Water clarity mapping of global lakes using a novel hybrid deep-learning-based recurrent model with Landsat OLI images.

Information regarding water clarity at large spatiotemporal scales is critical for understanding comprehensive changes in the water quality and status of ecosystems. Previous studies have suggested that satellite observation is an effective means of obtaining such information. However, a reliable model for accurately mapping the water clarity of global lakes (reservoirs) is still lacking due to the high optical complexity of lake waters. In this study, by using gated recurrent units (GRU) layers instead of full-connected layers from Artificial Neural Networks (ANNs) to capture the efficient sequence information of in-situ datasets, we propose a novel and transferrable hybrid deep-learning-based recurrent model (DGRN) to map the water clarity of global lakes with Landsat 8 Operational Land Imager (OLI) images. We trained and further validated the model using 1260 pairs of in-situ measured water clarity and surface reflectance of Landsat 8 OLI images with Google Earth Engine. The model was subsequently utilized to construct the global pattern of temporal and spatial changes in water clarity (lake area>10 km) from 2014 to 2020. The results show that the model can estimate water clarity with good performance (R = 0.84, MAE = 0.55, RMSE = 0.83, MAPE = 45.13%). The multi-year average of water clarity for global lakes (16,475 lakes) ranged from 0.0004 to 9.51 m, with an average value of 1.88 ± 1.24 m. Compared to the lake area, elevation, discharge, residence time, and the ratio of area to depth, water depth was the most important factor that determined the global spatial distribution pattern of water clarity. Water clarity of 15,840 global lakes between 2014 and 2020 remained stable (P ≥ 0.05); while there was a significant increase in 243 lakes (P < 0.05) and a decrease in 392 lakes (P < 0.05). However, water clarity in 2020 (COVID-19 period) showed a significant increase in most global lakes, especially in China and Canada, suggesting that the worldwide lockdown strategy due to COVID-19 might have improved water quality, espically water clarity, dueto the apparent reduction of anthropogenic activities.