Effects of Per-Pixel Variability on Uncertainties in Bathymetric Retrievals from High-Resolution Satellite Images

Botha, Elizabeth C.; Brando, Vittorio E.; Dekker, Arnold G.

doi:10.3390/rs8060459

Cited by 27 publications

(20 citation statements)

References 37 publications

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“…, Botha et al. ) and the radiance reflected from adjacent pixels (adjacency effect; e.g., Duan et al. ).…”

Section: Requirements For Observing Coastal Biodiversity and Ecosystementioning

confidence: 99%

“…To that end, atmospheric correction approaches for a coastal mission can leverage the maturity of operational algorithms for ocean color missions (Ahmad et al 2010), but need to be updated to address coastal and inland aerosol types (Pahlevan et al 2017b), hyperspectral data, and higher spatial resolution. Atmospheric correction should also incorporate procedures to evaluate and correct sun glint (e.g., Steinmetz et al 2011, Devred et al 2013, Botha et al 2016) and the radiance reflected from adjacent pixels (adjacency effect; e.g., Duan et al 2015).…”

Section: High Spectral Resolutionmentioning

confidence: 99%

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Satellite sensor requirements for monitoring essential biodiversity variables of coastal ecosystems

Muller‐Karger

Hestir

Ade

et al. 2018

Ecological Applications

119

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The biodiversity and high productivity of coastal terrestrial and aquatic habitats are the foundation for important benefits to human societies around the world. These globally distributed habitats need frequent and broad systematic assessments, but field surveys only cover a small fraction of these areas. Satellite‐based sensors can repeatedly record the visible and near‐infrared reflectance spectra that contain the absorption, scattering, and fluorescence signatures of functional phytoplankton groups, colored dissolved matter, and particulate matter near the surface ocean, and of biologically structured habitats (floating and emergent vegetation, benthic habitats like coral, seagrass, and algae). These measures can be incorporated into Essential Biodiversity Variables (EBVs), including the distribution, abundance, and traits of groups of species populations, and used to evaluate habitat fragmentation. However, current and planned satellites are not designed to observe the EBVs that change rapidly with extreme tides, salinity, temperatures, storms, pollution, or physical habitat destruction over scales relevant to human activity. Making these observations requires a new generation of satellite sensors able to sample with these combined characteristics: (1) spatial resolution on the order of 30 to 100‐m pixels or smaller; (2) spectral resolution on the order of 5 nm in the visible and 10 nm in the short‐wave infrared spectrum (or at least two or more bands at 1,030, 1,240, 1,630, 2,125, and/or 2,260 nm) for atmospheric correction and aquatic and vegetation assessments; (3) radiometric quality with signal to noise ratios (SNR) above 800 (relative to signal levels typical of the open ocean), 14‐bit digitization, absolute radiometric calibration <2%, relative calibration of 0.2%, polarization sensitivity <1%, high radiometric stability and linearity, and operations designed to minimize sunglint; and (4) temporal resolution of hours to days. We refer to these combined specifications as H4 imaging. Enabling H4 imaging is vital for the conservation and management of global biodiversity and ecosystem services, including food provisioning and water security. An agile satellite in a 3‐d repeat low‐Earth orbit could sample 30‐km swath images of several hundred coastal habitats daily. Nine H4 satellites would provide weekly coverage of global coastal zones. Such satellite constellations are now feasible and are used in various applications.

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“…, Botha et al. ) and the radiance reflected from adjacent pixels (adjacency effect; e.g., Duan et al. ).…”

Section: Requirements For Observing Coastal Biodiversity and Ecosystementioning

confidence: 99%

Section: High Spectral Resolutionmentioning

confidence: 99%

Satellite sensor requirements for monitoring essential biodiversity variables of coastal ecosystems

Muller‐Karger

Hestir

Ade

et al. 2018

Ecological Applications

119

View full text Add to dashboard Cite

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“…For either of the methods, it is desirable to have clear, glint-free satellite images with a flat sea state. For the radiative transfer approach, a nearly perpendicular look angle provides superior results [22]. For the stereo approach, the second image should be clear and as glint free as possible, preferably acquired simultaneously or close to simultaneously with the first [23].…”

Section: Satellite Imagesmentioning

confidence: 99%

“…For the photogrammetric approach, the stereo pair of WV2 images were acquired in December 2009, approximately two minutes apart, from the satellite's downwards and backwards pointing cameras. While the 2009 images could have also been used for the Semi-Analytical Model for Unmixing and Concentration Assessment (SAMBUCA) method, the 2012 image was deemed superior for the purpose (and thus for this study) according to criteria derived in [22].…”

Section: Satellite Imagesmentioning

confidence: 99%

Depth from Satellite Images: Depth Retrieval Using a Stereo and Radiative Transfer-Based Hybrid Method

et al. 2018

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Satellite imagery is increasingly being used to provide estimates of bathymetry in near-coastal (shallow) areas of the planet, as a more cost-effective alternative to traditional methods. In this paper, the relative accuracy of radiative-transfer and photogrammetric stereo methods applied to World View 2 imagery are examined, using LiDAR bathymetry and towed video as ground truth, and it is demonstrated, with a case study, that these methods are complementary; where one method might have limited accuracy, the other method often has improved accuracy. The depths of uniform, highly-reflective (sand) sea bed are better estimated with a radiative transfer-based method, while areas where there is high visual contrast in the scene, as identified by using a local standard deviation measure, are better estimated using the photogrammetric technique. In this paper, it is shown that a hybrid method can give a potential improvement in accuracy of more than 50% (from 2.84 m to 1.38 m RMSE in the ideal case) compared to either of the two methods alone. Metrics are developed that can be used to characterize regions of the scene where each technique is superior, realizing an improved overall depth accuracy over either method alone of between 16.9% and 19.7% (demonstrating a realised RMSE of 2.36 m).

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“…The analyses of Botha et al [5] examined the effects of per-pixel variability in sunglint, atmospheric path length and directional effects on uncertainties in bathymetric retrievals from high-resolution satellite images. They performed a case-study to highlight retrieval errors from images acquired at non-optimal geometrical combinations, and they quantified the effects of variations in the environmental noise on water surface reflectance and the accuracy of environmental variable retrievals, using two WorldView-2 satellite images.…”

Section: Highlights Of Research Articlesmentioning

confidence: 99%

Preface: Remote Sensing in Coastal Environments

Mishra

Gould

2016

Remote Sensing

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Abstract:The Special Issue (SI) on "Remote Sensing in Coastal Environments" presents a wide range of articles focusing on a variety of remote sensing models and techniques to address coastal issues and processes ranging for wetlands and water quality to coral reefs and kelp habitats. The SI is comprised of twenty-one papers, covering a broad range of research topics that employ remote sensing imagery, models, and techniques to monitor water quality, vegetation, habitat suitability, and geomorphology in the coastal zone. This preface provides a brief summary of each article published in the SI.Keywords: barrier island; sea turtle habitat; mangroves; Sahara Desert coast; kelp; spartina biomass; bathymetry; mangrove distribution; Brazilian Coast; hypoxia; Louisiana Shelf; UAV; harmful algal blooms; Yellow Sea; Po River delta; Venice Lagoon; suspended sediment dynamics; geostationary ocean color imager; mangrove leaf pigment; semi-analytical algorithm; seagrass biomass; shoreline change; northern Java Island; sun glint removal; bottom reflectance; coral reefs; coastal marsh; belowground biomass; root:shoot Overview and ScopeCoastal ecosystems are regions of remarkable primary and secondary productivity, biodiversity, and high accessibility. Apart from supporting numerous physical and biological processes, they also act as recreational, leisure, and tourism centers. Encompassing a broad range of habitat types and harboring a wealth of species and genetic diversity, coastal ecosystems perform numerous vital ecosystem functions. In addition to serving as nursery grounds for many birds and aquatic organisms, coastal ecosystems play roles in regulating global hydrology and climate; the biological, physical, and chemical modifications of the water column, sediment, and submerged and emergent vegetation; the storage and cycling of nutrients; the filtration of pollutants from inland freshwater systems; and the protection of shorelines from erosion and storms. Consequently, there is a need for accurate, cost effective, frequent, and synoptic methods of characterizing and monitoring these complex ecosystems.Remote sensing from in situ, airborne, and space-borne platforms can help satisfy the aforementioned criteria and can provide synoptic coverage over a range of spatial resolutions (coarse to fine), at regular temporal frequencies, to facilitate monitoring of coastal environments. This Special Issue on "Remote Sensing in Coastal Environments" is specifically aimed at addressing challenges related to assessing, quantifying, and monitoring near-shore shallow marine and open ocean processes, ecosystem productivity and biodiversity, interrelationships between vegetation and water quality, integrating remote sensing into coupled coastal biophysical forecast models, physical/biological interactions, hyperspectral applications, geomorphological changes, coastal hypoxia, and the use of LiDAR and unmanned aerial vehicles. The response to the Special Issue call was excellent, and following an extensive peer-review process, twenty-one...

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Effects of Per-Pixel Variability on Uncertainties in Bathymetric Retrievals from High-Resolution Satellite Images

Cited by 27 publications

References 37 publications

Satellite sensor requirements for monitoring essential biodiversity variables of coastal ecosystems

Satellite sensor requirements for monitoring essential biodiversity variables of coastal ecosystems

Depth from Satellite Images: Depth Retrieval Using a Stereo and Radiative Transfer-Based Hybrid Method

Preface: Remote Sensing in Coastal Environments

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