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Recent Ice Trends in Swiss Mountain Lakes: 20-Year Analysis of MODIS Imagery

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Recent Ice Trends in Swiss Mountain Lakes: 20-Year Analysis of MODIS Imagery Noname manuscript No. (will be inserted by the editor) Recent Ice Trends in Swiss Mountain Lakes: 20-year Analysis of MODIS Imagery Manu Tom 1,2,3 · Tianyu Wu 1 · Emmanuel Baltsavias 1 · Konrad Schindler 1 Received: date / Accepted: date Abstract Depleting lake ice can serve as an indicator products. We find a change in Complete Freeze Dura- for climate change, just like sea level rise or glacial re- tion of -0.76 and -0.89 days per annum for lakes Sils and treat. Several Lake Ice Phenological (LIP) events serve Silvaplana, respectively. Furthermore, we observe plau- as sentinels to understand the regional and global cli- sible correlations of the LIP trends with climate data mate change. Hence, it is useful to monitor long-term measured at nearby meteorological stations. We notice lake freezing and thawing patterns. In this paper we re- that mean winter air temperature has negative correla- port a case study for the Oberengadin region of Switzer- tion with the freeze duration and break-up events, and land, where there are several small- and medium-sized positive correlation with the freeze-up events. Addition- mountain lakes. We observe the LIP events, such as ally, we observe strong negative correlation of sunshine freeze-up, break-up and ice cover duration, across two during the winter months with the freeze duration and decades (2000-2020) from optical satellite images. We break-up events. analyse time-series of MODIS imagery by estimating spatially resolved maps of lake ice for these Alpine Keywords lake ice monitoring · machine learning · lakes with supervised machine learning (and addition- semantic segmentation · satellite image processing · ally cross-check with VIIRS data when available). To MODIS · VIIRS train the classifier we rely on reference data annotated manually based on webcam images. From the ice maps we derive long-term LIP trends. Since the webcam data 1 Introduction is only available for two winters, we also validate our results against the operational MODIS and VIIRS snow Scientists around the globe strive to understand the changing climate, to find ways to mitigate the impact of Manu Tom ( ) associated extreme weather conditions, and to protect E-mail: [email protected] the environment for future generations (Rolnick et al., Tianyu Wu 2019). The repercussions of climate change are fore- arXiv:2103.12434v2 [cs.CV] 3 Aug 2021 E-mail: [email protected] seen to amplify in the next few decades. Furthermore, Emmanuel Baltsavias the latest climate models underline the need for urgent E-mail: [email protected] mitigation (Forster et al., 2020). "Human activities are Konrad Schindler estimated to have caused approximately 1:0◦C of global E-mail: [email protected] warming above pre-industrial levels, with a likely range ◦ ◦ 1Photogrammetry and Remote Sensing Group, of 0:8 C to 1:2 C. Global warming is likely to reach ETH Zurich, 8093 Zurich, Switzerland 1:5◦C between 2030 and 2052 if it continues to increase at the current rate", said the IPCC special report on im- 2 Glaciology and Geomorphodynamics Group, pacts of global warming (Masson-Delmotte et al., 2018). University of Zurich, 8057 Zurich, Switzerland Many studies have reported on the response of LIP 3Remote Sensing Group, Swiss Federal Institute of Aquatic trends to climate variations (Brown and Duguay, 2010; Science and Technology, 8600 D¨ubendorf, Switzerland Duguay et al., 2006; Howell et al., 2009; Kang et al., 2 Tom et al. 2012; Sharma et al., 2019; Surdu et al., 2014). Lo- the spatial resolution is moderate (250-1000m Ground cal weather patterns and lake ice formation processes Sampling Distance, GSD). In addition, the global cov- are inter-connected (Brown and Duguay, 2010). Hence, erage is beneficial to eventually scale up to country- or monitoring the long-term LIP trends can provide in- world-wide monitoring. On the other hand, cloud cover tegral cues on the local and global climate. Increasing is a bottleneck for optical satellite data analysis. An temperatures cause decreasing trends in the lake ice important asset is the availability of large time-series, formation process. Air temperature in the vicinity of a e.g., MODIS data is available for the entire period since lake affects the ice formation process within the lake 2000, contrary to other sensor data like airborne or ter- and vice versa. Moreover, there are potential positive restrial photography, webcams etc. This makes it pos- feedbacks, as frozen lakes have higher albedo (especially sible to implement a 20-year analysis and to derive the when covered with snow), and thus lower absorption LIP trends. and evaporation (Slater et al., 2021; Wang et al., 2018). The last decades have seen the rise of machine learn- In addition to its usefulness for climate studies, lake ice ing as a tool for data analysis in remote sensing and monitoring is also crucial to organise safe transporta- the Earth sciences. That is, large-scale statistical data tion especially in lakes that freeze only partially, to con- analysis is used to capture the complex input-output re- serve freshwater ecology, to trigger warnings against ice lationships in a data-driven manner. Machine learning shoves caused by wind during the break-up period, and is a powerful tool to recognise the underlying patterns for winter tourism (Hampton et al., 2017; Hirose et al., in data where mechanistic models are lacking or too 2008; Knoll et al., 2019; Mullan et al., 2017). complicated. We leverage it to create a 20 year time- In the present case study, we aim to monitor lakes series of ice cover in Swiss mountain lakes primarily of the Oberengadin region in the Swiss Alps (which using the Terra MODIS (https://terra.nasa.gov reliably freeze every winter) on a daily basis during /about/terra-instruments/modis) data, and show the winter months, to derive the spatio-temporal ex- empirically that the ice formation indeed follows a de- tent of lake ice.1 Specifically, we estimate the four im- creasing trend. We cast lake ice detection as a 2-class portant LIP events: Freeze-Up Start (FUS), Freeze-Up (frozen, non-frozen) per-pixel supervised classification End (FUE), Break-Up Start (BUS) and Break-Up End problem. Class frozen represents both snow-on-ice and (BUE). Using these four dates, we also estimate the snow-free-ice pixels, while non-frozen denotes open wa- Complete Freeze Duration (CFD) and Ice Coverage Du- ter. As part of our study, we compare the performance ration (ICD), refer to Table 1 for definitions. Some pub- of three popular machine learning methods: Support lications have termed FUE and BUS as ice-on and ice- Vector Machine (Cortes and Vapnik, 1995, SVM), Ran- off dates, respectively (Hendricks Franssen and Scher- dom Forest (Breiman, 2001), and XGBoost (Chen and rer, 2008; Tom et al., 2020c). However, other researchers Guestrin, 2016). Additionally, we assess the sensitivity (and the NSIDC database, https://nsidc.org/) con- of these classifiers to the respective hyper-parameters. sider BUE as ice-off (Duguay et al., 2015). Regarding We find that a linear SVM offers the best generalisa- the ice-on/off dates, the Global Climate Observing Sys- tion across winters and lakes for our data, and derive tem (GCOS) requirements are daily observations at an LIP from the resulting time-series by fitting a piece-wise accuracy of ±2 days (https://gcos.wmo.int/en/es linear model per winter. sential-climate-variables/lakes/ecv-requirem ents). 1.1 Operational lake ice / snow products In this work, we focus on estimating the spatio- temporal extent of the ice cover from optical satel- To our knowledge, the only operational lake ice prod- 2 lite data. Compared to other sensors, MODIS and uct at present is the Climate Change Initiative Lake VIIRS satellite data have several advantages such as Ice Cover (Cr´etauxet al., 2020). A comparison of our wide area coverage, good spectral and fine temporal results with this product is however not possible, since resolution (daily), free availability etc. Additionally, none of our target lakes are included in the list of 250 compared to other optical satellites such as Landsat- lakes covered by the product. A second product, Coper- 8, Sentinel-2 and the like, MODIS and VIIRS offer the nicus Lake Ice Extent (LIE, https://land.coper best spatio-temporal resolution trade-off for the appli- nicus.eu/global/products/lie), is still in pre- cation of single-sensor lake ice monitoring, even though operational stage due to accuracy issues, and coverage only starts in 2017. Though not designed for lake ice, 1 we do not include the ice thickness. the MODIS Snow Product (Hall and Riggs, 2016) and 2 we have previously also used webcams (Tom et al., 2020c; Xiao et al., 2018) and Sentinel-1 Synthetic Aperture VIIRS Snow Product (https://nsidc.org/sites/ns Radar (Tom et al., 2020a, SAR) for lake ice monitoring. idc.org/files/technical-references/VIIRS-sno Recent Ice Trends in Swiss Mountain Lakes: 20-year Analysis of MODIS Imagery 3 w-products-user-guide-final.pdf) are also possi- and reported delayed FUS (0.58 d/a) as well as BUS ble options for comparison, since lakes in the Alps are (0.09 d/a), and reduced ice duration (-0.49 d/a) trends. typically snow-covered for most of the frozen period. We Another study (Yao et al., 2016) also noted increas- cross check our results with these two snow products, ingly shorter freeze duration during the period 2000{ see Section 5.2.4. More details on all the mentioned 2011 when investigating the lakes in Hoh Xil region products can be found in Table 7 (Appendix A).
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