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5. The measurement and model construction of complex permittivity of corn leaves at the main frequency points of L/S/C/X-band Zeng Jiangyuan Institute of Remote Sensing and Digital Earth Chinese Academy of Sciences 35th International Symposium on Remote Sensing of Environment April 23, 2013

6. Outline Background and Motivation Materials and methods Measurement results and model construction Comparisons and validations Conclusions and Discussions

7. Outline Background and Motivation Materials and methods Measurement results and model construction Comparisons and validations Conclusions and Discussions

8. Background and Motivation Complex permittivity of the targets Soil, Vegetation, Snow, et al The complex permittivity, shapes, and orientations of the vegetation elements together determine the scattering and emission by the canopy. (Ulaby, 1987,TGRS)

9. Background and Motivation The complex permittivity of the target can be expressed as: a complex number The real part The imaginary part Dielectric constantDielectric loss factor Therefore, the establishment of the relationship between the specific physical parameters of the vegetation and its complex permittivity is very important.

10. Outline Background and Motivation Materials and methods Measurement results and model construction Comparisons and validations Conclusions and Discussions

11. Materials and methods Collecting location: Zhangye City——the largest breeding base of hybrid corn seeds in China Corn types: Zhengdan 985, Jundan 20 and Xianyu 335 Corn heights: range from 0.95m to 2.05m Corn leaves collection from various corn types and different corn heights

12. Materials and methods lettuce leavespotato leaves apple-pear leaveswinter apple leaves green poplar leaves Collection of the other five types of vegetation

13. Materials and methods Instrument: the vector network analyzer E8362B Method: coaxial probe technique Materials: six types of vegetation samples Frequency range : 0.2~20GHz Temperature: 22 ℃ The vector network analyzer E8362B High temperature probe

14. Materials and methods Verification of the instrument accuracy after calibration at room temperature Reference: Li Liying et al "Laboratory measurement of the dielectric constant of frozen soil" Journal of Beijing Normal University(Natural Science), 2007

15. Materials and methods The measurement of the complex permittivity of white bond papers combines copper and Teflon (a) laboratory equipment (b) experimental results (a)(b) Methodological points—1.Determination of the thickness threshold Thickness threshold is 3 mm

16. Materials and methods Methodological points—2. Appropriate pressure ①.Overlay the leaves neatly ②.Apply appropriate pressure on the probe to measure the samples for the purpose of insuring no air gaps among them and avoiding excess pressure to alter the structure of the tissues ③.In order to reduce human errors and systematic errors,10 complex permittivity measurements of vegetation samples were averaged to represent the complex permittivity of the sample.

17. Materials and methods Acquisition of the vegetation gravimetric moisture How to obtain different gravimetric moisture? 1. Samples were dried by the automatic drying oven for different time ranging from 20 minutes to 2 hours after every measurement 2. After the drying finished, samples were placed for 30 minutes to allow their temperature to be equilibrate to room temperature and then measure their complex permittivity 3. Finally, the dry weights of samples were recorded after drying them in the oven for 48 h at 70 until their weights do not change any more gravimetric moisture Dry weight Fresh weight Why obtain the gravimetric moisture? 1. The gravimetric moisture is the dominant factor which influences the complex permittivity of vegetation; (Nelson S O et al,J Microw Power Electromagn Energy,2012) 2. For the purpose of facilitating the practical application. Finally, we get: The total number of measured data of the corn leaves with different gravimetric moisture is 26 and the water content ranges from 1.34% to 72.09%. While the number of the other five types of vegetation samples data is 6 or 7 and the water content ranges from 10.31% to 91.70%.

18. Outline Background and Motivation Materials and methods Measurement results and model construction Comparisons and validations Conclusions and Discussions

19. Measurement results and model construction Part of the measurement results (the percentages in the figure represent the gravimetric moisture of the samples)

20. Measurement results and model construction Goal of model construction Purpose: Establish empirical models of different vegetation types for describing the relationship between the gravimetric moisture and both the real and imaginary parts of complex permittivity of all vegetation types at the commonly used frequency points of microwave sensors. These microwave sensors include SAR, scatterometers and radiometers and the commonly used frequency points are as follow: L=1.26GHz (ALOS PALSAR sensor) L=1.4GHz (SMOS satellite) S=3.2GHz (Chinese HJ-1C radar satellite) C=5.3GHz (ERS-1/2 satellite) C=6.9GHz (Aqua AMSR-E sensor) X=9.6GHz (TerraSAR-X satellite and Cosmo-SkyMed satellite)

21. Measurement results and model construction Take f=5.3 GHz as an example (the results of remaining frequency points are similar)

22. Measurement results and model construction Thus, the empirical model used for describing the relationship between the gravimetric moisture and both the real and imaginary parts of complex permittivity at a specific frequency can be expressed as: Where a 1,a 2,b 1,b 2 are the coefficients which need to be calibrated The look-up table of the empirical model of corn leaves at the main frequency points of L/S/C/X-band

23. Outline Background and Motivation Materials and methods Measurement results and model construction Comparisons and validations Conclusions and Discussions

24. Comparisons and validations The comparison between the empirical models and the Debye-Cole model at f=5.3GHz (the results of remaining frequency points are similar) Colpitts and Coleman also found that the Debye-Cole model cannot fit the complex permittivity of potato leaves well, and the model overestimates the real and imaginary parts of complex permittivity of potato leaves (Colpitts and Coleman, TGRS,1997)

25. Comparisons and validations The corn leaves samples used for validation were collected from Huailai county in Hebei province, China Finally, the number of measured data with different gravimetric moisture is 30 and the moisture content ranges from 1.34% to 72.09%

26. Comparisons and validations The comparisons and validations between the empirical model of corn leaves and the Debye-Cole model by using measured data at f=5.3GHz (the results of remaining frequency points are similar)

27. Comparisons and validations The comparisons between the empirical model of corn leaves and the Debye-Cole model by using measured data at the main frequency points of the L/S/C/X-band

28. Outline Background and Motivation Materials and methods Measurement results and model construction Comparisons and validations Conclusions and discussions

29. Conclusions and Discussions ①. the variation of the complex permittivity of all the vegetation with frequency follows the same rules: the real part of the complex permittivity decreases monotonically as the frequency increases, while the imaginary part decreases as the frequency increases in the low frequency range, after reaching the minimum value, it increases as the frequency increases and finally tends to be stable; ②. At a specific frequency, the relationship between both the real and imaginary parts of the complex permittivity of all vegetation and their gravimetric moisture can fit well with a simple base e exponential function; ③. Debye-Cole model will overestimate the value of both the real and imaginary parts of the complex permittivity of all six types of vegetation in high water content (i.e. >30%); ④. By using the measured data for the comparison and validation between the empirical model of corn leaves and the Debye-Cole model, it was found that the empirical model of corn leaves has higher accuracy and is more practical than the Debye-Cole model.