Abstract
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 actualdistribution trend, indicating that the first-order derivative model after spectral smoothing can be used for Chl-a estimation in turbid lake.
Highlights
Freshwater lakes are the main source of drinking and agricultural water in many areas, and their water quality can greatly affect human health
The hyperspectral reflectance of turbid lake water is an expression of the compound information of the water components, including chlorophyll-a, suspended sediment and colored dissolved organic matter (CDOM) [4] and is often affected by certain factors, such as the measuring instruments and environment conditions
Spectral derivatives can distinguish the detailed information in the spectrum, but the direct derivative processing of the remote sensing reflectance can magnify the noise caused by the environment or measurement influence
Summary
Freshwater lakes are the main source of drinking and agricultural water in many areas, and their water quality can greatly affect human health. The main danger caused by eutrophication is the toxins produced by some algae, which are harmful for drinking and can poison or even kill humans and animals that consume contaminated water and food [1]. Compared with monitoring Chl-a through field water sampling, which is usually costly and time-consuming, remote sensing is a robust and effective means of monitoring large areas of lake water and is widely used in the assessment of. The hyperspectral reflectance of turbid lake water is an expression of the compound information of the water components, including chlorophyll-a, suspended sediment and colored dissolved organic matter (CDOM) [4] and is often affected by certain factors, such as the measuring instruments and environment conditions. The preprocessing of the spectrum is of great importance for extracting better information about Chl-a
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