Doppler Lidar with High Sensitivity and Large Dynamic Range for Atmospheric Wind Measurement.

Doppler wind Lidar is an important method for atmospheric wind measurement. The Doppler frequency shift of backscattering spectrum due to the wind is detected from the transmissions of frequency discrimination. High sensitivity and large dynamic range measurement is difficult for atmospheric wind measurement because of the limitationof frequency discriminator characteristic. In this paper, a frequency discriminator constructed with Dual fiber Mach-Zehnder interferometer is proposed. Two fiber Mach-Zehnder interferometers with different atmospheric wind detection dynamic range and sensitivity are designed and used to measure Doppler frequency shift at the same time. The optical path difference of FMZI-2 is 13.7 cm which can realize large dynamic range wind measurement (±100 m·s-1) and the optical path difference of FMZI-1is designed to be 74.8 cm which can realize high sensitivity detection. Moreover, the absolute wind velocity of FMZI-1 channel can be corrected by the measurement result of FMZI-2 channel. Thus the high sensitivity and large dynamic range wind detection can be realized. The sensitivity, SNR and wind error of two channels are simulated and analyzed for different parameters. The results indicate that the system can realize the wind error less than 1 m·s-1 for large dynamic range(±100 m·s-1) wind velocity, which is a beneficial exploration for high sensitivity and large dynamic range Doppler wind lidar.

[Research on Modeling Method for Chlorophyll Content Fine Measurement Based on Neural Network].

Aiming at SPAD values of living plant leaf chlorophyll content affected easily by the blade thickness, water content, etc, a fine retrieval method of chlorophyll content based on multiple parameters of neural network model is presented. The SPAD values and water index (WI) of leaves were obtained by the leaf transmittance under the irradiation of light central wavelength in 650 nm, 940 nm, 1450 nm respectively. Meanwhile, the corresponding blade thickness is got by micrometer and the chlorophyll content is measured by spectrophotometric method. To modeling samples, the single parameter model between SPAD values and chlorophyll content was built and the nonlinear model between WI, thickness, SPAD values and chlorophyll content was established based on BP neural network. The predicted value of chlorophyll content of test samples were calculated separately by two models, and the correlation and relative errors were analyzed between predicted values and actual values. 340 samples of three different plant leaves were tested by the method described above in experiment. The results showed that compared with single parameter model, the prediction accuracy of three different plant samples were improved in different degrees, the average absolute relative error of chlorophyll content of all pooled samples predicted by BP neural network model reduced from 7.55% to 5.22%. The fitting determination coefficient is increased from 0.83 to 0.93. The feasibility were verified in this paper that the prediction accuracy of living plant chlorophyll content can improved effectively using multiple parameter BP neural network model.

[Determination of sodium by near infrared spectroscopy].

The research on near infrared spectroscopy of sodium in biological and medicine is significant. Sodion is the main component of electrolytes in human blood and electrolytes help maintain the body's acid-base balance. In the present paper the concentration of sodium was determined with the use of NIR spectra. On the basis of NIR spectroscopic measurement mechanism of sodion, prediction models of the concentration of sodium were developed with linear regression using the absorbance at selected wavelengths. In order to reduce temperature perturbations to water bands with the measurement of sodium, Partial least squares regression (PLS) was adopted using select spectra area. The result shows that the determination coefficients (R2) = 99.82%, the root mean square error of cross validation (RMSECV) = 14.5, and the residual prediction deviation (RPD) = 23.7, for the calibration model. It meets the daily requirements of biochemical detection accuracy. This technique can be applied to quantitative analysis of sodion in the hospital laboratory.

[The Mie scattering lidar return signal denoising research based on EMD-DISPO].

Lidar echo signal is a typical non-steady-state, non-stationary signal, and difficult to be dealt with by the traditional filtering methods. As a new signal processing theory proposed in recent years, empirical mode decomposition method can adaptively divide the lidar echo signal into different intrinsic mode function (IMF) components according to different time scale, and noise mainly concentrates in the high-frequency component. However, when filtered with simply removing high frequency component, the useful signal will be possibly reduced. In the present paper, a new method which combines empirical mode decomposition (EMD) with Savitzky-Golay filter is proposed. With experiments, it is indicated that our approach not only removes the noise component effectively but also maintains the useful signal, then will improve the accuracy in the next phase of data processing.

[Lidar observations of atmospheric aerosol optical properties over Yinchuan area].

A compact Mie scattering lidar system has been developed to measure the optical properties and temporal-spatial distribution of atmospheric aerosol particles and some continuous experiments were carried out over Yinchuan area (38 degrees 29'N, 106 degrees 06'E) from 1 to 10 April in 2009 for the first time. The laser located at wavelength of 532 nm was selected as the light source and the Fernald method was used to retrieve the extinction coefficient. The aerosol extinction coefficient profiles and temporal-spatial variation properties of aerosol relative density were obtained and analyzed within the whole day at one hour interval, and also an obvious sand-dust-weather process over Yinchuan area was observed and analyzed. The observation results show that the compact Mie scattering lidar is capable of measuring efficiently the optical properties and temporal-spatial distribution of aerosol particles, and the measurement result is useful for studying the variation tendency of atmospheric aerosol and sand weather of Yinchuan area.