Algorithms for the remote estimation of chlorophyll-a in the Chesapeake Bay

摘要

Remote estimation of chlorophyll-a concentration [Chl-a] in the Chesapeake Bay from reflectance spectra is challenging because of the optical complexity and variability of the water composition as well as atmospheric corrections for this area. This work is focused on algorithms for near surface measurements. The performance and tuning of several well established global inversion algorithms that use the NIR and Blue-Green parts of the spectrum are analyzed together with recently proposed algorithm that use the Red-Green part of the spectrum. These algorithms are evaluated and tuned on our field data collected during summer 2013 field campaign in the in the Chesapeake Bay region . These data consist of a full range of water optical properties as well as chlorophyll concentrations and specific absorption spectra from in water samples. We then compare these algorithms with a multiband retrieval algorithm that was developed using neural networks (NN) and which was trained on simulated data generated through bio-optical modeling typical for a broad range of coastal water parameters, including those known for the Chesapeake Bay. This NN algorithm was then applied to our field measurements and used to retrieve the phytoplankton absorption at 443nm which was then related to [Chl-a]. hi this process, special attention was paid to field data consistency in terms of both measured reflectance and [Chl-a] values, to avoid undesirable biases and trends. All algorithm retrievals were finally evaluated by several statistical indicators to arrive at their relative merits and potential for further improvements and application to satellite data.