Department of Geomatics, National Cheng Kung University, Tainan, Taiwan.
Department of Geomatics Engineering, Institut Teknologi Sepuluh Nopember, Surabaya, Indonesia.
Environ Monit Assess. 2020 Apr 20;192(5):299. doi: 10.1007/s10661-020-08271-9.
An empirical approach through remote sensing generally produces a robust data model of water quality for inland and coastal water. Traditional regressions in water quality mapping fail because the bio-optical relationship of turbid water exhibits nonlinear and heterogeneous patterns. In addition, in situ data are generally insufficient in the water quality mapping. Mapping based on a relatively small amount of water quality samples is considered a practical issue in environmental monitoring. Learning-based algorithms that require a large amount of data are inapplicable in this case. According to the concept of Nadaraya-Watson estimator, the kernel model can estimate nonlinear and spatially varying water quality maps effectively in turbid water.Experiments indicate that the kernel estimator provides better goodness-of-fit between the observed and derived concentrations of water quality parameter, e.g., chlorophyll-a in turbid water. The kernel estimator is feasible for a relatively small size of ground observations. Approximately 30% improvement of cross-validation error was identified in this approach when compared with traditional regressions. The model offers a robust approach without further calibrations for estimating the spatial patterns of water quality by using remote sensing reflectance and a small set of observations, considering spatial and spectral information, e.g., multiple bands and band ratios.
通过遥感进行实证研究通常可以为内陆和沿海水域水质生成强大的数据模型。水质测绘中的传统回归方法之所以失败，是因为混浊水中的生物光学关系表现出非线性和异质模式。此外，在水质测绘中，现场数据通常不足。基于相对少量水质样本的测绘被认为是环境监测中的一个实际问题。在这种情况下，需要大量数据的基于学习的算法是不适用的。根据纳达雅-沃森估计量的概念，核模型可以有效地估计混浊水中的非线性和空间变化的水质图。实验表明，核估计量提供了更好的水质参数观测值和推导值之间的拟合优度，例如混浊水中的叶绿素-a。核估计量适用于相对较小规模的地面观测。与传统回归相比，该方法在交叉验证误差方面提高了约 30%。该模型提供了一种稳健的方法，无需进一步校准，即可通过使用遥感反射率和少量观测值（考虑空间和光谱信息，例如多个波段和波段比）来估计水质的空间模式。
