Science and Operational Applications Research (SOAR)
SOAR is a joint partnership program between MacDonald Dettwiler and Associates Ltd. - Geospatial Services Inc. (MDA GSI) and the Canadian Government through the Canadian Space Agency (CSA) and the Natural Resources Canada's Centre for Remote Sensing (CCRS). The program provides access to RADARSAT-2 data for research and testing purposes.
The SOAR program provides an opportunity to explore the enhanced capabilities of RADARSAT-2 and their potential contributions to various applications. This opportunity consists of a loan, of circumscribed amounts of RADARSAT-2 data to research projects. The main outcome pursued by SOAR is to ensure that Canadian stakeholders benefit, through research and development activities, from the $450 million investment made for the development of RADARSAT-2. Now that the satellite is fully operational, the Government of Canada would like to develop specific initiatives under the SOAR umbrella.
In , the CSA issued a joint Announcement of Opportunity with our long-time partner Agriculture and Agri-Food Canada to address the perceived gap or future need for RADARSAT agriculture applications. The following universities were selected to receive a total of $800,435 to develop new applications and innovative technologies using imagery from Canada's RADARSAT-2 satellite.
Project name: Mapping dynamic surface soil moisture content and contributing drainage area in vegetated agriculture fields
Pasture land on the South Saskatchewan River. (Credit: Dr. Karl-Erich Lindenschmidt, University of Saskatchewan)
University: University of Saskatchewan
CSA funding: $149,400
Scientist: Dr. Karl-Erich Lindenschmidt, Associate Professor, School of Environment and Sustainability and Global Institute for Water Security
Description: Sustainable agriculture and water resource management adapted to climate change is crucial for the welfare of an increasing world population. The purpose of this project is to develop a new approach to mapping the changing soil moisture content in vegetated agriculture fields by applying sophisticated mathematical techniques to images captured by the RADARSAT-2 satellite. The study will focus on selected sites in the Red River watershed in Manitoba and the South Saskatchewan River basin in Saskatchewan.
Benefits: The project will result in improved irrigation management, drought and flood forecasting, and water quality modelling to eventually enhance water resource management. This will fundamentally increase the ability to adapt to and manage the negative impacts of climate change in agriculture to achieve sustainable food production. The technique will also be transferrable to the new RADARSAT Constellation Mission.
Project name: Study of soil moisture and vegetation phenology: combination of radar data and hydro-agronomic modelling
University: University of Sherbrooke
Scientist: Dr. Mélanie Trudel, Assistant Professor, Department of Civil Engineering
CSA funding: $150,000
Description: Climate change can adversely affect the agriculture and agri-food sector, which are vital to the Canadian economy. Variations in the climate affect soil moisture, which, in turn, has a major impact on crop yield. Rainfall, irrigation, and soil and vegetation characteristics influence root-zone soil moisture. It can be estimated using hydro-agronomic computer models to simulate the different hydrological processes and vegetation growth. However, to be accurate, hydro-agronomic models must be calibrated with actual observations. This is where remote sensing satellites come in. Satellite observations can be used to track physical variables, which can then be used together with hydro-agronomic models to produce accurate soil moisture estimates.
Looking ahead, the RADARSAT Constellation Mission (RCM) will allow for better time-tracking of agricultural variables. This will be achieved thanks to a much lower time interval between imaging opportunities over the same spot on Earth's surface. It will also have a new imaging mode called compact polarimetry (CP), which will be particularly useful for estimating agricultural variables, as it provides important gains in information compared to other polarizations. This project will use RADARSAT-2 data to simulate the new CP imaging mode.
Benefits: The project aims to gain a better understanding of soil moisture and vegetation phenology characteristics in agricultural areas. Maps of soil moisture (surface and root) and vegetation growth will be produced by combining simulated RCM compact polarimetry data with a hydro-agronomic model. Ultimately, such tools will allow farmers to better plan irrigation and the timing of harvests to maximize crop yields.
What is vegetation phenology?
Vegetation phenology is the study of the life-cycle events of plants throughout the year (e.g., bud burst, canopy growth, flowering) and how they are influenced by variations in climate and other factors like elevation.
What is agronomy?
Agronomy is "a branch of agriculture dealing with field-crop production and soil management" (Credit: Merriam-Webster). Hydro-agronomy places an emphasis on the effects of soil moisture both at the surface and in the root zone of crops.
Project Name: Integration of RADARSAT-2 and Sentinel-2 datasets for crop identification and biomass estimation
RADARSAT-2 image of farmland near Carman, Manitoba - Different crops respond differently to the polarized waves emitted by the radar. This is represented in the map by different colours. Major crop types are identified using coloured outlines with yellow representing canola fields, orange representing soybean fields, green representing corn fields, and red representing wheat fields. (Credit: RADARSAT-2 Data and Products © MacDonald, Dettwiler and Associates Ltd. () – All Rights Reserved. RADARSAT is an official trademark of the Canadian Space Agency.)
Map courtesy of Dr. Jiali Shang, Agriculture and Agri-Food Canada
University: University of Toronto
Scientist: Dr. Jing M. Chen, Professor, Department of Geography and Planning
CSA funding: $150,000
Description: Unlike optical sensors, RADARSAT-2's Synthetic Aperture Radar (SAR) signals can penetrate cloud and darkness, hence data acquisition is not limited by weather conditions. This capability is especially important for agricultural applications when data acquisition is needed within specific time windows to capture crop information at key growth stages.
Agriculture and Agri-Food Canada (AAFC) currently uses RADARSAT-2 and optical satellite imagery to produce its national annual crop inventory. While crop classification accuracy is satisfactory, it can only be achieved by including satellite image data acquired late in the growing season. This has limited the timely production of the crop inventory. An improved methodology for crop classification, before the end of the growing season, is desirable. More timely and accurate information on Canadian agricultural land use and crop growth conditions is of great importance to all stakeholders in the agricultural production and marketing chain.
Benefits: This project aims to improve crop type identification in Canada by combining RADARSAT-2 and other sources of data (for example, the European Space Agency's Sentinel-1 & 2 radar satellites) with ecosystem modelling to develop an improved methodology for crop classification that does not require the late-season satellite acquisitions. This will allow production of a more timely crop inventory by AAFC. The project will also improve crop biomass estimation accuracy by assimilating biophysical and biochemical parameters derived from RADARSAT-2 and Sentinel-2 data into an ecosystem model. This information will help Canadian producers and commodity-trading agencies to better position themselves in the global marketplace. Two study sites are proposed, one in Ontario and one in Manitoba.
Project name: Application of RADARSAT-2 polarimetric Synthetic Aperture Radar (SAR) to crop biophysical variable retrieval and crop monitoring
University: Western University
Scientist: Dr. Jinfei Wang, Professor, Department of Geography
CSA funding: $150,000
Description: Canada is a major agricultural production country. Wheat, corn and soybean are amongst the most valuable crops in Canada. Assessing crop growth conditions by monitoring plant variables such as crop height, leaf area index and biomass is of great importance in forecasting crop yield. Accurate yield forecasting enables farmers to make timely decisions on crop management, and ultimately leads to better informed trading and agricultural policy decisions.
This project will develop new algorithms for estimating crop biophysical variables using RADARSAT-2 quad-pol data, combined with data from optical satellite Venµs and an unmanned aerial vehicle (UAV). The study site was selected as one of the 50 sites globally to collect time series of high-resolution Venµs images. RADARSAT-2 data have great potential for estimating crop variables because of the satellite's sensitivity to vegetation characteristics such as plant structure, leaf area index, biomass and plant water content.
Benefits: When fully developed, these algorithms will directly benefit farmers, traders and policy makers to generate more accurate yield forecasting.
Project name: Calibration of the Multi-Disciplinary Simulator for Standard Crops (STICS) growth simulator for estimating corn biomass and yields at the regional scale by integrating satellite Synthetic Aperture Radar (SAR) imagery
A farmer inspects his crops.
University: Institut national de recherche scientifique (national institute of scientific research), Centre Eau Terre Environnement (water Earth environment centre)
Scientist: Dr. Karem Chokmani, Professor, Remote Sensing and Hydrology
CSA funding: $149,435
Description: In the context of climate change and increases in both the world population and energy demand, monitoring of risks limiting the agricultural yield becomes essential for food security. The proposed solution will help improve the on-farm planning and management process by providing access to a reliable, accurate, low-cost technology solution that does not affect crop growth.
This project aims to develop a dynamic assimilation approach of RADARSAT-2 data acquired at various growth stages of corn crop into the STICS growth model. This is meant to improve yield predictions at the local and regional scales. The assimilation of RADARSAT-2 imagery into the STICS model is proving to be an avenue of interest for the continued development of crop forecasting and dynamic monitoring systems in Canada. These systems are used for agricultural insurance and other purposes.
Benefits: The main beneficiaries of this approach will be Canadian corn producers. However, the main users of the technology developed will be Canadian agri-consulting firms. It will allow these firms to provide farmers with yield estimates for their plot generated from the STICS model assimilating RADARSAT-2 imagery. Producers not equipped with yield sensors as well as those with such equipment will benefit from it since information on potential yields is not available other than by simulation.
Project name: Geospatial tools for downscaling passive microwave soil moisture products to field scale using RADARSAT-2
University: University of Guelph
Scientist: Dr. Aaron Berg, Professor, Department of Geography; Canada Research Chair in Hydrology and Remote Sensing
CSA funding: $51,600
Description: Soil moisture is one of the most important environmental parameters for understanding, monitoring and assessing climate change. Changes in soil moisture significantly impact ecosystem health and consequently the management of natural resources. Soil moisture data are needed at the local scale for agricultural monitoring (crop yield, crop growth, early drought detection, etc.), and regional to global scales for water and energy cycle applications (hydrological modelling, climate prediction, etc.).
Benefits: The proposed research will build a tool for accurate estimation of soil moisture at the agricultural field scale. It will use RADARSAT-2 data with detailed soil and weather information to downscale satellite derived estimates of soil moisture obtained at relatively coarse spatial scales to the resolution of farm fields. The higher resolution soil moisture data will benefit farmers in making decisions that are dependent on soil moisture, such as seeding, nutrient management and disease management.