Studies utilizing Global Positioning System (GPS) telemetry rarely result in 100% fix success rates (FSR). Many assessments of wildlife resource use do not account for missing data, either assuming data loss is random or because a lack of practical treatment for systematic data loss. Several studies have explored how the environment, technological features, and animal behavior influence rates of missing data in GPS telemetry, but previous spatially explicit models developed to correct for sampling bias have been specified to small study areas, on a small range of data loss, or to be species-specific, limiting their general utility. Here we explore environmental effects on GPS fix acquisition rates across a wide range of environmental conditions and detection rates for bias correction of terrestrial GPS-derived, large mammal habitat use. We also evaluate patterns in missing data that relate to potential animal activities that change the orientation of the antennae and characterize home-range probability of GPS detection for 4 focal species; cougars (Puma concolor), desert bighorn sheep (Ovis canadensis nelsoni), Rocky Mountain elk (Cervus elaphus ssp. nelsoni) and mule deer (Odocoileus hemionus). Part 1, Positive Openness Raster (raster dataset): Openness is an angular measure of the relationship between surface relief and horizontal distance. For angles less than 90 degrees it is equivalent to the internal angle of a cone with its apex at a DEM location, and is constrained by neighboring elevations within a specified radial distance. 480 meter search radius was used for this calculation of positive openness. Openness incorporates the terrain line-of-sight or viewshed concept and is calculated from multiple zenith and nadir angles-here along eight azimuths. Positive openness measures openness above the surface, with high values for convex forms and low values for concave forms (Yokoyama et al. 2002). We calculated positive openness using a custom python script, following the methods of Yokoyama et. al (2002) using a USGS National Elevation Dataset as input. Part 2, Northern Arizona GPS Test Collar (csv): Bias correction in GPS telemetry data-sets requires a strong understanding of the mechanisms that result in missing data. We tested wildlife GPS collars in a variety of environmental conditions to derive a predictive model of fix acquisition. We found terrain exposure and tall over-story vegetation are the primary environmental features that affect GPS performance. Model evaluation showed a strong correlation (0.924) between observed and predicted fix success rates (FSR) and showed little bias in predictions. The model's predictive ability was evaluated using two independent data-sets from stationary test collars of different make/model, fix interval programming, and placed at different study sites. No statistically significant differences (95% CI) between predicted and observed FSRs, suggest changes in technological factors have minor influence on the models ability to predict FSR in new study areas in the southwestern US. The model training data are provided here for fix attempts by hour. This table can be linked with the site location shapefile using the site field. Part 3, Probability Raster (raster dataset): Bias correction in GPS telemetry datasets requires a strong understanding of the mechanisms that result in missing data. We tested wildlife GPS collars in a variety of environmental conditions to derive a predictive model of fix aquistion. We found terrain exposure and tall overstory vegetation are the primary environmental features that affect GPS performance. Model evaluation showed a strong correlation (0.924) between observed and predicted fix success rates (FSR) and showed little bias in predictions. The models predictive ability was evaluated using two independent datasets from stationary test collars of different make/model, fix interval programing, and placed at different study sites. No statistically significant differences (95% CI) between predicted and observed FSRs, suggest changes in technological factors have minor influence on the models ability to predict FSR in new study areas in the southwestern US. We evaluated GPS telemetry datasets by comparing the mean probability of a successful GPS fix across study animals home-ranges, to the actual observed FSR of GPS downloaded deployed collars on cougars (Puma concolor), desert bighorn sheep (Ovis canadensis nelsoni), Rocky Mountain elk (Cervus elaphus ssp. nelsoni) and mule deer (Odocoileus hemionus). Comparing the mean probability of acquisition within study animals home-ranges and observed FSRs of GPS downloaded collars resulted in a approximatly 1:1 linear relationship with an r-sq= 0.68. Part 4, GPS Test Collar Sites (shapefile): Bias correction in GPS telemetry data-sets requires a strong understanding of the mechanisms that result in missing data. We tested wildlife GPS collars in a variety of environmental conditions to derive a predictive model of fix acquisition. We found terrain exposure and tall over-story vegetation are the primary environmental features that affect GPS performance. Model evaluation showed a strong correlation (0.924) between observed and predicted fix success rates (FSR) and showed little bias in predictions. The model's predictive ability was evaluated using two independent data-sets from stationary test collars of different make/model, fix interval programming, and placed at different study sites. No statistically significant differences (95% CI) between predicted and observed FSRs, suggest changes in technological factors have minor influence on the models ability to predict FSR in new study areas in the southwestern US. Part 5, Cougar Home Ranges (shapefile): Cougar home-ranges were calculated to compare the mean probability of a GPS fix acquisition across the home-range to the actual fix success rate (FSR) of the collar as a means for evaluating if characteristics of an animal’s home-range have an effect on observed FSR. We estimated home-ranges using the Local Convex Hull (LoCoH) method using the 90th isopleth. Data obtained from GPS download of retrieved units were only used. Satellite delivered data was omitted from the analysis for animals where the collar was lost or damaged because satellite delivery tends to lose as additional 10% of data. Comparisons with home-range mean probability of fix were also used as a reference for assessing if the frequency animals use areas of low GPS acquisition rates may play a role in observed FSRs. Part 6, Cougar Fix Success Rate by Hour (csv): Cougar GPS collar fix success varied by hour-of-day suggesting circadian rhythms with bouts of rest during daylight hours may change the orientation of the GPS receiver affecting the ability to acquire fixes. Raw data of overall fix success rates (FSR) and FSR by hour were used to predict relative reductions in FSR. Data only includes direct GPS download datasets. Satellite delivered data was omitted from the analysis for animals where the collar was lost or damaged because satellite delivery tends to lose approximately an additional 10% of data. Part 7, Openness Python Script version 2.0: This python script was used to calculate positive openness using a 30 meter digital elevation model for a large geographic area in Arizona, California, Nevada and Utah. A scientific research project used the script to explore environmental effects on GPS fix acquisition rates across a wide range of environmental conditions and detection rates for bias correction of terrestrial GPS-derived, large mammal habitat use.

This dataset provides global daily sea surface temperature (SST) data from the Group for High Resolution Sea Surface Temperature (GHRSST) multi-product ensemble (GMPE) produced by the European Space Agency SST Climate Change Initiative (ESA SST CCI). The GMPE system was designed to allow users to compare the outputs from different SST analysis systems and understand their similarities and differences. Although originally intended for comparison of near real time data, it has also been used to compare long historical datasets. Note that the dataset provided here is the climate version of the GMPE dataset. An operational version, with different input products and time coverage, also exists but is not distributed by the CDS. The SST analyses ingested into the GMPE system come from the following seven SST products and providers:

ESA SST CCI Analysis version 2.0 ESA SST CCI Analysis version 1.1 Copernicus Marine Environment Monitoring Service (CMEMS) Operational Sea Surface Temperature and Sea Ice Analysis (OSTIA) Reprocessing National Centers for Environmental Information (NCEI) Advanced Very High Resolution Radiometer (AVHRR) Optimal Interpolation (OI) Global Blended SST Analysis Canada Meteorological Center (CMC) 0.2-degree Global Foundation SST Analysis Hadley Centre Sea Ice and Sea Surface Temperature (HadISST) Analysis version 2.2.0.0 Japan Meteorological Agency (JMA) Merged satellite and in-situ Data Global Daily SST (MGDSST) Analysis

These products are all spatially complete (through use of infilling or reconstruction techniques) but were originally produced for different purposes and with different user requirements in mind. Therefore, each producer has made different choices on aspects of data production such as which input observations to use and what type of SST to represent. For example, the CMEMS OSTIA, CMC, and MGDSST analyses attempt to represent the foundation SST (water temperature free of diurnal temperature variability) while the ESA SST CCI and HadISST analyses estimate the SST at a standard depth of 20 cm. The AVHRR OI product, on the other hand, is bias-corrected to in situ observations and hence will be representative of their depths. The GMPE dataset provides the median and standard deviation of the input SST products, the differences between each input product and the median, and the horizontal gradients in each of the input SST products as well as the final ensemble product. The HadISST product consists of 10 different realisations, therefore the median and standard deviation are calculated for an ensemble of 16 input fields. All fields are provided on a common 0.25 degree regular latitude-longitude grid and extend from 1 September 1981 to 31 December 2016, although some of the individual input products cover shorter periods. The dataset will not be extended beyond 2016.

This dataset provides high-resolution gridded temperature and precipitation observations from a selection of sources. Additionally the dataset contains daily global average near-surface temperature anomalies. All fields are defined on either daily or monthly frequency. The datasets are regularly updated to incorporate recent observations. The included data sources are commonly known as GISTEMP, Berkeley Earth, CPC and CPC-CONUS, CHIRPS, IMERG, CMORPH, GPCC and CRU, where the abbreviations are explained below. These data have been constructed from high-quality analyses of meteorological station series and rain gauges around the world, and as such provide a reliable source for the analysis of weather extremes and climate trends. The regular update cycle makes these data suitable for a rapid study of recently occurred phenomena or events. The NASA Goddard Institute for Space Studies temperature analysis dataset (GISTEMP-v4) combines station data of the Global Historical Climatology Network (GHCN) with the Extended Reconstructed Sea Surface Temperature (ERSST) to construct a global temperature change estimate. The Berkeley Earth Foundation dataset (BERKEARTH) merges temperature records from 16 archives into a single coherent dataset. The NOAA Climate Prediction Center datasets (CPC and CPC-CONUS) define a suite of unified precipitation products with consistent quantity and improved quality by combining all information sources available at CPC and by taking advantage of the optimal interpolation (OI) objective analysis technique. The Climate Hazards Group InfraRed Precipitation with Station dataset (CHIRPS-v2) incorporates 0.05° resolution satellite imagery and in-situ station data to create gridded rainfall time series over the African continent, suitable for trend analysis and seasonal drought monitoring. The Integrated Multi-satellitE Retrievals dataset (IMERG) by NASA uses an algorithm to intercalibrate, merge, and interpolate “all'' satellite microwave precipitation estimates, together with microwave-calibrated infrared (IR) satellite estimates, precipitation gauge analyses, and potentially other precipitation estimators over the entire globe at fine time and space scales for the Tropical Rainfall Measuring Mission (TRMM) and its successor, Global Precipitation Measurement (GPM) satellite-based precipitation products. The Climate Prediction Center morphing technique dataset (CMORPH) by NOAA has been created using precipitation estimates that have been derived from low orbiter satellite microwave observations exclusively. Then, geostationary IR data are used as a means to transport the microwave-derived precipitation features during periods when microwave data are not available at a location. The Global Precipitation Climatology Centre dataset (GPCC) is a centennial product of monthly global land-surface precipitation based on the ~80,000 stations world-wide that feature record durations of 10 years or longer. The data coverage per month varies from ~6,000 (before 1900) to more than 50,000 stations. The Climatic Research Unit dataset (CRU v4) features an improved interpolation process, which delivers full traceability back to station measurements. The station measurements of temperature and precipitation are public, as well as the gridded dataset and national averages for each country. Cross-validation was performed at a station level, and the results have been published as a guide to the accuracy of the interpolation. This catalogue entry complements the E-OBS record in many aspects, as it intends to provide high-resolution gridded meteorological observations at a global rather than continental scale. These data may be suitable as a baseline for model comparisons or extreme event analysis in the CMIP5 and CMIP6 dataset.

This dataset contains information on the Surface Soil Moisture (SM) content derived from satellite observations in the microwave domain.

Dataset paper (public preprint)

A description of this dataset, including the methodology and validation results, is available at:

Preimesberger, W., Stradiotti, P., and Dorigo, W.: ESA CCI Soil Moisture GAPFILLED: An independent global gap-free satellite climate data record with uncertainty estimates, Earth Syst. Sci. Data Discuss. [preprint], https://doi.org/10.5194/essd-2024-610, in review, 2025.

Abstract

ESA CCI Soil Moisture is a multi-satellite climate data record that consists of harmonized, daily observations coming from 19 satellites (as of v09.1) operating in the microwave domain. The wealth of satellite information, particularly over the last decade, facilitates the creation of a data record with the highest possible data consistency and coverage.
However, data gaps are still found in the record. This is particularly notable in earlier periods when a limited number of satellites were in operation, but can also arise from various retrieval issues, such as frozen soils, dense vegetation, and radio frequency interference (RFI). These data gaps present a challenge for many users, as they have the potential to obscure relevant events within a study area or are incompatible with (machine learning) software that often relies on gap-free inputs.
Since the requirement of a gap-free ESA CCI SM product was identified, various studies have demonstrated the suitability of different statistical methods to achieve this goal. A fundamental feature of such gap-filling method is to rely only on the original observational record, without need for ancillary variable or model-based information. Due to the intrinsic challenge, there was until present no global, long-term univariate gap-filled product available. In this version of the record, data gaps due to missing satellite overpasses and invalid measurements are filled using the Discrete Cosine Transform (DCT) Penalized Least Squares (PLS) algorithm (Garcia, 2010). A linear interpolation is applied over periods of (potentially) frozen soils with little to no variability in (frozen) soil moisture content. Uncertainty estimates are based on models calibrated in experiments to fill satellite-like gaps introduced to GLDAS Noah reanalysis soil moisture (Rodell et al., 2004), and consider the gap size and local vegetation conditions as parameters that affect the gapfilling performance.

Summary

• Gap-filled global estimates of volumetric surface soil moisture from 1991-2023 at 0.25° sampling
• Fields of application (partial): climate variability and change, land-atmosphere interactions, global biogeochemical cycles and ecology, hydrological and land surface modelling, drought applications, and meteorology
• Method: Modified version of DCT-PLS (Garcia, 2010) interpolation/smoothing algorithm, linear interpolation over periods of frozen soils. Uncertainty estimates are provided for all data points.
• More information: See Preimesberger et al. (2025) and https://doi.org/10.5281/zenodo.8320869" target="_blank" rel="noopener">ESA CCI SM Algorithm Theoretical Baseline Document [Chapter 7.2.9] (Dorigo et al., 2023)

Programmatic Download

You can use command line tools such as wget or curl to download (and extract) data for multiple years. The following command will download and extract the complete data set to the local directory ~/Download on Linux or macOS systems.

#!/bin/bash

# Set download directory
DOWNLOAD_DIR=~/Downloads

base_url="https://researchdata.tuwien.at/records/3fcxr-cde10/files"

# Loop through years 1991 to 2023 and download & extract data
for year in {1991..2023}; do
  echo "Downloading $year.zip..."
  wget -q -P "$DOWNLOAD_DIR" "$base_url/$year.zip"
  unzip -o "$DOWNLOAD_DIR/$year.zip" -d $DOWNLOAD_DIR
  rm "$DOWNLOAD_DIR/$year.zip"
done

Data details

The dataset provides global daily estimates for the 1991-2023 period at 0.25° (~25 km) horizontal grid resolution. Daily images are grouped by year (YYYY), each subdirectory containing one netCDF image file for a specific day (DD), month (MM) in a 2-dimensional (longitude, latitude) grid system (CRS: WGS84). The file name has the following convention:

ESACCI-SOILMOISTURE-L3S-SSMV-COMBINED_GAPFILLED-YYYYMMDD000000-fv09.1r1.nc

Data Variables

Each netCDF file contains 3 coordinate variables (WGS84 longitude, latitude and time stamp), as well as the following data variables:

• sm: (float) The Soil Moisture variable reflects estimates of daily average volumetric soil moisture content (m3/m3) in the soil surface layer (~0-5 cm) over a whole grid cell (0.25 degree).
• sm_uncertainty: (float) The Soil Moisture Uncertainty variable reflects the uncertainty (random error) of the original satellite observations and of the predictions used to fill observation data gaps.
• sm_anomaly: Soil moisture anomalies (reference period 1991-2020) derived from the gap-filled values (`sm`)
• sm_smoothed: Contains DCT-PLS predictions used to fill data gaps in the original soil moisture field. These values are also available when an observation was initially available (compare `gapmask`). In this case, they provided a smoothed version of the original data.
• gapmask: (0 | 1) Indicates grid cells where a satellite observation is available (0), and where the interpolated value is used (1) in the 'sm' field.
• frozenmask: (0 | 1) Indicates grid cells where ERA5 soil temperature is <0 °C. In this case, a linear interpolation over time is applied.

Additional information for each variable is given in the netCDF attributes.

Version Changelog

Changes in v9.1r1 (previous version was v09.1):

• This version uses a novel uncertainty estimation scheme as described in Preimesberger et al. (2025).

Software to open netCDF files

These data can be read by any software that supports Climate and Forecast (CF) conform metadata standards for netCDF files, such as:

• https://github.com/pydata/xarray" target="_blank" rel="noopener">Xarray (python)
• https://unidata.github.io/netcdf4-python/" target="_blank" rel="noopener">netCDF4 (python)
• https://github.com/TUW-GEO/esa_cci_sm">esa_cci_sm (python)
• Similar tools exists for other programming languages (Matlab, R, etc.)
• Software packages and GIS tools can open netCDF files, e.g. CDO, NCO, QGIS, ArCGIS
• You can also use the GUI software Panoply to view the contents of each file

References

• Preimesberger, W., Stradiotti, P., and Dorigo, W.: ESA CCI Soil Moisture GAPFILLED: An independent global gap-free satellite climate data record with uncertainty estimates, Earth Syst. Sci. Data Discuss. [preprint], https://doi.org/10.5194/essd-2024-610, in review, 2025.
• Dorigo, W., Preimesberger, W., Stradiotti, P., Kidd, R., van der Schalie, R., van der Vliet, M., Rodriguez-Fernandez, N., Madelon, R., & Baghdadi, N. (2023). ESA Climate Change Initiative Plus - Soil Moisture Algorithm Theoretical Baseline Document (ATBD) Supporting Product Version 08.1 (version 1.1). Zenodo. https://doi.org/10.5281/zenodo.8320869
• Garcia, D., 2010. Robust smoothing of gridded data in one and higher dimensions with missing values. Computational Statistics & Data Analysis, 54(4), pp.1167-1178. Available at: https://doi.org/10.1016/j.csda.2009.09.020
• Rodell, M., Houser, P. R., Jambor, U., Gottschalck, J., Mitchell, K., Meng, C.-J., Arsenault, K., Cosgrove, B., Radakovich, J., Bosilovich, M., Entin, J. K., Walker, J. P., Lohmann, D., and Toll, D.: The Global Land Data Assimilation System, Bulletin of the American Meteorological Society, 85, 381 – 394, https://doi.org/10.1175/BAMS-85-3-381, 2004.

Related Records

The following records are all part of the Soil Moisture Climate Data Records from satellites community

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.

The version 4.0 SMAP-SSS level 3, monthly gridded product is based on the fourth release of the validated standard mapped sea surface salinity (SSS) data from the NASA Soil Moisture Active Passive (SMAP) observatory, produced operationally by Remote Sensing Systems (RSS) with a one-month latency. Enhancements with this release include: use of an improved 0.125 degree land correction table with land emission based on SMAP TB; replacement of the previous NCEP sea-ice mask with one based on RSS AMSR-2 and implementing a sea-ice threshold of 0.3% (gain weighted sea-ice fraction); revised solar flagging that depends on glint angle and wind speed; inclusion of estimated SSS-uncertainty; consolidation of both 40KM and 70KM SMAP-SSS datasets as variable fields in a single data product. Monthly data files for this product are averages over one-month time intervals. SMAP data begins on April 1,2015 and is ongoing, with a one-month latency in processing and availability. L3 products are global in extent and gridded at 0.25degree x 0.25degree with a default spatial feature resolution of approximately 70KM. Note that while a SSS 40KM variable is also included in the product, for most open ocean applications, the default SSS variable (70KM) is best used as they are significantly less noisy than the 40KM data. The SMAP satellite is in a near-polar orbit at an inclination of 98 degrees and an altitude of 685 km. It has an ascending node time of 6 pm and is sun-synchronous. With its 1000km swath, SMAP achieves global coverage in approximately 3 days, but has an exact orbit repeat cycle of 8 days. On board instruments include a highly sensitive L-band radiometer operating at 1.41GHz and an L-band 1.26GHz radar sensor providing complementary active and passive sensing capabilities. Malfunction of the SMAP scatterometer on 7 July, 2015, has necessitated the use of collocated wind speed, primarily from WindSat, for the surface roughness correction required for the surface salinity retrieval.

This metadata document describes the data contained in the "processedData" folder of this data package. This data package contains data collected by the Argos System from 12 satellite transmitters attached to Pacific Loons on their breeding range in arctic, 2015-2016. The raw data were processed to accomplish two goals: flag implausible location estimates and decode raw sensor data. Two Comma Separate Value (CSV) tables are included in the "processedData" folder of this data package: 1) the "diag_filteredLocations" table contains one record for every location estimate, accompanied by a binary flag that denotes an algorithm's plausibility check. Each record also includes a 'Tracking_Status' variable that denotes whether the location was collected from a live animal, a dead animal, or shed transmitter, and 2) the "deploymentAttributes" table contains one record for each transmitter deployment in a CSV formatted table. The deployment attributes file contains information such as when the transmitter was attached to the animal, when tracking of a live animal ended, and a variety of variables describing the animal and transmitter.

land and oceanic climate variables. The data cover the Earth on a 31km grid and resolve the atmosphere using 137 levels from the surface up to a height of 80km. ERA5 includes information about uncertainties for all variables at reduced spatial and temporal resolutions.

The National Centers for Environmental Information in partnership with the Cooperative Institute for Climate and Satellites - North Carolina is reprocessing the GOES (Geostationary Operational Environmental Satellite) Variable (GVAR) period of record: 1994-2015. GridSat GOES represents a reformatted, remapped and calibrated GOES brightness temperatures and reflectance provided in Climate and Forecasting (CF)-compliant netCDF format. This is similar to the current GridSat-B1 CDR, but at a higher spatial and temporal resolution. The data are provided near the original spatial resolution of the infrared channels (4 km) on an equal angle grid (0.04 degrees). Data are mapped to a region spanning the view of GOES East and West (150 deg East to 5 deg East). The data are provided hourly, with all data mapping to the nearest hour. Currently, the data are limited to variables including the observations from the GOES satellites: 5 total channels. However, future efforts are planned to include some basic cloud information (cloud probability, temperature, etc.). Other possible updates include: improved coverage by expanding the GOES inventory (currently, gaps exist in the CLASS archive) and expand to the predecessor to the GOES Imager: GOES VISSR, which would expand coverage back to the 1980s.

The new version of the Hamburg Ocean Atmosphere Parameters and Fluxes from Satellite Data set - HOAPS II - contains improved global fields of precipitation and evaporation over the oceans and all basic state variables needed for the derivation of the turbulent fluxes. Except for the NOAA Pathfinder SST data set, all variables are derived from SSM/I satellite data over the ice free oceans between 1987 and 2002. The earlier HOAPS version was improved and includes now the utilisation of multi-satellite averages with proper inter-satellite calibration, improved algorithms and a new ice detection procedure, resulting in more homogeneous and reliable spatial and temporal fields as before. The spatial resolution of 0.5 degree, makes them ideally suited for studies of climate variability over the global oceans. Pentade and climatological means are also public and available via the CERA database system. Further information under : https://www.cmsaf.eu/EN/Overview/OurProducts/Hoaps/Hoaps_node.html .

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).

This metadata document describes the data contained in the "processedData" folder of this data package. This data package contains all data collected by the Argos System from 53 satellite transmitters attached to Emperor geese on their breeding range in western Alaska, 1999-2003. The raw data were processed to accomplish two goals: flag implausible location estimates and decode raw sensor data. Three Comma Separate Value (CSV) tables are included in the "processedData" folder of this data package: 1) the "diag_filteredLocations" table contains one record for every Argos location estimate collected, accompanied by a binary flag that denotes an algorithm's plausibility check (based on distance, turning angle, and rate thresholds). Each record also includes a 'Tracking_Status' variable that denotes whether the location was collected from a live animal, a dead animal, or shed transmitter, 2) the "decodedSensor" table contains decoded sensor data such as the transmitter's temperature, battery voltage, and motion (activity), and 3) the "deploymentAttributes" table contains one record for each transmitter deployment in a CSV formatted table. The deployment attributes file contains information such as when the transmitter was attached to the animal, when tracking of a live animal ended, and a variety of variables describing the animal and transmitter. This table is identical to the "deploymentAttributes" table in the "rawData" folder of this data package.

EIGEN-6S4 (Version 2) is a satellite-only global gravity field model from the combination of LAGEOS, GRACE and GOCE data. All spherical harmonic coefficients up to degree/order 80 are time variable. Their time variable parameters consist of drifts as well as annual and semi-annual variations per year. The time series of the time variable spherical harmonic coefficients are based on the LAGEOS-1/2 solution (1985 to 2003) and the GRACE-LAGEOS monthly gravity fields RL03-v2 (August 2002 to July 2014) from GRGS/Toulouse (Bruinsma et al. 2009). The herein included GRACE/LAGEOS data were combined with all GOCE data which have been processed via the direct numerical approach (Pail et al. 2011). The polar gap instabilty has been overcome using the Sperical Cap Regularization (Metzler and Pail 2005). That means this model is a combination of LAGEOS/GACE with GO_CONS_GCF_2_DIR_R5 (Bruinsma et al. 2013). Version History: This data set is an updated version of Foerste et al. (2016, http://doi.org/10.5880/icgem.2016.004) Compared to the first version, EIGEN-6S4v2 contains an improved modelling of the time variable part, in particular for C20.

******************************************************************************************************************************************************NOTICE: all datasets and tools provided in this database can and should only be used to reproduce the original experiment for which the database was created. The use of any datasets and tools in this database is subject to third party restrictions. Before copying or using this database for other purposes than reproducing the original experiment for which it was created, please ask for adequate authorisations to the author (Moctar Dembélé, mocdembele@gmail.com), who might additionaly need the authorization of the providers of the data and the tools available in this database. ******************************************************************************************************************************************************

This database provides model inputs and outputs for the manuscript 'Potential of Satellite and Reanalysis Evaporation Datasets for Hydrological Modelling under Various Model Calibration Strategies' by Dembélé et al.

The content of each folder is as follows:

-Input contains the data needed to setup and run the mHM model.

-The folders MOD16A2, SSEBop, ALEXI, CMRSET, SEBS, GLEAM v3.2a, GLEAM v3.3a, GLEAM v3.2b, GLEAM v3.3b), ERA5, MERRA-2, and JRA-55 contain the model output files using different evaporation data to calibrate the mHM model. Each of these folders contains four sub-folders corresponding to four distint calibration strategies.

-refQ contains the model output files when the model is calibrated using only streamflow data.

-inputAnalysis contains the results and the files of the analysis of the model input datasets using the MATLAB software..

-combiEvapAnalysis contains the results and the files of the analysis of the model outputs using the MATLAB software.

For further information, please contact Moctar Dembélé, mocdembele@gmail.com

This dataset consists of satellite-derived habitat data tables used to quantify fine-scale landscape metrics in an estuarine environment undergoing rapid climate-driven habitat change. The data were generated as part of a study evaluating the effects of mangrove encroachment and marsh loss on species-landscape relationships in coastal Louisiana.

Habitat variables were derived for buffer zones ranging from 150 to 600 meters around 52 field sampling sites and edge zones 1, 3, and 5 meters from the water's edge, providing detailed metrics such as percent land cover, edge area, and proportional mangrove cover. The greater spatial coverage of the satellite imagery allowed for larger habitat scales to be encompassed in the analysis.

Satellite images used in this analysis were all taken during the year 2022, within a few months of our sampling season, in the region surrounding Port Fourchon, LA. This dataset enables testing of species-specific responses to habitat features at ecologically relevant fine scales, particularly for nekton species interacting with marsh edges and immediate surrounding areas.

The primary purpose of this dataset is to inform ecological research focused on habitat suitability, landscape ecology, and the impacts of fine-scale habitat changes on estuarine species distributions. Researchers and resource managers can use these data to improve habitat suitability models, identify critical habitat features, and guide conservation strategies. The data were collected and interpreted by Herbert Leavitt, Dr. James Nelson, and Alex Thomas, with institutional affiliation at the time of collection being the University of Louisiana at Lafayette.

This dataset provides monthly gridded data of sea ice thickness for the Arctic region based on satellite radar altimetry observations. 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 recognized by the Global Climate Observing System as an Essential Climate Variable. Sea ice thickness is one of the parameters commonly used to characterise sea ice, alongside sea ice concentration, sea ice edge, and sea ice type, also available in the Climate Data Store. Satellite radar altimeters provide measurements of the sea ice freeboard, which is the difference between the height of the surface of sea ice and the surface of water in open leads (areas of open water within the sea ice). Because of the buoyancy of ice in water, typically about 90% of the ice thickness remains under water and thus the total ice thickness is about 10 times the freeboard. However, snow on top of sea ice changes this ratio and complicates the estimation of the ice thickness, requiring the use of auxiliary information about snow depth and density. The retrieval of ice thickness uses the narrow radar swath at the nadir of the satellite at full resolution of approximately 1-10 km and a point spacing of 300 meters. This Level-2 sea-ice thickness products (not provided here) is then gridded for a period of a month to obtain full coverage of a north polar grid at a resolution of 25 km. The algorithm used was developed as part of the European Space Agency Climate Change Initiative (ESA CCI) on Sea Ice. The data provided here are Level-3 Collated (L3C) products: they contain monthly gridded values from orbit data from a single platform (Envisat or CryoSat-2) without interpolation or any other form of gap filling. The files also contain estimates of the algorithm uncertainty as well as a quality status flag indicating potential issues with the retrieval not captured in the algorithm uncertainty. Sources of uncertainty in the algorithm are related to the auxiliary data and to the use of different radar altimeter concepts in Envisat (pulse-limited) and CryoSat-2 (synthetic aperture radar). This dataset combines a Climate Data Record (CDR), which has sufficient length, consistency, and continuity to be used to assess climate variability and change, and an Interim Climate Data Record (ICDR), which provides regular temporal extensions to the CDR and where consistency with the CDR is expected but not extensively checked. Here, the CDR is based on measurements from the RA-2 altimeter on Envisat (October 2002 to October 2010) and the SIRAL altimeter on CryoSat-2 (November 2010 to April 2020). The ICDR is based on observations from CryoSat-2 only (from April 2015 onward) and is updated monthly with a one-month delay behind real time. Users should note that the quality and accuracy of the data record are higher during the CryoSat-2 period than during the Envisat period. As a result, care should be taken when combining the two missions to assess long-term changes and trends. More information can be found in the Product User Guide and Product Quality Assessment Report. This dataset is currently limited spatially to the Arctic region and temporally to the winter months of October through April due to unresolved bias originating from melting snow or open melt ponds in the remaining five months. For a similar reason, no sea-ice thickness data with sufficient quality exist for the Southern Hemisphere. The extension of the CDR/ICDR to other periods, regions, and radar altimeter missions is under development in the extension of the ESA CCI Sea Ice project (ESA CCI+). This dataset is produced on behalf of the Copernicus Climate Change Service (C3S).

This repository is for the datasets of “Resilience of Spanish forests to recent droughts and climate change” (Khoury and Coomes, 2020, GCB). The datasets include Satellite data, climate variables and elevation, all extracted and pre-processed in Google Earth Engine. They include species distribution maps and protect areas shapefiles used in the study. The code for the analyses done in R are available upon request. The datasets alongside the code can be used to reproduce the results of the paper. Please cite Khoury and Coomes (2020) or acknowledge this dataset 10.6084/m9.figshare.12612416.

This dataset provides daily estimates of global sea surface temperature (SST) based on observations from multiple satellite sensors since September 1981. SST is known to be a significant driver of global weather and climate patterns and to play important roles in the exchanges of energy, momentum, moisture and gases between the ocean and atmosphere. As such, its knowledge is essential to understand and assess variability and long-term changes in the Earth’s climate. The SST data provided here are based on measurements carried out by the following infrared sensors flown onboard multiple polar-orbiting satellites: the series of Advanced Very High Resolution Radiometers (AVHRRs), the series of Along Track Scanning Radiometers (ATSRs), and the Sea and Land Surface Temperature Radiometer (SLSTR). The dataset provides SST products of different processing levels. Only Level-3 Collated and Level-4 and served through this entry in the Catalogue. Due to the large number of files at Level-2 Pre-processed and Level-3 Collated these products are served through the Climate Data Store API. For more information on how to access these levels consult the documentation. The four types of products are:

Level-2 Pre-processed (L2P): SST data on the native satellite swath grid and derived from single-sensor measurements. Level-3 Uncollated (L3U): SST product generated by regridding L2P data onto a global latitude-longitude grid. Level-3 Collated (L3C): global daily (day and night) single-sensor SST product based on collated L3U data. Level-4 (L4): spatially complete global SST product based on data from multiple sensors.

These products are available as Climate Data Records (CDRs), which have sufficient length, consistency, and continuity to be used to assess climate variability and changes. These SST CDRs are identical to those produced as part of the European Space Agency (ESA) SST Climate Change Initiative (CCI) project. Interim CDRs (ICDRs) are produced at levels L3C and L4 on behalf of the Copernicus Climate Change Service (C3S) to extend the baseline CDRs. Both SST CDRs and ICDRs are generated using software and algorithms developed as part of the ESA SST CCI. Users should use the most recent version of the dataset whenever possible. Data from the previous version are also made available but cover shorter periods.

This metadata document describes the data contained in the "processedData" folder of this data package. This data package contains all data collected by the Argos System from 17 satellite transmitters attached to Whooper Swans at a non-breeding site in Miyagi Prefecture, Japan, 2009. The raw data were processed to accomplish two goals: flag implausible location estimates and decode raw sensor data. Three Comma Separate Value (CSV) tables are included in the "processedData" folder of this data package: 1) the "diag_filteredLocations" table contains one record for every Argos location estimate collected, accompanied by a binary flag that denotes an algorithm's plausibility check (based on distance, turning angle, and rate thresholds). Each record also includes a 'Tracking_Status' variable that denotes whether the location was collected from a live animal, a dead animal, or shed transmitter, 2) the "decodedSensor" table contains decoded sensor data such as the transmitter's temperature, battery voltage, and motion (activity), and 3) the "deploymentAttributes" table contains one record for each transmitter deployment in a CSV formatted table. The deployment attributes file contains information such as when the transmitter was attached to the animal, when tracking of a live animal ended, and a variety of variables describing the animal and transmitter. This table is identical to the "deploymentAttributes" table in the "rawData" folder of this data package.

This layer is a time series of the annual ESA CCI (Climate Change Initiative) land cover maps of the world. ESA has produced land cover maps for the years 1992-2020. These are available at the European Space Agency Climate Change Initiative website.Time Extent: 1992-2020Cell Size: 300 meter Source Type: ThematicPixel Type: 8 Bit UnsignedData Projection: GCS WGS84Mosaic Projection: Web Mercator Auxiliary Sphere Extent: GlobalSource: ESA Climate Change InitiativeUpdate Cycle: Annual until 2020, no updates thereafterWhat can you do with this layer? This layer may be added to ArcGIS Online maps and applications and shown in a time series to watch a "time lapse" view of land cover change since 1992 for any part of the world. The same behavior exists when the layer is added to ArcGIS Pro. In addition to displaying all layers in a series, this layer may be queried so that only one year is displayed in a map. This layer can be used in analysis. For example, the layer may be added to ArcGIS Pro with a query set to display just one year. Then, an area count of land cover types may be produced for a feature dataset using the zonal statistics tool. Statistics may be compared with the statistics from other years to show a trend. To sum up area by land cover using this service, or any other analysis, be sure to use an equal area projection, such as Albers or Equal Earth. Different Classifications Available to Map Five processing templates are included in this layer. The processing templates may be used to display a smaller set of land cover classes.Cartographic Renderer (Default Template)Displays all ESA CCI land cover classes.*Forested lands TemplateThe forested lands template shows only forested lands (classes 50-90).Urban Lands TemplateThe urban lands template shows only urban areas (class 190).Converted Lands TemplateThe converted lands template shows only urban lands and lands converted to agriculture (classes 10-40 and 190).Simplified RendererDisplays the map in ten simple classes which match the ten simplified classes used in 2050 Land Cover projections from Clark University.Any of these variables can be displayed or analyzed by selecting their processing template. In ArcGIS Online, select the Image Display Options on the layer. Then pull down the list of variables from the Renderer options. Click Apply and Close. In ArcGIS Pro, go into the Layer Properties. Select Processing Templates from the left hand menu. From the Processing Template pull down menu, select the variable to display. Using Time By default, the map will display as a time series animation, one year per frame. A time slider will appear when you add this layer to your map. To see the most current data, move the time slider until you see the most current year. In addition to displaying the past quarter century of land cover maps as an animation, this time series can also display just one year of data by use of a definition query. For a step by step example using ArcGIS Pro on how to display just one year of this layer, as well as to compare one year to another, see the blog called Calculating Impervious Surface Change. Hierarchical ClassificationLand cover types are defined using the land cover classification (LCCS) developed by the United Nations, FAO. It is designed to be as compatible as possible with other products, namely GLCC2000, GlobCover 2005 and 2009. This is a heirarchical classification system. For example, class 60 means "closed to open" canopy broadleaved deciduous tree cover. But in some places a more specific type of broadleaved deciduous tree cover may be available. In that case, a more specific code 61 or 62 may be used which specifies "open" (61) or "closed" (62) cover. Land Cover Processing To provide consistency over time, these maps are produced from baseline land cover maps, and are revised for changes each year depending on the best available satellite data from each period in time. These revisions were made from AVHRR 1km time series from 1992 to 1999, SPOT-VGT time series between 1999 and 2013, and PROBA-V data for years 2013, 2014 and 2015. When MERIS FR or PROBA-V time series are available, changes detected at 1 km are re-mapped at 300 m. The last step consists in back- and up-dating the 10-year baseline LC map to produce the 24 annual LC maps from 1992 to 2015. Source data The datasets behind this layer were extracted from NetCDF files and TIFF files produced by ESA. Years 1992-2015 were acquired from ESA CCI LC version 2.0.7 in TIFF format, and years 2016-2018 were acquired from version 2.1.1 in NetCDF format. These are downloadable from ESA with an account, after agreeing to their terms of use. https://maps.elie.ucl.ac.be/CCI/viewer/download.php CitationESA. Land Cover CCI Product User Guide Version 2. Tech. Rep. (2017). Available at: maps.elie.ucl.ac.be/CCI/viewer/download/ESACCI-LC-Ph2-PUGv2_2.0.pdfMore technical documentation on the source datasets is available here:https://cds.climate.copernicus.eu/cdsapp#!/dataset/satellite-land-cover?tab=doc*Index of all classes in this layer:10 Cropland, rainfed11 Herbaceous cover12 Tree or shrub cover20 Cropland, irrigated or post-flooding30 Mosaic cropland (>50%) / natural vegetation (tree, shrub, herbaceous cover) (<50%)40 Mosaic natural vegetation (tree, shrub, herbaceous cover) (>50%) / cropland (<50%) 50 Tree cover, broadleaved, evergreen, closed to open (>15%)60 Tree cover, broadleaved, deciduous, closed to open (>15%)61 Tree cover, broadleaved, deciduous, closed (>40%)62 Tree cover, broadleaved, deciduous, open (15-40%)70 Tree cover, needleleaved, evergreen, closed to open (>15%)71 Tree cover, needleleaved, evergreen, closed (>40%)72 Tree cover, needleleaved, evergreen, open (15-40%)80 Tree cover, needleleaved, deciduous, closed to open (>15%)81 Tree cover, needleleaved, deciduous, closed (>40%)82 Tree cover, needleleaved, deciduous, open (15-40%)90 Tree cover, mixed leaf type (broadleaved and needleleaved)100 Mosaic tree and shrub (>50%) / herbaceous cover (<50%)110 Mosaic herbaceous cover (>50%) / tree and shrub (<50%)120 Shrubland121 Shrubland evergreen122 Shrubland deciduous130 Grassland140 Lichens and mosses150 Sparse vegetation (tree, shrub, herbaceous cover) (<15%)151 Sparse tree (<15%)152 Sparse shrub (<15%)153 Sparse herbaceous cover (<15%)160 Tree cover, flooded, fresh or brakish water170 Tree cover, flooded, saline water180 Shrub or herbaceous cover, flooded, fresh/saline/brakish water190 Urban areas200 Bare areas201 Consolidated bare areas202 Unconsolidated bare areas210 Water bodies