Science and Operational Applications Research (SOAR)

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.

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.

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.

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.

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.