首页> 外文期刊>Remote Sensing >Retrieval of Chlorophyll- a and Total Suspended Solids Using Iterative Stepwise Elimination Partial Least Squares (ISE-PLS) Regression Based on Field Hyperspectral Measurements in Irrigation Ponds in Higashihiroshima, Japan
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Retrieval of Chlorophyll- a and Total Suspended Solids Using Iterative Stepwise Elimination Partial Least Squares (ISE-PLS) Regression Based on Field Hyperspectral Measurements in Irrigation Ponds in Higashihiroshima, Japan

机译:基于田间高光谱测量的日本东广岛灌溉池塘中基于场高光谱测量的迭代逐步消除偏最小二乘(ISE-PLS)回归提取叶绿素a和总悬浮固体

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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 ( R 2 ), 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 ( R 2 = 0.98, RMSECV = 6.15, RPD = 7.44) and TSS ( R 2 = 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.
机译:叶绿素a(Chl a)和总悬浮固体(TSS)的浓度是用于评估水质的重要参数。这项研究的目的是建立一个定量模型,通过田间高光谱测量和统计分析来估算日本东广岛县灌溉池塘中的Chl-a和TSS浓度。在六个池塘中进行了现场实验,并从2014年的六个现场观测中获得了Chla-和TSS的光谱读数。对于统计方法,我们使用了两个光谱指数,即比率光谱指数(RSI)和归一化差异光谱指数(NDSI) ),以及偏最小二乘(PLS)回归。使用确定系数(R 2),交叉验证的均方根误差(RMSECV)和残余预测偏差(RPD)比较预测能力。总体而言,使用Chr-a(R 2 = 0.98,RMSECV = 6.15,RPD = 7.44)和TSS的一阶导数反射率(FDR),基于PLS(ISE–PLS)的迭代逐步消除显示出最佳的预测精度。 (R 2 = 0.97,RMSECV = 1.91,RPD = 6.64)。 ISE–PLS从400–900 nm范围内的所有501个波段中选择了估计Chla(所有波段的16.97%)和TSS(所有波段的8.38%)的重要波段。这些发现表明,基于现场高光谱测量的ISE–PLS可用于估算灌溉池塘中水Chla和TSS的浓度。

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