River algal blooms can be estimated by remote sensing reflectance

Abstract

River eutrophication is difficult to diagnose and estimate quantitatively because of its complex degradation mechanism in large river systems. Conventional monitoring and modeling methods are limited to accurately revealing the evolution process and trends of river aquatic organisms. In the present study, based on HJ-1A/1B CCD sensor, combined with genetic algorithm (GA) and regression tree (GART), a remote sensing inversion prediction model was established; the model can estimate algal blooms in the Han River affected by China’s Middle Route of the South-to-North Water Diversion Project (SNWTP). During the outbreak of algal blooms, the near-infrared band reflectance evidently increased between 2009 and 2015, with increasing algal density. The algal density in the downstream of the Han River has a nearly synchronous positive change with the reflectance in the B4 (near-infrared) band and a nearly synchronous reverse change with the B1 (blue) band. B1 and B4 screened by GA reduced redundancy by 14%, leading to a good prediction performance ( R ^2 = 0.88). According to GART and partial dependence analysis, the B4 band is a crucial characterization factor of algal blooms in the Han River. When the remote sensing band was in the range of B1 ⩾ 0.085 and B4 ⩽ 0.101, the algal density was lower than 0.15 × 10 ^7 cells l ^−1 , indicating no algal bloom in the downstream of the Han River. When B4 was >0.103 and B1 ⩽ 0.076, algal density was higher than 1 × 10 ^7 cells l ^−1 and algal blooms were very likely to occur. These findings could provide a scientific reference for diagnosing and predicting large-scale water ecological degradation in similar watersheds.

Keywords

• algal blooms
• remote sensing inversion
• genetic algorithm and regression tree
• response threshold
• the Han River of China