Airborne Visible/Infrared Imaging spectrometer AVIS: Design, characterization and calibration

The Airborne Visible/Infrared imaging Spectrometer AVIS is a hyperspectral imager designed for environmental monitoring purposes. The sensor, which was constructed entirely from commercially available components, has been successfully deployed during several experiments between 1999 and 2007. We describe the instrument design and present the results of laboratory characterization and calibration of the system's second generation, AVIS-2, which is currently being operated. The processing of the data is described and examples of remote sensing reflectance data are presented

Earth Observations and Integrative Models in Support of Food and Water Security

Global food production depends upon many factors that Earth observing satellites routinely measure about water, energy, weather, and ecosystems. Increasingly sophisticated, publicly-available satellite data products can improve efficiencies in resource management and provide earlier indication of environmental disruption. Satellite remote sensing provides a consistent, long-term record that can be used effectively to detect large-scale features over time, such as a developing drought. Accuracy and capabilities have increased along with the range of Earth observations and derived products that can support food security decisions with actionable information. This paper highlights major capabilities facilitated by satellite observations and physical models that have been developed and validated using remotely-sensed observations. Although we primarily focus on variables relevant to agriculture, we also include a brief description of the growing use of Earth observations in support of aquaculture and fisheries

HIRIS (High-Resolution Imaging Spectrometer: Science opportunities for the 1990s. Earth observing system. Volume 2C: Instrument panel report

The high-resolution imaging spectrometer (HIRIS) is an Earth Observing System (EOS) sensor developed for high spatial and spectral resolution. It can acquire more information in the 0.4 to 2.5 micrometer spectral region than any other sensor yet envisioned. Its capability for critical sampling at high spatial resolution makes it an ideal complement to the MODIS (moderate-resolution imaging spectrometer) and HMMR (high-resolution multifrequency microwave radiometer), lower resolution sensors designed for repetitive coverage. With HIRIS it is possible to observe transient processes in a multistage remote sensing strategy for Earth observations on a global scale. The objectives, science requirements, and current sensor design of the HIRIS are discussed along with the synergism of the sensor with other EOS instruments and data handling and processing requirements

Hyperspectral remote sensing of cyanobacterial pigments as indicators for cell populations and toxins in eutrophic lakes

The growth of mass populations of toxin-producing cyanobacteria is a serious concern for the ecological status of inland waterbodies and for human and animal health. In this study we examined the performance of four semi-analytical algorithms for the retrieval of chlorophyll a (Chl a) and phycocyanin (C-PC) from data acquired by the Compact Airborne Spectrographic Imager-2 (CASI-2) and the Airborne Imaging Spectrometer for Applications (AISA) Eagle sensor. The retrieval accuracies of the semi-analytical models were compared to those returned by optimally calibrated empirical band-ratio algorithms. The best-performing algorithm for the retrieval of Chl a was an empirical band-ratio model based on a quadratic function of the ratio of re!ectance at 710 and 670 nm (R2=0.832; RMSE=29.8%). However, this model only provided a marginally better retrieval than the best semi-analytical algorithm. The best-performing model for the retrieval of C-PC was a semi-analytical nested band-ratio model (R2=0.984; RMSE=3.98 mg m−3). The concentrations of C-PC retrieved using the semi-analytical model were correlated with cyanobacterial cell numbers (R2=0.380) and the particulate and total (particulate plus dissolved) pools of microcystins (R2=0.858 and 0.896 respectively). Importantly, both the empirical and semi-analytical algorithms were able to retrieve the concentration of C-PC at cyanobacterial cell concentrations below current warning thresholds for cyanobacteria in waterbodies. This demonstrates the potential of remote sensing to contribute to early-warning detection and monitoring of cyanobacterial blooms for human health protection at regional and global scales

The NASA SBIR product catalog

The purpose of this catalog is to assist small business firms in making the community aware of products emerging from their efforts in the Small Business Innovation Research (SBIR) program. It contains descriptions of some products that have advanced into Phase 3 and others that are identified as prospective products. Both lists of products in this catalog are based on information supplied by NASA SBIR contractors in responding to an invitation to be represented in this document. Generally, all products suggested by the small firms were included in order to meet the goals of information exchange for SBIR results. Of the 444 SBIR contractors NASA queried, 137 provided information on 219 products. The catalog presents the product information in the technology areas listed in the table of contents. Within each area, the products are listed in alphabetical order by product name and are given identifying numbers. Also included is an alphabetical listing of the companies that have products described. This listing cross-references the product list and provides information on the business activity of each firm. In addition, there are three indexes: one a list of firms by states, one that lists the products according to NASA Centers that managed the SBIR projects, and one that lists the products by the relevant Technical Topics utilized in NASA's annual program solicitation under which each SBIR project was selected

Generation, validation, and application of abundance map reference data for spectral unmixing

Reference data ( ground truth ) maps traditionally have been used to assess the accuracy of imaging spectrometer classification algorithms. However, these reference data can be prohibitively expensive to produce, often do not include sub-pixel abundance estimates necessary to assess spectral unmixing algorithms, and lack published validation reports. Our research proposes methodologies to efficiently generate, validate, and apply abundance map reference data (AMRD) to airborne remote sensing scenes. We generated scene-wide AMRD for three different remote sensing scenes using our remotely sensed reference data (RSRD) technique, which spatially aggregates unmixing results from fine scale imagery (e.g., 1-m Ground Sample Distance (GSD)) to co-located coarse scale imagery (e.g., 10-m GSD or larger). We validated the accuracy of this methodology by estimating AMRD in 51 randomly-selected 10 m x 10 m plots, using seven independent methods and observers, including field surveys by two observers, imagery analysis by two observers, and RSRD using three algorithms. Results indicated statistically-significant differences between all versions of AMRD, suggesting that all forms of reference data need to be validated. Given these significant differences between the independent versions of AMRD, we proposed that the mean of all (MOA) versions of reference data for each plot and class were most likely to represent true abundances. We then compared each version of AMRD to MOA. Best case accuracy was achieved by a version of imagery analysis, which had a mean coverage area error of 2.0%, with a standard deviation of 5.6%. One of the RSRD algorithms was nearly as accurate, achieving a mean error of 3.0%, with a standard deviation of 6.3%, showing the potential of RSRD-based AMRD generation. Application of validated AMRD to specific coarse scale imagery involved three main parts: 1) spatial alignment of coarse and fine scale imagery, 2) aggregation of fine scale abundances to produce coarse scale imagery-specific AMRD, and 3) demonstration of comparisons between coarse scale unmixing abundances and AMRD. Spatial alignment was performed using our scene-wide spectral comparison (SWSC) algorithm, which aligned imagery with accuracy approaching the distance of a single fine scale pixel. We compared simple rectangular aggregation to coarse sensor point spread function (PSF) aggregation, and found that the PSF approach returned lower error, but that rectangular aggregation more accurately estimated true abundances at ground level. We demonstrated various metrics for comparing unmixing results to AMRD, including mean absolute error (MAE) and linear regression (LR). We additionally introduced reference data mean adjusted MAE (MA-MAE), and reference data confidence interval adjusted MAE (CIA-MAE), which account for known error in the reference data itself. MA-MAE analysis indicated that fully constrained linear unmixing of coarse scale imagery across all three scenes returned an error of 10.83% per class and pixel, with regression analysis yielding a slope = 0.85, intercept = 0.04, and R^2 = 0.81. Our reference data research has demonstrated a viable methodology to efficiently generate, validate, and apply AMRD to specific examples of airborne remote sensing imagery, thereby enabling direct quantitative assessment of spectral unmixing performance

SeaWiFS technical report series. Volume 6: SeaWiFS technical report series cumulative index: Volumes 1-5

The Sea-viewing Wide Field-of-view Sensor (SeaWiFS) is the follow-on ocean color instrument to the Coastal Zone Color Scanner (CZCS), which ceased operations in 1986, after an eight year mission. SeaWiFS is expected to be launched in August 1993, on the Sea Star satellite, being built by Orbital Sciences Corporation (OSC). The SeaWiFS Project at the NASA/Goddard Space Flight Center (GSFC) has undertaken the responsibility of documenting all aspects of this mission, which is critical to the ocean color and marine science communities. This documentation, entitled the SeaWiFS Technical Report Series, is in the form of NASA Technical Memoranda Number 104566. All reports published are volumes within the series. This volume serves as a reference, or guidebook, to the previous five volumes and consists of four main sections including an index to key words and phrases, a list of all references cited, and lists of acronyms and symbols used. It is our intention to publish a summary index of this type after every five volumes in the series. This will cover the topics published in all previous editions of the indices, that is, each new index will include all of the information contained in the preceding indices

NASA's surface biology and geology designated observable: A perspective on surface imaging algorithms

The 2017–2027 National Academies' Decadal Survey, Thriving on Our Changing Planet, recommended Surface Biology and Geology (SBG) as a “Designated Targeted Observable” (DO). The SBG DO is based on the need for capabilities to acquire global, high spatial resolution, visible to shortwave infrared (VSWIR; 380–2500 nm; ~30 m pixel resolution) hyperspectral (imaging spectroscopy) and multispectral midwave and thermal infrared (MWIR: 3–5 μm; TIR: 8–12 μm; ~60 m pixel resolution) measurements with sub-monthly temporal revisits over terrestrial, freshwater, and coastal marine habitats. To address the various mission design needs, an SBG Algorithms Working Group of multidisciplinary researchers has been formed to review and evaluate the algorithms applicable to the SBG DO across a wide range of Earth science disciplines, including terrestrial and aquatic ecology, atmospheric science, geology, and hydrology. Here, we summarize current state-of-the-practice VSWIR and TIR algorithms that use airborne or orbital spectral imaging observations to address the SBG DO priorities identified by the Decadal Survey: (i) terrestrial vegetation physiology, functional traits, and health; (ii) inland and coastal aquatic ecosystems physiology, functional traits, and health; (iii) snow and ice accumulation, melting, and albedo; (iv) active surface composition (eruptions, landslides, evolving landscapes, hazard risks); (v) effects of changing land use on surface energy, water, momentum, and carbon fluxes; and (vi) managing agriculture, natural habitats, water use/quality, and urban development. We review existing algorithms in the following categories: snow/ice, aquatic environments, geology, and terrestrial vegetation, and summarize the community-state-of-practice in each category. This effort synthesizes the findings of more than 130 scientists

NASA geology program bibliography

A bibliography of scientific papers, articles, and books based on research supported by the NASA Geology Program is given. The citations cover the period 1980 to 1990. An author index is included