MNR Key Laboratory for Geo-Environmental Monitoring of Great Bay Area & Guangdong Key Laboratory of Urban Informatics & Shenzhen Key Laboratory of Spatial Smart Sensing and Services, Shenzhen University, Shenzhen, 518060, China; College of Life Sciences and Oceanography, Shenzhen University, 518060, Shenzhen, China.
MNR Key Laboratory for Geo-Environmental Monitoring of Great Bay Area & Guangdong Key Laboratory of Urban Informatics & Shenzhen Key Laboratory of Spatial Smart Sensing and Services, Shenzhen University, Shenzhen, 518060, China; School of Architecture & Urban Planning, Shenzhen University, 518060, Shenzhen, China.
Environ Pollut. 2020 Nov;266(Pt 2):115412. doi: 10.1016/j.envpol.2020.115412. Epub 2020 Aug 14.
In arid and semi-arid regions, water-quality problems are crucial to local social demand and human well-being. However, the conventional remote sensing-based direct detection of water quality parameters, especially using spectral reflectance of water, must satisfy certain preconditions (e.g., flat water surface and ideal radiation geometry). In this study, we hypothesized that drone-borne hyperspectral imagery of emergent plants could be better applied to retrieval total nitrogen (TN) concentration in water regardless of preconditions possibly due to the spectral responses of emergent plants on nitrogen removal and water purification. To test this hypothesis, a total of 200 groups of bootstrap samples were used to examine the relationship between the extracted TN concentrations from the drone-borne hyperspectral imagery of emergent plants and the experimentally measured TN concentrations in Ebinur Lake Oasis using four machine learning (ML) models (Partial Least Squares (PLS), Random Forest (RF), Extreme Learning Machine (ELM), and Gaussian Process (GP)). Through the introduction of the fractional order derivative (FOD), we build a decision-level fusion (DLF) model to minimize the regression results' biases of individual ML models. For individual ML model, GP performed the best. Still, the amount of uncertainty in individual ML models renders their performance to be subpar. The introduction of the DLF model greatly minimizes the regression results' biases. The DLF model allows to reduce potential uncertainties without sacrificing accuracy. In conclusion, the spectral response caused by nitrogen removal and water purification on emergent plants could be used to retrieve TN concentration in water with a DLF model framework. Our study offers a new perspective and a basic scientific support for water quality monitoring in arid regions.
在干旱和半干旱地区，水质问题对当地社会需求和人类福祉至关重要。然而，传统的基于遥感的水质参数直接检测，特别是利用水的光谱反射率，必须满足某些前提条件（例如，水面平坦和理想的辐射几何形状）。在本研究中，我们假设无人机搭载的挺水植物高光谱图像可以更好地应用于检索水中总氮（TN）浓度，而无需满足这些前提条件，这可能是由于挺水植物对氮去除和水净化的光谱响应。为了验证这一假设，我们使用了 200 组 bootstrap 样本，利用四个机器学习（ML）模型（偏最小二乘法（PLS）、随机森林（RF）、极限学习机（ELM）和高斯过程（GP）），检验了从无人机搭载的挺水植物高光谱图像中提取的 TN 浓度与艾比湖绿洲实验测量的 TN 浓度之间的关系。通过引入分数阶导数（FOD），我们构建了一个决策级融合（DLF）模型，以最小化各个 ML 模型回归结果的偏差。对于单个 ML 模型，GP 表现最好。然而，单个 ML 模型的不确定性使得它们的性能欠佳。引入 DLF 模型可以大大减少回归结果的偏差。DLF 模型可以在不牺牲准确性的情况下降低潜在的不确定性。总之，挺水植物的氮去除和水净化引起的光谱响应可以用于通过 DLF 模型框架来检索水中的 TN 浓度。我们的研究为干旱地区的水质监测提供了一个新的视角和基本的科学支持。
