A physics-based method for the remote sensing of seagrasses
In: Remote Sensing of Environment. Elsevier: New York,. ISSN 0034-4257; e-ISSN 1879-0704, more |  |
| Keyword | | | Author keywords | Seagrass; Radiative transfer model; Inversion; Leaf area index; LAI; Uncertainty |
| Authors | | Top | - Hedley, J.
- Russell, B.
- Randolph, K.
- Dierssen, H., more
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| Abstract | 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. |
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