Abstract
Phytoplankton species composition research is key to understanding phytoplankton ecological and biogeochemical functions. Hyperspectral optical sensor technology allows us to obtain detailed information about phytoplankton species composition. In the present study, a transfer learning method to inverse phytoplankton species composition using in situ hyperspectral remote sensing reflectance and hyperspectral satellite imagery was presented. By transferring the general knowledge learned from the first few layers of a deep neural network (DNN) trained by a general simulation dataset, and updating the last few layers with an in situ dataset, the requirement for large numbers of in situ samples for training the DNN to predict phytoplankton species composition in natural waters was lowered. This method was established from in situ datasets and validated with datasets collected in different ocean regions in China with considerable accuracy (R2 = 0.88, mean absolute percentage error (MAPE) = 26.08%). Application of the method to Hyperspectral Imager for the Coastal Ocean (HICO) imagery showed that spatial distributions of dominant phytoplankton species and associated compositions could be derived. These results indicated the feasibility of species composition inversion from hyperspectral remote sensing, highlighting the advantages of transfer learning algorithms, which can bring broader application prospects for phytoplankton species composition and phytoplankton functional type research.
Highlights
Marine phytoplankton plays a crucial role in aquatic ecosystems [1]
We focused on the following four aspects in this research: (1) developing a prediction model of phytoplankton species composition through a deep neural network (DNN) with a simulated Rrs dataset for 11 phytoplankton species cultures and other optical components combined, so-called NNsim; (2) reforming the NNsim through the introduction of an in situ Rrs dataset using a transfer learning approach, so-called NNTL; (3) applying the NNTL to Hyperspectral Imager for the Coastal Ocean (HICO) imagery to predict the dominant phytoplankton species composition in the Changjiang Estuary and adjacent waters; and (4) analyzing the sensitivity of our method under conditions of various concentrations of suspended particulate matter (SPM), chlorophyll a and colored dissolved organic matter (CDOM), spectral resolutions, and signal-to-noise ratios (SNRs)
The DNN with transfer learning was tested; in addition, the conventional DNN was considered for comparative analysis
Summary
Marine phytoplankton plays a crucial role in aquatic ecosystems [1]. It contributes to primary production, affects the abundance and diversity of marine organisms [2], and exerts influence on climate processes [3]. Because the physiological processes of different phytoplankton species and associated compositions are distinct from each other, each individual phytoplankton species and its associated composition information are fundamental to its functional type [7]. They are indicative of variability in phytoplankton species diversity in the ocean [8]. With the development of satellite-based ocean color remote sensing, which provides the advantages of wide-range, long-term coverage, high efficiency, and low cost [9], phytoplankton species, composition, and related research from space have been carried out [10,11]
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