A physics-based method for the remote sensing of seagrasses

Seagrass meadows are important environments for the blue carbon budget and are potential early indicators for environmental change. Remote sensing is a viable monitoring tool for spatially extensive meadows but most current approaches are limited by the requirement for in situ calibration data or provide categorical level maps rather than quantitative estimates of direct physiological significance. In this paper we present a method for mapping water depth and the leaf area index (LAI, ratio of leaf area to substrate area) of Thalassia testudinum meadows, based on radiative transfer model inversion using an embedded three-dimensional aquatic canopy model. Variations in reflectance due to leaf length, leaf position, sediment coverage on leaves, water depth and solar zenith angle were included in the model to parameterise uncertainty propagation. The model revealed canopy reflectance as a function of LAI decreases exponentially at all wavelengths up to an LAI around four, beyond which increasing canopy density cannot be determined from reflectance. In addition, sediment coverage on leaves has surprisingly little effect on the reflectance of sparse canopies because shading is also a contributor to darkening. The capability of the method for image based mapping was assessed through sensitivity analyses and by application to hyperspectral airborne imagery of Florida Bay collected by the Portable Remote Imaging Spectrometer (PRISM), with the uncertainty propagation providing per-pixel confidence intervals on all the estimated parameters. Results were consistent across the sensitivity and image analyses and the agreement with field data was good, given the challenges in validation of submerged pixels at metre scale. Uncertainties were high for LAIs greater than two in water of depth 8 m, but lower for sparse canopies and shallower water. For water depths approaching 10 in the pixel-to-pixel variation arising from processes at the water surface upwards was greater than the uncertainties arising from the canopy or water column. The physics-based model inversion approach is readily adaptable to any sensor configuration and to different seagrass species and canopy morphologies. No site-specific in situ data is required and the uncertainty estimates can provide an objective basis for interpreting apparent changes in the distribution of seagrasses over time and space, as revealed by remote sensing techniques. (C) 2015 Elsevier Inc. All rights reserved.