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At least 19 records
All sourcesNASA NTRSDOE OSTI
NASA NTRS2018

NASA Fluid Lensing & MiDAR - Next-Generation Remote Sensing Technologies for Aquatic Remote Sensing

Piti's Tepungan Bay and Tumon Bay, two of five marine preserves in Guam, have not been mapped to a level of detail sufficient to support proposed management strategies. This project addresses this gap by providing high resolution maps to promote sustainable, responsible use of the area while protecting natural resources. Dr. Chirayath, a research scientist at the NASA Ames Laboratory, developed a theoretical model and algorithm called 'Fluid Lensing'. Fluid lensing removes optical distortions caused by moving water, improving the clarity of the images taken of the corals below the surface. We will also be using MiDAR, a next-generation remote sensing instrument that provides real-time multispectral video using an array of LED emitters coupled with NASA's FluidCam Imaging System, which may assist Guam's coral reef response team in understanding the severity and magnitude of coral bleaching events. This project will produce a 3D orthorectified model of the shallow water coral reef ecosystems in Tumon Bay and Piti marine preserves. These 3D models may be printed, creating a tactile diorama and increasing understanding of coral reefs among various audiences, including key decision makers. More importantly, the final data products can enable accurate and quantitative health assessment capabilities for coral reef ecosystems.

Aquatic↗
NASA NTRS2018

NASA Fluid Lensing & MiDAR: Next-Generation Remote Sensing Technologies for Aquatic Remote Sensing

We present two recent instrument technology developments at NASA, Fluid Lensing and MiDAR, and their application to remote sensing of Earth's aquatic systems. Fluid Lensing is the first remote sensing technology capable of imaging through ocean waves in 3D at sub-cm resolutions. MiDAR is a next-generation active hyperspectral remote sensing and optical communications instrument capable of active fluid lensing. Fluid Lensing has been used to provide 3D multispectral imagery of shallow marine systems from unmanned aerial vehicles (UAVs, or drones), including coral reefs in American Samoa and stromatolite reefs in Hamelin Pool, Western Australia. MiDAR is being deployed on aircraft and underwater remotely operated vehicles (ROVs) to enable a new method for remote sensing of living and nonliving structures in extreme environments. MiDAR images targets with high-intensity narrowband structured optical radiation to measure an objectâ€"TM"s non-linear spectral reflectance, image through fluid interfaces such as ocean waves with active fluid lensing, and simultaneously transmit high-bandwidth data. As an active instrument, MiDAR is capable of remotely sensing reflectance at the centimeter (cm) spatial scale with a signal-to-noise ratio (SNR) multiple orders of magnitude higher than passive airborne and spaceborne remote sensing systems with significantly reduced integration time. This allows for rapid video-frame-rate hyperspectral sensing into the far ultraviolet and VNIR wavelengths. Previously, MiDAR was developed into a TRL 2 laboratory instrument capable of imaging in thirty-two narrowband channels across the VNIR spectrum (400-950nm). Recently, MiDAR UV was raised to TRL4 and expanded to include five ultraviolet bands from 280-400nm, permitting UV remote sensing capabilities in UV A, B, and C bands and enabling mineral identification and stimulated fluorescence measurements of organic proteins and compounds, such as green fluorescent proteins in terrestrial and aquatic organics.

fluid lensing↗
NASA NTRSDate not supplied

Pushing the Limits of Aquatic Remote Sensing: Synthetic Data and Deep Learning for Fast Inverse Emulation of A Coupled Ocean-Atmosphere Radiative Transfer Model

The inversion of electromagnetic information to physical and biological properties of the water column is a notoriously difficult problem, yet fundamental to our ability of understanding aquatic processes on large time and space scales. There is now a growing necessity to develop pragmatic approaches that allow timely and effective extrapolation of local processes, to spatially resolved global products, and to promote operational and sustainable resource policy management. This presentation will discuss research integrating advanced biological and radiative modeling, high-end computation, and machine learning to develop a portable global processor for simultaneous retrieval of atmosphere and water optics for diverse aquatic systems from the open and coastal ocean to optically extreme inland waters and harmful algal blooms. We will discuss some of the basic concepts behind the forward modeling approach including DEAP, the novel Distributed Equivalent Algal Populations model, for developing large spectral libraries of aquatic particle optics to aid in our ability to distinguish phytoplankton functional types (PFTs) and inorganic material, as well as other factors which enable comprehensive modeling from the benthos to top-of-atmosphere (TOA). This information is being used to understand how we can leverage next-generation deep learning methods for maximum information retrieval and rapid image processing, while also providing capabilities to identify minimum sensor spectral requirements necessary for certain aquatic applications. Further, I will touch on how we envision this research to enable the aquatic community for science discovery and how we are moving closer towards the capability for high-fidelity global analysis of aquatic ecosystems.

Jeremy Alan Kravitz↗
NASA NTRS2014

On-Orbit Radiometric Characterization of OLI (Landsat 8) for Applications in Aquatic Remote Sensing

Landsat-8 carries two separate sensors, namely the Operational Land Imager (OLI) and the Thermal Infrared Radiometer Suite (TIRS), that image the earth surface throughout the visible and thermal portions of the spectrum. Compared to Landsat heritage sensors, the OLI has enhanced features, which include its 12-bit radiometric resolution and the addition of a band centered at 443 nm. The dramatically improved data quality/quantity expands existing applications of Landsat imagery in aquatic sciences from the retrieval of bio-geochemical properties, such as near-surface concentrations of chlorophyll-a (CHL) and total suspended solids (TSS), to benthic mapping. This study offers analysis of OLI's absolute radiometric performance over bodies ofwater using benchmark observations, namely the top-of-atmosphere (TOA) ocean color observations and marine in situ radiometric measurements. Sensor-to-sensor comparisons are performed to derive gain factors (g1) from near-concurrent observations in TOA radiance and reflectance domains. The gains in the radiance domain were further validated/adjusted by determining a second set of gains (g2) via analysis of OLI-derived water-leaving radiance, i.e., L(sub w) (gamma), against in situ measurements made either at the Ocean Color AErosol RObotic NETwork (AERONETOC) sites or during field campaigns. The analyses yield the OLI calibration uncertainties that need to be accounted forwhen studying aquatic environments. Itwas found that, for the visible and near-infrared channels, the OLI radiometric responses, on average, are well in agreement (b 2 % discrepancies) with the TOA radiances estimated by ocean color satellites or those predicted by models based onmeasurements of aquatic and atmospheric properties. However, the TOA radiance at the new 443-nm band is found to be, on average, 3.4 % larger than the reference observations. The inter-sensor comparisons in the reflectance domain, however, indicated slightly different results with the OLI responses being low in the blue bands. To enhance the retrieval accuracy of aquatic-science products from OLI datasets, sets of temporally averaged gains (radiance and reflectance) are derived and recommended for use prior to the retrieval of in-water products.

Remote Sensing↗
NASA NTRS1978

Inferring nutrient loading of estuarine systems by remote sensing of aquatic vegetation

THe use of remote sensing to record algal and vascular aquatic plant growths in estuarine waters is discussed. A technique is proposed that uses a combination of data to hierarchically classify watersheds with regard to severity of potential pollution. Specific nonpoint sources of nutrients in tributaries of the watershed are identified with lower altitude photography of vegetation and selected ground sampling. It is concluded that excessive growths of some aquatic plants may be related to nutrient pollution.

Anderson, R. R.↗
NASA NTRS2017

Fluid Lensing and Applications to Remote Sensing of Aquatic Environments

The use of fluid lensing technology on UAVs is presented as a novel means for 3D imaging of aquatic ecosystems from above the water's surface at the centimeter scale. Preliminary results are presented from airborne fluid lensing campaigns conducted over the coral reefs of Ofu Island, American Samoa (2013) and the stromatolite reefs of Shark Bay, Western Australia (2014), covering a combined area of 15km2. These reef ecosystems were revealed with centimetre-scale 2D resolution, and an accompanying 3D bathymetry model was derived using fluid lensing, Structure from Motion and UAV position data. Data products were validated from in-situ survey methods including underwater calibration targets, depth measurements and millimetre-scale high-dynamic range gigapixel photogrammetry. Fluid lensing is an experimental technology that uses water transmitting wavelengths to passively image underwater objects at high-resolution by exploiting time-varying optical lensing events caused by surface waves. Fluid lensing data are captured from low-altitude, cost-effective electric UAVs to achieve multispectral imagery and bathymetry models at the centimetre scale over regional areas. As a passive system, fluid lensing is presently limited by signal-to-noise ratio and water column inherent optical properties to approximately 10 m depth over visible wavelengths in clear waters. The datasets derived from fluid lensing present the first centimetre-scale images of a reef acquired from above the ocean surface, without wave distortion. The 3D multispectral data distinguish coral, fish and invertebrates in American Samoa, and reveal previously undocumented, morphologically distinct, stromatolite structures in Shark Bay. These findings suggest fluid lensing and multirotor electric drones represent a promising advance in the remote sensing of aquatic environments at the centimetre scale, or 'reef scale' relevant to the conservation of reef ecosystems. Pending further development and validation of fluid lensing methods, these technologies present a solution for large-scale 3D surveys of shallow aquatic habitats with centimetre-scale spatial resolution and hourly temporal sampling.

Aquatic↗
NASA NTRS2015

Coastal and Inland Aquatic Data Products for the Hyperspectral Infrared Imager (HyspIRI)

The HyspIRI Aquatic Studies Group (HASG) has developed a conceptual list of data products for the HyspIRI mission to support aquatic remote sensing of coastal and inland waters. These data products were based on mission capabilities, characteristics, and expected performance. The topic of coastal and inland water remote sensing is very broad. Thus, this report focuses on aquatic data products to keep the scope of this document manageable. The HyspIRI mission requirements already include the global production of surface reflectance and temperature. Atmospheric correction and surface temperature algorithms, which are critical to aquatic remote sensing, are covered in other mission documents. Hence, these algorithms and their products were not evaluated in this report. In addition, terrestrial products (e.g., land use land cover, dune vegetation, and beach replenishment) were not considered. It is recognized that coastal studies are inherently interdisciplinary across aquatic and terrestrial disciplines. However, products supporting the latter are expected to already be evaluated by other components of the mission. The coastal and inland water data products that were identified by the HASG, covered six major environmental and ecological areas for scientific research and applications: wetlands, shoreline processes, the water surface, the water column, bathymetry and benthic cover types. Accordingly, each candidate product was evaluated for feasibility based on the HyspIRI mission characteristics and whether it was unique and relevant to the HyspIRI science objectives.

Infrared↗
NASA NTRS1973

The remote sensing of aquatic macrophytes Part 1: Color-infrared aerial photography as a tool for identification and mapping of littoral vegetation. Part 2: Aerial photography as a quantitative tool for the investigation of aquatic ecosystems

Research was initiated to use aerial photography as an investigative tool in studies that are part of an intensive aquatic ecosystem research effort at Lake Wingra, Madison, Wisconsin. It is anticipated that photographic techniques would supply information about the growth and distribution of littoral macrophytes with efficiency and accuracy greater than conventional methods.

Gustafson, T. D.↗
NASA NTRS1977

Remote sensing of aquatic plants

Various sensors were tested in terms of their ability to detect and discriminate among noxious aquatic macrophytes. A survey of researchers currently studying the problem and a brief summary of their work is included. Results indicated that the sensor types best suited to assessment of the aquatic environment are color, color infrared, and black-and-white infrared film, which furnish consistently high contrasts between aquatic plants and their surroundings.

Long, K. S.↗
NASA NTRS2020

Robust Algorithm for Estimating Total Suspended Solids (TSS) in Inland and Nearshore Coastal Waters

One of the challenging tasks in modern aquatic remote sensing is the retrieval of near-surface concentrations of Total Suspended Solids (TSS). This study aims to present a Statistical, inherent Optical property (IOP) -based, and muLti-conditional Inversion proceDure (SOLID) for enhanced retrievals of satellite-derived TSS under a wide range of in-water bio-optical conditions in rivers, lakes, estuaries, and coastal waters. In this study, using a large in situ database (N > 3500), the SOLID model is devised using a three-step procedure: (a) water-type classification of the input remote sensing reflectance (R(sub rs)), (b) retrieval of particulate backscattering (b(sub bp)) in the red or near-infrared (NIR) regions using semi-analytical, machine-learning, and empirical models, and (c) estimation of TSS from b(sub bp) via water-type-specific empirical models. Using an independent subset of our in situ data (N = 2729) with TSS ranging from 0.1 to 2626.8 [g/m (exp 3)], the SOLID model is thoroughly examined and compared against several state-of-the-art algorithms (Miller and McKee, 2004; Nechad et al., 2010; Novoa et al., 2017; Ondrusek et al., 2012; Petus et al., 2010). We show that SOLID outperforms all the other models to varying degrees, i.e., from 10 to > 100%, depending on the statistical attributes (e.g., global versus water-type-specific metrics). For demonstration purposes, the model is implemented for images acquired by the MultiSpectral Imager aboard Sentinel-2A/B over the Chesapeake Bay, San-Francisco-Bay-Delta Estuary, Lake Okeechobee, and Lake Taihu. To enable generating consistent, multimission TSS products, its performance is further extended to, and evaluated for, other missions, such as the Ocean and Land Color Instrument (OLCI), Moderate Resolution Imaging Spectroradiometer (MODIS), Visible Infrared Imaging Radiometer Suite (VIIRS), and Operational Land Imager (OLI). Sensitivity analyses on uncertainties induced by the atmospheric correction indicate that 10% uncertainty in Rrs leads to < 20% uncertainty in TSS retrievals from SOLID. While this study suggests that SOLID has a potential for producing TSS products in global coastal and inland waters, our statistical analysis certainly verifies that there is still a need for improving retrievals across a wide spectrum of particle loads.

Total suspended solids↗
DOE OSTI2019

Performance of Landsat-8 and Sentinel-2 surface reflectance products for river remote sensing retrievals of chlorophyll-a and turbidity

Rivers and other freshwater systems play a crucial role in ecosystems, industry, transportation and agriculture. Despite the > 40 years of inland water observations made possible by optical remote sensing, a standardized reflectance product for inland waters is yet forthcoming. The aim of this work is to compare the standard USGS land surface reflectance product to two Landsat-8 and Sentinel-2 aquatic remote sensing reflectance products over the Amazon, Columbia and Mississippi rivers. Landsat-8 reflectance products from all three routines are then evaluated for their comparative performance in retrieving chlorophyll-a and turbidity in reference to ship- borne, underway in situ validation measurements. The land surface product shows the best agreement (4% Mean Absolute Percent Difference) with field measurements of radiometry collected on the Amazon River and gen- erates 36% higher reflectance values in the visible bands compared to aquatic methods (ACOLITE and SeaDAS) with larger differences between land and aquatic products observed in Sentinel-2 (0.01sr-1) compared to Landsat-8 (0.001 sr-1). Choice of atmospheric correction routine can bias Landsat-8 retrievals of chlorophyll-a and turbidity by as much as 59% and 35% respectively. Using a more restrictive time window for matching in situ and satellite imagery can reduce differences by 5–31% depending on correction technique. This work highlights the challenges of satellite retrievals over rivers and underscores the need for future optical and biogeochemical research aimed at improving our understanding of the absorbing and scattering properties of river water and their relationships to remote sensing reflectance.

Amazon↗
NASA NTRS1986

Satellite data in aquatic area research - Some ideas for future studies

Attempts to apply aquatic remote sensing to the preparation of parametric map-like presentations, quantitative evaluations and time-related investigations in various water areas in Finland are presented. The potential use of Landsat MSS data in aquatic area studies, including limology, aquatic botany, geomorphology and engineering is evaluated using computer-aided digital remote sensing techniques. MSS data may provide information about depth, Secchi disc values, humus content in water, and productivity. Aquatic vegetation classification using MSS is possible only where vegetation units are large enough in respect to the 0.5 hectares ground resolution. Multitemporal satellite imagery has been used to evaluate alterations in the littoral areas of some Finnish water reservoirs between successive periods of high water. It is concluded that although MSS data can be of use in aquatic studies, it should be used in connection with field data and/or TM and SPOT data.

Raitala, Jouko T.↗
NASA NTRS2019

Performance of Landsat-8 and Sentinel-2 Surface Reflectance Products for River Remote Sensing Retrievals of Chlorophyll-A and Turbidity

Rivers and other freshwater systems play a crucial role in ecosystems, industry, transportation and agriculture. Despite the more than 40 years of inland water observations made possible by optical remote sensing, a standardized reflectance product for inland waters is yet forthcoming. The aim of this work is to compare the standard USGS land surface reflectance product to two Landsat-8 and Sentinel-2 aquatic remote sensing reflectance products over the Amazon, Columbia and Mississippi rivers. Landsat-8 reflectance products from all three routines are then evaluated for their comparative performance in retrieving chlorophyll-a and turbidity in reference to shipborne, underway in situ validation measurements. The land surface product shows the best agreement (4 percent Mean Absolute Percent Difference) with field measurements of radiometry collected on the Amazon River and generates 36 percent higher reflectance values in the visible bands compared to aquatic methods (ACOLITE (Atmospheric Correction for OLI (Operational Land Imager) 'lite') and SeaDAS (Sea-viewing Wide Field-of-View Sensor (SeaWiFS) Data Analysis System)) with larger differences between land and aquatic products observed in Sentinel-2 (0.01 per steraradian) compared to Landsat-8 (0.001 per steraradian). Choice of atmospheric correction routine can bias Landsat-8 retrievals of chlorophyll-a and turbidity by as much as 59 percent and 35 percent respectively. Using a more restrictive time window for matching in situ and satellite imagery can reduce differences by 5–31 percent depending on correction technique. This work highlights the challenges of satellite retrievals over rivers and underscores the need for future optical and biogeochemical research aimed at improving our understanding of the absorbing and scattering properties of river water and their relationships to remote sensing reflectance.

Landsat-8↗
NASA NTRS2021

ACIX-Aqua: A Global Assessment of Atmospheric Correction Methods for Landsat-8 and Sentinel-2 Over Lakes, Rivers, and Coastal Waters

Atmospheric correction over inland and coastal waters is one of the major remaining challenges in aquatic remote sensing, often hindering the quantitative retrieval of biogeochemical variables and analysis of their spatial and temporal variability within aquatic environments. The Atmospheric Correction Intercomparison Exercise (ACIX-Aqua), a joint NASA – ESA activity, was initiated to enable a thorough evaluation of eight state-of-the-art atmospheric correction (AC) processors available for Landsat-8 and Sentinel-2 data processing. Over 1000 radiometric matchups from both freshwaters (rivers, lakes, reservoirs) and coastal waters were utilized to examine the quality of derived aquatic reflectances (ρ̂w). This dataset originated from two sources: Data gathered from the international scientific community (henceforth called Community Validation Database, CVD), which captured predominantly inland water observations, and the Ocean Color component of AERONET measurements (AERONET-OC), representing primarily coastal ocean environments. This volume of data permitted the evaluation of the AC processors individually (using all the matchups) and comparatively (across seven different Optical Water Types, OWTs) using common matchups. We found that the performance of the AC processors differed for CVD and AERONET-OC matchups, likely reflecting inherent variability in aquatic and atmospheric properties between the two datasets. For the former, the median errors in ρ̂w560 and ρ̂w664 were found to range from 20 to 30% for best-performing processors. Using the AERONET-OC matchups, our performance assessments showed that median errors within the 15–30% range in these spectral bands may be achieved. The largest uncertainties were associated with the blue bands (25 to 60%) for best-performing processors considering both CVD and AERONET-OC assessments. We further assessed uncertainty propagation to the downstream products such as near-surface concentration of chlorophyll-a (Chla) and Total Suspended Solids (TSS). Using satellite matchups from the CVD along with in situ Chla and TSS, we found that 20–30% uncertainties in ρ̂w490≤λ≤743nm yielded 25–70% uncertainties in derived Chla and TSS products for top-performing AC processors. We summarize our results using performance matrices guiding the satellite user community through the OWT-specific relative performance of AC processors. Our analysis stresses the need for better representation of aerosols, particularly absorbing ones, and improvements in corrections for sky- (or sun-) glint and adjacency effects, in order to achieve higher quality downstream products in freshwater and coastal ecosystems.

Nima Pahlevan↗
NASA NTRS2021

A Chlorophyll-a Algorithm for Landsat-8 Based on Mixture Density Networks

Retrieval of aquatic biogeochemical variables, such as the near-surface concentration of chlorophyll-a (Chla) in inland and coastal waters via remote observations, has long been regarded as a challenging task. This manuscript applies Mixture Density Networks (MDN) that use the visible spectral bands available by the Operational Land Imager (OLI) aboard Landsat-8 to estimate Chla. We utilize a database of co-located in situ radiometric and Chla measurements (N = 4,354), referred to as Type A data, to train and test an MDN model (MDN(A)). This algorithm’s performance, having been proven for other satellite missions, is further evaluated against other widely used machine learning models (e.g., support vector machines), as well as other domain-specific solutions (OC3), and shown to offer significant advancements in the field. Our performance assessment using a held-out test data set suggests that a 49% (median) accuracy with near-zero bias can be achieved via the MDN(A) model, offering improvements of 20 to 100% in retrievals with respect to other models. The sensitivity of the MDN(A) model and benchmarking methods to uncertainties from atmospheric correction (AC) methods, is further quantified through a semi-global matchup dataset (N = 3,337), referred to as Type B data. To tackle the increased uncertainties, alternative MDN models (MDN(B)) are developed through various features of the Type B data (e.g., Rayleigh-corrected reflectance spectra ρ(s)). Using held-out data, along with spatial and temporal analyses, we demonstrate that these alternative models show promise in enhancing the retrieval accuracy adversely influenced by the AC process. Results lend support for the adoption of MDN(B) models for regional and potentially global processing of OLI imagery, until a more robust AC method is developed. Index Terms—Chlorophyll-a, coastal water, inland water, Landsat-8, machine learning, ocean color, aquatic remote sensing.

Brandon Smith↗
NASA NTRS1975

Remote sensing of submerged aquatic vegetation in the lower Chesapeake Bay

An experimental water penetration film and black and white near infrared film were used to study the distribution of submerged aquatic vegetation in the lower Chesapeake Bay. Detailed description of the grass beds was obtained by flying at an altitude of 5,000 feet, at low tide when wind conditions were minimal. Results show that there was a 36% reduction in the amount of submerged aquatic vegetation in the lower Chesapeake Bay from 1971 to 1974, the greatest losses occurring in the York, Piankatank and Rappahannock rivers (tabulated data is given). Recovery of some grass beds occurs primarily through seedling recruitment and subsequent vegetative growth. Cownose rays are suspected as a main factor for the decimation of some of the grass beds. Maps and photographs of the areas studied are given.

Orth, R. J.↗
NASA NTRS1987

Remote sensing of submerged aquatic vegetation in lower Chesapeake Bay - A comparison of Landsat MSS to TM imagery

Landsat MSS and TM imagery, obtained simultaneously over Guinea Marsh, VA, as analyzed and compares for its ability to detect submerged aquatic vegetation (SAV). An unsupervised clustering algorithm was applied to each image, where the input classification parameters are defined as functions of apparent sensor noise. Class confidence and accuracy were computed for all water areas by comparing the classified images, pixel-by-pixel, to rasterized SAV distributions derived from color aerial photography. To illustrate the effect of water depth on classification error, areas of depth greater than 1.9 m were masked, and class confidence and accuracy recalculated. A single-scattering radiative-transfer model is used to illustrate how percent canopy cover and water depth affect the volume reflectance from a water column containing SAV. For a submerged canopy that is morphologically and optically similar to Zostera marina inhabiting Lower Chesapeake Bay, dense canopies may be isolated by masking optically deep water. For less dense canopies, the effect of increasing water depth is to increase the apparent percent crown cover, which may result in classification error.

Ackleson, S. G.↗
NASA NTRS1976

Remote sensing analysis of Lake Livingston aquatic plants

Results obtained during 1975 to monitor the growth of aquatic plants in the Lake Livingston area, using remote sensing photographic imagery, were described. Sequential total coverage was provided of the Jungle and White Rock Creek, plus coverage of smaller areas of localized infestation downlake, including Brushy Creek, KOA Kampground Marina, Penwaugh Slough, Memorial Point Marina, the Beacon Bay marinas and Pine Island. The imagery was generally good, photographic exposure being increased as the season progressed in order to obtain better pictures of the submerged vegetation. Some very significant differences in growth patterns, species interaction, and species dominance were observed when compared to 1974. Observation of the following plants was discussed: water hyacinth, hydrilla, coontail, potamageton. In general, the level of infestation was lower in 1975 than in 1974, due to the combined effect of more systematic application of herbicides and harsher intervening winter weather conditions.

Benton, A. R., Jr.↗