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NASA NTRS2017

Development of Multi-Sensor Global Cloud and Radiance Composites for DSCOVR EPIC Imager with Subpixel Definition

The Deep Space Climate Observatory (DSCOVR) enables analysis of the daytime Earth radiation budget via the onboard Earth Polychromatic Imaging Camera (EPIC) and National Institute of Standards and Technology Advanced Radiometer (NISTAR). EPIC delivers adequate spatial resolution imagery but only in shortwave bands (317-780 nm), while NISTAR measures the top-of-atmosphere (TOA) whole-disk radiance in shortwave and longwave broadband windows. Accurate calculation of albedo and outgoing longwave flux requires a high-resolution scene identification such as the radiance observations and cloud properties retrievals from low earth orbit (LEO, including NASA Terra and Aqua MODIS, Suomi-NPP VIIRS, and NOAA AVHRR) and geosynchronous (GEO, including GOES east and west, METEOSAT, INSAT-3D, MTSAT-2, and Himawari-8) satellite imagers. The cloud properties are derived using the Clouds and the Earth's Radiant Energy System (CERES) mission Cloud Subsystem group algorithms. These properties have to be co-located with EPIC pixels to provide the scene identification and to select anisotropic directional models (ADMs), which are then used to adjust the NISTAR-measured radiance and subsequently obtain the global daytime shortwave and longwave fluxes. This work presents an algorithm for optimal merging of selected radiance and cloud property parameters derived from multiple satellite imagers to obtain seamless global hourly composites at 5-km resolution. Selection of satellite data for each 5-km pixel is based on an aggregated rating that incorporates five parameters: nominal satellite resolution, pixel time relative to the EPIC time, viewing zenith angle, distance from day/night terminator, and probability of sun glint. To provide a smoother transition in the merged output, in regions where candidate pixel data from two satellite sources have comparable aggregated rating, the selection decision is defined by the cumulative function of the normal distribution so that abrupt changes in the visual appearance of the composite data are avoided. Higher spatial accuracy in the composite product is achieved by using the inverse mapping with gradient search during reprojection and bicubic interpolation for pixel resampling.

Khlopenkov, Konstantin V.↗
NASA NTRS1996

Subpixel Snow-Covered-Area and Snow Grain Size from Mixture Analysis with Aviris Data

Snow-covered-area (SCA) and snow grain size are crucial inputs to hydrologic and climatologic modeling of alpine and other seasonally snow-covered regions. SCA is necessary to parameterize energy budget calculations in climate models, to determine in which regions point snowmelt models are to be run for distributed snowmelt modeling efforts and to provide a basis from which estimates of snow water equivalent (SWE) may be made.

hydrologic↗
NASA NTRS2005

Spatial Scaling of Snow Observations and Microwave Emission Modeling During CLPX and Appropriate Satellite Sensor Resolution

Accurate estimates of snow water equivalent and other properties play an important role in weather, natural hazard, and hydrological forecasting and climate modeling over a range of scales in space and time. Remote sensing-derived estimates have traditionally been of the "snapshot" type, but techniques involving models with assimilation are also being explored. In both cases, forward emission models are useful to understand the observed passive microwave signatures and developing retrieval algorithms. However, mismatches between passive microwave sensor resolutions and the scales of processes controlling subpixel heterogeneity can affect the accuracy of the estimates. Improving the spatial resolution of new passive microwave satellite sensors is a major desire in order to (literally) resolve such subpixel heterogeneity, but limited spacecraft and mission resources impose severe constraints and tradeoffs. In order to maximize science return while mitigating risk for a satellite concept, it is essential to understand the scaling behavior of snow in terms of what the sensor sees (brightness temperature) as well as in terms of the actual variability of snow. NASA's Cold Land Processes Experiment-1 (CLPX-1: Colorado, 2002 and 2003) was designed to provide data to measure these scaling behaviors for varying snow conditions in areas with forested, alpine, and meadow/pasture land cover. We will use observations from CLPX-1 ground, airborne, and satellite passive microwave sensors to examine and evaluate the scaling behavior of observed and modeled brightness temperatures and observed and retrieved snow parameters across scales from meters to 10's of kilometers. The conclusions will provide direct examples of the appropriate spatial sampling scales of new sensors for snow remote sensing. The analyses will also illustrate the effects and spatial scales of the underlying phenomena (e.g., land cover) that control subpixel heterogeneity.

Kim, Edward J.↗
NASA NTRS2006

Photodetector Arrays for Multicolor Visible/Infrared Imaging

Monolithic focal-plane arrays of photodetectors capable of imaging the same scenes simultaneously in multiple wavelength bands in the visible and infrared spectral regions have been proposed. In prior visible/infrared imaging systems, it has been standard practice to use separate optical trains to form images in visible and infrared wavelength bands on separate visibleand infrared-photodetector arrays. Because the proposal would enable the detection of images in multiple wavelength bands on the same focal plane, the proposal would make it unnecessary to use multiple optical trains. Hence, multispectral imaging systems could be made more compact and the difficulties of aligning multiple optical trains would be eliminated. Each pixel in an array according to the proposal would contain stacks of several photodetectors. The proposal is a logical extension of prior concepts of arrays of stacked photodetectors for imaging in two or three wavelength bands. For example, such an array was described in Three-Color Focal-Plane Array of Infrared QWIPs (NPO-20683), NASA Tech Briefs, Vol. 24, No. 5 (May 2000), page 26a. In one proposed design, (see figure), each pixel would be divided into four subpixels, one being dedicated to a visible- and-near-infrared (V) band, one to a combination of the V band and a verylong- wavelength infrared (VLWIR) band, one to a combination of the V band and a long-wavelength infrared (LWIR) band, and one to a combination of the V band and a medium-wavelength infrared (MWIR) band. For this purpose, each subpixel would include a GaAs-based positive/intrinsic/negative (PIN) photodiode for detection in the V band stacked with three quantum-well infrared photodetectors (QWIPs), each optimized for one of the aforementioned infrared bands. The stacks of photodetectors in all the subpixels would be identical except for the electrical connections, which would be configured to activate the various wavelengthband combinations.

Gunapala, Sarath↗
NASA NTRS1978

Processing techniques development, volume 3

The author has identified the following significant results. Analysis of the geometric characteristics of the aircraft synthetic aperture radar (SAR) relative to LANDSAT indicated that relatively low order polynominals would model the distortions to subpixel accuracy to bring SAR into registration for good quality imagery. Also the area analyzed was small, about 10 miles square, so this is an additional constraint. For the Air Force/ERIM data, none of the tested methods could achieve subpixel accuracy. Reasons for this is unknown; however, the noisy (high scintillation) nature of the data and attendent unrecognizability of features contribute to this error. It is concluded that the quadratic model would adequately provide distortion modeling for small areas, i.e., 10 to 20 miles square.

Landgrebe, D. A.↗
NASA NTRS1988

Use of LANDSAT images of vegetation cover to estimate effective hydraulic properties of soils

This work focuses on the characterization of natural, spatially variable, semivegetated landscapes using a linear, stochastic, canopy-soil reflectance model. A first application of the model was the investigation of the effects of subpixel and regional variability of scenes on the shape and structure of red-infrared scattergrams. Additionally, the model was used to investigate the inverse problem, the estimation of subpixel vegetation cover, given only the scattergrams of simulated satellite scale multispectral scenes. The major aspects of that work, including recent field investigations, are summarized.

Eagleson, Peter S.↗
NASA NTRS1988

The structure of red-infrared scattergrams of semivegetated landscapes

A physically based linear stochastic geometric canopy soil reflectance model is presented for characterizing spatial variability of semivegetated landscapes at subpixel and regional scales. Landscapes are conceptualized as stochastic geometric surfaces, incorporating not only the variability in geometric elements, but also the variability in vegetation and soil background reflectance which can be important in some scenes. The model is used to investigate several possible mechanisms which contribute to the often observed characteristic triangular shape of red-infrared scattergrams of semivegetated landscapes. Scattergrams of simulated and semivegetated scenes are analyzed with respect to the scales of the satellite pixel and subpixel components. Analysis of actual aerial radiometric data of a pecan orchard is presented in comparison with ground observations as preliminary confirmation of the theoretical results.

Jasinski, Michael F.↗
NASA NTRS1989

The structure of red-infrared scattergrams of semivegetated landscapes

A physically based linear stochastic geometric canopy soil reflectance model is presented for characterizing spatial variability of semivegetated landscapes at subpixel and regional scales. Landscapes are conceptualized as stochastic geometric surfaces, incorporating not only the variability in geometric elements, but also the variability in vegetation and soil background reflectance which can be important in some scenes. The model is used to investigate several possible mechanisms which contribute to the often observed characteristic triangular shape of red-infrared scattergrams of semivegetated landscapes. Scattergrams of simulated semivegetated scenes are analyzed with respect to the scales of the satellite pixel and subpixel components. Analysis of actual aerial radiometric data of a pecan orchard is presented in comparison with ground observations as preliminary confirmation of the theoretical results.

Jasinski, Michael F.↗
NASA NTRS1989

Physically-based parameterization of spatially variable soil and vegetation using satellite multispectral data

A stochastic-geometric landsurface reflectance model is formulated and tested for the parameterization of spatially variable vegetation and soil at subpixel scales using satellite multispectral images without ground truth. Landscapes are conceptualized as 3-D Lambertian reflecting surfaces consisting of plant canopies, represented by solid geometric figures, superposed on a flat soil background. A computer simulation program is developed to investigate image characteristics at various spatial aggregations representative of satellite observational scales, or pixels. The evolution of the shape and structure of the red-infrared space, or scattergram, of typical semivegetated scenes is investigated by sequentially introducing model variables into the simulation. The analytical moments of the total pixel reflectance, including the mean, variance, spatial covariance, and cross-spectral covariance, are derived in terms of the moments of the individual fractional cover and reflectance components. The moments are applied to the solution of the inverse problem: The estimation of subpixel landscape properties on a pixel-by-pixel basis, given only one multispectral image and limited assumptions on the structure of the landscape. The landsurface reflectance model and inversion technique are tested using actual aerial radiometric data collected over regularly spaced pecan trees, and using both aerial and LANDSAT Thematic Mapper data obtained over discontinuous, randomly spaced conifer canopies in a natural forested watershed. Different amounts of solar backscattered diffuse radiation are assumed and the sensitivity of the estimated landsurface parameters to those amounts is examined.

Jasinski, Michael F.↗
NASA NTRS1989

Applications of Landsat Thematic Mapper and ground-based spectrometer data to a study of the Skaergaard and other mafic intrusions of East Greenland

Landsat TM data have been used in conjunction with field spectrometer data to map the lithologic units associated with a series of gabbroic intrusions in the East Greenland Tertiary Igneous Province. The general lack of vegetation combined with the difficulty of access to these intrusions make them ideal candidates for lithologic mapping using remote sensing techniques. In addition, these bodies are of interest as possible precious metal ore deposits. The intrusions are spectrally distinct from the surrounding Precambrian gneisses; however, subpixel contamination by snow, oxide surface coatings, and lichen cover and severe topography limit the discrimination of lithologic units within the gabbro. The spectral nature of the surface contaminants was evaluated with a Barringer Hand Held Ratioing Radiometer (HHRR). These HHRR data indicate that bare rock exposures have distinct TM signatures for each lithologic unit but that even small amounts of subpixel contamination are enough to mask these differences because of the large differences between the TM signatures of the rocks and the contaminants.

Birnie, Richard W.↗
NASA NTRS1991

Using image area to control CCD systematic errors in spaceborne photometric and astrometric time-series measurements

The effect of some systematic errors for high-precision time-series spaceborne photometry and astrometry has been investigated with a CCD as the detector. The 'pixelization' of the images causes systematic error in astrometric measurements. It is shown that this pixelization noise scales as image radius r exp -3/2. Subpixel response gradients, not correctable by the 'flat field', and in conjunction with telescope pointing jitter, introduce further photometric and astrometric errors. Subpixel gradients are modeled using observed properties of real flat fields. These errors can be controlled by having an image span enough pixels. Large images are also favored by CCD dynamic range considerations. However, magnified stellar images can overlap, thus introducing another source of systematic error. An optimum image size is therefore a compromise between these competing factors.

Buffington, Andrew↗
NASA NTRS1991

3-D laser radar simulation for autonomous spacecraft landing

A sophisticated 3D laser radar sensor simulation, developed and applied to the task of autonomous hazard detection and avoidance, is presented. This simulation includes a backward ray trace to sensor subpixels, incoherent subpixel integration, range dependent noise, sensor point spread function effects, digitization noise, and AM-CW modulation. Specific sensor parameters, spacecraft lander trajectory, and terrain type have been selected to generate simulated sensor data.

Reiley, Michael F.↗
NASA NTRS1994

The Area Coverage of Geophysical Fields as a Function of Sensor Field-of View

In many remote sensing studies of geophysical fields such as clouds, land cover, or sea ice characteristics, the fractional area coverage of the field in an image is estimated as the proportion of pixels that have the characteristic of interest (i.e., are part of the field) as determined by some thresholding operation. The effect of sensor field-of-view on this estimate is examined by modeling the unknown distribution of subpixel area fraction with the beta distribution, whose two parameters depend upon the true fractional area coverage, the pixel size, and the spatial structure of the geophysical field. Since it is often not possible to relate digital number, reflectance, or temperature to subpixel area fraction, the statistical models described are used to determine the effect of pixel size and thresholding operations on the estimate of area fraction for hypothetical geophysical fields. Examples are given for simulated cumuliform clouds and linear openings in sea ice, whose spatial structures are described by an exponential autocovariance function. It is shown that the rate and direction of change in total area fraction with changing pixel size depends on the true area fraction, the spatial structure, and the thresholding operation used.

Key, Jeffrey R.↗
NASA NTRS1998

Laboratory Studies of Carbon Emission from Biomass Burning for use in Remote Sensing

Biomass burning is a significant source of many trace gases in the atmosphere. Up to 25% of the total anthropogenic carbon dioxide added to the atmosphere annually is from biomass burning. However, this gaseous emission from fires is not directly detectable from satellite. Infrared radiance from the fires is. In order to see if infrared radiance can be used as a tracer for these emitted gases, we made laboratory measurements to determine the correlation of emitted carbon dioxide, carbon monoxide and total burned biomass with emitted infrared radiance. If the measured correlations among these quantities hold in the field, then satellite-observed infrared radiance can be used to estimate gaseous emission and total burned biomass on a global, daily basis. To this end, several types of biomass fuels were burned under controlled conditions in a large-scale combustion laboratory. Simultaneous measurements of emitted spectral infrared radiance, emitted carbon dioxide, carbon monoxide, and total mass loss were made. In addition measurements of fuel moisture content and fuel elemental abundance were made. We found that for a given fire, the quantity of carbon burned can be estimated from 11 (micro)m radiance measurements only within a factor of five. This variation arises from three sources, 1) errors in our measurements, 2) the subpixel nature of the fires, and 3) inherent differences in combustion of different fuel types. Despite this large range, these measurements can still be used for large-scale satellite estimates of biomass burned. This is because of the very large possible spread of fire sizes that will be subpixel as seen by Moderate Resolution Imaging Spectroradiometer (MODIS). Due to this large spread, even relatively low-precision correlations can still be useful for large-scale estimates of emitted carbon. Furthermore, such estimates using the MODIS 3.9 (micro)m channel should be even more accurate than our estimates based on 11 (micro)m radiance.

Wald, Andrew E.↗
NASA NTRS2006

The Australian National Airborne Field Experiment 2005: Soil Moisture Remote Sensing at 60 Meter Resolution and Up

Spatially-distributed soil moisture observations have applications spanning a wide range of spatial resolutions from the very local needs of individual farmers to the progressively larger areas of interest to weather forecasters, water resource managers, and global climate modelers. To date, the most promising approach for space-based remote sensing of soil moisture makes use of passive microwave emission radiometers at L-band frequencies (1-2 GHz). Several soil moisture-sensing satellites have been proposed in recent years, with the European Space Agency's Soil Moisture Ocean Salinity (SMOS) mission scheduled to be launched first in a couple years. While such a microwave-based approach has the advantage of essentially allweather operation, satellite size limits spatial resolution to 10's of km. Whether used at this native resolution or in conjunction with some type of downscaling technique to generate soil moisture estimates on a finer-scale grid, the effects of subpixel spatial variability play a critical role. The soil moisture variability is typically affected by factors such as vegetation, topography, surface roughness, and soil texture. Understanding and these factors is the key to achieving accurate soil moisture retrievals at any scale. Indeed, the ability to compensate for these factors ultimately limits the achievable spatial resolution and/or accuracy of the retrieval. Over the last 20 years, a series of airborne campaigns in the USA have supported the development of algorithms for spaceborne soil moisture retrieval. The most important observations involved imagery from passive microwave radiometers. The early campaigns proved that the retrieval worked for larger and larger footprints, up to satellite-scale footprints. These provided the solid basis for proposing the satellite missions. More recent campaigns have explored other aspects such as retrieval performance through greater amounts of vegetation. All of these campaigns featured extensive ground truth collection over a range of grid spacings, to provide a basis for examining the effects of subpixel variability. However, the native footprint size of the airborne L-band radiometers was always a few hundred meters. During the recently completed (November, 2005) National Airborne Field Experiment (NAFE) campaign in Australia, a compact L-band radiometer was deployed on a small aircraft. This new combination permitted routine observations at native resolutions as high as 60 meters, substantially finer than in previous airborne soil moisture campaigns, as well as satellite footprint areal coverage. The radiometer, the Polarimetric L-band Microwave Radiometer (PLMR) performed extremely well and operations included extensive calibration-related observations. Thus, along with the extensive fine-scale ground truth, the NAFE dataset includes all the ingredients for the first scaling studies involving very-high-native resolution soil moisture observations and the effects of vegetation, roughness, etc. A brief overview of the NAFE will be presented, then examples of the airborne observations with resolutions from 60 m to 1 km will be shown, and early results from scaling studies will be discussed.

Kim, E. J.↗
NASA NTRS2007

Visual End-Effector Position Error Compensation for Planetary Robotics

This paper describes a vision-guided manipulation algorithm that improves arm end-effector positioning to subpixel accuracy and meets the highly restrictive imaging and computational constraints of a planetary robotic flight system. Analytical, simulation-based, and experimental analyses of the algorithm's effectiveness and sensitivity to camera and arm model error is presented along with results on several prototype research systems and 'ground-in-the-loop' technology experiments on the Mars Exploration Rover (MER) vehicles. A computationally efficient and robust subpixel end-effector fiducial detector that is instrumental to the algorithm's ability to achieve high accuracy is also described along with its validation results on MER data.

vision-guided manipulation algorithm↗
NASA NTRS2016

Avoiding Stair-Step Artifacts in Image Registration for GOES-R Navigation and Registration Assessment

In developing software for independent verification and validation (IVV) of the Image Navigation and Registration (INR) capability for the Geostationary Operational Environmental Satellite R Series (GOES-R) Advanced Baseline Imager (ABI), we have encountered an image registration artifact which limits the accuracy of image offset estimation at the subpixel scale using image correlation. Where the two images to be registered have the same pixel size, subpixel image registration preferentially selects registration values where the image pixel boundaries are close to lined up. Because of the shape of a curve plotting input displacement to estimated offset, we call this a stair-step artifact. When one image is at a higher resolution than the other, the stair-step artifact is minimized by correlating at the higher resolution. For validating ABI image navigation, GOES-R images are correlated with Landsat-based ground truth maps. To create the ground truth map, the Landsat image is first transformed to the perspective seen from the GOES-R satellite, and then is scaled to an appropriate pixel size. Minimizing processing time motivates choosing the map pixels to be the same size as the GOES-R pixels. At this pixel size image processing of the shift estimate is efficient, but the stair-step artifact is present. If the map pixel is very small, stair-step is not a problem, but image correlation is computation-intensive. This paper describes simulation-based selection of the scale for truth maps for registering GOES-R ABI images.

stair-step artifact↗