Constructing a High Temporal Resolution Global Lakes Dataset via Swin-Unet with Applications to Area Prediction
This work addresses the need for continuous global lake monitoring for environmental and climate research, though it is incremental as it builds on an existing dataset with improved temporal resolution.
The paper tackles the problem of insufficient temporal resolution in global lake monitoring by introducing GLAKES-Additional, a dataset providing biennial delineations and area measurements for 152,567 lakes from 1990 to 2021, and achieves an RMSE of 0.317 km² in predicting future lake area changes using an LSTM model.
Lakes provide a wide range of valuable ecosystem services, such as water supply, biodiversity habitats, and carbon sequestration. However, lakes are increasingly threatened by climate change and human activities. Therefore, continuous global monitoring of lake dynamics is crucial, but remains challenging on a large scale. The recently developed Global Lakes Area Database (GLAKES) has mapped over 3.4 million lakes worldwide, but it only provides data at decadal intervals, which may be insufficient to capture rapid or short-term changes.This paper introduces an expanded lake database, GLAKES-Additional, which offers biennial delineations and area measurements for 152,567 lakes globally from 1990 to 2021. We employed the Swin-Unet model, replacing traditional convolution operations, to effectively address the challenges posed by the receptive field requirements of high spatial resolution satellite imagery. The increased biennial time resolution helps to quantitatively attribute lake area changes to climatic and hydrological drivers, such as precipitation and temperature changes.For predicting lake area changes, we used a Long Short-Term Memory (LSTM) neural network and an extended time series dataset for preliminary modeling. Under climate and land use scenarios, our model achieved an RMSE of 0.317 km^2 in predicting future lake area changes.