Abstract

The envelope removal method has the advantage of suppressing the background spectrum and expanding the weak absorption characteristic information. However, for second-class water bodies with a relatively complex water quality, there are few studies on the inversion of chlorophyll a (Chl-a) concentration in water bodies that consider the spectral absorption characteristics. In addition, the current research on the inversion of the Chl-a concentration was carried out under the condition of sample concentration equilibrium. For areas with a highly variable Chl-a concentration, it is still challenging to establish a highly applicable and accurate Chl-a concentration inversion model. Taking Dongting Lake in China as an example, this study used high-concentration samples and spectral absorption characteristics to invert the Chl-a concentration. The decap method was used to preprocess the high-concentration samples with large deviations, and the envelope removal method was used to extract the spectral absorption characteristic parameters of the water body. On the basis of the correlation analysis between the water Chl-a concentration and the spectral absorption characteristics, the water Chl-a concentration was inverted. The results showed the following: (1) The bands that were significantly related to the Chl-a concentration and had a large correlation coefficient were mainly located in the three absorption valleys (400–580, 580–650, and 650–710 nm) of the envelope removal curve. Moreover, the correlation between the Chl-a concentration and the absorption characteristic parameters at 650–710 nm was better than that at 400–580 nm and 580–650 nm. (2) Compared with the conventional inversion model, the uncapped inversion model had a higher RP2 and a lower RMSEP, and was closer to the predicted value of the 1:1 line. Moreover, the performance of the uncapped inversion model was better than that of the conventional inversion model, indicating that the uncapped method is an effective preprocessing method for high-concentration samples with large deviations. (3) The predictive capabilities of the ER_New model were significantly better than those of the R_New model. This shows that the envelope removal method can significantly amplify the absorption characteristics of the original spectrum, which can significantly improve the performance of the prediction model. (4) From the inversion models for the absorption characteristic parameters, the prediction models of A650–710 nm_New and D650–710 nm_New exhibited the best performance. The three combined models (A650–710 nm&D650–710 nm_New, A650–710 nm&NI_New, A650–710 nm&DI_New) also demonstrated good predictive capabilities. This demonstrates the feasibility of using the spectral absorption feature to retrieve the chlorophyll concentration.

Highlights

  • Lake eutrophication is one of the most important water-related environmental problems facing humanity [1,2,3]

  • Chlorophyll is an important component of phytoplankton, and chlorophyll a (Chl-a) is the type of chlorophyll contained in all phytoplankton categories

  • The current water quality monitoring method can accurately determine the various indicators of water quality at a certain location, but it is costly, time-consuming, and the range of monitoring points is limited, which does not reflect the distribution of water quality in time and space [9,10,11]

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Summary

Introduction

Lake eutrophication is one of the most important water-related environmental problems facing humanity [1,2,3]. The current degree of eutrophication in Dongting. It can be used to estimate the biomass and productivity of phytoplankton, and it is an important parameter reflecting the degree of nutrition of water bodies [6,7,8]. Hyperspectral remote sensing has the advantages of multiple and narrow bands. It can quickly obtain surface reflection spectra of water bodies, detect the relationship between spectral characteristics and water quality indicators, and provide a powerful tool for real-time, rapid, and large-scale water quality monitoring [12,13,14,15,16]

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