Brief 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.
Notes
Supplemental InformationFilenames follow a simple convention: cropmap_
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.
Notes
CreditWe 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.
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.
Data Quality Assessment Scope
local :
dataset
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
local :
Quality 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.
Created: 2014-04-11
Issued: 2022-07-22
Modified: 2024-09-23
Data time period: 1988-01-01
text: Note that a larger extent is covered by the summer crop mapping compared with the winter.
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Point-of-truth metadata URL
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
- URI : geonetwork.tern.org.au/geonetwork/srv/eng/catalog.search#/metadata/bae77dd5-ba0a-41b4-973b-a800236b8476
- global : bae77dd5-ba0a-41b4-973b-a800236b8476
