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Winter and summer crop mapping – Landsat, Sentinel-2 and MODIS, Queensland, 1988 - ongoing

Ver: 0.0.3
Status of Data: on going
Update Frequency: quarterly
Security Classification: unclassified
Viewed: 0
Accessed: 0

Description

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 (Schmidt et al., 2016).

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.

For optimum display symbology files have been provided for both QGIS and ArcGIS.


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.
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.

Purpose

The statistical classifier predicts these classes of crops in summer: "Cotton", "Sugarcane", and "OtherCrop" (predominantly sorghum, but also includes, e.g., maize, mungbean, peanut).

In winter, the statistical classifier predicts only a "Crop" class (i.e. whether a crop was grown or not).

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.


Supplemental Information
Filenames follow a simple convention: cropmap_.gpkg
Example: cropmap_winter2020.gpkg

Lineage
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.

Data Creation
Attributes:
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').
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.

Temporal coverage

From 1/1/1988

Dates

Creation date

4/11/2014

Publication date

7/22/2022

Revision date

9/23/2024

Citation information

How to cite this collection:

Pringle, M. & Tindall, D. (2022): Winter and summer crop mapping – Landsat, Sentinel-2 and MODIS, Queensland, 1988 - ongoing. Version 0.0.3. Terrestrial Ecosystem Research Network. (Dataset). https://portal.tern.org.au/metadata/TERN/bae77dd5-ba0a-41b4-973b-a800236b8476

Access data

These data can be accessed from following links:

Rights and Licensing

CC-BY

Creative Commons Attribution 4.0 International Licence
http://creativecommons.org/licenses/by/4.0

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.
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.

Please advise any work or publications that use this data via the online form at https://www.tern.org.au/research-publications/#reporting
Please cite this dataset as {Author} ({PublicationYear}). {Title}. {Version, as appropriate}. Terrestrial Ecosystem Research Network. Dataset. {Identifier}.
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/

Keywords

Parameters
Platforms
Instruments
GCMD Science
ANZSRC - FOR
Horizontal Resolution
Temporal Resolution
Topic Categories

People and Organisation

AuthorsAuthors
Co-authorsCo-authors
Point of ContactPoint of Contact
PublisherPublisher
Distributor's OrganisationDistributor's Organisation

Publications

PublicationsPublications

Data Quality

Data QualityData Quality
  • Data Quality Assessment Scope:
    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.
  • Data Quality Assessment Result:
    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.

Additional information

Method DocumentationMethod Documentation

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Contacts
  • Terrestrial Ecosystem Research Network
  • Building 1019, 80 Meiers Rd
  • QLD 4068
  • Australia
Spatial coverage
Spatial description
Note that a larger extent is covered by the summer crop mapping compared with the winter.