estimation of chlorophyll-a concentration in turbid lake using spectral smoothing and derivative analysis

As a major indicator of lake eutrophication that is harmful to human health, the chlorophyll-a concentration (Chl-a) is often estimated using remote sensing, and one method often used is the spectral derivative algorithm. Direct derivative processing may magnify the noise, thus making spectral smoothing necessary. This study aims to use spectral smoothing as a pretreatment and to test the applicability of the spectral derivative algorithm for Chl-a estimation in Taihu Lake, China, based on the in situ hyperspectral reflectance. Data from July–August of 2004 were used to build the model, and data from July–August of 2005 and March of 2011 were used to validate the model, with Chl-a ranges of 5.0–156.0 mg/m3, 4.0–98.0 mg/m3 and 11.4–35.8 mg/m3, respectively. The derivative model was first used and then compared with the band ratio, three-band and four-band models. The results show that the first-order derivative model at 699 nm had satisfactory accuracy (R2 = 0.75) after kernel regression smoothing and had smaller validation root mean square errors of 15.21 mg/m3 in 2005 and 5.85 mg/m3 in 2011. The distribution map of Chl-a in Taihu Lake based on the HJ1/HSI image showed the actual distribution trend, indicating that the first-order derivative model after spectral smoothing can be used for Chl-a estimation in turbid lake.