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NASA NTRS2016

SMAP Data Assimilation at NASA SPoRT

The NASA Short-Term Prediction Research and Transition (SPoRT) Center maintains a near-real- time run of the Noah Land Surface Model within the Land Information System (LIS) at 3-km resolution. Soil moisture products from this model are used by several NOAA/National Weather Service Weather Forecast Offices for flood and drought situational awareness. We have implemented assimilation of soil moisture retrievals from the Soil Moisture Ocean Salinity (SMOS) and Soil Moisture Active/ Passive (SMAP) satellites, and are now evaluating the SMAP assimilation. The SMAP-enhanced LIS product is planned for public release by October 2016.

soil mositure↗
NASA NTRS2016

Assimilation of Gridded Terrestrial Water Storage Observations from GRACE into a Land Surface Model

Observations of terrestrial water storage (TWS) from the Gravity Recovery and Climate Experiment (GRACE) satellite mission have a coarse resolution in time (monthly) and space (roughly 150,000 km(sup 2) at midlatitudes) and vertically integrate all water storage components over land, including soil moisture and groundwater. Data assimilation can be used to horizontally downscale and vertically partition GRACE-TWS observations. This work proposes a variant of existing ensemble-based GRACE-TWS data assimilation schemes. The new algorithm differs in how the analysis increments are computed and applied. Existing schemes correlate the uncertainty in the modeled monthly TWS estimates with errors in the soil moisture profile state variables at a single instant in the month and then apply the increment either at the end of the month or gradually throughout the month. The proposed new scheme first computes increments for each day of the month and then applies the average of those increments at the beginning of the month. The new scheme therefore better reflects submonthly variations in TWS errors. The new and existing schemes are investigated here using gridded GRACE-TWS observations. The assimilation results are validated at the monthly time scale, using in situ measurements of groundwater depth and soil moisture across the U.S. The new assimilation scheme yields improved (although not in a statistically significant sense) skill metrics for groundwater compared to the open-loop (no assimilation) simulations and compared to the existing assimilation schemes. A smaller impact is seen for surface and root-zone soil moisture, which have a shorter memory and receive smaller increments from TWS assimilation than groundwater. These results motivate future efforts to combine GRACE-TWS observations with observations that are more sensitive to surface soil moisture, such as L-band brightness temperature observations from Soil Moisture Ocean Salinity (SMOS) or Soil Moisture Active Passive (SMAP). Finally, we demonstrate that the scaling parameters that are applied to the GRACE observations prior to assimilation should be consistent with the land surface model that is used within the assimilation system.

GRACE↗
NASA NTRS2016

RFI Detection and Mitigation using Independent Component Analysis as a Pre-Processor

Radio-frequency interference (RFI) has negatively impacted scientific measurements of passive remote sensing satellites. This has been observed in the L-band radiometers Soil Moisture and Ocean Salinity (SMOS), Aquarius and more recently, Soil Moisture Active Passive (SMAP). RFI has also been observed at higher frequencies such as K band. Improvements in technology have allowed wider bandwidth digital back ends for passive microwave radiometry. A complex signal kurtosis radio frequency interference detector was developed to help identify corrupted measurements. This work explores the use of Independent Component Analysis (ICA) as a blind source separation (BSS) technique to pre-process radiometric signals for use with the previously developed real and complex signal kurtosis detectors.

independent component analysis↗
NASA NTRS2016

The Flux-Anomaly-Forced Model Intercomparison Project (FAFMIP) Contribution to CMIP6: Investigation of Sea-Level and Ocean Climate Change in Response to CO2 Forcing

The Flux-Anomaly-Forced Model Intercomparison Project (FAFMIP) aims to investigate the spread in simulations of sea-level and ocean climate change in response to CO2 forcing by atmosphere-ocean general circulation models (AOGCMs). It is particularly motivated by the uncertainties in projections of ocean heat uptake, global-mean sealevel rise due to thermal expansion and the geographical patterns of sea-level change due to ocean density and circulation change. FAFMIP has three tier-1 experiments, in which prescribed surface flux perturbations of momentum, heat and freshwater respectively are applied to the ocean in separate AOGCM simulations. All other conditions are as in the pre-industrial control. The prescribed fields are typical of pattern and magnitude of changes in these fluxes projected by AOGCMs for doubled CO2 concentration. Five groups have tested the experimental design with existing AOGCMs. Their results show diversity in the pattern and magnitude of changes, with some common qualitative features. Heat and water flux perturbation cause the dipole in sea-level change in the North Atlantic, while momentum and heat flux perturbation cause the gradient across the Antarctic Circumpolar Current. The Atlantic meridional overturning circulation (AMOC) declines in response to the heat flux perturbation, and there is a strong positive feedback on this effect due to the consequent cooling of sea-surface temperature in the North Atlantic, which enhances the local heat input to the ocean. The momentum and water flux perturbations do not substantially affect the AMOC. Heat is taken up largely as a passive tracer in the Southern Ocean, which is the region of greatest heat input, while the weakening of the AMOC causes redistribution of heat towards lower latitudes. Future analysis of these and other phenomena with the wider range of CMIP6 FAFMIP AOGCMs will benefit from new diagnostics of temperature and salinity tendencies, which will enable investigation of the model spread in behaviour in terms of physical processes as formulated in the models.

thermosteric sea-level change↗
NASA NTRS2016

Assimilation of SMOS Brightness Temperatures or Soil Moisture Retrievals into a Land Surface Model

Three different data products from the Soil Moisture Ocean Salinity (SMOS) mission are assimilated separately into the Goddard Earth Observing System Model, version 5 (GEOS-5) to improve estimates of surface and root-zone soil moisture. The first product consists of multi-angle, dual-polarization brightness temperature (Tb) observations at the bottom of the atmosphere extracted from Level 1 data. The second product is a derived SMOS Tb product that mimics the data at a 40 degree incidence angle from the Soil Moisture Active Passive (SMAP) mission. The third product is the operational SMOS Level 2 surface soil moisture (SM) retrieval product. The assimilation system uses a spatially distributed ensemble Kalman filter (EnKF) with seasonally varying climatological bias mitigation for Tb assimilation, whereas a time-invariant cumulative density function matching is used for SM retrieval assimilation. All assimilation experiments improve the soil moisture estimates compared to model-only simulations in terms of unbiased root-mean-square differences and anomaly correlations during the period from 1 July 2010 to 1 May 2015 and for 187 sites across the US. Especially in areas where the satellite data are most sensitive to surface soil moisture, large skill improvements (e.g., an increase in the anomaly correlation by 0.1) are found in the surface soil moisture. The domain-average surface and root-zone skill metrics are similar among the various assimilation experiments, but large differences in skill are found locally. The observation-minus-forecast residuals and analysis increments reveal large differences in how the observations add value in the Tb and SM retrieval assimilation systems. The distinct patterns of these diagnostics in the two systems reflect observation and model errors patterns that are not well captured in the assigned EnKF error parameters. Consequently, a localized optimization of the EnKF error parameters is needed to further improve Tb or SM retrieval assimilation.

SMOS↗
NASA NTRS2017

Assimilation of Global Radar Backscatter and Radiometer Brightness Temperature Observations to Improve Soil Moisture and Land Evaporation Estimates

Active radar backscatter (s◦) observations from the Advanced Scatterometer (ASCAT) and passive radiometer brightness temperature (TB) observations from the Soil Moisture Ocean Salinity (SMOS) mission are assimilated either individually or jointly into the Global Land Evaporation Amsterdam Model (GLEAM) to improve its simulations of soil moisture and land evaporation. To enable s◦ and TB assimilation, GLEAM is coupled to the Water Cloud Model and the L-band Microwave Emission from the Biosphere (L-MEB) model. The innovations, i.e. differences between observations and simulations, are mapped onto the model soil moisture states through an Ensemble Kalman Filter. The validation of surface (0-10 cm) soil moisture simulations over the period 2010-2014 against in situ measurements from the International Soil Moisture Network (ISMN) shows that assimilating s◦ or TB alone improves the average correlation of seasonal anomalies (Ran) from 0.514 to 0.547 and 0.548, respectively. The joint assimilation further improves Ran to 0.559. Associated enhancements in daily evaporative flux simulations by GLEAM are validated based on measurements from 22 FLUXNET stations. Again, the singular assimilation improves Ran from 0.502 to 0.536 and 0.533, respectively for s◦ and TB, whereas the best performance is observed for the joint assimilation (Ran = 0.546). These results demonstrate the complementary value of assimilating radar backscatter observations together with brightness temperatures for improving estimates of hydrological variables, as their joint assimilation outperforms the assimilation of each observation type separately.

GLEAM↗
NASA NTRS2016

Considering Combined or Separated Roughness and Vegetation Effects in Soil Moisture Retrievals

For more than six years, the Soil Moisture and Ocean Salinity (SMOS) mission has provided multi angular and full-polarization brightness temperature (TB) measurements at L-band. Geophysical products such as soil moisture (SM) and vegetation optical depth at nadir (tau(sub nad)) are retrieved by an operational algorithm using TB observations at different angles of incidence and polarizations. However, the quality of the retrievals depends on several surface effects, such as vegetation, soil roughness and texture, etc. In the microwave forward emission model used in the retrievals (L-band Microwave Emission Model, L-MEB),soil roughness is modeled with a semi-empirical equation using four main parameters (Q(sub r), H(sub r), N(sub rp), with p = H or V polarizations). At present, these parameters are calibrated with data provided by airborne studies and in situ measurements made at a local scale that is not necessarily representative of the large SMOS footprints (43 km on average) at global scale. In this study, we evaluate the impact of the calibrated values of N(sub rp) and H(sub r) on the SM and tau(sub nad) retrievals based on SMOS TB measurements (SMOS Level 3 product) over the Soil Climate Analysis Network (SCAN) network located in North America over five years (2011-2015). In this study, Qr was set equal to zero and we assumed that N(sub rH)= N(sub rV). The retrievals were performed by varying N(sub rp) from −1 to 2 by steps of 1 and H(sub r) from 0 to 0.6 by steps of 0.1. At satellite scale, the results show that combining vegetation and roughness effects in a single parameter provides the best results in terms of soil moisture retrievals, as evaluated against the in situ SM data. Even though our retrieval approach was very simplified, as we did not account for pixel heterogeneity, the accuracy we obtained in the SM retrievals was almost systematically better than those of the Level 3 product. Improved results were also obtained in terms of optical depth retrievals. These new results may have key consequences in terms of calibration of roughness effects within the algorithms of the SMOS (ESA) and the SMAP (NASA) space missions.

full-polarization↗
NASA NTRS2016

Evaluating ESA CCI Soil Moisture in East Africa

To assess growing season conditions where ground based observations are limited or unavailable, food security and agricultural drought monitoring analysts rely on publicly available remotely sensed rainfall and vegetation greenness. There are also remotely sensed soil moisture observations from missions like the European Space Agency (ESA) Soil Moisture and Ocean Salinity (SMOS) and NASAs Soil Moisture Active Passive (SMAP), however these time series are still too short to conduct studies that demonstrate the utility of these data for operational applications, or to provide historical context for extreme wet or dry events. To promote the use of remotely sensed soil moisture in agricultural drought and food security monitoring, we use East Africa as a case study to evaluate the quality of a 30+ year time series of merged active-passive microwave soil moisture from the ESA Climate Change Initiative (CCI-SM). Compared to the Normalized Difference Vegetation index (NDVI) and modeled soil moisture products, we found substantial spatial and temporal gaps in the early part of the CCI-SM record, with adequate data coverage beginning in 1992. From this point forward, growing season CCI-SM anomalies were well correlated (R greater than 0.5) with modeled, seasonal soil moisture, and in some regions, NDVI. We use correlation analysis and qualitative comparisons at seasonal time scales to show that remotely sensed soil moisture can add information to a convergence of evidence framework that traditionally relies on rainfall and NDVI in moderately vegetated regions.

Amy McNally↗
NASA NTRS2016

NASA Soil Moisture Active Passive Mission Status and Science Performance

The Soil Moisture Active Passive (SMAP) observatory was launched January 31, 2015, and its L-band radiometer and radar instruments became operational since mid-April 2015. The SMAP radiometer has been operating flawlessly, but the radar transmitter ceased operation on July 7. This paper provides a status summary of the calibration and validation of the SMAP instruments and the quality assessment of its soil moisture and freeze/thaw products. Since the loss of the radar in July, the SMAP project has been conducting two parallel activities to enhance the resolution of soil moisture products. One of them explores the Backus Gilbert optimum interpolation and de-convolution techniques based on the oversampling characteristics of the SMAP radiometer. The other investigates the disaggregation of the SMAP radiometer data using the European Space Agency's Sentinel-1 C-band synthetic radar data to obtain soil moisture products at about 1 to 3 kilometers resolution. In addition, SMAP's L-band data have found many new applications, including vegetation opacity, ocean surface salinity and hurricane ocean surface wind mapping. Highlights of these new applications will be provided.

hydroclimatology↗
NASA NTRS2016

RFI and Remote Sensing of the Earth from Space

Passive microwave remote sensing of the Earth from space provides information essential for understanding the Earth's environment and its evolution. Parameters such as soil moisture, sea surface temperature and salinity, and profiles of atmospheric temperature and humidity are measured at frequencies determined by the physics (e.g. sensitivity to changes in desired parameters) and by the availability of suitable spectrum free from interference. Interference from manmade sources (radio frequency interference) is an impediment that in many cases limits the potential for accurate measurements from space. A review is presented here of the frequencies employed in passive microwave remote sensing of the Earth from space and the associated experience with RFI.

RFI↗
NASA NTRS2016

L-band Radiometer Calibration Consistency Assessment for the SMOS, SMAP, and Aquarius Instruments

Three L-band radiometers have been observing the Earth in order to retrieve soil moisture and ocean salinity. They use different instrument configurations and calibration and retrieval algorithms. In any case, the brightness temperature retrieved at the Earth surface should be consistent between all instruments. One reason for inconsistency would be the use of different approaches for the instrument calibration or the use of different models to retrieve surface brightness temperature. We report on the different approaches used for the SMOS, SMAP and Aquarius instruments and their impact on the observations consistency.

Aquarius↗
NASA NTRS2016

L-Band RFI in Japan

In recent years, three instruments have been launched into orbit with the aim of producing global maps of sea surface salinity and soil moisture using the 1400-1427 MHz band: SMOS, Aquarius and SMAP. Although this frequency band is allocated to passive measurements only, RFI (Radio-Frequency Interference) is present in the data of all three missions. On a global scale, the three sensors have observed approximately the same distribution of RFI. Japan is an important exception that has implications for the design of RFI detection algorithms. RFI in Japan is caused by a large number of emitters belonging to the same system (TV receivers) and for this reason some traditional RFI detection strategies detect little to no RFI over Japan. The study of this case has led to an improvement of the approach to detect RFI in Aquarius data.

Aquarius↗
NASA NTRS2016

Faraday Rotation Measurement with the SMAP Radiometer

Faraday rotation is an issue that needs to be taken into account in remote sensing of parameters such as soil moisture and ocean salinity at L-band. This is especially important for SMAP because Faraday rotation varies with azimuth around the conical scan. SMAP retrieves Faraday rotation in situ using the ratio of the third and second Stokes parameters, a procedure that was demonstrated successfully by Aquarius. This manuscript reports the performance of this algorithm on SMAP. Over ocean the process works reasonably well and results compare favorably with expected values. But over land, the inhomogeneous nature of the scene results in much noisier, and in some cases unreliable estimates of Faraday rotation.

microwave remote sensing↗
NASA NTRS2016

Evaluation of Assimilated SMOS Soil Moisture Data for US Cropland Soil Moisture Monitoring

Remotely sensed soil moisture data can provide timely, objective and quantitative crop soil moisture information with broad geospatial coverage and sufficiently high resolution observations collected throughout the growing season. This paper evaluates the feasibility of using the assimilated ESA Soil Moisture Ocean Salinity (SMOS)Mission L-band passive microwave data for operational US cropland soil surface moisture monitoring. The assimilated SMOS soil moisture data are first categorized to match with the United States Department of Agriculture (USDA)National Agricultural Statistics Service (NASS) survey based weekly soil moisture observation data, which are ordinal. The categorized assimilated SMOS soil moisture data are compared with NASSs survey-based weekly soil moisture data for consistency and robustness using visual assessment and rank correlation. Preliminary results indicate that the assimilated SMOS soil moisture data highly co-vary with NASS field observations across a large geographic area. Therefore, SMOS data have great potential for US operational cropland soil moisture monitoring.

SMOS↗
NASA NTRS2017

X-Ray Diffraction on Mars: Scientific Discoveries Made by the CheMin Instrument

The Mars Science Laboratory Curiosity landed in Gale crater in August 2012 with the goal to identify and characterize habitable environments on Mars. Curiosity has been studying a series of sedimentary rocks primarily deposited in fluviolacustrine environments approximately 3.5 Ga. Minerals in the rocks and soils on Mars can help place further constraints on these ancient aqueous environments, including pH, salinity, and relative duration of liquid water. The Chemistry and Mineralogy (CheMin) X-ray diffraction and X-ray fluorescence instrument on Curiosity uses a Co X-ray source and charge-coupled device detector in transmission geometry to collect 2D Debye-Scherrer ring patterns of the less than 150 micron size fraction of drilled rock powders or scooped sediments. With an angular range of approximately 2.52deg 20 and a 20 resolution of approximately 0.3deg, mineral abundances can be quantified with a detection limit of approximately 1-2 wt. %. CheMin has returned quantitative mineral abundances from 16 mudstone, sandstone, and aeolian sand samples so far. The mineralogy of these samples is incredibly diverse, suggesting a variety of depositional and diagenetic environments and different source regions for the sediments. Results from CheMin have been essential for reconstructing the geologic history of Gale crater and addressing the question of habitability on ancient Mars.

Rampe, E. B.↗
NASA NTRS2017

Evaluating Soil Moisture Retrievals from ESA's SMOS and NASA's SMAP Brightness Temperature Datasets

Two satellites are currently monitoring surface soil moisture (SM) using L-band observations: SMOS (Soil Moisture and Ocean Salinity), a joint ESA (European Space Agency), CNES (Centre national d'tudes spatiales), and CDTI (the Spanish government agency with responsibility for space) satellite launched on November 2, 2009 and SMAP (Soil Moisture Active Passive), a National Aeronautics and Space Administration (NASA) satellite successfully launched in January 2015. In this study, we used a multilinear regression approach to retrieve SM from SMAP data to create a global dataset of SM, which is consistent with SM data retrieved from SMOS. This was achieved by calibrating coefficients of the regression model using the CATDS (Centre Aval de Traitement des Donnes) SMOS Level 3 SM and the horizontally and vertically polarized brightness temperatures (TB) at 40 deg incidence angle, over the 2013 - 2014 period. Next, this model was applied to SMAP L3 TB data from Apr 2015 to Jul 2016. The retrieved SM from SMAP (referred to here as SMAP_Reg) was compared to: (i) the operational SMAP L3 SM (SMAP_SCA), retrieved using the baseline Single Channel retrieval Algorithm (SCA); and (ii) the operational SMOSL3 SM, derived from the multiangular inversion of the L-MEB model (L-MEB algorithm) (SMOSL3). This inter-comparison was made against in situ soil moisture measurements from more than 400 sites spread over the globe, which are used here as a reference soil moisture dataset. The in situ observations were obtained from the International Soil Moisture Network (ISMN; https:ismn.geo.tuwien.ac.at) in North of America (PBO_H2O, SCAN, SNOTEL, iRON, and USCRN), in Australia (Oznet), Africa (DAHRA), and in Europe (REMEDHUS, SMOSMANIA, FMI, and RSMN). The agreement was analyzed in terms of four classical statistical criteria: Root Mean Squared Error (RMSE),Bias, Unbiased RMSE (UnbRMSE), and correlation coefficient (R). Results of the comparison of these various products with in situ observations show that the performance of both SMAP products i.e. SMAP_SCA and SMAP_Reg is 48 similar and marginally better to that of the SMOSL3 product particularly over the PBO_H2O, SCAN, and USCRN sites. However, SMOSL3 SM was closer to the in situ observations over the DAHRA and Oznet sites. We found that the correlation between all three datasets and in situ measurements is best (R 0.80) over the Oznet sites and worst (R 0.58) over the SNOTEL sites for SMAP_SCA and over the DAHRA and SMOSMANIA sites (R 0.51 and R 0.45 for SMAP_Reg and SMOSL3, respectively). The Bias values showed that all products are generally dry, except over RSMN, DAHRA, and Oznet (and FMI for SMAP_SCA). Finally, our analysis provided interesting insights that can be useful to improve the consistency between SMAP and SMOS datasets.

SMOS↗
NASA NTRS2016

Everglades Ecological Forecasting II: Utilizing NASA Earth Observations to Enhance the Capabilities of Everglades National Park to Monitor & Predict Mangrove Extent to Aid Current Restoration Efforts

Mangroves act as a transition zone between fresh and salt water habitats by filtering and indicating salinity levels along the coast of the Florida Everglades. However, dredging and canals built in the early 1900s depleted the Everglades of much of its freshwater resources. In an attempt to assist in maintaining the health of threatened habitats, efforts have been made within Everglades National Park to rebalance the ecosystem and adhere to sustainably managing mangrove forests. The Everglades Ecological Forecasting II team utilized Google Earth Engine API and satellite imagery from Landsat 5, 7, and 8 to continuously create land-change maps over a 25 year period, and to allow park officials to continue producing maps in the future. In order to make the process replicable for project partners at Everglades National Park, the team was able to conduct a supervised classification approach to display mangrove regions in 1995, 2000, 2005, 2010 and 2015. As freshwater was depleted, mangroves encroached further inland and freshwater marshes declined. The current extent map, along with transition maps helped create forecasting models that show mangrove encroachment further inland in the year 2030 as well. This project highlights the changes to the Everglade habitats in relation to a changing climate and hydrological changes throughout the park.

Kirk, Donnie↗
NASA NTRS2017

NASA Soil Moisture Active Passive Mission Status and Science Highlights

The Soil Moisture Active Passive (SMAP) observatory was launched January 31, 2015, and its L-band radiometer and radar instruments became operational during April 2015. This paper provides a summary of the quality assessment of its baseline soil moisture and freeze/thaw products as well as an overview of new products. The first new product explores the Backus Gilbert optimum interpolation based on the oversampling characteristics of the SMAP radiometer. The second one investigates the disaggregation of the SMAP radiometer data using the European Space Agency's Sentinel-1 C-band synthetic aperture radar (SAR) data to obtain soil moisture products at about 1 to 3 km resolution. In addition, SMAPs L-band data have been found useful for many scientific applications, including depictions of water cycles, vegetation opacity, ocean surface salinity and hurricane ocean surface wind mapping. Highlights of these new applications will be provided.The SMAP soil moisture, freeze/taw state and SSSprovide a synergistic view of water cycle. For example, Fig.7 illustrates the transition of freeze/thaw state, change of soilmoisture near the pole and SSS in the Arctic Ocean fromApril to October in 2015 and 2016. In April, most parts ofAlaska, Canada, and Siberia remained frozen. Melt onsetstarted in May. Alaska, Canada, and a big part of Siberia havebecome thawed at the end of May; some freshwater dischargecould be found near the mouth of Mackenzie in 2016, but notin 2015. The soil moisture appeared to be higher in the Oband Yenisei river basins in Siberia in 2015. As a result,freshwater discharge was more widespread in the Kara Seanear the mouths of both rivers in June 2015 than in 2016. TheNorth America and Siberia have become completely thawedin July. After June, the freshwater discharge from other riversinto the Arctic, indicated by blue, also became visible. Thefreeze-up started in September and the high latitude regionsin North America and Eurasia became frozen. Comparing thespread of freshwater in August 2015 and 2016 suggests thatthere was more discharge from Ob and Yenisei in 2015,which appeared to correspond to a higher soil moisturecontent in the Ob and Yenisei basins. In contrast, Mackenzieappeared to have more discharge in September 2016.

soil moisture↗