A Remote Sensing Approach to Estimate Vertical Profile Classes of Phytoplankton in a Eutrophic Lake

Remote Sensing

Kun Xue¹  Yuchao Zhang¹  Hongtao Duan¹  Ronghua Ma¹  Steven Loiselle²  Minwei Zhang⁴  Deepak R. Mishra³  Eurico J. D’Sa³  Sachidananda Mishra³  Raphael M. Kudela³  Magaly Koch³ 

[1] State Key Laboratory of Lake Science and Environment, Nanjing Institute of Geography and Limnology, Chinese Academy of Sciences, 73 East Beijing Road, Nanjing 210008, China; E-Mails:;Dipartimento di Biotecnologie, Chimica e Farmacia, University of Siena, CSGI, Via Aldo Moro 2, Siena 53100, Italy; E-Mail:State Key Laboratory of Lake Science and Environment, Nanjing Institute of Geography and Limnology, Chinese Academy of Sciences, 73 East Beijing Road, Nanjing 210008, China;;College of Marine Science, University of South Florida, 140 Seventh Avenue, South, St. Petersburg, FL 33701, USA; E-Mail:

关键词: vertical distribution profile classes;    chlorophyll-a;    Normalized Difference algal Bloom Index (NDBI);    classification and regression tree (CART);    remote sensing;   

DOI  :  10.3390/rs71114403

来源: mdpi

【 摘 要 】

The extension and frequency of algal blooms in surface waters can be monitored using remote sensing techniques, yet knowledge of their vertical distribution is fundamental to determine total phytoplankton biomass and understanding temporal variability of surface conditions and the underwater light field. However, different vertical distribution classes of phytoplankton may occur in complex inland lakes. Identification of the vertical profile classes of phytoplankton becomes the key and first step to estimate its vertical profile. The vertical distribution profile of phytoplankton is based on a weighted integral of reflected light from all depths and is difficult to determine by reflectance data alone. In this study, four Chla vertical profile classes (vertically uniform, Gaussian, exponential and hyperbolic) were found to occur in three in situ vertical surveys (28 May, 19–24 July and 10–12 October) in a shallow eutrophic lake, Lake Chaohu. We developed and validated a classification and regression tree (CART) to determine vertical phytoplankton biomass profile classes. This was based on an algal bloom index (Normalized Difference algal Bloom Index, NDBI) applied to both in situ remote sensing reflectance (R_rs) and MODIS Rayleigh-corrected reflectance (R_rc) data in combination with data of local wind speed. The results show the potential of retrieving Chla vertical profiles information from integrated information sources following a decision tree approach.

【 授权许可】

CC BY   
© 2015 by the authors; licensee MDPI, Basel, Switzerland.