This dataset provides daily gridded data of sea ice concentration for both hemispheres derived from satellite passive microwave brightness temperatures. Sea ice is an important component of our climate system and a sensitive indicator of climate change. Its presence or its retreat has a strong impact on air-sea interactions, the Earth’s energy budget as well as marine ecosystems. It is recognised by the Global Climate Observing System as an Essential Climate Variable. Sea ice concentration is defined as the fraction of a pixel or grid cell in a satellite image or other gridded product that is covered with sea ice. It is one of the parameters commonly used to characterise sea ice. Other sea ice parameters include sea ice thickness, sea ice edge, and sea ice type, also available in the Climate Data Store. The dataset consists of two products:

The Global Sea Ice Concentration Climate Data Record produced by the European Organisation for the Exploitation of Meteorological Satellites (EUMETSAT) Ocean and Sea Ice Satellite Application Facility (OSI SAF). This is a coarse-resolution product based on measurements from the following sensors: Scanning Multichannel Microwave Radiometer (SMMR; 1979–1987), Special Sensor Microwave/Imager (SSM/I; 1987–2006), and Special Sensor Microwave Imager/Sounder (SSMIS; 2005 onward). This product spans the period from 1979 to present and is updated daily. In the following, it is referred to as the SSMIS product. The Global Sea Ice Concentration Climate Data Record produced by the European Space Agency Climate Change Initiative Phase 2 project (ESA CCI). This is a medium-resolution product based on measurements from the Advanced Microwave Scanning Radiometer - Earth Observing System (AMSR-E) sensor (2002–2011) and its successor, AMSR2 (2012–2017). This product spans the 2002–2017 period and is not updated. In the following, it is referred to as the AMSR product.

Both products are provided on the same polar projection with a grid resolution of 25 km. However, the AMSR product has a true spatial resolution (as resolved by the sensor) of about 15–25 km versus 30–60 km for the SSMIS product. Therefore, the AMSR product provides a much more detailed view of the sea ice cover than the SSMIS product, especially along the marginal ice zone, the transitional zone between open water and the dense sea ice pack. On the other hand, the clear strength of the SSMIS product is its more than 40-year long and consistent record with daily updates. Although originating from different projects, the two products share the same algorithm baseline, which is both a continuation of the EUMETSAT OSI SAF approach and a series of innovations contributed mostly by ESA CCI activities. For both products, the underlying algorithm makes use of a combination of the same three temperature channels near 19 GHz and 37 GHz. The data also share a common data format so that interested users can revert some of the filtering steps and access the raw output of the SIC algorithms. Both are level-4 products in the sense that gaps are filled by temporal and spatial interpolation. However, gap filling is not applied to fill in days when no input satellite data are available. Further details about each product can be found below as well as in the Documentation section.

This dataset provides estimates of surface soil moisture over the globe from a large set of satellite sensors. It is based on the methodology developed in the ESA Climate Change Initiative for Soil Moisture and represents the current state-of-the-art for satellite-based soil moisture climate data record production, in line with the “Systematic observation requirements for satellite-based products for climate” as defined by GCOS (Global Climate Observing System). Data are on a regular latitude/longitude grid expectedly with gaps in space and time. When dealing with satellite data it is common to encounter references to Climate Data Records (CDR) and interim-CDR (ICDR). For this dataset, both the ICDR and CDR parts of each product were generated using the same software and algorithms. The CDR is intended to have sufficient length, consistency, and continuity to detect climate variability and change. The ICDR provides a short-delay access to current data where consistency with the CDR baseline is expected but was not extensively checked. The dataset contains the following products: "active", "passive" and "combined". The "active" and "passive" products were created by using scatterometer and radiometer soil moisture products, respectively. The "Combined" product results from a blend based on both scatterometer and radiometer soil moisture products. This dataset is produced on behalf of the Copernicus Climate Change Service (C3S).

Qing Yang, Xinyi Shen, Kang He, Qingyuan Zhang, Sean Helfrich, William Straka III, Josef M. Kellndorfer, Emmanouil N. AnagnostouAbstract:On June 6, 2023, the Kakhovka Dam in Ukraine experienced a catastrophic breach that led to the loss of life and substantial economic values. Prior to the breach, the supporting structures downstream of the spillway had shown signs of being compromised. Here, we use multi-source satellite data, meteorological reanalysis, and dam design criteria to document the dam’s pre-failure condition. We find that anomalous operation of the Kakhovka dam began in November 2022, following the destruction of a bridge segment, which led to persistent overtopping from late April 2023 up to the breach, contributing to the erosion of the spillway foundation. Moreover, our findings also highlight safety and risk-reduction measures pivotal in avoiding such scenarios. To help prevent future disasters, we advocate for greater transparency in the design parameters of key water structures to enable risk management, and conclude that remote sensing technology can help ensuring water infrastructure safety.Data repository corresponding to: yang2473@uwm.eduThis repository contains underlying data and code for the main manuscript figures. Data for Figure 2 is from PlanetScope. Other raw data source are also concluded as follows:The Sentinel-2 and Landsat images used in this study are accessible at https://www.sentinel-hub.com/index.html. The PlanetScope images are obtained through https://www.planet.com/. The Sentinel-1 images were acquired from https://asf.alaska.edu/. The reservoir water levels can be found at https://ipad.fas.usda.gov/cropexplorer/global_reservoir/. The GloFAS-ERA5 river discharge reanalysis data are available at https://cds.climate.copernicus.eu/cdsapp#!/dataset/cems-glofas-historical?tab=overview.

No description is available. Visit https://dataone.org/datasets/%7BDB1CC082-A321-4F73-8824-2A14656128B2%7D for complete metadata about this dataset.

This dataset provides global maps describing the land surface into 22 classes, which have been defined using the United Nations Food and Agriculture Organization’s (UN FAO) Land Cover Classification System (LCCS). In addition to the land cover (LC) maps, four quality flags are produced to document the reliability of the classification and change detection. In order to ensure continuity, these land cover maps are consistent with the series of global annual LC maps from the 1990s to 2015 produced by the European Space Agency (ESA) Climate Change Initiative (CCI), which are also available on the ESA CCI LC viewer. To produce this dataset, the entire Medium Resolution Imaging Spectrometer (MERIS) Full and Reduced Resolution archive from 2003 to 2012 was first classified into a unique 10-year baseline LC map. This is then back- and up-dated using change detected from (i) Advanced Very-High-Resolution Radiometer (AVHRR) time series from 1992 to 1999, (ii) SPOT-Vegetation (SPOT-VGT) time series from 1998 to 2012 and (iii) PROBA-Vegetation (PROBA-V), Sentinel-3 OLCI (S3 OLCI) and Sentinel-3 SLSTR (S3 SLSTR) time series from 2013. Beyond the climate-modelling communities, this dataset’s long-term consistency, yearly updates, and high thematic detail on a global scale have made it attractive for a multitude of applications such as land accounting, forest monitoring and desertification, in addition to scientific research.

Sea level data were collected using satellite data in a world-wide distribution from October 4, 1992 to December 27, 1999. Data were submitted by Dr. Charles Sun of National Oceanographic Data Center (NODC) as a part of the World Ocean Circulation Experiment (WOCE) project.

This dataset provides estimates of surface elevation change over the Greenland and Antarctic ice sheets since 1992, utilizing satellite radar altimetry from five missions: ERS-1, ERS-2, ENVISAT, CryoSat-2, and Sentinel-3A. The surface elevation change is modelled over successive, overlapping periods and reported monthly. The dataset production method is an evolution of those employed by the European Space Agency (ESA)'s Greenland and Antarctic Ice Sheet Climate Change Initiatives and is guided by the Global Climate Observing System targets for the Ice Sheets Essential Climate Variable. An annual Climate Data Record (CDR), and monthly intermediate CDRs (ICDRs) are issued. Each monthly record includes all previous data, from 1992 onwards, as well as that month's update. This product is designed to provide data stability, so changes in the historic data, eg. if a satellite's elevation data is reprocessed or if inter-satellite cross-calibration is revised, are only introduced in the annual CDR. Each annual CDR is given a version number. The differences in the geographical location of the two sheets result in site-specific processing: Greenland: Data consists of surface elevation change rate and its uncertainty in a five-year (for the early satellites: ERS-1, ERS-2, and ENVISat) or three-year (for CryoSat-2 and Sentinel-3A) moving window. The moving window is advanced at one-month steps. Elevation measurements from satellite radar altimetry are used to build time-series of elevation change by the most optimal combination of the crossover-, repeat-track- and plane-fitting methods. The timeseries is derived for each cell on a 25km by 25km polar stereographic grid, covering the main Greenland ice sheet, and not including peripheral glaciers and ice caps. Data gaps have been filled using an ordinary Kriging interpolation method, and the distance to the nearest observational point is provided as utility information. The distance can be used to flag filled data. Antarctica: Data consists of surface elevation change rate over a five-year moving window that advances in one-month steps. It covers the Antarctic ice sheet, ice shelves and associated ice rises and islands on a 25km by 25km polar stereographic grid. Elevation measurements from five satellite radar altimetry missions, ERS1, ERS2, EnviSat, CryoSat-2 and Sentinel-3A, are used to produce timeseries of surface elevation change by the crossover method for each grid cell. The mission timeseries are cross-calibrated into a consistent record, which is used to derive surface elevation change rates and their uncertainty estimates in each cell and time-window. Data gaps are flagged but not filled.