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Remote sensing of ice albedo using harmonized Landsat and Sentinel 2 datasets: validation
International Journal of Remote Sensing
Abstract
Albedo plays a key role in regulating the absorption of solar radiation within ice surfaces and hence strongly regulates the production of meltwater. A combination of Landsat and Sentinel 2 data provides the longest continuous medium resolution (10–30 m) earth surface observatory records. An albedo product (harmonized satellite albedo, hereafter HSA) has already been developed and validated for the Greenland Ice Sheet (GrIS), using harmonized Landsat 4–8 and Sentinel 2 datasets. In this paper, the HSA was validated for various Arctic and alpine glaciers and ice caps using in situ measurements. We determine the optimal spatial window size in point-to-pixel analysis, the best practices in evaluating remote sensing algorithms with groundtruth data, and cross sensor comparison of the Landsat 9 (L9) and Landsat 8 (L8) data. The impact of the spatial window size on measured ice surface homogeneity and albedo validation was analysed at both local and regional scales. Homogeneity statistics calculated from the grey-level co-occurrence matrix (GLCM) suggest that the ice surface becomes more homogeneous as the image resolution becomes coarser. The optimal spatial window size was found to be 90 m, based on maximizing the statistical and graphical measures while minimizing the root mean square error and bias. HSAs generally agree closely with in situ albedo measurements (e.g. Pearson’s R ranges from 0.68 to 0.92) across various Arctic and alpine glaciers and ice caps. Cross sensor differences between L9 and L8 are minor, and we suggest that no harmonization is necessary to add L9 to our HSA product.
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Cryoconite holes (CHs) are water-filled cylindrical holes with cryoconite (dark-coloured sediment) deposited at their bottoms, forming on ablating ice surfaces of glaciers and ice sheets worldwide. Because the collapse of CHs may disperse cryoconite on the ice surface, thereby decreasing the ice surface albedo, accurate simulation of the temporal changes in CH depth is essential for understanding ice surface melt. We established a novel model that simulates the temporal changes in CH depth using heat budgets calculated independently at the ice surface and CH bottom based on hole-shaped geometry. We evaluated the model with in situ observations of the CH depths on the Qaanaaq ice cap in northwestern Greenland during the 2012, 2014, and 2017 melt seasons. The model reproduced the observed depth changes and timing of CH collapse well. Although earlier models have shown that CH depth tends to be deeper when downward shortwave radiation is intense, our sensitivity tests suggest that deeper CH tends to form when the diffuse component of downward shortwave radiation is dominant, whereas CHs tend to be shallower when the direct component is dominant. In addition, the total heat flux to the CH bottom is dominated by shortwave radiation transmitted through ice rather than that directly from the CH mouths when the CH is deeper than 0.01 m. Because the shortwave radiation transmitted through ice can reach the CH bottom regardless of CH diameter, CH depth is unlikely to be correlated with CH diameter. The relationship is consistent with previous observational studies. Furthermore, the simulations highlighted that the difference in albedo between ice surface and CH bottom was a key factor for reproducing the timing of CH collapse. It implies that lower ice surface albedo could induce CH collapse and thus cause further lowering of the albedo. Heat component analysis suggests that CH depth is governed by the balance between the intensity of the diffuse component of downward shortwave radiation and the turbulent heat transfer. Therefore, these meteorological conditions may be important factors contributing to the recent surface darkening of the Greenland ice sheet and other glaciers via the redistribution of CHs.
Albedo is a key factor in modulating the absorption of solar radiation on ice surfaces. Satellite measurements have shown a general reduction in albedo across the Greenland ice sheet over the past few decades, particularly along the western margin of the ice sheet, a region known as the Dark Zone (albedo < 0.45). Here we chose a combination of Landsat 4–8 and Sentinel 2 imagery to enable us to derive the longest record of albedo variations in the Dark Zone, running from 1984 to 2020. We developed a simple, pragmatic and efficient sensor transformation to provide a long time series of consistent, harmonized satellite imagery. Narrow to broadband conversion algorithms were developed from regression models of harmonized satellite data and in situ albedo from the Program for Monitoring of the Greenland Ice Sheet (PROMICE) automatic weather stations. The albedo derived from the harmonized Landsat and Sentinel 2 data shows that the maximum extent of the Dark Zone expanded rapidly between 2005 and 2007, increasing to ~280% of the average annual maximum extent of 2900 km ² to ~8000 km ² since. The Dark Zone is continuing to darken slowly, with the average annual minimum albedo decreasing at a rate of ( p = 0.16, 2001–2020).
Blooms of pigmented algae darken the surface of glaciers and ice sheets, thereby enhancing solar energy absorption and amplifying ice and snow melt. The impacts of algal pigment and community composition on surface darkening are still poorly understood. Here, we characterise glacier ice and snow algal pigment signatures on snow and bare ice surfaces and study their role in photophysiology and energy absorption on three glaciers in Southeast Greenland. Purpurogallin and astaxanthin esters dominated the glacier ice and snow algal pigment pools (mass ratios to chlorophyll a of 32 and 56, respectively). Algal biomass and pigments impacted chromophoric dissolved organic matter concentrations. Despite the effective absorption of astaxanthin esters at wavelengths where incoming irradiance peaks, the cellular energy absorption of snow algae was 95% lower than anticipated from their pigmentation, due to pigment packaging. The energy absorption of glacier ice algae was consequently ~ 5 × higher. On bare ice, snow algae may have locally contributed up to 13% to total biological radiative forcing, despite contributing 44% to total biomass. Our results give new insights into the impact of algal community composition on bare ice energy absorption and biomass accumulation during snow melt.
Glacier phototroph blooms on the surfaces of ice sheets and glaciers cause albedo reduction, leading to increased melting rates. We observed seasonal changes in the abundance of phototrophs on the Qaanaaq Ice Cap in northwestern Greenland from June to August 2014, and reproduced these changes using numerical and empirical models. The phototroph community on the ice surface mainly consisted of the glacier alga Ancylonema nordenskioldii and the cyanobacterium Phormidesmis priestleyi . The glacier alga appeared on the ice surface in late June, after which its abundance increased exponentially throughout the melting period. A logistic growth model designed for snow algal growth reproduced the measured exponential increases, suggesting that growth could be explained using the model as a function of the ice melting duration. Cyanobacteria appeared and their abundance increased in late July but did not change exponentially thereafter. The abundance of cyanobacteria was explained with an empirical model expressed as a function of the amount of mineral dust on the bare ice surface. Our numerical and empirical models for reproducing glacier algae and cyanobacteria could be useful for quantifying the albedo reduction caused by their growth and the melt rates of the Greenland ice sheet and glaciers in the future.
Pigmented microalgae inhabiting snow and ice environments lower the albedo of glacier and ice-sheet surfaces, significantly enhancing surface melt. Our ability to accurately predict their role in glacier and ice-sheet surface mass balance is limited by the current lack of empirical data to constrain their representation in predictive models. Here we present new empirical optical properties for snow and ice algae and incorporate them in a radiative transfer model to investigate their impact on snow and ice surface albedo. We found ice algal cells to be more efficient absorbers than snow algal cells, but their blooms had comparable impact on surface albedo due to the different photic conditions of their habitats. We then used the model to reconstruct the effect of ice algae on bare ice albedo spectra collected at our field site in southern Greenland, where blooms dropped the albedo locally by between 3 and 43%, equivalent to 1–10 L m d of melted ice. Using the newly parametrized model, future studies could investigate biological albedo reduction and algal quantification from remote hyperspectral and multispectral imagery.
Since 1972, the Landsat program has been continually monitoring the Earth, to now provide 50 years of digital, multispectral, medium spatial resolution observations. Over this time, Landsat data were crucial for many scientific and technical advances. Prior to the Landsat program, detailed, synoptic depictions of the Earth's surface were rare, and the ability to acquire and work with large datasets was limited. The early years of the Landsat program delivered a series of technological breakthroughs, pioneering new methods, and demonstrating the ability and capacity of digital satellite imagery, creating a template for other global Earth observation missions and programs. Innovations driven by the Landsat program have paved the way for subsequent science, application , and policy support activities. The economic and scientific value of the knowledge gained through the Landsat program has been long recognized, and despite periods of funding uncertainty, has resulted in the program's 50 years of continuity, as well as substantive and ongoing improvements to payload and mission performance. Free and open access to Landsat data, enacted in 2008, was unprecedented for medium spatial resolution Earth observation data and substantially increased usage and led to a proliferation of science and application opportunities. Here, we highlight key developments over the past 50 years of the Landsat program that have influenced and changed our scientific understanding of the Earth system. Major scientific and pro-grammatic impacts have been realized in the areas of agricultural crop mapping and water use, climate change 2 drivers and impacts, ecosystems and land cover monitoring, and mapping the changing human footprint. The introduction of Landsat collection processing, coupled with the free and open data policy, facilitated a transition in Landsat data usage away from single images and towards time series analyses over large areas and has fostered the widespread use of science-grade data. The launch of Landsat-9 on September 27, 2021, and the advanced planning of its successor mission, Landsat-Next, underscore the sustained institutional support for the program. Such support and commitment to continuity is recognition of both the historic impact the program, and the future potential to build upon Landsat's remarkable 50-year legacy.
Ice surface albedo is a primary modulator of melt and runoff, yet our understanding of how reflectance varies over time across the Greenland Ice Sheet remains poor. This is due to a disconnect between point or transect scale albedo sampling and the coarser spatial, spectral and/or temporal resolutions of available satellite products. Here, we present time-series of bare-ice surface reflectance data that span a range of length scales, from the 500 m for Moderate Resolution Imaging Spectrometer’s MOD10A1 product, to 10 m for Sentinel-2 imagery, 0.1 m spot measurements from ground-based field spectrometry, and 2.5 cm from uncrewed aerial drone imagery. Our results reveal broad similarities in seasonal patterns in bare-ice reflectance, but further analysis identifies short-term dynamics in reflectance distribution that are unique to each dataset. Using these distributions, we demonstrate that areal mean reflectance is the primary control on local ablation rates, and that the spatial distribution of specific ice types and impurities is secondary. Given the rapid changes in mean reflectance observed in the datasets presented, we propose that albedo parameterizations can be improved by (i) quantitative assessment of the representativeness of time-averaged reflectance data products, and, (ii) using temporally-resolved functions to describe the variability in impurity distribution at daily time-scales. We conclude that the regional melt model performance may not be optimally improved by increased spatial resolution and the incorporation of sub-pixel heterogeneity, but instead, should focus on the temporal dynamics of bare-ice albedo.
An increase in the temporal revisit of satellite data is often sought to increase the likelihood of obtaining cloud- and shadow-free observations as well as to improve mapping of rapidly- or seasonally-changing features. Currently, as a tandem, Landsat-7 Enhanced Thematic Mapper Plus (ETM+) and −8 Operational Land Imager (OLI) provide an acquisition opportunity on an 8-day revisit interval. Sentinel-2A and -2B MultiSpectral Instrument (MSI), with a wider swath, have a 5-day revisit interval at the equator. Due to robust pre- and post-launch cross-calibration, it has been possible for NASA to produce the Harmonized Landsat Sentinel-2 (HLS) data product from Landsat-8 OLI and Sentinel-2 MSI: L30 and S30, respectively. Knowledge of the agreement of HLS outputs (especially S30) with historic Landsat surface reflectance products will inform the ability to integrate historic time-series information with new and more frequent measures as delivered by HLS. In this research, we control for acquisition date and data source to cross-compare the HLS data (L30, S30) with established Landsat-8 OLI surface-reflectance measures as delivered by the USGS (hereafter BAP, Best Available Pixel). S30 and L30 were found to have high agreement (R = 0.87–0.96) for spectral channels and an r = 0.99 for Normalized Burn Ratio (NBR) with low relative root-mean-square difference values (1.7%–3.3%). Agreement between L30 and BAP was lower, with R values ranging from 0.85 to 0.92 for spectral channels and R = 0.94 for NBR. S30 and BAP had the lowest agreement, with R values ranging from 0.71 to 0.85 for spectral channels and r = 0.90 for NBR. Comparisons indicated a stronger agreement at latitudes above 55° N. Some dependency between spectral agreement and land cover was found, with stronger correspondence for non-vegetated cover types. The level of agreement between S30 and BAP reported herein would enable integration of HLS outputs with historic Landsat data. The resulting increased temporal frequency of data allows for improvements to current cloud screening practices and increases data density and the likelihood of temporal proximity to target date for pixel compositing approaches. Furthermore, additional within-year observations will enable change products with a higher temporal fidelity and allow for the incorporation of phenological trends into land cover classification algorithms.
Background
Determining the appropriate window size is a critical step in the estimation process of stand structural variables based on remote sensing data. Because the value of the reference laser and image metrics that affect the quality of the prediction model depends on window size. However, suitable window sizes are usually determined by trial and error. There are a limited number of published studies evaluating appropriate window sizes for different remote sensing data. This research investigated the effect of window size on predicting forest structural variables using airborne LiDAR data, digital aerial image and WorldView-3 satellite image.
Results
In the WorldView-3 and digital aerial image, significant differences were observed in the prediction accuracies of the structural variables according to different window sizes. For the estimation based on WorldView-3 in black pine stands, the optimal window sizes for stem number (N), volume (V), basal area (BA) and mean height (H) were determined as 1000 m ² , 100 m ² , 100 m ² and 600 m ² , respectively. In oak stands, the R ² values of each moving window size were almost identical for N and BA. The optimal window size was 400 m ² for V and 600 m ² for H. For the estimation based on aerial image in black pine stands, the 800 m ² window size was optimal for N and H, the 600 m ² window size was optimal for V and the 1000 m ² window size was optimal for BA. In the oak stands, the optimal window sizes for N, V, BA and H were determined as 1000 m ² , 100 m ² , 100 m ² and 600 m ² , respectively. The optimal window sizes may need to be scaled up or down to match the stand canopy components. In the LiDAR data, the R ² values of each window size were almost identical for all variables of the black pine and the oak stands.
Conclusion
This study illustrated that the window size has an effect on the prediction accuracy in estimating forest structural variables based on remote sensing data. Moreover, the results showed that the optimal window size for forest structural variables varies according to remote sensing data and tree species composition.
Since 2017, the orbit of Landsat 7 has drifted outside its nominal mission requirement toward an earlier acquisition time because of limited onboard fuel resources. This makes quantitative analyses from Landsat 7 data potentially unreliable for many scientific studies. To comprehensively understand the effect of ongoing (2018–2020) orbit drift on Landsat 7 data, we compared surface reflectance and Top-Of-Atmosphere (TOA) reflectance of growing season observations (July 1 ± 30 days) from Landsat 7 with orbit drift and Landsat 8 with nominal orbit using a total of 10,000 randomly selected Northern Hemisphere (0–75⁰ N) terrestrial pixels. To evaluate the future (2021–2023) effect of Landsat 7's orbit drift, we analyzed the historical Northern Hemisphere terrestrial growing season Earth Observing-1 (EO-1) TOA reflectance images, which shared a similar orbit drift as Landsat 7 but occurred much earlier. Results suggest that Landsat 7's orbit drift has already led to a general decrease in surface reflectance and TOA reflectance in 2019 and 2020, with a limited impact (overall reflectance changes less than 0.007). The influence of orbit drift is more substantial for the two shortwave infrared (SWIR) bands and the near infrared (NIR) band, but less for the three visible bands (i.e., Red, Green, and Blue). The Normalized Difference Vegetation Index (NDVI), derived from either surface reflectance or TOA reflectance, increased less than 0.003 in 2020. According to the historical EO-1 TOA reflectance data, we estimate that the effect of Landsat 7's orbit drift will be much more dramatic in the future (e.g., the NIR and SWIR bands will decrease more than 0.015 since July 1, 2021), and for different land cover types, the effects of orbit drift are also quite different. To reduce this influence, we examined the c-factor Bidirectional Reflectance Distribution Function (BRDF) normalization approach to correct the orbit drift impact for Landsat 7 surface reflectance data collected between 2019 and 2020. We found that the c-factor BRDF can reduce the data difference substantially, but how this approach works after Landsat 7's orbit drifts further still requires more investigation. Therefore, we recommended that Landsat 7 can preserve its science capability until 2020, but will be less reliable for remote sensing applications that need accurate absolute radiometric values after 2020. Correction methods such as c-factor BRDF could be a potential viable approach to maintain its science capability going forward.
The Programme for Monitoring of the Greenland Ice Sheet (PROMICE) has been measuring climate and ice sheet properties since 2007. Currently, the PROMICE automatic weather station network includes 25 instrumented sites in Greenland. Accurate measurements of the surface and near-surface atmospheric conditions in a changing climate are important for reliable present and future assessment of changes in the Greenland Ice Sheet. Here, we present the PROMICE vision, methodology, and each link in the production chain for obtaining and sharing quality-checked data. In this paper, we mainly focus on the critical components for calculating the surface energy balance and surface mass balance. A user-contributable dynamic web-based database of known data quality issues is associated with the data products at https://github.com/GEUS-Glaciology-and-Climate/PROMICE-AWS-data-issues/ (last access: 7 April 2021). As part of the living data option, the datasets presented and described here are available at 10.22008/promice/data/aws.
We evaluated the performance of a variety of time series models, that include the harmonic (HR) model, autoregressive (AR) model, linear Gaussian state-space (LGSS) model, cubic spline (SP) model, double logistic (DL) model, and asymmetric Gaussian (AG) model, for reconstructing (all six models) and forecasting (HR, AR, and LGSS models) dense Landsats 5–7 time series based on 4562 samples. To remove the impact of land change and human interventions on data reconstruction and forecasting, this evaluation excluded croplands and samples changed between 2000 and 2011. Results show that the widely used HR model is not a good model for data reconstruction but outperforms other models in data forecasting. The DL and AG models have the best performance in data reconstruction but cannot forecast Landsat observations. The AR and LGSS models shared similar performance in reconstructing Landsat data but are less ideal for data forecasting, particularly for the LGSS model. Integrating the HR (for forecasting) and DL or AG (for reconstruction) is recommended to improve land change detection and land cover classification results. We also evaluated the impact of data density and irregularity on reconstructing and forecasting Landsat observations. When the data density is low (
Glacier albedo determines the net shortwave radiation absorbed at the glacier surface and plays a crucial role in glacier energy and mass balance. Remote sensing techniques are efficient means to retrieve glacier surface albedo over large and inaccessible areas and to study its variability. However, corrections of anisotropic reflectance of glacier surface have been established for specific shortwave bands only, such as Landsat 5 Thematic Mapper (L5/TM) band 2 and band 4, which is a major limitation of current retrievals of glacier broadband albedo. In this study, we calibrated and evaluated four anisotropy correction models for glacier snow and ice, applicable to visible, near-infrared and shortwave-infrared wavelengths using airborne datasets of Bidirectional Reflectance Distribution Function (BRDF). We then tested the ability of the best-performing anisotropy correction model, referred to from here on as the ‘updated model’, to retrieve albedo from L5/TM, Landsat 8 Operational Land Imager (L8/OLI) and Moderate Resolution Imaging Spectroradiometer (MODIS) imagery, and evaluated these results with field measurements collected on eight glaciers around the world. Our results show that the updated model: (1) can accurately estimate anisotropic factors of reflectance for snow and ice surfaces; (2) generally performs better than prior approaches for L8/OLI albedo retrieval but is not appropriate for L5/TM; (3) generally retrieves MODIS albedo better than the MODIS standard albedo product (MCD43A3) in both absolute values and glacier albedo temporal evolution, i.e., exhibiting both fewer gaps and better agreement with field observations. As the updated model enables anisotropy correction of a maximum of 10 multispectral bands and is implemented in Google Earth Engine (GEE), it is promising for observing and analyzing glacier albedo at large spatial scales.
The Programme for Monitoring of the Greenland Ice Sheet (PROMICE) provides surface meteorological and glaciological measurements from widespread on-ice automatic weather stations since mid-2007. In this study, we use 105 PROMICE ice-ablation time series to identify the timing of seasonal bare-ice onset preceded by snow cover conditions. From this collection, we find a bare-ice albedo at ice-ablation onset (here called bare-ice-onset albedo) of 0.565 ± 0.109 that has no apparent spatial dependence among 20 sites across Greenland. We then apply this snow-to-ice albedo transition value to measure the variations in daily Greenland bare-ice area in Sentinel-3 optical satellite imagery covering the extremely low and high respective melt years of 2018 and 2019. Daily Greenland bare-ice area peaked at 153 489 km² in 2019, 1.9 times larger than in 2018 (80 220 km²), equating to 9.0% (in 2019) and 4.7% (in 2018) of the ice sheet area.
The history of Earth observation from space is well reflected through the Landsat program. With data collection beginning with Landsat-1 in 1972, the program has evolved technical capabilities while maintaining continuity of land observations. In so doing, Landsat has provided a critical reference for assessing long-term changes to Earth's land environment due to both natural and human forcing. Poised for launch in mid-2021, the joint NASA-USGS Landsat 9 mission will continue this important data record. In many respects Landsat 9 is a clone of Landsat-8. The Operational Land Imager-2 (OLI-2) is largely identical to Landsat 8 OLI, providing calibrated imagery covering the solar reflected wavelengths. The Thermal Infrared Sensor-2 (TIRS-2) improves upon Landsat 8 TIRS, addressing known issues including stray light incursion and a malfunction of the instrument scene select mirror. In addition, Landsat 9 adds redundancy to TIRS-2, thus upgrading the instrument to a 5-year design life commensurate with other elements of the mission. Initial performance testing of OLI-2 and TIRS-2 indicate that the instruments are of excellent quality and expected to match or improve on Landsat 8 data quality. Landsat-9 will maintain the current data acquisition rate of up to 740 scenes per day, with these scenes available from the Landsat archive at no cost to users. In this communication, we provide background and rationale for the Landsat 9 mission, describe the instrument payloads and ground system, and discuss data products available from the Landsat 9 mission through USGS.
We examine the spatial patterns of near-surface air temperature (T a) over a melting glacier using a multi-annual dataset from McCall Glacier, Alaska. The dataset consists of a 10-year (2005-2014) meteorological record along the glacier centreline up to an upper glacier cirque, spanning an elevation difference of 900 m. We test the validity of on-glacier linear lapse rates, and a model that calculates T a based on the influence of katabatic winds and other heat sources along the glacier flow line. During the coldest hours of each summer (10% of time), average lapse rates across the entire glacier range from −4.7 to −6.7°C km −1 , with a strong relationship between T a and elevation (R 2 > 0.7). During warm conditions, T a shows more complex, non-linear patterns that are better explained by the flow line-dependent model, reducing errors by up to 0.5°C compared with linear lapse rates, although more uncertainty might be associated with these observations due to occasionally poor sensor ventilation. We conclude that T a spatial distribution can vary significantly from year to year, and from one glacier section to another. Importantly, extra-polations using linear lapse rates from the ablation zone might lead to large underestimations of T a on the upper glacier areas.
Melting of the Greenland Ice Sheet (GrIS) is the largest single contributor to eustatic sea level and is amplified by the growth of pigmented algae on the ice surface, which increases solar radiation absorption. This biological albedo-reducing effect and its impact upon sea level rise has not previously been quantified. Here, we combine field spectroscopy with a radiative-transfer model, supervised classification of unmanned aerial vehicle (UAV) and satellite remote-sensing data, and runoff modelling to calculate biologically driven ice surface ablation. We demonstrate that algal growth led to an additional 4.4–6.0 Gt of runoff from bare ice in the south-western sector of the GrIS in summer 2017, representing 10 %–13 % of the total. In localized patches with high biomass accumulation, algae accelerated melting by up to 26.15±3.77 % (standard error, SE). The year 2017 was a high-albedo year, so we also extended our analysis to the particularly low-albedo 2016 melt season. The runoff from the south-western bare-ice zone attributed to algae was much higher in 2016 at 8.8–12.2 Gt, although the proportion of the total runoff contributed by algae was similar at 9 %–13 %. Across a 10 000 km2 area around our field site, algae covered similar proportions of the exposed bare ice zone in both years (57.99 % in 2016 and 58.89 % in 2017), but more of the algal ice was classed as “high biomass” in 2016 (8.35 %) than 2017 (2.54 %). This interannual comparison demonstrates a positive feedback where more widespread, higher-biomass algal blooms are expected to form in high-melt years where the winter snowpack retreats further and earlier, providing a larger area for bloom development and also enhancing the provision of nutrients and liquid water liberated from melting ice. Our analysis confirms the importance of this biological albedo feedback and that its omission from predictive models leads to the systematic underestimation of Greenland's future sea level contribution, especially because both the bare-ice zones available for algal colonization and the length of the biological growth season are set to expand in the future.
We present a simplified atmospheric correction algorithm for snow/ice albedo retrievals using single view satellite measurements. The validation of the technique is performed using Ocean and Land Colour Instrument (OLCI) on board Copernicus Sentinel-3 satellite and ground spectral or broadband albedo measurements from locations on the Greenland ice sheet and in the French Alps. Through comparison with independent ground observations, the technique is shown to perform accurately in a range of conditions from a 2100 m elevation mid-latitude location in the French Alps to a network of 15 locations across a 2390 m elevation range in seven regions across the Greenland ice sheet. Retrieved broadband albedo is accurate within 5% over a wide (0.5) broadband albedo range of the (N = 4155) Greenland observations and with no apparent bias.
The climate in Svalbard is undergoing amplified change compared to the global mean. This has major implications for runoff from glaciers and seasonal snow on land. We use a coupled energy balance–subsurface model, forced with downscaled regional climate model fields, and apply it to both glacier-covered and land areas in Svalbard. This generates a long-term (1957–2018) distributed dataset of climatic mass balance (CMB) for the glaciers, snow conditions, and runoff with a 1km×1km spatial and 3-hourly temporal resolution. Observational data including stake measurements, automatic weather station data, and subsurface data across Svalbard are used for model calibration and validation. We find a weakly positive mean net CMB (+0.09 m w.e. a-1) over the simulation period, which only fractionally compensates for mass loss through calving. Pronounced warming and a small precipitation increase lead to a spatial-mean negative net CMB trend (-0.06 m w.e. a-1 decade-1), and an increase in the equilibrium line altitude (ELA) by 17 m decade-1, with the largest changes in southern and central Svalbard. The retreating ELA in turn causes firn air volume to decrease by 4 % decade-1, which in combination with winter warming induces a substantial reduction of refreezing in both glacier-covered and land areas (average -4 % decade-1). A combination of increased melt and reduced refreezing causes glacier runoff (average 34.3 Gt a-1) to double over the simulation period, while discharge from land (average 10.6 Gt a-1) remains nearly unchanged. As a result, the relative contribution of land runoff to total runoff drops from 30 % to 20 % during 1957–2018. Seasonal snow on land and in glacier ablation zones is found to arrive later in autumn (+1.4 d decade-1), while no significant changes occurred on the date of snow disappearance in spring–summer. Altogether, the output of the simulation provides an extensive dataset that may be of use in a wide range of applications ranging from runoff modelling to ecosystem studies.
Snow algal bloom is a common phenomenon on melting snowpacks in polar and alpine regions and can substantially increase snow melt rates due to the effect of albedo reduction on the snow surface. In order to reproduce algal growth on the snow surface using a numerical model, temporal changes in snow algal abundance were investigated on the Qaanaaq Glacier in north-western Greenland from June to August 2014. Snow algae first appeared at the study sites in late June, which was approximately 94 h after air temperatures exceeded the melting point. Algal abundance increased exponentially after this appearance, but the increasing rate became slow after late July, and finally reached 3.5 × 107 cells m-2 in early August. We applied a logistic model to the algal growth curve and found that the algae could be reproduced with an initial cell concentration of 6.9 × 102 cells m-2, a growth rate of 0.42 d-1, and a carrying capacity of 3.5 × 107 cells m-2 on this glacier. This model has the potential to simulate algal blooms from meteorological data sets and to evaluate their impact on the melting of seasonal snowpacks and glaciers.
A multilayer, daily ice surface temperature (IST)-albedo-water vapor product of Greenland, extending from March 2000 through December 2016, has been developed using standard MODerate-resolution Imaging Spectroradiometer (MODIS) data products from the Terra satellite. To meet the needs of the ice sheet modeling community, this new Earth Science Data Record (ESDR) is provided in a polar stereographic projection in NetCDF format, and includes the existing standard MODIS Collection 6.1 IST and derived melt maps, and Collection 6 snow albedo and water vapor maps, along with ancillary data, and is provided at a spatial resolution of ~0.78 km. This ESDR enables relationships between IST, surface melt, albedo, and water vapor to be evaluated easily. We show examples of the components of the ESDR and describe some uses of the ESDR such as for comparison with skin temperature, albedo, and water vapor output from Modern Era Retrospective analysis for Research and Applications, Version 2 (MERRA-2). Additionally, we show validation of the MODIS IST using in situ and aircraft data, and validation of MERRA-2 skin temperature maps using MODIS IST and in situ data. The ESDR has been assigned a DOI and will be available through the National Snow and Ice Data Center by the summer of 2018.
In this study we analyse a 15-year long time series of surface mass-balance (SMB) measurements performed between 2001 and 2016 in the ablation zone of the Morteratsch glacier complex (Engadine, Switzerland). For a better understanding of the SMB variability and its causes, multiple linear regressions analyses are performed with temperature and precipitation series from nearby meteorological stations. Up to 85% of the observed SMB variance can be explained by the mean May–June–July temperature and the total precipitation from October to March. A new method is presented where the contribution of each month's individual temperature and precipitation to the SMB can be examined in a total sample of 2 ²⁴ (16.8 million) combinations. More than 90% of the observed SMB can be explained with particular combinations, in which the May–June–July temperature is the most recurrent, followed by October temperature. The role of precipitation is less pronounced, but autumn, winter and spring precipitation are always more important than summer precipitation. Our results indicate that the length of the ice ablation season is of larger importance than its intensity to explain year-to-year variations. The widely used June–July–August temperature index may not always be the best option to describe SMB variability through statistical correlation.
The Arctic is being disproportionally affected by climate change compared with other geographic locations, and is currently experiencing unprecedented melt rates. The Greenland Ice Sheet (GrIS) can be regarded as the largest supraglacial ecosystem on Earth, and ice algae are the dominant primary producers on bare ice surfaces throughout the course of a melt season. Ice-algal-derived pigments cause a darkening of the ice surface, which in turn decreases albedo and increases melt rates. The important role of ice algae in changing melt rates has only recently been recognized, and we currently know little about their community compositions and functions. Here, we present the first analysis of ice algal communities across a 100 km transect on the GrIS by high-throughput sequencing and subsequent oligotyping of the most abundant taxa. Our data reveal an extremely low algal diversity with Ancylonema nordenskiöldii and a Mesotaenium species being by far the dominant taxa at all sites. We employed an oligotyping approach and revealed a hidden diversity not detectable by conventional clustering of operational taxonomic units and taxonomic classification. Oligotypes of the dominant taxa exhibit a site-specific distribution, which may be linked to differences in temperatures and subsequently the extent of the melting. Our results help to better understand the distribution patterns of ice algal communities that play a crucial role in the GrIS ecosystem.
Albedo-a primary control on surface melt-varies considerably across the Greenland Ice Sheet yet the specific surface types that comprise its dark zone remain unquantified. Here we use UAV imagery to attribute seven distinct surface types to observed albedo along a 25 km transect dissecting the western, ablating sector of the ice sheet. Our results demonstrate that distributed surface impurities-an admixture of dust, black carbon and pigmented algae-explain 73% of the observed spatial variability in albedo and are responsible for the dark zone itself. Crevassing and supraglacial water also drive albedo reduction but due to their limited extent, explain just 12 and 15% of the observed variability respectively. Cryoconite, concentrated in large holes or fluvial deposits, is the darkest surface type but accounts for <1% of the area and has minimal impact. We propose that the ongoing emergence and dispersal of distributed impurities, amplified by enhanced ablation and biological activity, will drive future expansion of Greenland's dark zone.
The darkening effects of biological impurities on ice and snow have been recognised as a control on the surface energy balance of terrestrial snow, sea ice, glaciers and ice sheets. With a heightened interest in understanding the impacts of a changing climate on snow and ice processes, quantifying the impact of biological impurities on ice and snow albedo (“bioalbedo”) and its evolution through time is a rapidly growing field of research. However, rigorous quantification of bioalbedo has remained elusive because of difficulties in isolating the biological contribution to ice albedo from that of inorganic impurities and the variable optical properties of the ice itself. For this reason, isolation of the biological signature in reflectance data obtained from aerial/orbital platforms has not been achieved, even when ground-based biological measurements have been available. This paper provides the cell-specific optical properties that are required to model the spectral signatures and broadband darkening of ice. Applying radiative transfer theory, these properties provide the physical basis needed to link biological and glaciological ground measurements with remotely sensed reflectance data. Using these new capabilities we confirm that biological impurities can influence ice albedo, then we identify 10 challenges to the measurement of bioalbedo in the field with the aim of improving future experimental designs to better quantify bioalbedo feedbacks. These challenges are (1) ambiguity in terminology, (2) characterising snow or ice optical properties, (3) characterising solar irradiance, (4) determining optical properties of cells, (5) measuring biomass, (6) characterising vertical distribution of cells, (7) characterising abiotic impurities, (8) surface anisotropy, (9) measuring indirect albedo feedbacks, and (10) measurement and instrument configurations. This paper aims to provide a broad audience of glaciologists and biologists with an overview of radiative transfer and albedo that could support future experimental design.
Surface ablation of the Greenland ice sheet is amplified by surface darkening caused by light-absorbing impurities such as mineral dust, black carbon, and pigmented microbial cells. We present the first quantitative assessment of the microbial contribution to the ice sheet surface darkening, based on field measurements of surface reflectance and concentrations of light-absorbing impurities, including pigmented algae, during the 2014 melt season in the southwestern part of the ice sheet. The impact of algae on bare ice darkening in the study area was greater than that of non-algal impurities and yielded a net albedo reduction of 0.038 ± 0.0035 for each algal population doubling. We argue that algal growth is a crucial control of bare ice darkening, and incorporating the algal darkening effect will improve mass balance and sea level projections of the Greenland ice sheet and ice masses elsewhere.
In the frame of the Copernicus programme, ESA has developed and launched the Sentinel-2 optical imaging mission that delivers optical data products designed to feed downstream services mainly related to land monitoring, emergency management and security. The Sentinel-2 mission is the constellation of two polar orbiting satellites Sentinel-2A and Sentinel-2B, each one equipped with an optical imaging sensor MSI (Multi-Spectral Instrument). Sentinel-2A was launched on June 23rd, 2015 and Sentinel-2B followed on March 7th, 2017. With the beginning of the operational phase the constellation of both satellites enable image acquisition over the same area every 5 days or less. To use unique potential of the Sentinel-2 data for land applications and ensure the highest quality of scientific exploitation, accurate correction of satellite images for atmospheric effects is required. Therefore the atmospheric correction processor Sen2Cor was developed by Telespazio VEGA Deutschland GmbH on behalf of ESA. Sen2Cor is a Level-2A processor which main purpose is to correct single-date Sentinel-2 Level-1C Top-Of-Atmosphere (TOA) products from the effects of the atmosphere in order to deliver a Level-2A Bottom-Of-Atmosphere (BOA) reflectance product. Additional outputs are an Aerosol Optical Thickness (AOT) map, a Water Vapour (WV) map and a Scene Classification (SCL) map with Quality Indicators for cloud and snow probabilities. Telespazio France and DLR have teamed up in order to provide the calibration and validation of the Sen2Cor processor. Here we provide an overview over the Sentinel-2 data, processor and products. It presents some processing examples of Sen2Cor applied to Sentinel-2 data, provides up-to-date information about the Sen2Cor release status and recent validation results at the time of the SPIE Remote Sensing 2017.
To better understand the processes controlling recent mass loss of peripheral glaciers and ice caps in northwestern Greenland, we measured surface mass balance (SMB), ice velocity and near-surface ice temperature on Qaanaaq Ice Cap in the summers of 2012–16. The measurements were performed along a survey route spanning the terminus of an outlet glacier to the upper reaches (243–968 m a.s.l.). The ice-cap-wide SMB ranged from −1.10 ± 0.29 to −0.13 ± 0.26 m w.e. a ⁻¹ for the years from 2012/13 to 2015/16. Mass balance showed substantially large fluctuations over the study period under the influence of summer temperature and snow accumulation. Ice velocity showed seasonal speedup only in the summer of 2012, suggesting an extraordinary amount of meltwater penetrated to the bed and enhanced basal ice motion. Ice temperature at a depth of 13 m was −8.0°C at 944 m a.s.l., which was 2.5°C warmer than that at 243 m a.s.l., suggesting that ice temperature in the upper reaches was elevated by refreezing and percolation of meltwater. Our study provided in situ data from a relatively unstudied region in Greenland, and demonstrated the importance of continued monitoring of these processes for longer timespans in the future.
Glaciers and ice sheets, like other biomes, occupy a significant area of the planet and harbour biological communities with distinct interactions and feedbacks with their physical and chemical environment. In the case of the glacial biome, the biological processes are dominated almost exclusively by microbial communities. Habitats on glaciers and ice sheets with enough liquid water to sustain microbial activity include snow, surface ice, cryoconite holes, englacial systems and the interface between ice and overridden rock/ soil. There is a remarkable similarity between the different specific glacial habitats across glaciers and ice sheets worldwide, particularly regarding their main primary producers and ecosystem engineers. At the surface, cyanobacteria dominate the carbon production in aquatic/sediment systems such as cryoconite holes, while eukaryotic Zygnematales and Chlamydomonadales dominate ice surfaces and snow dynamics, respectively. Microbially driven chemolithotrophic processes associated with sulphur and iron cycle and C transformations in subglacial ecosystems provide the basis for chemical transformations at the rock interface under the ice that underpin an important mechanism for the delivery of nutrients to downstream ecosystems. In this review, we focus on the main ecosystem engineers of glaciers and ice sheets and how they interact with their chemical and physical environment. We then discuss the implications of this microbial activity on the icy microbiome to the biogeochemistry of downstream ecosystems.
Surface albedo partitions the amount of energy received by glacier surfaces from shortwave fluxes and modulates the energy available for melt processes. The ice-albedo feedback, influenced by the contamination of bare-ice surfaces with light-absorbing impurities, plays a major role in the melting of mountain glaciers in a warming climate. However, little is known about the spatial and temporal distribution and variability of bare-ice glacier surface albedo under changing conditions. In this study, we focus on two mountain glaciers located in the western Swiss Alps and perform a cross-comparison of different albedo products. We take advantage of high spectral and spatial resolution (284 bands, 2 m) imaging spectrometer data from the Airborne Prism Experiment (APEX) and investigate the applicability and potential of Sentinel-2 and Landsat 8 data to derive broadband albedo products. The performance of shortwave broadband albedo retrievals is tested and we assess the reliability of published narrow-to-broadband conversion algorithms. The resulting albedo products from the three sensors and different algorithms are further cross compared. Moreover, the impact of the anisotropy correction is analysed depending on different surface types. While degradation of the spectral resolution impacted glacier-wide mean albedo by about 5%, reducing the spatial resolution resulted in changes of less than 1%. However, in any case, coarser spatial resolution was no longer able to represent small-scale variability of albedo on glacier surfaces. We discuss the implications when using Sentinel-2 and Landsat 8 to map dynamic glaciological processes and to monitor glacier surface albedo on larger spatial and more frequent temporal scales.
Sentinel-2A MSI is the Landsat-like spatial resolution (10-60 m) super-spectral instrument of the European Space Agency (ESA), aimed at additional data continuity for global land surface monitoring with Landsat and Satellite Pour l'Observation de la Terre (SPOT) missions. Several simulation studies have been conducted in the last several years to show the potential of Sentinel-2A MSI (MultiSpectral Instrument). Now that real data are available, the first confirmations of this potential and comparisons with other operational systems are being made. This paper aims at evaluating Sentinel-2A MSI band ratio products that are relevant for geological remote sensing. A Sentinel-2A MSI and a Landsat 8 OLI (Operational Land Imager) scene were processed from their respective levels L1C and L1T to level L2A (bottom of atmosphere reflectance). Then, three band ratios originally defined for Landsat TM (Thematic Mapper) were used to map mineralogy associated with a hydrothermal alteration system in southeast Spain. The results obtained with Sentinel-2A MSI were compared with those obtained with Landsat 8 OLI and a simulated Sentinel-2A MSI dataset that was used before actual data were released. Results show that the images appear similar to the human eye having a correlation of approximately 0.8 and higher, but that the associated data ranges differ significantly. The resulting products are also compared to a published geologic map of the study area, and it is shown that the resulting maps correspond with the conceptual geologic model of the epithermal deposit.
A 21-yr record is presented of surface mass balance measurements along the K-transect. The series covers the period 1990–2011. Data are available at eight sites along a transect over an altitude range of 380–1850 m at approximately 67° N in West Greenland. The surface mass balance gradient is on average 3.8 × 10−3 m w.e. m−1, and the mean equilibrium line altitude is 1553 m a.s.l. Only the lower three sites within 10 km of the margin up to an elevation of 700 m experience a significant increasing trend in the ablation over the entire period. Data are available at: doi:10.1594/PANGAEA.779181 .
With its temporal resolution of 10 days (five days with two satellites, and significantly more at high latitudes), its swath width of 290 km, and its 10 m and 20 m spatial resolution bands from the visible to the shortwave infrared, the European Sentinel-2 satellites have significant potential for glacier remote sensing, in particular mapping of glacier outlines and facies, and velocity measurements. Testing Level 1C commissioning and ramp-up phase data for initial sensor quality experiences, we find a high radiometric performance, but with slight striping effects under certain conditions. Through co-registration of repeat Sentinal-2 data we also find lateral offset patterns and noise on the order of a few metres. Neither of these issues will complicate most typical glaciological applications. Absolute geo-location of the data investigated was on the order of one pixel at the time of writing. The most severe geometric problem stems from vertical errors of the DEM used for ortho-rectifying Sentinel-2 data. These errors propagate into locally varying lateral offsets in the images, up to several pixels with respect to other georeferenced data, or between Sentinel-2 data from different orbits. Finally, we characterize the potential and limitations of tracking glacier flow from repeat Sentinel-2 data using a set of typical glaciers in different environments: Aletsch Glacier, Swiss Alps; Fox Glacier, New Zealand; Jakobshavn Isbree, Greenland; Antarctic Peninsula at the Larsen C ice shelf.
The Greenland ice sheet is reacting to climate change. Yet, mass-budget estimates differ considerably, partly due to climatic variability and partly to uncertainties in the techniques of assessing mass change (IPCC 2007). Nevertheless, all recent estimates agree that the ice sheet is losing mass (e.g. 286 Gt/yr; Velicogna 2009) at an accelerating rate (Rignot et al. 2011). On top of this, the area with a negative mass budget is expanding rapidly (Khan et al. 2010). The mass loss is attributed equally to increases in both iceberg production and melting of the ice sheet (Van den Broeke et al. 2009).
At over 40 years, the Landsat satellites provide the longest temporal record of space-based land surface observations, and the successful 2013 launch of the Landsat-8 is continuing this legacy. Ideally, the Landsat data record should be consistent over the Landsat sensor series. The Landsat-8 Operational Land Imager (OLI) has improved calibration, signal to noise characteristics, higher 12-bit radiometric resolution, and spectrally narrower wavebands than the previous Landsat-7 Enhanced Thematic Mapper (ETM+). Reflective wavelength differences between the two Landsat sensors depend also on the surface reflectance and atmospheric state which are difficult to model comprehensively. The orbit and sensing geometries of the Landsat-8 OLI and Landsat-7 ETM+ provide swath edge overlapping paths sensed only one day apart. The overlap regions are sensed in alternating backscatter and forward scattering orientations so Landsat bi-directional reflectance effects are evident but approximately balanced between the two sensors when large amounts of time series data are considered. Taking advantage of this configuration a total of 59 million 30m corresponding sensor observations extracted from 6,317 Landsat-7 ETM+ and Landsat-8 OLI images acquired over three winter and three summer months for all the conterminous United States (CONUS) are compared. Results considering different stages of cloud and saturation filtering, and filtering to reduce one day surface state differences, demonstrate the importance of appropriate per-pixel data screening. Top of atmosphere (TOA) and atmospherically corrected surface reflectance for the spectrally corresponding visible, near infrared and shortwave infrared bands, and derived normalized difference vegetation index (NDVI), are compared and their differences quantified. On average the OLI TOA reflectance is greater than the ETM+ TOA reflectance for all bands, with greatest differences in the near-infrared (NIR) and the shortwave infrared bands due to the quite different spectral response functions between the sensors. The atmospheric correction reduces the mean difference in the NIR and shortwave infrared but increases the mean difference in the visible bands. Regardless of whether TOA or surface reflectance are used to generate NDVI, on average, for vegetated soil and vegetation surfaces (0 ≤ NDVI ≤ 1), the OLI NDVI is greater than the ETM+ NDVI. Statistical functions to transform between the comparable sensor bands and sensor NDVI values are presented so that the user community may apply them in their own research to improve temporal continuity between the Landsat-7 ETM+ and Landsat-8 OLI sensor data. The transformation functions were developed using ordinary least squares (OLS) regression and were fit quite reliably (r
2
values >0.7 for the reflectance data and >0.9 for the NDVI data, p-values <0.0001).
Performance measures (PMs) and corresponding performance evaluation criteria (PEC) are important as-pects of calibrating and validating hydrologic and water quality models and should be updated with advances in modeling science. We synthesized PMs and PEC from a previous special collection, performed a meta-analysis of performance data reported in recent peer-reviewed literature for three widely published watershed-scale models (SWAT, HSPF, WARMF), and one field-scale model (ADAPT), and provided guidelines for model performance evaluation. Based on the synthesis, meta-analysis, and personal modeling experiences, we recommend coefficient of determination (R2; in conjunction with gradient and intercept of the corresponding regression line), Nash Sutcliffe efficiency (NSE), index of agreement (d), root mean square error (RMSE; alongside the ratio of RMSE and standard deviation of measured data, RSR), percent bias (PBIAS), and several graphical PMs to evaluate model performance. We recommend that model performance can be judged “satisfactory” for flow simulations if monthly R2 > 0.70 and d > 0.75 for field-scale models, and daily, monthly, or annual R2 > 0.60, NSE > 0.50, and PBIAS ≤±15% for watershed-scale models. Model performance at the watershed scale can be evaluated as “satisfactory” if monthly R2 > 0.40 and NSE > 0.45 and daily, monthly, or annual PBIAS ≤±20% for sediment; monthly R2 > 0.40 and NSE > 0.35 and daily, monthly, or annual PBIAS ≤±30% for phosphorus (P); and monthly R2 > 0.30 and NSE > 0.35 and daily, monthly, or annual PBIAS ≤±30% for nitrogen (N). For RSR, we rec-ommend that previously published PEC be used as detailed in this article. We also recommend that these PEC be used primarily for the four models for which there were adequate data, and used only with caution for other models. These PEC can be adjusted within acceptable bounds based on additional considerations, such as quality and quantity of avail-able measured data, spatial and temporal scales, and project scope and magnitude, and updated based on the framework presented herein. This initial meta-analysis sets the stage for more comprehensive meta-analysis to revise PEC as new PMs and more data become available.
Accurate measurements and simulations of Greenland Ice Sheet (GrIS) surface albedo are essential, given the role of surface albedo in modulating the amount of absorbed solar radiation and meltwater production. In this study, we assess the spatio-temporal variability of GrIS albedo during June, July, and August (JJA) for the period 2000–2013. We use two remote sensing products derived from data collected by the Moderate Resolution Imaging Spectroradiometer (MODIS), as well as outputs from the Modèle Atmosphérique Régionale (MAR) regional climate model (RCM) and data from in situ automatic weather stations. Our results point to an overall consistency in spatio-temporal variability between remote sensing and RCM albedo, but reveal a difference in mean albedo of up to ~0.08 between the two remote sensing products north of 70° N. At low elevations, albedo values simulated by the RCM are positively biased with respect to remote sensing products by up to ~0.1 and exhibit low variability compared with observations. We infer that these differences are the result of a positive bias in simulated bare ice albedo. MODIS albedo, RCM outputs, and in situ observations consistently indicate a decrease in albedo of −0.03 to −0.06 per decade over the period 2003–2013 for the GrIS ablation area. Nevertheless, satellite products show a decline in JJA albedo of −0.03 to −0.04 per decade for regions within the accumulation area that is not confirmed by either the model or in situ observations. These findings appear to contradict a previous study that found an agreement between in situ and MODIS trends for individual months. The results indicate a need for further evaluation of high elevation albedo trends, a reconciliation of MODIS mean albedo at high latitudes, and the importance of accurately simulating bare ice albedo in RCMs.
Field activities of the “Snow Impurity and Glacial Microbe effects on abrupt warming in the Arctic” (SIGMA) Project in Greenland in the summer season of 2011-2013 are reported;this consists of (1) glaciological and meteorological observations and (2) biological observations. In 2011, we conducted a field reconnaissance in the Qaanaaq, Ilulissat and Kangerlussuaq areas to enable continuous meteorological observations with automatic weather stations (AWS), campaign observations for glaciology, meteorology and Biology and shallow ice core drilling, which were planned for 2012-2014. Based on the results, we chose the Qaanaaq area in northwest Greenland as our main activity area and the Kangerlussuaq area in mid-west Greenland partly for biological observations. In 2012, we conducted field observations for (1) and (2) mentioned above together with installations of two AWSs at site SIGMA-A on The Greenland ice sheet (GrIS) and at site SIGMA-B on the Qaanaaq ice cap (QIC) from June to August. Surface snow and ice over all of the QIC melted in July and August 2012, and most of the Glacier surface appeared to be dark-colored, probably due to mineral dust and glacial microbial products. In 2013, we carried out similar observations in the Qaanaaq area. However, the weather and Glacier surface conditions were considerably different from those in 2012. Snow cover over the summer of 2013 remained over large areas with elevations higher than about 700 m on QIC. Biological activity on the Glacier surface appears to be substantially lower as compared to that in 2012.
Commonly known as the Asian Water Tower, glaciers in the Tibetan Plateau (TP) and its surrounding regions are vital to the regional water cycle and water resources in the downstream areas. Recently, these glaciers have been experiencing significant shrinkage mostly due to climate warming, which is also profoundly modulated by the surface snow albedos. In this study, we summarized the current status of the glaciers in the TP and its surrounding region, focusing on glacier retreat and mass balance. Furthermore, based on glacier surface snow albedo data retrieved from MODIS (moderate resolution imaging spectroradiometer, with resolution of 500 m × 500 m), we investigated the potential impact of glacier surface snow albedo changes on glacier melting. The results demonstrated that glacier shrinkage was pronounced over the Himalayas and the southeast TP. The regional distribution of the average albedos on the glacier surface (during summer) exhibited similar patterns to those of glacier retreat and mass balance changes, indicating a significant relationship between the annual glacier mass balance and glacier surface albedos during the past decades (2001–2018). This reflected that albedo reduction, in addition with rising temperatures and changing precipitation, was a significant driver of glacier melting in the TP. Estimations based on glacier surface summer albedos and snowmelt models further suggested that the effect of surface albedo reduction can drive about 30% to 60% of glacier melting. Due to its strong light absorption, black carbon (BC) in snow can be a substantial contributor to albedo reduction, which enhanced glacier melting in summer in the TP by approximately 15%. This study improved our insights into the causes of glacier melting in the Tibetan Plateau.
Long-term ground LAI measurements from the global networks of sites (e.g. FLUXNET) have emerged as a promising data source to validate remotely sensed global LAI product time-series. However, the spatial scale-mismatch issue between site and satellite observations hampers the use of such invaluable ground measurements in validation practice. Here, we propose an approach (Grading and Upscaling of Ground Measurements, GUGM) that integrates a spatial representativeness grading criterion and a spatial upscaling strategy to resolve this scale-mismatch issue and maximize the utility of time-series of site-based LAI measurements. The performance of GUGM was carefully evaluated by comparing this method to both benchmark LAI and other widely used conventional approaches. The uncertainty of three global LAI products (i.e. MODIS, GLASS and GEOV1) was also assessed based on the LAI time-series validation dataset derived from GUGM. Considering all the evaluation results together, this study suggests that the proposed GUGM approach can significantly reduce the uncertainty from spatial scale mismatch and increase the size of the available validation dataset. In particular, the proposed approach outperformed other widely used approaches in these two respects. Furthermore, GUGM was successfully implemented to validate global LAI products in various ways with advantaging frequent time-series validation dataset. The validation results of the global LAI products show that GLASS has the lowest uncertainty, followed by GEOV1 and MODIS for the overall biome types. However, MODIS provides more consistent uncertainties across different years than GLASS and GEOV1. We believe that GUGM enables us to better understand the structure of LAI product uncertainties and their evolution across seasonal or annual contexts. In turn, this method can provide fundamental information for further LAI algorithm improvements and the broad application of LAI product time-series.
The roles of quantitative remote sensing products in scientific research and quantitative applications lie in their ability to record the real states of earth surface features. Therefore, it is crucial to quantify the performance of remote sensing products. However, validation is not straightforward due to the scale effects caused by surface heterogeneity and the spatial scale mismatch between satellite- and ground-based observations, but a challenging scientific issue in the field of remote sensing. Although validation works have been widely carried out in the past decades, it is difficult to reach an accordant and compelling conclusion about the performance of different satellite products due to the inconsistencies in spatial and temporal extents, the type of in situ data sources, and the validation strategies, which hinder effective applications of remote sensing products. Therefore, it is necessary to give an overview of validation, simultaneously to point out its problems and insufficiencies, and finally to put forward the suggestions for future researches. The in situ data acquisition, the upscaling method, the uncertainties implied in the validation process, together with future challenges, are included in this paper. It is expected to promote the validation technique development and improve the application accuracy of remote sensing products.
ArcticDEM is a collection of 2-m resolution, repeat digital surface models created from stereoscopic satellite imagery. To demonstrate the potential of ArcticDEM for measuring river stages and discharges, we estimate river surface heights along a reach of Tanana River near Fairbanks, Alaska, by the precise detection of river shorelines and mapping of shorelines to land surface elevation. The river height profiles over a 15-km reach agree with in situ measurements to a standard deviation less than 30 cm. The time series of ArcticDEM-derived river heights agree with the U.S. Geological Survey gage measurements with a standard deviation of 32 cm. Using the rating curve for that gage, we obtain discharges with a validation accuracy (root-mean-square error) of 234 m³/s (23% of the mean discharge). Our results demonstrate that ArcticDEM can accurately measure spatial and temporal variations of river surfaces, providing a new and powerful data set for hydrologic analysis.
Satellite data provide a large range of information on glacier dynamics and changes. Results are often reported, provided and used without consideration of measurement accuracy (difference to a true value) and precision (variability of independent assessments). Whereas accuracy might be difficult to determine due to the limited availability of appropriate reference data and the complimentary nature of satellite measurements, precision can be obtained from a large range of measures with a variable effort for determination. This study provides a systematic overview on the factors influencing accuracy and precision of glacier area, elevation change (from altimetry and DEM differencing), and velocity products derived from satellite data, along with measures for calculating them. A tiered list of recommendations is provided (sorted for effort from Level 0 to 3) as a guide for analysts to apply what is possible given the datasets used and available to them. The more simple measures to describe product quality (Levels 0 and 1) can often easily be applied and should thus always be reported. Medium efforts (Level 2) require additional work but provide a more realistic assessment of product precision. Real accuracy assessment (Level 3) requires independent and coincidently acquired reference data with high accuracy. However, these are rarely available and their transformation into an unbiased source of information is challenging. This overview is based on the experiences and lessons learned in the ESA project Glaciers_cci rather than a review of the literature.
The temporal and spatial variation in the surface albedo of the Greenland ice sheet during the ablation season of 1991 is investigated. The study focuses on an area east of Søndre Strømfjord measuring 200 km by 200 km and centred at 67°5′ N, 48° 13′W. The analysis is based on satellite radiance measurements carried out by the Advanced Very High Resolution Radiometer (AVHRR). The broad-band albedo is estimated from the albedos in channel 1 (visible) and channel 2 (near-infrared). The results are calibrated with the surface albedo of sea and dry snow.
Satellite-derived albedos are compared with GIMEX ground measurements at three stations. There is a high degree of consistency in temporal variation at two of the three stations. Large systematic differences are attributed to albedo variations on sub-pixel scale.
In the course of the ablation season four zones appear, each parallel to the ice edge. It is proposed that these are, in order of increasing altitude: (I) clean and dry ice, (II) ice with surface water, (III) superimposed ice, and (IV) snow. An extensive description of these zones is given on the basis of the situation on 25 July 1991. Zones I, III and IV reveal fairly constant albedos (0.46, 0.65 and 0.75 on average), whereas zone II is characterised by an albedo minimum (0.34). Survey of the western margin of the Greenland ice sheet (up to 71° N) shows that the zonation occurs between 66° and 70° N.
Glacier surfaces are not only composed of ice or snow but are heterogeneous mixtures of different materials. The occurrence and dynamics of light-absorbing impurities affect ice surface characteristics and strongly influence glacier melt processes. However, our understanding of the spatial distribution of impurities and their impact on ice surface characteristics and the glacier's energy budget is still limited. We use imaging spectroscopy in combination with in-situ experiments to assess the composition of ice surface materials and their respective impact on surface albedo and glacier melt rates. Spectroscopy data were acquired in August 2013 using the Airborne Prism EXperiment (APEX) imaging spectrometer and were used to map the abundances of six predominant surface materials on Glacier de la Plaine Morte, Swiss Alps. A pixel-based classification revealed that about 10% of the ice surface is covered with snow, water or debris. The remaining 90% of the surface can be divided into three types of glacier ice, namely ~7% dirty ice, ~43% pure ice and ~39% bright ice. Spatially distributed spectral albedo derived from APEX reflectance data in combination with in-situ multi-angular spectroscopic measurements was used to analyse albedo patterns present on the glacier surface. About 85% of all pixels exhibit a low albedo between 0.1 and 0.4 (mean albedo 0.29 ± 0.12), indicating that Glacier de la Plaine Morte is covered with a significant amount of light-absorbing impurities, resulting in a strong ice-albedo feedback during the ablation season. Using a pixel-based albedo map instead of a constant albedo for ice (0.34) as input for a mass balance model revealed that the glacier-wide total ablation remained similar (10% difference). However, the large local variations in mass balance can only be reproduced using the pixel-based albedo derived from APEX, emphasizing the need to quantify spatial albedo differences as an important input for glacier mass balance models.
Calibration and validation of satellite-derived ice sheet albedo data require high-quality, in-situ measurements commonly acquired by up- and down-facing pyranometers mounted on automated weather stations (AWS). However, direct comparison between ground and satellite-derived albedo can only be justified when the measured surface is homogeneous at the length-scale of both satellite pixel and in-situ footprint. Here, we use digital imagery acquired by an unmanned aerial vehicle to evaluate point-to-pixel albedo comparisons across the western, ablating margin of the Greenland Ice Sheet. Our results reveal that in-situ measurements overestimate albedo by up to 0.10 at the end of the melt-season because the ground footprints of AWS-mounted pyranometers are insufficient to capture the spatial heterogeneity of the ice surface as it progressively ablates and darkens. Statistical analysis of 21 AWS across the entire Greenland Ice Sheet reveals that almost half suffer from this bias, including some AWS located within the wet snow zone
Following the lead of Haggett and Chorley (1967), Rayner (1974), Terjung (1976), Strahler (1980) and many others, physical geography has adopted a “model-based paradigm” and, as a result, the development and application of a wide variety of models is now commonplace within virtually every sub-field from geomorphology to bioclimatology. Within climatology, many models have a predominately deductive genesis while other models are collages of statistical and empirical reasoning and, in a few cases, “best-fit” functions are extracted from data with seemingly little regard for the safeguards of a deductive stance. Still other models combine the mathematics of probability theory with empirically derived probabilities to create stochastic simulation models, e.g., Markov or Monte Carlo models. These categories of models are, by no means, mutually exclusive (or exhaustive for that matter) and a number of recent models may be considered combinatorial in that they incorporate two or more of the above-mentioned strategies into a single model.