Abstract
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.
Highlights
Monitoring crop productivity is critical in determining risks to regional and global food security
Correlations between RADARSAT-2 responses and optically derived leaf area index (LAI) are presented in Table 2 and Figure 1
As observed for broadleaf crops, synthetic aperture radar (SAR) parameters which characterize volume scattering from the canopy are most sensitive to grain LAI
Summary
Monitoring crop productivity is critical in determining risks to regional and global food security. Crop management applications during active crop growth, as well as ever changing meteorological conditions, mean that crop condition must be monitored continuously through the growing season. Crop descriptors such as leaf area index (LAI) are good indicators of crop condition and productivity. LAI can be linked with crop yield through process models Derivation of these crop descriptors from remote sensing data can be used to drive these crop yield models, to validate model estimates and to update or adjust model predictions. LAI can be derived from optical sensors (Liu et al, 2010) the reliability of access to data to monitor continuously through the season is questionable due to cloud cover. The methods to estimate LAI from radar response are not as developed as those that use optical data and significant research is required
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