Lake Ice Detection From Sentinel-1 Sar With Deep Learning

Abstract

Abstract. Lake ice, as part of the Essential Climate Variable (ECV) lakes, is an important indicator to monitor climate change and global warming. The spatio-temporal extent of lake ice cover, along with the timings of key phenological events such as freeze-up and break-up, provide important cues about the local and global climate. We present a lake ice monitoring system based on the automatic analysis of Sentinel-1 Synthetic Aperture Radar (SAR) data with a deep neural network. In previous studies that used optical satellite imagery for lake ice monitoring, frequent cloud cover was a main limiting factor, which we overcome thanks to the ability of microwave sensors to penetrate clouds and observe the lakes regardless of the weather and illumination conditions. We cast ice detection as a two class (frozen, non-frozen) semantic segmentation problem and solve it using a state-of-the-art deep convolutional network (CNN).We report results on two winters (2016–17 and 2017–18) and three alpine lakes in Switzerland. The proposed model reaches mean Intersection-over-Union (mIoU) scores >90% on average, and >84% even for the most difficult lake. Additionally, we perform cross-validation tests and show that our algorithm generalises well across unseen lakes and winters.

Monitoring the Ice Phenology of Qinghai Lake from 1980 to 2018 Using Multisource Remote Sensing Data and Google Earth Engine

“…Some scholars detect lake ice using deep learning and machine learning. For example, [19] presented a lake ice monitoring system in 2020 based on the automatic analysis of Sentinel-1 Synthetic Aperture Radar (SAR) data by a deep neural network. The authors of [20] also proposed a machine learning-based semantic segmentation approach in 2018 for lake ice detection using low spatial resolution (250 m-1000 m) optical satellite data (MODIS and VIIRS).…”

Ice Monitoring in Swiss Lakes from Optical Satellites and Webcams Using Machine Learning

“…One way to circumvent the problem of clouds with optical satellite sensors is to use microwave data. In particular, Sentinel-1 SAR data (GSD 10 m, freely available) looks very promising [37]. Optical sensors such as Sentinel-2 and Landsat-8 are visually easier to interpret w.r.t.…”

Formulas for calculating ice cover thickness on selected spring lakes on the upper Radunia (Kashubian Lakeland, northern Poland)

“…That is why the monitoring of lakes not under hydrometric supervision is a particularly interesting issue. Such studies usually involve analysing ice cover with the use of satellite images or remote video monitoring data (Murfitt and Duguay 2020;Prabha et al 2020) and then ice models are created using such indirectly collected data sets (Tom et al 2020). There are only a few publications on monitoring lakes that are under hydrological control with the use of benchmark lakes (Barańczuk and Barańczuk 2018;Barańczuk 2019).…”