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EO in Orbit: Scientific webinars – Soil moisture


  • Type: Webinar
  • Theme: Soil moisture
  • Date:
  • Time: 11:00 a.m. to noon ET
  • Duration: 1 hour
  • Cost: Free
  • Location: Virtual
  • Language: English
  • Target audience: Industry, academic institutions, media, not-for-profit organizations, scientists, government.


Presentations will focus on scientific developments in the field of Earth observation (EO) and soil moisture monitoring.

Detailed description


  1. EO of Land Surface for Weather and Environmental Prediction at ECCC
  2. Integrating Satellite Soil Moisture into Agricultural Climate Risk Analysis

EO of Land Surface for Weather and Environmental Prediction at ECCC

Presentation 1 (in English)

From 11:00 to 11:30 a.m.

Stéphane Bélair
Environment and Climate Change Canada (ECCC), Atmospheric Science and Technology

The land surface is an important component of ECCC's analysis and prediction systems. Featuring soils, vegetation, and cities, the land surface is at the interface of several environmental applications like weather, hydrology, and air quality. The increase in satellite EO has led to considerable improvement in the last decade to the initialization of land surface variables related to surface temperature, soil moisture, and terrestrial snow. Inclusion of EO data is traditionally performed in the context of land data assimilation systems in which observations are combined in an optimal manner with a first guess from numerical modelling. A few examples of land surface analyses from ECCC's Canadian Land Data Assimilation System (CaLDAS) will be presented during the webinar, together with their impact on numerical prediction.

With the impressive rise of artificial intelligence (AI) for a wide range of applications, there is an opportunity to revolutionize environmental analysis and prediction, with expectations for more accurate forecasts based on methods very different from ECCC's current approach, using models that can run faster and with less computational power (at least in prediction mode). Importantly, as will be discussed in the webinar, AI has the potential to change how EO can influence (and improve) land surface analyses and enhance their impact on environmental predictions.

Integrating Satellite Soil Moisture into Agricultural Climate Risk Analysis

Presentation 2 (in English)

From 11:30 a.m. to noon

Catherine Champagne
Agriculture and Agri-Food Canada

Soil moisture is a critical hydrological variable in agricultural systems and is often the key limiting factor determining crop yields. In the past decades, estimation of soil moisture using microwave satellite data and land surface models has expanded the information that is available to support assessment of soil moisture conditions for assessing crop yields, drought severity, and excess moisture in agricultural regions. These networks are limited spatially, and fail to capture the high variability in soil moisture resulting from soil physical properties and land management practices.

While the dynamics of precipitation and temperature under climate extremes have been explored extensively, the patterns of soil moisture associated with drought and excess moisture events are less well understood, as are the impacts of errors resulting from using satellite soil moisture as a proxy measure. Soil moisture characteristics leading into and out of drought and excess moisture conditions for regions in Canada were examined over a period of 15 years using data from the European Space Agency Climate Change Initiative (ESA-CCI), the Soil Moisture and Ocean Salinity (SMOS) and the Soil Moisture Active/Passive (SMAP) missions to look at the temporal dynamics of soil moisture under extreme conditions. These dynamics were then used to develop critical thresholds and characteristics for soil moisture that lead to crop losses under extreme events.

The presenter will outline these results and how these are being used to inform risk assessment at Agriculture and Agri-Food Canada. She will also touch on how this information is being used to assess food security at the global scale.

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