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NASA NTRS2019

Evaluating the Operational Application of SMAP for Global Agricultural Drought Monitoring

Over the past two decades, remote sensing has made possible the routine global monitoring of surface soil moisture. Regionalagricultural drought monitoring is one of the most logicalapplication areas for such monitoring. However, remote sensing alone provides soil moisture information for only the top few centimetersof the soil profile, while agricultural drought monitoring requires knowledge of the amount of water present in the entireroot zone. The assimilation of remotely sensed soil moisture productsinto continuous soil water balance models provides a way ofaddressing this shortcoming. Here, we describe the assimilationof NASA's soil moisture active passive (SMAP) surface soil moisture data into the United States Department of Agriculture Foreign Agricultural Service (USDA FAS) Palmer model and assess the impactof SMAP on USDA FAS drought monitoring capabilities. Theassimilation of SMAP is specifically designed to enhance the model skill and the USDA FAS drought capabilities by correcting for randomerrors inherent in its rainfall forcing data. The performanceof this SMAP-based assimilation system is evaluated using two approaches.At global scale, the accuracy of the system is assessed by examining the lagged correlation agreement between soil moistureand the normalized difference vegetation index (NDVI). Additional regional-scale evaluation using in situ-based soil moisture estimatesis carried out at seven of the SMAP core Cal/Val sites located in theUSA. Both types of analysis demonstrate the value of assimilating SMAP into the USDA FAS Palmer model and its potential to enhance operational USDA FAS root-zone soil moisture information.

Mladenova, Iliana E.↗
NASA NTRS2019

Consistency Between NASS Surveyed Soil Moisture Conditions and SMAP Soil Moisture Observations

The USDA National Agricultural Statistics Survey (NASS) collects and publishes crop growth status and soil moisture conditions in major US agricultural regions. The operationally-produced weekly reports are based on survey information. The surveys are based on visual assessments and ? in the case of soil moisture ? report soil moisture levels in one of four categories (Very Short, Short, Adequate and Surplus). In this study, we show that these reports have remarkable correspondence with the NASA Soil Moisture Active Passive (SMAP) Level-4 Soil Moisture (L4SM) product. This consistency allows the combining the two distinct types of data to produce a value-added combination, which is mapped fields rather than State-by-State tables and it is refreshed daily rather than weekly. In this study classification thresholds are derived for L4SM by mapping cumulative distribution functions of L4SM surface and root-zone SM to the categorical NASS SM conditions. The results show that, year-over-year, the SMAP cumulative SM distributions are consistent with the NASS SM conditions and, furthermore, that the temporal evolution of the SMAP-derived thresholds is consistent with the seasonal crop growth cycles from year to year. The results signify that the SMAP SM retrievals are relatable to SM estimation conducted in agriculture by land managers and farmers, which underlines the general applicability of the SMAP data.

Andreas Colliander↗
NASA NTRS2019

Definition of a Technology Validation Mission for P-band Reflectometry using Signals of Opportunity

Root-Zone Soil Moisture (RZSM) (moisture profile in the top meter of soil) and Snow Water Equivalent (SWE) (total snow pack water content) are identified as priority target variables in the ESAS 2017 decadal survey [1] with critical roles in hydrology and water management. RZSM estimates are vital for understanding multiple Earth system processes and forecasting (for example, droughts [2]). Simultaneous knowledge of surface and RZSM could enable a breakthrough in estimating key unobserved hydrologic fluxes and reduce uncertainty in net ecosystem exchange (NEE), carbon balance [3] discharge estimates, and crop yield forecasts [4] .With the high albedo and insulating properties of snow, monitoring, SWE accumulation would provide a key constraint on the potential runoff during spring ablation while monitoring SWE disappearance rates would provide a key constraint on SWE partition into runoff vs. infiltration/recharge. [5] demonstrated that knowledge of early-spring SWE generally contributes most to streamflow forecast skill in the Western U.S. SWE is also a source of water storage that provides the water resources during spring snowmelt. Despite such potentially transformative contributions, accurate RZSM and SWE measurements are unattainable with current technology. While active/passive L-band methods (e.g. SMAP, SMOS) can reliably retrieve surface soil moisture in the top 5 cm of soil [6], [7]. RZSM estimates are only available through model assimilation of brightness temperatures with a radiative transfer and land surface models [8]. SWE estimation uses multi-frequency passive microwave techniques (e.g. [9]-[11]), which have significant problems with deeper snow and in forested and mountainous environments [12]. Signals of opportunity (SoOp) in P-band (200-400 MHz) is a new remote sensing technique with the capability of estimating both essential hydrologic variables, RZSM and SWE, circumventing many of the aforementioned limitations under all weather conditions day and night. SoOp is the re-utilization of existing powerful satellite transmissions within bands allocated for communications or navigation. P-band SoOp sensitivity to soil moisture has been demonstrated in an airborne experiment over Oklahoma in 2016 [13]. Recent theory [14] and experiments [15] have also confirmed that the reflection coefficient phase is proportional to SWE.

Garrison, J. L.↗
NASA NTRS2020

Impact of Gauge-Based Precipitation Corrections on the Skill of SMAP Level-4 Soil Moisture Estimates

The NASA Soil Moisture Active Passive (SMAP) mission provides observations of L-band (1.4 GHz) passive microwave brightness temperature (Tb) observations at a resolution of ~40 km globally every 2-3 days. These observations are routinely assimilated into the NASA Catchment land surface model to generate the Level-4 Soil Moisture (L4_SM) product, which provides global estimates of surface and root-zone soil moisture, soil temperature, and surface fluxes (among others) at 9-km, 3-hourly resolution with ~2.5-day latency. The Catchment land surface model in the L4_SM algorithm is driven with 0.25°, hourly surface meteorological forcing data from the NASA Goddard Earth Observing System (GEOS) "forward-processing" product. Outside of Africa and the high latitudes, the GEOS precipitation forcing is corrected using the Climate Prediction Center Unified (CPCU) gauge-based, 0.5°, daily precipitation product.Soil moisture estimates from the L4_SM product were previously shown to improve over land model-only estimates that do not benefit from the assimilation of Tb observations, thereby demonstrating the value of assimilating SMAP observations for soil moisture estimation. In this presentation, we further isolate the contribution of the gauge-based precipitation corrections to the skill of the L4_SM soil moisture estimates. Specifically, we compare the skill of the L4_SM soil moisture to that of separate model-only and assimilation estimates obtained without the benefit of the gauge-based precipitation corrections.Preliminary results suggest that the soil moisture skill added by the CPCU-based precipitation corrections primarily depends on the quality of the CPCU precipitation product and is greatest in regions where the CPCU gauge network is dense and reliable. Conversely, in regions where the CPCU product is known to be of poor quality, for example in central Australia, the assimilation of SMAP Tb observations provides the most benefit. The presentation will provide an in-depth evaluation of the soil moisture skill of the model-only and assimilation estimates vs. independent in situ and satellite measurements.

Reichle, Rolf↗
NASA NTRS2020

Exploring Spatiotemporal Relations Between Soil Moisture, Precipitation, and Streamflow for a Large Set of Watersheds Using Google Earth Engine

An understanding of streamflow variability and its response to changes in climate conditions is essential for water resource planning and management practices that will help to mitigate the impacts of extreme events such as floods and droughts on agriculture and other human activities. This study investigated the relationship between precipitation, soil moisture, and streamflow over a wide range of watersheds across the United States using Google Earth Engine (GEE). The correlation analyses disclosed a strong association between precipitation, soil moisture, and streamflow, however, soil moisture was found to have a higher correlation with the streamflow relative to precipitation. Results indicated different strength of the association depends on the watershed classes and lag times assessments. The perennial watersheds showed higher coherence compared to intermittent watersheds. Previous month precipitation and soil moisture have a stronger influence on the current month streamflow, particularly in the snow-dominated watersheds. Monthly streamflow forecasting models were developed using an autoregressive integrated moving average (ARIMA) and support vector machine (SVM). The results showed that the SVM model generally performed better than the ARIMA model. Overall streamflow forecasting model performance varied considerably among watershed classes, and perennial watersheds tend to exhibit better predictably compared to intermittent watersheds due to lower streamflow variability. The SVM models with precipitation and streamflow inputs performed better than those with streamflow input only. Results indicated that the inclusion of antecedent root-zone soil moisture improved the streamflow forecasting in most of the watersheds, and the largest improvements occurred in the intermittent watersheds. In conclusion, this work demonstrated that knowing the relationship between precipitation, soil moisture, and streamflow in different watershed classes will enhance the understanding of the hydrologic process and can be effectively utilized in improving streamflow forecasting for better satellite-based water resource management strategies.

Nazmus Sazib↗
NASA NTRS2020

Assimilation of SMAP and ASCAT Soil Moisture Retrievals into the JULES Land Surface Model Using the Local Ensemble Transform Kalman Filter

A land data assimilation system is developed to merge satellite soil moisture retrievals into the Joint U.K. Land Environment Simulator (JULES) land surface model (LSM) using the Local Ensemble Transform Kalman Filter (LETKF). The system assimilates microwave soil moisture retrievals from the Soil Moisture Active Passive (SMAP) radiometer and the Advanced Scatterometer (ASCAT) after bias correction based on cumulative distribution function fitting. The soil moisture assimilation estimates are evaluated with ground-based soil moisture measurements over the continental U.S. for five consecutive warm seasons (May–September of 2015–2019). The result shows that both SMAP and ASCAT retrievals improve the accuracy of soil moisture estimates. Especially, the SMAP single-sensor assimilation experiment shows the best performance with the increase of temporal anomaly correlation by ΔR ~ 0.05 for surface soil moisture and ΔR ~ 0.03 for root-zone soil moisture compared with the LSM simulation without satellite data assimilation. SMAP assimilation is more skillful than ASCAT assimilation primarily because of the greater skill of the assimilated SMAP retrievals compared to the ASCAT retrievals. The skill improvement also depends significantly on the region; the higher skill improvement in the western U.S. compared to the eastern U.S. is explained by the Kalman gain in the two experiments. Additionally, the regional skill differences in the single-sensor assimilation experiments are attributed to the number of assimilated observations. Finally, the soil moisture assimilation estimates provide more realistic land surface information than model-only simulations for the 2015 and the 2016 western U.S. droughts, suggesting the advantage of using satellite soil moisture retrievals in the current drought monitoring system.

SMAP↗
NASA NTRS2017

HydroCube Mission Concept: P-Band Signals of Opportunity for Remote Sensing of Snow and Root Zone Soil Moisture

We have developed the HydroCube mission concept with a constellation of small satellites to remotely sense Snow Water Equivalent (SWE) and Root Zone Soil Moisture (RZSM). The HydroCube satellites would operate at sun-synchronous 3-day repeat polar orbits with a spatial resolution of about 1-3 Km. The mission goals would be to improve the estimation of terrestrial water storage and weather forecasts. Root-zone soil moisture and snow water storage in land are critical parameters of the water cycle. The HydroCube Signals of Opportunity (SoOp) concept utilizes passive receivers to detect the reflection of strong existing P-band radio signals from geostationary Mobile Use Objective System (MUOS) communication satellites. The SWE remote sensing measurement principle using the P-band SoOp is based on the propagation delay (or phase change) of radio signals through the snowpack. The time delay of the reflected signal due to the snowpack with respect to snow-free conditions is directly proportional to the snowpack SWE. To address the ionospheric delay at P-band frequencies, the signals from both MUOS bands (360-380 MHz and 250-270 MHz) would be used. We have conducted an analysis to trade off the spatial resolution for a space-based sensor and measurement accuracy. Through modeling analysis, we find that the dual-band MUOS signals would allow estimation of soil moisture and surface roughness together. From the two MUOS frequencies at 260 MHz and 370 MHz, we can retrieve the soil moisture from the reflectivity ratio scaled by wavenumbers using the two P-band frequencies for MUOS. A modeling analysis using layered stratified model has been completed to determine the sensitivity requirements of HydroCube measurements. For mission concept demonstration, a field campaign has been conducted at the Fraser Experimental Forest in Colorado since February 2016. The data acquired has provided support to the HydroCube concept.

Derksen, Chris↗
NASA NTRS2018

UAS-Based P-Band Signals of Opportunity for Remote Sensing of Snow and Root Zone Soil Moisture

We have developed the P-band Signals of Opportunity (SoOp) sensor based on the Unmanned Aircraft System (UAS) to remotely sense Snow Water Equivalent (SWE) and Root Zone Soil Moisture (RZSM). The P-band UAS SoOp sensor for Hydrology (UASHydro) would operate on the S2 aircraft developed by Black Swift Technologies for sensing of SWE and RZSM with a spatial resolution of about 10m. Root-zone soil moisture and snow water storage in land are critical parameters of the water cycle. The long-term goal of our development would be to use small UAS to perform regional high resolution observation of two key hydrological measurements to improve the estimation of terrestrial water storage for water management, crop production and forecasts of natural hazard. The UASHydro concept utilizes passive receivers to detect the reflection of strong existing P-band radio signals at the 360-380 MHz band from geostationary Mobile Use Objective System (MUOS) communication satellites launched by the US Navy. The SWE remote sensing measurement principle using the P-band SoOp is based on the propagation delay (or phase change) of radio signals through the snowpack. The time delay of the reflected signal due to the snowpack with respect to snow-free conditions is directly proportional to the snowpack SWE, while the soil moisture can be retrieved from the reflectivity at the P-band frequencies for MUOS. We have been conducting ground-based campaigns to test the instrumentation and data processing methods at the Fraser Experimental Forest in Colorado since February 2016. The field campaign data has provided support to the measurement concept. To install the SoOp technologies on the UAS, a lightweight antenna has been built and interfaces with the S2 built by Black Swift Technologies have been completed. A set of flights have been planned starting April 2018 through the end of 2018 in Colorado.

Elston, Jack↗
NASA NTRS2021

Assimilation of SMAP Brightness Temperature Observations in the GEOS Land–Atmosphere Data Assimilation System

Errors in soil moisture adversely impact the modeling of land–atmosphere water and energy fluxes and, consequently, near-surface atmospheric conditions in atmospheric data assimilation systems (ADAS). To mitigate such errors, a land surface analysis is included in many such systems, although not yet in the currently operational NASA Goddard Earth Observing System (GEOS) ADAS. This article investigates the assimilation of L-band brightness temperature (Tb) observations from the Soil Moisture Active Passive (SMAP) mission in the GEOS weakly coupled land–atmosphere data assimilation system (LADAS) during boreal summer 2017. The SMAP Tb analysis improves the correlation of LADAS surface and root-zone soil moisture versus in situ measurements by ∼0.1–0.26 over that of ADAS estimates; the unbiased root-mean-square error of LADAS soil moisture is reduced by 0.002–0.008 m^3 /m^3 from that of ADAS. Furthermore, the global land average RMSE versus in situ measurements of screen-level air specific humidity (q2m) and daily maximum temperature (T2m_max ) is reduced by 0.05 g/kg and 0.04 K, respectively, for LADAS compared to ADAS estimates. Regionally, the RMSE of LADAS q2m and T2m_max is improved by up to 0.4 g/kg and 0.3 K, respectively. Improvement in LADAS specific humidity extends into the lower troposphere (below ∼700 mb), with relative improvements in bias of 15–25%, although LADAS air temperature bias slightly increases relative to that of ADAS. Finally, the root mean square of the LADAS Tb observation-minus-forecast residuals is smaller by up to ∼0.1 K than in a land-only assimilation system, corroborating the positive impact of the Tb analysis on the modeled land–atmosphere coupling.

microwave remote sensing↗
NASA NTRS2021

Validation of Soil Moisture Data Products from the NASA SMAP Mission

The National Aeronautics and Space Administration (NASA)Soil Moisture Active Passive(SMAP) mission has been validatingits soil moisture (SM) products since the start of data production onMarch 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 <0.04 m3/m3). Thevalidation approach also includes other (“sparse network”) in situSM 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 thesereference data,and the analysis approaches themselves have been scrutinizedin an effort to best understand the products’ performance. Validation of themost recent SMAP Level 2 and 3 SMretrievalproducts (R17000) shows that the L-band (1.4 GHz) radiometer-based SM record continues to meet mission requirements. The products aregenerallyconsistentwith SM retrievals from the European Space Agency (ESA)Soil Moisture Ocean Salinity mission, althoughthere aredifferencesin some regions. The high-resolution (3-km) SM retrieval product,generated by combining Copernicus Sentinel-1 data with SMAP observations,performswithin expectations. Currently, however,there is limited availability of3-kmCVSdatato support extensive validation at this spatial scale. The most recent (version 5)SMAP Level4 SMdata assimilation productprovidingsurface and root-zone SM with complete spatio-temporal coverageat 9-km resolution also meets performance requirements. The SMAP SM validation program will continue throughout the mission life; futureplans include expanding ittoforestedand high-latituderegions

SMAP↗
NASA NTRS2021

SNOOPI: Demonstrating P-Band Reflectometry from Orbit

SigNals Of Opportunity: P-band Investigation (SNOOPI)will be the first on-orbit demonstration of remote sensing using Signals of Opportunity (SoOp) in P-band (240-380 MHz). P-band is needed to penetrate through dense vegetation and into the root zone. The longer wavelength of P-band also increases the unwrapping interval for phase observations. These observations hold the potential for spaceborne remote sensing of root-zone soil moisture (RZSM) and snow water equivalent(SWE), two variables identified as priorities in the 2017-2027 Decadal Survey for Earth Science and Applications from Space. SNOOPI will provide in-space validation of both the P-band SoOp technique and a science instrument prototype. SNOOPI technology validation goals will be met by targeting observations within 9 km of the SMAP calibration/validation sites in the continental United States. A secondary priority is collection of continuous phase data over snow-covered regions. These goals are evaluated under constraints of a limited data budget and mission lifetime, with a launch readiness in early2022. Updates on the development of measurement models and mission planning to support SNOOPI are provided. Aground-based station will be deployed to monitor the non-cooperative sources, in order to reduce risk due to uncertainty in knowledge of the broadcast power, spectrum shape, and orbital position.

J L Garrison↗
NASA NTRS2023

Non-Gaussian Ensemble Filtering and Adaptive Inflation for Soil Moisture Data Assimilation

The rank histogram filter (RHF) and the ensemble Kalman filter (EnKF) are assessed for soil moisture estimation using perfect model (identical twin) synthetic data assimilation experiments. The primary motivation is to gauge the impact on analysis quality attributable to the consideration of non-Gaussian forecast error distributions. Using the NASA Catchment land surface model, the two filters are compared at 18 globally-distributed single-catchment locations for a 10-year experiment period. It is shown that both filters yield adequate estimates of soil moisture, with the RHF having a small but significant performance advantage. Most notably, the RHF systematically increases the normalized information contribution (NIC) score of the mean absolute bias by 0.05 over that of the EnKF for surface, root-zone and profile soil moisture. The RHF also increases the NIC score for the anomaly correlation of surface soil moisture by 0.02 over that of the EnKF (at a 5% significance level). Results also demonstrate that the performance of both filters is somewhat improved when the ensemble priors are adaptively inflated to offset the negative effects of systematic errors.

Soil moisture↗
NASA NTRSDate not supplied

An Operational Product for Peatland Applications: The Version 7 of the SMAP Level-4 Soil Moisture Data Assimilation Product

The NASA Soil Moisture Active Passive (SMAP) mission Level-4 Soil Moisture (L4_SM) product provides global, 9-km resolution, 3-hourly surface (0-5 cm) and root-zone (0-100 cm) soil moisture estimates from April 2015 to present with a mean latency of 2.5 days from the time of observation. The L4_SM estimates are derived from the assimilation of SMAP L-band (1.4 GHz) brightness temperature (Tb) observations into the NASA Catchment Land Surface Model (CLSM). The recently released Version 7 of the L4_SM product comprises a key advancement that is of interest for peatland applications. CLSM now includes the recently developed PEATCLSM hydrology module for peatlands and uses an updated global peatland distribution. In this presentation, we first give an overview of the operational product for the peatland community. We further show how the incorporation of PEATCLSM considerably improves the dynamics of water table depth, surface soil moisture and evapotranspiration in L4_SM Version 7 over Version 6 when evaluated against in situ measurements in peatlands. These improvements are also manifested in smaller Tb observation-minus-forecast residuals. Eventually, we provide three research examples in which the data assimilation product is used to gain insights into: peatland hydrological processes, the peat moisture dependency of the carbon cycle, and wildfire occurrence in peatlands.

Michel Bechtold↗
NASA NTRSDate not supplied

Improved Peatland Hydrology and L-Band Microwave Radiative Transfer Modeling in Version 7 of the SMAP Level-4 Soil Moisture Data Assimilation Product

The NASA Soil Moisture Active Passive (SMAP) mission Level-4 Soil Moisture (L4_SM) product provides global, 9-km resolution, 3-hourly surface (0-5 cm) and root-zone (0-100 cm) soil moisture from April 2015 to present with a mean latency of 2.5 days from the time of observation. The L4_SM estimates are derived from the assimilation of SMAP L-band (1.4 GHz) brightness temperature (Tb) observations into the NASA Catchment land surface model. The recently released Version 7 of L4_SM comprises two key advances. First, the Catchment model now includes the recently developed PEATCLSM hydrology module for peatlands and uses an updated global map of peatlands. Second, revised parameters are used in the L-band radiative transfer model that converts the simulated soil moisture and temperature estimates into Tb predictions for use in the radiance-based L4_SM analysis. Specifically, the L-band scattering albedo, soil roughness, and vegetation opacity climatology are obtained from the SMAP Level-2 dual-channel soil moisture retrieval product. In this presentation, we examine the improvements gained with the model and parameter changes. In peatlands, the dynamics of water table depth, surface soil moisture and evapotranspiration are considerably improved in L4_SM Version 7 over Version 6 when evaluated against in situ measurements. These improvements are also manifested in smaller Tb observation-minus-forecast residuals.

SMAP↗
NASA NTRS2022

Instrument Science Experiments on the SNOOPI P-Band Reflectometry Mission

SigNals Of Opportunity: P-band Investigation (SNOOPI) will be the first in-space validation of P-band (240–380 MHz) SoOp techniques and a prototype science instrument. These techniques have the potential to enable remote sensing of root-zone soil moisture (RZSM) and snow water equivalent (SWE). SNOOPI technology validation goals will be met by targeting observations within 9 km of the SMAP calibration/validation sites in the continental United States. A second priority is collection of continuous phase data over snow-covered regions. These goals are evaluated under constraints of a limited data budget and mission lifetime, with a launch readiness in August 2022. This presentation will review the instrument science plans aimed at achieving the validation objectives defined for the mission. Mission planning and data processing approaches are described.

Soil Moisture↗
NASA NTRSDate not supplied

Systematic Errors in Simulated L-Band Brightness Temperature in the SMAP Level-4 Soil Moisture Analysis

The NASA Soil Moisture Active Passive (SMAP) mission has been providing L-band (1.4 GHz) brightness temperature (Tb) observations since April 2015. By assimilating the Tb observations into the NASA Catchment land surface model using a spatially distributed ensemble Kalman filter (EnKF), the SMAP Level-4 Soil Moisture (L4_SM) product provides global, 3-hourly, 9-km resolution estimates of surface (0-5 cm) and root-zone (0-100 cm) soil moisture with ~2.5-day latency for use in research and applications. The EnKF-based L4_SM analysis assumes unbiased forecast errors. Consequently, the seasonally varying bias between the model forecast Tb and the observed values is removed prior to the assimilation of the SMAP Tb observations. The L4_SM system is thus designed to only correct errors in synoptic-scale and interannual variations from the long-term mean seasonal cycle while maintaining the model’s (possibly wrong) climatology. In this paper, we examine the Tb observation-minus-forecast (O-F) residuals from the L4_SM Version 7 product (computed after rescaling the Tb observations to the mean seasonal cycle of the simulated Tb). The long-term average of the Tb O-F residuals has a global mean of only 0.13 K and locally small values, ranging from -1 to 3 K. The model forecast Tb, however, still exhibits undesirable systematic errors relative to the (rescaled) Tb observations. At some locations, the time-average Tb O-F values strongly depend on surface soil moisture (SM). At the Yanco SMAP core validation site, for example, the Tb O-F residuals typically range from 5 to 15 K under dry soil moisture conditions (SM < 0.15 m3 m-3) yet are predominantly negative under wet soil moisture conditions (SM > 0.25 m3 m-3), with values ranging from 0 to -40 K. This results in soil moisture analysis increments that persistently make the soil drier under dry SM conditions and persistently make the soil wetter under wet SM conditions, suggesting an error in the dynamic range of the simulated Tb, soil moisture or soil temperature. In this paper, we describe the higher-order systematic Tb forecast errors in more detail, examine their impact on the L4_SM product quality, and explore potential avenues to improve the L4_SM algorithm.

Rolf Reichle↗
NASA NTRSDate not supplied

Examination of L-Band Brightness Temperature Forecasts in the SMAP Level-4 Soil Moisture Analysis

The NASA Soil Moisture Active Passive (SMAP) mission [4] has been providing L-band (1.4 GHz) passive microwave brightness temperature (Tb) observations since April 2015. By assimilating the Tb observations into the NASA Catchment land surface model [5] using a spatially distributed ensemble Kalman filter, the NASA Global Modeling and Assimilation Office generates the SMAP Level-4 Soil Moisture (L4_SM) product, which provides global, 3-hourly, 9-km resolution estimates of surface (0-5 cm) and root-zone (0-100 cm) soil moisture with ~2.5-day latency for use in research and applications [6]. The L4_SM product also includes estimates of soil temperature, land surface fluxes, and assimilation diagnostics such as the model forecast and observed Tb values [7]. The output from the L4_SM system is routinely monitored by the L4_SM team. Such monitoring provides valuable information; instances of unusually large Tb observation-minus-forecast (O-F) residuals can indicate events during which soil moisture conditions are poorly described in the land modeling system [7,8]. For example, repeated occurrences of very large Tb O-F values in central Australia were traced back to deficiencies in the gauge-based precipitation product used in the land modeling system through L4_SM Version 5; this discovery prompted the use, beginning in Version 6, of satellite- and gauge-based precipitation observations outside of North America [9]. Recently, a statistical analysis of the Tb O-F residuals revealed systematic errors in the “tau-omega” L-band radiative transfer model that converts the land-model simulated soil moisture and temperature into the forecast Tb prior to the L4_SM analysis. The L4_SM analysis is built on the ensemble Kalman filter and assumes unbiased forecast errors. The presence of systematic Tb forecast errors could thus adversely impact the quality of the analyzed soil moisture. In this paper, we examine the Tb O-F residuals of the latest Version 7 L4_SM data (Science Version ID Vv7030 and Vv7032) [10,11,12]. In the L4_SM Version 7 algorithm, key parameters of the L-band radiative transfer model, including soil roughness, scattering albedo, and a (seasonally varying) climatology of vegetation opacity, are obtained from the SMAP Level-2 Radiometer retrieval product (Version 5) [1]. Additionally, we also use ground measurements of surface soil moisture and soil temperature from the SMAP core validation sites as in situ reference of soil conditions [2,3]. As part of the L4_SM system calibration, the seasonally-varying bias between the model forecast Tb and the observed values is removed prior to the assimilation of the SMAP Tb observations [7]. That is, the L4_SM system is designed to only correct errors in synoptic-scale and interannual variations from the long-term mean seasonal cycle while maintaining the model’s (potentially erroneous) climatology. The Tb O-F residuals examined here are thus computed using SMAP Tb observations after they are rescaled to the mean seasonal cycle of Tb from the modeling system without SMAP data assimilation. Consequently, the long-term average of the Tb O-F residuals has a global mean of only 0.13 K and locally small values, ranging from -1 to 3 K. Despite the small time-average values of the Tb O-F residuals, the model forecast Tb was nevertheless found to exhibit undesirable systematic errors. At some locations, the time-average Tb O-F values strongly depend on surface soil moisture (SM). At the Yanco SMAP core validation site, for example, the Tb O-F residuals typically range from 5 to 15 K under dry soil moisture conditions (SM < 0.15 m3 m-3) yet are predominantly negative under wet soil moisture conditions (SM > 0.25 m3 m-3), with values ranging from 0 to -40 K. This results in soil moisture analysis increments that persistently make the soil drier under dry SM conditions and persistently make the soil wetter under wet SM conditions, suggesting an error in the dynamic range of the simulated Tb, soil moisture or soil temperature. In this paper, we describe the higher-order systematic Tb forecast errors in more detail, examine their impact on the L4_SM product quality, and explore potential avenues to further improve the L4_SM algorithm.

Rolf Reichle↗
NASA NTRS2023

A Spaceborne Demonstration of P-Band Signals-of-Opportunity (SoOp) Reflectometry

Land-reflected signals from a geosynchronous communication satellite broadcasting in P-band (367.5 MHz) were captured in low Earth orbit (LEO) using a simple dipole antenna. A delay-Doppler map (DDM) was generated through autocorrelation. Estimates of the specular point delay were obtained from the lag of the second peak in the DDM with a bias of 239.4 m and a standard deviation of 44 m (12 m over a frozen lake) with respect to a predicted orbit model. Relative magnitudes of the first and second DDM peaks fell within the range of values predicted using dielectric models for the frozen ground and lake. Finally, retrievals of surface reflection coefficient were generated using a range of realistic values for the transmitter link budget G/T , and these also fell within the range of possible values for the antenna gain pattern. Given the lack of calibration and the large uncertainties in the receiver orbit and attitude, this agreement is sufficient to conclude a successful demonstration of the fundamental principle of single-antenna reflectometry in P-band. P-band reflectometry may offer a new approach to remote sensing of sub-canopy and root-zone soil moisture.

Delays↗