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A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Remote Sensing in Geology, Geomorphology and Hydrology".

Deadline for manuscript submissions: closed (31 December 2021) | Viewed by 56759

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Special Issue Editors

Dr. Davide Fugazza

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Guest Editor

Department of Environmental Science and Policy, Università degli Studi di Milano, Milano, Italy
Interests: remote sensing; glacier

Dr. Roberto Sergio Azzoni

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Guest Editor

Department of Earth Sciences, Università degli Studi di Milano, 20133 Milan, Italy
Interests: glacial geomorphology; glaciology; remote-sensing of the cryosphere

Dr. Antonella Senese

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Guest Editor

Dipartimento di Scienze e Politiche Ambientali, Università degli Studi di Milano, Via Celoria 10, 20133 Milan, Italy
Interests: glaciology; cryosphere; climate change impacts on high mountain areas; sustainability of techniques for mitigating climate change effetcs
Special Issues, Collections and Topics in MDPI journals

Dr. Giovanni Baccolo

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Guest Editor

Department of Environmental and Earth Sciences, University of Milano-Bicocca, Milano, Italy
Interests: glaciology; climatology; geochemistry; environmental radioactivity; analytical chemistry

Special Issue Information

Dear Colleagues,

The cryosphere holds about 69% percent of the world’s freshwater, providing an invaluable resource for civil and industrial use, and feeding large river systems in high-mountain and densely populated regions of the world where other sources of water are scarce. In light of the Alpine Glaciology Meeting, held in Milan on 27–28 February 2020 and dedicated to advances in the study of the cryosphere, encompassing glaciers, ice caps, snow, permafrost, and glacial geomorphology, with this Special Issue, we hope to provide an updated picture of new discoveries in cryospheric science pertaining to the fields of remote sensing and hydrology.

In view of the often remote location of glaciers, ice sheets, and ice caps, the cryosphere has always lent itself to remote sensing observations and has been quick to adopt new methods and datasets made available through progress in remote observations. Remote sensing has in fact already enabled the scientific community to gain several insights into the evolution of the Earth’s icy regions, understand the effects of climate change, and project its future impacts on water availability. The development of remote sensing, however, is ever-increasing, as attested by the availability of new satellites, sensors, and the recent application of UAVs to cryospheric science. Likewise, improvement in the modeling of glacier melt and mass balance is expected to contribute to an enhanced understanding of the hydrological cycle and availability of water for downstream populations. We therefore solicit papers that contribute to advancing our understanding of cryospheric processes by taking advantage of satellite, aerial (including from UAVs), and terrestrial platforms, using active or passive data, and/or provide a significant contribution to the modeling of glacier melt and its impact on the hydrological regime and ecology of river systems. Contributions from participants to the AGM 2020 as well as external scientists are equally welcome.

You may choose our Joint Special Issue in Hydrology.

Dr. Davide Fugazza
Dr. Roberto Sergio Azzoni
Dr. Antonella Senese
Dr. Giovanni Baccolo
Guest Editors

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Keywords

cryosphere
glaciers
snow cover
permafrost
glacial geomorphology
snow/ice melt
glacier mass balance

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Published Papers (13 papers)

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18 pages, 2923 KiB

Open AccessArticle

Flow Velocity Variations and Surface Change of the Destabilised Plator Rock Glacier (Central Italian Alps) from Aerial Surveys

by Francesca Bearzot, Roberto Garzonio, Roberto Colombo, Giovanni Battista Crosta, Biagio Di Mauro, Matteo Fioletti, Umberto Morra Di Cella and Micol Rossini

Remote Sens. 2022, 14(3), 635; https://doi.org/10.3390/rs14030635 - 28 Jan 2022

Cited by 4 | Viewed by 3622

Abstract

Flow velocities were measured on the Plator rock glacier in the Central Italian Alps using a correlation image analysis algorithm on orthophotos acquired by drones between the years 2016 and 2020. The spatial patterns of surface creep were then compared to the Bulk Creep Factor (BCF) spatial variability to interpret the rock glacier dynamics as a function of material properties and geometry. The rock glacier showed different creep rates in the rooting zone (0.40–0.90 m/y) and in the frontal zone (>4.0 m/y). Close to the rock glacier front, the BCF assumed the highest values, reaching values typical of rock glaciers experiencing destabilisation. Conversely, in the rooting zone the small rates corresponded to lowest BCFs, about five times smaller than in the frontal zone. The Plator rock glacier revealed a substantial advancement from 1981 to 2020 and distinct geomorphological features typical of rock glaciers exhibiting destabilising processes. Given the fast-moving phase, the advancement of both the front line and the front toe of the rock glacier, and the contrasting spatial distribution in the BCFs, the Plator could be considered a destabilised rock glacier. Full article

(This article belongs to the Special Issue Recent Advances in Cryospheric Sciences)

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20 pages, 6698 KiB

Open AccessArticle

Assessment of Recent Flow, and Calving Rate of the Perito Moreno Glacier Using LANDSAT and SENTINEL2 Images

by Daniele Bocchiola, Francesco Chirico, Andrea Soncini, Roberto Sergio Azzoni, Guglielmina Adele Diolaiuti and Antonella Senese

Remote Sens. 2022, 14(1), 52; https://doi.org/10.3390/rs14010052 - 23 Dec 2021

Viewed by 4100

Abstract

We mapped flow velocity and calving rates of the iconic Perito Moreno Glacier (PMG), belonging to the Southern Patagonian Icefield (SPI) in the Argentinian Patagonia. We tracked PMG from 2001 to 2017, focusing mostly upon the latest images from 2016–2017. PMG delivers about ca. 10⁶ m³ day⁻¹ of ice in the Lago Argentino, and its front periodically reaches the Peninsula Magallanes. Therein, the PMG causes an ice-dam, clogging Brazo Rico channel, and lifting water level by about 10 m, until ice-dam failure, normally occurring in March. Here, we used 36 pairs of satellite images with a resolution of 10 m (SENTINEL2, visible, 9 pairs of images) and 15 m (LANDSAT imagery, panchromatic, 27 pairs of images) to calculate surface velocity (V_S). We used Orientation Correlation technique, implemented via the ImGRAFT^® TemplateMatch tool. Calving rates were then calculated with two methods, namely, (i) M1, by ice flow through the glacier front, and (ii) M2, by ice flow at 7.5 km upstream of the front minus ablation losses. Surface velocity ranged from about 4 m day⁻¹ in the accumulation area to about 2 m day⁻¹ in the calving front, but it is variable seasonally with maxima in the summer (December–January–February). Calving rate (CR_M) ranges from 7.72 × 10⁵ ± 32% to 8.76 × 10⁵ ± 31% m³ day⁻¹, in line with recent studies, also with maxima in the summer. We found slightly lower flow velocity and calving rates than previously published values, but our estimates cover a different period, and a generally large uncertainty in flow assessment suggests a recent overall stability of the glacier. Full article

(This article belongs to the Special Issue Recent Advances in Cryospheric Sciences)

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26 pages, 11840 KiB

Open AccessArticle

Snow Cover Variability in the Greater Alpine Region in the MODIS Era (2000–2019)

by Davide Fugazza, Veronica Manara, Antonella Senese, Guglielmina Diolaiuti and Maurizio Maugeri

Remote Sens. 2021, 13(15), 2945; https://doi.org/10.3390/rs13152945 - 27 Jul 2021

Cited by 19 | Viewed by 2810

Abstract

Snow cover is particularly important in the Alps for tourism and the production of hydroelectric energy. In this study, we investigate the spatiotemporal variability in three snow cover metrics, i.e., the length of season (LOS), start of season (SOS) and end of season (EOS), obtained by gap-filling of MOD10A1 and MYD10A1, daily snow cover products of MODIS (Moderate-resolution Imaging Spectroradiometer). We analyze the period 2000–2019, evaluate snow cover patterns in the greater Alpine region (GAR) as a whole and further subdivide it into four subregions based on geographical and climate divides to investigate the drivers of local variability. We found differences both in space and time, with the northeastern region having generally the highest LOS (74 ± 4 days), compared to the southern regions, which exhibit a much shorter snow duration (48/49 ± 2 days). Spatially, the variability in LOS and the other metrics is clearly related to elevation (r² = 0.85 for the LOS), while other topographic (slope, aspect and shading) and geographic variables (latitude and longitude) play a less important role at the MODIS scale. A high interannual variability was also observed from 2000 to 2019, as the average LOS in the GAR ranged between 41 and 85 days. As a result of high variability, no significant trends in snow cover metrics were seen over the GAR when considering all grid cells. Considering 500-m elevation bands and subregions, as well as individual grid points, we observed significant negative trends above 3000 m a.s.l., with an average of −17 days per decade. While some trends appeared to be caused by glacierized areas, removing grid cells covered by glaciers leads to an even higher frequency of grid cells with significant trends above 3000 m a.s.l., reaching 100% at 4000 m a.s.l. Trends are however to be considered with caution because of the limited length of the observation period. Full article

(This article belongs to the Special Issue Recent Advances in Cryospheric Sciences)

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28 pages, 12500 KiB

Open AccessArticle

Novel Machine Learning Method Integrating Ensemble Learning and Deep Learning for Mapping Debris-Covered Glaciers

by Yijie Lu, Zhen Zhang, Donghui Shangguan and Junhua Yang

Remote Sens. 2021, 13(13), 2595; https://doi.org/10.3390/rs13132595 - 2 Jul 2021

Cited by 27 | Viewed by 4491

Abstract

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. Full article

(This article belongs to the Special Issue Recent Advances in Cryospheric Sciences)

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19 pages, 2702 KiB

Open AccessArticle

Landsat 8 OLI Broadband Albedo Validation in Antarctica and Greenland

by Giacomo Traversa, Davide Fugazza, Antonella Senese and Massimo Frezzotti

Remote Sens. 2021, 13(4), 799; https://doi.org/10.3390/rs13040799 - 22 Feb 2021

Cited by 19 | Viewed by 5776

Abstract

The albedo is a fundamental component of the processes that govern the energy budget, and particularly important in the context of climate change. However, a satellite-based high-resolution (30 m) albedo product which can be used in the polar regions up to 82.5° latitude during the summer seasons is lacking. To cover this gap, in this study we calculate satellite-based broadband albedo from Landsat 8 OLI and validate it against broadband albedo measurements from in situ stations located on the Antarctic and Greenland icesheets. The model to derive the albedo from raw satellite data includes an atmospheric and topographic correction and conversion from narrow-band to broadband albedo, and at each step different options were taken into account, in order to provide the best combination of corrections. Results, after being cleaned from anomalous data, show a good agreement with in situ albedo measurements, with a mean absolute error between in situ and satellite albedo of 0.021, a root mean square error of 0.026, a standard deviation of 0.015, a correlation coefficient of 0.995 (p < 0.01) and a bias estimate of −0.005. Considering the structure of the model, it could be applied to data from previous sensors of the Landsat family and help construct a record to analyze albedo variations in the polar regions. Full article

(This article belongs to the Special Issue Recent Advances in Cryospheric Sciences)

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17 pages, 25614 KiB

Open AccessArticle

Mapping Frozen Ground in the Qilian Mountains in 2004–2019 Using Google Earth Engine Cloud Computing

by Yuan Qi, Shiwei Li, Youhua Ran, Hongwei Wang, Jichun Wu, Xihong Lian and Dongliang Luo

Remote Sens. 2021, 13(1), 149; https://doi.org/10.3390/rs13010149 - 5 Jan 2021

Cited by 13 | Viewed by 4541

Abstract

The permafrost in the Qilian Mountains (QLMs), the northeastern margin of the Qinghai–Tibet Plateau, changed dramatically in the context of climate warming and increasing anthropogenic activities, which poses significant influences on the stability of the ecosystem, water resources, and greenhouse gas cycles. Yet, the characteristics of the frozen ground in the QLMs are largely unclear regarding the spatial distribution of active layer thickness (ALT), the maximum frozen soil depth (MFSD), and the temperature at the top of the permafrost or the bottom of the MFSD (TTOP). In this study, we simulated the dynamics of the ALT, TTOP, and MFSD in the QLMs in 2004–2019 in the Google Earth Engine (GEE) platform. The widely-adopted Stefan Equation and TTOP model were modified to integrate with the moderate-resolution imaging spectroradiometer (MODIS) land surface temperature (LST) in GEE. The N-factors, the ratio of near-surface air to ground surface freezing and thawing indices, were assigned to the freezing and thawing indices derived with MODIS LST in considerations of the fractional vegetation cover derived from MODIS normalized difference vegetation index (NDVI). The results showed that the GEE platform and remote sensing imagery stored in Google cloud could be quickly and effectively applied to obtain the spatial and temporal variation of permafrost distribution. The area with TTOP < 0 °C is 8.4 × 10⁴ km² (excluding glaciers and lakes) and accounts for 46.6% of the whole QLMs, the regional mean ALT is 2.43 ± 0.44 m, while the regional mean MFSD is 2.54 ± 0.45 m. The TTOP and ALT increase with the decrease of elevation from the sources of the sub-watersheds to middle and lower reaches. There is a strong correlation between TTOP and elevation (slope = −1.76 °C km⁻¹, p < 0.001). During 2004–2019, the area of permafrost decreased by 20% at an average rate of 0.074 × 10⁴ km²·yr⁻¹. The regional mean MFSD decreased by 0.1 m at a rate of 0.63 cm·yr⁻¹, while the regional mean ALT showed an exception of a decreasing trend from 2.61 ± 0.45 m during 2004–2005 to 2.49 ± 0.4 m during 2011–2015. Permafrost loss in the QLMs in 2004–2019 was accelerated in comparison with that in the past several decades. Compared with published permafrost maps, this study shows better calculation results of frozen ground in the QLMs. Full article

(This article belongs to the Special Issue Recent Advances in Cryospheric Sciences)

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26 pages, 13648 KiB

Open AccessArticle

Comparison of Surface Solar Irradiance from Ground Observations and Satellite Data (1990–2016) over a Complex Orography Region (Piedmont—Northwest Italy)

by Veronica Manara, Elia Stocco, Michele Brunetti, Guglielmina Adele Diolaiuti, Davide Fugazza, Uwe Pfeifroth, Antonella Senese, Jörg Trentmann and Maurizio Maugeri

Remote Sens. 2020, 12(23), 3882; https://doi.org/10.3390/rs12233882 - 26 Nov 2020

Cited by 6 | Viewed by 2735

Abstract

Climate Monitoring Satellite Application Facility (CM SAF) surface solar irradiance (SSI) products were compared with ground-based observations over the Piedmont region (north-western Italy) for the period 1990–2016. These products were SARAH-2.1 (Surface Solar Radiation DataSet—Heliosat version 2.1) and CLARA-A2 (Cloud, Albedo and Surface Radiation dataset version A2). The aim was to contribute to the discussion on the representativeness of satellite SSI data including a focus on high-elevation areas. The comparison between SSI averages shows that for low OCI (orographic complexity index) stations, satellite series have higher values than corresponding ground-based observations, whereas for high OCI stations, SSI values for satellite records are mainly lower than for ground stations. The comparison between SSI anomalies highlights that satellite records have an excellent performance in capturing SSI day-to-day variability of ground-based low OCI stations. In contrast, for high OCI stations, the agreement is much lower, due to the higher uncertainty in both satellite and ground-based records. Finally, if the temporal trends are considered, average low-elevation ground-based SSI observations show a positive trend, whereas satellite records do not highlight significant trends. Focusing on high-elevation stations, the observed trends for ground-based and satellite records are more similar with the only exception of summer. This divergence seems to be due to the relevant role of atmospheric aerosols on SSI trends. Full article

(This article belongs to the Special Issue Recent Advances in Cryospheric Sciences)

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18 pages, 16846 KiB

Open AccessArticle

Comparing Measured Incoming Shortwave and Longwave Radiation on a Glacier Surface with Estimated Records from Satellite and Off-Glacier Observations: A Case Study for the Forni Glacier, Italy

by Antonella Senese, Veronica Manara, Maurizio Maugeri and Guglielmina Adele Diolaiuti

Remote Sens. 2020, 12(22), 3719; https://doi.org/10.3390/rs12223719 - 12 Nov 2020

Cited by 11 | Viewed by 2411

Abstract

The development of methods for quantifying meltwater from glaciated areas is very important for better management of water resources and because of the strong impact of current and expected climate change on the Alpine cryosphere. Radiative fluxes are the main melt-drivers, but they can generally not be derived from in situ measures because glaciers are usually located in remote areas where the number of meteorological stations is very low. For this reason, focusing, as a case study, on one of the few glaciers with a supraglacial automatic weather station (Forni Glacier), we investigated methods based on both satellite records and off-glacier surface observations to estimate incoming short- and long-wave radiation at the glacier surface (SW_in and LW_in). Specifically, for SW_in, we considered CM SAF SARAH satellite gridded surface solar irradiance fields and data modeled by cloud transmissivity parametrized from both CM SAF COMET satellite cloud fractional cover fields and daily temperature range observed at the closest off-glacier station. We then used the latter two data sources to derive LW_in too. Finally, we used the estimated SW_in and LW_in records to assess the errors obtained when introducing estimated rather than measured incoming radiation data to quantify glacier melting by means of an energy balance model. Our results suggest that estimated SW_in and LW_in records derived from satellite measures are in better agreement with in situ observations than estimated SW_in and LW_in records parametrized from observations performed at the closest off-glacier station. Moreover, we find that the former estimated records permit a significantly better quantification of glacier melting than the latter estimated ones. Full article

(This article belongs to the Special Issue Recent Advances in Cryospheric Sciences)

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19 pages, 11883 KiB

Open AccessArticle

Estimating Fractional Snow Cover in Open Terrain from Sentinel-2 Using the Normalized Difference Snow Index

by Simon Gascoin, Zacharie Barrou Dumont, César Deschamps-Berger, Florence Marti, Germain Salgues, Juan Ignacio López-Moreno, Jesús Revuelto, Timothée Michon, Paul Schattan and Olivier Hagolle

Remote Sens. 2020, 12(18), 2904; https://doi.org/10.3390/rs12182904 - 8 Sep 2020

Cited by 44 | Viewed by 9353

Abstract

Sentinel-2 provides the opportunity to map the snow cover at unprecedented spatial and temporal resolutions on a global scale. Here we calibrate and evaluate a simple empirical function to estimate the fractional snow cover (FSC) in open terrains using the normalized difference snow index (NDSI) from 20 m resolution Sentinel-2 images. The NDSI is computed from flat surface reflectance after masking cloud and snow-free areas. The NDSI–FSC function is calibrated using Pléiades very high-resolution images and evaluated using independent datasets including SPOT 6/7 satellite images, time lapse camera photographs, terrestrial lidar scans and crowd-sourced in situ measurements. The calibration results show that the FSC can be represented with a sigmoid-shaped function 0.5 × tanh(a × NDSI + b) + 0.5, where a = 2.65 and b = −1.42, yielding a root mean square error (RMSE) of 25%. Similar RMSE are obtained with different evaluation datasets with a high topographic variability. With this function, we estimate that the confidence interval on the FSC retrievals is 38% at the 95% confidence level. Full article

(This article belongs to the Special Issue Recent Advances in Cryospheric Sciences)

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23 pages, 6993 KiB

Open AccessArticle

Consistent Comparison of Remotely Sensed Sea Ice Concentration Products with ERA-Interim Reanalysis Data in Polar Regions

by Shuang Liang, Jiangyuan Zeng, Zhen Li, Dejing Qiao, Ping Zhang and Haiyun Bi

Remote Sens. 2020, 12(18), 2880; https://doi.org/10.3390/rs12182880 - 5 Sep 2020

Cited by 4 | Viewed by 3214

Abstract

Sea ice concentration (SIC) plays a significant role in climate change research and ship’s navigation in polar regions. Satellite-based SIC products have become increasingly abundant in recent years; however, the uncertainty of these products still exists and needs to be further investigated. To comprehensively evaluate the consistency of the SIC derived from different SIC algorithms in long time series and the whole polar regions, we compared four passive microwave (PM) satellite SIC products with the ERA-Interim sea ice fraction dataset during the period of 2015–2018. The PM SIC products include the SSMIS/ASI, AMSR2/BT, the Chinese FY3B/NT2, and FY3C/NT2. The results show that the remotely sensed SIC products derived from different SIC algorithms are generally in good consistency. The spatial and temporal distribution of discrepancy among satellite SIC products for both Arctic and Antarctic regions are also observed. The most noticeable difference for all the four SIC products mostly occurs in summer and at the marginal ice zone, indicating that large uncertainties exist in satellite SIC products in such period and areas. The SSMIS/ASI and AMSR2/BT show relatively better consistency with ERA-Interim in the Arctic and Antarctic, respectively, but they exhibit opposite bias (dry/wet) relative to the ERA-Interim data. The sea ice extent (SIE) and sea ice area (SIA) derived from PM and ERA-Interim SIC were also compared. It is found that the difference of PM SIE and SIA varies seasonally, which is in line with that of PM SIC, and the discrepancy between PM and ERA-Interim data is larger in Arctic than in Antarctic. We also noticed that different algorithms have different performances in different regions and periods; therefore, the hybrid of multiple algorithms is a promising way to improve the accuracy of SIC retrievals. It is expected that our findings can contribute to improving the satellite SIC algorithms and thus promote the application of these useful products in global climate change studies. Full article

(This article belongs to the Special Issue Recent Advances in Cryospheric Sciences)

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22 pages, 6252 KiB

Open AccessArticle

Estimation of Snow Depth in the Hindu Kush Himalayas of Afghanistan during Peak Winter and Early Melt Season

by Abdul Basir Mahmoodzada, Divyesh Varade and Sawahiko Shimada

Remote Sens. 2020, 12(17), 2788; https://doi.org/10.3390/rs12172788 - 27 Aug 2020

Cited by 18 | Viewed by 5780

Abstract

The Pamir ranges of the Hindu Kush regions in Afghanistan play a substantial role in regulating the water resources for the Middle Eastern countries. Particularly, the snowmelt runoff in the Khanabad watershed is one of the critical drivers for the Amu River, since it is a primary source of available water in several Middle Eastern countries in the off monsoon season. The purpose of this study is to devise strategies based on active microwave remote sensing for the monitoring of snow depth during the winter and the melt season. For the estimation of snow depth, we utilized a multi-temporal C-band (5.405 GHz) Sentinel-1 dual polarimetric synthetic aperture radar (SAR) with a differential interferometric SAR (DInSAR)-based framework. In the proposed approach, the estimated snowpack displacements in the vertical transmit-vertical receive (VV) and vertical transmit-horizonal receive (VH) channels were improved by incorporating modeled information of snow permittivity, and the scale was enhanced by utilizing snow depth information from the available ground stations. Two seasonal datasets were considered for the experiments corresponding to peak winter season (February 2019) and early melt season (March 2019). The results were validated with the available nearest field measurements. A good correlation determined by the coefficient of determination of 0.82 and 0.57, with root mean square errors of 2.33 and 1.44 m, for the peak winter and the early melt season, respectively, was observed between the snow depth estimates and the field measurements. Further, the snow depth estimates from the proposed approach were observed to be significantly better than the DInSAR displacements based on the correlation with respect to the field measurements. Full article

(This article belongs to the Special Issue Recent Advances in Cryospheric Sciences)

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23 pages, 5183 KiB

Open AccessArticle

Effects of Winter Snow Cover on Spring Soil Moisture Based on Remote Sensing Data Product over Farmland in Northeast China

by Shuang Liang, Xiaofeng Li, Xingming Zheng, Tao Jiang, Xiaojie Li and Dejing Qiao

Remote Sens. 2020, 12(17), 2716; https://doi.org/10.3390/rs12172716 - 22 Aug 2020

Cited by 16 | Viewed by 3207

Abstract

Spring soil moisture (SM) is of great importance for monitoring agricultural drought and waterlogging in farmland areas. While winter snow cover has an important impact on spring SM, relatively little research has examined the correlation between winter snow cover and spring SM in great detail. To understand the effects of snow cover on SM over farmland, the relationship between winter snow cover parameters (maximum snow depth (MSD) and average snow depth (ASD)) and spring SM in Northeast China was examined based on 30 year passive microwave snow depth (SD) and SM remote-sensing products. Linear regression models based on winter snow cover were established to predict spring SM. Moreover, 4 year SD and SM data were applied to validate the performance of the linear regression models. Additionally, the effects of meteorological factors on spring SM also were analyzed using multiparameter linear regression models. Finally, as a specific application, the best-performing model was used to predict the probability of spring drought and waterlogging in farmland in Northeast China. Our results illustrated the positive effects of winter snow cover on spring SM. The average correlation coefficient (R) of winter snow cover and spring SM was above 0.5 (significant at a 95% confidence level) over farmland. The performance of the relationship between snow cover and SM in April was better than that in May. Compared to the multiparameter linear regression models in terms of fitting coefficient, MSD can be used as an important snow parameter to predict spring drought and waterlogging probability in April. Specifically, if the relative SM threshold is 50% when spring drought occurs in April, the prediction probability of the linear regression model concerning snow cover and spring SM can reach 74%. This study improved our understanding of the effects of winter snow cover on spring SM and will be beneficial for further studies on the prediction of spring drought. Full article

(This article belongs to the Special Issue Recent Advances in Cryospheric Sciences)

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18 pages, 8545 KiB

Open AccessTechnical Note

Ten-Year Monitoring of the Grandes Jorasses Glaciers Kinematics. Limits, Potentialities, and Possible Applications of Different Monitoring Systems

by Niccolò Dematteis, Daniele Giordan, Fabrizio Troilo, Aleksandra Wrzesniak and Danilo Godone

Remote Sens. 2021, 13(15), 3005; https://doi.org/10.3390/rs13153005 - 30 Jul 2021

Cited by 13 | Viewed by 3034

Abstract

In the Ferret Valley (NW Italy), anthropic activities coexist close to the Grandes Jorasses massif’s glaciological complex. In the past, break-off events have caused damage to people and infrastructure. These events concerned two specific sectors: the Montitaz Lobe (Planpincieux Glacier) and the Whymper Serac (Grandes Jorasses Glacier). Since 2010, permanent and discontinuous survey campaigns have been conducted to identify potential failure precursors, investigate the glacier instability processes, and explore different monitoring approaches. Most of the existing terrestrial apparatuses that measure the surface kinematics have been adopted in the Grandes Jorasses area. The monitoring sites in this specific area are characterized by severe weather, complex geometry, logistic difficulties, and rapid processes dynamics. Such exceptional conditions highlighted the limitations and potentialities of the adopted monitoring approaches, including robotic total station (RTS), GNSS receivers, digital image correlation applied to time-lapse imagery, and terrestrial radar interferometry (TRI). We examined the measurement uncertainty of each system and their monitoring performances. We discussed their principal limitations and possible use for warning purposes. In the Grandes Jorasses area, the use of a time-lapse camera appeared to be a versatile and cost-effective solution, which, however is not suitable for warning applications, as it does not guarantee data continuity. RTS and GNSS have warning potentialities, but the target installation and maintenance in remote environments remain challenging. TRI is the most effective monitoring system for early warning purposes in such harsh conditions, as it provides near-real-time measurements. However, radar equipment is very costly and requires extreme logistic effort. In this framework, we present data integration strategies to overcome the abovementioned limits and we demonstrate that these strategies are optimal solutions to obtain data continuity and robustness. Full article

(This article belongs to the Special Issue Recent Advances in Cryospheric Sciences)

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