These data have been created by the Department for Environment, Food and Rural Affairs (Defra) and Joint Nature Conservation Committee (JNCC) in order to cost effectively provide high quality, Analysis Ready Data (ARD) for a wide range of applications. The dataset contains modified Copernicus Sentinel-1 data processed into a normalised radar backscatter product on a linear scale in dB. Products acquired from ESA are Ground-Range Detected (GRD) Interferometric Wide-swath (IW) in the dual VV+VH polarisation (DV) mode, where both VV and VH polarisations are collected. Defra and JNCC data were processed on separate platforms using a common specification to produce complementary outputs up to and including the acquisition date 23/06/2023. Data acquired after that date were processed on a single platform to the same specification. Sentinel-1 scenes processed before July 2021 have had a strip of data clipped from their northern edge to remove an artefact caused by a deprecated processing method. Details can be found in the lineage statement of the metadata for all affected scenes.

These data have been created by the Department for Environment, Food and Rural Affairs (Defra) and Joint Nature Conservation Committee (JNCC) in order to cost-effectively provide high quality, Analysis Ready Data (ARD) for a wide range of applications. The dataset contains modified Copernicus Sentinel-2 (Level 1C data processed into a surface reflectance product (Level 2)). Defra and JNCC data were processed on separate platforms using a common specification to produce complementary outputs up to and including the acquisition date 23/06/2023. Data acquired after that date were processed on a single platform to the same specification. The majority of data captured between July 2015 and August 2017 was processed by Aberystwyth University for Defra and later updated by JNCC to the same specification as the rest of this dataset. Please see the image-level metadata for details of data lineage and processing. The Sentinel-2 ARD filename format was changed in April 2023. Filenames of data acquired on or after 01/04/2023 include the timestamp of data generation and display image latitude and longitude to a consistent number of significant figures preceded by ‘n’ (North) and ‘e/w’ (East / West). Filenames of data acquired before this date do not include the data generation timestamp and display latitude and longitude to varying significant figures not preceded by ‘n’ and ‘e/w’.

5km gridded resolution data for four climate projection scenarios, produced in support of the UK Climate Impacts Programme 2002 (UKCIP02) by the Met Office Hadley Centre. There are data for monthly and seasonal average anomalies with respect to simulated 1961~90 average for four alternative future climates for the UK. The four emissions scenarios are Low (LO), Medium-Low (ML), Medium-High (MH) and High (HI). Monthly data for cloud, precipitation (prec), temperature (temp), maximum temperature (tmax), minimum temperature (tmin) and wind are available using the following convention in the filename: 2020s = predictions for 2011 to 2040, 2050s = predictions for 2041 to 2070, 2080s = predictions for 2071 to 2100.

The 50km gridded resolution data for four climate projection scenarios produced in support of the UK Climate Impacts Programme 2002 (UKCIP02). There are data for monthly and seasonal average anomalies with respect to simulated 1961~90 average for four alternative future climates for the UK. The four emissions scenarios are Low (LO), Medium-Low (ML), Medium-High (MH) and High (HI). The monthly time-slice data are available for the following variables: Maximum temperature (TMAX), Minimum temperature (TMIN), Daily mean temperature (TEMP), Total precipitation rate (PREC), Snowfall rate (SNOW), Wind speed (WIND), Relative humidity (RHUM), Total cloud in longwave radiation (TCLW), Net surface longwave flux (NSLW), Net surface shortwave flux (NSSW), Total downward surface shortwave flux (DSWF), Soil moisture content (SMOI), Mean sea level pressure (MSLP), Surface latent heat flux (SLHF), Specific humidity (SPHU), Inter-annual variability: temperature (IAVT) and Inter-annual variability: precipitation (IAVP). The data are available over the following time periods using the following convention in the filename: 6190 = 1961 to 1990, i.e. observed climate, 2020s = predictions for 2011 to 2040, 2050s = predictions for 2041 to 2070, 2080s = predictions for 2071 to 2100.

Sentinel-Hub NDVI description: NDVI is a simple, but effective index for quantifying green vegetation. It normalizes green leaf scattering in Near Infra-red wavelengths with chlorophyll absorption in red wavelengths. The value range of the NDVI is -1 to 1. Negative values of NDVI (values approaching -1) correspond to water. Values close to zero (-0.1 to 0.1) generally correspond to barren areas of rock, sand, or snow. Low, positive values represent shrub and grassland (approximately 0.2 to 0.4), while high values indicate temperate and tropical rainforests (values approaching 1). It is a good proxy for live green vegetation. NDVI = (NIR – Red) / (NIR + RED) Sentinel-2 NDVI = (B8 - B4) / (B8 + B4) These data have been created by the Joint Nature Conservation Committee (JNCC) as part of a Defra Natural Capital and Ecosystem Assessment (NCEA) project to produce a regional, and ultimately national, system for detecting a change in habitat conditions at a land parcel level. The first stage of the project is focused on Yorkshire, UK, and therefore the dataset includes granules and scenes covering Yorkshire and surrounding areas only. The dataset contains Normalised Difference Vegetation Index (NDVI) data derived from Defra and JNCC Sentinel-2 Analysis Ready Data. NDVI files are generated for the following Sentinel-2 granules: • T30UWE • T30UXF • T30UWF • T30UXE • T31UCV • T30UYE • T31UCA As the project continues, JNCC will expand the geographical coverage of this dataset and will provide continuous updates as ARD becomes available.

Sentinel-Hub NDWI description: The NDWI is used to monitor changes related to water content in water bodies. As water bodies strongly absorb light in visible to the infrared electromagnetic spectrum, NDWI uses green and near-infrared bands to highlight water bodies. It is sensitive to built-up land and can result in the over-estimation of water bodies. Index values greater than 0.5 usually correspond to water bodies. Vegetation usually corresponds to much smaller values and built-up areas to values between zero and 0.2. NDWI = (GREEN – NIR) / (GREEN + NIR) Sentinel-2 NDWI = (B03 - B08) / (B03 + B08) These data have been created by the Joint Nature Conservation Committee (JNCC) as part of a Defra Natural Capital and Ecosystem Assessment (NCEA) project to produce a regional, and ultimately national, system for detecting a change in habitat conditions at a land parcel level. The first stage of the project is focused on Yorkshire, UK, and therefore the dataset includes granules and scenes covering Yorkshire and surrounding areas only. The dataset contains Normalised Difference Water Index (NDWI) data derived from Defra and JNCC Sentinel-2 Analysis Ready Data. NDWI files are generated for the following Sentinel-2 granules: • T30UWE • T30UXF • T30UWF • T30UXE • T31UCV • T30UYE • T31UCA As the project continues, JNCC will expand the geographical coverage of this dataset and will provide continuous updates as ARD becomes available.

Sentinel Hub NBR description: To detect burned areas, the NBR-RAW index is the most appropriate choice. Using bands 8 and 12 it highlights burnt areas in large fire zones greater than 500 acres. To observe burn severity, you may subtract the post-fire NBR image from the pre-fire NBR image. Darker pixels indicate burned areas. NBR = (NIR – SWIR) / (NIR + SWIR) Sentinel-2 NBR = (B08 - B12) / (B08 + B12) These data have been created by the Joint Nature Conservation Committee (JNCC) as part of a Defra Natural Capital & Ecosystem Assessment (NCEA) project to produce a regional, and ultimately national, system for detecting a change in habitat condition at a land parcel level. The first stage of the project is focused on Yorkshire, UK, and therefore the dataset includes granules and scenes covering Yorkshire and surrounding areas only. The dataset contains the following indices derived from Defra and JNCC Sentinel-2 Analysis Ready Data. NDVI, NDMI, NDWI, NBR, and EVI files are generated for the following Sentinel-2 granules: • T30UWE • T30UXF • T30UWF • T30UXE • T31UCV • T30UYE • T31UCA As the project continues, JNCC will expand the geographical coverage of this dataset and will provide continuous updates as ARD becomes available.

Earth-system modelling data from the UK-Japan Climate Collaboration (UJCC). The project is a joint project between the Hadley Centre (DEFRA) and the NCAS-CGAM (Centre for Global Atmospheric Modelling) at the University of Reading. UJCC makes use of a broad group of models in order to systematically explore the role and value of resolution in climate system research. The dataset comprises of UJCC 30 year simulations from models at resolutions of either (1.25 lat x 1.875 lon) or (0.83 lat x 1.25 lon) with differing degrees of atmosphere-ocean coupling (1 degree ocean or 1/3 degree ocean). The dataset also includes NUGAM (Nihon-UK Global Environmental Model) Atmosphere only simulations and NUGEM Coupled atmosphere and ocean simulations which are both at the same resolution (0.83 lat x 0.56 lon, corresponding to ~60 km in mid-latitudes).

Sentinel-Hub NDMI description: The NDMI is a normalized difference moisture index, that uses NIR and SWIR bands to display moisture. The SWIR band reflects changes in both the vegetation water content and the spongy mesophyll structure in vegetation canopies, while the NIR reflectance is affected by leaf internal structure and leaf dry matter content but not by water content. The combination of the NIR with the SWIR removes variations induced by leaf internal structure and leaf dry matter content, improving the accuracy in retrieving the vegetation water content. The amount of water available in the internal leaf structure largely controls the spectral reflectance in the SWIR interval of the electromagnetic spectrum. SWIR reflectance is therefore negatively related to leaf water content. In short, NDMI is used to monitor changes in the water content of leaves and was proposed by Gao. NDWI is computed using the near-infrared (NIR) and the short wave infrared (SWIR) reflectances: NDMI = (NIR – SWIR) / (NIR + SWIR) Sentinel-2 NDMI = (B08 - B11) / (B08 + B11) These data have been created by the Joint Nature Conservation Committee (JNCC) as part of a Defra Natural Capital and Ecosystem Assessment (NCEA) project to produce a regional, and ultimately national, system for detecting a change in habitat conditions at a land parcel level. The first stage of the project is focused on Yorkshire, UK, and therefore the dataset includes granules and scenes covering Yorkshire and surrounding areas only. The dataset contains Normalised Difference Moisture Index data derived from Defra and JNCC Sentinel-2 Analysis Ready Data. NDMI files are generated for the following Sentinel-2 granules: • T30UWE • T30UXF • T30UWF • T30UXE • T31UCV • T30UYE • T31UCA As the project continues, JNCC will expand the geographical coverage of this dataset and will provide continuous updates as ARD becomes available.

The UK Climate Impacts Programme 2002 (UKCIP02) comprises a set of four scenarios of future climate change produced for assessing climate change vulnerability, impacts and adaptation in the UK based on the understanding of the science of climate change in 2002. Data are provided at two resolutions 50km and 5km. The 5km resolution data are provided in both a gridded and time-series format. The four alternative future climates for the UK are labelled respectively, Low Emissions, Medium-Low Emissions, Medium-High Emissions and High Emissions. No probabilities can be attached to these four climate futures – in line with the IPCC, UKCIP02 do not suggest that one is more likely than another. While they represent a wide range of possible future climates, the UKCIP02 scenarios do not capture the entire range of future possibilities. The scenarios are designed to be used in conjunction with other UKCIP reports and products.