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All sourcesNASA NTRSDOE OSTI
NASA NTRS2021

Augmenting Landsat time series with Harmonized Landsat Sentinel-2 data products: Assessment of spectral correspondence

An increase in the temporal revisit of satellite data is often sought to increase the likelihood of obtaining cloud- and shadow-free observations as well as to improve mapping of rapidly- or seasonally-changing features. Currently, as a tandem, Landsat-7 Enhanced Thematic Mapper Plus (ETM+) and −8 Operational Land Imager (OLI) provide an acquisition opportunity on an 8-day revisit interval. Sentinel-2A and -2B MultiSpectral Instrument (MSI), with a wider swath, have a 5-day revisit interval at the equator. Due to robust pre- and post-launch cross-calibration, it has been possible for NASA to produce the Harmonized Landsat Sentinel-2 (HLS) data product from Landsat-8 OLI and Sentinel-2 MSI: L30 and S30, respectively. Knowledge of the agreement of HLS outputs (especially S30) with historic Landsat surface reflectance products will inform the ability to integrate historic time-series information with new and more frequent measures as delivered by HLS. In this research, we control for acquisition date and data source to cross-compare the HLS data (L30, S30) with established Landsat-8 OLI surface-reflectance measures as delivered by the USGS (hereafter BAP, Best Available Pixel). S30 and L30 were found to have high agreement (R = 0.87–0.96) for spectral channels and an r = 0.99 for Normalized Burn Ratio (NBR) with low relative root-mean-square difference values (1.7%–3.3%). Agreement between L30 and BAP was lower, with R values ranging from 0.85 to 0.92 for spectral channels and R = 0.94 for NBR. S30 and BAP had the lowest agreement, with R values ranging from 0.71 to 0.85 for spectral channels and r = 0.90 for NBR. Comparisons indicated a stronger agreement at latitudes above 55° N. Some dependency between spectral agreement and land cover was found, with stronger correspondence for non-vegetated cover types. The level of agreement between S30 and BAP reported herein would enable integration of HLS outputs with historic Landsat data. The resulting increased temporal frequency of data allows for improvements to current cloud screening practices and increases data density and the likelihood of temporal proximity to target date for pixel compositing approaches. Furthermore, additional within-year observations will enable change products with a higher temporal fidelity and allow for the incorporation of phenological trends into land cover classification algorithms.

Michael A. Wulder↗
NASA NTRS1978

Application of multispectral scanner data to the study of an abandoned surface coal mine

The utility of aircraft multispectral scanner data for describing the land cover features of an abandoned contour-mined coal mine is considered. The data were obtained with an 11 band multispectral scanner at an altitude of 1.2 kilometers. Supervised, maximum-likelihood statistical classifications of the data were made to establish land-cover classes and also to describe in more detail the barren surface features as they may pertain to the reclamation or restoration of the area. The scanner data for the surface-water areas were studied to establish the variability and range of the spectral signatures. Both day and night thermal images of the area are presented. The results of the study show that a high degree of statistical separation can be obtained from the multispectral scanner data for the various land-cover features.

Spisz, E. W.↗
NASA NTRS1973

Structural lineaments of Gaspe from ERTS imagery

A test study was conducted to assess the value of ERTS images for mapping geologic features of the Gaspe Peninsula, Quebec. The specific objectives of the study were: 1) to ascertain the best procedure to follow in order to obtain valuable geologic data as a result of interpretation; and 2) to indicate in which way these data could relate to mineral exploration. Of the four spectral bands of the Multispectral scanner, the band from 700 to 800 nanometers, which seems to possess the best informational content for geologic study, was selected for analysis. The original ERTS image at a scale of 1:3,700,000 was enlarged about 15 times and reproduced on film. Geologically meaningful lines, called structural lineaments, were outlined and classified according to five categories: morpho-lithologic boundaries, morpho-lithologic lineaments, fault traces, fracture zones and undefined lineaments. Comparison with the geologic map of Gaspe shows that morpho-lithologic boundaries correspond to contacts between regional stratigraphic units. Morpholithologic lineaments follow bedding trends, whereas fracture traces appear as sets of parallel lineaments, intersecting at high angles the previous category of lineaments. Fault traces mark more precisely the location of faults already mapped and spot the presence of presumable faults, not indicated on the geologic map.

Steffensen, R.↗
NASA NTRS1973

Textural features for image classification

Description of some easily computable textural features based on gray-tone spatial dependances, and illustration of their application in category-identification tasks of three different kinds of image data - namely, photomicrographs of five kinds of sandstones, 1:20,000 panchromatic aerial photographs of eight land-use categories, and ERTS multispectral imagery containing several land-use categories. Two kinds of decision rules are used - one for which the decision regions are convex polyhedra (a piecewise-linear decision rule), and one for which the decision regions are rectangular parallelpipeds (a min-max decision rule). In each experiment the data set was divided into two parts, a training set and a test set. Test set identification accuracy is 89% for the photomicrographs, 82% for the aerial photographic imagery, and 83% for the satellite imagery. These results indicate that the easily computable textural features probably have a general applicability for a wide variety of image-classification applications.

Haralick, R. M.↗
NASA NTRS1984

Geodetic Accuracy of LANDSAT-4 Multispectral Scanner and Thematic Mapper Data

The geodetic accuracy of an MSS or TM scene is assessed using a minicomputer and appropriate software, a digitizer, and an image display device. The calculated image location of a selected feature is compared with the actual image location obtained though visual inspection of the image on the display. Measurements of 15 to 20 features evenly distributed throughout the image provide an estimate of the geodetic accuracy of the scene. Tests of two system-corrected MSS scenes measured geodetic registration root-mean-square (RMS) errors of approximately 3,200 m or 57 pixels. Tests of two TM system-corrected scenes measured RMS errors of approximately 1,250 and 1,000 m, or 44 and 35 pixels, respectively. All errors were primarily translational, implying good internal scene registration of both MSS and TM data. The one MSS GCP-corrected scene which was evaluated had an RMS error of approximately 325 m or 6 pixels.

Thormodsgard, J. M.↗
NASA NTRS1982

Honeywell multispectral linear array (MLA) instrument design A system overview

In support of NASA's Experimental Land Observing System, a simple, reliable and relatively inexpensive multispectral linear array (MLA) instrument concept has been developed. The design satisfies requirements for multichannel radiometry and wide field imaging. A reflective off-axis anastigmat telescope is mounted in a highly strength-to-weight efficient truss structure, coupled to a solid beamsplitter assembly. Attention is given to the optical design of the MLA instrument, the telescope structure, the detector arrays, detector cooling, on-orbit calibration, signal processing electronics, the spacecraft interfaces, the modulation transfer function, the signal-to-noise ratio, radiometric accuracy, and spatial registration.

Eyring, D.↗
NASA NTRS1994

Space Shuttle orbit determination using empirical force modeling of attitude maneuvers for the German MOMS-02/D2 mission

In the spring of 1993, the MOMS-02 (modular Optoelectronic Multispectral Scanner) camera, as part of the second German Spacelab mission aboard STS-55, successfully took digital threefold stereo images of the surface of the Earth. While the mission is experimental in nature, its primary goals are to produce high quality maps and three-dimensional digital terrain models of the Earth's surface. Considerable improvement in the quality of the terrain model can be attained if information about the position and attitude of the camera is included during the adjustment of the image data. One of the primary sources of error in the Shuttle's position is due to the significant attitude maneuvers conducted during the course of the mission. Various arcs, using actual Tracking and Data Relay Satellite (TDRSS) Doppler data of STS-55, were processed to determine how effectively empirical force modeling could be used to solve for the radial, transverse, and normal components of the orbit perturbations caused by these routine maneuvers. Results are presented in terms of overlap-orbit differences in the three components. Comparisons of these differences, before and after the maneuvers are estimated, show that the quality of an orbit can be greatly enhanced with this technique, even if several maneuvers are present. Finally, a discussion is made of some of the difficulties encountered with this approach, and some ideas for future studies are presented.

Vonbraun, C.↗
NASA NTRS1998

Remote Sensing Measurements of Vertical and Horizontal Moisture Variations from Aircraft Instruments

The research in this paper focuses on describing vertical and horizontal of water vapor variability using two remote sensing aircraft instruments. To achieve this goal we will compare precipitable water and upper level humidity estimates derived from the each of the instruments. The Multispectral Atmospheric Mapping Sensor (MAMS) is a visible and infrared radiometer with similar channels to that of the GOES imager. MAMS has flown aboard the NASA ER-2 numerous times. It has been used to validate features observed with the previous series of GOES satellites. MAMS data has been used to study precipitable water and upper level water vapor as well as other geophysical parameters. MAMS provides the opportunity to obtain water vapor Imagery at 6.7 mm. Upper tropospheric humidity can be computed using this channel in a similar fashion to that of Soden and Bretherton. In addition to the water vapor channel, MAMS records data In 3 other Infrared channels and 8 visible and near Infrared bands at high spatial resolution (I 00 Abstract: m). The 1 1 and 12 mm infrared channels allow for the application of a split technique to derive total precipitable water. The Udar Atmospheric Sensing Experiment (LASE) which uses the Differential Absorption Udar (DIAL) technique for obtaining simultaneous water vapor and aerosol profiles through the entire troposphere. LASE operates In the 81 5 nm wavelength region and uses a double pulsed Ti:sapphire laser that is locked onto a water vapor line. LASE has good horizontal (IO km) and excellent vertical (300 m) resolution. MAMS and LASE collected data simultaneously on several ER-2 flights in September 1995. LASE mixing ratio profiles will be Integrated for comparison with MAMS precipitable water estimates and the upper tropospheric humidity will be computed for the layer observed by the MAMS 6.7 mm channel for comparison for this time period. Results show a significant correlation between the measurements of the two Instruments. Regions of high/low upper tropospheric humidity are apparent In measurements from both instruments. Also changes in boundary layer moisture depicted by LASE are reflected in the total precipitable water measured by MAMS.

Atkinson, R. J.↗
NASA NTRS2003

Validation of ADAR System 5500 Digital Imagery: Delivery Task Order #1, Task Request #857 - Brookings, SD

This work was performed under NASA's Verification and Validation Program as an independent check of data supplied by Positive Systems, Inc. through the Earth Science Enterprise's Scientific Data Purchase (SDP) Program. This document serves as the basis for reporting results associated with validation of multispectral imagery according to the specifications of contract NAS 13-98049. The validation was performed under the Positive Systems Imaging System Validation Work Instruction CRSP-WI-28: Spectral registration, spatial resolution, endlaps, sidelaps, and image quality were evaluated. The validation was proceded by Shipment Verification, as described in the Work Instruction CRSP-WI-22: Every image was passed through an automatic ingest verification and thumbnail review process to identify omissions, problems with media integrity, and gross errors in data quality. Validation of metadata files is not within the scope of this report, but it was performed separately.

Blonski, Slawomir↗
NASA NTRS2004

Combined Infrared Stereo and Laser Ranging Cloud Measurements from Shuttle Mission STS-85

Multiangle remote sensing provides a wealth of information for earth and climate monitoring, such as the ability to measure the height of cloud tops through stereoscopic imaging. As technology advances so do the options for developing spacecraft instrumentation versatile enough to meet the demands associated with multiangle measurements. One such instrument is the infrared spectral imaging radiometer, which flew as part of mission STS-85 of the space shuttle in 1997 and was the first earth- observing radiometer to incorporate an uncooled microbolometer array detector as its image sensor. Specifically, a method for computing cloud-top height with a precision of +/- 620 m from the multispectral stereo measurements acquired during this flight has been developed, and the results are compared with coincident direct laser ranging measurements from the shuttle laser altimeter. Mission STS-85 was the first space flight to combine laser ranging and thermal IR camera systems for cloud remote sensing.

Lancaster, R. S.↗
NASA NTRS2021

Assessing within-Field Corn and Soybean Yield Variability from WorldView-3, Planet, Sentinel-2, and Landsat 8 Satellite Imagery

Crop yield monitoring is an important component in agricultural assessment. Multispectral remote sensing instruments onboard space-borne platforms such as Advanced Very High Resolution Radiometer (AVHRR), Moderate Resolution Imaging Spectroradiometer (MODIS), and Visible Infrared Imaging Radiometer Suite (VIIRS) have shown to be useful for efficiently generating timely and synoptic information on the yield status of crops across regional levels. However, the coarse spatial resolution data inherent to these sensors provides little utility at the management level. Recent satellite imagery collection advances toward finer spatial resolution (down to 1 m) alongside increased observational cadence (near daily) implies information on crops obtainable at field and within-field scales to support farming needs is now possible. To test this premise, we focus on assessing the efficiency of multiple satellite sensors, namely WorldView-3, Planet/Dove-Classic, Sentinel-2, and Landsat 8 (through Harmonized Landsat Sentinel-2 (HLS)), and investigate their spatial, spectral (surface reflectance (SR) and vegetation indices (VIs)), and temporal characteristics to estimate corn and soybean yields at sub-field scales within study sites in the US state of Iowa. Precision yield data as referenced to combine harvesters’ GPS systems were used for validation. We show that imagery spatial resolution of 3 m is critical to explaining 100% of the within-field yield variability for corn and soybean. Our simulation results show that moving to coarser resolution data of 10 m, 20 m, and 30 m reduced the explained variability to 86%, 72%, and 59%, respectively. We show that the most important spectral bands explaining yield variability were green (0.560 µm), red-edge (0.726 µm), and near-infrared (NIR - 0.865 µm). Furthermore, the high temporal frequency of Planet and a combination of Sentinel-2/Landsat 8 (HLS) data allowed for optimal date selection for yield map generation. Overall, we observed mixed performance of satellite-derived models with the coefficient of determination (R^2) varying from 0.21 to 0.88 (averaging 0.56) for the 30 m HLS and from 0.09 to 0.77 (averaging 0.30) for 3 m Planet. R^2 was lower for fields with higher yields, suggesting saturation of the satellite-collected reflectance features in those cases. Therefore, other biophysical variables, such as soil moisture and evapotranspiration, at similar fine spatial resolutions are likely needed alongside the optical imagery to fully explain the yields.

agriculture; yield; within-field; corn; soybean; r↗
NASA NTRS2023

Bhutan Agriculture III: Monitoring Cropland Changes in Bhutan using Remote Sensing to Bolster Food Security and Support Crop Monitoring

The Bhutan Agriculture III team aimed to improve agricultural efficiency in Bhutan. Bhutan is a nation heavily reliant on agriculture, but it faces challenges such as geophysical limitations and lack of scientific agricultural practice. The team partnered with a primary end user, Bhutan’s Department of Agriculture (DoA), and with collaborators; the Bhutan Foundation, National Plant Protection Centre (NPPC), Agricultural Research Department Centre (ARDC), National Statistics Bureau (NSB), and the Ugyen Wangchuck Institute for Conservation and Environment Research (UWICER). Advised by NASA SERVIR, the team developed crop masks and monitored rice distribution from 2015 to 2022 utilizing Earth observations such as Landsat 8 Operational Land Imager (OLI), Landsat 9 OLI-2, Sentinel-1 C-Band Synthetic Aperture Radar (C-SAR), Sentinel-2 MultiSpectral Instrument (MSI) and Shuttle Radar Topography Mission (SRTM). The team gathered 5,000 points from the five dzongkhags that yield the most rice in Bhutan (Paro, Punakha, Samtse, Sarpang and Wangue Phodrang) using Collect Earth Online (CEO). With the data collected, the team split the data into training and validation data on Google Earth Engine (GEE) for a random forest (RF) classifier for rice and non-rice classification. After running the data on the Random Forest (RF) model, the team got an accuracy score of 81.48%, a kappa score of 55.75% and an F1 score of 86.11%. This data supports better agricultural decision-making for the governing body of Bhutan, helps enhance farming efficiency and foster sustainable practices, assists in overcoming data inaccuracy and bolsters food security in the country.

Sonam Seldon Tshering↗
NASA NTRS2000

Earth Observing-1 Advanced Land Imager: Radiometric Response Calibration

The Advanced Land Imager (ALI) is one of three instruments to be flown on the first Earth Observing mission (EO-1) under NASA's New Millennium Program (NMP). ALI contains a number of innovative features, including a wide field of view optical design, compact multispectral focal plane arrays, non-cryogenic HgCdTe detectors for the short wave infrared bands, and silicon carbide optics. This document outlines the techniques adopted during ground calibration of the radiometric response of the Advanced Land Imager. Results from system level measurements of the instrument response, signal-to-noise ratio, saturation radiance, and dynamic range for all detectors of every spectral band are also presented.

Mendenhall, J. A.↗
NASA NTRS2005

Techniques for Producing Coastal Land Water Masks from Landsat and Other Multispectral Satellite Data

Coastal erosion and land loss continue to threaten many areas in the United States. Landsat data has been used to monitor regional coastal change since the 1970s. Many techniques can be used to produce coastal land water masks, including image classification and density slicing of individual bands or of band ratios. Band ratios used in land water detection include several variations of the Normalized Difference Water Index (NDWI). This poster discusses a study that compares land water masks computed from unsupervised Landsat image classification with masks from density-sliced band ratios and from the Landsat TM band 5. The greater New Orleans area is employed in this study, due to its abundance of coastal habitats and its vulnerability to coastal land loss. Image classification produced the best results based on visual comparison to higher resolution satellite and aerial image displays. However, density sliced NDWI imagery from either near infrared (NIR) and blue bands or from NIR and green bands also produced more effective land water masks than imagery from the density-sliced Landsat TM band 5. NDWI based on NIR and green bands is noteworthy because it allows land water masks to be generated from multispectral satellite sensors without a blue band (e.g., ASTER and Landsat MSS). NDWI techniques also have potential for producing land water masks from coarser scaled satellite data, such as MODIS.

Spruce, Joseph P.↗
NASA NTRS1992

The simulation of multispectral composite satellite imagery on 8-bit color workstations

A technique is described which combines up to three channels of imagery into a single 8-bit composite color image. Each image controls a specific color or colors (red, green, or blue) in the resultant color composite image, allowing for improved interpretation of scene characteristics. The technique is a useful tool for the forecaster and satellite image analyst. In addition to providing the capability of displaying multichannel images on an 8-bit desktop color workstation, the technique allows sophisticated image manipulation to be performed.

Griffin, Michael K.↗
NASA NTRS2005

Techniques for Producing Coastal Land Water Masks from Landsat and Other Multispectral Satellite Data

Coastal erosion and land loss continue to threaten many areas in the United States. Landsat data has been used to monitor regional coastal change since the 1970's. Many techniques can be used to produce coastal land water masks, including image classification and density slicing of individual bands or of band ratios. Band ratios used in land water detection include several variations of the Normalized Difference Water Index (NDWI). This poster discusses a study that compares land water masks computed from unsupervised Landsat image classification with masks from density-sliced band ratios and from the Landsat TM band 5. The greater New Orleans area is imployed in this study, due to its abundance of coastal habitats and ist vulnerability to coastal land loss. Image classification produced the best results based on visual comparison to higher resolution satellite and aerial image displays. However, density-sliced NDWI imagery from either near infrared (NIR) and blue bands or from NIR and green bands also produced more effective land water masks than imagery from the density-sliced Landsat TM band 5. NDWI based on NIR and green bands is noteworthy because it allows land water masks to be generated form multispectral satellite sensors without a blue band (e.g., ASTER and Landsat MSS). NDWI techniques also have potential for producing land water masks from coarser scaled satellite data, such as MODIS.

Spruce, Joe↗
NASA NTRS2008

Advanced Land Imager Assessment System

The Advanced Land Imager Assessment System (ALIAS) supports radiometric and geometric image processing for the Advanced Land Imager (ALI) instrument onboard NASA s Earth Observing-1 (EO-1) satellite. ALIAS consists of two processing subsystems for radiometric and geometric processing of the ALI s multispectral imagery. The radiometric processing subsystem characterizes and corrects, where possible, radiometric qualities including: coherent, impulse; and random noise; signal-to-noise ratios (SNRs); detector operability; gain; bias; saturation levels; striping and banding; and the stability of detector performance. The geometric processing subsystem and analysis capabilities support sensor alignment calibrations, sensor chip assembly (SCA)-to-SCA alignments and band-to-band alignment; and perform geodetic accuracy assessments, modulation transfer function (MTF) characterizations, and image-to-image characterizations. ALIAS also characterizes and corrects band-toband registration, and performs systematic precision and terrain correction of ALI images. This system can geometrically correct, and automatically mosaic, the SCA image strips into a seamless, map-projected image. This system provides a large database, which enables bulk trending for all ALI image data and significant instrument telemetry. Bulk trending consists of two functions: Housekeeping Processing and Bulk Radiometric Processing. The Housekeeping function pulls telemetry and temperature information from the instrument housekeeping files and writes this information to a database for trending. The Bulk Radiometric Processing function writes statistical information from the dark data acquired before and after the Earth imagery and the lamp data to the database for trending. This allows for multi-scene statistical analyses.

Chander, Gyanesh↗
NASA NTRS1973

Performance of optical surfaces in space.

The Multispectral Scanner carried by the ERTS-1 satellite (launched on July 23, 1972) is a very high resolution line scanning imaging radiometer operating in the visible and near infrared spectral regions. The instrument contains optical surfaces that are exposed to both direct and reflected sunlight, and the reflectivity and transmission of some of these surfaces have decreased in orbit. Details of the optical system are explained, and data on the degradation are reviewed as a function of both spectral interval and time in orbit. The history of the scanner during testing (when hydrocarbons from an external source may have been deposited on the optical surfaces) is also examined, and a mechanism is postulated for the observed degradation in orbit.

Horan, J. J.↗