Shalei Song

Double sodium layers observation over Beijing, China

The altitude of the sodium layer in the mesosphere and lower thermosphere is usually from 80 km to 105 km. In this paper, we report a set of double sodium layer (DSL) events observed by sodium lidar over Beijing, China. In these DSL events, the normal sodium layer and secondary sodium layer (SeSL) present separately. There were about 17 DSL events occurred in 319 observation nights during 2009 similar to 2011. All DSL events were observed in spring and summer. The SeSL appeared independently within the altitude range from 105 km to 130 km. The density of the SeSL is very high. The maximum ratio of peak density and the ratio of column density for the SeSL to the normal sodium layer are up to similar to 60% and similar to 47%, respectively. The SeSL lasted several hours, and then merged into the normal sodium layer. After the SeSL, a sporadic sodium layer occurred in the normal sodium layer. Citation: Wang, J., Y. Yang, X. Cheng, G. Yang, S. Song, and S. Gong (2012), Double sodium layers observation over Beijing, China, Geophys. Res. Lett., 39, L15801, doi:10.1029/2012GL052134.

Estimation of rice leaf nitrogen contents based on hyperspectral LIDAR

Precision agriculture has become a global research hotspot in recent years. Thus, a technique for rapidly monitoring a farmland in a large scale and for accurately monitoring the growing status of crops needs to be established. In this paper, a novel technique, i.e., hyperspectral LIDAR (HL) which worked based on wide spectrum emission and a 32-channel detector was introduced, and its potential in vegetation detection was then evaluated. These spectra collected by HL were used to classify and derive the nitrogen contents of rice under four different nitrogen content levels with support vector machine (SVM) regression. Meanwhile the wavelength selection and channel correction method for achieving high spectral resolution were discussed briefly. The analysis results show that: (1) the reflectance intensity of the selected characteristic wavelengths of HL system has high correlation with different nitrogen contents levels of rice. (2) By increasing the number of wavelengths in calculation, the classification accuracy is greatly improved (from 54% with 4 wavelengths to 83% with 32 wavelengths) and so the regression coefficient r(2) is (from 0.51 with 4 wavelengths to 0.75 with 32 wavelengths). (3) Support vector machine (SVM) is a useful regression method for rice leaf nitrogen contents retrieval. These analysis results can help farmers to make fertilization strategies more accurately. The receiving channels and characteristic wavelengths of HL system can be flexibly selected according to different requirements and thus this system will be applied in other fields, such as geologic exploration and environmental monitoring

Improving Backscatter Intensity Calibration for Multispectral LiDAR

A wavelength-dependent light detection and ranging (LiDAR) backscatter intensity calibration method was developed to maximize the advantages of a multispectral LiDAR system. We established a spectral ratio calibration method for multispectral LiDAR and investigated the effective calibration procedure for the mixed measurement of the effect of incident angle and surface roughness. Experiment results showed that the proposed LiDAR spectral ratio is insensitive to sensor-related factors and advantageous in calibrating the effect of incidence angle and surface roughness. As the product of the LiDAR calibration procedure based on spectral ratio, extended vegetation indexes significantly improve the classification accuracy.

Application of fluorescence spectrum to precisely inverse paddy rice nitrogen content

Paddy rice is important for Chinese agriculture and crop production, which largely depends on the leaf nitrogen (N) levels. The purpose of this study is to discuss the relationship between the fluorescence parameters and leaf N content of paddy rice and to test their performance in inversing N content of crops through back-propagation (B-P) neural network. In the correlative analysis of the fluorescence parameters and the N content, we found that the correlation between fluorescence ratios (F740/F685 and F685/F525 (F740, F685, F525 - intensity of fluorescence at 740, 685 and 525 nm, respectively)) and the N content (R-2 are 0.735 and 0.4342, respectively) is weaker than that between the intensity of fluorescence peaks (F685 and F740) and N content (R-2 are 0.9743 and 0.9686, respectively). Our studies show that the accuracy and precision of N content inversion which is acquired from the intensity of fluorescence peaks through the B-P neural network model are significantly improved (root mean square error (MSRE) = 0.1702, the residual changes between -0.1-0.1 mg/g) compared with the fluorescence ratio (MSRE = 0.3655, the residual changes from -0.3-0.3 mg/g). Results demonstrate that the intensity of fluorescence peaks can be as a characteristic parameter to estimate N content of crops leaf. The B-P neural network model will be serviceable approach in inversing N content of paddy leaf.

Accurate identification of nitrogen fertilizer application of paddy rice using laser-induced fluorescence combined with support vector machine

To identify accurately the doses of nitrogen (N) fertilizer and improve the photosynthetic efficiency of paddy rice, laser induced fluorescence (LIF) technique combined with the support vector machine (SVM) and principal component analysis (PCA) is proposed in this paper. The LIF technology, in which the ultraviolet light (355 nm) is applied as an excitation light source, is employed to measure fluorescence spectra of paddy rice. These fluorescence spectra demonstrate that the fluorescence spectral characteristics of paddy rice leaves with different doses of N fertilizer have distinct differences from each other. Then, PCA and SVM are implemented to extract the features of fluorescence spectra and to recognize different doses of N fertilizer, respectively. The overall recognition accuracy can reach 95%. The results show that the LIF technology combined with PCA and SVM is a convenient, rapid, and sensitive diagnostic method for detecting N levels of paddy rice. Thus, it will also be convenient for farmers to manage accurately their fertilization strategies.

Estimation of nitrogen content based on fluorescence spectrum and principal component analysis in paddy rice.

Paddy rice is one of the most important cereal crops in China. Nitrogen (N) is closely related to crops production by influencing the photosynthetic efficiency of paddy rice. In this study, laser-induced fluorescence (LIF) technology with the help of principal component analysis (PCA) and back-propagation neural network (BPNN) is proposed to monitor leaf N content (LNC) of paddy rice. The PCA is utilized to extract the characteristic variables of LIF spectra by analysing the major attributes. The results showed that the first three principal components (PCs) can explain 95.76% and 93.53% of the total variance contained in the fluorescence spectra for tillering stage and shooting stage, respectively. Then, BPNN was utilized to inverse the LNC on the basis of the first three PCs as input variables and can obtain the satisfactory inversion results (R-2 of tillering stage and shooting stage are 0.952 and 0.931, respectively; residual main range from -0.2 to 0.2 mg/g). The experimental results demonstrated that LIF technique combined with multivariate analysis will be a useful method for monitoring the LNC of paddy rice, which can provide consultations for the decision-making of peasants in their N fertilization strategies.

Evaluation of hyperspectral LiDAR for monitoring rice leaf nitrogen by comparison with multispectral LiDAR and passive spectrometer

Fast and nondestructive assessment of leaf nitrogen concentration (LNC) is critical for crop growth diagnosis and nitrogen management guidance. In the last decade, multispectral LiDAR (MSL) systems have promoted developments in the earth and ecological sciences with the additional spectral information. With more wavelengths than MSL, the hyperspectral LiDAR (HSL) system provides greater possibilities for remote sensing crop physiological conditions. This study compared the performance of ASD FieldSpec Pro FR, MSL, and HSL for estimating rice (Oryza sativa) LNC. Spectral reflectance and biochemical composition were determined in rice leaves of different cultivars (Yongyou 4949 and Yangliangyou 6) throughout two growing seasons (2014–2015). Results demonstrated that HSL provided the best indicator for predicting rice LNC, yielding a coefficient of determination (R2) of 0.74 and a root mean square error of 2.80 mg/g with a support vector machine, similar to the performance of ASD (R2 = 0.73). Estimation of rice LNC could be significantly improved with the finer spectral resolution of HSL compared with MSL (R2 = 0.56).

Vegetation identification based on characteristics of fluorescence spectral spatial distribution

To differentiate and analyze plant types and species, a spectral identification approach is proposed founded on the characteristics of fluorescence spectral spatial distribution. Pseudo-color images of fluorescence spectral spatial distribution outperforming steady-state fluorescence spectra are constructed to serve as individual fingerprints for plant types and species, which can be utilized to accurately discriminate different plant types and species especially the different species of the same family. The introduced method provides a more reliable and stabilized means for identifying and analyzing plant species in the fields of vegetation-ecology and remote sensing. Stability and reliability are validated by using the spatial distribution of fluorescence spectra measurements of paddy rice and Dracaena sanderiana at two different incident angles of excitation light source in an additional experiment.

Estimating the leaf nitrogen content of paddy rice by using the combined reflectance and laser-induced fluorescence spectra

Paddy rice is one of the most important crops in China, and leaf nitrogen content (LNC) serves as a significant indictor for monitoring crop status. A reliable method is needed for precise and fast quantification of LNC. Laser-induced fluorescence (LIF) technology and reflectance spectra of crops are widely used to monitor leaf biochemical content. However, comparison between the fluorescence and reflectance spectra has been rarely investigated in the monitoring of LNC. In this study, the performance of the fluorescence and reflectance spectra for LNC estimation was discussed based on principal component analysis (PCA) and back-propagation neural network (BPNN). The combination of fluorescence and reflectance spectra was also proposed to monitor paddy rice LNC. The fluorescence and reflectance spectra exhibited a high degree of multi-collinearity. About 95.38%, and 97.76% of the total variance included in the spectra were efficiently extracted by using the first three PCs in PCA. The BPNN was implemented for LNC prediction based on new variables calculated using PCA. The experimental results demonstrated that the fluorescence spectra (R2 = 0.810, 0.804 for 2014 and 2015, respectively) are superior to the reflectance spectra (R2 = 0.721, 0.671 for 2014 and 2015, respectively) for estimating LNC based on the PCA-BPNN model. The proposed combination of fluorescence and reflectance spectra can greatly improve the accuracy of LNC estimation (R2 = 0.912, 0.890 for 2014 and 2015, respectively).

Multispectral LiDAR Point Cloud Classification: A Two-Step Approach

Target classification techniques using spectral imagery and light detection and ranging (LiDAR) are widely used in many disciplines. However, none of the existing methods can directly capture spectral and 3D spatial information simultaneously. Multispectral LiDAR was proposed to solve this problem as its data combines spectral and 3D spatial information. Point-based classification experiments have been conducted with the use of multispectral LiDAR; however, the low signal to noise ratio creates salt and pepper noise in the spectral-only classification, thus lowering overall classification accuracy. In our study, a two-step classification approach is proposed to eliminate this noise during target classification: routine classification based on spectral information using spectral reflectance or a vegetation index, followed by neighborhood spatial reclassification. In an experiment, a point cloud was first classified with a routine classifier using spectral information and then reclassified with the k-nearest neighbors (k-NN) algorithm using neighborhood spatial information. Next, a vegetation index (VI) was introduced for the classification of healthy and withered leaves. Experimental results show that our proposed two-step classification method is feasible if the first spectral classification accuracy is reasonable. After the reclassification based on the k-NN algorithm was combined with neighborhood spatial information, accuracies increased by 1.50–11.06%. Regarding identification of withered leaves, VI performed much better than raw spectral reflectance, with producer accuracy increasing from 23.272% to 70.507%.