 2050. With increases in income, population, and demand for more nutrient-dense foods, global meat production is projected to increase by 206 million tons per year during the next 35 years. These changes in population and dietary practices have led to a tremendous rise in the demand for food protein, especially animal-source protein. Consuming the required amounts of protein is fundamental to human growth and health. Protein needs can be met through intakes of animal and plant-source foods. Increased consumption of food proteins is associated with increased greenhouse gas emissions and overutilization of water. Consequently, concerns exist regarding impacts of agricultural production, processing and distribution of food protein on the environment, ecosystem, and sustainability. To address these challenging issues, the New York Academy of Sciences organized the conference "Frontiers in Agricultural Sustainability: Studying the Protein Supply Chain to Improve Dietary Quality" to explore sustainable innovations in food science and programming aimed at producing the required quality and quantity of protein through improved supply chains worldwide. This report provides an extensive discussion of these issues and summaries of the presentations from the conference.

ABSTRACT: Precipitation and streamflow data from three nested subwatersheds within the Little Washita River Experimental Watershed (LWREW) in southwestern Oklahoma were used to evaluate the capabilities of the Soil and Water Assessment Tool (SWAT) to predict streamflow under varying climatic conditions. Eight years of precipitation and streamflow data were used to calibrate parameters in the model, and 15 years of data were used for model validation. SWAT was calibrated on the smallest and largest sub‐watersheds for a wetter than average period of record. The model was then validated on a third subwatershed for a range in climatic conditions that included dry, average, and wet periods. Calibration of the model involved a multistep approach. A preliminary calibration was conducted to estimate model parameters so that measured versus simulated yearly and monthly runoff were in agreement for the respective calibration periods. Model parameters were then fine tuned based on a visual inspection of daily hydrographs and flow frequency curves. Calibration on a daily basis resulted in higher baseflows and lower peak runoff rates than were obtained in the preliminary calibration. Test results show that once the model was calibrated for wet climatic conditions, it did a good job in predicting streamflow responses over wet, average, and dry climatic conditions selected for model validation. Monthly coefficients of efficiencies were 0.65, 0.86, and 0.45 for the dry, average, and wet validation periods, respectively. Results of this investigation indicate that once calibrated, SWAT is capable of providing adequate simulations for hydrologic investigations related to the impact of climate variations on water resources of the LWREW.

Abstract The 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 m 3 /m 3 for surface and 0.026 m 3 /m 3 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 m 3 /m 3 ) 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.

Methods developed to process raster digital elevation models (DEM) automatically in order to delineate and measure the properties of drainage networks and drainage basins are being recognized as potentially valuable tools for the topographic parameterization of hydrological models. All of these methods ultimately rely on some form of overland flow simulation to define drainage courses and catchment areas and, therefore, have difficulty dealing with closed depressions and flat areas on digital land surface models. Some fundamental assumptions about the nature of these problem topographic features in DEM are implicit in the various techniques developed to deal with them in automated drainage analysis. The principal assumptions are: (1) that closed depressions and flat areas are spurious features that arise from data errors and limitations of DEM resolution; (2) that flow directions across flat areas are determined solely by adjacent cells of lower elevation; and (3) that closed depressions are caused exclusively by the underestimation of DEM elevations. It is argued that while the first of these assumptions is reasonable, given the quality of DEMs generally available for hydrological analysis, the others are not. Rather it seems more likely that depressions are caused by both under- and overestimation errors and that flow directions across flat areas are determined by the distribution of both higher and lower elevations surrounding flat areas. Two new algorithms are introduced that are based on more reasonable assumptions about the nature of flat areas and depressions, and produce more realistic results in application. These algorithms allow breaching of depression outlets and consider the distribution of both higher and lower elevations in assigning flow directions on flat areas. The results of applying these algorithms to some real and hypothetical landscapes are presented. © 1998 John Wiley & Sons, Ltd.

Soil organic carbon (SOC) is closely tied to soil health. However, additional biological indicators may also provide insight about C dynamics and microbial activity. We used SOC and the other C indicators (potential C mineralization, permanganate oxidizable C, water extractable organic C, and β-glucosidase enzyme activity) from the North American Project to Evaluate Soil Health Measurements to examine the continental-scale drivers of these indicators, the relationships among indicators, and the effects of soil health practices on indicator values. All indicators had greater values at cooler temperatures, and most were greater with increased precipitation and clay content. The indicators were strongly correlated with each other at the site-level, with the strongest relationship between SOC and permanganate oxidizable C. The indicator values responded positively to decreased tillage, inclusion of cover crops, application of organic nutrients, and retention of crop residue, but not the number of harvested crops in a rotation. The effect of decreased tillage on the C indicators was generally greater at sites with higher precipitation. The magnitude and direction of the response to soil health practices was consistent across indicators within a site but measuring at least two indicators would provide additional confidence of the effects of management, especially for tillage. All C indicators responded to management, an essential criterion for evaluating soil health. Balancing the cost, sensitivity, interpretability, and availability at commercial labs, a 24-hr potential C mineralization assay could deliver the most benefit to measure in conjunction with SOC.

Precise estimation of sediment transport capacity ( T c ) is critical to the development of physically based erosion models. Few data are available for estimating T c on steep slopes. The objectives of this study were to evaluate the effects of unit flow discharge ( q ), slope gradient ( S ), and mean flow velocity on T c in shallow flows and to investigate the relationship between T c and shear stress, stream power, and unit stream power on steep slopes using a 5‐m‐long and 0.4‐m‐wide nonerodible flume bed. Unit flow discharge ranged from 0.625 × 10 −3 to 5 × 10 −3 m 2 s −1 and slope gradient from 8.8 to 46.6%. The diameter of the test riverbed sediment varied from 20 to 2000 μm, with a median diameter of 280 μm. The results showed that T c increased as a power function with discharge and slope gradient with a coefficient of Nash–Sutcliffe model efficiency (NSE) of 0.95. The influences of S on T c increased as S increased, with T c being slightly more sensitive to q than to S The T c was well predicted by shear stress (NSE = 0.97) and stream power (NSE = 0.98) but less satisfactorily by unit stream power (NSE = 0.92) for the slope range of 8.8 to 46.6%. Mean flow velocity was also a good predictor of T c (NSE = 0.95). Mean flow velocity increased as q and S increased in this study. Overall, stream power seems to be the preferred predictor for estimating T c for steep slopes; however, the predictive relationships derived in this study need to be evaluated further in eroding beds using a range of soil materials under various slopes.

Soils harbor highly diverse microbial communities that are critical to soil health, but agriculture has caused extensive land use conversion resulting in negative effects on critical ecosystem processes. However, the responses and adaptations of microbial communities to land use conversion have not yet been understood. Here, we examined the effects of land conversion for long-term crop use on the network complexity and stability of soil microbial communities over 19 months. Despite reduced microbial biodiversity in comparison with native tallgrass prairie, conventionally tilled (CT) cropland significantly increased network complexity such as connectivity, connectance, average clustering coefficient, relative modularity, and the number of species acting at network hubs and connectors as well as resulted in greater temporal variation of complexity indices. Molecular ecological networks under CT cropland became significantly more robust and less vulnerable, overall increasing network stability. The relationship between network complexity and stability was also substantially strengthened due to land use conversion. Lastly, CT cropland decreased the number of relationships between network structure and environmental properties instead being strongly correlated to management disturbances. These results indicate that agricultural disturbance generally increases the complexity and stability of species "interactions", possibly as a trade-off for biodiversity loss to support ecosystem function when faced with frequent agricultural disturbance.

The National Aeronautics and Space Administration Soil Moisture Active Passive (SMAP) mission has been validating its soil moisture (SM) products since the start of data production on March 31, 2015. Prior to launch, the mission defined a set of criteria for core validation sites (CVS) that enable the testing of the key mission SM accuracy requirement (unbiased root-mean-square error &lt;0.04 m³&#x002F;m³). The validation approach also includes other (&#x201C;sparse network&#x201D;) <i>in situ</i> SM measurements, satellite SM products, model-based SM products, and field experiments. Over the past six years, the SMAP SM products have been analyzed with respect to these reference data, and the analysis approaches themselves have been scrutinized in an effort to best understand the products&#x2019; performance. Validation of the most recent SMAP Level 2 and 3 SM retrieval products (R17000) shows that the <i>L</i>-band (1.4 GHz) radiometer-based SM record continues to meet mission requirements. The products are generally consistent with SM retrievals from the European Space Agency Soil Moisture Ocean Salinity mission, although there are differences in some regions. The high-resolution (3-km) SM retrieval product, generated by combining Copernicus Sentinel-1 data with SMAP observations, performs within expectations. Currently, however, there is limited availability of 3-km CVS data to support extensive validation at this spatial scale. The most recent (version 5) SMAP Level 4 SM data assimilation product providing surface and root-zone SM with complete spatio&#x2013;temporal coverage at 9-km resolution also meets performance requirements. The SMAP SM validation program will continue throughout the mission life; future plans include expanding it to forested and high-latitude regions.

Abstract During 2012, flash drought developed and subsequently expanded across large areas of the Central United States (US) with severe impacts to overall water resources and warm-season agricultural production. Recent efforts have yielded a methodology to detect and quantify flash drought occurrence and rate of intensification from climatological datasets via the standardized evaporative stress ratio (SESR). This study utilizes the North American Regional Reanalysis and applied the SESR methodology to quantify the spatial and temporal development and expansion of flash drought conditions during 2012. Critical results include the identification of the flash drought epicenter and subsequent spread of flash drought conditions radially outward with varying rates of intensification. Further, a comparison of the SESR analyses with surface-atmosphere coupling metrics demonstrated that a hostile environment developed across the region, which limited the formation of deep atmospheric convection, exacerbated evaporative stress, and perpetuated flash drought development and enhanced its radial spread across the Central US.

The validation of the soil moisture retrievals from the recently launched National Aeronautics and Space Administration (NASA) Soil Moisture Active/Passive (SMAP) satellite is important prior to their full public release. Uncertainty in attempts to characterize footprint-scale surface-layer soil moisture using point-scale ground observations has generally limited past validation of remotely sensed soil moisture products to densely instrumented sites covering an area approximating the satellite ground footprint. However, by leveraging independent soil moisture information obtained from land surface modeling and/or alternative remote sensing products, triple collocation (TC) techniques offer a strategy for characterizing upscaling errors in sparser ground measurements and removing the impact of such error on the evaluation of remotely sensed soil moisture products. Here, we propose and validate a TC-based strategy designed to utilize existing sparse soil moisture networks (typically with a single sampling point per satellite footprint) to obtain an unbiased correlation validation metric for satellite surface soil moisture retrieval products. Application of this TC strategy at five SMAP core validation sites suggests that unbiased estimates of correlation between the satellite product and the true footprint average can be obtained - even in cases where ground observations provide only one single reference point within the footprint. An example of preliminary validation results from the application of this TC strategy to the SMAP Level 2 Soil Moisture Passive (beta release version) product is presented.

The soil moisture active passive (SMAP) mission was designed to acquire L-band radiometer measurements for the estimation of soil moisture (SM) with an average ubRMSD of not more than 0.04 m <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</sup> /m <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</sup> volumetric accuracy in the top 5 cm for vegetation with a water content of less than 5 kg/m <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> . Single-channel algorithm (SCA) and dual-channel algorithm (DCA) are implemented for the processing of SMAP radiometer data. The SCA using the vertically polarized brightness temperature (SCA-V) has been providing satisfactory SM retrievals. However, the DCA using prelaunch design and algorithm parameters for vertical and horizontal polarization data has a marginal performance. In this article, we show that with the updates of the roughness parameter h and the polarization mixing parameters Q, a modified DCA (MDCA) can achieve improved accuracy over DCA; it also allows for the retrieval of vegetation optical depth (VOD or τ). The retrieval performance of MDCA is assessed and compared with SCA-V and DCA using four years (April 1, 2015 to March 31, 2019) of in situ data from core validation sites (CVSs) and sparse networks. The assessment shows that SCA-V still outperforms all the implemented algorithms.