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The winter regime of river-ice covers in high northern latitude regions is often a determining factor in the management of water resources, conservation of aquatic ecosystems and preservation of traditional and cultural lifestyles of local peoples. As ground-based monitoring of river-ice regimes in high northern latitudes is expensive and restricted to a few locations due to limited accessibility to most places along rivers from shorelines, remote sensing techniques are a suitable approach for monitoring. This study developed a RADARSAT-2 based method to monitor the spatio-temporal variation of ice covers, as well as ice types during the freeze-up period, along the main channel of the Slave River Delta in the Northwest Territories of Canada. The spatio-temporal variation of ice covers along the river was analyzed using the backscatter-based coefficient of variation (CV) in the 2013-2014 and 2014-2015 winters. As a consequence of weather and flow conditions, the ice cover in the 2013-2014 winter had the higher variation than the 2014-2015 winter, particularly in the potential areas of flooded/cracked ice covers. The river sections near active channels (e.g., Middle Channel and Nagle Channel), Big Eddy, and Great Slave Lake also yielded higher intra-annual variation of ice cover characteristics during the winters. With the inclusion of backscatter and texture analysis from RADARSAT-2 data, four water and ice cover classes consisting of open water, thermal ice, juxtaposed ice, and consolidated ice, were discriminated in the images acquired between November and March in both the studied winters. In addition to river geomorphology and climatic conditions such as river width, sinuosity or air temperature, the fluctuation of water flows during the winter has a significant impact on the variation of ice cover as well as the formation of different ice types in the Slave River. The RADARSAT-2 based monitoring algorithm can also be applied to other river systems in high latitude ecosystems to annually monitor their river-ice variation and formation during the freeze-up and ice cover progression period. [ABSTRACT FROM AUTHOR]

The Tizinafu watershed has a complex mountainous terrain in the western Kunlun Mountains; little study has been done on the spatial and temporal characteristics of snow cover in the region. Daily snow cover data of 10 hydrological years (October 2002 to September 2012) in the watershed were generated by combining MODIS Terra (MOD10A1) and Aqua (MYD10A1) snow cover products and employing a nine-day temporal filter for cloud reduction. The accuracy and window size of the temporal filter were assessed using a simulation approach. Spatial and temporal characteristics of snow cover in the watershed were then analyzed. Our results showed that snow generally starts melting in March and reaches the minimum in early August in the watershed. Snow cover percentages (SCPs) in all five elevation zones increase consistently with the rise of elevation. Slope doesn't play a major role in snow cover distribution when it exceeds 10°. The largest SCP difference is between the south and the other aspects and occurs between mid-October and mid-November with decreasing SCP, indicating direct solar radiation may cause the reduction of snow cover. While both the mean snow cover durations (SCDs) of the hydrological years and of the snowmelt seasons share a similar spatial pattern to the topography of the watershed, the coefficient of variation of the SCDs exhibits an opposite spatial distribution. There is a significant correlation between annual mean SCP and annual total stream flow, indicating that snowmelt is a major source of stream runoff that might be predictable with SCP. [ABSTRACT FROM AUTHOR]

The formation of oriented thermokarst lakes on the Arctic Coastal Plain of northern Alaska has been the subject of debate for more than half a century. The striking elongation of the lakes perpendicular to the prevailing wind direction has led to the development of a preferred wind-generated gyre hypothesis, while other hypotheses include a combination of sun angle, topographic aspect, and/or antecedent conditions. A spatio-temporal analysis of oriented thermokarst lake gyres with recent (Landsat 8) and historical (Landsat 4, 5, 7 and ASTER) satellite imagery of the Arctic Coastal Plain of northern Alaska indicates that wind-generated gyres are both frequent and regionally extensive. Gyres are most common in lakes located near the Arctic coast after several days of sustained winds from a single direction, typically the northeast, and decrease in number landward with decreasing wind energy. This analysis indicates that the conditions necessary for the Carson and Hussey (1962) wind-generated gyre for oriented thermokarst lake formation are common temporally and regionally and correspond spatially with the geographic distribution of oriented lakes on the Arctic Coastal Plain. Given an increase in the ice-free season for lakes as well as strengthening of the wind regime, the frequency and distribution of lake gyres may increase. This increase has implications for changes in northern high latitude aquatic ecosystems, particularly if wind-generated gyres promote permafrost degradation and thermokarst lake expansion. [ABSTRACT FROM AUTHOR]

Knowledge of surface hydrology is essential for many applications, including studies that aim to understand permafrost response to changing climate and the associated feedback mechanisms. Advanced remote sensing techniques make it possible to retrieve a range of land-surface variables, including radar retrieved soil moisture (SSM). It has been pointed out before that soil moisture retrieval from satellite data can be challenging at high latitudes, which correspond to remote areas where ground data are scarce and the applicability of satellite data of this type is essential. This study investigates backscatter variability other than associated with changing soil moisture in order to examine the possible impact on soil moisture retrieval. It focuses on issues specific to SSM retrieval in the Arctic, notably variations related to tundra lakes. ENVISAT Advanced Synthetic Aperture Radar (ASAR) Wide Swath (WS, 120 m) data are used to understand and quantify impacts on Metop (AAdvanced Scatterometer (ASCAT, 25 km) soil moisture retrieval during the snow free period. Sites of interest are chosen according to ASAR WS availability, high or low agreement between output from the land surface model ORCHIDEE and ASCAT derived SSM. Backscatter variations are analyzed with respect to the ASCAT footprint area. It can be shown that the low model agreement is related to water fraction in most cases. No difference could be detected between periods with floating ice (in snow off situation) and ice free periods at the chosen sites. The mean footprint backscatter is however impacted by partial short term surface roughness change. The water fraction correlates with backscatter deviations (relative to a smooth water surface reference image) within the ASCAT footprint areas (R = 0.91??0.97). Backscatter deviations of up to 5 dB can occur in areas with less than 50% water fraction and an assumed soil moisture related range (sensitivity) of 7 dB in the ASCAT data. The sensitivity is also positively correlated with water fraction in regions with low land-surface model agreement (R = 0.68). A precise quantification of the impact on soil moisture retrieval would, however, need to consider actual soil moisture changes and sensor differences. The study demonstrates that the usage of higher spatial resolution data than currently available for SSM is required in lowland permafrost environments. [ABSTRACT FROM AUTHOR]

Abstract: This paper aims to introduce the main types and sources of remotely sensed data that are freely available and have cryospheric applications. We describe aerial and satellite photography, satellite-borne visible, near-infrared and thermal infrared sensors, synthetic aperture radar, passive microwave imagers and active microwave scatterometers. We consider the availability and practical utility of archival data, dating back in some cases to the 1920s for aerial photography and the 1960s for satellite imagery, the data that are being collected today and the prospects for future data collection; in all cases, with a focus on data that are openly accessible. Derived data products are increasingly available, and we give examples of such products of particular value in polar and cryospheric research. We also discuss the availability and applicability of free and, where possible, open-source software tools for reading and processing remotely sensed data. The paper concludes with a discussion of open data access within polar and cryospheric sciences, considering trends in data discoverability, access, sharing and use. [ABSTRACT FROM AUTHOR]

For successful applications of microwave remote sensing endeavors it is essential to understand how surface targets respond to changing synthetic aperture radar (SAR) parameters. The purpose of the study is to examine how two particular parameters, acquisition time and incidence angle, influences the response from various land use/land cover types (forests, urban infrastructure, surface water and marsh wetland targets) using nine RADARSAT-2 C-band fine-beam (FQ7 and FQ21) fully polarimetric SAR data acquired during the 2011 growing season over northern Ontario, Canada. The results indicate that backscatter from steep incidence angle acquisitions was typically higher than shallow angles. Wetlands showed an increase in HH and HV intensity due to the growth of emergent vegetation over the course of the summer. The forest and urban targets displayed little variation in backscatter over time. The surface water target showed the greatest difference with respect to incidence angle, but was also determined to be the most affected by wind conditions. Analysis of the co-polarized phase difference revealed the urban target as greatly influenced by the incidence angle. The observed phase differences of the wetland target for all acquisitions also suggested evidence of double-bounce interactions, while the forest and surface water targets showed little to no phase difference. In addition, Cloude-Pottier and Freeman-Durden decompositions, when analyzed in conjunction with polarimetric response plots, provided supporting information to confidently identify the various targets and their scattering mechanisms. [ABSTRACT FROM AUTHOR]

Daytime measurements of reflected sunlight in the visible spectrum have been a staple of Earth-viewing radiometers since the advent of the environmental satellite platform. At night, these same optical-spectrum sensors have traditionally been limited to thermal infrared emission, which contains relatively poor information content for many important weather and climate parameters. These deficiencies have limited our ability to characterize the full diurnal behavior and processes of parameters relevant to improved monitoring, understanding and modeling of weather and climate processes. Visible-spectrum light information does exist during the nighttime hours, originating from a wide variety of sources, but its detection requires specialized technology. Such measurements have existed, in a limited way, on USA Department of Defense satellites, but the Suomi National Polar-orbiting Partnership (NPP) satellite, which carries a new Day/Night Band (DNB) radiometer, offers the first quantitative measurements of nocturnal visible and near-infrared light. Here, we demonstrate the expanded potential for nocturnal low-light visible applications enabled by the DNB. Via a combination of terrestrial and extraterrestrial light sources, such observations are always available-expanding many current existing applications while enabling entirely new capabilities. These novel low-light measurements open doors to a wealth of new interdisciplinary research topics while lighting a pathway toward the optimized design of follow-on satellite based low light visible sensors. [ABSTRACT FROM AUTHOR]

Snow is an important component of the water cycle, and its estimation in hydrological models is of great significance concerning the simulation and forecasting of flood events due to snow-melt. The assimilation of Snow Cover Area (SCA) in physical distributed hydrological models is a possible source of improvement of snowmelt-related floods. In this study, the assimilation in the LISFLOOD model of the MODIS sensor SCA has been evaluated, in order to improve the streamflow simulations of the model. This work is realized with the final scope of improving the European Flood Awareness System (EFAS) pan-European flood forecasts in the future. For this purpose daily 500 m resolution MODIS satellite SCA data have been used. Tests were performed in the Morava basin, a tributary of the Danube, for three years. The particle filter method has been chosen for assimilating the MODIS SCA data with different frequencies. Synthetic experiments were first performed to validate the assimilation schemes, before assimilating MODIS SCA data. Results of the synthetic experiments could improve modelled SCA and discharges in all cases. The assimilation of MODIS SCA data with the particle filter shows a net improvement of SCA. The Nash of resulting discharge is consequently increased in many cases. [ABSTRACT FROM AUTHOR]

The present article is an introductory paper in this special issue on synthetic aperture radar (SAR). A short review is presented on the recent trend and development of SAR and related techniques with selected topics, including the fields of applications, specifications of airborne and spaceborne SARs, and information contents in and interpretations of amplitude data, interferometric SAR (InSAR) data, and polarimetric SAR (PolSAR) data. The review is by no means extensive, and as such only brief summaries of of each selected topics and key references are provided. For further details, the readers are recommended to read the literature given in the references theirin [ABSTRACT FROM AUTHOR]

RADARSAT and ERS-2 data collected at multiple incidence angles are used to characterize the seasonal variations in the backscatter of snow-covered landscapes in the northern Hudson Bay Lowlands during the winters of 1997/98 and 1998/99. The study evaluates the usefulness of C-band SAR systems for retrieving the snow water equivalent under dry snow conditions in the forest-tundra ecotone. The backscatter values are compared against ground measurements at six sampling sites, which are taken to be representative of the land-cover types found in the region. The contribution of dry snow to the radar return is evident when frost penetrates the first 20 cm of soil. Only then does the backscatter respond positively to changes in snow water equivalent, at least in the open and forested areas near the coast, where 1-dB increases in backscatter for each approximate 5-10 mm of accumulated water equivalent are observed at 20-31° incidence angles. Further inland, the backscatter shows either no change or a negative change with snow accumulation, which suggests that the radar signal there is dominated by ground surface scattering (e.g., fen) when not attenuated by vegetation (e.g., forested and transition). With high-frequency ground-penetrating radar, we demonstrate the presence of a 10-20-cm layer of black ice underneath the snow cover, which causes the reduced radar returns (-15 dB and less) observed in the inland fen. A correlation between the backscattering and the snow water equivalent cannot be determined due to insufficient observations at similar incidence angles. To establish a relationship between the snow water equivalent and the backscatter, only images acquired with similar incidence angles should be used, and they must be corrected for both vegetation and ground effects. [ABSTRACT FROM AUTHOR]

A nonlinear least squares fitting algorithm is used to estimate both snow depth and snow density for a snow-layer above a flat ground reflector. The product of these two quantities, snow depth and density, provides an estimate of the snow water equivalent. The input to this algorithm is a simple ray model that includes a speculary reflected signal along with a direct signal. These signals are transmitted from the global positioning system satellites at 1.57542 GHz with right-hand circularly polarization. The elevation angles of interest at the GPS receiving antenna are between 5° and 30°. The results from this nonlinear algorithm show potential for inferring snow water equivalent using GPS multipath signals. [ABSTRACT FROM AUTHOR]

This paper presents two space detected Global Positioning System (GPS) signals reflected off sea ice and compares the returned power profiles with independent estimates of ice concentration provided by the Advanced Microwave Scanning Radiometer (AMSR-E) and sea ice charts from the National Ice Center. The results of the analysis show significantly different signals received for each of the GPS reflections. For the first collection, comparisons with ice concentration estimates from AMSR-E and the National Ice Centers reveal a very strong GPS signal return off high concentration sea ice. The second GPS data collection occurs over a region of changing sea ice concentration, and the GPS signal level responds at roughly the same point that the AMSR-E data and National Ice Center charts indicate a change in ice concentration. However, the very strong signal of the first GPS collection is not consistent in magnitude with similar ice concentrations during the second GPS data collection. This demonstration shows the potential and the difficulties of this new technique as a valuable low-cost compliment to existing sea ice monitoring instruments. Additionally, a general method for calculating the location of the specular reflection point on the Earth's surface and the received Doppler frequencies and code phase delays is presented as part of an on-board open-loop signal tracking technique. [ABSTRACT FROM AUTHOR]

The scatterometer SeaWinds on QuikSCAT provided regular measurements at Ku-band from 1999 to 2009. Although it was designed for ocean applications, it has been frequently used for the assessment of seasonal snowmelt patterns aside from other terrestrial applications such as ice cap monitoring, phenology and urban mapping. This paper discusses general data characteristics of SeaWinds and reviews relevant change detection algorithms. Depending on the complexity of the method, parameters such as long-term noise and multiple event analyses were incorporated. Temporal averaging is a commonly accepted preprocessing step with consideration of diurnal, multi-day or seasonal averages. [ABSTRACT FROM AUTHOR]

Altimetry is probably one of the most powerful tools for ice sheet observation. Our vision of the Antarctic ice sheet has been deeply transformed since the launch of the ERS1 satellite in 1991. With the launch of ERS2 and Envisat, the series of altimetric observations now provides 19 years of continuous and homogeneous observations that allow monitoring of the shape and volume of ice sheets. The topography deduced from altimetry is one of the relevant parameters revealing the processes acting on ice sheet. Moreover, altimeter also provides other parameters such as backscatter and waveform shape that give information on the surface roughness or snow pack characteristics. [ABSTRACT FROM AUTHOR]

The topic of this paper is the airborne evaluation of ICESat-2 Advanced Topographic Laser Altimeter System (ATLAS) measurement capabilities and surface-height-determination over crevassed glacial terrain, with a focus on the geodetical accuracy of geophysical data collected from a helicopter. To obtain surface heights over crevassed and otherwise complex ice surface, ICESat-2 data are analyzed using the density-dimension algorithm for ice surfaces (DDA-ice), which yields surface heights at the nominal 0.7 m along-track spacing of ATLAS data. As the result of an ongoing surge, Negribreen, Svalbard, provided an ideal situation for the validation objectives in 2018 and 2019, because many different crevasse types and morphologically complex ice surfaces existed in close proximity. Airborne geophysical data, including laser altimeter data (profilometer data at 905 nm frequency), differential Global Positioning System (GPS), Inertial Measurement Unit (IMU) data, on-board-time-lapse imagery and photographs, were collected during two campaigns in summers of 2018 and 2019. Airborne experiment setup, geodetical correction and data processing steps are described here. To date, there is relatively little knowledge of the geodetical accuracy that can be obtained from kinematic data collection from a helicopter. Our study finds that (1) Kinematic GPS data collection with correction in post-processing yields higher accuracies than Real-Time-Kinematic (RTK) data collection. (2) Processing of only the rover data using the Natural Resources Canada Spatial Reference System Precise Point Positioning (CSRS-PPP) software is sufficiently accurate for the sub-satellite validation purpose. (3) Distances between ICESat-2 ground tracks and airborne ground tracks were generally better than 25 m, while distance between predicted and actual ICESat-2 ground track was on the order of 9 m, which allows direct comparison of ice-surface heights and spatial statistical characteristics of crevasses from the satellite and airborne measurements. (4) The Lasertech Universal Laser System (ULS), operated at up to 300 m above ground level, yields full return frequency (400 Hz) and 0.06–0.08 m on-ice along-track spacing of height measurements. (5) Cross-over differences of airborne laser altimeter data are −0.172 ± 2.564 m along straight paths, which implies a precision of approximately 2.6 m for ICESat-2 validation experiments in crevassed terrain. (6) In summary, the comparatively light-weight experiment setup of a suite of small survey equipment mounted on a Eurocopter (Helicopter AS-350) and kinematic GPS data analyzed in post-processing using CSRS-PPP leads to high accuracy repeats of the ICESat-2 tracks. The technical results (1)–(6) indicate that direct comparison of ice-surface heights and crevasse depths from the ICESat-2 and airborne laser altimeter data is warranted. Numerical evaluation of height comparisons utilizes spatial surface roughness measures. The final result of the validation is that ICESat-2 ATLAS data, analyzed with the DDA-ice, facilitate surface-height determination over crevassed terrain, in good agreement with airborne data, including spatial characteristics, such as surface roughness, crevasse spacing and depth, which are key informants on the deformation and dynamics of a glacier during surge. [ABSTRACT FROM AUTHOR]

Sea ice is an important part of the global cryosphere and an important variable in the global climate system. Sea ice also presents one of the major natural disasters in the world. The automatic and accurate extraction of sea ice extent is of great significance for the study of climate change and disaster prevention. The accuracy of sea ice extraction in the Yellow River Estuary is low due to the large dynamic changes in the suspended particulate matter (SPM). In this study, a set of sea ice automatic extraction method systems combining image spectral information and textural information is developed. First, a sea ice spectral information index that can adapt to sea areas with different turbidity levels is developed to mine the spectral information of different types of sea ice. In addition, the image's textural feature parameters and edge point density map are extracted to mine the spatial information concerning the sea ice. Then, multi-scale segmentation is performed on the image. Finally, the OTSU algorithm is used to determine the threshold to achieve automatic sea ice extraction. The method was successfully applied to Gaofen-1 (GF1), Sentinel-2, and Landsat 8 images, where the extraction accuracy of sea ice was over 93%, which was more than 5% higher than that of SVM and K-Means. At the same time, the method was applied to the Liaodong Bay area, and the extraction accuracy reached 99%. These findings reveal that the method exhibits good reliability and robustness. [ABSTRACT FROM AUTHOR]

Accurate cloud-masking procedures to distinguish cloud-free pixels from cloudy pixels are essential for optical satellite remote sensing. Many studies on satellite-based cloud-detection have been performed using the spectral characteristics of clouds in terms of reflectance and temperature. This study proposes a cloud-detection method using reflectance in four bands: 0.56 μm, 0.86 μm, 1.38 μm, and 1.61 μm. Methodologically, we present a conversion relationship between the normalized difference water index (NDWI) and the green band in the visible spectrum for thick cloud detection using moderate-resolution imaging spectroradiometer (MODIS) observations. NDWI consists of reflectance at the 0.56 and 0.86 μm bands. For thin cloud detection, the 1.38 and 1.61 μm bands were applied with empirically determined threshold values. Case study analyses for the four seasons from 2000 to 2019 were performed for the sea surface area of the Yellow Sea and Bohai Sea. In the case studies, the comparison of the proposed cloud-detection method with the MODIS cloud mask (CM) and Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation data indicated a probability of detection of 0.933, a false-alarm ratio of 0.086, and a Heidke Skill Score of 0.753. Our method demonstrated an additional important benefit in distinguishing clouds from sea ice or yellow dust, compared to the MODIS CM products, which usually misidentify the latter as clouds. Consequently, our cloud-detection method could be applied to a variety of low-orbit and geostationary satellites with 0.56, 0.86, 1.38, and 1.61 μm bands. [ABSTRACT FROM AUTHOR]

Temperature and precipitation are considered to be the most important indicators affecting the green-up date. Sensitivity of the green-up date to temperature and precipitation is considered to be one of the key indicators to characterize the response of terrestrial ecosystems to climate change. We selected the main grassland types for analysis, including temperate steppe, temperate meadow steppe, upland meadow, and lowland meadow. This study investigates the variation in key meteorological indicators (daily maximum temperature (Tmax), daily minimum temperature (Tmin), and precipitation) between 2001 and 2018. We then examined the partial correlation and sensitivity of green-up date (GUD) to Tmax, Tmin, and precipitation. Our analysis indicated that the average GUD across the whole area was DOY 113. The mean GUD trend was −3.1 days/decade and the 25% region advanced significantly. Tmax and Tmin mainly showed a decreasing trend in winter (p > 0.05). In spring, Tmax mainly showed an increasing trend (p > 0.05) and Tmin a decreasing trend (p > 0.05). Precipitation showed no significant (p > 0.05) change trend and the trend range was ±10 mm/decade. For temperate steppe, the increase in Tmin in March promotes green-up (27.3%, the proportion of significant pixels), with a sensitivity of −0.17 days/°C. In addition, precipitation in April also promotes green-up (21.7%), with a sensitivity of −0.32 days/mm. The GUDs of temperate meadow steppe (73.9%), lowland meadow (65.9%), and upland meadow (22.1%) were mainly affected by Tmin in March, with sensitivities of −0.15 days/°C, −0.13 days/°C, and −0.14 days/°C, respectively. The results of this study reveal the response of vegetation to climate warming and contribute to improving the prediction of ecological changes as temperatures increase in the future. [ABSTRACT FROM AUTHOR]

Landslides can be triggered by natural and human activities, threatening the safety of buildings and infrastructures. Mapping potential landslide runout zones are critical for regional risk evaluation. Although remote sensing technology has been widely used to discover unstable areas, an entire landslide runout zone is difficult to identify using these techniques alone. Some simplified methods based on empirical models are used to simulate full-scale movements, but these methods do not consider the kinematic uncertainties caused by random particle collisions in practice. In this paper, we develop a semi-empirical landslide dynamics method considering kinematic uncertainties to solve this problem. The uncertainties caused by the microtopography and anisotropy of the material are expressed by the diffusion angle. Monte Carlo (MC) simulations are adopted to calculate the probability of each cell. Compared with the existing Flow-R model, this method can more accurately and effectively estimate runout zones of the Yigong landslide where random particle collisions are intense. Combining the D-InSAR technique, we evaluate the runout zones in the Jinsha River from June 2019 to December 2020. This result shows that the method is of great significance in early warning and risk mitigation, especially in remote areas. The source area of the landslide and DEM resolution together affect the number of MC simulations required. A landslide with a larger volume requires a larger diffusion angle and more MC simulations. [ABSTRACT FROM AUTHOR]

Water area segmentation is an important branch of remote sensing image segmentation, but in reality, most water area images have complex and diverse backgrounds. Traditional detection methods cannot accurately identify small tributaries due to incomplete mining and insufficient utilization of semantic information, and the edge information of segmentation is rough. To solve the above problems, we propose a multi-scale feature aggregation network. In order to improve the ability of the network to process boundary information, we design a deep feature extraction module using a multi-scale pyramid to extract features, combined with the designed attention mechanism and strip convolution, extraction of multi-scale deep semantic information and enhancement of spatial and location information. Then, the multi-branch aggregation module is used to interact with different scale features to enhance the positioning information of the pixels. Finally, the two high-performance branches designed in the Feature Fusion Upsample module are used to deeply extract the semantic information of the image, and the deep information is fused with the shallow information generated by the multi-branch module to improve the ability of the network. Global and local features are used to determine the location distribution of each image category. The experimental results show that the accuracy of the segmentation method in this paper is better than that in the previous detection methods, and has important practical significance for the actual water area segmentation. [ABSTRACT FROM AUTHOR]

Lake surface water temperature (LSWT) is sensitive to climate change. Previous studies have found that LSWT warming is occurring on a global scale and is expected to continue in the future. Recently, new global LSWT data products have been generated using satellite remote sensing, which provides an inimitable opportunity to study the LSWT response to global warming. Based on the satellite observations, we found that the warming rate of global lakes is uneven, with apparent regional differences. Indeed, comparing the LSWT warming in different climate zones (from arid to humid), the lakes in drylands experienced more significant warming (0.28 °C decade−1) than those in semi-humid and humid regions (0.19 °C decade−1) during previous decades (1995–2016). By further quantifying the impact factors, it showed that the LSWT warming is attributed to air temperature (74.4%), evaporation (4.1%), wind (9.9%), cloudiness (4.3%), net shortwave (3.1%), and net longwave (4.0%) over the lake surface. Air temperature is the main driving force for the warming of most global lakes, so the first estimate quantification of future LSWT trends can be determined from air temperature projections. By the end of the 21st century, the summer air temperature would warm up to 1.0 °C (SSP1-2.6) and 6.3 °C (SSP5-8.5) over lakes, with a more significant warming trend over the dryland lakes. Combined with their higher warming sensitivity, the excess summer LSWT warming in drylands is expected to continue, which is of great significance because of their high relevance in these water-limited regions. [ABSTRACT FROM AUTHOR]

In recent decades, the melting of the Greenland Ice Sheet (GrIS) has become one of the major causes of global sea-level rise. Supraglacial lakes (SGLs) are typical hydrological features produced on the surface of the GrIS during the melt seasons. The existence and evolution of SGLs play an important role in the melting process of the ice sheet surface. To understand the distribution and recent changes of SGLs in Greenland, this study developed a random forest (RF) algorithm incorporating the texture and morphological features to automatically identify SGLs based on the Google Earth Engine (GEE) platform. Sentinel-2 imagery was used to map the SGLs inventory in Greenland during the 2016–2018 melt seasons and to explore the spatial and temporal variability characteristics of SGLs. Our results show changes in SGLs from 2016 to 2018, with the total area decreasing by ~1152.22 km2 and the number increasing by 1134; SGLs are mainly distributed in western Greenland (SW, CW, NW) and northeastern Greenland (NE), where the NE region has the largest number of observed SGLs and the largest SGL was with the surface area of 16.60 km2 (2016). SGLs were found to be most active in the area with the elevation of 800–1600 m and the slope of 0–5°, and showed a phenomenon of retreating to lower elevation areas and developing to steeper slope areas. Our work provided a method for rapid inventory of SGLs. This study will help monitor the mass balance of the GrIS and predict future rapid ice loss from Greenland. [ABSTRACT FROM AUTHOR]

Arctic tundra landscapes are highly complex and are rapidly changing due to the warming climate. Datasets that document the spatial and temporal variability of the landscape are needed to monitor the rapid changes. Synthetic Aperture Radar (SAR) imagery is specifically suitable for monitoring the Arctic, as SAR, unlike optical remote sensing, can provide time series regardless of weather and illumination conditions. This study examines the potential of seasonal backscatter mechanisms in Arctic tundra environments for improving land cover classification purposes by using a time series of HH/HV TerraSAR-X (TSX) imagery. A Random Forest (RF) classification was applied on multi-temporal Sigma Nought intensity and multi-temporal Kennaugh matrix element data. The backscatter analysis revealed clear differences in the polarimetric response of water, soil, and vegetation, while backscatter signal variations within different vegetation classes were more nuanced. The RF models showed that land cover classes could be distinguished with 92.4% accuracy for the Kennaugh element data, compared to 57.7% accuracy for the Sigma Nought intensity data. Texture predictors, while improving the classification accuracy on the one hand, degraded the spatial resolution of the land cover product. The Kennaugh elements derived from TSX winter acquisitions were most important for the RF model, followed by the Kennaugh elements derived from summer and autumn acquisitions. The results of this study demonstrate that multi-temporal Kennaugh elements derived from dual-polarized X-band imagery are a powerful tool for Arctic tundra land cover mapping. [ABSTRACT FROM AUTHOR]

In this study, the harbor aquaculture area tested is Zhanjiang coast, and for the remote sensing data, we use images from the GaoFen-1 satellite. In order to achieve a superior extraction performance, we propose the use of an integration-enhanced gradient descent (IEGD) algorithm. The key idea of this algorithm is to add an integration gradient term on the basis of the gradient descent (GD) algorithm to obtain high-precision extraction of the harbor aquaculture area. To evaluate the extraction performance of the proposed IEGD algorithm, comparative experiments were performed using three supervised classification methods: the neural network method, the support vector machine method, and the maximum likelihood method. From the results extracted, we found that the overall accuracy and F-score of the proposed IEGD algorithm for the overall performance were 0.9538 and 0.9541 , meaning that the IEGD algorithm outperformed the three comparison algorithms. Both the visualized and quantitative results demonstrate the high precision of the proposed IEGD algorithm aided with the CEM scheme for the harbor aquaculture area extraction. These results confirm the effectiveness and practicality of the proposed IEGD algorithm in harbor aquaculture area extraction from GF-1 satellite data. Added to that, the proposed IEGD algorithm can improve the extraction accuracy of large-scale images and be employed for the extraction of various aquaculture areas. Given that the IEGD algorithm is a type of supervised classification algorithm, it relies heavily on the spectral feature information of the aquaculture object. For this reason, if the spectral feature information of the region of interest is not selected properly, the extraction performance of the overall aquaculture area will be extremely reduced. [ABSTRACT FROM AUTHOR]

Climate warming and concomitant glacier recession in the High Mountain Asia (HMA) have led to widespread development and expansion of glacial lakes, which reserved the freshwater resource, but also may increase risks of glacial lake outburst floods (GLOFs) or debris floods. Using 46 moderate- and high-resolution satellite images, including declassified Keyhole and Landsat missions between 1964 and 2020, we provide a comprehensive area mapping of glaciers and glacial lakes in the Tama Koshi (Rongxer) basin, a highly glacierized China-Nepal transnational catchment in the central Himalayas with high potential risks of glacier-related hazards. Results show that the 329.2 ± 1.9 km2 total area of 271 glaciers in the region has decreased by 26.2 ± 3.2 km2 in the past 56 years. During 2000–2016, remarkable ice mass loss caused the mean glacier surface elevation to decrease with a rate of −0.63 m a−1, and the mean glacier surface velocity slowed by ~25% between 1999 and 2015. The total area of glacial lakes increased by 9.2 ± 0.4 km2 (~180%) from 5.1 ± 0.1 km2 in 1964 to 14.4 ± 0.3 km2 in 2020, while ice-contacted proglacial lakes have a much higher expansion rate (~204%). Large-scale glacial lakes are developed preferentially and experienced rapid expansion on the east side of the basin, suggesting that in addition to climate warming, the glacial geomorphological characters (aspect and slope) are also key controlling factors of the lake growing process. We hypothesize that lake expansion will continue in some cases until critical local topography (i.e., steepening icefall) is reached, but the lake number may not necessarily increase. Further monitoring should be focused on eight rapidly expanding proglacial lakes due to their high potential risks of failure and relatively high lake volumes. [ABSTRACT FROM AUTHOR]

Timely identifying and detecting water bodies from SAR images are significant for flood monitoring and water resources management. In recent decades, deep learning has been applied to water extraction but is subject to the large difficulty of acquiring SAR dataset of various water bodies types, as well as heavy labeling work. In addition, the traditional methods mostly occur over the large, open lakes and rivers, rarely focusing on complex areas such as the urban water, and cannot automatically acquire the classification threshold. To address these issues, a novel water extraction method is proposed with high accuracy in this paper. Firstly, a multiscale feature extraction using a Gabor filter is conducted to reduce the noise and roughly identify water feature. Secondly, we apply the Otsu algorithm as well as a voting strategy to initially extract the homogeneous regions and for subsequent Gaussian mixture model (GMM). Finally, the dual threshold is obtained from the fitted Gaussian distribution of water and non-water, which is integrated into the graph cut model to redefine the weights of the edges, then constructing the energy function of the water map. The dual-threshold graph cut (DTGC) model precisely pinpoints the water location by minimizing the energy function. To verify the efficiency and robustness, our method and comparison methods, including the IGC method and IACM method, are tested on six different types of water bodies, by performing the accuracy assessment via comparing outcomes with the manually labeled ground truth. The qualitative and quantitative results show that the overall accuracy of our method for the whole dataset all surpasses 99%, along with an obvious improvement of the Kappa, F1-score, and IoU indicators. Therefore, DTGC method has the absolute advantage of automatically capturing water maps in different scenes of SAR images without specific prior knowledge and can also determine the optimal threshold range. [ABSTRACT FROM AUTHOR]

It is possible to detect various microplastics (MPs) floating on water or contained in ice due to the unique optical characteristics of plastics of various chemical compositions and structures. When the MPs are measured in the spectral region between 800 and 1000 nm, which has relatively little influence on the temperature change in water, they are frequently perceived as noise or obscured by the surrounding reflection spectra because of the small number and low intensity of the representative peak wavelengths. In this study, we have applied several mathematical methods, including the convex hull, Gaussian deconvolution, and curve fitting to amplify and normalize the reflectance and thereby find the spectral properties of each polymer, namely polypropylene (PP), polyethylene terephthalate (PET), methyl methacrylate (PMMA), and polyethylene (PE). Blunt-shaped spectra with a relatively large maximum of normalized reflectance (NRmax) can be decomposed into several Gaussian peak wavelengths: 889, 910, and 932 nm for the PP and 898 and 931 nm for the PE. Moreover, unique peak wavelengths with the meaningful measure at 868 and 907 nm for the PET and 887 nm for the PMMA were also obtained. Based on the results of the study, one can say that each plastic can be identified with up to 81% precision by compensating based on the spectral properties even when they are hidden in water or ice. [ABSTRACT FROM AUTHOR]

Despite an increase in heatwaves and rising air temperatures in the Arctic, little research has been conducted into the temperatures of proglacial lakes in the region. An assumption persists that they are cold and uniformly feature a temperature of 1 °C. This is important to test, given the rising air temperatures in the region (reported in this study) and potential to increase water temperatures, thus increasing subaqueous melting and the retreat of glacier termini from where they are in contact with lakes. Through analysis of ASTER surface temperature product data, we report warm (>4 °C) proglacial lake surface water temperatures (LSWT) for both ice-contact and non-ice-contact lakes, as well as substantial spatial heterogeneity. We present in situ validation data (from problematic maritime areas) and a workflow that facilitates the extraction of robust LSWT data from the high-resolution (90 m) ASTER surface temperature product (AST08). This enables spatial patterns to be analysed in conjunction with surrounding thermal influences, such as parent glaciers and topographies. This workflow can be utilised for the analysis of the LSWT data of other small lakes and crucially allows high spatial resolution study of how they have responded to changes in climate. Further study of the LSWT is essential in the Arctic given the amplification of climate change across the region. [ABSTRACT FROM AUTHOR]

The use of unmanned aerial vehicle (UAV)-mounted radar for obtaining snowpack parameters has seen considerable advances over recent years. However, a robust method of snow density estimation still needs further development. The objective of this work is to develop a method to reliably and remotely estimate snow water equivalent (SWE) using UAV-mounted radar and to perform initial field experiments. In this paper, we present an improved scheme for measuring SWE using ultra-wide-band (UWB) (0.7 to 4.5 GHz) pseudo-noise radar on a moving UAV, which is based on airborne snow depth and density measurements from the same platform. The scheme involves autofocusing procedures with the frequency–wavenumber (F–K) migration algorithm combined with the Dix equation for layered media in addition to altitude correction of the flying platform. Initial results from field experiments show high repeatability (R > 0.92) for depth measurements up to 5.5 m, and good agreement with Monte Carlo simulations for the statistical spread of snow density estimates with standard deviation of 0.108 g/cm3. This paper also outlines needed system improvements to increase the accuracy of a snow density estimator based on an F–K migration technique. [ABSTRACT FROM AUTHOR]

Glaciers in High Mountain Asia (HMA) have a significant impact on human activity. Thus, a detailed and up-to-date inventory of glaciers is crucial, along with monitoring them regularly. The identification of debris-covered glaciers is a fundamental and yet challenging component of research into glacier change and water resources, but it is limited by spectral similarities with surrounding bedrock, snow-affected areas, and mountain-shadowed areas, along with issues related to manual discrimination. Therefore, to use fewer human, material, and financial resources, it is necessary to develop better methods to determine the boundaries of debris-covered glaciers. This study focused on debris-covered glacier mapping using a combination of related technologies such as random forest (RF) and convolutional neural network (CNN) models. The models were tested on Landsat 8 Operational Land Imager (OLI)/Thermal Infrared Sensor (TIRS) data and the Advanced Spaceborne Thermal Emission and Reflection Radiometer Global Digital Elevation Model (ASTER GDEM), selecting Eastern Pamir and Nyainqentanglha as typical glacier areas on the Tibetan Plateau to construct a glacier classification system. The performances of different classifiers were compared, the different classifier construction strategies were optimized, and multiple single-classifier outputs were obtained with slight differences. Using the relationship between the surface area covered by debris and the machine learning model parameters, it was found that the debris coverage directly determined the performance of the machine learning model and mitigated the issues affecting the detection of active and inactive debris-covered glaciers. Various classification models were integrated to ascertain the best model for the classification of glaciers. [ABSTRACT FROM AUTHOR]

The accurate monitoring of forest cover and its changes are essential for environmental change research, but current satellite products for forest coverage carry many uncertainties. This study used 30-m Landsat-8 data, and aggregated 1-m GaoFen-2 (GF-2) satellite images to construct the training samples and used multiple machine learning algorithms (MLAs) to estimate the fractional forest cover (FFC) in China's Three North Region (TNR). In this study, multiple MLAs were merged to construct stacked generalization (SG) models based on the idea of SG, and the performances of the MLAs in the FFC estimation were evaluated. The results of the 10-fold cross-validation showed that all non-linear algorithms had a good performance, with an R2 value of greater than 0.8 and a root-mean square error (RMSE) of less than 0.05. In the bagging ensemble, the random forest (RF) (R2 = 0.993, RMSE = 0.020) model performed the best and in the boosting ensemble, the light gradient boosted machine (LGBM) (R2 = 0.992, RMSE = 0.022) performed the best. Although the evaluation index of the RF is slightly better than that of the LGBM, the independent validation results show that the two models have similar performances. The model evaluation results of the independent datasets showed that, in the SG model, the performance of the SG(LGBM) (R2 = 0.991, RMSE = 0.034) was better than that of the single or non-ensemble model. Comparing the FFC estimates of our model with those of existing datasets showed that our model exhibited more forest spatial distribution details and higher accuracy in complex landscapes. Overall, in this study, the method of using high-resolution remote sensing (RS) images to extract samples for FFC estimation is feasible. Our results demonstrate the potential of the ensemble MLAs to map the FFC. The research results also show that among many MALs, the RF algorithm is the most suitable algorithm for estimating FFC, which provides a reference for future research. [ABSTRACT FROM AUTHOR]

Daily Normalized Difference Vegetation Index (NDVI) values from the MODIS Aqua and Terra satellites were compared with on-the-ground camera observations at five locations in northern Alaska. Over half of the spring rise in NDVI was due to the transition from the snow-covered landscape to the snow-free surface prior to the deciduous leaf-out. In the fall after the green season, NDVI fluctuated between an intermediate level representing senesced vegetation and lower values representing clouds and intermittent snow, and then dropped to constant low levels after establishment of the permanent winter snow cover. The NDVI value of snow-free surfaces after fall leaf senescence was estimated from multi-year data using a 90th percentile smoothing spline curve fit to a plot of daily NDVI values vs. ordinal date. This curve typically showed a flat region of intermediate NDVI values in the fall that represent cloud- and snow-free days with senesced vegetation. This "fall plateau" was readily identified in a large systematic sample of MODIS NDVI values across the study area, in typical tundra, shrub, and boreal forest environments. The NDVI level of the fall plateau can be extrapolated to the spring rising leg of the annual NDVI curve to approximate the true start of green season. [ABSTRACT FROM AUTHOR]

Lake formation and drainage are pervasive phenomena in permafrost regions. Drained lake basins (DLBs) are often the most common landforms in lowland permafrost regions in the Arctic (50% to 75% of the landscape). However, detailed assessments of DLB distribution and abundance are limited. In this study, we present a novel and scalable remote sensing-based approach to identifying DLBs in lowland permafrost regions, using the North Slope of Alaska as a case study. We validated this first North Slope-wide DLB data product against several previously published sub-regional scale datasets and manually classified points. The study area covered >71,000 km2, including a >39,000 km2 area not previously covered in existing DLB datasets. Our approach used Landsat-8 multispectral imagery and ArcticDEM data to derive a pixel-by-pixel statistical assessment of likelihood of DLB occurrence in sub-regions with different permafrost and periglacial landscape conditions, as well as to quantify aerial coverage of DLBs on the North Slope of Alaska. The results were consistent with previously published regional DLB datasets (up to 87% agreement) and showed high agreement with manually classified random points (64.4–95.5% for DLB and 83.2–95.4% for non-DLB areas). Validation of the remote sensing-based statistical approach on the North Slope of Alaska indicated that it may be possible to extend this methodology to conduct a comprehensive assessment of DLBs in pan-Arctic lowland permafrost regions. Better resolution of the spatial distribution of DLBs in lowland permafrost regions is important for quantitative studies on landscape diversity, wildlife habitat, permafrost, hydrology, geotechnical conditions, and high-latitude carbon cycling. [ABSTRACT FROM AUTHOR]

Glaciers and numerous glacial lakes that are produced by glacier melting are key indicators of climate change. Often overlooked, supra-glacial lakes develop in the melting area in the low-lying part of a glacier and appear to be highly variable in their size, shape, and location. The lifespan of these lakes is thought to be quite transient, since the lakes may be completely filled by water and burst out within several weeks. Changes in supra-glacial lake outlines and other surface features such as supra-glacial rivers and crevasses on the glaciers are useful indicators for the direct monitoring of glacier changes. Synthetic aperture radar (SAR) is not affected by weather and climate, and is an effective tool for study of glaciated areas. The development of the Chinese GaoFen-3 (GF-3) SAR, which has high spatial and temporal resolution and high-precision observation performance, has made it possible to obtain dynamic information about glaciers in more detail. In this paper, the classical Canny operator, the variational B-spline level-set method, and U-Net-based deep-learning model were applied and compared to extract glacial lake outlines and other surface features using different modes and Chinese GF-3 SAR imagery in the Mount Everest Region of the Himalayas. Particularly, the U-Net-based deep-learning method, which was independent of auxiliary data and had a high degree of automation, was used for the first time in this context. The experimental results showed that the U-Net-based deep-learning model worked best in the segmentation of supra-glacial lakes in terms of accuracy (Precision = 98.45% and Recall = 95.82%) and segmentation efficiency, and was good at detecting small, elongated, and ice-covered supra-glacial lakes. We also found that it was useful for accurately identifying the location of supra-glacial streams and ice crevasses on glaciers, and quantifying their width. Finally, based on the time series of the mapping results, the spatial characteristics and temporal evolution of these features over the glaciers were comprehensively analyzed. Overall, this study presents a novel approach to improve the detection accuracy of glacier elements that could be leveraged for dynamic monitoring in future research. [ABSTRACT FROM AUTHOR]

Sea ice observations through satellite imaging have led to advancements in environmental research, ship navigation, and ice hazard forecasting in cold regions. Machine learning and, recently, deep learning techniques are being explored by various researchers to process vast amounts of Synthetic Aperture Radar (SAR) data for detecting potential hazards in navigational routes. Detection of hazards such as sea ice floes in Marginal Ice Zones (MIZs) is quite challenging as the floes are often embedded in a multiscale ice cover composed of ice filaments and eddies in addition to floes. This study proposes a segmentation model tailored for detecting ice floes in SAR images. The model exploits the advantages of both convolutional neural networks and convolutional conditional random field (Conv-CRF) in a combined manner. The residual UNET (RES-UNET) computes expressive features to generate coarse segmentation maps while the Conv-CRF exploits the spatial co-occurrence pairwise potentials along with the RES-UNET unary/segmentation maps to generate final predictions. The whole pipeline is trained end-to-end using a dual loss function. This dual loss function is composed of a weighted average of binary cross entropy and soft dice loss. The comparison of experimental results with the conventional segmentation networks such as UNET, DeepLabV3, and FCN-8 demonstrates the effectiveness of the proposed architecture. [ABSTRACT FROM AUTHOR]

This study investigates the growth and displacement of landfast ice along the shoreline of the Mackenzie Delta in Northwest Territories, Canada, by synthetic aperture radar (SAR) speckle offset tracking (SPO). Three-dimensional (3D) offsets were reconstructed from Sentinel-1 ascending and descending SAR images acquired on the same dates during the November 2017–April 2018 and October 2018–May 2019 annual cycles. The analysis revealed both horizontal and vertical offsets. The annual horizontal offsets of up to ~8 m are interpreted as landfast ice displacements caused by wind and ocean currents. The annual vertical offsets of approximately −1 to −2 m were observed from landfast ice, which are likely due to longer radar penetration up to the ice–water interface with increasing landfast ice thickness. Numerical ice thickness model estimates supported the conclusion that the cumulative vertical negative offsets correspond to the growth of freshwater ice. Time-series analysis showed that the significant growth and displacement of landfast ice in the Mackenzie Delta occurred between November and January during the 2017–2018 and 2018–2019 cycles. [ABSTRACT FROM AUTHOR]

Northern latitudes of Pakistan are warming at faster rate as compared to the rest of the country. It has induced irregular and sudden glacier fluctuations leading to the progression of glacial lakes, and thus enhancing the risk of Glacier Lake Outbursts Floods (GLOF) in the mountain systems of Pakistan. Lack of up-to-date inventory, classification, and susceptibility profiles of glacier lakes and newly formed GLOFs, are few factors which pose huge hindrance towards disaster preparedness and risk reduction strategies in Pakistan. This study aims to bridge the existing gap in data and knowledge by exploiting satellite observations, and efforts are made to compile and update glacier lake inventories. GLOF susceptibility assessment is evaluated by using Analytical Hierarchy Process (AHP), a multicriteria structured technique based on three susceptibility contributing factors: Geographic, topographic, and climatic. A total of 294 glacial lakes are delineated with a total area of 7.85 ± 0.31 km2 for the year 2018. Analysis has identified six glacier lakes as potential GLOF and met the pre-established criteria of damaging GLOFs. The historical background of earlier GLOF events is utilized to validate the anticipated approach and found this method appropriate for first order detection and prioritization of potential GLOFs in Northern Pakistan. [ABSTRACT FROM AUTHOR]

Climate change and associated Arctic amplification cause a degradation of permafrost which in turn has major implications for the environment. The potential turnover of frozen ground from a carbon sink to a carbon source, eroding coastlines, landslides, amplified surface deformation and endangerment of human infrastructure are some of the consequences connected with thawing permafrost. Satellite remote sensing is hereby a powerful tool to identify and monitor these features and processes on a spatially explicit, cheap, operational, long-term basis and up to circum-Arctic scale. By filtering after a selection of relevant keywords, a total of 325 articles from 30 international journals published during the last two decades were analyzed based on study location, spatio-temporal resolution of applied remote sensing data, platform, sensor combination and studied environmental focus for a comprehensive overview of past achievements, current efforts, together with future challenges and opportunities. The temporal development of publication frequency, utilized platforms/sensors and the addressed environmental topic is thereby highlighted. The total number of publications more than doubled since 2015. Distinct geographical study hot spots were revealed, while at the same time large portions of the continuous permafrost zone are still only sparsely covered by satellite remote sensing investigations. Moreover, studies related to Arctic greenhouse gas emissions in the context of permafrost degradation appear heavily underrepresented. New tools (e.g., Google Earth Engine (GEE)), methodologies (e.g., deep learning or data fusion etc.) and satellite data (e.g., the Methane Remote Sensing LiDAR Mission (Merlin) and the Sentinel-fleet) will thereby enable future studies to further investigate the distribution of permafrost, its thermal state and its implications on the environment such as thermokarst features and greenhouse gas emission rates on increasingly larger spatial and temporal scales. [ABSTRACT FROM AUTHOR]

Hydrological connectivity is an important characteristic of wetlands that maintains the stability and functions of an ecosystem. This study investigates the temporal variations of hydrological connectivity and their driving mechanism in Baiyangdian Lake, a large shallow wetland in North China, using a time series of open water surface area data derived from 36 Landsat 8 multispectral images from 2013–2019 and in situ measured water level data. Water area classification was implemented using the Google Earth Engine. Six commonly used indexes for extracting water surface data from satellite images were compared and the best performing index was selected for the water classification. A composite hydrological connectivity index computed from open water area data derived from Landsat 8 images was developed based on several landscape pattern indices and applied to Baiyangdian Lake. The results show that, reflectance in the near-infrared band is the most accurate index for water classification with >98% overall accuracy because of its sensitivity to different land cover types. The slopes of the best-fit linear relationships between the computed hydrological connectivity and observed water level show high variability between years. In most years, hydrological connectivity generally increases when water levels increase, with an average R2 of 0.88. The spatial distribution of emergent plants also varies year to year owing to interannual variations of the climate and hydrological regime. This presents a possible explanation for the variations in the annual relationship between hydrological connectivity and water level. For a given water level, the hydrological connectivity is generally higher in spring than summer and autumn. This can be explained by the fact that the drag force exerted by emergent plants, which reduces water flow, is smaller than that for summer and autumn owing to seasonal variations in the phenological characteristics of emergent plants. Our study reveals that both interannual and seasonal variations in the hydrological connectivity of Baiyangdian Lake are related to the growth of emergent plants, which occupy a large portion of the lake area. Proper vegetation management may therefore improve hydrological connectivity in this wetland. [ABSTRACT FROM AUTHOR]

Accurate ice segmentation is one of the most crucial techniques for intelligent ice monitoring. Compared with ice segmentation, it can provide more information for ice situation analysis, change trend prediction, and so on. Therefore, the study of ice segmentation has important practical significance. In this study, we focused on fine-grained river ice segmentation using unmanned aerial vehicle (UAV) images. This has the following difficulties: (1) The scale of river ice varies greatly in different images and even in the same image; (2) the same kind of river ice differs greatly in color, shape, texture, size, and so on; and (3) the appearances of different kinds of river ice sometimes appear similar due to the complex formation and change procedure. Therefore, to perform this study, the NWPU_YRCC2 dataset was built, in which all UAV images were collected in the Ningxia–Inner Mongolia reach of the Yellow River. Then, a novel semantic segmentation method based on deep convolution neural network, named ICENETv2, is proposed. To achieve multiscale accurate prediction, we design a multilevel features fusion framework, in which multi-scale high-level semantic features and lower-level finer features are effectively fused. Additionally, a dual attention module is adopted to highlight distinguishable characteristics, and a learnable up-sampling strategy is further used to improve the segmentation accuracy of the details. Experiments show that ICENETv2 achieves the state-of-the-art on the NWPU_YRCC2 dataset. Finally, our ICENETv2 is also applied to solve a realistic problem, calculating drift ice cover density, which is one of the most important factors to predict the freeze-up data of the river. The results demonstrate that the performance of ICENETv2 meets the actual application demand. [ABSTRACT FROM AUTHOR]

The usability of multispectral satellite data for detecting and monitoring supraglacial meltwater ponds has been demonstrated for western Greenland. For a multitemporal analysis of large regions or entire Greenland, largely automated processing routines are required. Here, we present a sequence of algorithms that allow for an automated Sentinel-2 data search, download, processing, and generation of a consistent and dense melt pond area time-series based on open-source software. We test our approach for a ~82,000 km2 area at the 79 °N Glacier (Nioghalvfjerdsbrae) in northeast Greenland, covering the years 2016, 2017, 2018 and 2019. Our lake detection is based on the ratio of the blue and red visible bands using a minimum threshold. To remove false classification caused by the similar spectra of shadow and water on ice, we implement a shadow model to mask out topographically induced artifacts. We identified 880 individual lakes, traceable over 479 time-steps throughout 2016–2019, with an average size of 64,212 m2. Of the four years, 2019 had the most extensive lake area coverage with a maximum of 333 km2 and a maximum individual lake size of 30 km2. With 1.5 days average observation interval, our time-series allows for a comparison with climate data of daily resolution, enabling a better understanding of short-term climate-glacier feedbacks. [ABSTRACT FROM AUTHOR]

Analysis of the radar echoes from a spaceborne altimeter gives information on sea surface height, wave height and wind speed, as well as other parameters over land and ice. The first spaceborne radar altimeter was pioneered on Skylab in 1974. Since then, there have been about 20 further missions, with several advances in the sophistication of hardware and complexity of processing with the aim of increased accuracy and precision. Because of that, the importance of regular and precise calibration and validation ("cal/val") remains undiminished, especially with efforts to merge altimetric records from multiple missions spanning different domains and time periods. This special issue brings together 19 papers, with a focus on the recent missions (Jason-2, Jason-3, Sentinel-3A and HY-2B) as well as detailing the issues for anticipated future missions such as SWOT. This editorial provides a brief guide to the approaches and issues for cal/val of the various different derived parameters, including a synopsis of the papers in this special issue. [ABSTRACT FROM AUTHOR]

Wetlands are ranked as very diverse ecosystems, covering about 4–6% of the global land surface. They occupy the transition zones between aquatic and terrestrial environments, and share characteristics of both zones. Wetlands play critical roles in the hydrological cycle, sustaining livelihoods and aquatic life, and biodiversity. Poor management of wetlands results in the loss of critical ecosystems goods and services. Globally, wetlands are degrading at a fast rate due to global environmental change and anthropogenic activities. This requires holistic monitoring, assessment, and management of wetlands to prevent further degradation and losses. Remote-sensing data offer an opportunity to assess changes in the status of wetlands including their spatial coverage. So far, a number of studies have been conducted using remotely sensed data to assess and monitor wetland status in semi-arid and arid regions. A literature search shows a significant increase in the number of papers published during the 2000–2020 period, with most of these studies being in semi-arid regions in Australia and China, and few in the sub-Saharan Africa. This paper reviews progress made in the use of remote sensing in detecting and monitoring of the semi-arid and arid wetlands, and focuses particularly on new insights in detection and monitoring of wetlands using freely available multispectral sensors. The paper firstly describes important characteristics of wetlands in semi-arid and arid regions that require monitoring in order to improve their management. Secondly, the use of freely available multispectral imagery for compiling wetland inventories is reviewed. Thirdly, the challenges of using freely available multispectral imagery in mapping and monitoring wetlands dynamics like inundation, vegetation cover and extent, are examined. Lastly, algorithms for image classification as well as challenges associated with their uses and possible future research are summarised. However, there are concerns regarding whether the spatial and temporal resolutions of some of the remote-sensing data enable accurate monitoring of wetlands of varying sizes. Furthermore, it was noted that there were challenges associated with the both spatial and spectral resolutions of data used when mapping and monitoring wetlands. However, advancements in remote-sensing and data analytics provides new opportunities for further research on wetland monitoring and assessment across various scales. [ABSTRACT FROM AUTHOR]

Multi-mission satellite altimetry (e.g., ERS, Envisat, TOPEX/Poseidon, Jason) data have enabled a synoptic-scale view of ocean variations in past decades. Since 2016, the Sentinel-3 mission has provided better spatial and temporal sampling compared to its predecessors. The Sentinel-3 Ku/C Radar Altimeter (SRAL) is one of the synthetic aperture radar altimeters (SAR Altimeter) which is more precise for coastal and lake observations. The article studies the performance of the Sentinel-3 Level-2 sea level altimetry products in the coastal areas of the Baltic Sea and on two lakes of Estonia. The Sentinel-3 data were compared with (i) collocated Global Navigation Satellite System (GNSS) ship measurements, (ii) the Estonian geoid model (EST-GEOID2017) together with sea-level anomaly corrections from the tide gauges, and (iii) collocated buoy measurements. The comparisons were carried out along seven Sentinel-3A/B tracks across the Baltic Sea and Estonian lakes in 2019. In addition, the Copernicus Marine Environment Monitoring Service (CMEMS) Level-3 sea-level products and the Nucleus for European Modelling of the Ocean (NEMO) reanalysis outcomes were compared with measurements from Estonia's 21 tide gauges and the buoy deployed offshore. Our results showed that the uncertainty of the Sentinel-3 Level-2 altimetry product was below decimetre level for the seacoast and the selected lakes of Estonia. Results from CMEMS Level-3 altimetry products showed a correlation of 0.83 (RMSE 0.18 m) and 0.91 (RMSE 0.27 m) when compared against the tide gauge measurements and the NEMO model, respectively. The overall performance of the altimetry products was very good, except in the immediate vicinity of the coastline and for the lakes, where the accuracy was nearly three times lower than for the open sea, but still acceptably good. [ABSTRACT FROM AUTHOR]

Glacial lakes (GLs), a vital link between the hydrosphere and the cryosphere, participate in the local hydrological process, and their interannual dynamic evolution is an objective reflection and an indicator of regional climate change. The complex terrain and climatic conditions in mountainous areas where GLs are located make it difficult to employ conventional remote sensing observation means to obtain stable, accurate, and comprehensive observation data. In view of this situation, this study presents an algorithm with a high generalization ability established by optimizing and improving a deep learning (DL) semantic segmentation network model for extracting GL contours from combined synthetic-aperture radar (SAR) amplitude and multispectral imagery data. The aim is to use the high penetrability and all-weather advantages of SAR to reduce the effects of cloud cover as well as to integrate the multiscale and detail-oriented advantages of multispectral data to facilitate accurate, quantitative extraction of GL contours. The accuracy and reliability of the model and algorithm were examined by employing them to extract the contours of GLs in a large region of south-eastern Tibet from Landsat 8 optical remote sensing images and Sentinel-1A amplitude images. In this study, the contours of a total 8262 GLs in south-eastern Tibet were extracted. These GLs were distributed predominantly at altitudes of 4000–5500 m. Only 17.4% of these GLs were greater than 0.1 km2 in size, while a large number of small GLs made up the majority. Through analysis and validation, the proposed method was found highly capable of distinguishing rivers and lakes and able to effectively reduce the misidentification and extraction of rivers. With the DL model based on combined optical and SAR images, the intersection-over-union (IoU) score increased by 0.0212 (to 0.6207) on the validation set and by 0.038 (to 0.6397) on the prediction set. These validation data sufficiently demonstrate the efficacy of the model and algorithm. The technical means employed in this study as well as the results and data obtained can provide a reference for research and application expansion in related fields. [ABSTRACT FROM AUTHOR]

This study aims to monitor spatiotemporal changes of spring phenology using the green-up start dates based on the accumulated growing degree days (AGDD) and the enhanced vegetation index (EVI), which were deducted from moderate resolution imaging spectroradiometer (MODIS) land surface temperature (LST) data. The green-up start dates were extracted from the MODIS-derived AGDD and EVI for 30 Mongolian oak (Quercus mongolica Fisch.) stands throughout South Korea. The relationship between green-up day of year needed to reach the AGDD threshold (DoYAGDD) and air temperature was closely maintained in data in both MODIS image interpretation and from 93 meteorological stations. Leaf green-up dates of Mongolian oak based on the AGDD threshold obtained from the records measured at five meteorological stations during the last century showed the same trend as the result of cherry observed visibly. Extrapolating the results, the spring onset of Mongolian oak and cherry has become earlier (14.5 ± 4.3 and 10.7 ± 3.6 days, respectively) with the rise of air temperature over the last century. The temperature in urban areas was consistently higher than that in the forest and the rural areas and the result was reflected on the vegetation phenology. Our study expanded the scale of the study on spring vegetation phenology spatiotemporally by combining satellite images with meteorological data. We expect our findings could be used to predict long-term changes in ecosystems due to climate change. [ABSTRACT FROM AUTHOR]

Avalanche disasters are extremely destructive and catastrophic, often causing serious casualties, economic losses and surface erosion. However, far too little attention has been paid to utilizing remote sensing mapping avalanches quickly and automatically to mitigate calamity. Such endeavors are limited by formidable natural conditions, human subjective judgement and insufficient understanding of avalanches, so they have been incomplete and inaccurate. This paper presents an objective and widely serviceable method for regional auto-detection using the scattering and interference characteristics of avalanches extracted from Sentinel-1 SLC images. Six indices are established to distinguish avalanches from surrounding undisturbed snow. The active avalanche belts in Kizilkeya and Aktep of the Western TianShan Mountains in China lend urgency to this research. Implementation found that smaller avalanches can be consistently identified more accurately in descending images. Specifically, 281 and 311 avalanches were detected in the ascending and descending of Kizilkeya, respectively. The corresponding numbers on Aktep are 104 and 114, respectively. The resolution area of single avalanche detection can reach 0.09 km2. The performance of the model was excellent in all cases (areas under the curve are 0.831 and 0.940 in descending and ascending of Kizilkeya, respectively; and 0.807 and 0.938 of Aktep, respectively). Overall, the evaluation of statistical indices are POD > 0.75, FAR < 0.34, FOM < 0.13 and TSS > 0.75. The results indicate that the performance of the innovation proposed in this paper, which employs multivariate comprehensive descriptions of avalanche characteristics to actualize regional automatic detection, can be more objective, accurate, applicable and robust to a certain extent. The latest and more complete avalanche inventory generated by this design can effectively assist in addressing the increasingly severe avalanche disasters and improving public awareness of avalanches in alpine areas. [ABSTRACT FROM AUTHOR]

Supraglacial liquid water at the margins of ice sheets has an important impact on the surface energy balance and can also influence the ice flow when supraglacial lakes drain to the bed. Optical imagery is able to monitor supraglacial lakes during the summer season. Here we developed an alternative method using polarimetric SAR from Sentinel-1 during 2017–2020 to distinguish between liquid water and other surface types at the margin of the Northeast Greenland Ice Stream. This allows the supraglacial hydrology to be monitored during the winter months too. We found that the majority of supraglacial lakes persist over winter. When comparing our results to optical data, we found significantly more water. Even during summer, many lakes are partly or fully covered by a lid of ice and snow. We used our classification results to automatically map the outlines of supraglacial lakes, create time series of water area for each lake, and hence detect drainage events. We even found several winter time drainages, which might have an important effect on ice flow. Our method has problems during the peak of the melt season, but for the rest of the year it provides crucial information for better understanding the component of supraglacial hydrology in the glaciological system. [ABSTRACT FROM AUTHOR]

This paper reviews the scientific motivation and challenges, development, and use of underwater robotic vehicles designed for use in ice-covered waters, with special attention paid to the navigation systems employed for under-ice deployments. Scientific needs for routine access under fixed and moving ice by underwater robotic vehicles are reviewed in the contexts of geology and geophysics, biology, sea ice and climate, ice shelves, and seafloor mapping. The challenges of under-ice vehicle design and navigation are summarized. The paper reviews all known under-ice robotic vehicles and their associated navigation systems, categorizing them by vehicle type (tethered, untethered, hybrid, and glider) and by the type of ice they were designed for (fixed glacial or sea ice and moving sea ice). [ABSTRACT FROM AUTHOR]

Space-based radar sensors have transformed Earth observation since their first use by Seasat in 1978. Radar instruments are less affected by daylight or weather conditions than optical counterparts, suitable for continually monitoring the global biosphere. The current trends in synthetic aperture radar (SAR) platform design are distinct from traditional approaches in that miniaturized satellites carrying SAR are launched in multiples to form a SAR constellation. A systems engineering perspective is presented in this paper to track the transitioning of space-based SAR platforms from large satellites to small satellites. Technological advances therein are analyzed in terms of subsystem components, standalone satellites, and satellite constellations. The availability of commercial satellite constellations, ground stations, and launch services together enable real-time SAR observations with unprecedented details, which will help reveal the global biomass and their changes owing to anthropogenic drivers. The possible roles of small satellites in global biospheric monitoring and the subsequent research areas are also discussed. [ABSTRACT FROM AUTHOR]