Remote Estimation of Trophic State Index for Inland Waters Using Landsat-8 OLI Imagery

Remote monitoring of trophic state for inland waters is a hotspot water quality studies worldwide. However, the complex optical properties limit potential algorithms. This research aims to develop an algorithm estimate in waters. First, turbid index was applied determination types on each pixel, and bodies are divided into two categories: algae-dominated (Type I) II). The algal biomass (ABI) then established based classification derive (TSI) proposed by Carlson (1977). results showed considerable precision Type I (R2 = 0.62, N 282) II 0.57, 132). ABI-derived TSI outperformed several band-ratio algorithms machine learning method (RMSE 4.08, MRE 5.46%, MAE 3.14, NSE 0.64). Such model employed generate 146 lakes (> 10 km2) eastern China from 2013 2020 using Landsat-8 surface reflectance data. number hypertrophic oligotrophic decreased 45.89% 21.92% 4.11% 1.37%, respectively, while mesotrophic eutrophic increased 12.33% 23.97% 37.67% 52.74%. annual mean lower reaches Yangtze River basin higher than that middle Huai basin. retrieval illustrated applicability other sensors with overall accuracy 83.27% moderate-resolution imaging spectroradiometer (MODIS) 82.92% Sentinel-3 OLCI sensor, demonstrating high-frequency observation large-scale simulation capability. Our study can provide effective assessment support management.