气候变化领域集成服务门户(CCPortal): Hyperspectral retrievals of phytoplankton absorption and chlorophyll-a in inland and nearshore coastal waters

CCPortal

DOI	10.1016/j.rse.2020.112200
	Hyperspectral retrievals of phytoplankton absorption and chlorophyll-a in inland and nearshore coastal waters
	Pahlevan N.; Smith B.; Binding C.; Gurlin D.; Li L.; Bresciani M.; Giardino C.
发表日期	2021
ISSN	00344257
卷号	253
英文摘要	Following more than two decades of research and developments made possible through various proof-of-concept hyperspectral remote sensing missions, it has been anticipated that hyperspectral imaging would enhance the accuracy of remotely sensed in-water products. This study investigates such expected improvements and demonstrates the utility of hyperspectral radiometric measurements for the retrieval of near-surface phytoplankton properties, i.e., phytoplankton absorption spectra (aph) and biomass evaluated through examining the concentration of chlorophyll-a (Chla). Using hyperspectral data (409–800 nm at ~5 nm resolution) and a class of neural networks known as Mixture Density Networks (MDN) (Pahlevan et al., 2020), we show that the median error in aph retrievals is reduced two-to-three times (N = 722) compared to that from heritage ocean color algorithms. The median error associated with our aph retrieval across all the visible bands varies between 20 and 30%. Similarly, Chla retrievals exhibit significant improvements (i.e., more than two times; N = 1902), with respect to existing algorithms that rely on select spectral bands. Using an independent matchup dataset acquired near-concurrently with the acquisition of the Hyperspectral Imager for the Coastal Ocean (HICO) images, the models are found to perform well, but at reduced levels due to uncertainties in the atmospheric correction. The mapped spatial distribution of Chla maps and aph spectra for selected HICO swaths further solidify MDNs as promising machine-learning models that have the potential to generate highly accurate aquatic remote sensing products in inland and coastal waters. For aph retrieval to improve further, two immediate research avenues are recommended: a) the network architecture requires additional optimization to enable a simultaneous retrieval of multiple in-water parameters (e.g., aph, Chla, absorption by colored dissolved organic matter), and b) the training dataset should be extended to enhance model generalizability. This feasibility analysis using MDNs provides strong evidence that high-quality, global hyperspectral data will open new pathways toward a better understanding of biodiversity in aquatic ecosystems. © 2020 The Author(s)
英文关键词	Algorithm development; Chlorophyll-a; HICO; Hyperspectral; Inland and coastal waters; Machine learning; Phytoplankton absorption
语种	英语
scopus关键词	Aquatic ecosystems; Biodiversity; Chlorophyll; Internet protocols; Network architecture; Phytoplankton; Quality control; Remote sensing; Spectroscopy; Water absorption; Aquatic remote sensing; Atmospheric corrections; Colored dissolved organic matter; Hyperspectral remote sensing; Machine learning models; Phytoplankton absorptions; Radiometric measurements; Research and development; Hyperspectral imaging; absorption; aquatic ecosystem; biodiversity; chlorophyll a; coastal water; multispectral image; optimization; phytoplankton; remote sensing; spatial distribution
来源期刊	Remote Sensing of Environment
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/179021
作者单位	NASA Goddard Space Flight Center, Greenbelt, MD, United States; Science Systems and Applications, Inc. (SSAI), Lanham, MD, United States; Environment and Climate Change Canada, Burlington, ON, Canada; Wisconsin Department of Natural Resources, Madison, WI, United States; Purdue School of Science, Indiana University-Purdue UniversityIN, United States; National Research Council of Italy, IREA, Milan, Italy
推荐引用方式 GB/T 7714	Pahlevan N.,Smith B.,Binding C.,et al. Hyperspectral retrievals of phytoplankton absorption and chlorophyll-a in inland and nearshore coastal waters[J],2021,253.
APA	Pahlevan N..,Smith B..,Binding C..,Gurlin D..,Li L..,...&Giardino C..(2021).Hyperspectral retrievals of phytoplankton absorption and chlorophyll-a in inland and nearshore coastal waters.Remote Sensing of Environment,253.
MLA	Pahlevan N.,et al."Hyperspectral retrievals of phytoplankton absorption and chlorophyll-a in inland and nearshore coastal waters".Remote Sensing of Environment 253(2021).