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All sourcesNASA NTRSDOE OSTI
NASA NTRS2004

Estimation of Soil Moisture with L-band Multi-polarization Radar

Through analyses of the model simulated data-base, we developed a technique to estimate surface soil moisture under HYDROS radar sensor (L-band multi-polarizations and 40deg incidence) configuration. This technique includes two steps. First, it decomposes the total backscattering signals into two components - the surface scattering components (the bare surface backscattering signals attenuated by the overlaying vegetation layer) and the sum of the direct volume scattering components and surface-volume interaction components at different polarizations. From the model simulated data-base, our decomposition technique works quit well in estimation of the surface scattering components with RMSEs of 0.12,0.25, and 0.55 dB for VV, HH, and VH polarizations, respectively. Then, we use the decomposed surface backscattering signals to estimate the soil moisture and the combined surface roughness and vegetation attenuation correction factors with all three polarizations.

Shi, J.↗
NASA NTRS2006

Observed Scaling Behavior of Snow and Soil Moisture Signatures from the CLPX and NAFE Campaigns

Seasonal snow and soil moisture (SM) are important elements of the Earth's water cycle, acting as storage elements and by modulating water and energy exchanges between the land surface and the atmosphere and space. Accurate estimates of snow water equivalent (SWE) and SM also play an important role in weather and climate models over a range of scales. There are many advantages for the retrieval of snow parameters using passive microwave sensors. However, the current scale mismatch between many high-resolution applications and available satellite snow sensor footprints (approx.25km) presents challenges. There is also a scale mismatch for planned L-band soil moisture satellite measurements with their large (50-100 km) passive microwave footprints. Combined radiometer-radar schemes are being explored as possible aids, but this is still an area of ongoing research. Retrieval accuracies are limited in part by complex, and potentially nonlinear relationships between SWE or SM and microwave emission signatures. We will use airborne and satellite observations from two field campaigns to examine the scaling behavior of brightness temperatures and SWE and SM across scales from 100 meters to 10's of kilometers. The results will provide examples of the appropriate spatial sampling scales for new sensors, and guidance for downscaling schemes. The two campaigns are NASA's Cold Land Processes Experiment- 1 (CLPX- 1 : Colorado, 2002 & 2003), and the Australian National Airborne Field Experiment (NAFE, Nov, 2005). The analyses will also illustrate the roles and spatial scales of the underlying phenomena (e.g., vegetation, topography) that control subpixel heterogeneity.

Kim, Edward J.↗
NASA NTRS2007

Identification of Human-Induced Changes in Atmospheric Moisture Content

Data from the satellite-based Special Sensor Microwave Imager (SSM/I) show that the total atmospheric moisture content over oceans has increased by 0.41 kg/sq m per decade since 1988. Results from current climate models indicate that water vapor increases of this magnitude cannot be explained by climate noise alone. In a formal detection and attribution analysis using the pooled results from 22 different climate models, the simulated "fingerprint" pattern of anthropogenically caused changes in water vapor is identifiable with high statistical confidence in the SSM/I data. Experiments in which forcing factors are varied individually suggest that this fingerprint "match" is primarily due to human-caused increases in greenhouse gases and not to solar forcing or recovery from the eruption of Mount Pinatubo. Our findings provide preliminary evidence of an emerging anthropogenic signal in the moisture content of earth's atmosphere.

Santer, B.D.↗
NASA NTRS2009

Microstrip Antenna for Remote Sensing of Soil Moisture and Sea Surface Salinity

This compact, lightweight, dual-frequency antenna feed developed for future soil moisture and sea surface salinity (SSS) missions can benefit future soil and ocean studies by lowering mass, volume, and cost of the antenna system. It also allows for airborne soil moisture and salinity remote sensors operating on small aircraft. While microstrip antenna technology has been developed for radio communications, it has yet to be applied to combined radar and radiometer for Earth remote sensing. The antenna feed provides a key instrument element enabling high-resolution radiometric observations with large, deployable antennas. The design is based on the microstrip stacked-patch array (MSPA) used to feed a large, lightweight, deployable, rotating mesh antenna for spaceborne L-band (approximately equal to 1 GHz) passive and active sensing systems. The array consists of stacked patches to provide dual-frequency capability and suitable radiation patterns. The stacked-patch microstrip element was designed to cover the required L-band center frequencies at 1.26 GHz (lower patch) and 1.413 GHz (upper patch), with dual-linear polarization capabilities. The dimension of patches produces the required frequencies. To achieve excellent polarization isolation and control of antenna sidelobes for the MSPA, the orientation of each stacked-patch element within the array is optimized to reduce the cross-polarization. A specialized feed-distribution network was designed to achieve the required excitation amplitude and phase for each stacked-patch element.

Ramhat-Samii, Yahya↗
NASA NTRS2009

Moisture-Induced Delayed Alumina Scale Spallation on a Ni(Pt)Al Coating

Delayed interfacial scale failure takes place after cooling for samples of a Ni(Pt)Al-coated CMSX4 single crystal superalloy, cycled at 1150 C for up to 2000 hr. One sample exhibited premature coating grain boundary wrinkling, alumina scale spallation to bare metal, and a final weight loss of 3.3 mg/cm2 . Spallation under ambient conditions was monitored with time after cooldown and was found to continue for 24 hr. This produced up to 0.05 mg/cm2 additional loss for each hold, accumulating 0.7 mg/cm 2 (20 percent of the total) over the course of the test. After test termination, water immersion produced an additional 0.15 mg/cm2 loss. (A duplicate sample produced much less wrinkling and time dependent spalling, maintaining a net weight gain.) The results are consistent with the general phenomena of moisture-induced delayed spallation (MIDS) of mature, distressed alumina scales formed on oxidation resistant M-Al alloys. Relative ambient humidity is discussed as the factor controlling adsorbed moisture, reaction with the substrate, and hydrogen effects on interface strength.

Smialek, James L.↗
NASA NTRS2010

Portable Dew Point Mass Spectrometry System for Real-Time Gas and Moisture Analysis

A portable instrument incorporates both mass spectrometry and dew point measurement to provide real-time, quantitative gas measurements of helium, nitrogen, oxygen, argon, and carbon dioxide, along with real-time, quantitative moisture analysis. The Portable Dew Point Mass Spectrometry (PDP-MS) system comprises a single quadrupole mass spectrometer and a high vacuum system consisting of a turbopump and a diaphragm-backing pump. A capacitive membrane dew point sensor was placed upstream of the MS, but still within the pressure-flow control pneumatic region. Pressure-flow control was achieved with an upstream precision metering valve, a capacitance diaphragm gauge, and a downstream mass flow controller. User configurable LabVIEW software was developed to provide real-time concentration data for the MS, dew point monitor, and sample delivery system pressure control, pressure and flow monitoring, and recording. The system has been designed to include in situ, NIST-traceable calibration. Certain sample tubing retains sufficient water that even if the sample is dry, the sample tube will desorb water to an amount resulting in moisture concentration errors up to 500 ppm for as long as 10 minutes. It was determined that Bev-A-Line IV was the best sample line to use. As a result of this issue, it is prudent to add a high-level humidity sensor to PDP-MS so such events can be prevented in the future.

Arkin, C.↗
NASA NTRS2010

Retrospective Analog Year Analyses Using NASA Satellite Precipitation and Soil Moisture Data to Improve USDA's World Agricultural Supply and Demand Estimates

A primary goal of the U.S. Department of Agriculture (USDA) is to expand markets for U.S. agricultural products and support global economic development. The USDA World Agricultural Outlook Board (WAOB) supports this goal by coordinating monthly World Agricultural Supply and Demand Estimates (WASDE) for the U.S. and major foreign producing countries. Because weather has a significant impact on crop progress, conditions, and production, WAOB prepares frequent agricultural weather assessments, in a GIS-based, Global Agricultural Decision Support Environment (GLADSE). The main goal of this project, thus, is to improve WAOB's estimates by integrating NASA remote sensing soil moisture observations and research results into GLADSE (See diagram below). Soil moisture is currently a primary data gap at WAOB.

Teng, William↗
NASA NTRS2004

Radar for Measuring Soil Moisture Under Vegetation

A two-frequency, polarimetric, spaceborne synthetic-aperture radar (SAR) system has been proposed for measuring the moisture content of soil as a function of depth, even in the presence of overlying vegetation. These measurements are needed because data on soil moisture under vegetation canopies are not available now and are necessary for completing mathematical models of global energy and water balance with major implications for global variations in weather and climate.

Moghaddam, Mahta↗
NASA NTRS2005

Feed-Back Moisture Sensor Control for the Delivery of Water to Plants Cultivated in Space

The development of a spaceflight-rated Porous Tube Insert Module (PTIM) nutrient delivery tray has facilitated a series of studies evaluating various aspects of water and nutrient delivery to plants as they would be cultivated in space. We report here on our first experiment using the PTIM with a software-driven feedback moisture sensor control strategy for maintaining root zone wetness level set-points. One-day-old wheat seedlings (Tritium aestivum cv Apogee; N=15) were inserted into each of three Substrate Compartments (SCs) pre-packed with 0.25-1 . mm Profile(TradeMark) substrate and maintained at root zone relative water content levels of 70, 80 and 90%. The SCs contained a bottom-situated porous tube around which a capillary mat was wrapped. Three Porous Tubes. were planted using similar protocols (but without the substrate) and also maintained at these three moisture level set-points. Half-strength modified Hoagland's nutrient solution was used to supply water and nutrients. Results on hardware performance, water usage rates and wheat developmental differences between the different experimental treatments are presented.

Levine, Howard G.↗
NASA NTRS2011

The Soil Moisture Active Passive (SMAP) Applications Activity

The Soil Moisture Active Passive (SMAP) mission is one of the first-tier satellite missions recommended by the U.S. National Research Council Committee on Earth Science and Applications from Space. The SMAP mission 1 is under development by NASA and is scheduled for launch late in 2014. The SMAP measurements will allow global and high-resolution mapping of soil moisture and its freeze/thaw state at resolutions from 3-40 km. These measurements will have high value for a wide range of environmental applications that underpin many weather-related decisions including drought and flood guidance, agricultural productivity estimation, weather forecasting, climate predictions, and human health risk. In 2007, NASA was tasked by The National Academies to ensure that emerging scientific knowledge is actively applied to obtain societal benefits by broadening community participation and improving means for use of information. SMAP is one of the first missions to come out of this new charge, and its Applications Plan forms the basis for ensuring its commitment to its users. The purpose of this paper is to outline the methods and approaches of the SMAP applications activity, which is designed to increase and sustain the interaction between users and scientists involved in mission development.

Brown, Molly E.↗
NASA NTRS2012

Assimilating Remote Sensing Observations of Leaf Area Index and Soil Moisture for Wheat Yield Estimates: An Observing System Simulation Experiment

Observing system simulation experiments were used to investigate ensemble Bayesian state updating data assimilation of observations of leaf area index (LAI) and soil moisture (theta) for the purpose of improving single-season wheat yield estimates with the Decision Support System for Agrotechnology Transfer (DSSAT) CropSim-Ceres model. Assimilation was conducted in an energy-limited environment and a water-limited environment. Modeling uncertainty was prescribed to weather inputs, soil parameters and initial conditions, and cultivar parameters and through perturbations to model state transition equations. The ensemble Kalman filter and the sequential importance resampling filter were tested for the ability to attenuate effects of these types of uncertainty on yield estimates. LAI and theta observations were synthesized according to characteristics of existing remote sensing data, and effects of observation error were tested. Results indicate that the potential for assimilation to improve end-of-season yield estimates is low. Limitations are due to a lack of root zone soil moisture information, error in LAI observations, and a lack of correlation between leaf and grain growth.

Nearing, Grey S.↗
NASA NTRS2014

Assimilation of SMOS Soil Moisture Retrievals in the Land Information System

Soil moisture is a crucial variable for weather prediction because of its influence on evaporation. It is of critical importance for drought and flood monitoring and prediction and for public health applications. The NASA Short-term Prediction Research and Transition Center (SPoRT) has implemented a new module in the NASA Land Information System (LIS) to assimilate observations from the ESA's Soil Moisture and Ocean Salinity (SMOS) satellite. SMOS Level 2 retrievals from the Microwave Imaging Radiometer using Aperture Synthesis (MIRAS) instrument are assimilated into the Noah LSM within LIS via an Ensemble Kalman Filter. The retrievals have a target volumetric accuracy of 4% at a resolution of 35-50 km. Parallel runs with and without SMOS assimilation are performed with precipitation forcing from intentionally degraded observations, and then validated against a model run using the best available precipitation data, as well as against selected station observations. The goal is to demonstrate how SMOS data assimilation can improve modeled soil states in the absence of dense rain gauge and radar networks.

Blankenship, Clay↗
NASA NTRS2014

Assimilation of SMOS Soil Moisture Retrievals in the Land Information System

Soil moisture is a crucial variable for weather prediction because of its influence on evaporation. It is of critical importance for drought and flood monitoring and prediction and for public health applications. The NASA Short-term Prediction Research and Transition Center (SPoRT) has implemented a new module in the NASA Land Information System (LIS) to assimilate observations from the ESA's Soil Moisture and Ocean Salinity (SMOS) satellite. SMOS Level 2 retrievals from the Microwave Imaging Radiometer using Aperture Synthesis (MIRAS) instrument are assimilated into the Noah LSM within LIS via an Ensemble Kalman Filter. The retrievals have a target volumetric accuracy of 4% at a resolution of 35-50 km. Parallel runs with and without SMOS assimilation are performed with precipitation forcing from intentionally degraded observations, and then validated against a model run using the best available precipitation data, as well as against selected station observations. The goal is to demonstrate how SMOS data assimilation can improve modeled soil states in the absence of dense rain gauge and radar networks.

Blakenship, Clay↗
NASA NTRS2013

Assessment of Soil Moisture Data Requirements by the Potential SMAP Data User Community: Review of SMAP Mission User Community

NASA's Soil Moisture Active and Passive (SMAP) mission is planned for launch in October 2014 and will provide global measurements of soil moisture and freeze thaw state. The project is driven by both basic research and applied science goals. Understanding how application driven end-users will apply SMAP data, prior to the satellite's launch, is an important goal of NASA's applied science program and SMAP mission success. Because SMAP data are unique, there are no direct proxy data sets that can be used in research and operational studies to determine how the data will interact with existing processes. The objective of this study is to solicit data requirements, accuracy needs, and current understanding of the SMAP mission from the potential user community. This study showed that the data to be provided by the SMAP mission did substantially meet the user community needs. Although there was a broad distribution of requirements stated, the SMAP mission fit within these requirements.

applications↗
NASA NTRS2014

How Accurate is Land/Ocean Moisture Transport Variability in Reanalyses?

Quantifying the global hydrological cycle and its variability across various time scales remains a challenge to the climate community. Direct measurements of evaporation (E), evapotranspiration (ET), and precipitation (P) are not feasible on a global scale, nor is the transport of water vapor over the global oceans and sparsely populated land areas. Expanding satellite data streams have enabled development of various water (and energy) flux products, complementing reanalyses and facilitating observationally constrained modeling. But the evolution of the global observing system has produced additional complications--improvements in satellite sensor resolution and accuracy have resulted in "epochs" of observational quasi-uniformity that can adversely affect reanalysis trends. In this work we focus on vertically integrated moisture flux convergence (VMFC) variations within the period 1979 - present integrated over global land. We show that VMFC in recent reanalyses (e.g. ERA-I, NASA MERRA, NOAA CFSR and JRA55) suffers from observing system changes, though differently in each product. Land Surface Models (LSMs) forced with observations-based precipitation, radiation and near-surface meteorology share closely the interannual P-ET variations of the reanalyses associated with ENSO events. (VMFC over land and P-ET estimates are equivalent quantities since atmospheric storage changes are small on these scales.) But the long-term LSM trend over the period since 1979 is approximately one-fourth that of the reanalyses. Additional reduced observation reanalyses assimilating only surface pressure and /or specifying seasurface temperature also have a much smaller trend in P-ET like the LSMs. We explore the regional manifestation of the reanalysis P-ET / VMFC problems, particularly over land. Both principal component analysis and a simple time series changepoint analysis highlight problems associated with data poor regions such as Equatorial Africa and, for one reanalysis, the Equatorial Andes region. Onset of the availability of passive microwave Special Sensor Microwave Imager (SSMI) moisture data in July 1987 and the transition from the Microwave Sounder Unit (MSU) to an advanced version (AMSU) have significant impacts on VMFC variability. Simple accounting for these errors of leading importance results in modified reanalysis VMFC estimates that agree much better with the LSM results. Regional details of the modified reanalysis VMFC and LSM P-ET are related to changes in Pacific Decadal Variability as manifest in SST changes after the late 1990s.

Robertson, F. R.↗
NASA NTRS2013

Monte Carlo Bayesian Inference on a Statistical Model of Sub-gridcolumn Moisture Variability Using High-resolution Cloud Observations : Sensitivity Tests and Results - Part II

Part I presented a Monte Carlo Bayesian method for constraining a complex statistical model of GCM sub-gridcolumn moisture variability using high-resolution MODIS cloud data, thereby permitting large-scale model parameter estimation and cloud data assimilation. This part performs some basic testing of this new approach, verifying that it does indeed significantly reduce mean and standard deviation biases with respect to the assimilated MODIS cloud optical depth, brightness temperature and cloud top pressure, and that it also improves the simulated rotational-Ramman scattering cloud optical centroid pressure (OCP) against independent (non-assimilated) retrievals from the OMI instrument. Of particular interest, the Monte Carlo method does show skill in the especially difficult case where the background state is clear but cloudy observations exist. In traditional linearized data assimilation methods, a subsaturated background cannot produce clouds via any infinitesimal equilibrium perturbation, but the Monte Carlo approach allows finite jumps into regions of non-zero cloud probability. In the example provided, the method is able to restore marine stratocumulus near the Californian coast where the background state has a clear swath. This paper also examines a number of algorithmic and physical sensitivities of the new method and provides guidance for its cost-effective implementation. One obvious difficulty for the method, and other cloud data assimilation methods as well, is the lack of information content in the cloud observables on cloud vertical structure, beyond cloud top pressure and optical thickness, thus necessitating strong dependence on the background vertical moisture structure. It is found that a simple flow-dependent correlation modification due to Riishojgaard (1998) provides some help in this respect, by better honoring inversion structures in the background state.

Monte Carlo Bayesian inference↗