Winter and summer crop mapping – Landsat, Sentinel-2 and MODIS, Queensland, 1988 - ongoing
<p>This dataset shows the crops grown in Queensland's main cropping areas, for the winter and summer growing-seasons, from 1988 to the current year. The winter growing-season is defined as June to October, and the summer growing-season is November to May. The basis of the maps is imagery from the (when available) Landsat-5 TM, Landsat-7 ETM+, Landsat-(8,9) OLI, and Sentinel-2(A,B) satellites; MODIS MOD13Q1 imagery was used as a backup in the case of large, temporal data gaps. Clusters of temporally similar pixels, termed 'segments', were identified in the imagery for each growing season, and served as an approximation of field boundaries. Per-segment phenological information, derived from the satellite imagery, was then combined with a tiered, tree-based statistical classifier, using >10000 field observations as training data, and >4000 independent observations for validation. The dataset supersedes a former crop-mapping effort <a href =" https://doi.org/10.3390/rs8040 312">(Schmidt et al., 2016)</a>.</p>
<p>Each season has 2 maps: an end-of-season prediction and a mid-season prediction. The mid-season prediction is labelled "_vInterim" to indicate that it is based on a relatively short time series, and should be used with caution.</p>
<p>For optimum display symbology files have been provided for both QGIS and ArcGIS.</p>
Simple
Identification info
- Date (Creation)
- 2014-04-11
- Date (Publication)
- 2022-07-22
- Date (Revision)
- 2024-09-23
- Edition
- 0.0.3
Publisher
Author
Department of Environment and Science (2017-2023), Queensland Government - Pringle, Matthew ()
41 Boggo Road, Dutton Park, 4102, Queensland, Australia
Dutton Park
Queensland
4102
Australia
Co-author
Department of Environment and Science (2017-2023), Queensland Government - Tindall, Dan ()
41 Boggo Road, Dutton Park, 4102, Queensland, Australia
Dutton Park
Queensland
4102
Australia
- Website
- https://www.tern.org.au/
- Purpose
- <p>The statistical classifier predicts these classes of crops in summer: "Cotton", "Sugarcane", and "OtherCrop" (predominantly sorghum, but also includes, e.g., maize, mungbean, peanut).</p> <p>In winter, the statistical classifier predicts only a "Crop" class (i.e. whether a crop was grown or not).</p> <p>Note that the extent of the mapping changes by season: in winter the maps are restricted to what we define as the 'western' cropping zone only; in summer, predictions extend further, into the potential sugarcane-growing areas of the 'coastal' zone (which includes northern NSW). Any other crops grown in the coastal zone, apart from sugarcane, are not considered.</p>
- Credit
- We at TERN acknowledge the Traditional Owners and Custodians throughout Australia, New Zealand and all nations. We honour their profound connections to land, water, biodiversity and culture and pay our respects to their Elders past, present and emerging.
- Credit
- Landsat imagery was obtained from the US Geological Survey. Modified-Copernicus-Sentinel-2 imagery was obtained from the European Space Agency. MODIS MOD13Q1 imagery was obtained from the LP DAAC Data Pool.
- Status
- On going
Point of contact
Department of Environment and Science (2017-2023), Queensland Government - Pringle, Matthew ()
41 Boggo Road, Dutton Park, 4102, Queensland, Australia
41 Boggo Road
Dutton Park
Queensland
4102
Australia
Point of contact
Department of Environment and Science (2017-2023), Queensland Government - Buck, Rosalie ()
41 Boggo Road, Dutton Park, 4102, Queensland, Australia
41 Boggo Road
Dutton Park
Queensland
4102
Australia
Spatial resolution
- Spatial resolution
- 30
- Topic category
-
- Imagery base maps earth cover
- Farming
Extent
- Description
- Note that a larger extent is covered by the summer crop mapping compared with the winter.
N
S
E
W
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Temporal extent
- Time period
- 1988-01-01
- Title
- Schmidt, M., Pringle, M., Devadas, R., Denham, J., and Tindall, D. (2016). A Framework for Large-Area Mapping of Past and Present Cropping Activity Using Seasonal Landsat Images and Time Series Metrics. Remote Sensing, 8, 312
- Website
-
Schmidt, M., Pringle, M., Devadas, R., Denham, J., and Tindall, D. (2016). A Framework for Large-Area Mapping of Past and Present Cropping Activity Using Seasonal Landsat Images and Time Series Metrics. Remote Sensing, 8, 312
Related documentation
- Title
- Queensland Department of Environment and Science Crop mapping project
- Website
-
Queensland Department of Environment and Science Crop mapping project
Related documentation
- Title
- Long Paddock Forage Reports
- Website
-
Long Paddock Forage Reports
Related documentation
- Title
- Queensland Spatial Catalog
- Website
-
Queensland Spatial Catalog
Related documentation
- Title
- Pringle, M.J., Schmidt, M., and Tindall, D.R. (2018): Multi-decade, multi-sensor time-series modelling--based on geostatistical concepts--to predict broad groups of crops. Remote Sensing of Environment, 216, 183--200.
- Website
-
Pringle, M.J., Schmidt, M., and Tindall, D.R. (2018): Multi-decade, multi-sensor time-series modelling--based on geostatistical concepts--to predict broad groups of crops. Remote Sensing of Environment, 216, 183--200.
Related documentation
- Title
- Pringle, M. (2021) Detecting the annual areal extent of sugarcane crops in Queensland, Australia. Remote Sensing Applications: Society and Environment Volume 22
- Website
-
Pringle, M. (2021) Detecting the annual areal extent of sugarcane crops in Queensland, Australia. Remote Sensing Applications: Society and Environment Volume 22
Related documentation
- Maintenance and update frequency
- Quarterly
- GCMD Science Keywords
- ANZSRC Fields of Research
- TERN Platform Vocabulary
- TERN Instrument Vocabulary
- TERN Parameter Vocabulary
- QUDT Units of Measure
- GCMD Horizontal Resolution Ranges
- GCMD Temporal Resolution Ranges
Resource constraints
- Use limitation
- The Creative Commons Attribution 4.0 International (CC BY 4.0) license allows others to copy, distribute, display, and create derivative works provided that they credit the original source and any other nominated parties. Details are provided at https://creativecommons.org/licenses/by/4.0/
- File name
- 88x31.png
- File description
- CCBy Logo from creativecommons.org
- File type
- png
- Title
- Creative Commons Attribution 4.0 International Licence
- Alternate title
- CC-BY
- Edition
- 4.0
- Access constraints
- License
- Use constraints
- Other restrictions
- Other constraints
- TERN services are provided on an "as-is" and "as available" basis. Users use any TERN services at their discretion and risk. They will be solely responsible for any damage or loss whatsoever that results from such use including use of any data obtained through TERN and any analysis performed using the TERN infrastructure. <br />Web links to and from external, third party websites should not be construed as implying any relationships with and/or endorsement of the external site or its content by TERN. <br /><br />Please advise any work or publications that use this data via the online form at https://www.tern.org.au/research-publications/#reporting
- Other constraints
- Please cite this dataset as {Author} ({PublicationYear}). {Title}. {Version, as appropriate}. Terrestrial Ecosystem Research Network. Dataset. {Identifier}.
- Other constraints
- The Department of Environment and Science requests attribution in the following manner. State of Queensland (Department of Environment and Science) 2023.Update data available at http://qldspatial.information.qld.gov.au/catalogue/
Resource constraints
- Classification
- Unclassified
- Supplemental Information
- Filenames follow a simple convention: cropmap_<season><year>.gpkg <br> Example: cropmap_winter2020.gpkg
Distribution Information
Distributor
Distributor
Terrestrial Ecosystem Research Network
80 Meiers Road, Indooroopilly, Queensland, 4068, Australia
Indooroopilly
Queensland
4068
Australia
- Distribution format
-
- OnLine resource
- Download crop files
- OnLine resource
- ro-crate-metadata.json
Data quality info
- Hierarchy level
- Dataset
- Other
- Pastures may be incorrectly classified as cropping, particularly in wet years or in seasons when rapid green-up occurs at a similar time to actively growing crops. Land-in-transition: formerly-cropped land may still appear as cropped if the vegetation greens-up during the growing period, e.g. weeds or redundant or abandoned crops and crop residue are dominating the ground cover. Failed crop: a failed crop will be detected if it reached a certain level of greenness, which may vary between region, seasons and years. Water: vegetated areas around watercourses and dams may be classified as crop due to the strong greening-up phase after rainfall events. Some areas of shallow water or water with emergent or floating vegetation may be incorrectly classified as cropped, due to seasonal patterns in greenness that may be similar to crops. Topographic effects: areas with steep slopes (i.e. a slope of greater than 10%) are excluded. DEM inaccuracies may result in some areas being excluded.
Report
Result
- Statement
- Based on independent validation data, in winter the user's accuracy of the "Crop" class is 89% (dropping to 83% for vInterim files). In summer, the corresponding user's accuracies are: "Cotton" = 85% (77% for vInterim), "Sugarcane" = 93% (86% for vInterim), and "OtherCrop" = 67% (53% for vInterim). The mid-season predictions held by the vInterim files are less accurate than the end-of-season predictions, because of the shorter time-series (and hence less information) involved.
Resource lineage
- Statement
- All the data described here has been generated from the analysis of satellite imagery at a spatial resolution of approximately 30 m. A grid of Landsat TM, ETM+ and OLI data were supplemented by Sentinel-2 (after 2016) and MODIS (after 2000) imagery when large temporal data gaps occurred.
- Hierarchy level
- Dataset
- Title
- Clevers J. G. P. W. and Gitelson A. A. (2013). Remote estimation of crop and grass chlorophyll and nitrogen content using red-edge bands on Sentinel-2 and -3. International Journal of Applied Earth Observation and Geoinformation, 23: 344-351
- Website
-
https://doi.org/10.1016/j.jag.2012.10.008
Method documentation
Process step
- Description
- Attributes: <br> The predicted class is stored in the attribute table (field 'CLASS'), along with the probability of the prediction (field 'P_CLASS'; the larger this value, the more certain is 'CLASS'). <br> Also included in the attribute table is the field ‘RCI’, which is the red-edge chlorophyll index (Clevers and Gitelson, 2013), integrated at weekly intervals over the growing season. The larger the value of RCI, the greater the plant productivity; a negative value indicates that, due to imagery constraints, RCI was not actually calculated.
Reference System Information
- Reference system identifier
- EPSG/EPSG:4326
- Reference system type
- Geodetic Geographic 2D
Metadata
- Metadata identifier
-
urn:uuid/bae77dd5-ba0a-41b4-973b-a800236b8476
- Title
- TERN GeoNetwork UUID
- Language
- English
- Character encoding
- UTF8
Point of contact
Type of resource
- Resource scope
- Dataset
- Metadata linkage
-
https://geonetwork.tern.org.au/geonetwork/srv/eng/catalog.search#/metadata/bae77dd5-ba0a-41b4-973b-a800236b8476
Point-of-truth metadata URL
- Date info (Creation)
- 2014-04-11T00:00:00
- Date info (Revision)
- 2024-09-23T00:00:00
Metadata standard
- Title
- ISO 19115-1:2014/AMD 1:2018 Geographic information - Metadata - Fundamentals
- Edition
- 1
Metadata standard
- Title
- ISO/TS 19115-3:2016
- Edition
- 1.0
Metadata standard
- Title
- ISO/TS 19157-2:2016
- Edition
- 1.0
- Title
- Terrestrial Ecosystem Research Network (TERN) Metadata Profile of ISO 19115-3:2016 and ISO 19157-2:2016
- Date (published)
- 2021
- Edition
- 1.0
Identifier
Overviews
Spatial extent
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W
Provided by
Associated resources
Not available