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NASA NTRS2021

Error Propagation in Microwave Soil Moisture and Vegetation Optical Depth Retrievals

Satellite soil moisture and vegetation optical depth (VOD; related to the total vegetation water mass per unit area) are increasingly being used to study water relations in the soil-plant continuum across the globe. However, soil moisture and VOD are typically jointly estimated, where errors in the optimization approach can cause compensation between both variables and confound such studies. It is thus critical to quantify how satellite microwave measurement errors propagate into soil moisture and VOD. Such a study is especially important for VOD given limited investigations of whether VOD reflects in-situ plant physiology. Furthermore, despite new approaches that constrain (or regularize) VOD dynamics to reduce soil moisture errors, there is limited study of whether regularization reduces VOD errors without obscuring true vegetation temporal dynamics. Here, we find that, across the globe, VOD is less robust to measurement error (more difficult for optimization methods to find the true solution) than soil moisture in their joint estimation. However, a moderate degree of regularization (via time-constrained VOD) reduces errors in VOD to a greater degree than soil moisture and reduces spurious soil moisture-VOD coupling. Furthermore, despite constraining VOD time dynamics, regularized VOD variations on sub-weekly scales are both closer to simulated true VOD time series and have global VOD post-rainfall responses with reduced error signatures compared to VOD retrievals without regularization. Ultimately, we recommend moderately regularized VOD for use in large scale studies of soil-plant water relations because it suppresses noise and spurious soil moisture-VOD coupling without removing the physical signal.

Vegetation optical depth↗

NASA NTRS1994

Factors affecting the estimate of primary production from space

Remote sensing of primary production in the euphotic zone has been based mostly on visible-band and water-leaving radiance measured with the coastal zone color scanner. There are some robust, simple relationships for calculating integral production based on surface measurements, but they also require knowledge for photoadaptive parameters such as maximum photosynthesis which currently cannot be obtained from spave. A 17,000-station data set is used to show that space-based estimates of maximum photosynthesis could improve predictions of psi, the water column light utiliztion index, which is an important term in many primary productivity models. Temperature is also examined as a factor for predicting hydrographic structure and primary production. A simple model is used to relate temperature and maximum photosynthesis; the model incorporates (1) the positive relationship between maximum photosynthesis and temperature and (2) the strongly negative relationship between temperature and nitrate in the ocean (which directly affects maximum growth rates via nitrogen limitation). Since these two factors relate to carbon and nitrogen, 'balanced carbon/nitrogen assimilation' was calculated using the Redfield ratio, It is expected that the relationship between maximum balanced carbon assimilation versus temperature is concave-down, with the peak dependent on nitrate uptake kinetics, temperature-nitrate relationships,a nd the carbon chlorophyll ration. These predictions were compared with the sea truth data. The minimum turnover time for nitrate was also calculated using this approach. Lastly, sea surface temperature gradients were used to predict the slope of isotherms (a proxy for the slope of isopycnals in many waters). Sea truth data show that at size scales of several hundred kilometers, surface temperature gradients can provide information on the slope of isotherms in the top 200 m of the water column. This is directly relevant to the supply of nutrients into the surface mixed layer, which is useful for predicting integral biomass and primary production.

Balch, W. M.↗

NASA NTRS2005

State Estimation of International Space Station Centrifuge Rotor With Incomplete Knowledge of Disturbance Inputs

This thesis develops a state estimation algorithm for the Centrifuge Rotor (CR) system where only relative measurements are available with limited knowledge of both rotor imbalance disturbances and International Space Station (ISS) thruster disturbances. A Kalman filter is applied to a plant model augmented with sinusoidal disturbance states used to model both the effect of the rotor imbalance and the 155 thrusters on the CR relative motion measurement. The sinusoidal disturbance states compensate for the lack of the availability of plant inputs for use in the Kalman filter. Testing confirms that complete disturbance modeling is necessary to ensure reliable estimation. Further testing goes on to show that increased estimator operational bandwidth can be achieved through the expansion of the disturbance model within the filter dynamics. In addition, Monte Carlo analysis shows the varying levels of robustness against defined plant/filter uncertainty variations.

SINUSOIDAL DISTURBANCE STATES↗

NASA NTRS1990

On estimating scale invariance in stratocumulus cloud fields

Examination of cloud radiance fields derived from satellite observations sometimes indicates the existence of a range of scales over which the statistics of the field are scale invariant. Many methods were developed to quantify this scaling behavior in geophysics. The usefulness of such techniques depends both on the physics of the process being robust over a wide range of scales and on the availability of high resolution, low noise observations over these scales. These techniques (area perimeter relation, distribution of areas, estimation of the capacity, d0, through box counting, correlation exponent) are applied to the high resolution satellite data taken during the FIRE experiment and provides initial estimates of the quality of data required by analyzing simple sets. The results of the observed fields are contrasted with those of images of objects with known characteristics (e.g., dimension) where the details of the constructed image simulate current observational limits. Throughout when cloud elements and cloud boundaries are mentioned; it should be clearly understood that by this structures in the radiance field are meant: all the boundaries considered are defined by simple threshold arguments.

Seze, Genevieve↗

NASA NTRS2017

Evidence for a Low Bulk Crustal Density for Mars from Gravity and Topography

Knowledge of the average density of the crust of a planet is important in determining its interior structure. The combination of high-resolution gravity and topography data has yielded a low density for the Moon’s crust, yet for other terrestrial planets the resolution of the gravity field models has hampered reasonable estimates. By using well-chosen constraints derived from topography during gravity field model determination using satellite tracking data, we show that we can robustly and independently determine the average bulk crustal density directly from the tracking data, using the admittance between topography and imperfect gravity. We find a low average bulk crustal density for Mars, 2582 ± 209 kgm−3. This bulk crustal density is lower than that assumed until now. Densities for volcanic complexes are higher, consistent with earlier estimates, implying large lateral variations in crustal density. In addition, we find indications that the crustal density increases with depth.

Goossens, Sander J.↗

NASA NTRS1987

Controller design for the improvement of feedback properties: A method for tuning the weight in the evaluation function of the LQG theory

In the controller design of a linear time-invariant system, it is important to improve the feedback properties such as robust stability and sensitivity. In the multi-input multi-output case, these properties can be estimated by using the singular values of return difference matrix. The design method to obtain better singular-value-points is desired. How to tune the weight matrix of performance index and/or covariance matrix of noise in the Linear Quadratic Gaussian (LQC) theory to get a desired singular-value-plot was studied. First, the property of singular-value plots of return difference matrix of a system designed by LQG theory is examined from the viewpoint of tuning weight. Second, a distance between real singular-value-plots and a desired plot is defined, and the weight of performance index is numerically determined by quasi-Newton method so that the distance is minimized.

Saeki, Masami↗

NASA NTRS1997

Atmospheric Angular Momentum Fluctuations During 1979-1988 Simulated by Global Circulation Models

Changes in major global dynamical phenomena in the Earth's atmosphere are manifested in the time series of atmospheric angular momentum (AAM), as determined directly from meteorological observations and indirectly from geodetic observations of small fluctuations in the rotation of the solid Earth which are proportional to length of day. AAM fluctuations are intimately linked with energetic processes throughout the whole atmosphere and also with the stresses at the Earth's surface produced largely by turbulent momentum transport in the oceanic and continental boundary layers and by the action of normal pressure forces on orographic features. A stringent test of any numerical global circulation model (GCM) is therefore provided by a quantitative assessment of its ability to represent AAM fluctuations on all relevant timescales, ranging from months to several years. From monthly data provided by the Atmospheric Model Intercomparison Project (AMIP) of the World Climate Research Programme, we have investigated seasonal and interannual fluctuations and the decadal mean in the axial component of AAM in 23 AMIP GCMs over the period 1979-1 988. The decadal means are generally well simulated, with the model median value (1.58 x 10(exp 26) kg sq m/s) being only 3.5% larger than the observed mean and with 10 of the models being within 5% of the observed. The seasonal cycle is well reproduced, with the median amplitude of the models' seasonal standard deviations being only 2.4% larger than observed. Half the seasonal amplitudes lie within 15% of the observed, and the median correlation found between the observed and model seasonal cycles is 0.95. The dominant seasonal error is an under- estimation of AAM during northern hemisphere winter associated with errors in the position of subtropical jets. Less robust are the modeled interannual variations, although the median correlation of 0.61 between model simulations and observed AAM is statistically significant. The two El Nino-Southern Oscillation events that occurred during the AMIP decade 1979-1988 have the expected positive AAM anomalies, although the AAM signature of the 1982-1983 event tends to be underestimated and that of the 1986-1987 event overestimated.

Hide, R.↗

NASA NTRS2004

Automated Detection of Solar Loops by the Oriented Connectivity Method

An automated technique to segment solar coronal loops from intensity images of the Sun s corona is introduced. It exploits physical characteristics of the solar magnetic field to enable robust extraction from noisy images. The technique is a constructive curve detection approach, constrained by collections of estimates of the magnetic fields orientation. Its effectiveness is evaluated through experiments on synthetic and real coronal images.

Lee, Jong Kwan↗

NASA NTRS2008

Symmetric Phase-Only Filtering in Particle-Image Velocimetry

Symmetrical phase-only filtering (SPOF) can be exploited to obtain substantial improvements in the results of data processing in particle-image velocimetry (PIV). In comparison with traditional PIV data processing, SPOF PIV data processing yields narrower and larger amplitude correlation peaks, thereby providing more-accurate velocity estimates. The higher signal-to-noise ratios associated with the higher amplitude correlation peaks afford greater robustness and reliability of processing. SPOF also affords superior performance in the presence of surface flare light and/or background light. SPOF algorithms can readily be incorporated into pre-existing algorithms used to process digitized image data in PIV, without significantly increasing processing times. A summary of PIV and traditional PIV data processing is prerequisite to a meaningful description of SPOF PIV processing. In PIV, a pulsed laser is used to illuminate a substantially planar region of a flowing fluid in which particles are entrained. An electronic camera records digital images of the particles at two instants of time. The components of velocity of the fluid in the illuminated plane can be obtained by determining the displacements of particles between the two illumination pulses. The objective in PIV data processing is to compute the particle displacements from the digital image data. In traditional PIV data processing, to which the present innovation applies, the two images are divided into a grid of subregions and the displacements determined from cross-correlations between the corresponding sub-regions in the first and second images. The cross-correlation process begins with the calculation of the Fourier transforms (or fast Fourier transforms) of the subregion portions of the images. The Fourier transforms from the corresponding subregions are multiplied, and this product is inverse Fourier transformed, yielding the cross-correlation intensity distribution. The average displacement of the particles across a subregion results in a displacement of the correlation peak from the center of the correlation plane. The velocity is then computed from the displacement of the correlation peak and the time between the recording of the two images. The process as described thus far is performed for all the subregions. The resulting set of velocities in grid cells amounts to a velocity vector map of the flow field recorded on the image plane. In traditional PIV processing, surface flare light and bright background light give rise to a large, broad correlation peak, at the center of the correlation plane, that can overwhelm the true particle- displacement correlation peak. This has made it necessary to resort to tedious image-masking and background-subtraction procedures to recover the relatively small amplitude particle-displacement correlation peak. SPOF is a variant of phase-only filtering (POF), which, in turn, is a variant of matched spatial filtering (MSF). In MSF, one projects a first image (denoted the input image) onto a second image (denoted the filter) as part of a computation to determine how much and what part of the filter is present in the input image. MSF is equivalent to cross-correlation. In POF, the frequency-domain content of the MSF filter is modified to produce a unitamplitude (phase-only) object. POF is implemented by normalizing the Fourier transform of the filter by its magnitude. The advantage of POFs is that they yield correlation peaks that are sharper and have higher signal-to-noise ratios than those obtained through traditional MSF. In the SPOF, these benefits of POF can be extended to PIV data processing. The SPOF yields even better performance than the POF approach, which is uniquely applicable to PIV type image data. In SPOF as now applied to PIV data processing, a subregion of the first image is treated as the input image and the corresponding subregion of the second image is treated as the filter. The Fourier transforms from both the firs and second- image subregions are normalized by the square roots of their respective magnitudes. This scheme yields optimal performance because the amounts of normalization applied to the spatial-frequency contents of the input and filter scenes are just enough to enhance their high-spatial-frequency contents while reducing their spurious low-spatial-frequency content. As a result, in SPOF PIV processing, particle-displacement correlation peaks can readily be detected above spurious background peaks, without need for masking or background subtraction.

Wemet, Mark P.↗

NASA NTRS2020

Multi-Objective Gust Load Alleviation Control Designs for an Aeroelastic Wind Tunnel Demonstration Wing

This paper presents several control and gust disturbance estimation techniques applied to a mathematical model of a physical flexible wing wind tunnel model used in ongoing tests at the University of Washington Aeronautical Laboratory's Kirsten Wind Tunnel. Three methods of gust disturbance estimation are presented, followed by three control methods: LQG, Basic Multi-Objective (BMO), and a novel Multi-Objective Prediction Correction (MOPC) controller. The latter of which augments a multi-objective controller, and attempts to correct for errors in the disturbance estimate. A simplified linear simulation of the three controllers is performed and a simple MIMO stability and robustness assessment is performed. Then, the same controllers are simulated in a higher fidelity Simulink environment that captures sampling, saturation and noise effects. This preliminary analysis indicates that the BMO controller provides the best performance and largest stability margins.

Drew, Michael C.↗

NASA NTRS2016

Snow Process Estimation Over the Extratropical Andes Using a Data Assimilation Framework Integrating MERRA Data and Landsat Imagery

A data assimilation framework was implemented with the objective of obtaining high resolution retrospective snow water equivalent (SWE) estimates over several Andean study basins. The framework integrates Landsat fractional snow covered area (fSCA) images, a land surface and snow depletion model, and the Modern Era Retrospective Analysis for Research and Applications (MERRA) reanalysis as a forcing data set. The outputs are SWE and fSCA fields (1985-2015) at a resolution of 90 m that are consistent with the observed depletion record. Verification using in-situ snow surveys showed significant improvements in the accuracy of the SWE estimates relative to forward model estimates, with increases in correlation (0.49-0.87) and reductions in root mean square error (0.316 m to 0.129 m) and mean error (-0.221 m to 0.009 m). A sensitivity analysis showed that the framework is robust to variations in physiography, fSCA data availability and a priori precipitation biases. Results from the application to the headwater basin of the Aconcagua River showed how the forward model versus the fSCA-conditioned estimate resulted in different quantifications of the relationship between runoff and SWE, and different correlation patterns between pixel-wise SWE and ENSO. The illustrative results confirm the influence that ENSO has on snow accumulation for Andean basins draining into the Pacific, with ENSO explaining approximately 25% of the variability in near-peak (1 September) SWE values. Our results show how the assimilation of fSCA data results in a significant improvement upon MERRA-forced modeled SWE estimates, further increasing the utility of the MERRA data for high-resolution snow modeling applications.

SWE↗

NASA NTRS2019

Challenges and Solutions for Precision Solar Pointing on the ISS for the TSIS Instrument

The International Space Station offers an attractive platform for external payloads in many regards, but its large, complex structure is challenging for payloads that require precise, unobstructed pointing. As an instrument that measures solar irradiance on a continual basis, the TSIS payload was designed to robustly account for these ISS challenges and is now performing its mission on the ISS with great success.This paper first describes how TSIS was able to measure the ISS background jitter using High-rate Fine Sun Sensor data that was taken during passive solar transits through its field of view. The measurements showed that ISS jitter at the TSIS location on ELC-3 Site 5 is relatively benign with less than 4 arcseconds 1 at frequencies less than 0.5 Hz. This value is lower than previous estimates but agrees well with recent measurements from other external payloads, including OPALS and SAGE III. Because TSIS was designed to be robust to base motion jitter, it has achieved less than 4 arcseconds 1 of pointing error, which provides an extremely precise pointing platform for the solar irradiance measurements. The other challenging ISS characteristic is its large, complex structure that obscures solar viewing at sunrise and sunset from the zenith-facing location of TSIS. Although TSIS was able to predict solar viewing durations during the design phase with relatively high accuracy, the exact timing and causes of obscuration were difficult to predict and understand after TSIS began operating on the ISS. To aid in the understanding of blockage by ISS structure, detailed obscuration maps were created using HFSS measurements that clearly show the exact gimbal angles where blockage occurs and the specific structural features of the ISS that cause the blockage. These maps have helped TSIS to maximize its solar tracking durations each orbit, which has supported the mission in meeting its science objectives.

background jitter↗

NASA NTRS1992

Enhanced orbital gyrocompassing by the optical flow sensed by an Earth-pointing camera

A new method for improving the orbital gyrocompassing process involving the attitude angle estimation of an earth-pointing satellite in low-Earth orbit uses an electro-optical sensor for direct measurement of the satellite azimuth angle. Simulations have shown that this additional measurement drastically reduces the estimator convergence time, especially when the sun sensor is rendered ineffective, e.g., by high solar elevations. The azimuth-sensing method is based on estimation of the image shift between successive picture frames of an on-board, Earth-pointing, charge-coupled device (CCD) full-matrix camera. The shift-estimation algorithm is based on minimizing a cost function which expresses mean-squared differences in brightness patterns of selected areas of the two frames. An extensive evaluation program with a computer-controlled 2-axis light table and actual satellite images has demonstrated high robustness for a wide range of variation of parameters including image texture content; camera focal length; sampling rate; and number of pixels processed. It was shown to be possible to estimate the azimuth angle within 0.1-0.2 degrees, for a suitably chosen parameter set.

Topaz, Leora↗

NASA NTRS1999

Satellite-Derived Water Vapor Winds for Regional Climate Studies

The retrieval of winds and humidity in the upper-troposphere has matured to the point where it may now be possible to better understand and diagnose regional climate variations from geostationary satellites than from conventional measurements or model analysis, especially in data sparse regions. In this poster paper, upper-tropospheric circulation features and moisture transport covering ENSO periods are presented and discussed. Precursor and other detectable interannual climate signals are analyzed and compared to model diagnosed features. Estimates of winds and humidity over data-rich regions (from conventional measurements) are used to show the robustness of the data and its value over regions which are currently poorly sampled.

Jedlovce, Gary J.↗

NASA NTRS2003

Wavelet-Based Real-Time Diagnosis of Complex Systems

A new method of robust, autonomous real-time diagnosis of a time-varying complex system (e.g., a spacecraft, an advanced aircraft, or a process-control system) is presented here. It is based upon the characterization and comparison of (1) the execution of software, as reported by discrete data, and (2) data from sensors that monitor the physical state of the system, such as performance sensors or similar quantitative time-varying measurements. By taking account of the relationship between execution of, and the responses to, software commands, this method satisfies a key requirement for robust autonomous diagnosis, namely, ensuring that control is maintained and followed. Such monitoring of control software requires that estimates of the state of the system, as represented within the control software itself, are representative of the physical behavior of the system. In this method, data from sensors and discrete command data are analyzed simultaneously and compared to determine their correlation. If the sensed physical state of the system differs from the software estimate (see figure) or if the system fails to perform a transition as commanded by software, or such a transition occurs without the associated command, the system has experienced a control fault. This method provides a means of detecting such divergent behavior and automatically generating an appropriate warning.

Gulati, Sandeep↗

NASA NTRS2019

Radar Altimetry as a Proxy for Determining Terrestrial Water Storage Variability in Tropical Basins

The Gravity Recovery and Climate Experiment (GRACE) mission has provided us with unforeseen information on terrestrial water-storage (TWS) variability, contributing to our understanding of global hydrological processes, including hydrological extreme events and anthropogenic impacts on water storage. Attempts to decompose GRACE-based TWS signals into its different water storage layers, i.e., surface water storage (SWS), soil moisture, groundwater and snow, have shown that SWS is a principal component, particularly in the tropics, where major rivers flow over arid regions at high latitudes. Here, we demonstrate that water levels, measured with radar altimeters at a limited number of locations, can be used to reconstruct gridded GRACE-based TWS signals in the Amazon basin, at spatial resolutions ranging from 0.5 to 3°, with mean absolute errors (MAE) as low as 2.5 cm and correlations as high as 0.98. We show that, at 3° spatial resolution, spatially-distributed TWS time series can be precisely reconstructed with as few as 41 water-level time series located within the basin. The proposed approach is competitive when compared to existing TWS estimates derived from physically based and computationally expensive methods. Also, a validation experiment indicates that TWS estimates can be extrapolated to periods beyond that of the model regression with low errors. The approach is robust, based on regression models and interpolation techniques, and offers a new possibility to reproduce spatially and temporally distributed TWS that could be used to fill inter-mission gaps and to extend GRACE-based TWS time series beyond its timespan.

Terrestrial water storage↗

NASA NTRS2020

Spatiotemporal Characteristics of the Association between AOD and PM over the California Central Valley

Many air pollution health effects studies rely on exposure estimates of particulate matter (PM) concentrations derived from remote sensing observations of aerosol optical depth (AOD). Simple but robust calibration models between AOD and PM are therefore important for generating reliable PM exposures. We conduct an in-depth examination of the spatial and temporal characteristics of the AOD-PM2.5 relationship by leveraging data from the Distributed Regional Aerosol Gridded Observation Networks (DRAGON) field campaign where eight NASA Aerosol Robotic Network (AERONET) sites were co-located with EPA Air Quality System (AQS) monitoring sites in California’s Central Valley from November 2012 to April 2013. With this spatiotemporally rich data we found that linear calibration models (R(exp 2) = 0.35, RMSE = 10.38 µg/cu.m) were significantly improved when spatial (R(exp 2) = 0.45, RMSE = 9.54 µg/cu.m), temporal (R(exp 2) = 0.62, RMSE = 8.30 µg/cu.m), and spatiotemporal (R(exp 2) = 0.65, RMSE = 7.58 µg/cu.m) functions were included. As a use-case we applied the best spatiotemporal model to convert space-borne MultiAngle Imaging Spectroradiometer (MISR) AOD observations to predict PM2.5 over the region (R(exp 2) = 0.60, RMSE = 8.42 µg/cu.m). Our results imply that simple AERONET AOD-PM2.5 calibrations are robust and can be reliably applied to space-borne AOD observations, resulting in PM2.5 prediction surfaces for use in downstream applications.

Meytar Sorek-Hamer↗

NASA NTRSDate not supplied

Global Carbon Consumption Database for Wildland Fire

Fire plays a significant role on both national and global scales, profoundly impacting landscapes shaped by human activity as well as those left wild. Even though fire can be devastating, wildland fire is a natural and integral force on our landscapes. Fires can also serve to reduce fuels to mitigate wildfire risk and maintain healthy ecosystem functions. However, the smoke produced by fires, regardless of their size or purpose, can pose adverse effects on human health when inhaled downwind. Understanding the influence of smoke on air quality and human well-being necessitates the quantification of emissions that fires release into the atmosphere. In response to this need, we have established a comprehensive global consumption database directly linked to distinct fuels within various fire danger categories. This database, featuring a spatial resolution of approximately 300 meters, builds upon the foundations of the Pettinari, M. Lucrecia (2015) Global Fuelbed database, a global fuel map with standardized Fuel Characteristic Classification System (FCCS) biomass parameters. Consumption is broken down into five Fire Danger categories (Low, Moderate, High, Very High, Extreme), for both ‘new’ and ‘residual’ burning scenarios. We define ‘residual burned area’ as area burning in a region that has burned on a previous day for the same fire season, and ‘new burned area’ as area burning in a region that has not recently burned. This product serves as a valuable tool when used in conjunction with burned area data to rapidly estimate the carbon consumed and released into the atmosphere. Previously, we developed a similar emissions method utilizing satellite information, in conjunction with the FCCS 30-meter United States fuelbed dataset. We implemented this approach on fires, documented during the 2019 Fire Influence on Regional to Global Environments and Air Quality (FIREX-AQ) campaign to estimate daily carbon emissions. Our emissions estimates were rigorously compared against in-situ measurements of CO2, CO, and black carbon aerosols, revealing a robust agreement between the two datasets.

Emily Gargulinski↗