Brief description
This dataset shows the broad groups of crops grown in the main cropping areas of Queensland, for the winter and summer growing seasons from 1990 to the current year. The winter growing-season is defined as June to October, and the summer growing-season is November to May. The predicted group 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').
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.
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. An algorithm then interpolates pixel-wise data to weekly averages and determines the best match to one of the seasonal classes. The algorithm was trained with >10000 field observations and validated against >4000 independent observations. The predicted group 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'). These datasets are GDA2020 compliant.
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').
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 classification algorithm predicts these classes of crops in summer: “Banana”, "Cotton", "Sugarcane", and "OtherCrop" (predominantly sorghum, but also includes, e.g., maize, mungbean, peanut). In winter, the classification algorithm predicts “Cereal” and “Chickpea”.
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
In summer, notional user's accuracies are: “Banana” = 99%, "Cotton" = 87%, "Sugarcane" = 98%, and "OtherCrop" = 98%. In winter the user's accuracies are: "Cereal" = 89%, and “Chickpea” = 73%. User’s accuracies cannot be reported as anything more than notional approximations, due to sampling constraints.
Created: 2024-12-16
Issued: 2022-07-22
Modified: 2026-03-02
Data time period: 1990-01-01
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- URI : geonetwork.tern.org.au/geonetwork/srv/eng/catalog.search#/metadata/bae77dd5-ba0a-41b4-973b-a800236b8476
- global : bae77dd5-ba0a-41b4-973b-a800236b8476
