This dataset is a monthly mean gridded total ozone data record (level 3) produced by the ESA Ozone Climate Change Initiative project (Ozone CCI). The dataset is a prototype of a merged harmonised ozone data record combining ozone data from the GOME instrument on ERS-2, the SCIAMACHY instrument on ENVISAT and the GOME-2 instrument on METOP-A, and covers the period between April 1996 to June 2011.

• Data link
• Data link
• undefined
• 0d2260ad4e2c42b6b14fe5b3308f5eaa

FEDEO Short Name: 0d2260ad4e2c42b6b14fe5b3308f5eaa Version ID: NA Unique ID: C2548143081-FEDEO

This dataset contains Daily Snow Cover Fraction (snow on ground) from AVHRR, produced by the Snow project of the ESA Climate Change Initiative programme. Snow cover fraction on ground (SCFG) indicates the area of snow observed from space over land surfaces, in forested areas corrected for the transmissivity of the forest canopy. The SCFG is given in percentage (%) per pixel. The global SCFG product is available at about 5 km pixel size for all land areas, excluding Antarctica and Greenland ice sheets. The coastal zones of Greenland are included.The SCFG time series provides daily products for the period 1979-2023. The product V4.0 is based on EUMETSAT Fundamental Data Record (FDR) medium resolution optical satellite data from the Advanced Very High Resolution Radiometer (AVHRR). Clouds are masked using the CLARA-A3 cloud product. The retrieval method of the snow_cci SCFG product from AVHRR data has been further developed and improved based on the ESA GlobSnow approach described by Metsämäki et al. (2015) and complemented with a pre-classification module. All cloud free pixels are then used for the snow extent mapping, using spectral bands centred at about 0.63 µm and 1.61 µm (channel 3a or the reflective part of channel 3b (ref3b)), and an emissive band centred at about 10.8 µm. The snow_cci snow cover mapping algorithm is a three-step approach: first, a strict pre-classification is applied to identify all cloud free pixels which are certainly snow free. For all remaining pixels, the snow_cci SCFG retrieval method is applied using dynamic reference reflectance values (snow, forest, ground) temporally and spatially adapted to consider angle dependencies (sun, view). Finally, a post-processing removes erroneous snow pixels caused either by falsely classified clouds in the tropics or by unreliable ref3b values at a global scale. The following auxiliary data sets are used for product generation: i) ESA CCI Land Cover from 2000; water bodies and permanent snow and ice areas are masked based on this dataset. Both classes were separately aggregated to the pixel spacing of the SCF product. Water areas are masked if more than 50 percent of the pixel is classified as water, permanent snow and ice areas are masked if more than 50 percent are identified as such areas in the aggregated map; ii) Forest canopy transmissivity map; this layer is based on the tree cover classes of the ESA CCI Land Cover 2000 data set and the tree cover density map from Landsat data for the year 2000 (Hansen et al., Science, 2013, DOI: 10.1126/science.1244693). This layer is used to apply a forest canopy correction and estimate in forested areas the fractional snow cover on ground. RMSE is retrieved from a statistical model and added as pixel-wise information. The SCFG product is aimed to serve the needs of users working in cryosphere and climate research and monitoring activities, including the detection of variability and trends, climate modelling and aspects of hydrology, meteorology, and biology.The Remote Sensing Research Group of the University of Bern, in cooperation with Gamma Remote Sensing is responsible for the SCFG product development and generation. ENVEO (ENVironmental Earth Observation IT GmbH) developed and prepared all auxiliary data sets used for the product generation.The SCFG AVHRR product comprises a few data gaps in 1979 – 1986 (1979: 22.-24.Feb.; 01.-07.Oct.; 03.-04.Nov.; 07.Nov.; 17.-18.Nov.; 1980: 22.-27.Feb.; 01.March; 03.March; 15.-20.March; 30.March – 02.April; 26.-29.June; 12.-19.July; 12.-18.Dec.; 1981: 09.-11.May; 01.-03.Aug.; 14.-23.Aug.; 1982: 28.- 31.May; 25.-26. Oct.; 1983: 27.- 31. July; 01.- 02. and 06. Aug.; 1984: 14.-15.Jan.; 06. Dec.; 1985: 01.- 24.Feb; 1986: 15. March), resulting in a 99% data coverage over the entire study period of 43 years.

• Data link
• Data link
• undefined
• 80d96e3a14854420b6f742d70877c431

FEDEO Short Name: 80d96e3a14854420b6f742d70877c431 Version ID: NA Unique ID: C4017534900-FEDEO

This dataset contains the CryoClim Daily Snow Cover Fraction (snow on ground) product, produced by the Snow project of the ESA Climate Change Initiative programme.Fractional snow cover (FSC) on the ground indicates the area of snow observed from space on land surfaces, in forested areas compensated for the effect of trees hiding the ground surface snow cover under the forest canopy. The FSC is given in percentage (%) per grid cell. The global snow_cci CryoClim fractional snow cover (FSC) product is available at 0.05° grid size (about 5 km) for all land areas, excluding Antarctica and Greenland ice sheet. The coastal zones of Greenland are included. The CryoClim FSC time series provides daily products for the period 1982 – 2019. The CryoClim FSC product is based on a multi-sensor time-series fusion algorithm combining observations by optical and passive microwave radiometer (PMR) data. The product combines an historical record of AVHRR sensor data with PMR data from the SMMR, SSM/I and SSMIS sensors. The overall aim of the CryoClim FSC climate data record is to provide one of the longest snow cover extent time series available with global coverage and without hindrance from clouds and polar night. This has been achieved by utilising the best features of optical and passive microwave radiometer observations of snow using a sensor-fusion algorithm generating a consistent time series of global FSC products (Solberg et al. 2014, 2015; Rudjord et al. 2015). The snow_cci project has advanced the original CryoClim binary product to an FSC product. The thematic variable represents snow on the ground (SCFG). AVHRR sensors aboard the satellites NOAA-7, -9, -11, -14, -16, -18, -19 have been used as the optical data source, and SMMR, SSM/I and SSMIS sensors aboard the Nimbus-7, DMSP F8, DMSP F10, DMSP F11, DMSP F13, DMSP F14, DMSP F15, DMSP F16, DMSP F17 and DMSP F18 satellites, respectively, have been used as PMR data source. To have the best possible input data quality, we have used fundamental climate data records (FCDRs) developed by EUMETSAT CM SAF for AVHRR (Karlson et al. 2020) and PMR (Fenning et al. 2017).The optical algorithm component processes all available swaths from AVHRR GAC. The calculations are based on a Bayesian approach using a set of signatures (instrument channel combinations) and statistical coefficients. For each pixel of the swath, the probabilities for the surface classes snow, bare ground and cloud are estimated. The statistical coefficients are based on pre-knowledge of the typical behaviour of the surface classes in the different parts of the electromagnetic spectrum.The algorithm for PMR is also based on a Bayesian estimation approach. For SSM/I and SSMIS four snow classes were defined to model the snow surface state. For SMMR two classes were considered. The algorithm estimates the probability for each snow class given the PMR measurements. Land cover data are included to improve the performance of the Bayesian algorithm. This made it possible to construct a Bayesian estimator for each land cover regime. The multi-sensor multi-temporal fusion algorithm (Rudjord et al. 2015; Solberg et al. 2017) is based on a hidden Markov model (HMM) simulating the snow states based on observations with PMR and optical sensors. The basic idea is to simulate the states the snow surface goes through during the snow season with a state model. The states are not directly observable, but the remote sensing observations give data describing the snow conditions, which are related to the snow states. The HMM solution represents not only a multi-sensor model but also a multi-temporal model. The sequence of states over time is conditioned to follow certain optimisation criteria.The advancement from binary to fractional snow cover carried out by snow_cci has followed two main paths: First, we introduced more HMM states to be able to classify the snow cover into 10% FSC intervals. However, introducing 100 primary states to obtain 1% FSC intervals would not give a stable model. For obtaining higher precision, we have interpolated between HMM states using a secondary Viterbi sequence. The two probabilities are used as weights to estimate the FSC.Permanent snow and ice, and water areas are masked based on the Land Cover CCI data set of the year 2000. Both classes were separately aggregated to the grid size of the FSC product. Water areas are masked if more than 30% of the grid cell is classified as water, permanent snow and ice areas are masked if more than 50% is identified as such areas in the aggregated map. The product uncertainty for observed land areas is provided as unbiased root mean square error (RMSE) per grid cell in the ancillary variable.The FSC product aims to serve the needs of users working with the cryosphere and climate research and monitoring activities, including the assessment of variability and trends, climate modelling and aspects of hydrology, meteorology, and biology.The Norwegian Computing Center (Norsk Regnesentral, NR) is together with the Norwegian Meteorological Institute (MET Norway) responsible for the FSC product development and generation from satellite data. ENVEO IT GmbH developed and prepared all auxiliary data sets used for the product generation.For the whole time series, there are 27 days with neither optical nor PMR retrieval. These are individual days and not series of days in a row. The multi-sensor time-series algorithm handles this by making a best estimate of snow cover, based on days both prior to and following after the lack of data. This will not reduce the quality of the snow maps much for days without data as long as they are just individual days.The algorithm estimating the uncertainty associated with the FSC maps needs observations of covariates from the same day as the time stamp of the FSC product. These covariates are partly based on data from PMR sensors. Hence, estimates of uncertainty could not be produced for days lacking PMR acquisitions. Most days lacking PMR are in the period 1982-1988 (53 days), and there are only two cases after that (in 2008).

• Data link
• Data link
• undefined
• f4654030223445b0bac63a23aaa60620

FEDEO Short Name: f4654030223445b0bac63a23aaa60620 Version ID: NA Unique ID: C3327359735-FEDEO

The Soil Moisture CCI ACTIVE dataset is one of the three datasets created as part of the European Space Agency's (ESA) Soil Moisture Essential Climate Variable (ECV) Climate Change Initiative (CCI) project. The product has been created by fusing scatterometer soil moisture products, derived from the instruments AMI-WS and ASCAT. PASSIVE and COMBINED products have also been created.The v05.3 ACTIVE product, provided as global daily images in NetCDF-4 classic file format, presents a global coverage of surface soil moisture at a spatial resolution of 0.25 degrees. It is provided in percent of saturation [%] and covers the period (yyyy-mm-dd) 1991-08-05 to 2020-12-31. For information regarding the theoretical and algorithmic base of the product, please see the Algorithm Theoretical Baseline Document. Other additional reference documents and information relating to the dataset can also be found on the CCI Soil Moisture project website.The data set should be cited using all three of the following references:1. Gruber, A., Scanlon, T., van der Schalie, R., Wagner, W., and Dorigo, W. (2019). Evolution of the ESA CCI Soil Moisture climate data records and their underlying merging methodology, Earth Syst. Sci. Data, 11, 717–739, https://doi.org/10.5194/essd-11-717-20192. Dorigo, W.A., Wagner, W., Albergel, C., Albrecht, F., Balsamo, G., Brocca, L., Chung, D., Ertl, M., Forkel, M., Gruber, A., Haas, E., Hamer, D. P. Hirschi, M., Ikonen, J., De Jeu, R. Kidd, R. Lahoz, W., Liu, Y.Y., Miralles, D., Lecomte, P. (2017). ESA CCI Soil Moisture for improved Earth system understanding: State-of-the art and future directions. In Remote Sensing of Environment, 2017, ISSN 0034-4257, https://doi.org/10.1016/j.rse.2017.07.0013. Gruber, A., Dorigo, W. A., Crow, W., Wagner W. (2017). Triple Collocation-Based Merging of Satellite Soil Moisture Retrievals. IEEE Transactions on Geoscience and Remote Sensing. PP. 1-13. 10.1109/TGRS.2017.2734070

• Data link
• Data link
• undefined
• 1da8dadcdfb642f4aad2384f02efe756

FEDEO Short Name: 1da8dadcdfb642f4aad2384f02efe756 Version ID: NA Unique ID: C2548142594-FEDEO

The Soil Moisture CCI COMBINED dataset is one of three datasets created as part of the European Space Agency's (ESA) Soil Moisture Essential Climate Variable (ECV) Climate Change Initiative (CCI) project. The product has been created by directly merging Level 2 scatterometer and radiometer soil moisture products derived from the AMI-WS, ASCAT, SMMR, SSM/I, TMI, AMSR-E, WindSat, AMSR2, SMOS and SMAP satellite instruments. PASSIVE and ACTIVE products have also been created.The v05.2 COMBINED product, provided as global daily images in NetCDF-4 classic file format, presents a global coverage of surface soil moisture at a spatial resolution of 0.25 degrees. It is provided in volumetric units [m3 m-3] and covers the period (yyyy-mm-dd) 1978-11-01 to 2019-12-31. For information regarding the theoretical and algorithmic base of the product, please see the Algorithm Theoretical Baseline Document. Other additional reference documents and information relating to the dataset can also be found on the CCI Soil Moisture project website.The data set should be cited using all three of the following references:1. Gruber, A., Scanlon, T., van der Schalie, R., Wagner, W., and Dorigo, W. (2019). Evolution of the ESA CCI Soil Moisture climate data records and their underlying merging methodology, Earth Syst. Sci. Data, 11, 717–739, https://doi.org/10.5194/essd-11-717-20192. Dorigo, W.A., Wagner, W., Albergel, C., Albrecht, F., Balsamo, G., Brocca, L., Chung, D., Ertl, M., Forkel, M., Gruber, A., Haas, E., Hamer, D. P. Hirschi, M., Ikonen, J., De Jeu, R. Kidd, R. Lahoz, W., Liu, Y.Y., Miralles, D., Lecomte, P. (2017). ESA CCI Soil Moisture for improved Earth system understanding: State-of-the art and future directions. In Remote Sensing of Environment, 2017, ISSN 0034-4257, https://doi.org/10.1016/j.rse.2017.07.0013. Gruber, A., Dorigo, W. A., Crow, W., Wagner W. (2017). Triple Collocation-Based Merging of Satellite Soil Moisture Retrievals. IEEE Transactions on Geoscience and Remote Sensing. PP. 1-13. 10.1109/TGRS.2017.2734070

• Data link
• Data link
• undefined
• 057dd6c36f0741d3b97f9eee688b7835

FEDEO Short Name: 057dd6c36f0741d3b97f9eee688b7835 Version ID: NA Unique ID: C2548143472-FEDEO