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34 results for “crop mapping”
Field-Level California Crop Maps 2007-2021
<p>Field-Level California Crop Maps 2007-2021</p> <p><strong>Abstract</strong></p> <p>Technological advances in satellite image processing have made crop maps readily available over the last decade. Because of the diversity and complexity of crop production in California, however, reliable crop maps for the state are still scant. To fill this gap, we created field-level crop maps of California (hereinafter, Field-Level California Crop Map (FLCCM)) for 2007-2021. We leverage highly accurate ground-truth labels that exist in 2014, 2016, and 2018 to train our crop classifier using probability random forests. We then feed to our classifier the data for predictors that are available from 2007 to 2021. We release three types of crop predictions, and their corresponding accuracy measures in three formats (.csv, .shp, .rds). Our training algorithm can be applied to other settings in which field-level ground-truth data are scarce but fine-resolution pixel-level data are relatively more abundant. </p> <p> </p> <p><strong>Disclamer: </strong>The dataset is in the process of being peer-reviewed. </p>
Map of 2016 agricultural crops in Camargue
<p>Agricultural landscape mapping (with focus on wheat and rice fields) based on a classification using random forest algorithm with field data and zonal statistics from Sentinel 1 and 2 as inputs.</p>
Map of 2018 agricultural crops in Camargue
<p>Agricultural landscape mapping (with focus on wheat and rice fields) based on a classification using random forest algorithm with field data and zonal statistics from Sentinel 1 and 2 as inputs.</p>
Map of 2017 agricultural crops in Camargue
<p>Agricultural landscape mapping (with focus on wheat and rice fields) based on a classification using random forest algorithm with field data and zonal statistics from Sentinel 1 and 2 as inputs.</p>
Argentina National Map of Crops 2021/2022
<p>Crop type map covering the main agricultural areas of Argentina for growing season 2021/2022. This version includes two different maps for winter and summer crops. Maps were generated using supervised classification methods with samples obtained from on-road surveys and Landsat satellite images along the growing season. Files provided include a Geotiff version of each map with a resolution of 30 m. The report includes the methodological details, map legend and accuracy assessments (in Spanish). A web visualizer can be accessed through the following link: <a href="https://intalulc.users.earthengine.app/view/mnc21-22">https://intalulc.users.earthengine.app/view/mnc21-22</a></p>
Argentina National Map of Crops 2018/2019
<p>Crop type map covering the main agricultural areas of Argentina for growing season 2018/2019. This version includes a unique map for the complete growing season considering single and double crops. Map was generated using supervised classification methods with samples obtained from on-road surveys and Landsat satellite images along the growing season. Files provided include a Geotiff version with a resolution of 30 m. The report includes the methodological details, map legend and accuracy assessments (in Spanish). A web visualizer can be accessed through the following link: <a href="https://deabelle.users.earthengine.app/view/mncv1r1">https://deabelle.users.earthengine.app/view/mncv1r1</a></p>
Argentina National Map of Crops 2019/2020
<p>Crop type map covering the main agricultural areas of Argentina for growing season 2019/2020. This version includes two different maps for winter and summer crops. Maps were generated using supervised classification methods with samples obtained from on-road surveys and Landsat satellite images along the growing season. Files provided include a Geotiff version of each map with a resolution of 30 m. The report includes the methodological details, map legend and accuracy assessments (in Spanish). A web visualizer can be accessed through the following link: <a href="https://intalulc.users.earthengine.app/view/mnc19-20">https://intalulc.users.earthengine.app/view/mnc19-20 </a></p>
Argentina National Map of Crops 2020/2021
<p>Crop type map covering the main agricultural areas of Argentina for growing season 2020/2021. This version includes two different maps for winter and summer crops. Maps were generated using supervised classification methods with samples obtained from on-road surveys and Landsat satellite images along the growing season. Files provided include a Geotiff version of each map with a resolution of 30 m. The report includes the methodological details, map legend and accuracy assessments (in Spanish). A web visualizer can be accessed through the following link: <a href="https://intalulc.users.earthengine.app/view/mnc20-21">https://intalulc.users.earthengine.app/view/mnc20-21</a></p>
Kenya Cropland Map and Non-Crop Labelled dataset
<p>This dataset provides a 10m resolution map of cropland in Kenya for the 2019-2020 growing season (kenya_cropland_binary_2019.tif.zip) and a 10m resolution map of cropland in Busia County, Kenya for the 2020-2021 growing season (busia_cropland_binary_2020.tif.zip). Each pixel has a binary value, 0 if it does not contain crops and 1 if it does. These values were obtained by thresholding the predictions of an LSTM classifier trained on multi-spectral time series of Sentinel-2 satellite observations. A thresholding value of 0.5 was used.</p> <p>This dataset also provides the hand-labelled non-crop points used for training, which were created by labelling high-resolution satellite imagery in QGIS and Google Earth Pro.</p> <p>For more information, or if you use any part of this dataset, please refer to / cite the following paper: Gabriel Tseng, Hannah Kerner, Catherine Nakalembe and Inbal Becker-Reshef. 2020. Annual and in-season mapping of cropland at field scale with sparse labels. Tackling Climate Change with Machine Learning workshop at NeurIPS ’20: December 11th, 2020</p>
Landsat-based maps of irrigated dry season cropping in Southeastern Anatolia, Turkey
<p><strong>Landsat-based maps of irrigated dry-season cropping in Southeastern Anatolia, Turkey</strong></p> <p>Long-term monitoring of the extent and intensity of irrigation systems is needed to track crop water consumption and to optimize land use in a changing climate. We mapped the expansion and land use intensity of irrigated dry season cropping in Turkey´s Southeastern Anatolia Project annually from 1990 to 2018 using Landsat time series and Google Earth Engine.</p> <p>This dataset includes multiple maps documenting the expansion and land use intensity of irrigated dry season cropping in Turkey´s largest irrigation scheme. We aggregated all Landsat imagery acquired during the July through September for the period 1990 to 2018 into spectral-temporal metrics and predicted dry season cropping annually using a machine learning classifier. We performed several post-processing steps to derive multiple map products for all areas with at least two dry season cropping cycles in the study period. The dataset comes in .zip format and includes the following map products:</p> <ul> <li>gap_dsc_fst.tif: first year of dry season cropping</li> <li>gap_dsc_yrs.tif: number of years with dry season cropping</li> <li>gap_dsc_frq.tif: dry season cropping frequency (% of years since first dry season cultivation):</li> <li>gap_dsc_trd.tif: five-year dry season cropping frequency trend magnitude</li> <li>gap_dsc_pvl.tif: significance level (p-values)</li> </ul> <p><strong>Spatial coverage</strong><br> The maps come in 30m spatial resolution and cover the Southeastern Anatolia Project (Güneydoğu Anadolu Projesi, GAP) region. The region consists of nine provinces which account for approximately 10% of the Turkish land area. </p> <p><strong>Temporal coverage</strong><br> The analyses cover the period 1990-2018. The first year of dry season cropping and the number of years with dry season cropping represent the time period 1990-2017. The temporal coverage of dry season cropping frequency varies on a pixel level, depending on the initial year of dry-season cultivation. The temporal coverage of the trend indicators also vary on a pixel level and furthermore have a constrained maximum temporal coverage of 1992-2012 due to the post-processing steps involved.</p> <p><strong>Data format</strong><br> The data are delivered as 16bit single layer GeoTIFFs in EPSG:3035 projection. The images are LZW compressed, and have NoData value 0.</p> <p><strong>Publication & further information</strong><br> Please see the publication for further information on the methodology and accuracy of the map products:</p> <p>Rufin, P.; Müller, D.; Schwieder, M.; Pflugmacher, D.; Hostert, P. (2020): Landsat time series reveal simultaneous expansion and intensification of irrigated dry season cropping in Southeastern Turkey. <em>Journal of Land Use Science. </em>DOI: http://dx.doi.org/10.1080/1747423X.2020.1858198</p> <p><strong>Acknowledgments</strong><br> This research contributes to the Landsat Science Team 2018-2023 (http://www.usgs.gov/land-resources/nli/landsat/landsat-science-teams) and the Global Land Programme (https://glp.earth/). We gratefully acknowledge the open cloud processing platform provided by Google. </p>
Landsat-based maps of cropping practices in the irrigated drylands of the Aral Sea Basin (1987-2019)
<p><strong>Overview</strong></p> <p>A set of maps revealing agricultural land use patterns in the irrigated drylands of the Amu Darya and Syr Darya basin between 1987 and 2019. The maps were produced using time series of 30m Landsat TM, ETM+ and OLI Collection 1 surface reflectance products and a Random Forest classification model trained with ~30,000 samples from the years 1987, 1998, 2008, and 2018. All processing steps were conducted in Google Earth Engine. The target classes of this product are "wet season cropping", "dry season cropping", "double cropping including fodder crops", and "non-cropland". Mapping was conducted across nine provinces in Uzbekistan, two in Turkmenistan, and three in Tajikistan, and post-processing was used to constrain the study area to regions which were irrigated in at least two years in the study period, areas below 2,000 m above sea level, and regions/years with a sufficient number of cloud-free Landsat images (n>6). Annual maps were aggregated temporally and spatially, resulting in different datasets described below.</p> <p>We advise map users to read the <a href="https://doi.org/10.1088/1748-9326/ac8daa">open access paper</a> and the associated supplementary materials for detailed insights. In case of further questions please contact the lead author of the work.</p> <p><strong>Data</strong></p> <p>This download contains three folders:</p> <ul> <li><em>landuse_30m</em>: Land use layers with 30m spatial resolution, representing the percentage (0-100%) of the three key land use types ("wet season cropping", "dry season cropping", "double cropping including fodder crops") within two time frames (1987-2000 and 2001-2019). Years with insufficient cloud-free Landsat observations were excluded from the calculation of percentages.</li> <li><em>landuse_3km</em>: Land use metrics with 3km spatial resolution, representing - for each grid cell and year - the "percentage of cropland", "percentage of dry-season cultivation", and "cropping frequency". The layers in these datasets are ordered chronologically, with the layer 1 representing the year 1987, and layer 33 the year 2019. Gaps in the time series were filled using linear interpolation for subsequent trend calculation using <a href="https://github.com/morrowcj/remotePARTS/">remotePARTS</a>.</li> <li><em>mask</em>: A mask with 30m spatial resolution constraining the study area to regions below 2,000 m above sea level and regions which were irrigated at least twice in the study period.</li> </ul> <p><strong>Map accuracy</strong></p> <p>We conducted an area-adjusted accuracy assessment based on a stratified random sample (n = 2,784 per year), which yielded important insights regarding accuracies and error types. The median area-adjusted overall accuracy of the maps across the study period is 91.4%, but class-specific user´s and producer´s accuracies vary substantially. Users should consult the supplementary materials of the article for details on accuracies, confusion matrices, and the most important error types.</p> <p><strong>Further resources</strong><br> The production of this map was made possible through the Landsat Program of the United States Geological Survey (USGS) and the Google Earth Engine cloud computing platform for preprocessing of the satellite data and classification. The code for preprocessing the Landsat time series is based on the Google Earth Engine Python API and made available at <a href="https://github.com/philipperufin/eepypr/">https://github.com/philipperufin/eepypr/</a>.</p>
Georeferenced and cropped "63k Maps of Burma"
<p>Georeferenced (to WGS1984) and cropped set of about 820 historic maps of Burma at a scale of 1 inch per mile (63,360) covering about 75% of the country. Those topographic maps, originally produced and published by the Great Trigonometrical Survey of India between 1899 and 1946, have been scanned and shared with the public as part of the "Old Survey Of India Maps” Community under a CC BY 4.0 International Licence. Many of these maps are reprints of earlier maps produced before the war. Most mapsheets are early editions (edition 1 or edition 2).</p> <p>Each of the 820 map sheet scans was georeferenced using the Latitude-Longitude corner coordinates in Everest 1830 projection. Those map sheets were cropped, keeping only the map area - to allow a seamless mosaic without the mapframe overlapping adjacent map sheets when several map sheets are put together in a GIS. Those cropped map sheets were projected from Everest 1830 to WGS1984 (EPSG4326) - standard GPS - projection to make them easier to use and combine with other GIS data.</p> <p>Those map sheets can be loaded directly in any GIS such as QGIS or ESRI ArcGIS as well as Google Earth.</p> <ul> <li>The mm_OI_JBv2024 folder contains the cropped end georeferenced map sheets in jpg-format as well as accompagning georeference and metadata incl.<br> <ul> <li>The mm_OI_JBv2024_kmlLinks contains kml files to easily load the mapsheets into Google Earth</li> <li>The mm_historicOI_EPSG4326.gdb contains an ESRI mosaic dataset to easily load all mapsheets into ArcGIS</li> </ul> </li> <li>The mm_OI_JBv2024_scanMaps folder contains the uncropped original map scans (renamed though) in jpg-format.</li> <li>The mm_topoOI_JBv7_masterlist.xlsx is a masterlist cataloguing all map sheets for easier use and matching them with the original source files as shared as part of the "Old Survey Of India Maps" (e.g. to identify new mapsheets should new maps be released)</li> <li>The indexMaps folder contains small scale index maps to locate the map sheets using their map sheet Grid-Letter-nomenclature</li> </ul> <p>All georeferenced map scans are based on maps shared by John Brown via Zenodo</p> <ul> <li><a href="../records/8040798">https://zenodo.org/records/8040798</a> (63k Maps of Burma, version 7, Published June 14, 2023)</li> <li><a href="../records/10463372">https://zenodo.org/records/10463372</a> (63k Maps of Burma--additional 1--20240105, version 1, Published January 5, 2024)</li> </ul> <p>The file naming convention is to first give the <strong><em>number</em></strong> of the 4 degree x 4 degree block followed by the <strong><em>letter (A to P)</em></strong> of the sixteen 1 degree x 1 degree blocks in each 4 degree block eg. 38 D, and this is followed by a <strong><em>number</em></strong> from 1 to 16 to indicate the number of the map in the 1 degree block. </p> <p>This <strong><em>Number Letter Number</em></strong> designation is followed by the <strong>map series type</strong> either OI (contains a LCC grid) or OILatLon (only has a Lat-Lon grid), followed by the <strong>edition and year of the edition</strong>, followed by the <strong><em>date of publication/print</em></strong>. If the information is not available an "X" (for edition) or "0000" (for an unknown year) is used. A best-guess approach was used if the edition and print year and version information was ambiguous.</p> <p>The files as shared via the "<a href="https://zenodo.org/records/11661876">Old Survey Of India Maps</a>" have been renamed to standardize the file naming, sometimes correcting them and to make them unique in the case several editions of the same map sheet were available. </p> <p>A topographical index produced by the Survey of India is provided to assist the viewer in selecting a particular map of interest.</p>
Cropping intensity maps for Vojvodina, Serbia
<p>This dataset represents cropping intensity maps in Vojvodina (Serbia) for 2022 and 2023, characterized by different weather conditions. These maps have a resolution of 10 meters and were created using machine learning techniques applied to Sentinel-2 data, along with on-site collected ground truth data.<br>The maps are in .tiff format and include the following classes:</p> <ul> <li>single summer cropping (0)</li> <li>single winter cropping (1)</li> <li>double-cropping (2)</li> <li>clover (3)</li> <li>other (4).</li> </ul>
Fig. 2 in (Hem.: Pentatomidae) in flooded rice crop in Southern Brazil Mapping of spatiotemporal distribution of Tibraca limbativentris Stal
Fig. 2. Interpolation maps by multiquadric equations of spatiotemporal distribution of occurrence categories of Tibraca limbativentris [I = no insect (green), II = adult (red), III = nymphs (pink), IV = adult + nymphs (blue)] in flooded rice crop in Southern Brazil, 2011/2012 crop season. *Thematic maps: (A) 11/19/11 [V4]; (B) 12/03/11 [V6]; (C) 12/17/11 [V8/V9]; (D) 01/07/12 [V11]; (E) 01/21/12 [R1]; (F) 02/02/12 [R5]; (G) 02/15/12 [R9]; (H) 02/29/12 [post-harvest = crop residues destroyed]. Phenological stage according to Counce et al. (2000).
Georeferenced and cropped "Half Inch" (1:126,720) maps of Burma (colonial period)
<p>Georeferenced (to WGS1984) and cropped set of about 555 historic maps of Burma at a scale of 1 inch per two miles (1:126,720) covering most of the country. Those topographic maps, originally produced and published by the Great Trigonometrical Survey of India between 1878 and 1949, have been scanned and shared with the public as "Old Survey Of India Maps” Community under a CC BY 4.0 International Licence.</p> <p>Each of the map sheet scans was georeferenced using the Latitude-Longitude corner coordinates in Everest 1830 projection. Those map sheets were cropped, keeping only the map area - to allow a seamless mosaic without the mapframe overlapping adjacent map sheets when several map sheets are put together in a GIS. Those cropped map sheets were projected from Everest 1830 to WGS1984 (EPSG:4326) - standard GPS - projection to make them easier to use and combine with other GIS data.</p> <p>Many grid cells in this dataset are covered by 2 versions of map sheets - those with hill shade and only lat-lon grid and those without hill shade and featuring a LCC map grid. </p> <p>Those map sheets can be loaded directly in any GIS such as QGIS or ESRI ArcGIS.</p> <ul> <li>The mm_HI_JBv2024_epsg4326 folder contains the cropped end georeferenced map sheets in jpg-format as well as accompagning georeference and metadata incl.<br> <ul> <li>The mm_HI_JBv2024_epsg4326_kmlLinks contains a KML file for each map sheet facilitating their easy use in Google Earth byt linking them the georeferenced map sheet file located in the mm_HI_JBv2024_epsg4326 folder. </li> <li>The mm_historicHI_EPSG4326.gdb contains three ESRI mosaic datasets to easily load all mapsheets, only mapheets with hillshading and lat-lon grid and only "regular" mapsheets without hillshading and LCC grid into ArcGIS</li> </ul> </li> <li>The mm_HI_JBv2024_scanMaps folder contains the uncropped original map scans (renamed though) in jpg-format.</li> <li>The mm_historicTopoHI_JBv2024 is a masterlist cataloguing all map sheets for easier use and matching them with the original source files as shared via the "Old Survey Of India Maps” Community (e.g. to identify new mapsheets should new maps be released)</li> </ul> <p>All georeferenced map scans are based on maps shared as part of the "Old Survey Of India Maps” via Zenodo. Links to each file can be found in the above mentined excel file and most can be also accessed through the zenodo repository below.</p> <ul> <li><a href="../records/7894128">https://zenodo.org/records/8040798</a> (128k Maps of South Asia, version 4, Published May 3, 2023)</li> </ul> <p>The file naming convention is to first give the <strong><em>number</em></strong> of the 4 degree x 4 degree block followed by the <strong><em>letter (A to P)</em></strong> of the sixteen 1 degree x 1 degree blocks in each 4 degree block eg. 38 D, and this is followed by the <strong>cardinal direction letters</strong> (NE, NW, SE, SW) to indicate the 30x30 minutes sized map position in the 1 degree block. </p> <p>This <strong><em>Number - Letter - Cardinal direction letter </em></strong>designation is followed by the <strong>year of the edition, </strong>followed by the <strong>map series type</strong> either HI-hs (hillshaded) or HI-reg (regular), followed by the <strong><em>map sheet title/name</em></strong>.</p> <p>The original files as shared as part of the "<a href="https://zenodo.org/records/11661876">Old Survey Of India Maps</a>” have been renamed to further standardize the file naming, sometimes correcting them and to make them unique in the case several editions of the same map sheet were available.</p> <p>Lineage: This version (1.01, Upload 2024-08-20) has some file attributes fixed.</p>
Argentina National Map of Crops 2022/2023
<p>Crop type map covering the main agricultural areas of Argentina for growing season 2022/2023. This version includes two different maps for winter and summer crops. Maps were generated using supervised classification methods with samples obtained from on-road surveys and Landsat satellite images along the growing season. Files provided include a Geotiff version of each map with a resolution of 30 m and legend style files. The report includes the methodological details, map legend and accuracy assessments (in Spanish). A web visualizer can be accessed through the following link: <a href="https://deabelle.users.earthengine.app/view/mnc22-23">https://deabelle.users.earthengine.app/view/mnc22-23</a></p>
Open-SEA-Rice-10: Open Access High-Resolution Maps of Rice Harvested Area and Cropping Intensity in Southeast Asia
<h2>Cite this article</h2> <p>Ginting, F.I., Rudiyanto, R., Fatchurrachman, F., Mohd Shah, R., Che Soh, N., Goh Eng Giap, S., Fiantis, D., Setiawan, B.I., Schiller, S., Davitt, A., Minasny, B. High-resolution maps of rice cropping intensity across Southeast Asia. Scientific Data [12, 1408] (2025). <a href="https://www.nature.com/articles/s41597-025-05722-1">https://doi.org/10.1038/s41597-025-05722-1</a></p> <p> </p> <h2>Data Description</h2> <p>The datasets are high-resolution mapping of rice cropping intensity across Southeast Asia using the integration of Sentinel-1 and Sentinel-2 data</p> <ul> <li>The data file is in “.tif" format</li> <li>Spatial extent: Southeast Asia</li> <li>Pixel size: 10 m</li> <li>Projection information: EPSG: 4326</li> <li>CropType: Paddy rice.</li> <li>Year: Values from 2021</li> <li>Raster class:<br>-1 is single rice cropping area; <br>-2 is double rice cropping area and<br>-3 is triple rice cropping area</li> <li>The data also can be viewed on the GEE App (<a href="https://ee-rudiyanto.projects.earthengine.app/view/open-sea-rice-10">https://ee-rudiyanto.projects.earthengine.app/view/open-sea-rice-10</a>) and the Climate TRACE platform (<a href="https://climatetrace.org/">https://climatetrace.org/</a>) </li> <li>Correspondence to: Rudiyanto (rudiyanto@umt.edu.my) and Budiman Minasny (budiman.minasny@sydney.edu.au)</li> </ul> <p> </p> <p> </p>
Mapping of spatiotemporal distribution of Tibraca limbativentris Stal in (Hem.: Pentatomidae) in flooded rice crop in Southern Brazil Mapping of spatiotemporal distribution of Tibraca limbativentris Stal
Mapping of spatiotemporal distribution of Tibraca limbativentris Stal
Probabilistic Data Generating Process-based Crop Type Map for the EU 2010-2020
<h3>General Description</h3> <p>This dataset consists of probabilistic crop type maps for the EU-28 for the years 2010-2020 that distinguish 28 crop types at 1km resolution (EPSG:3035). The maps were generated using the Data Generating Process-based procedure developed by Baumert, Heckelei and Storm (2024) [<em><span><a href="https://doi.org/10.1016/j.ecoinf.2024.102836">https://doi.org/10.1016/j.ecoinf.2024.102836</a></span></em>]. We refer to this paper for details on the generation and validation of the maps. The code used to create the maps including a detailed list of the input data can be found here: <a href="https://github.com/JoBaumert/Probabilistic_Crop_Mapping_EU">GitHub - JoBaumert/Probabilistic_Crop_Mapping_EU</a> . </p> <h3>Downloadable Data</h3> <p>The file “EU_expected_crop_shares.zip” consists of 11 raster files, one for each year from 2010 – 2020. The raster files indicate the expected shares for each of the 28 distinguished crop types in a grid cell for the entire EU-28 (see readme.txt contained in the zipped folder). Note that this raster file does not contain uncertainty information.</p> <p>The other 28 zip files contain the entire crop map ensemble (i.e., including uncertainty information), each for one of the EU countries and the United Kingdom. Each of those zip files contain 11 raster files, one for each year from 2010 – 2020. Each raster file has 2830 bands: the first two bands indicate the weight of the cell (proportional to the utilized agricultural area in a cell) and the estimated number of agricultural fields in a cell, respectively. The next 28 bands indicate the expected shares for each of the 28 crops in the respective cell. The remaining 2800 bands compose the crop type map ensemble, i.e., 100 simulated crop shares for each of the 28 crops. The zipped country folder also includes a csv file named “bands” that describes which band refers to which crop. Note that all crop shares were multiplied by 1000 when writing them to the raster files (saving them as integers requires less storage capacity), i.e., if a crop share is 0.325 or 32.5% it will appear as 325 in the raster files. </p> <p>The distinguished crops are (with abbreviation used in "bands.csv"):</p> <ul> <li>Apples and other fruits, nuts and berries (APPL+OFRU)</li> <li>Barley (BARL)</li> <li>Citrus fruits (CITR)</li> <li>Durum wheat (DWHE)</li> <li>Flowers and ornamental plants (FLOW)</li> <li>Grassland (GRAS)</li> <li>Maize (both green maize as well as grain maize, LMAIZ)</li> <li>Rape and turnip (LRAPE)</li> <li>Nurseries (NURS)</li> <li>Oats (OATS)</li> <li>Other cereals (OCER)</li> <li>Other permanent crops (OCRO)</li> <li>Other forage plants (OFAR)</li> <li>Other industrial plants (OIND)</li> <li>Olives (OLIVGR)</li> <li>Rice (PARI)</li> <li>Potatoes (POTA)</li> <li>Pulses (PULS)</li> <li>Fodder roots and brassicas (ROOF)</li> <li>Rye (RYEM)</li> <li>Soybeans (SOYA)</li> <li>Sugar beets (SUGB)</li> <li>Sunflowers (SUNF)</li> <li>Soft/common wheat (SWHE)</li> <li>Other oilseeds and fibre crops (TEXT)</li> <li>Tobacco (TOBA)</li> <li>Fresh vegetables, melons, strawberries (TOMA+OVEG)</li> <li>Vineyards (VINY)</li> </ul> <p> </p> <p> </p>
Land surface phenology based crop maps for Continental United States 2000-2018
<p>This data collection contains annual maps of crop types for Continental United States for the period 2000-2018 at spatial resolution of ~231m derived using <a href="https://doi.org/10.3334/ORNLDAAC/1299">MODIS Land Surface Phenology</a>. This data collection is a companion to the paper <strong><em>Konduri, V., Kumar, J., Hargrove, W. W., Hoffman, F. M., Ganguly, A. R. (2020) <a href="https://doi.org/10.1016/j.rse.2020.112048">Mapping Crops Within the Growing Season Across the United States. Remote Sensing of Environment</a>, Vol 251, 2020 </em></strong><a href="https://doi.org/10.1016/j.rse.2020.112048">https://doi.org/10.1016/j.rse.2020.112048</a>, which describes the methodology for development of these datasets, validation metrics and analysis.</p> <p> </p> <p><strong><strong>Files in collection (28): </strong></strong></p> <ul> <li><strong><em>crop_map_predicted_[YEAR].nc</em>: </strong>Annual crop type maps for YEAR = 2000-2018</li> <li><strong><em>Crop_map_legend.csv</em>: </strong>Legend for crop type categories in the maps (Category numbers and legends for crop types are consistent with those used by <a href="https://www.nass.usda.gov/Research_and_Science/Cropland/SARS1a.php">USDA Crop Data Layer</a>)</li> <li>Maps of earliest date (Day of Year) of classification for eight dominant crop types for year 2015: <ul> <li><strong>earliest_classification_date_corn_CONUS.nc</strong> : Earliest date of classification for corn</li> <li><strong>earliest_classification_date_soybeans_CONUS.nc</strong> : Earliest date of classification for soybeans</li> <li><strong>earliest_classification_date_winter_wheat_CONUS.nc</strong> : Earliest date of classification for winter wheat</li> <li><strong>earliest_classification_date_fallow_CONUS.nc</strong> : Earliest date of classification for fallow</li> <li><strong>earliest_classification_date_other_hay_non_alfalfa_CONUS.nc</strong> : Earliest date of classification for other hay/non-alfalfa</li> <li><strong>earliest_classification_date_alfalfa_CONUS.nc</strong> : Earliest date of classification for alfalfa</li> <li><strong>earliest_classification_date_sorghum_CONUS.nc</strong> : Earliest date of classification for sorghum</li> <li><strong>earliest_classification_date_rice_CONUS.nc</strong> : Earliest date of classification for rice</li> </ul> </li> </ul> <p> </p> <p><strong><strong>Data formats:</strong></strong></p> <ul> <li>All map products are in gridded <a href="https://www.unidata.ucar.edu/software/netcdf/">NetCDF</a> format.</li> <li>Annual crop type maps use category types described in Crop_map_legend.csv. </li> <li>Earliest date of classification maps are encoded as Day of the Year.</li> </ul> <p> </p> <p><strong><strong>Projection for geospatial data</strong> (in <a href="https://live.osgeo.org/en/overview/proj4_overview.html">PROJ4 format</a>):</strong></p> <pre><code class="language-bash">PROJCS["US_National_Atlas_Equal_Area", GEOGCS["sphere", DATUM["unknown", SPHEROID["Spherical_Earth",6370997,"inf"]], PRIMEM["Greenwich",0], UNIT["degree",0.0174532925199433]], PROJECTION["Lambert_Azimuthal_Equal_Area"], PARAMETER["latitude_of_center",45], PARAMETER["longitude_of_center",-100], PARAMETER["false_easting",0], PARAMETER["false_northing",0], UNIT["Meter",1]]</code></pre> <p><strong>Paper Citation:</strong></p> <blockquote> <p><strong><em>Konduri, V., Kumar, J., Hargrove, W. W., Hoffman, F. M., Ganguly, A. R. (2020) Mapping Crops Within the Growing Season Across the United States. Remote Sensing of Environment (in revision)</em></strong></p> </blockquote>
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Allen Brain Atlas
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DANDI Archive for NWB datasets
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International Brain Laboratory public data
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OpenNeuro
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