Radar’s Potential Studied For Gathering Crop Data

Anew eye in the sky is being prepared for launch, and Canadian researchers will be among the first to figure out how to make it work for farmers.

Ahead of the launch of the European Space Agency’s (ESA) Sentinel-1 satellite in 2011, an international team of researchers will collect space-borne radar images throughout the 2009 growing season from sites in the Netherlands and Spain, as well as crops within a 25-km radius of Agriculture and Agri-Food Canada’s research farm at Indian Head, Sask.

Data gleaned from the project, dubbed AgriSAR 2009, will be examined by the researchers with an eye on potential uses for land cover mapping, crop management and environmental monitoring.

Sentinel-1 will be the first of five missions planned by the ESA as part of the European Union’s Global Monitoring for Environment and Security initiative, aimed at providing more accurate and timely information about the environment and climate change and ensuring civil security.

Radar images used in the project this year will come from RADARSAT-2, which belongs to British Columbiabased MacDonald, Dettwiler and Associates (MDA), the company that built the Canadarm.

Unlike optical satellites hindered by cloud cover and darkness, MDA’s microwave-based Synthetic Aperture Radar (SAR) provides high-definition images night or day and can “see” through clouds.

Guy LaFond, a production system agronomist at AAFC Indian Head, and other Canadian scientists will collect and analyze ground-based information about land cover, crop type and condition, crop biomass and yield, and other production variables such as soil moisture and weather.

The eyes on the ground will compare the RADARSAT images from space to see if the data can be used or adapted for agricultural purposes, especially in terms of classifying crop type and estimating crop yield.

Such information would be handy for government agencies and grain companies in the midst of the growing season. For example, if the radar can be calibrated effectively, it might be used in the future to get a better handle on how crops are doing in the former Soviet Union, or other major grain-producing areas.

“When you’re talking about satellites, you’re really talking about regional and national issues,” LaFond said.

Furthermore, just as solar panels were a byproduct of the investments in the 1960s’ space race, there is potential for farmers in the form of new applications making use of radar-based imagery that could provide an “extra edge” in decision-making.

Working with the engineers who created the technology gives researchers an opportunity to open the developers’ eyes to new possibilities in agronomy that they may not have considered, LaFond added.

For example, he said, “rather than using satellite, maybe all you need is to have some type of an on-board sensor that you put on the sprayer, just like we do with the GreenSeeker sensor,” referring to the ground-based optical sensor units made by California-based NTech.

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Right now, radar technology works well for mapping surface moisture because it is very sensitive to water. But with some tweaking, or by adapting the technology to work with a ground-based sensor, it may be possible to penetrate deeper to measure and map subsurface moisture levels.

A clearer soil moisture profile after fall freeze-up would give farmers a tool for knowing how much fertilizer to apply or to book for the next growing season, he added.

Another part of the project involves collecting RapidEye satellite images and correlating, or associating, NDVI data from the RapidEye images with NDVI data from a GreenSeeker sensor.

The NDVI (Normalized Difference Vegetation Index) is the most well-known and -used index to identify vegetative (crop) areas and their condition from remote sensing data. LaFond is using it in experiments aimed at perfecting the practice of applying variable-rate foliar nitrogen mid-season.

Other scientists will test whether the RapidEye NDVI can be used for yield predictions, and will look at Leaf Area Index, which measures the total amount of plant foliage covering the ground.

The Leaf Area Index is considered a good indicator of crop growth stage and can be measured on the ground or estimated from imagery data – making it another important tool in interpreting radar images.

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