ABSTRACT

Estimation of surface water quality parameters (SWQPs) from satellite imagery is a cost-effective method. Remote sensing algorithms are developed to map the relationship between satellite reflectance and concentrations of SWQPs. This study exploited principal component analysis (PCA) to decorrelate multispectral bands prior to developing algorithms for SWQPs. Landsat-8 surface reflectances are first achieved, then, principal components are extracted to develop algorithms for turbidity and total suspended solids (TSS). The developed Landsat-8-based-PCA technique enhanced the accuracy of retrieving concentrations of turbidity and TSS by 12.585% and 12.626%, respectively. These findings demonstrated the significant use of Landsat-8-based-PCA technique to consider/estimate other SWQPs.

摘要

从卫星图像估算地表水质量参数 (SWQP) 是一种具有成本效益的方法。开发了遥感算法来绘制卫星反射率与 SWQP 浓度之间的关系。本研究在开发 SWQP 算法之前，利用主成分分析 (PCA) 对多光谱波段进行去相关。首先实现 Landsat-8 表面反射率，然后提取主成分以开发浊度和总悬浮固体 (TSS) 算法。开发的基于 Landsat-8 的 PCA 技术将反演浊度和 TSS 浓度的精度分别提高了 12.585% 和 12.626%。这些发现证明了基于 Landsat-8 的 PCA 技术在考虑/估计其他 SWQP 方面的重要用途。