Aug 2026· Remote Sensing· Vol 18, pp. 2643· 1 citation· 47 references
Abstract
Detecting changes in coral and seagrass habitat composition from satellite imagery places exceptionally high demands on sensor design due to low underwater reflectance signals and variable water column conditions. Recent multispectral satellite-based attempts to assess such changes across large spatial extents illustrate this challenge through an inability to reliably distinguish live coral from algae, often resulting in broad confidence intervals. This study quantifies the radiometric sensitivity requirements for detecting 10% absolute changes in live coral and seagrass fractional cover from spaceborne imaging spectroscopy using representative parameters for an aquatic imaging spectrometer. The analysis combined representative benthic spectra with realistic, management-relevant co-occurrence scenarios informed by extensive regional knowledge and field measurements from Fiji, Australia, and the Solomon Islands to evaluate detection performance across depths from 0 to 30 m. We show that live coral is the most demanding of the benthic targets evaluated because of its low reflectance and spectral similarity to algal cover types, requiring SNRs of approximately 300–700 to detect 10% absolute changes in cover at depths up to 10 m. In contrast, the greater spectral separation between seagrass and adjacent sandy substrates allows detection of 10% absolute changes in cover to depths exceeding 20 m in clear water. These results highlight the importance of high radiometric sensitivity and contiguous spectral sampling for future aquatic imaging spectrometers intended to monitor benthic change. Approaches that increase effective SNR, such as ground motion compensation (GMC), can extend the depth and confidence with which changes in benthic composition are detected, supporting a transition from broad-area habitat mapping toward quantitative monitoring of benthic change from space.
Sea ice algal aggregates represent a poorly characterized but crucial component of the Arctic marine ecosystem during the melt season, contributing to productivity and playing a key role in sympagic-benthic coupling. However, their patchy distribution and temporary nature make them difficult to study using traditional...
Janina Osanen, Benjamin A. Lange, Karley Campbell et al.· Elem Sci Anth· 0 citations
Coastal ecosystems support high biological diversity while experiencing rapid change caused by shoreline dynamics, sea-level rise, extreme events, pollution, urban development, and intensive resource use. Conventional field surveys provide essential ecological observations but often lack the spatial extent or repetitio...
This review synthesizes recent regional applications of satellite, unmanned aerial vehicle (UAV), and hyperspectral/spectroradiometric remote sensing for vegetation cover discrimination, including crop and natural vegetation discrimination, plant disease and pest detection, and yield assessment. The reviewed evidence d...
G. Khdery, M. Shokr, Aleksandra O. Utkina· Sustainability· 0 citations
The vegetation red edge (VRE) - a sharp increase in reflectance between approximately 700 and 750 nm arising from chlorophyll absorption of visible light and strong near-infrared scattering within leaf tissues - represents the most extensively studied surface biosignature for remote detection of photosynthetic life. Th...
I. Petrescu-Mag, J. R. Arévalo Sierra, Adrian Cîmpean· Extreme Life, Biospeology &a...· 1 citation
This study evaluated imagery acquired by an unmanned aerial vehicle (UAV) equipped with red-green-blue (RGB) and red-green-near-infrared (RGN) sensors for rapid detection and monitoring of cyanobacterial blooms. Water samples were collected at 26 sites across four contrasting reservoirs in trophic status to quantify...
Will J. M. S. Silva, D. P. Costa, Welves M. S. V. Assis et al.· ACS ES&T Water· 0 citations
Phytoplankton diagnostic pigments are critical for identifying phytoplankton functional types, yet their retrieval is limited by sparse in situ data and low spectral resolution. This study retrieved phytoplankton diagnostic pigments in the East China Sea using hyperspectral PACE ocean color data with 5 nm resolution in...
Han Wang, Fang Shen, Ren-Hu Li· IEEE Journal of Selected Top...· 0 citations
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