For a full description of the methodology and results, please see the following article:

Demarquet, Q., Rapinel, S., Gore, O., Dufour, S., Hubert-Moy, L., 2024. Continuous change detection outperforms traditional post-classification change detection for long term monitoring of wetlands. International Journal of Applied Earth Observation and Geoinformation 133, 104142. https://doi.org/10.1016/j.jag.2024.104142

Datasets

Points datasets are projected in WGS84 (EPSG:4326), and are provided in the open source GeoPackage format.

The first dataset (Dataset_1.gpkg) contains training and validation points for random forest classification of EUNIS habitats in the Poitevin Marsh. This dataset consists of 3360 training and 840 validation points (total: 4200).Fields description:

"ID": unique identifier

"CLASS": EUNIS first level habitat type, classified as following:

1: EUNIS habitat A

2: EUNIS habitat B

3: EUNIS habitat C1J5

4: EUNIS habitat C3

5: EUNIS habitat E

6: EUNIS habitat G

7: EUNIS habitat I

8: EUNIS habitat J

"DATE": Date associated with EUNIS habitat sample

"LON": Point longitude in decimal degrees

"LAT": Point latitude in decimal degrees

"TYPE": Either training ("train") or validation ("test") sample

The second dataset (Dataset_2.gpkg) contains points for the Olofsson correction method. This dataset consists of 326 points where the change classes are classified as following: -10 (wetland loss), 10 (wetland gain), 100 (stable existing wetland), and 200 (stable damaged wetland).Fields description:

"ID": unique identifier

"LON": Point longitude in decimal degrees

"LAT": Point latitude in decimal degrees

"REFERENCE": Change class reference

"CCDC": Change class obtained from the Continuous Change Detection and Classification approach

"PCCD": Change class obtained from the Post-Classification Change Detection approach

Supplementary layout files (Dataset_1.qml and Dataset_2.qml) support formatting of the points in QGIS software.

EUNIS habitat

Maps are projected in WGS84 (EPSG:4326), and are provided in the GeoTiff format at 30m of spatial resolution.

Habitat maps are given for the two approaches in years 1984 and 2022:

CCDC: Continuous Change Detection and Classification (CCDC_HABITAT_1984.tif and CCDC_HABITAT_2022.tif)

PCCD: Traditional post-classification approach (PCCD_HABITAT_1984.tif and PCCD_HABITAT_2022.tif)

Supplementary layout files (CCDC_HABITAT_1984.qml, CCDC_HABITAT_2022.qml, PCCD_HABITAT_1984.qml, PCCD_HABITAT_2022.qml) support formatting of raster layers in QGIS software.

Change detection during the 1984-2022 period

Maps are projected in WGS84 (EPSG:4326), and are provided in the GeoTiff format at 30m of spatial resolution. Raster values follow the classification scheme used in Dataset_2.

Change detection maps are given for the two approaches:

CCDC: Continuous Change Detection and Classification (CCDC_CHANGE_1984_2022.tif)

PCCD: Traditional post-classification approach (PCCD_CHANGE_1984_2022.tif)

Supplementary layer files (CCDC_CHANGE_1984_2022.qml and PCCD_CHANGE_1984_2022.qml) support formatting of the raster layers in QGIS software.

GEE repository

To get direct access to GEE scripts and assets, please follow those two links:

https://code.earthengine.google.com/?accept_repo=users/demarquetquentin/CCDC_Poitevin

https://code.earthengine.google.com/?asset=projects/ee-quen-dem/assets/CCDC_Poitevin

Developed by SOLARGIS and provided by the Global Solar Atlas (GSA), this data resource contains diffuse horizontal irradiation (DIF) in kWh/m² covering the globe. Data is provided in a geographic spatial reference (EPSG:4326). The resolution (pixel size) of solar resource data (GHI, DIF, GTI, DNI) is 9 arcsec (nominally 250 m), PVOUT and TEMP 30 arcsec (nominally 1 km) and OPTA 2 arcmin (nominally 4 km). The data is hyperlinked under 'resources' with the following characeristics: DIF LTAy_AvgDailyTotals (GeoTIFF) Data format: GEOTIFF File size : 198.94 MB There are two temporal representation of solar resource and PVOUT data available: • Longterm yearly/monthly average of daily totals (LTAym_AvgDailyTotals) • Longterm average of yearly/monthly totals (LTAym_YearlyMonthlyTotals) Both type of data are equivalent, you can select the summarization of your preference. The relation between datasets is described by simple equations: • LTAy_YearlyTotals = LTAy_DailyTotals * 365.25 • LTAy_MonthlyTotals = LTAy_DailyTotals * Number_of_Days_In_The_Month For individual country or regional data downloads please see: https://globalsolaratlas.info/download (use the drop-down menu to select country or region of interest) For data provided in AAIGrid please see: https://globalsolaratlas.info/download/world. For more information and terms of use, please, read metadata, provided in PDF and XML format for each data layer in a download file. For other data formats, resolution or time aggregation, please, visit Solargis website. Data can be used for visualization, further processing, and geo-analysis in all mainstream GIS software with raster data processing capabilities (such as open source QGIS, commercial ESRI ArcGIS products and others).

The Worldclim dataset is the most widely used set of long term bioclimatic data, with long term averages for key bioclimatic variables used in climate modelling, species distribution modelling and countless other applications (Fick et al., 2017). However, unlike temperature and precipitation based values, wind, solar radiation and water vapor pressure are provided as monthly, rather than annual averages.This limits their use cases. Here, I used QGIS (ver 3.40.5-Bratislava) cell statistic function to calculate mean, maximum and minimum per cell values for wind speed, water vapor pressure and solar radiation. These are provided at 10min, 5min, 2.5min and 30 second resolution as in the Worldclim dataset. These are provided as geotiff files, but can be readily converted into ASCII if required (such as for Maxents GUI) (Gkantiragas, 2025). Each file has an accompanying log for the raster calculation and can be used to check that the file was correctly generated.There are three zip folders, one for wind, solar radiation and water vapor pressure. Each zip folder contains the data at all 4 resolutions. For questions or issues with the dataset please address them to: alexis.gkantiragas@gmail.comReferencesFick, S.E. and Hijmans, R.J., 2017. WorldClim 2: new 1‐km spatial resolution climate surfaces for global land areas. International journal of climatology, 37(12), pp.4302-4315.Gkantiragas, A. (2025) AlexisGGk/maxent_scripts: Useful tools for working with Maxent, GitHub. Available at: https://github.com/AlexisGGk/maxent_scripts (Accessed: 17 October 2025).

EN: This dataset provides local public transit frequency indicator for all German cities with populations exceeding 50,000. The spatial resolution is based on 1 km grid cells. Open-source public transit feed data (GTFS) served as input for an advanced geoprocessing workflow. A spatio-temporal filtering approach was applied to model GTFS data on a weekly basis—averaging values for weekdays, Saturdays, and Sundays—as well as during peak traffic hours (morning: 06:00–08:59; afternoon: 14:00–16:59). The resulting feature layers contain hourly departure counts for each public transit service mode (local and regional) at every transit stop within the selected time periods. This point-based dataset was used to calculate a public transit frequency indicator. Regional transit services were assigned a higher weight than local modes, based on the assumption that regional transport typically offers greater capacity than local options such as buses and trams. A maximum walking distance of 500 meters was assumed for transit users. The input Iso-Area size was set to 1,000 meters in the point-based network interpolation algorithm, implemented using the QNEAT3 plugin in QGIS. This algorithm utilized OpenStreetMap road network data and shortest-path optimization criteria. Additionally, open source data from the INSPIRE grid was employed as a standardized raster mask for geoprocessing and adminitrative boundary of cites to provide aggregated statistics at city scale. DE: Dieses Datensatz enthält Informationen zur Häufigkeit des öffentlichen Nahverkehrs für alle deutschen Städte mit mehr als 50.000 Einwohnern. Die räumliche Auflösung basiert auf einem Raster mit 1-km-Zellen. Als Grundlage für den fortgeschrittenen Geoverarbeitungsprozess dienten offene Datenquellen aus dem öffentlichen Verkehrsfeed (GTFS). Ein raum-zeitliches Filterverfahren wurde angewendet, um die GTFS-Daten auf Wochenbasis zu modellieren – mit Durchschnittswerten für Werktage, Samstage und Sonntage sowie für Stoßzeiten (morgens: 06:00–08:59; nachmittags: 14:00–16:59). Die daraus resultierenden Feature-Layer enthalten stündliche Abfahrtszahlen für jede Verkehrsart (lokal und regional) an allen Haltestellen innerhalb der ausgewählten Zeiträume. Dieses punktbasierte Datenset wurde verwendet, um einen Indikator für die Häufigkeit des öffentlichen Verkehrs zu berechnen. Regionalverkehrsdienste erhielten ein höheres Gewicht als lokale Verkehrsmittel, basierend auf der Annahme, dass der Regionalverkehr in der Regel eine höhere Kapazität bietet als lokale Optionen wie Busse und Straßenbahnen. Für die Nutzer des öffentlichen Verkehrs wurde eine maximale Gehentfernung von 500 Metern angenommen. Die Eingabegröße der Iso-Area wurde im punktbasierten Netzwerkinterpolationsalgorithmus auf 1.000 Meter festgelegt, der mit dem QNEAT3-Plugin in QGIS implementiert wurde. Dieser Algorithmus nutzte das Straßennetz von OpenStreetMap sowie Kriterien zur Optimierung der kürzesten Wege. Zusätzlich wurden offene Daten aus dem INSPIRE-Raster als standardisierte Rastermaske für die Geoverarbeitung und die administrativen Stadtgrenzen verwendet, um aggregierte Statistiken auf Stadtebene bereitzustellen.

This data sets include yearly maps of land cover classification for the state of Mato Grosso, Brasil, from 2001 to 2016, based on MODIS image time series at 250 meter spatial resolution. Ground samples consisting of 2,115 time series with known labels are used as training data for a support vector machine classifier. The classes include natural and human-transformed land areas, discriminating among different agricultural crops in state of Mato Grosso, Brazil's agricultural frontier. The results provide spatially explicit estimates of productivity increases in agriculture as well as the trade-offs between crop and pasture expansion. Quality assessment using a 5-fold cross-validation of the training samples indicates an overall accuracy of 93% and the following user's and producer's accuracy for the land cover classes: Cerrado: UA - 99% PA - 98% Fallow_Cotton UA - 100% PA - 100% Forest UA - 99% PA - 98% Pasture UA - 95% PA - 96% Soy-Corn UA- 87% PA - 97% Soy-Cotton UA - 99% PA - 94% Soy-Fallow UA - 100% PA - 100% Soy-Millet UA- 84% PA - 84% Soy-Sunflower UA - 85% PA - 85%

The correlation coefficients between the agricultural areas classified by our method and the estimates by IBGE (Brazil's Census Bureau) for the harvests from 2005 to 2016, were equal to 0.98. At state level the soybean, cotton, corn and sunflower areas had a correlation equal 0.98, 0.73, 0.96 and 0.80.

The following data sets are provided:

(a) The classified maps in compressed TIFF format (one per year) at MODIS resolution. (b) A QGIS style file for displaying the data in the QGIS software (c) An RDS file (R compressed format) with the training data set (2,115 ground samples).

The software used to produce the analysis is available as open source on https://github.com/e-sensing.

Note: The TIFF raster files use the Sinusoidal Projection, which is the same cartographical projection used by the input MODIS images. When opening the TIFF raster maps in QGIS, to ensure correct navigation please use the Sinusoidal Projection, by selecting in QGIS projection menu, the following option: "Generated CRS (+proj=sinu +lon_0=0 +x_0=0 +y_0=0 +a=6371007.181 +b=6371007.181 +units=m +no_defs)"

Developed by SOLARGIS (https://solargis.com) and provided by the Global Solar Atlas (GSA), this data resource contains photovoltaic power potential (PVOUT) in kWh/kWp covering the globe. Data is provided in a geographic spatial reference (EPSG:4326). The resolution (pixel size) of solar resource data (GHI, DIF, GTI, DNI) is 9 arcsec (nominally 250 m), PVOUT and TEMP 30 arcsec (nominally 1 km) and OPTA 2 arcmin (nominally 4 km). The data is hyperlinked under 'resources' with the following characteristics: PVOUT - LTAy_AvgDailyTotals (GeoTIFF) Data format: GEOTIFF File size : 3.6 GB There are two temporal representation of solar resource and PVOUT data available: • Longterm yearly/monthly average of daily totals (LTAym_AvgDailyTotals) • Longterm average of yearly/monthly totals (LTAym_YearlyMonthlyTotals) Both type of data are equivalent, you can select the summarization of your preference. The relation between datasets is described by simple equations: • LTAy_YearlyTotals = LTAy_DailyTotals * 365.25 • LTAy_MonthlyTotals = LTAy_DailyTotals * Number_of_Days_In_The_Month For individual country or regional data downloads please see: https://globalsolaratlas.info/download (use the drop-down menu to select country or region of interest) For data provided in AAIGrid please see: https://globalsolaratlas.info/download/world. For more information and terms of use, please, read metadata, provided in PDF and XML format for each data layer in a download file. For other data formats, resolution or time aggregation, please, visit Solargis website. Data can be used for visualization, further processing, and geo-analysis in all mainstream GIS software with raster data processing capabilities (such as open source QGIS, commercial ESRI ArcGIS products and others).

Developed by SOLARGIS and provided by the Global Solar Atlas (GSA), this data resource contains direct normal irradiation (DNI) in kWh/m² covering the globe. Data is provided in a geographic spatial reference (EPSG:4326). The resolution (pixel size) of solar resource data (GHI, DIF, GTI, DNI) is 9 arcsec (nominally 250 m), PVOUT and TEMP 30 arcsec (nominally 1 km) and OPTA 2 arcmin (nominally 4 km). The data is hyperlinked under 'resources' with the following characteristics: DNI LTAy_AvgDailyTotals (GeoTIFF) Data format: GEOTIFF File size : 343.99 MB There are two temporal representation of solar resource and PVOUT data available: • Longterm yearly/monthly average of daily totals (LTAym_AvgDailyTotals) • Longterm average of yearly/monthly totals (LTAym_YearlyMonthlyTotals) Both type of data are equivalent, you can select the summarization of your preference. The relation between datasets is described by simple equations: • LTAy_YearlyTotals = LTAy_DailyTotals * 365.25 • LTAy_MonthlyTotals = LTAy_DailyTotals * Number_of_Days_In_The_Month For individual country or regional data downloads please see: https://globalsolaratlas.info/download (use the drop-down menu to select country or region of interest) For data provided in AAIGrid please see: https://globalsolaratlas.info/download/world. For more information and terms of use, please, read metadata, provided in PDF and XML format for each data layer in a download file. For other data formats, resolution or time aggregation, please, visit Solargis website. Data can be used for visualization, further processing, and geo-analysis in all mainstream GIS software with raster data processing capabilities (such as open source QGIS, commercial ESRI ArcGIS products and others).

These spatial datasets represent a continental-scale approach to ecosystem condition monitoring using Earth observations and deep learning. This was undertaken for the years 2010, 2015, 2020, 2021 and 2022 for Australia at 100 m resolution. For each predicted ecosystem condition dataset (e.g. predicted_ecosystem_condition_2022.tif), a condition score of 1 indicates at or near reference condition, where a condition score of 0 indicates a fully degraded condition. Accompanying the predicted ecosystem condition datasets are QGIS colour schema (.qml). We also include datasets on spatial and temporal model sensitivity, including mean absoluate error (MAE) of predicted ecosystem condition by bioregions, as well as standard deviation of each pixels predicted ecosystem condition value over time, for all years mapped.

These datasets are associated with a manuscript currently undergoing peer-review. Lineage: The methods used to generate these datasets are described in full in Owers et al. (in review). In this study we use an innovative deep learning architecture to pair time series satellite imagery to predict ecosystem condition across Australia for several years (2010, 2015, 2020, 2021, 2022) at 100 m resolution. Each dataset represents a continuum of ecosystem condition from close to reference condition (1) to fully degraded (0), where a pixel’s value corresponds to the prediction of ecosystem condition.

These datasets were generated using EO data and deep learning techniques. Our model was developed using 209,041 on-ground records of ecosystem condition, coupled with Landsat time series data and topographic and climatological datasets. Input data was collated from several sources including the harmonised Australia vegetated plot (HAVPlot) dataset for at or near reference condition (Mokany et al., 2022). Further input data considered as fully degraded condition were obtained by using features such as roads, parking lots and buildings from Open Street Map (OpenStreetMap contributors, 2017), plantation and vineyard locations (AARSC, 2021; ABARES, 2021), cultivated areas (Owers et al., 2022).

Earth observation (EO) data were accessed using the CSIRO Earth Analytics Science and Innovation (EASI) platform. Spectral reflectance values of 6 reflectance bands (Blue, Green, Red, NIR, SWIR1, SWIR2) were extracted for each observation in specified calendar years and 17 common spectral indices were generated relevant for landscape and vegetation characteristics relating to ecosystem condition (NDVI, EVI, LAI, SAVI, MSAVI, TCG, NDWI, MNDWI, TCW, BUI, NBI, NDBI, TCB, BAI, NBR, BSI, NDMI). Additional nationally available spatial data layers were also used to provide topographical and climatological context. These included a digital elevation model (Farr et al., 2007) and topographic wetness index (Gallant and Austin, 2015), latitude and longitude, as well as climatology datasets including mean annual potential evaporation, mean annual precipitation, mean maximum temperature of the warmest month, and mean minimum temperature of the coldest month (Harwood et al., 2018).

Our deep learning model architecture was inspired the temporal convolutional neural network proposed by Pelletier et al. (2019). The selected final model was applied to make predictions across the Australian continent for the calendar years 2010, 2015, 2020, 2021, 2022. EO time series data were generated using the same approach as input data for model training, including normalisation of data variables and interpolation of values where required in time series observations. The model was applied to Landsat data resampled to 100 m resolution.

Spatial and temporal sensitivity of the model was evaluated across the Australian continent for the five calendar year maps. Spatial variation was mapped, stratified on bioregions, where mean absolute error (MAE) of predicted and actual ecosystem condition values were summarised. Temporal variation was mapped to evaluate predicted ecosystem condition values over time. This was achieved by mapping the standard deviation of each pixel for all years mapped. These sensitivity datasets are also included in this collection.

The datasets including in this collection are geotiffs of predicted ecosystem condition predicted_ecosystem_condition_

All geotiffs as continental mosaics at 100 m resolution projected in the Australia Albers coordinate system for Australia (EPSG:3577).

References -- see attached document --

Summary: This repository contains spatial data files representing the density of vegetation cover within a 200 meter radius of points on a grid across the land area of New York City (NYC), New York, USA based on 2017 six-inch resolution land cover data, as well as SQL code used to carry out the analysis. The 200 meter radius was selected based on a study led by researchers at the NYC Department of Health and Mental Hygiene, which found that for a given point in the city, cooling benefits of vegetation only begin to accrue once the vegetation cover within a 200 meter radius is at least 32% (Johnson et al. 2020). The grid spacing of 100 feet in north/south and east/west directions was intended to provide granular enough detail to offer useful insights at a local scale (e.g., within a neighborhood) while keeping the amount of data needed to be processed for this manageable. The contained files were developed by the NY Cities Program of The Nature Conservancy and the NYC Environmental Justice Alliance through the Just Nature NYC Partnership. Additional context and interpretation of this work is available in a blog post.

References: Johnson, S., Z. Ross, I. Kheirbek, and K. Ito. 2020. Characterization of intra-urban spatial variation in observed summer ambient temperature from the New York City Community Air Survey. Urban Climate 31:100583. https://doi.org/10.1016/j.uclim.2020.100583

Files in this Repository: Spatial Data (all data are in the New York State Plane Coordinate System - Long Island Zone, North American Datum 1983, EPSG 2263): Points with unique identifiers (fid) and data on proportion tree canopy cover (prop_canopy), proportion grass/shrub cover (prop_grassshrub), and proportion total vegetation cover (prop_veg) within a 200 meter radius (same data made available in two commonly used formats, Esri File GeoDatabase and GeoPackage): nyc_propveg2017_200mbuffer_100ftgrid_nowater.gdb.zip nyc_propveg2017_200mbuffer_100ftgrid_nowater.gpkg Raster Data with the proportion total vegetation within a 200 meter radius of the center of each cell (pixel centers align with the spatial point data) nyc_propveg2017_200mbuffer_100ftgrid_nowater.tif Computer Code: Code for generating the point data in PostgreSQL/PostGIS, assuming the data sources listed below are already in a PostGIS database. nyc_point_buffer_vegetation_overlay.sql

Data Sources and Methods: We used two openly available datasets from the City of New York for this analysis: Borough Boundaries (Clipped to Shoreline) for NYC, from the NYC Department of City Planning, available at https://www.nyc.gov/site/planning/data-maps/open-data/districts-download-metadata.page Six-inch resolution land cover data for New York City as of 2017, available at https://data.cityofnewyork.us/Environment/Land-Cover-Raster-Data-2017-6in-Resolution/he6d-2qns All data were used in the New York State Plane Coordinate System, Long Island Zone (EPSG 2263). Land cover data were used in a polygonized form for these analyses. The general steps for developing the data available in this repository were as follows: Create a grid of points across the city, based on the full extent of the Borough Boundaries dataset, with points 100 feet from one another in east/west and north/south directions Delete any points that do not overlap the areas in the Borough Boundaries dataset. Create circles centered at each point, with a radius of 200 meters (656.168 feet) in line with the aforementioned paper (Johnson et al. 2020). Overlay the circles with the land cover data, and calculate the proportion of the land cover that was grass/shrub and tree canopy land cover types. Note, because the land cover data consistently ended at the boundaries of NYC, for points within 200 meters of Nassau and Westchester Counties, the area with land cover data was smaller than the area of the circles. Relate the results from the overlay analysis back to the associated points. Create a raster data layer from the point data, with 100 foot by 100 foot resolution, where the center of each pixel is at the location of the respective points. Areas between the Borough Boundary polygons (open water of NY Harbor) are coded as "no data." All steps except for the creation of the raster dataset were conducted in PostgreSQL/PostGIS, as documented in nyc_point_buffer_vegetation_overlay.sql. The conversion of the results to a raster dataset was done in QGIS (version 3.28), ultimately using the gdal_rasterize function.

This data contains values of bare sand area, modelled wind speed, aspect and slope at a 2.5 m spatial resolution for four UK coastal dune fields, Abberfraw (Wales), Ainsdale (England), Morfa Dyffryn (Wales), Penhale (England). Data is stored as a .csv file. Data is available for 620,756.25 m2 of dune at Abberfraw, 550,962.5 m2 of dune at Ainsdale, 1,797,756.25 m2 of dune at Morfa Dyffryn and 2,275,056.25 m2 of dune at Penhale. All values were calculated from aerial imagery and digital terrain models collected between 2014 and 2016. For each location, areas of bare sand were mapped in QGIS using the semi-automatic classification plugin (SCP) and the minimum distance algorithm on true-colour RGB images. The slope and aspect of the dune surface at each site was calculated in QGIS from digital terrain models. Wind speed at 0.4 m above the surface of the digital terrain model at each site was calculated using a steady state computational fluid dynamics (CFD). Data was collected to statistically test the relationship between bare sand and three abiotic physical factors on coastal dunes (wind speed, dune slope and dune slope aspect). Vertical aerial imagery was sourced from EDINA Aerial Digimap Service and digital terrain models from EDINA LIDAR Digimap Service. Wind speed data was generated and interpreted by Dr Thomas Smyth (University of Huddersfield). Full details about this dataset can be found at https://doi.org/10.5285/972599af-0cc3-4e0e-a4dc-2fab7a6dfc85

This is a gridded dataset of monthly industrial water withdrawal (IWW) for China, namely, the China industrial water withdrawal dataset (CIWW). The dataset begins in January 1965 and is ongoing (currently up to December 2020) with a temporal resolution of a month and a spatial resolution of 0.1°/0.25°. The CIWW dataset, together with its auxiliary data, will be useful for water resource management and hydrological models.Version history:V1.1_20240403Update the seasonal variability.Compared to version 1.0, we estimated the seasonality of the subsector (Electricity and Heating Power Production and Supply,) based on spatial classification and then recreated the CIWW data with the updated seasonal variability. More details are described in Hou et al. (2023). The seasonal variation in the updated version is less different from the previous one.V1.0_20230209Using notes：Updated notes about opening the data with ArcGIS and other software (Jan 13, 2025)When opening the CIWW dataset (NetCDF format) in ArcGIS, the following issues may appear, as reported by users:1) The file cannot be successfully opened in ArcGIS.2) The time dimension value could not be properly displayed (e.g., time fixed to January 1, 1965).a) For ArcGIS users, it is recommended to utilize the Multidimension Tools in the toolbox and select the Make NetCDFRaster Layer tool. During the import process:Choose iww_layer as the variable.Select time as the third dimension in addition to longitude and latitude.After importing, open the Properties of the layer and navigate to the Symbology tab. You will see 672 different bands, representing the monthly data from January 1965 to December 2020.If the dataset is directly dragged into ArcGIS, the variable cell_area will be opened by default. This variable represents the area of each grid cell at a resolution of 0.25°/0.1° within the longitude and latitude range of China. The industrial water withdrawal is provided in units of mm/month. If needed, you can convert this to m³/month by multiplying the values by the corresponding grid cell area. For detailed variable descriptions, refer to the readme.txt file.b) The CIWW dataset can be opened using QGIS. Users can select the relevant dimensions and drag the dataset directly into QGIS. The time dimension includes 672 bands, with each band representing the number of days since January 1, 1965.c) The NetCDF format CIWW dataset can be easily opened by any programing language with NetCDF capabilities, for example, the xarry package in Python, Matlab, R, and others).Authors: Chengcheng Hou (cch@mail.bnu.edu.cn), Yan Li (yanli@bnu.edu.cn).Reference: Hou, C., Li, Y., Sang, S., Zhao, X., Liu, Y., Liu, Y., and Zhao, F.: High-resolution mapping of monthly industrial water withdrawal in China from 1965 to 2020, Earth Syst. Sci. Data Discuss. [preprint], https://doi.org/10.5194/essd-2023-66, in review, 2023.