Document Type

Article

Publication Date

2015

Keywords

remote sensing; MERIS; chlorophyll-a; Poyang Lake; eutrophication; suspended sediments; SeaDAS; BEAM; atmospheric correction; algorithms

Digital Object Identifier (DOI)

https://doi.org/10.3390/rs70100275

Abstract

A new empirical Chl-a algorithm has been developed and validated for the largest freshwater lake of China (Poyang Lake) using a normalized green-red difference index (NGRDI), where the uncertainty was estimated to be <45% for Chl-a ranging between 1.3 and 10.5 mg·m−3. The combined approach of using the NGRDI algorithm and atmospherically-corrected Medium Resolution Imaging Spectrometer (MERIS) data showed an advantage over other popular approaches. The algorithm was then applied to 325 carefully-selected MERIS full-resolution (300-m) scenes between 2003 and 2012, with pixels of extreme turbidity (NGRDI < 0.06, corresponding to >~25 mg·L−1 total suspended sediments or TSS) masked. The long-term Chl-a distribution showed significant spatial gradient and temporal variability, with Chl-a ranging between 2.4 ± 0.2 mg·m−3 in April and 4.4 ± 1.0 mg·m−3 in July and no significant increasing or decreasing trend during the 10-year period. In waters where Chl-a was retrievable (i.e., where TSS is <25 mg·L−1), Chl-a concentration indicated a significant negative correlation with TSS concentration on a seasonal scale and a significant positive correlation with precipitation over the years. Potential eutrophic regions in the southern and eastern lake, thought to be results of limited water exchange with the main lake, were delineated based on the occurrence frequency of high Chl-a (>10 mg·m−3) in summer. The study not only provides, for the first time, synoptic baseline information on the lake’s Chl-a distributions and potential eutrophic regions, but also demonstrates a practical approach that might be extended to assess eutrophication conditions in other inland waters.

Rights Information

Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 License.

Was this content written or created while at USF?

Yes

Citation / Publisher Attribution

Remote Sensing, v. 7, issue 1, p. 275-299

Included in

Life Sciences Commons

Share

COinS