Remote retrieval of dissolved organic carbon in rivers using a hyperspectral drone system

Abstract

ABSTRACTRivers act as the principal channels for transporting terrigenous dissolved organic carbon (DOC) to lakes and reservoirs. Satellite remote sensing-based river monitoring is difficult due to the narrow river form and high spatiotemporal heterogeneity of DOC components. The unique advantages of unmanned aerial vehicles (UAVs) facilitate river DOC concentration monitoring. The DOC concentration in 8 major tributaries (average width: 109.62 m) and shoreside of the Lake Chaohu Basin were retrieved via a hyperspectral UAV. The results showed that (1) the DOC concentration was significantly correlated with the water remote sensing reflectance ([Formula: see text]) of 402, 429-438, 440–451 and 458–462 nm in the blue band (r2: 0.11 to 0.13; p<0.05), and 620–621 and 623–693 nm in the red band (r2: 0.12 to 0.20; p<0.05). The water quality parameters chlorophyll-a (Chl-a) and suspended particulate matter (SPM) and environmental parameters wind speed and temperature 3 days delay sampling date, also showed a significant correlation. (2) The random forest regression (RFR) model attained the best performance (r2: 0.64; RMSE: 0.30 mg/L; MAPE: 7.02%). (3) DOC concentration in Lake Chaohu Basin was highest in the northeast (8.19 mg/L), followed by the northwest and west (7.13 mg/L), and it was lowest in the south (6.70 mg/L).

Keywords

• DOC monitoring; hyperspectral UAV; random forest regression; water quality; remote sensing