Snow Cover Mapping Research Articles

A Review of the Current State and Recent Changes of the Andean Cryosphere

ANIGL-ACONICET Service National d'Observation GLACIOCLIM UGA Centre National de la Recherche Scientifique (CNRS) IRD INRAE IPEV LMI GREAT ICE (IRD) French National Research Agency (ANR) ANR-10-LABX56 Comision Nacional de Investigacion Cientifica y Tecnologica (CONICYT) CONICYT FONDECYT 1171832 France Space Agency (CNES) Region Occitanie for the Ph.D. fellowship of ID ANID R16a10003

Snow cover mapping, topographic controls and integration of meteorological data sets in Din-Gad Basin, Central Himalaya

Din-Gad basin, located in central Himalaya is occupied by the Dokriani glacier in upper reaches. Landsat TM, OLI and Sentinel MSI data sets of seven years from 2009 to 2011 and 2013 to 2016 are utilized to map the areal extent of snow-cover, its seasonal variability and topographical controls (elevation, aspect, and slope). Coefficient of Variation (CV) in Snow Cover Area (SCA) below the elevation of ~5000 m asl is high (~2.0), and is low at higher elevations. CV is also higher for southern as compared to northern aspects. Meteorological data (temperature, relative humidity, and solar radiation) from Automatic Weather Stations (AWSs) is utilized to compute isotherms (~0, 1, and 2 °C) by extrapolation method using Near-surface Temperature Lapse Rate (NSTLR). These data sets are integrated and presented by graphical as well as on Geographical Information System (GIS). Month-wise isotherms closely correspond with snow-cover extent during ablation season, suggesting that the extrapolated temperatures represent a significant proxy for depletion pattern, while month-wise and hourly isotherms deviate largely from the SCA during winter months due to snow events. Elevation zones above 5200 m asl retain snow throughout the year, indicating proximity with Equilibrium Line Altitude (ELA). The effects of Relative Humidity (RH) and solar radiation on melting history during different seasons have also emerged. The proposed methodology is efficient and reliable in terms of estimating the depletion patterns, and deriving the isotherms that can be applied to other comparable snow-covered areas.

On the Value of Available MODIS and Landsat8 OLI Image Pairs for MODIS Fractional Snow Cover Mapping Based on an Artificial Neural Network

This article investigates how to select the optimal Moderate-Resolution Imaging Spectroradiometer (MODIS) and Landsat 8 OLI image pairs for MODIS fractional snow cover (FSC) mapping using an artificial neural network (ANN). Four issues are discussed, including date selection, location selection, priority of date and location, and global and regional monitoring of MODIS FSC with ANNs. We propose using the histogram quadratic distance to define the similarity between the ANN training and the target test scene, which was used to quantify the representativeness of the training samples. We use the case study of MODIS FSC mapping of North Xinjiang, China, in the 2014–2015 snow season as an example. Thirty-eight experiments were designed. The experimental results demonstrate that the ANN-based FSC estimation accuracy outperformed the MODIS FSC product, with an average RMSE of 0.17, ${R}$ exceeds 0.8, and the total snow cover area was estimated more accurately in most cases. For a target test scene, we preliminarily inferred that the best method is to develop an ANN using image pairs of another location with the highest similarity in the same acquisition time, using historical image pairs of the target scene with the highest similarity is the second choice, and using historical image pairs from another location with a high similarity is the third choice. For global- and regional-scale MODIS FSC mapping with ANNs, we formulated the strategy of initially determining a reasonable location and subsequently selecting the acquisition date of the image pairs to guarantee that the training data set represents the entire study area well.

Canopy Adjustment and Improved Cloud Detection for Remotely Sensed Snow Cover Mapping

AbstractMaps of snow cover serve as early indicators for hydrologic forecasts and as inputs to hydrologic models that inform water management strategies. Advances in snow cover mapping have led to increasing accuracy, but unsatisfactory treatment of vegetation's interference when mapping snow has led to maps that have limited utility for water forecasting. Vegetation affects snow mapping because ground surfaces not visible to the satellite produce uncertainty as to whether the ground is snow covered. At nadir, the forest canopy obscures the satellite view below the canopy. At oblique viewing angles, the forest floor is obscured by both the canopy and the projection of tree profiles onto the forest floor. We present a canopy correction method based on Moderate Resolution Imaging Spectroradiometer satellite imagery validated with field observations that mitigates geometric and georegistration issues associated with changing satellite acquisition angles in forested areas. The largest effect from a variable viewing zenith angle on the viewable gap fraction in forested areas occurs in moderately forested areas with 30–40% tree canopy coverage. Cloud cover frequently causes errors in snow identification, with some clouds identified as snow and some snow identified as cloud. A snow‐cloud identification method utilizes a time series of fractional vegetation and rock land‐surface data to flag snow‐cloud identification errors and improve snow‐map accuracy reducing bias by 20% over previous methods. Together, these contributions to snow‐mapping techniques could advance hydrologic forecasting in forested, snow‐dominated basins that comprise an estimated one fifth of Northern Hemisphere snow‐covered areas.

Mapping snow cover using landsat data: toward a fine-resolution water-resistant snow index

Snow cover is of significant importance to freshwater supplies and influences the hydrology of different altitudes in mountainous regions. The monitoring of snow cover over the Mediterranean region of Turkey is of high priority due to its rapid irregularities experienced during the past decades. These irregularities in snow cover might especially lead to severe risks for the local ecosystems, such as rivers, and irrigated agriculture. Water-Resistant Snow Index (WSI) is a new and powerful spectral index for mapping snow cover using Moderate Resolution Imaging Spectroradiometer (MODIS) data. There still exists a large knowledge gap about how to improve the finer resolution applications of WSI. This study aimed to explore the applicability of WSI using Landsat TM and OLI images with a better spatial resolution to a complex catchment in the eastern Mediterranean region of Turkey. The WSI maps derived from both MODIS/Terra-MOD09A1 and Landsat images from 2005 to 2018 were compared with the high-resolution Sentinel-2A data. The comparative analysis of the MODIS and Landsat WSI maps for the snowy areas was also presented with error distribution patterns. The baseline snow cover led to the coefficient of determination values of 0.77 with the MODIS data and 0.78 with the Landsat data. The results indicated that Landsat images offered a suitable spatial resolution for the snow cover mapping using the WSI approach in regional studies. A finer resolution mapping of snow cover with the Landsat data can provide essential insights into the spatiotemporal dynamics at the local and regional scales.

Analysis of Melt and Freeze in Shyok Sub-Basin, Indus Basin of Indian Himalaya using Ku Band Scatterometer SCATSAT-1

The spatio temporal evaluation of melting snow and ice is important, among other things for studying climatological and hydrological applications. The knowledge of melting and freezing cycles of the seasonal snow cover is essential for water resources management and flood forecasting. Microwave remote sensing is a promising tool to characterise the melt and freeze over a large spatial scale and frequent temporal basis. This study attempts to use the Ku band dual polarization satellite SCATSAT-1 launched by ISRO with a scatterometer on-board with an enhanced spatial resolution of 2.2 Km to analyse the melt and freeze conditions in the Shyok basin, which is a sub-basin of the Indus river basin. The backscatter measurements from SCATSAT are helpful in identifying melt and freeze due to its sensitivity to the water present in the illuminated region. This study aims to adopt an adaptive threshold technique to identify the melt and freeze conditions, where the winter mean and maximum standard deviation and drop in backscatter due to the water content in the snow forms the basis to identify the melt and freeze conditions of the snow covered area. Spatio-temporal variability was observed in melt onset with 18% area under melt onset in the month of June, 2018. Onset in the month of April and May was less in comparison to June. Melt incidence maps were generated for major melt months of April, May and June and were categorised into 5 categories. April observed melt for 1 – 5 days, whereas a part of the study area was in melt condition throughout the month of June. A derived snow cover map was validated with Soumi_NPP derived snow cover with correlation of 0.96, indicating that SCATSAT can be used for snow cover retrieval over Himalayas by observing melt/freeze. Availability of daily SCATSAT data makes it possible to generate daily melt incidence maps. https://doi.org/10.23953/cloud.ijarsg.467

Towards a webcam-based snow cover monitoring network: methodology and evaluation

Abstract. Snow cover variability has a significant impact on climate and the environment and is of great socioeconomic importance for the European Alps. Terrestrial photography offers a high potential to monitor snow cover variability, but its application is often limited to small catchment scales. Here, we present a semiautomatic procedure to derive snow cover maps from publicly available webcam images in the Swiss Alps and propose a procedure for the georectification and snow classification of such images. In order to avoid the effort of manually setting ground control points (GCPs) for each webcam, we implement a novel registration approach that automatically resolves camera parameters (camera orientation; principal point; field of view, FOV) by using an estimate of the webcams' positions and a high-resolution digital elevation model (DEM). Furthermore, we propose an automatic image-to-image alignment to correct small changes in camera orientation and compare and analyze two recent snow classification methods. The resulting snow cover maps indicate whether a DEM grid is snow-covered, snow-free, or not visible from webcams' positions. GCPs are used to evaluate our novel automatic image registration approach. The evaluation reveals a root mean square error (RMSE) of 14.1 m for standard lens webcams (FOV<48∘) and a RMSE of 36.3 m for wide-angle lens webcams (FOV≥48∘). In addition, we discuss projection uncertainties caused by the mapping of low-resolution webcam images onto the high-resolution DEM. Overall, our results highlight the potential of our method to build up a webcam-based snow cover monitoring network.

Snow cover estimation from MODIS and Sentinel-1 SAR data using machine learning algorithms in the western part of the Tianshan Mountains

Obtaining the spatial distribution of snow cover in mountainous areas using the optical image of remote sensing technology is difficult because of cloud and fog. In this study, the object-based principle component analysis-support vector machine (PCA-SVM) method is proposed for snow cover mapping through the integration of moderateresolution imaging spectroradiometer (MODIS) snow cover products and the Sentinel-1 synthetic aperture radar (SAR) scattering characteristics. First, derived from the Sentinel-1A SAR images, the feature parameters, including VV/VH backscatter, scattering entropy, and scattering alpha, were used to describe the variations of snow and non-snow covers. Second, the optimum feature combinations of snow cover were formed from the feature parameters using the principle component analysis (PCA) algorithm. Finally, using the optimum feature combinations, a snow cover map with a 20 m spatial resolution was extracted by means of an object-based SVM classifier. This method was applied in the study area of the Xinyuan County, which is located in the western part of the Tianshan Mountains in Xinjiang, China. The accuracies in this method were analyzed according to the data observed at different experimental sites. Results showed that the snow cover pixels of the extraction were less than those in the actual situation (FB1=93.86, FB2=59.78). The evaluation of the threat score (TS), probability of detection (POD), and false alarm ratio (FAR) for the snow-covered pixels obtained from the two-stage SAR images were different (TS1=86.84, POD1=90.10, FAR1=4.01; TS2=56.40, POD2=57.62, FAR2=3.62). False and misclassifications of the snow cover and non-snow cover pixels were found. Although the classifications were not highly accurate, the approach showed potential for integrating different sources to retrieve the spatial distribution of snow covers during a stable period.

An Improved UWB Microwave Radar for Very Long-Range Measurements of Snow Cover

We present an ultrawideband (UWB) frequency-modulated (FM) microwave radar instrument capable of preserving cm-scale vertical resolution from a very long range (verified up to ~6 km). The system can operate either as a conventional continuous-wave FM radar (2-18 GHz) or in time-delayed stretch processing mode (2-14 GHz), providing high sensitivity, fine range resolution, and very low-range sidelobes. We present an overview of the system and laboratory test results to validate its performance. As a practical demonstration of its capabilities, we tested the system's ability to map snow cover thickness on the Arctic marine ice. We present sample results from airborne measurements carried out at altitudes between ~500 m and ~6 km above the ground level onboard a long-range fixed-wing platform.

Intercomparison and Validation of MIRS, MSPPS, and IMS Snow Cover Products

We evaluate the agreement between automated snow products generated from satellite observations in the microwave bands within NESDIS Microwave Integrated Retrieval System (MIRS) and Microwave Surface and Precipitation Products System (MSPPS), on the one hand, and snow cover maps produced with manual input by the NOAA’s Interactive Multisensor Snow and Ice Mapping System (IMS), on the other. MIRS uses physically based retrievals of atmospheric and surface state parameters to provide daily global maps of snow cover and snow water equivalent at 50 km resolution. The older MSPPS delivers daily global maps at the spatial resolution of 45 km and utilizes mostly simple empirical algorithms to retrieve information. IMS daily maps of snow and sea ice cover for the Northern Hemisphere are produced interactively through the analysis of satellite imagery in the visible, infrared, and microwave spectral bands. We compare the performances of these products across the Northern Hemisphere for 2014–2017, using IMS as the standard. In this intercomparison, the daily overall agreement of the automated snow products with IMS ranges between 88% and 99% for MIRS and 87% and 99% for MSPPS. However, daily snow sensitivity is lower, ranging between 36% and 90% for MIRS and 26% and 91% for MSPPS. We analyze this disagreement rate as a function of terrain and land cover type, finding that, relative to IMS, MIRS shows fewer false positives but more false negatives than MSPPS over high elevation and grassland areas.

Comparative evaluation of VIIRS daily snow cover product with MODIS for snow detection in China based on ground observations

Accurate spatiotemporal information of snow cover not only is important for investigating the mechanisms of climate change but also greatly contributes to hydrological modelling in mountainous regions. The Suomi-National Polar-orbiting Partnership (S-NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) (referred to as VNP) daily snow cover product is recently released and expected to take place of Moderate Resolution Imaging Spectroradiometer (MODIS) snow cover products in near future. As an important addition to the widely used MODIS products, there is also an urgent need for a reliable accuracy evaluation and comparison of VNP for future large-scale daily snow cover mapping. This study for the first time evaluates the accuracy of VNP daily snow cover data in China using daily snow depth observations from 330 stations. The accuracy of VNP data is generally good with the averaged CK (Cohen's Kappa) and FS (F-Score) as high as 0.72 and 0.75, respectively, but considerably decreases to 0.50 and 0.52 for the Tibetan Plateau. VNP shows slightly better accuracy than MODIS TERRA for stations outside the Tibetan Plateau owing to its higher spatial resolution, but its accuracy is lower than TERRA for those within the Tibetan Plateau possibly due to its longer time interval between ground observation and satellite overpass time. By contrast, VNP shows much better accuracy than MODIS AQUA in China including both outside and within the Tibetan Plateau. This study provides important implications for optimal use of VNP and MODIS daily snow cover products in China, which may further contribute to more accurate snow variation information for climate analysis and cryospheric hydrological modelling.

MODIS Fractional Snow Cover Mapping Using Machine Learning Technology in a Mountainous Area

To improve the poor accuracy of the MODIS (Moderate Resolution Imaging Spectroradiometer) daily fractional snow cover product over the complex terrain of the Tibetan Plateau (RMSE = 0.30), unmanned aerial vehicle and machine learning technologies are employed to map the fractional snow cover based on MODIS over this terrain. Three machine learning models, including random forest, support vector machine, and back-propagation artificial neural network models, are trained and compared in this study. The results indicate that compared with the MODIS daily fractional snow cover product, the introduction of a highly accurate snow map acquired by unmanned aerial vehicles as a reference into machine learning models can significantly improve the MODIS fractional snow cover mapping accuracy. The random forest model shows the best accuracy among the three machine learning models, with an RMSE (root-mean-square error) of 0.23, especially over forestland and shrubland, with RMSEs of 0.13 and 0.18, respectively. Although the accuracy of the support vector machine and back-propagation artificial neural network models are worse over forestland and shrubland, their average errors are still better than that of MOD10A1. Different fractional snow cover gradients also affect the accuracy of the machine learning algorithms. Nevertheless, the random forest model remains stable in different fractional snow cover gradients and is, therefore, the best machine learning algorithm for MODIS fractional snow cover mapping in Tibetan Plateau areas with complex terrain and severely fragmented snow cover.

Comparing MODIS snow products Collection 5 with Collection 6 over Italian Central Apennines

ABSTRACTRemotely sensed snow-cover information has become a key tool to study temporal and spatial snow-cover patterns and to develop regional snow-cover climatologies. Aqua/Terra MODIS products provide about 20-year daily snow-cover data with 500 m spatial resolution. MODIS Collection 6 represents the most recent release of global snow-cover mapping algorithms and could further increase the high accuracy of previous collections: snow cover is now reported by its NDSI (Normalized Difference Snow Index) values, allowing more flexibility in using the datasets for specific regions than previous releases. We quantified the potential-added value of tuning the NDSI threshold as opposed to a global snow-detection algorithm by developing a 16-year snow-cover climatology for the Central Apennines (Italy) from daily observations in MODIS snow products Collection 5 (C5) and Collection 6 (C6). Seven ground-based stations were used as independent benchmark. Three versions of binary snow-cover maps were generated from the NDSI Snow Cover product (C6), using NDSI-threshold tests for snow detection. The most accurate snow-cover maps show an agreement with available ground data of 88% for Aqua and 89% for Terra MODIS, with an improvement compared to snow-cover maps obtained from C5 Snow Cover Area (SCA) products (yielding 86% for Aqua and 88% for Terra). NDSI thresholds in the range 0.10–0.40 provide an agreement higher than 83% but snow-cover duration, distribution, and spatial extent are sensible to the NDSI threshold: if compared to the optimal NDSI threshold for this region (0.20), the value of 0.40 reduces by 15% the snow-cover extent in all seasons due to increased underestimation. The lowest tested threshold (0.10) estimates at least 10% larger snow-cover fraction in winter and spring but increases commission errors. This high sensitivity to the NDSI threshold makes its choice an essential step for using MODIS C6 snow products in hydrologic or climatologic studies.

Identification of Snow Using Fully Polarimetric SAR Data Based On Entropy and Anisotropy

AbstractRecently, with the extensive availability of fully polarimetric synthetic aperture radar (SAR) data, methods that are simple and efficient, and involve lesser computation and data processing, are needed to be explored for snow cover mapping. This paper analyzes different polarimetric parameters such as entropy, anisotropy, and the mean scattering angle for the identification of snow cover area. We present a novel index for mapping snow cover based on the assessment of entropy (H) and anisotropy (A) using fully polarimetric SAR data and refer to it as the radar snow fraction (RSF). The RSF is proposed as an extension of the H(1‐A) metric by applying a sigmoidal function to this metric. The experiments to evaluate the applicability of the proposed RSF are carried out using fully polarimetric SAR data of L‐band ALOS‐2/PALSAR‐2 and C‐band RADARSAT‐2 data sets corresponding to different geographical locations in the Indian Himalayas. The developed snow cover maps from the proposed method were validated with respect to reference snow cover maps derived by thresholding the Normalized Differenced Snow Index developed from multispectral data (e.g., Landsat‐8 imagery). These maps were also statistically compared with those obtained from the conventional radar snow index, which is based on the polarization fraction. We determined a mean overall accuracy of 0.8 between the developed snow cover maps and the reference maps for the different data sets used for experiments. The results showed that, in general, the RSF outperformed the other polarimetric parameters for snow cover detection.

Processes governing snow ablation in alpine terrain – detailed measurements from the Canadian Rockies

Abstract. The spatial distribution of snow water equivalent (SWE) and melt are important for estimating areal melt rates and snow-cover depletion (SCD) dynamics but are rarely measured in detail during the late ablation period. This study contributes results from high-resolution observations made using large numbers of sequential aerial photographs taken from an unmanned aerial vehicle on an alpine ridge in the Fortress Mountain Snow Laboratory in the Canadian Rocky Mountains from May to July in 2015. Using structure-from-motion and thresholding techniques, spatial maps of snow depth, snow cover and differences in snow depth (dHS) during ablation were generated in very high resolution as proxies for spatial SWE, spatial ablation rates and SCD. The results indicate that the initial distribution of snow depth was highly variable due to overwinter snow redistribution; thus, the subsequent distribution of dHS was also variable due to albedo, slope/aspect and other unaccountable differences. However, the initial distribution of snow depth was 5 times more variable than that of the subsequent dHS values, which varied by a factor of 2 between the north and south aspects. dHS patterns were somewhat spatially persistent over time but had an insubstantial impact on SCD curves, which were overwhelmingly governed by the initial distribution of snow depth. The reason for this is that only a weak spatial correlation developed between the initial snow depth and dHS. Previous research has shown that spatial correlations between SWE and ablation rates can strongly influence SCD curves. Reasons for the lack of a correlation in this study area were analysed and a generalisation to other regions was discussed. The following questions were posed: what is needed for a large spatial correlation between initial snow depth and dHS? When should snow depth and dHS be taken into account to correctly model SCD? The findings of this study suggest that hydrological and atmospheric models need to incorporate realistic distributions of SWE, melt energy and cold content; therefore, they must account for realistic correlations (i.e. not too large or too small) between SWE and melt in order to accurately model SCD.

Evaluating satellite retrieved fractional snow-covered area at a high-Arctic site using terrestrial photography

The seasonal snow-cover is one of the most rapidly varying natural surface features on Earth. It strongly modulates the terrestrial water, energy, and carbon balance. Fractional snow-covered area (fSCA) is an essential snow variable that can be retrieved from multispectral satellite imagery. In this study, we evaluate fSCA retrievals from multiple sensors that are currently in polar orbit: the operational land imager (OLI) on-board Landsat 8, the multispectral instrument (MSI) on-board the Sentinel-2 satellites, and the moderate resolution imaging spectroradiometer (MODIS) on-board Terra and Aqua. We consider several retrieval algorithms that fall into three classes: thresholding of the normalized difference snow index (NDSI), regression on the NDSI, and spectral unmixing. We conduct the evaluation at a high-Arctic site in Svalbard, Norway, by comparing satellite retrieved fSCA to coincident high-resolution snow-cover maps obtained from a terrestrial automatic camera system. For the lower resolution MODIS retrievals, the regression-based retrievals outperformed the unmixing-based retrievals for all metrics but the bias. For the higher resolution sensors (OLI and MSI), retrievals based on NDSI thresholding overestimated the fSCA due to the mixed pixel problem whereas spectral unmixing retrievals provided the most reliable estimates across the board. We therefore encourage the operationalization of spectral unmixing retrievals of fSCA from both OLI and MSI.

Threshold-Based Adaptive Gaussian Mixture Model Integration (TA-GMMI) Algorithm for Mapping Snow Cover in Mountainous Terrain

Threshold-Based Adaptive Gaussian Mixture Model Integration (TA-GMMI) Algorithm for Mapping Snow Cover in Mountainous Terrain

Daily MODIS Snow Cover Maps for the European Alps from 2002 onwards at 250 m Horizontal Resolution Along with a Nearly Cloud-Free Version

Snow cover dynamics impact a whole range of systems in mountain regions, from society to economy to ecology; and they also affect downstream regions. Monitoring and analyzing snow cover dynamics has been facilitated with remote sensing products. Here, we present two high-resolution daily snow cover data sets for the entire European Alps covering the years 2002 to 2019, and with automatic updates. The first is based on moderate resolution imaging spectroradiometer (MODIS) and its implementation is specifically tailored to the complex terrain, exploiting the highest possible resolution available of 250 m. The second is a nearly cloud-free product derived from the first using temporal and spatial filters, which reduce average cloud cover from 41.9% to less than 0.1%. Validation has been performed using an extensive network of 312 ground stations, and for the cloud filtering also with cross-validation. Average overall accuracies were 93% for the initial and 91.5% for the cloud-filtered product using the ground stations; and 95.3% for the cross-validation of the cloud-filter. The data can be accessed online and via the R and python programming languages. Possible applications of the data include but are not limited to hydrology, cryosphere and climate.

Simulation of the meltwater under different climate change scenarios in a poorly gauged snow and glacier-fed Chitral River catchment (Hindukush region)

Seasonal and annual water supplies of the rivers originating in the Hindukush-Karakoram-Himalaya (HKH) region of Pakistan are important to manage the Indus basin irrigation system for better agricultural production and its dependent agrarian economy. In this study, we simulated the current and future snowmelt runoff in a poorly gauged river basin of the Hindukush region under Representative Concentration Pathways (RCP) climate change scenarios. Snowmelt Runoff Model (SRM) furnished with satellite snow cover maps and hydro-meteorological data were used to simulate the daily river discharge for the period 2000‒2005. The results indicated that SRM has effectually simulated the runoff in Chitral River with Nash-Sutcliffe model efficiency coefficient of 0.85 (0.84) and 0.88 (0.83) in the basin-wide (zone-wise) application during the calibration and validation periods, respectively. The results obtained under future climate change scenario showed ∼14‒19% increase in mean summer discharge under three mid-21st century RCP (2.6, 4.5 and 8.5) scenarios. While an increase of ∼13‒37% is expected under late-21st century RCP scenarios. This study can help water resource managers to plan and manage peak discharges from the Chitral River Basin in the future and can thus prevent major losses due to floods in the area.

Earth Observation Open Science: Enhancing Reproducible Science Using Data Cubes

Earth Observation Data Cubes (EODC) have emerged as a promising solution to efficiently and effectively handle Big Earth Observation (EO) Data generated by satellites and made freely and openly available from different data repositories. The aim of this Special Issue, “Earth Observation Data Cube”, in Data, is to present the latest advances in EODC development and implementation, including innovative approaches for the exploitation of satellite EO data using multi-dimensional (e.g., spatial, temporal, spectral) approaches. This Special Issue contains 14 articles covering a wide range of topics such as Synthetic Aperture Radar (SAR), Analysis Ready Data (ARD), interoperability, thematic applications (e.g., land cover, snow cover mapping), capacity development, semantics, processing techniques, as well as national implementations and best practices. These papers made significant contributions to the advancement of a more Open and Reproducible Earth Observation Science, reducing the gap between users’ expectations for decision-ready products and current Big Data analytical capabilities, and ultimately unlocking the information power of EO data by transforming them into actionable knowledge.