With climate change posing a real threat to food supplies worldwide, sustainability of food production and the ability to feed the world's population are becoming increasingly critical — not only for for producers, distributers and sellers but also for governments and communities globally. Remote sensing can bring certainty to food supply estimation with its ability to detect what crops are growing and area coverage, enabling more accurate yield prediction months before harvest and also facilitating more accurate carbon footprint modelling of agriculture.
Crop inventories are collated seasonally to provide invaluable information that enables yield prediction, as well as other insights such as national and regional trends. This information is usually gathered and reported by farmers and agronomists, before being recorded by grain handling companies and governments. However, the task of gathering the data requires vast resources to be deployed, often over wide areas, which is especially challenging in large and sparsely populated countries such as Australia. Records therefore tend to suffer from inaccuracies, gaps, and assumptions based on historical data. Alternatively, using remote sensing, crop detection and classification can be performed over a whole country in a matter of hours without needing large-scale human resource.
Thanks to D-CAT's cloud computing infrastructure and data analysis solutions, crop classification maps can be generated on-demand for any business or institution interested in knowing what crops are being grown, where they are being grown, and how much area they cover. To generate accurate crop maps, D-CAT uses Sentinel-2 multispectral data (European Space Agency, ESA), our deep knowledge of spectral science, and machine learning models that have been extensively validated across very large areas over a number of years using substantial ground truth data and benchmarked at country level against official published statistics from government sources.
The image below is a sample output from of our service, showing the crop classification at the field/paddock level in an area of the Eyre Peninsula, Australia.
Increasingly, crop classification is also forming an essential component of monitoring, reporting and verifying sustainable farming practices. Proving crop rotational practices and the use of cover crops, for example, can aid growers as they move to new funding models or auditing regimes, and save regulators from costly site visits.
Closely coupled to sustainability practices is, of course, the carbon footprint of any agricultural enterprise. Understanding what crops or pastures are being grown is one of the most fundamental components of the complex calculation required to fully model the greenhouse gas emissions and carbon sequestration of agricultural land, and so the crop classification service is also an essential input to modellers and analysts seeking trusted data sources for GHG and carbon accounting.
By using this service clients:
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