Abstract
Concentrations of chlorophyll-a (Chl-a) and total suspended solids (TSS) are significant parameters used to assess water quality. The objective of this study is to establish a quantitative model for estimating the Chl-a and the TSS concentrations in irrigation ponds in Higashihiroshima, Japan, using field hyperspectral measurements and statistical analysis. Field experiments were conducted in six ponds and spectral readings for Chl-a and TSS were obtained from six field observations in 2014. For statistical approaches, we used two spectral indices, the ratio spectral index (RSI) and the normalized difference spectral index (NDSI), and a partial least squares (PLS) regression. The predictive abilities were compared using the coefficient of determination (R2), the root mean squared error of cross validation (RMSECV) and the residual predictive deviation (RPD). Overall, iterative stepwise elimination based on PLS (ISE–PLS), using the first derivative reflectance (FDR), showed the best predictive accuracy, for both Chl-a (R2 = 0.98, RMSECV = 6.15, RPD = 7.44) and TSS (R2 = 0.97, RMSECV = 1.91, RPD = 6.64). The important wavebands for estimating Chl-a (16.97% of all wavebands) and TSS (8.38% of all wavebands) were selected by ISE–PLS from all 501 wavebands over the 400–900 nm range. These findings suggest that ISE–PLS based on field hyperspectral measurements can be used to estimate water Chl-a and TSS concentrations in irrigation ponds.
Highlights
Agriculture is by far the greatest water consumer in the world, and a major cause of water pollution
36 samples were collected from six irrigation ponds in six sets of field measurements (3 January, 19 January, 24 March, 9 April, 24 May, and 28 June in 2014)
Our results showed 16.97% of all available wavelengths that were selected for predicting Chl-a and 8.38% were selected for predicting total suspended solids (TSS) by ISE–partial least squares (PLS), which indicates that less than 20% of the waveband information from field hyperspectral data contributes to the prediction for water quality parameters (Chl-a and TSS) and over 80% were redundant
Summary
Agriculture is by far the greatest water consumer in the world, and a major cause of water pollution. The primary pollutants from agriculture are excess nutrients and pesticides [1]. Non-point source pollution, such as irrigation water and surface runoff water containing fertilizer from farmland, contributes to excessive nutrient concentrations [2]. Excess nutrients that cause eutrophication, hypoxia and algal blooms in surface water bodies and Remote Sens. 2017, 9, 264 coastal areas contribute to the primary global water quality problem [1]. Since Chl-a is the primary photosynthetic pigment of all plant life [5], the concentration of Chl-a indicates phytoplankton biomass and eutrophication in lakes [6]. The concentration of total suspended solids (TSS) is another commonly used indicator for water quality assessment [7]. Increased TSS decrease light transmission through the water [9], and affect light availability to phytoplankton, resulting in a decrease of phytoplankton primary production [10]
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