Lei Shaohua, Xu Jie, Li Yunmei, Li Lin, Lyu Heng, Liu Ge, Chen Yu, Lu Chunyan, Tian Chao, Jiao Wenzhe
Jiangsu Center for Collaborative Innovation in Geographical Information Resource Development and Application, Key Laboratory of Virtual Geographical Environment of Ministry of Education, School of Geography, Nanjing Normal University, Nanjing, 210023, China; Department of Earth Sciences, Indiana University-Purdue University Indianapolis (IUPUI), IN, 46202, USA.
Jiangsu Center for Collaborative Innovation in Geographical Information Resource Development and Application, Key Laboratory of Virtual Geographical Environment of Ministry of Education, School of Geography, Nanjing Normal University, Nanjing, 210023, China.
Environ Pollut. 2021 Feb 1;270:116288. doi: 10.1016/j.envpol.2020.116288. Epub 2020 Dec 11.
The particle size distribution (PSD) slope (ξ) can indicate the predominant particle size, material composition, and inherent optical properties (IOPs) of inland waters. However, few semi-analytical methods have been proposed for deriving ξ from the surface remote sensing reflectance due to the variable optical state of inland waters. A semi-analytical algorithm was developed for inland waters having a wide range of turbidity and ξ in this study. Application of the proposed model to Ocean and Land Color Instrument (OLCI) imagery of the water body resulted in several important observations: (1) the proposed algorithm (754 nm and 779 nm combination) was capable of retrieving ξ with R2 being 0.72 (p < 0.01, n = 60), and MAPE and RMSE being 4.37% and 0.22 (n = 30) respectively; (2) the ξ in HZL was lower in summer than other seasons during the period considered, this variation was driven by the phenological cycle of algae and the runoff caused by rainfall; (3) the band optimization proposed in this study is important for calculating the particle backscattering slope (η) and deriving ξ because it is feasible for both algae dominant and sediment governed turbid inland lakes. These observations help improve our understanding of the relationship between IOPs and ξ, which are affected by different bio-optic processes and algal phenology in the lake environment.
粒径分布（PSD）斜率（ξ）可以指示内陆水体的主要粒径、物质组成和固有光学特性（IOPs）。然而，由于内陆水体光学状态的变化，很少有半分析方法被提出用于从表面遥感反射率中推导ξ。本研究针对具有广泛浊度和ξ的内陆水体开发了一种半分析算法。将所提出的模型应用于水体的海洋和陆地颜色仪器（OLCI）图像，得到了几个重要的观测结果：（1）所提出的算法（754nm和779nm组合）能够反演ξ，R2为0.72（p<0.01，n = 60），平均绝对百分比误差（MAPE）和均方根误差（RMSE）分别为4.37%和0.22（n = 30）；（2）在所考虑的时期内，夏季HZL中的ξ低于其他季节，这种变化是由藻类的物候周期和降雨引起的径流驱动的；（3）本研究中提出的波段优化对于计算颗粒后向散射斜率（η）和推导ξ很重要，因为它对于藻类主导和沉积物控制的浑浊内陆湖泊都是可行的。这些观测结果有助于提高我们对IOPs与ξ之间关系的理解，它们在湖泊环境中受到不同生物光学过程和藻类物候的影响。
