This v2.0 SST_cci Advanced Very High Resolution Radiometer (AVHRR) level 3 uncollated data (L3U) Climate Data Record (CDR) consists of stable, low-bias sea surface temperature (SST) data from the AVHRR series of satellite instruments. It covers the period between 08/1981 - 12/2016. This Level 3 Uncollated (L3U) product provides these SST data on a 0.05 regular latitude-longitude grid with a single orbit per file. The dataset has been produced as part of the European Space Agency (ESA) Climate Change Initiative Sea Surface Temperature project(ESA SST_cci). The data products from SST CCI accurately map the surface temperature of the global oceans over the period 1981 to 2016 using observations from many satellites. The data provide independently quantified SSTs to a quality suitable for climate research. Data are made freely and openly available under a Creative Commons License by Attribution (CC By 4.0) https://creativecommons.org/licenses/by/4.0/ .

These data are a copy of MODIS data from the NASA Level-1 and Atmosphere Archive & Distribution System (LAADS) Distributed Active Archive Center (DAAC). The copy is potentially only a subset. Below is the description from https://ladsweb.modaps.eosdis.nasa.gov/missions-and-measurements/products/MYD06_L2 The MODIS Level-2 Cloud product consists of cloud optical and physical parameters. These parameters are derived using remotely sensed infrared, visible and near infrared solar reflected radiances. MODIS infrared channel radiances are used to derive cloud top temperature, cloud top height, effective emissivity, cloud phase (ice vs. water, opaque vs. non-opaque), and cloud fraction under both daytime and nighttime conditions. MODIS visible radiances are used to derive cloud optical thickness and effective particle radius and cloud shadow effects. Near-infrared solar reflected radiance provides additional information for the retrieval of cloud particle phase (ice vs. water, clouds vs. snow). The shortname for this Level-2 MODIS cloud product is MYD06_L2. MYD06_L2 consists of parameters at a spatial resolution of either 1km or 5km (at nadir). Each MYD06_L2 product file covers a 5-minute time interval. This means that for 5km resolution parameters, the output grid is 270 pixels wide by 406 pixels in length. Every tenth granule has an output grid size of 270 by 408 pixels. For 1-km resolution parameters, the output grid is 1354 pixels in width by 2030 pixels in length and every tenth granule has an output grid size of 1354 by 2040 pixels. MYD06_L2 product files are stored in Hierarchical Data Format (HDF-EOS). All gridded cloud parameters are stored as Scientific Data Sets (SDS) within the file, except two (band number and statistics). These are stored as Vdata (table arrays). Approximately 288 files are produced daily. Nighttime files are smaller than their daytime counterparts since only the cloud top properties are retrieved at night. The MODIS Cloud Product will be used to investigate seasonal and inter-annual changes in cirrus (semi-transparent) global cloud cover and cloud phase with multispectral observations at 1km spatial resolution. For additional details see the MODIS Atmospheres web site page onCollection 6.1 Updates. Shortname: MYD06_L2 , Platform: Aqua , Instrument: MODIS , Processing Level: Level-2 , Spatial Resolution: 1 km , Temporal Resolution: 5 minute , ArchiveSets: 61 , Collection: MODIS Collection 6.1 - Level 1, Atmosphere, Land (ArchiveSet 61) , PGE Number: PGE06 , File Naming Convention: MYD06_L2.AYYYYDDD.HHMM.CCC.YYYYDDDHHMMSS.hdf AYYYYDDD = Acqusition Year and Day of Year HHMM = Hour and Minute of acquisition CCC = Collection number YYYYDDDHHMMSS = Production Date and Time AYYYYDDD = Year and Day of Year of acquisition , Citation: Platnick, S., Ackerman, S., King, M., et al., 2015. MODIS Atmosphere L2 Cloud Product (06_L2). NASA MODIS Adaptive Processing System, Goddard Space Flight Center, USA: http://dx.doi.org/10.5067/MODIS/MYD06_L2.061 , Keywords: Water Vapor, Precipitable Water

The Airborne Research & Survey Facility (ARSF, formerly Airborne Remote Sensing Facility) is managed by NERC Scientific Services and Programme Management. It provides the UK environmental science community, and other potential users, with the means to obtain remotely-sensed data in support of research, survey and monitoring programmes. The ARSF is a unique service providing environmental researchers, engineers and surveyors with synoptic analogue and digital imagery of high spatial and spectral resolution.The NEODC holds the entire archive of Airborne Thematic Mapper (ATM) and Compact Airborne Spectrographic Imager (CASI) data acquired by the NERC ARSF. High-resolution scanned digital versions of the entire collection of analogue photographs are now also available as well as selected LiDAR-derived elevation and terrain models for selected sites flown using the sensor.

The Airborne Research & Survey Facility (ARSF, formerly Airborne Remote Sensing Facility) is managed by NERC Scientific Services and Programme Management. It provides the UK environmental science community, and other potential users, with the means to obtain remotely-sensed data in support of research, survey and monitoring programmes. The ARSF is a unique service providing environmental researchers, engineers and surveyors with synoptic analogue and digital imagery of high spatial and spectral resolution.The NEODC holds the entire archive of Airborne Thematic Mapper (ATM) and Compact Airborne Spectrographic Imager (CASI) data acquired by the NERC ARSF. High-resolution scanned digital versions of the entire collection of analogue photographs are now also available as well as selected LiDAR-derived elevation and terrain models for selected sites flown using the sensor.

Raw Landsat 4/5 data covering the UK were acquired from Infoterra by the Landmap project. Landmap subsequently orthorectified and mosaiced the images. Two types of image data from the satellite are available for the UK from 1988 to 1992: MultiSpectral Scanner (MSS) and Thematic Mapper (TM). Landsat 4 and 5 carry both the MSS and the TM sensors; however, routine collection of MSS data was terminated in late 1992. The MSS and TM sensors primarily detect reflected radiation from the Earth's surface in the visible and near-infrared (IR) wavelengths, but the TM sensor with its seven spectral bands provides more radiometric information than the MSS sensor. The Landsat Program is one of the longest running programmes for image acquisition from space, first launched in 1972 the program is managed between the U.S. Geological Survey (USGS) and NASA. Eight satellites have so far been launched, the most recent being Landsat 8, on February 11th, 2013. Landsat satellite imagery offers a unique resource for global change research and applications in agriculture, geology, forestry, regional planning, education, and national security. The Joint Information Systems Committee (JISC) funded Landmap service which ran from 2001 to July 2014 collected and hosted a large amount of earth observation data for the majority of the UK. After removal of JISC funding in 2013, the Landmap service is no longer operational, with the data now held at the NEODC. When using these data please also add the following copyright statement: Original Landsat 4 & 5 Landsat data copyright NOAA. Distributed by CHEST under licence from Infoterra International.

This dataset contains level 2 vector formatted data derived from the Centre of Ecology and Hydrology's (CEH) Land Cover Map 2000 (LCM2000) data for the Thorney Island, South Coast of England, UK, NCAVEO calibration/validation (cal/val) test site. The NERC funded Network for Calibration and Validation of EO (NCAVEO) campaign was designed to illustrate and explain the processes involved in cal/val of earth observation data.

ARSF project 04/19: The impact of managed retreat for inter-tidal habitat restoration. PI: S. Winterbottom. Site: Nigg Bay.

ARSF project MC04/19: A study of landslide failure mechanisms and their relationship to topography: Collazzone, central Umbria, Italy. PI: Niels Hovius. Site: Collazzone, Umbria Italy.

ARSF project MC04/15: Coastal Marine Habitat Differentiation using Hyperspectral Remote Sensing Data. PI: Graham Ferrier. Site: Sitia

This dataset contains land surface temperatures (LSTs) and their uncertainty estimates from the Moderate Resolution Imaging Spectroradiometer (MODIS) on Earth Observing System – Terra (Terra). Satellite land surface temperatures are skin temperatures which means, for example, the temperature of the ground surface in bare soil areas, the temperature of the canopy over forests, and a mix of the soil and leaf temperature over sparse vegetation. The skin temperature is an important variable when considering surface fluxes of, for instance, heat and water. Daytime and night-time temperatures are provided in separate files corresponding to the morning and evening Terra equator crossing times which are 10:30 and 22:30 local solar time. Per pixel uncertainty estimates are given in two forms, first, an estimate of the total uncertainty for the pixel and second, a breakdown of the uncertainty into components by correlation length. Also provided in the files, on a per pixel basis, are the observation time, the satellite viewing and solar geometry angles, a quality flag, and land cover class. The dataset coverage is global over the land surface. LSTs are provided on a global equal angle grid at a resolution of 0.01° longitude and 0.01° latitude. MODIS achieves full Earth coverage nearly twice per day so the daily files have small gaps primarily close to the equator where the surface is not covered by the satellite swath on that day. Furthermore, LSTs are not produced where clouds are present since under these circumstances the IR radiometer observes the cloud top which is usually much colder than the surface. Dataset coverage starts on 24th February 2000 and ends on 31st December 2018. There are minor interruptions (1-2 days) during satellite/instrument maintenance periods. The dataset was produced by the University of Leicester (UoL) and LSTs were retrieved using a generalised split window retrieval algorithm and data were processed in the UoL processing chain. The dataset was produced as part of the ESA Land Surface Temperature Climate Change Initiative which strives to improve satellite datasets to Global Climate Observing System (GCOS) standards.