Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XXXIX-B8, 283-287, 2012
© Author(s) 2012. This work is distributed under
the Creative Commons Attribution 3.0 License.
28 Jul 2012
H. McNairn1, J. Shang1, X. Jiao1, and B. Deschamps2 1Agriculture and Agri-Food Canada, 960 Carling Avenue, Ottawa, Canada K2K 2C2
2MDA Geospatial Services Inc., 57 Auriga Drive Suite 201, Ottawa, Canada K2E 8B2
Keywords: agriculture, crop, monitoring, SAR, RADARSAT, modelling, retrieval Abstract. Crop productivity is influenced by a number of management and environmental conditions, and variations in crop growth can occur in-season due to, for example, unfavourable meteorological conditions. Consequently information on crop growth must be temporally frequent in order to adequately characterize crop productivity. Leaf Area Index (LAI) is a key indicator of crop productivity and a number of methods have been developed to derive LAI from optical satellite data. Integration of LAI estimates from synthetic aperture radar (SAR) sensors would assist in efforts to monitor crop production through the growing season, particularly during periods of persistent cloud cover. Consequently, Agriculture and Agri-Food Canada has assessed the capability of RADARSAT-2 data to estimate LAI. The results of a sensitivity analysis revealed that several SAR polarimetric variables were strongly correlated with LAI derived from optical sensors for small grain crops. As the growing season progressed, contributions from volume scattering from the crop canopies increased. This led to the sensitivity of the intensity of linear cross-polarization backscatter, entropy and the Freeman-Durden volume scattering component, to LAI. For wheat and oats, correlations above 0.8 were reported. Following this sensitivity analysis, the Water Cloud Model (WCM) was parameterized using LAI, soil moisture and SAR data. A look up table inversion approach to estimate LAI from SAR parameters, using the WCM, was subsequently developed. This inversion approach can be used to derive LAI from sensors like RADARSAT-2 to support the monitoring of crop condition throughout the cropping season.
Conference paper (PDF, 761 KB)

Citation: McNairn, H., Shang, J., Jiao, X., and Deschamps, B.: ESTABLISHING CROP PRODUCTIVITY USING RADARSAT-2, Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XXXIX-B8, 283-287,, 2012.

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