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At least 73 records · Page 4

Uncertainties in the Geostationary Ocean Color Imager (GOCI) Remote Sensing Reflectance for Assessing Diurnal Variability of Biogeochemical Processes

Short-term (sub-diurnal) biological and biogeochemical processes cannot be fully captured by the current suite of polar-orbiting satellite ocean color sensors, as their temporal resolution is limited to potentially one clear image per day. Geostationary sensors, such as the Geostationary Ocean Color Imager (GOCI) from the Republic of Korea, allow the study of these short-term processes because their orbit permit the collection of multiple images throughout each day for any area within the sensor’s field of regard. Assessing the capability to detect sub-diurnal changes in in-water properties caused by physical and biogeochemical processes characteristic of open ocean and coastal ocean ecosystems, however, requires an understanding of the uncertainties introduced by the instrument and/or geophysical retrieval algorithms. This work presents a study of the uncertainties during the daytime period for an ocean region with characteristically low-productivity with the assumption that only small and undetectable changes occur in the in-water properties due to biogeochemical processes during the daytime period. The complete GOCI mission data were processed using NASA’s SeaDAS/l2gen package. The assumption of homogeneity of the study region was tested using three-day sequences and diurnal statistics. This assumption was found to hold based on the minimal diurnal and day-to-day variability in GOCI data products. Relative differences with respect to the midday value were calculated for each hourly observation of the day in order to investigate what time of the day the variability is greater. Also, the influence of the solar zenith angle in the retrieval of remote sensing reflectances and derived products was examined. Finally, we determined that the uncertainties in water-leaving “remote-sensing” reflectance (Rrs) for the 412,443, 490, 555, 660 and 680 nm bands on GOCI are 8.05 x 10(exp -4), 5.49 x 10(exp -4), 4.48 x 10(exp -4), 2.51 x 10(exp -4), 8.83 x 10(exp -5), and 1.36 x 10(exp -4)/sr, respectively, and 1.09 x 10(exp -2)/cu.mgm for the chlorophyll-a concentration (Chl-a), 2.09 x 10(exp -3)/m for the absorption coefficient of chromophoric dissolved organic matter at 412 nm (a(sub g) (412)), and 3.7 mg/cu.m for particulate organic carbon (POC). These R(sub rs) values can be considered the threshold values for detectable changes of the in-water properties due to biological, physical or biogeochemical processes from GOCI.

Concha, Javier↗

Remote sensing images, DEM, and point clouds associated with “Accuracy evaluation of cost-effective 3D reconstruction approaches for hydrobiogeochemical processes in non-perennial stream riverbeds”

This data package is associated with the publication “Accuracy evaluation of cost-effective 3D reconstruction approaches for hydrobiogeochemical processes in non-perennial stream riverbeds” published in Frontiers in Environmental Science, Environmental Informatics and Remote Sensing (Bao et al., 2026; doi: 10.3389/fenvs.2026.1725258). This data package includes the drone photos for a section of Umtanum Creek in Washington, Unted States. The photos were used to reconstruct the 3-dimensional (3D) digital elevation model (DEM) of the riverbed for the investigated stream section. The reconstruction results from four approaches are provided: (1) unoccupied aerial vehicle (UAV, colloquially known as drone) imagery-based Structure-from-Motion (SfM), (2) a machine learning-based 3D reconstruction model, Visual Geometry Grounded Deep Structure from Motion (VGGSfM), (3) Visual Geometry Grounded Transformer for long sequence of images (VGGT-Long), and (4) handheld smartphone LiDAR scanning. The ground truth measurements by tripod-mounted optical level kit and ground control points GPS locations for evaluating the accuracy of the four reconstruction approaches are also provided in this data package. A preliminary version of this data package was published in October 2025 at the time of manuscript submission. It was updated in March 2026, at the time of manuscript acceptance, to include additional metadata (this readme, data dictionary, and file level metadata). The data did not change. For details on how to navigate data packages generated by this project, see https://data.ess-dive.lbl.gov/portals/PNNLRiverCorridorSFA/About. In addition to a readme, this data package also includes a file-level metadata (FLMD) file that describes each file and a data dictionary (DD) that describes all column/row headers and variable definitions. This dataset is comprised of (1) 8 folders; (2) the detailed flight configuration html files; (3) field metadata; (4) a readme; (5) a data dictionary; and (6) file-level metadata. The folders “2024_10_18_d01” and “2024_10_18_d02” contain the original drone photos for the two drone flights (d01 and d02) on October 18, 2024. The reconstruction results from each of the approaches are in the folders called “ODM_SfM”, “VGGSfM”, “VGGTLong”, and “LiDAR”. The ground truth measurements are in the folder called “optical_level_kit”. Lastly, results comparing the different approaches are in the folder called “comparisons”. All files are .csv, .html, .jpg, .obj, .txt, and .npy. For information on using the .obj and .npy files, see the readme files within the same folder as the files.

54 ENVIRONMENTAL SCIENCES↗

FLIPPER: Validation for Remote Ocean Imaging

one of the determining factors in the planet s ability to support life is the same factor that makes the Blue Planet blue: water. Therefore, NASA researchers have a focused interest in understanding Earth s oceans and their ability to continue sustaining life. A critical objective in this study is to understand the global processes that control the changes of carbon and associated living elements in the oceans. Since oceans are so large, one of the most widely used methods of this research is remote sensing, using satellites to observe changes in the ocean color that may be indicative of changes occurring at the surface. Major changes in carbon are due to photosynthesis conducted by phytoplankton, showing, among other things, which areas are sustaining life. Although valuable for large-scale pictures of an ocean, remote sensing really only provides a surface, and therefore incomplete, depiction of that ocean s sustainability. True and complete testing of the water requires local testing in conjunction with the satellite images in order to generate the necessary algorithm parameters to calculate ocean health. For this reason, NASA has spearheaded research to provide onsite validation for its satellite imagery surveys.

Source record↗

Atmospheric correction of remotely sensed image data by a simplified model

The interaction of radiation with the atmosphere is complex and has proved difficult to calculate without reference to measurements made at or close to the time and location of interest. In this paper we describe the use of data from an airborne-tracking sunphotometer mounted on a NASA C-130 aircraft to derive optical properties of the atmospheric aerosols above the aircraft when it is on the ground as well as when the aircraft is at altitude collecting remote sensing data. Furthermore, we describe the use of these optical properties for quantitative atmospheric correction of Landsat Thematic Mapper (TM) and NS001 TM simulator radiances in a simplified radiative transfer model appropriate for each pixel of entire images. Both qualitative and quantitative comparisons of results from use of the model show it provides good to excellent correction for atmospheric effects in TM and NS001 TM simulator data. Quantitative comparisons with measurements of surface radiances measured near the time of the TM overpass showed agreement within a few percent for bands 2, 3, and 4. Band 1 yielded radiances within 15 percent of the surface radiances. We think the simplified atmospheric correction model shows great promise for applying atmospheric corrections to entire images instead of individual points within images to yield surface radiances within a few percent in a computationally efficient manner.

Wrigley, R. C.↗

A Vision of Quantitative Imaging Technology for Validation of Advanced Flight Technologies

Flight-testing is traditionally an expensive but critical element in the development and ultimate validation and certification of technologies destined for future operational capabilities. Measurements obtained in relevant flight environments also provide unique opportunities to observe flow phenomenon that are often beyond the capabilities of ground testing facilities and computational tools to simulate or duplicate. However, the challenges of minimizing vehicle weight and internal complexity as well as instrumentation bandwidth limitations often restrict the ability to make high-density, in-situ measurements with discrete sensors. Remote imaging offers a potential opportunity to noninvasively obtain such flight data in a complementary fashion. The NASA Hypersonic Thermodynamic Infrared Measurements Project has demonstrated such a capability to obtain calibrated thermal imagery on a hypersonic vehicle in flight. Through the application of existing and accessible technologies, the acreage surface temperature of the Shuttle lower surface was measured during reentry. Future hypersonic cruise vehicles, launcher configurations and reentry vehicles will, however, challenge current remote imaging capability. As NASA embarks on the design and deployment of a new Space Launch System architecture for access beyond earth orbit (and the commercial sector focused on low earth orbit), an opportunity exists to implement an imagery system and its supporting infrastructure that provides sufficient flexibility to incorporate changing technology to address the future needs of the flight test community. A long term vision is offered that supports the application of advanced multi-waveband sensing technology to aid in the development of future aerospace systems and critical technologies to enable highly responsive vehicle operations across the aerospace continuum, spanning launch, reusable space access and global reach. Motivations for development of an Agency level imagery-based measurement capability to support cross cutting applications that span the Agency mission directorates as well as meeting potential needs of the commercial sector and national interests of the Intelligence, Surveillance and Reconnaissance community are explored. A recommendation is made for an assessment study to baseline current imaging technology including the identification of future mission requirements. Development of requirements fostered by the applications suggested in this paper would be used to identify technology gaps and direct roadmapping for implementation of an affordable and sustainable next generation sensor/platform system.

Horvath, Thomas J.↗

Global Auroral Imaging as a Remote Diagnostic of Geospace

Images of the Earth's aurora, taken from space, can be used to examine plasma behavior throughout the magnetospheric regions surrounding the earth. The coupling of the magnetospheric plasmas through the ionosphere are discussed. A summary of past and current imaging technology is given and then specific examples of remote sensing are given using images from the Ultraviolet Imager aboard the POLAR satellite.

Germany, G. A.↗

Balanced Expertise Distribution in Remote Ultrasound Imaging Aboard The International Space Station (ISS)

Astronaut training for ISS operations usually ensures independent performance. With small crew size same crews also conduct all science work onboard. With diverse backgrounds, a good "match" between the existing and required skills can only be anecdotal. Furthermore, full proficiency in most of the complex tasks can be attained only through long training and practice, which may not be justified and may be impossible given the scarcity of training time. To enable a number of operational and science advancements, authors have developed a new approach to expertise distribution in time and among the space and ground personnel. Methods: As part of NASA Operational Ultrasound Project (1998-2003) and the NASA-solicited experiment "Advanced Diagnostic Ultrasound in Microgravity-ADUM" (P.I. -S.D., ongoing), the authors have created a "Balanced Expertise Distribution" approach to perform complex ultrasound imaging tasks on ISS for both operational and science use. The four components of expertise are a) any pre-existing pertinent expertise; b) limited preflight training c) adaptive onboard proficiency enhancement tools; d) real-time ' guidance from the ground. Throughout the pre-flight training and flight time preceding the experiments, the four components are shaped in a dynamic fashion to meet in an optimum combination during the experiment sessions. Results: Procedure validation sessions and feasibility studies have given encouraging results. While several successful real-time remote guidance sessions have been conducted on ISS, Expedition 8 is the first to use an "on-orbit proficiency enhancement" tool. Conclusions: In spite of severely limited training time, daring peer-reviewed research and operational enhancements are feasible through a balanced distribution of expertise in time, as well as among the crewmembers and ground personnel. This approach shows great promise for biomedical research, but may be applicable for other areas of micro gravity-based science

Sargsyan, Ashot↗

A solid state multispectral imager.

Photodiode arrays used as image sensors in satellite remote imaging systems offer the advantages of geometric accuracy, precise registration of scene elements, enhanced operational reliability, and wide spectral response. Principles of photodiode-array operation are reviewed, and the latest developments in self-scanned array chip technology are described along with optical system requirements for multispectral coverage. Results of development efforts with a spaceborne solid-state multispectral camera based on linear sensing array technology are outlined to demonstrate the capabilities of this approach to multispectral sensing.

Green, A.↗

Preparation of remotely-sensed image data for land use planning

Preliminary processing operations required before computer compatible tapes (CCT) can be utilized routinely to realize the full potential of ERTS imagery for rapid mensuration, quantitative estimation and for detecting and monitoring changes over time are described. Geographic referencing, geometric manipulations, classification, and association overlays (as of political boundaries) are discussed.

Bond, A. D.↗

An application of expert systems technology to remotely sensed image analysis

In October of 1983, a project was initiated at NASA/Ames Research Center to design a prototype expert system capable of producing a preliminary land cover classification from an unsupervised classification of Landsat Multispectral Scanner (MSS) imagery and associated collateral data. This paper outlines the problem of determining land cover classes from an unsupervised classification, presents a brief overview of expert systems, and continues with a description of the design and current state of implementation of NASA/Ames' prototype Landsat MSS image analysis expert system.

Erickson, W. K.↗

Applications of high-resolution remote sensing image data

There are many situations in which the image resolution of satellite data is insufficient to provide the detail required for resource management and environmental monitoring. This paper will focus on applications of high-resolution (0.4 to 10 m) airborne multispectral and imaging spectrometer data acquired in Canada using the MEIS II multispectral line imager and the PMI imaging spectrometer. Applications discussed will include forestry, mapping, and geobotany.

Strome, W. M.↗

An investigative study of multispectral data compression for remotely-sensed images using vector quantization and difference-mapped shift-coding

A study is conducted to investigate the effects and advantages of data compression techniques on multispectral imagery data acquired by NASA's airborne scanners at the Stennis Space Center. The first technique used was vector quantization. The vector is defined in the multispectral imagery context as an array of pixels from the same location from each channel. The error obtained in substituting the reconstructed images for the original set is compared for different compression ratios. Also, the eigenvalues of the covariance matrix obtained from the reconstructed data set are compared with the eigenvalues of the original set. The effects of varying the size of the vector codebook on the quality of the compression and on subsequent classification are also presented. The output data from the Vector Quantization algorithm was further compressed by a lossless technique called Difference-mapped Shift-extended Huffman coding. The overall compression for 7 channels of data acquired by the Calibrated Airborne Multispectral Scanner (CAMS), with an RMS error of 15.8 pixels was 195:1 (0.41 bpp) and with an RMS error of 3.6 pixels was 18:1 (.447 bpp). The algorithms were implemented in software and interfaced with the help of dedicated image processing boards to an 80386 PC compatible computer. Modules were developed for the task of image compression and image analysis. Also, supporting software to perform image processing for visual display and interpretation of the compressed/classified images was developed.

Jaggi, S.↗

Remotely sensed image compression based on wavelet transform

In this paper, we present an image compression algorithm that is capable of significantly reducing the vast amount of information contained in multispectral images. The developed algorithm exploits the spectral and spatial correlations found in multispectral images. The scheme encodes the difference between images after contrast/brightness equalization to remove the spectral redundancy, and utilizes a two-dimensional wavelet transform to remove the spatial redundancy. the transformed images are then encoded by Hilbert-curve scanning and run-length-encoding, followed by Huffman coding. We also present the performance of the proposed algorithm with the LANDSAT MultiSpectral Scanner data. The loss of information is evaluated by PSNR (peak signal to noise ratio) and classification capability.

Kim, Seong W.↗