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RSS FeedsRemote Sensing, Vol. 14, Pages 508: Insect-Equivalent Radar Cross-Section Model Based on Field Experimental Results of Body Length and Orientation Extraction (Remote Sensing)

 
 

21 january 2022 12:38:41

 
Remote Sensing, Vol. 14, Pages 508: Insect-Equivalent Radar Cross-Section Model Based on Field Experimental Results of Body Length and Orientation Extraction (Remote Sensing)
 


Migratory insects constitute a valuable component of atmospheric and terrestrial biomass, and their migratory behavior provides abundant information for insect management and ecological effect assessment. Effective monitoring of migratory insects contributes to the evaluation and forecasting of catastrophic migration events, such as pest outbreaks. With a large-scale monitoring technique using S-band weather radar, the insect density is estimated based on the linear relationship between radar reflectivity and the average radar cross-section (RCS) of the insects. However, the average RCS model neglects the morphological and observation parameters of the insects, which reduces the estimation accuracy. In this paper, we established an insect-equivalent RCS model based on the joint probability distribution of “body length–incident angle”. Then, we observed and extracted the morphological and observational parameters of the migratory insects by conducting a 69-day field experiment, using a Ku-band fully polarimetric entomological radar, in Dongying, Shandong Province, China. Finally, combined with the experimental results and the simulated scattering characteristics of individual insects with different body lengths, the typical insect-equivalent RCS model was established. The RCS of the model fluctuates between 0.233 mm2 and 0.514 mm2, with different incident angles. Our results lay a data foundation for the quantitative analysis of insects by weather radar.


 
147 viewsCategory: Geology, Physics
 
Remote Sensing, Vol. 14, Pages 505: Small Sample Hyperspectral Image Classification Based on Cascade Fusion of Mixed Spatial-Spectral Features and Second-Order Pooling (Remote Sensing)
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