An Empirical Algorithm for Hyperspectral Remote Sensing of Chlorophyll-a in Turbid Waters: A Case Study on Hyperion Sensor

In this study, an empirical chlorophyll-a (chl-a) concentration estimation algorithm based on stepwise regression analysis method (ECSRA) are developed for estimating chl-a in turbid waters. The ECSRA algorithm is calibrated and validated by bio-optical dataset collected from Yellow River Estuary, China and Chesapeake Bay, USA. According to the study results, it can get the following conclusions: (1) the spectra reflectance recorded in Yellow River Estuary much more than in Chesapeake Bay; (2) the significant (F < 0.05) spectral bands of ECSRA algorithm are 671 nm, 691 nm, 732 nm, 742 nm and 834 nm in Yellow River Estuary and 447 nm, 498 nm, 508 nm, 641 nm and 681 nm in Chesapeake Bay; (3) the ECSRA algorithm has a superior performance to both two-band and three-band algorithms; (4) the two-band, three-band and ECSRA algorithms in Chesapeake Bay have a superior performance than it in Yellow River Estuary, and (5) the water quality characterizes with low chl-a concentration and high suspended sediment concentration make Yellow River Estuary be more challenge than Chesapeake Bay in chl-a estimation.