Deep Learning-Based Water Quality Retrieval in an Impounded Lake Using Landsat 8 Imagery: An Application in Dongping Lake

Abstract

Attempts have been made to incorporate remote sensing techniques and in situ observations for enhanced water quality assessments. Estimations of nonoptical indicators sensitive to water environment changes, however, have not been fully studied, mainly due to complex nonlinear relationships between the observed values and surface reflectance. In this study, we applied a novel deep learning approach driven by a range of spectral properties to retrieve 6-year changes in water quality variables, i.e., Chl-a, BOD, TN, CODMn, NH3-N, and TP, on a monthly basis between 2013 and 2018 at Dongping Lake, an impounded lake located in the Yellow River in China. Band arithmetic was used to compute 26 predictors from Landsat 8 OLI imagery for model inputs. The results showed generally strong agreement between in situ and ConvLSTM-derived lake variables, generating R2 of 0.92, 0.88, 0.84, 0.80, 0.83, and 0.77 for TN, NH3-N, CODMn, Chl-a, TP, and BOD, which suggest good performance of the developed model. We then used statistical analysis to identify the spatial and temporal heterogeneity. The framework established in this study has applications in effective water quality monitoring and serves as an alarming tool for water-environment management in the complex inland lake waters.

Keywords

• machine learning
• Landsat 8 OLI
• water quality retrieval
• temporal–spatial dynamics
• Dongping Lake