Evaluating the driving forces of spectral inversion methods used for assessing water quality parameters in Poyang Lake, China

Abstract

AbstractWater quality parameters are key indicators of quality of water and can indicate the algal biomass and eutrophication in lakes. Therefore, this study spectrally inverted and evaluated the water quality of the Poyang Lake in China by analysing the differences between the measured water quality parameters and the observed image spectra of Sentinel‐2 remote sensing. This analysis was done using statistical regression models (SRMs) and various machine learning models (e.g., support vector machine [SVM] and random forest [RF]). The following major conclusions were drawn: (1) nutrients accumulated more in summer (June–August) than in winter (December–February). For example, in local areas of the main river, total nitrogen (TN) concentration reached 3.4 mg/L in summer of 2016, whereas total phosphorus (TP) concentrations were below 0.052 mg/L in winters of 2016 and 2017. (2) The three models, SRM, RF and SVM, achieved good results in the inversions of Secchi depth and permanganate index. However, differences were noted in the inversions of other parameters. For example, the goodness‐of‐fit (r2) between the inversion and measured values of TN concentration from RF was 0.81, while that from SR was 0.73. (3) The spatial distribution patterns of the water quality parameters showed differences. The ammoniacal nitrogen concentration was higher in the central region than that in the western region of the lake, whereas TP concentration was higher at the shoreline of the lake at 0.07 mg/L on 2 April 2017 but was relatively low in the main channel.