Publications

Zhang, FF; Li, JS; Shen, Q; Zhang, B; Tian, LQ; Ye, HP; Wang, SL; Lu, ZY (2019). A soft-classification-based chlorophyll-a estimation method using MERIS data in the highly turbid and eutrophic Taihu Lake. INTERNATIONAL JOURNAL OF APPLIED EARTH OBSERVATION AND GEOINFORMATION, 74, 138-149.

Abstract
Soft-classification-based methods for estimating chlorophyll-a concentration (Cad.) by satellite remote sensing have shown great potential in turbid coastal and inland waters. However, one of the most important water color sensors, the MEdium Resolution Imaging Spectrometer (MERIS), has not been applied to the study of turbid or eutrophic lakes. In this study, we developed a new soft-classification-based C-chla, estimation method using MERIS data for the highly turbid and eutrophic Taihu Lake. We first developed a decision tree to classify Taihu Lake into three optical water types (OWTs) using MERIS reflectance data, which were quasi-synchronous (+/- 3 h) with in situ measured C-chla, data from 91 sample stations. Secondly, we used MERIS reflectance and in situ measured Ca. data in each OWT to calibrate the optimal C-chla, estimation model for each OWT. We then developed a soft classification -based C-chla estimation method, which blends the C-chla , estimation results in each OWT by a weighted average, where the weight for each MERIS spectra in each OWT is the reciprocal value of the spectral angle distance between the MERIS spectra and the centroid spectra of the OWT. Finally, the soft-classification based Cad. estimation algorithm was validated and compared with no-classification and hard-classification based methods by the leave-one-out cross-validation (LOOCV) method. The soft-classification-based method exhibited the best performance, with a correlation coefficient (R-2), average relative error (ARE), and root-mean square error (RMSE) of 0.81, 33.8%, and 7.0 mu g/L, respectively. Furthermore, the soft- classification-based method displayed smooth values at the edges of OWT boundaries, which resolved the main problem with the hard-classification-based method. The seasonal and annual variations of Cath, were computed in Taihu Lake from 2003 to 2011, and agreed with the results of previous studies, further indicating the stability of the algorithm. We therefore propose that the soft-classification-based method can be effectively used in Taihu Lake, and that it has the potential for use in other optically-similar turbid and eutrophic lakes, and using spectrally-similar satellite sensors.

DOI:
10.1016/j.jag.2018.07.018

ISSN:
0303-2434