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Canadian SAR Mini-MOOC: Winter-Water-Warming

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SAR Research and Operational Use at Agriculture and Agri-Food Canada

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Space-based Earth Observation is one of the few reliable methods to get detailed information describing the changing state of Canada’s agricultural landscapes from coast to coast. In this topic Dr. Andrew Davidson familiarizes you with how the federal department of Agriculture and Agri-Food Canada (AAFC) uses SAR data operationally to monitor Canada’s vast agricultural landscapes and provide an annual inventory of major crops.

Below video introduces you to various lesson components and the Earth observation operations at AAFC.

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Agriculture and Earth Observation (EO)

AAFC has taken advantage of recent advances in EO data acquired by a multitude of satellites. Their sensors are spanning the optical and microwave regions of the electromagnetic spectrum and a range of spatial resolutions.

New SAR sensors such as the Canadian RADARSAT Constellation Mission and the European Sentinel-1 are critical to the development of the next generation reliable information products of Agriculture Canada.

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Canada’s Annual Crop Inventory (ACI)

One of the most valuable EO-based information products produced operationally by AAFC is the Annual Crop Inventory. The ACI is updated annually and available free of charge to the public via the Government of Canada’s Open Data Portal.

The product comprises a gridded map of agricultural land use and non-agricultural land cover of Canada. In the video below you will gain insight into Canada’s Annual Crop Inventory.

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Some notes on producing the ACI

The extent and complexity of the Canadian agricultural landscape means that earth-orbiting satellites are central to maintaining information flow by delivering timely and relevant information on Canadian agricultural production trends.

In 2009, AAFC took its first steps towards the operational delivery of this information by developing a software system for mapping crop types using satellite observations. This system is based on two decades of remote sensing research and applications development at AAFC and elsewhere; it has been used for the Prairie Provinces of Alberta, Saskatchewan and Manitoba during 2009 and 2010, and it has been extended for all Provinces from 2011 onwards.

To create the digital crop inventory, a Decision Tree (DT) methodology is applied to optical (Landsat-8, Sentinel-2) and radar (RCM, Radarsat-2) satellite images. The DT algorithm uses the known crop types of certain locations on the ground to spectrally differentiate each of the crop types being mapped. These relationships are then applied to the satellite image data to identify the most likely crop type of each field in the study area.

More than 2000 satellite images – each linked to thousands of ground data points – are required to map Canada’s entire agricultural extent annually and validate the resulting product. Hundreds of hours of computer processing time are required to produce a final high-quality classification. So far, AAFC can consistently deliver a crop inventory that meets the overall target accuracy of at least 85%. The annual crop inventory maps have already been applied to address many needs for the sector.

Source: AAFC ??

Recent changes in soybean acreages

A changing climate is expected to create shifts in where and how crops are grown. In Canada, climate change could open up new acreages to production and change cropping patterns.
Soybeans are a long-season, heat-loving crop and the Province of Manitoba is experiencing fewer early killing fall frosts. This development has encouraged growers to plant soybeans. Soybean acreages have increased over the last 10 year, including a 90 per cent jump in the last five years1. [ what’s the “1” for?? ]


You can follow the expansion of soybean fields in the Province of Manitoba (MB) from 2009-2015 in the animation below. The distribution maps are based on SAR image analysis and classification.

Source: Product derived from the AAFC‘s Annual Crop Inventory, tracking changes in soybean acreages from 2009-2015

Pre-Processing of Satellite Data

Research suggest that optical and SAR data are required in order to characterize the key crop-growing stages with high accuracy. Both optical and SAR data provide unique and valuable information relating to plant type and growth.

Before using optical and SAR data as input to generate the Annual Crop Inventory, some pre-processing of these data are required. The video introduces you to the various pre-processing steps.

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Further references and links related to this sections are located here.


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