The capability for mapping two species of seagrass, Thalassia testudinium and Syringodium filiforme, by remote sensing using a physics based model inversion method was investigated. The model was based on a three-dimensional canopy model combined with a model for the overlying water column. The model included uncertainty propagation based on variation in leaf reflectances, canopy structure, water column properties, and the air-water interface. The uncertainty propagation enabled both a-priori predictive sensitivity analysis of potential capability and the generation of per-pixel error bars when applied to imagery. A primary aim of the work was to compare the sensitivity analysis to results achieved in a practical application using airborne hyperspectral data, to gain insight on the validity of sensitivity analyses in general. Results showed that while the sensitivity analysis predicted a weak but positive discrimination capability for species, in a practical application the relevant spectral differences were extremely small compared to discrepancies in the radiometric alignment of the model with the imagery—even though this alignment was very good. Complex interactions between spectral matching and uncertainty propagation also introduced biases. Ability to discriminate LAI was good, and comparable to previously published methods using different approaches. The main limitation in this respect was spatial alignment with the imagery with in situ data, which was heterogeneous on scales of a few meters. The results provide insight on the limitations of physics based inversion methods and seagrass mapping in general. Complex models can degrade unpredictably when radiometric alignment of the model and imagery is not perfect and incorporating uncertainties can have non-intuitive impacts on method performance. Sensitivity analyses are upper bounds to practical capability, incorporating a term for potential systematic errors in radiometric alignment may be advisable. While T. testudinium and S. filiforme were too spectrally similar to be discriminated purely on spectral grounds, mapping of these, and other species may be achievable by exploiting co-incident factors based on ecological zonation.

Shading and inter-reflections created by the three-dimensional coral canopy structure play an important role on benthic reflectance and its propagation above the water. Here, a plane parallel model was coupled with a three-dimensional radiative transfer canopy model, incorporating measured coral shapes and hyperspectral benthic reflectances, to investigate this question under different illumination and water column conditions. Results indicated that a Lambertian treatment of the bottom reflectance can be a reasonable assumption if a variable shading factor is included. Without flexibility in the shading treatment, nadir view bottom reflectances can vary by as much as ±20% (or ±9% in above-water remote sensing reflectance) under solar zenith angles (SZAs) up to 50°. Spectrally-independent shading factors are developed for benthic coral reflectance measurements based on the rugosity of the coral. In remote sensing applications, where the rugosity is unknown, a shading factor could be incorporated as an endmember for retrieval in the inversion scheme. In dense coral canopies in clear shallow waters, the benthos cannot always be treated as Lambertian, and for large solar-view angles the bi-directional reflectance distribution functions (BRDF) hotspot propagated to above water reflectances can create up to a 50% or more difference in water-leaving reflectances, and discrepancies of 20% even for nadir-view geometries.

The seagrass Thalassia testudinum is the dominant habitat-builder in coastal reef lagoons of the Caribbean, and provides vital ecosystem services including coastal protection and carbon storage. We used a remote sensing methodology to map T. testudinum canopies over 400 km of coastline of the eastern Yucatán Peninsula, comparing the depth distribution of canopy density, in terms of leaf area index (LAI), to a previously established ecological model of depth and LAI for this species in oligotrophic conditions. The full archive of Sentinel-2 imagery from 2016 to 2020 was applied in an automated model inversion method to simultaneously estimate depth and LAI, covering ∼900 km² of lagoon with approximately 800 images. Data redundancy allowed for statistical tests of change detection. Achieved accuracy was sufficient for the objectives: LAI estimates compared to field data had mean absolute error of 0.59, systematic error of 0.04 and r² > 0.67 over a range of 0–5. Bathymetry compared to 46,000 ICESat-2 data points had a mean absolute error of 1 m, systematic error less than 0.5 m, and r² > 0.88 over a range of 0–15 m. The estimated total area of seagrass canopy was consistent with previously published estimates of ∼580 km², but dense canopies (LAI > 3), which are the primary contributors to below-ground carbon storage, comprise only ∼40 km². Within the year-to-year variation there was no change in overall seagrass abundance 2017–2020, but localised statistically significant (p < 0.01) patches of canopy extension and retraction occurred. 2018 and 2019 were affected by beaching of pelagic Sargassum and dispersion as organic matter into the lagoon. The multi-year analysis enabled excluding this influence and provided an estimate of its extent along the coast. Finally, the distribution of LAI with depth was consistent with the ecological model and showed a gradient from north to south which mirrored a well-established gradient in anthropogenic pressure due to touristic development. Denser canopies were more abundant in developed areas, the expected growth response to nutrient enrichment. This increase in canopy density may be a useful early bio-indicator of environmental eutrophication, detectable by remote sensing before habitat deterioration is observed.

Nearshore bathymetric data are used in many coastal monitoring applications, but acquisition conditions can be challenging. Shipborne surveys are prone to the risk of grounding in shallow waters, and scheduled airborne surveys often fail to coincide with optimal atmospheric and water conditions. As an alternative, since its launch in 2018, ICESat-2 satellite laser profile altimetry data provide free and readily available data on a 91-day repeat cycle, which may contain incidental bathymetric returns when suitable environmental conditions prevail. In this paper, the vertical accuracy of extracted, refraction-adjusted ICESat-2 nearshore marine bathymetric data is evaluated at four test sites in a Northern hemisphere, temperate latitude location. Multiple ICEsat-2 bathymetric values that occurred in close horizontal proximity to one another were averaged at a spatial scale of 1 m and compared with Multibeam Echosounder bathymetric survey data and Global Navigation Satellite System reference data. Mean absolute errors of less than 0.15 m were observed up to depths of 5 m, with errors of less than 0.24 m (to 6 m), 0.39 m (to 7 m) and 0.52 m (to 10 m). The occurrence of larger bathymetric errors with depth, which increase to 0.54 m at maximum photon depths of 11 m, appears to be primarily related to reduced numbers of geolocated photons with depth. The accuracies achieved up to 6 m suggest that the manual extraction, refraction adjustment and bathymetric filtering steps were effective. Overall, the results suggest that ICESat-2 bathymetric data accuracy may be sufficient to be considered for use in nearshore coastal monitoring applications where shipborne and airborne bathymetric data might otherwise be applied.