Hyperspectral Remote Sensing of Phytoplankton Species Composition Based on Transfer Learning

Remote Sensing

Mengyu Li¹  Qing Zhu¹  Fang Shen¹  Yanqun Pan¹  Pei Shang¹ 

[1] State Key Laboratory of Estuarine and Coastal Research, East China Normal University, Shanghai 200241, China;

关键词: phytoplankton species composition;    hyperspectral remote sensing;    transfer learning;    HICO;   

DOI  :  10.3390/rs11172001

来源: DOAJ

【 摘 要 】

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.

【 授权许可】

Unknown