Estimating chlorophyll-a concentration based on a four-band model using field spectral measurements and HJ-1A hyperspectral data of Qiandao Lake, China

Abstract

Accurate estimation of phytoplankton chlorophyll-a (chl-a) concentration from remote sensing data is challenging due to the complex optical properties of case II waters. Recently, a novel semi-analytical four-band model was developed to estimate chl-a concentration in turbid productive waters. The objective of this study was to evaluate the performance of the four-band model and extend its application to hyperspectral satellite data for estimating chl-a concentration in Qiandao Lake of China. Based on field spectral measurements and in situ water sampling, the four-band model expressed as [Rrs−1(661.6) – Rrs−1(706.7)] [Rrs−1(714.8) – Rrs−1(682.2)]−1 was calibrated after band tuning, where Rrs−1 represents the reciprocal of the remote sensing reflectance. The spectral-based four-band model accounted for more than 88% of variance in chl-a concentration with a root mean square error (RMSE) of 1.47 μg l−1. To justify the potential of this model with satellite data, comparable wavelengths selected from HJ-1A Hyperspectral Imager (HSI) imagery were utilized to calibrate the four-band model. The HSI-based model explained about 80% of chl-a variation with an RMSE of 1.35 μg l−1. Experimental results also showed that the four-band model outperformed its three-band counterpart. The results validated the rationale of the four-band model and demonstrated the effectiveness of this model for estimating chl-a concentration from both in situ spectral data and HJ-1A hyperspectral satellite imagery.