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Leader: François Charbonneau
Context
Groundwater recharge to regional aquifers generally takes into account daily climate data, soil physical properties, vegetative growth and density as well as surface drainage properties. The applied water-balance method is based on the mass conservation as follows:
Recharge = Precipitation - Evapotranspiration - Runoff ± ΔStorage.
Precipitation constitutes the main water inflow. Evapotranspiration accounts for plant transpiration and soil evaporation and depends on vegetation type, physical properties of soils and climate data. Runoff is also influenced by vegetation and soil type in addition to drainage distance and slope. ΔStorage is the amount of water stored into the soil depending on precipitation intensity, vegetation type and soils properties (in the unsaturated zone). Land use and land cover (LULC), Leaf Area Index (LAI) information and soil properties are essential for the estimation of all of these water-balance parameters.
The use of Earth Observation (EO) data to extract this information is a practical approach because of their extensive coverage and relatively low purchase and processing costs. Land cover and LAI are operational products derived from EO (Landsat). However, current Landsat-based products cannot always provide information on demand either due to limited temporal sampling or limited sensitivity to soil surface parameters. Both sensors on Envisat such as ASAR and MERIS as well as current and planned RADARSAT sensors are investigated to mitigate against these limitations. Specific land use mapping, moisture pattern detection, soil permeability mapping and vegetation index using radar and optical medium-low spatial resolution sensors are prototype products in development to be integrated in recharge modeling (Figure 1).
Figure 1: Recharge assessment using new surface parameter maps
Goal
The activity is an operational R&D phase toward the development of Earth Observation (EO) applications and products that will help fill significant gaps in current groundwater mapping efforts. The purpose is to develop hydrological prototype products that support the groundwater mapping of key aquifers in Canada. Modern tools and techniques are encouraged to improve and support the detailed assessment of national aquifers.
Objectives
ESS is developing methods and prototypes products for mapping land surface and soil conditions using radar and optical medium-low spatial resolution sensors to improve or support recharge modeling. This is a fundamental input to water budget models. Some developments will be done in order to sustain specific map product already use in recharge modelling (hydrogeological LAI is produced only with Landsat data). ESS is also developing new surface parameter products through modern earth observation sensors.
The activities aim at developing and producing 4 prototype surface maps:
- Activity A: Prototype maps of moisture change and recharge area using weather station and rain gauge data with new and previous captured satellite data (RADAR);
- Activity B: Prototype maps of land surface (land cover and biomass) and soil conditions (permeability) using new satellite data (RADAR).
- Activity C: Validated land cover maps using MODIS imagery (250 meters).
- Activity D: Validated Leaf Area Index (LAI) maps using MODIS imagery (250 meters).
Material and method
Activity A: Prototype maps of soil moisture pattern
Figure 2 shows a ratio between 2 temporal RADARSAT-1 s1 Ascending images showing differences moisture levels in soil surface. The objective is to optimize the use of radar imageries for soil moisture characterization.
Figure 2: Soil moisture pattern detection.
Activity B: Prototype maps of land surface (land cover and biomass) and soil conditions (permeability) using new satellite data (RADAR).
B-1: Specific land use for the study of groundwater
Figure 3 presents preliminary results on detecting orchard area using polarimetric dataset. Polarimetric (SAR) information allows decomposing a scene in three dominant types of radar signal interaction (Surface, Double-bounce and Volume). Volume interaction component (e.g. vegetation) is used to discriminate between vegetated areas. In this examples: Dense forest (Green and Blue); Medium Biomass Farmland and Orchard (Orange); Low Vegetation and Plough Fields (Brown).
Figure 3: Specific land use mapping (orchard)
B-2 Soil permeability mapping
Figure 4 (right) presents a relative surface soil permeability map product using intensive radar Monitoring. Intensive monitoring (every 2-3 days) by radar sensor (figure 4, left), allows understanding how soil surface reacts to precipitation events. Relative soil permeability is estimated from multi-temporal RADARSAT-1 or ENVISAT-ASAR alternated polarizations. This example shows a prototype relative permeability map over a sub-area of the Châteauguay river watershed, Québec.
Figure 4: Soil permeability
B-3 Vegetation index
Figure 5 presents a radar vegetation index. The objective is to evaluate SAR dual-polarisation and polarimetry techniques for vegetation characteristic retrieval. A radar vegetation index can be estimated from ENVISAT-ASAR alternated polarizations SAR acquisitions. This index allows reducing the vegetation backscattering component from the total power, in order to maximize the surface soil information. This information is used in the permeability mapping methodology.
Figure 5: Radar Vegetation Index
External Collaborator
- Agriculture and Agri-Food Canada (AAFC)
- Land Information Ontario (LRC)
- University of British Columbia (UBC)
- Canadian Space Agency (ASC)