A conditional random fields-based identification for small lakes over multiple melt seasons using Sentinel-2 imagery in the Larsemann Hills, East Antarctica

This study provides the first long-time series of spatial and temporal distributions for small lakes in the Larsemann Hills (69°23′S, 76°20′E) in the East Antarctic. In the Larsemann oasis, there is a significant number of over 150 small lakes, which can be observed with high spatial resolution in remote sensing imagery. However, accurately identifying and analyzing these small water bodies and elongated rivers has been challenging due to the mixed pixels effect and limitations in available middle spatial resolution imagery. In our study, we propose a data-driven approach within the Conditional Random Fields (CRF) framework, which considers three scales: superpixel, pixel, and subpixel, to refine the boundaries of small water bodies efficiently. The superpixel level quickly identifies the main water body and NDWI provides a buffer region, while the pixel level employs SVM to obtain a more precise boundary. Subpixel mapping technology within the pixel level further reduces mixed pixel effects for improved accuracy. The waterbodies were extracted from Sentinel-2 images with a spatial resolution of 10 m. The lake boundaries derived from the proposed algorithm in this study showed good agreement with in-situ measurements of the lake shoreline delineated by aerial images from the 39th Chinese Antarctic Scientific Expedition. The analysis revealed distinct seasonal patterns across the Larsemann Hills while the lake areas achieved their peak extents earlier, specifically in February before 2020 and in January after 2020. The water body mapping based on the proposed algorithm can contribute to Antarctic remote sensing hydrological observations, particularly in the monitoring of outburst events. These findings demonstrate the potential of extending this method to other Antarctic oases to enhance intra-annual lake observations. Moreover, Sentinel-2 images provide valuable remote sensing data for studying the seasonal cycles of water bodies, including those of varying sizes in the Larsemann Hills, based on long-term time series imagery.