ABSTRACT Updating the physicochemical properties of surface water is important to water quality researchers and decision-makers. Routine surface water quality monitoring methods are inadequate; whereas, remote sensing (RS) is a source of processing data in both spatial and temporal domains. However, RS is a good tool of monitoring freshwater ecosystems, accurate modelling of surface water quality parameters from satellite imagery is still a challenging task. High correlation between the adjacent satellite signals, which creates redundancy, is one of the main challenges that negatively affect the stability of the developed models. Therefore, this paper focuses on the development of a novel approach for modelling concentrations of both turbidity (T) and total suspended solids (TSS) using Landsat-8 tasselled cap transformation (TCT). As a result, robust models were developed between measurements from satellite-derived T and TSS and in situ measurements with coefficient of determination values of 0.854 and 0.812, respectively. These findings confirmed the possibility of using TCT to establish strong models for assessing surface water quality due to its capability of transforming a set of correlated data onto a convenient set of uncorrelated data, which maintain the major information associated with a physical phenomenon.

中文翻译：

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基于 Landsat-8 流苏帽变换的地表水质建模新方法

摘要 更新地表水的理化特性对水质研究人员和决策者来说非常重要。常规地表水水质监测方法不足；而遥感 (RS) 是处理空间和时间域数据的来源。然而，RS 是监测淡水生态系统的好工具，从卫星图像准确建模地表水质参数仍然是一项具有挑战性的任务。相邻卫星信号之间的高度相关性会产生冗余，这是对开发模型的稳定性产生负面影响的主要挑战之一。因此，本文侧重于开发一种使用 Landsat-8 流苏帽变换 (TCT) 模拟浊度 (T) 和总悬浮固体 (TSS) 浓度的新方法。因此，在来自卫星的 T 和 TSS 的测量值与确定系数值分别为 0.854 和 0.812 的原位测量值之间开发了稳健模型。这些发现证实了使用 TCT 建立用于评估地表水质量的强大模型的可能性，因为它能够将一组相关数据转换为一组方便的不相关数据，这些数据保留了与物理现象相关的主要信息。