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Lake ice phenology is a sensitive indicator reflecting global warming. In this article, the long-term changes in lake ice phenology of the second-largest lake at Tibetan Plateau (i.e., the Nam Co) in response to climate change are investigated based on mathematical modeling. The model has been testified capable of reproducing daily surface temperature of the lake in both frozen and unfrozen seasons. The lake ice phenology determined according to the simulated lake surface temperature is found consistent with that derived from satellite observations. For the Nam Co during the period 1978–2017, the freezing date has been delayed (reached up to 0.57 days/year), whereas the thawing date became earlier (−0.23 days/year). The trend of lake ice phenology is significantly correlated to the annual minimal lake surface and air temperature. Modeling experiment suggests that lake ice phenology of the Nam Co is very sensitive to a warmer climate. Under warmer future, much later freezing date and earlier thawing date are expected; hence the ice-covering duration would be significantly shortened.

Studies of lake ice phenology have historically relied on limited in situ data. Satellite-derived temperature data provide an opportunity to better understand the climatological factors and trends behind ice phenology. Here we developed a model that uses daytime and nighttime surface temperature observations from the Moderate Resolution Imaging Spectroradiometer (MODIS) sensor onboard Aqua to approximate ice in for 13 lakes and ice out for 58 lakes in Maine from 2002–2018. Ice-in and ice-out approximations were signaled by a moving average of the data crossing the 0°C threshold. The comparison of in situ and satellite-derived ice-out and ice-in dates were both highly correlated, but the amount of bias in those estimates was influenced by the number of days in a moving average of temperature. Systematic biases in the approximation model toward predicting ice in too early and ice out too late point to the importance of in-lake dynamics in modifying the timing of stabilization or melting of ice.

Lake ice is a sensitive indicator for the environment and climate change. In the comparative study of climate change from the three poles (Antarctic, Arctic, and the Tibetan Plateau), the lake ice phenology which is mirrored the seasonal changes of lake ice, and it is an important index for assessing the synchronization and difference of climate change. Based on passive microwave, lake ice phenology of 48 lakes from 1978 to 2017 were extracted for northern Europe, Tibetan Plateau and Mongolia Plateau. A comparative analysis of the temporal variation for the lake ice phenology shows that from 1978 to 2017, the average ice cover duration showed a shortening trend of all regions, and the Northern Europe with the most dramatic shortening. The year of 2000 may be a turning of the lake ice cover duration in three regions. Especially in the northern Tibetan Plateau where is a trend of prolonged ice cover duration after 2000, and the delay of the break-up end date plays a more important role than the advance of the freeze-up start date. For the spatial changes, the variation of lake ice cover duration in the Tibetan Plateau shows a spatially aggregate phenomenon, and both the Northern Europe and Mongolian Plateau show changes with latitude. The comparison of lake ice phenology changes of three regions can provide more basis for climate characteristics analysis or climate change research. In the future work, various remote sensing data will be comprehensively used to obtain more information on lake ice phenology in High Mountain and cold regions. Meanwhile, it will combine other reanalysis data to find the reasons that affect the lake ice phenology, and provide support for the comparative study of the three-pole environment.

One proposed method to detect the presence of spilled or leaked oil under the surface ice of frozen waters would rely on measuring the reverberation time of an impulsive acoustic source generated beneath that surface. Although intended for application to expansive environments such as rivers, lakes, and oceans, where deployment and subsequent recovery of equipment would make experimental testing economically difficult to perform, a low-cost, scaled experiment to study the efficacy of this method was conducted. In the design of the experiment, realistic modeling of a large underwater environment and the ability to accurately measure reverberation time within such an environment were of paramount concern. The required scaling, the dimensions of the experimental platform, and its adaptation to mimic a realistic environment are discussed. The design of an appropriate acoustic source along with practical matters such as growing the ice and injecting the oil under the ice are also discussed. Finally, experimental results are presented.

Lake ice phenology are considered as a sensitive indicator of global warming. In Tibetan Plateau (TP), differences exist in identifying lake ice phenology using remote sensing data because of the limited ground observation and difference in the sources of remote sensing and the available algorithms. In this study, four methods based on different remote sensed data (MODIS reflectance data, snow product, land surface temperature data, and microwave data) were used to identify the lake ice phenology of 18 lakes in the TP from 2002 to 2015. Lake ice duration was longer from the south to the north, and the highest consistency is occurred in monitoring the completely ice-covering duration. Temperature plays a greater role among several climatic-environmental elements. The lake ice period was shortened in the northeastern part of the TP, and extended in the middle and northern part.

Synthetic Aperture Radar (SAR) remote sensing has become a valuable tool for sea ice monitoring. A recently proposed SAR configuration for Earth observation called compact polarimetric (CP) SAR could be a good compromised choice between conventional (single or dual) and fully polarimetric SAR for operational sea ice observation. Given its enhanced radar target information compared to conventional SAR systems over wider swath coverage compared to fully polarimetric SAR systems, CP SAR systems could play important role in the new generation of Earth observation systems. In this study, fully polarimetric SAR images were collected over the Resolute Bay area during the fall of 2017. Acquired images are used for the simulation of CP SAR images and the derivation of a set of 23 CP SAR parameters from each image. The derived CP parameters were analysed in relation to the ice thickness and salinity of lake ice and fast sea ice. Results are compared against backscattering and decomposition parameters derived from the fully polarimetric SAR imagery.

The goal of this research was to evaluate the effect of Arctic climate change on the surrounding land area. In this research, we investigated a method to monitor the ice condition of shallow lakes in the Arctic region using C-band synthetic aperture radar (SAR) data and provide a strategy to evaluate the effect of climate change on the Arctic land area. A method to monitor the ice condition of these lakes was investigated using the SAR data acquired by Sentinel-1 from 2015 to 2018. We confirmed that the C-band SAR data from Sentinel-1 could monitor the ice condition of shallow lakes in northern Alaska. The backscattering threshold between floating and grounded ice was -14.8 to -16.8 dB depending on the incidence angle. We could also successfully monitor the growth of floating ice by using the interferometric SAR approach.

Algorithms for the generation of a bedfast/floating lake ice product from Sentinel-1A/B synthetic aperture radar (SAR) data were implemented, cross-compared, and validated for various permafrost regions (Alaska, Canada and Russia). The algorithms consisted of: 1) thresholding; 2) Iteration Region Growing with Semantics (IRGS); and 3) K-means. The thresholding algorithm (92.4%) was found to perform slightly better on average than the IRGS algorithm (90.1%), and to outperform K-means (85.3%). The thresholding algorithm was therefore selected for implementation of a processing chain to generate a novel bedfast/floating lake ice product. Using a time series of Sentinel-1 SAR data, the new map product shows the day of year (DOY) when the ice becomes bedfast or remains afloat for individual lake sections.

The goal of this research was to evaluate the effect of Arctic climate change on the surrounding land area. In this research, we developed a method to monitor the freezing and thawing of shallow lakes in the Arctic region using C-band synthetic aperture radar (SAR) data and provide a strategy to evaluate the effect of climate change on the Arctic land area. A method to monitor the ice condition of these lakes was investigated using the SAR data acquired by Sentinel-1 from 2014 to 2017. We confirmed that the C-band SAR data from Sentinel-1 could monitor the freezing and thawing ice condition of shallow lakes in northern Alaska. The backscattering threshold between floating and grounded ice was −14 dB, which was approximately 2 dB less than that of ers-1/2. We envision continuing to monitor lake ice over a long period of time using future available Sentinel-1 data.

We have conducted a multiyear (2011–2014) airborne microwave radiometer measurement program of snow on lake ice, regularly using our HUTRAD instrument (6.9 to 36.5 GHz, vertical and horizontal polarization). Occasionally, we have also used our interferometric 1.4 GHz HUT-2D radiometer. Our data consist of observations along a 4-km test line over two lakes in southern Finland, and include data also for adjacent land areas, whose land-cover types range from forest to short vegetation. A wide variety of snow and ice conditions were encountered, covering early winter, mid-winter and late winter conditions, and the melting season. In this paper we focus on the comparison of brightness temperatures observed for snow on lake ice and snow on terrain.

The thawing time and lake ice completely covered time can directly reflect the climate change in the region, and the duration of the lake ice can also reflect the local climate change. The traditional field monitoring of lake ice requires a lot of manpower and material resources, and the timeliness is poor, so it is not suitable for long-term ice condition monitoring. Since 1960s, remote sensing image has been used because of its wide range, fast speed, low cost and so on. More and more widely used in all kinds of research, the long-term monitoring of lake ice can also use remote sensing monitoring. Based on the Landsat series of remote sensing data, the Neural Network Algorithm was used to study the remote sensing image of Namco lake in the lake ice segmentation. The lake ice condition of Namco lake was monitored from 1988 to 2016.

We have conducted a multiyear airborne multifrequency microwave radiometer data collection program over a test site comprising two lakes and adjacent land areas in southern Finland. A variety of snow and ice conditions were encountered, covering early winter, mid-winter, and late winter conditions, and the melting season. A semi-empirical radiative transfer forward model has been used for simulations and the theoretical values have been compared with experimental brightness temperature observations.

We have conducted airborne multi-frequency radiometer measurements over two lakes and adjacent land areas in southern Finland over a period of several winters using a frequency range of 1.4 to 36.5 GHz. Data have been collected under a variety of snow, ice, and weather conditions in order to determine the behavior of the snow-ice-water system. This paper presents an overview of the airborne campaigns and results confirming that the brightness temperature behavior of the snow/lake ice/water system is different from that of the snow/terrain system. This needs to be taken into account in algorithms for retrieval of snow characteristics from space-borne radiometer data for northern lake-rich areas. Comparisons between experimental brightness temperatures and theoretical results show that the HUT snow emission model performs well for lake ice.

The final goal of this research is to evaluate an effect of Arctic climate change into surrounding land area. In this research, we will develop a method to monitor freezing and thawing of tundra lakes in Arctic region with C-band and L-band synthetic aperture radar (SAR) data, and give a strategy to evaluate the effect of climate change to Arctic land area. The method to monitor the lake ice of tundra lakes was investigated by using SAR data acquired by ers-1, ENVISAT, and ALOS. We found that the increase of backscattering coefficients with ice growth for typical floating ice in 2008 was slower than that in 1992, which was probably caused by the decrease of accumulated freezing index in 2008. We also indicated a possibility to detect the change of backscattering mechanisms for growing and thawing lake ice by using L-band fully polarimetric SAR data acquired in 2009. In our further research, we will evaluate a time series of C-band and L-band backscattering coefficients of tundra lakes in Alaska and Siberia for long period of time by using the archived and future available SAR data.

Winter season backscatter (?°) evolution of snow covered lake ice was observed by ground-based University of Waterloo X-(9.6 GHz) and Ku-band (17.2 GHz) scatterometers (UW-SCAT) during the winter of 2010-11. The UW-SCAT post-processing procedure allowed for the observation of ?° at the surface (snow/ice interface, ice types) and the ice volume. Observations indicated that: (1) ?° associated with the development of tubular bubbles within the ice volume causes double-bounce of the signal and high returns at X- and Ku-bands; (2) ice types at the surface (grey ice) composed of high density spherical micro-bubbles result in ?° increases at both X- and Ku-bands; and (3) the removal of snow overlying ice results in a drop in Ku-band ?° up to 5.5 dB, exhibiting sensitivity to snow water equivalent.

We have investigated the brightness temperature behavior of snow on lake ice by conducting airborne radiometer measurements over two lakes in southern Finland since 2004 and, with a more intense schedule, since 2011. The flight path includes two lakes of different sizes and, additionally, a forested area and an open, mostly agricultural area next to the lakes. This allows us to compare the behavior of the brightness temperature of snow on lake ice with that of snow on terrain. The main airborne instrument is our HUTRAD system, which operates at 6.9, 10.65, 18.7, 23.8, and 36.5 GHz, providing data for both vertical and horizontal polarization at an incidence angle of 50 degrees off nadir. The antenna beamwidth is 3 to 5 degrees depending on frequency. Additional data have been collected with our HUT-2D interferometric imaging radiometer, which operates at 1.4 GHz and looks straight down covering an incidence angle range up to 25 degrees with a swath of 95 per cent of the flight altitude. The radiometers are accommodated on our Skyvan research aircraft. Insitu measurements have been made simultaneously with airborne data collection. We have covered a variety of conditions including dry snow/ice, occasional water on top of ice, moist snow surface, and the melting season. In order to study the effect of diurnal temperature variations during the melting season we have collected data both in the morning (dry refrozen snow) and afternoon (moist topmost snow layer).

In this work we propose a model for the polarimetric backscattering of shallow sub-arctic lakes, which are frozen usually up to two meters depth during winter. The model takes into account the inhomogeneities in the ice layer introduced mainly by CH 4 bubbles trapped in the lake ice. The model is validated against experimental data acquired by ALOS-PalSAR.

Airborne microwave radiometer measurements of lake ice have been performed in 2004, 2007, 2011, and 2012 over two lakes in southern Finland using radiometer systems that cover frequencies from 1.4 to 36.5 GHz. Airborne and surface data have been collected under circumstances ranging from early winter dry snow to late winter dry and wet snow conditions. Water and slush on top of ice and dry snow grain size have been determined to be the two most important parameters affecting brightness temperature.

In this paper, we propose a general classification method for determining ice cover on lakes using ENVISAT ASAR data. The method assumes a Gaussian mixture model for feature vectors extracted from a local neighborhood around each pixel. We focus on Lake Paijanne in this work, and the classification results are validated against in situ observations of freeze up dates and a snow cover product from the optical MODIS sensor. The method is fully automatic, but it requires a manual post-processing step to remove poor results. The classification of the selected scenes compared fairly well to both sources of validation data. However, the manual selection step is time-consuming and subjective.

This article presents some results from the snow surface roughness measurements that were made during the Snow Reflectance Transition Experiment -campaign in Sodankyla, Finnish Lapland, in 2009 and 2010. The surface roughness was measured with a photogrammetry-based method where a black plate was inserted into the snow and photographed. After field work a 2D profile was automatically extracted from the image. The profiles extracted from the photographs were analyzed using root mean square height and correlation length as function of measured length. 777 profiles were measured during the SORTEX -campaign in different locations with different canopy type, including marshland, pine, birch and mixed forest and lake ice. In addition to this some profiles were measured in February 2010. The results show a clear shift from midwinter to melting season conditions as well as the effect of fresh snow on the surface.

The multi-polarized and polarimetric capabilities of RADARSAT-2 are evaluated to enhance river and lake ice monitoring. Multi-year time series of polarimetric images for selected rivers and lakes in north-western Canada are analyzed for their information potential. Meteorological and geophysical field measurements support the interpretation of polarimetric imagery. Statistically estimated thresholds for selected polarimetric parameters are used in unsupervised classification and subsequent ice mapping.

Historic freeze-up and break-up dates of lake can record the regional climate variability. Observations of lake ice from different regional or country's stations are inconsistent and their number was declined in past two decades, which makes climate change analysis difficult. This paper proposes a dynamic threshold method to derive the freeze-up and break-up dates of big lakes using passive microwave brightness temperature data. Lake ice information from 35 big lakes in Northern Hemisphere is retrieved in past three decades. These freeze-up and break-up dates were validated from 18 stations' observations. The linear correlation coefficients between observations and retrievals are 0.926 for freeze-up dates and 0.936, respectively.

Airborne microwave radiometer measurements of lake ice have been performed in 2004, 2007, and 2011 in southern Finland. The HUTRAD radiometer system provided data in the 6.9 to 36.5 GHz range using an incidence angle of 50 degrees off nadir. In 2011 also the interferometric HUT-2D radiometer was used to provide 1.4 GHz imagery of lake ice.

This paper presents a novel method to estimate freeze-thaw dates of a small lake, with area 10 km2, using SAR data from RADARSAT-1 remote sensing spacecraft. The algorithms analyze an annual time series of the difference in average backscatter, between the lake and the surrounding region, to estimate the day of occurrence of the respective lake ice event. The initial results obtained, using multi-year data from a northern high-latitude lake, are promising. The mean absolute error in estimating the thaw date is 0.7 days and for the freeze date it is 4 days. These results encourage further testing of these algorithms on lakes in other geographic regions.

The observational data on hydrometeorologic stations, satellite images of low and middle resolutions, ice aircraft survey data collected over a long-term period have allowed the author to develop model of ice dynamics on the Ladoga lake for the spring period. The variety and complexity of natural processes, insufficiency of the operating information received from hydrological ground network of stations and the irregular character of the satellite information hinder forecasting of the ice situation on large lakes. In such conditions it is convenient to use a computer simulation. The offered model is a first step of an iterative process of simulation, which includes modeling and assessment of its adequate to real conditions.

Great Lakes small-craft harbor structures are damaged by ice. Information obtained from extended winter observations in two hundred harbors is presented. This information, together with design experiences, field tests, laboratory tests, and literature reviews are used to prepare design recommendations for northern harbors.