Visible and near-infrared (Vis-NIR) spectroscopy technique has been recognized as a cost-effective, rapid, non-destructive alternative to traditional soil physicochemical analysis to estimate soil properties over the past few decades. Most efforts are devoted to the selection of characteristic wavelengths to eliminate the uninformative variables while ignoring the impact of the spectral resolution of these wavelengths on the prediction accuracy of soil properties. Therefore, the originality of this study is to identify the characteristic wavelengths with the optimal spectral resolution to achieve a better prediction performance. A 'two-step' wavelength selection method was proposed to select the characteristic wavelengths. Then, we simulated 1 nm-100 nm spectral resolution based on the spectral database measured by a portable ASD spectroradiometer and adopted the artificial bee colony (ABC) algorithm to further improve the prediction ability by configuring the most appropriate spectral resolution for each characteristic wavelength. The soil databases for this study consisted of 112 soil samples collected from Songnen Plain area in northeast China, and partial least squares regression (PLSR) was used to establish relations between pretreatment spectra and soil properties, including soil organic matter (SOM), available phosphorus (AP), and available potassium (AK). The independent validation results of this strategy effectively favored the prediction accuracy of SOM ( [Formula: see text] ), AP ( [Formula: see text] ), and AK ( [Formula: see text] ) compared with the PLSR models developed with full-spectra. In general, the method presented in this study suggested a framework for selecting characteristic wavelengths with optimal spectral solutions to predict SOM, AP, AK, and perhaps some other soil properties. The results of this paper also will provide guidance for the development of the low-cost specialized spectroscopic instruments for soil properties measurement.

The variation and the spatial–temporal distribution of soil water content have significant effects on heat balance, agricultural moisture, etc. A soil moisture (SM) retrieval model can provide a theoretical basis for realizing a rapid test and revealing the spatial–temporal variation of the surface water. However, remote sensors do not measure soil water content directly. Therefore, it is of great importance to establish a SM retrieval model. In this paper, the relationship between SM and diffuse reflectance was first derived using the absorption coefficient and scattering coefficient related to SM. Then, based on Kubelka–Munk (KM) theory, the SM retrieval model using reflectance information was further derived, which is a semi-empirical model with an unknown parameter obtained either from fitting or from experimental measurements. The validity and reliability of the model were confirmed with the validation set. The results showed that the root mean square errors of prediction (RMSEPs) of four soils were generally less than 0.017, while the coefficients of determination (R²s) of four soils were generally more than 0.85, and the ratios of the performance to deviation (RPDs) of four soils were greater than 2.5 (470–2400 nm). Therefore, the model has high prediction accuracy, and can be well applied to the prediction of water content in different sorts of soils.

Considering that large quantities of soil hyperspectral data include the redundancy and overlap of spectral information, the technology of selecting feature wavelength can effectively solve these problems, and improve the accuracy and stability of the soil organic matter (SOM) content retrieval model. Traditional methods of wavelength selection mainly attempt to establish the empirical relationship between reflectance and SOM contents, and the performance is directly related to the quality and representativeness of the "training data". This study first distinguished the sensitive wavelength interval of SOM through the sensitivity analysis (SA) of the SOM to soil reflectance in radiative transfer model. Then sensitive wavelength points of SOM were ascertained using the successive projection algorithm (SPA): 468, 476, 496, 599, 775 and 900nm. Results show that SOM content can be estimated with high accuracy (root-mean-square error of prediction (RMSEP) < 0.234%, coefficient of determination (R2)> 82.9%) by adopting the selected six wavelengths. Especially at 599nm, the accuracy of SOM content estimation is the highest (RMSEP: 0.176%, R2: 90.4%). Compared with traditional empirical wavelength selection methods, the wavelength selection based on the SA-SPA with the SOM radiative transfer model improves the generalizability and accuracy of the result. The research provides theoretical basis and technical support for the remote sensing retrieval of SOM, the development of rapid spectral instruments, and the bands setting of sensor instrument.

Marine oil spills accidents has caused a large amount of crude oil to leak into the marine environment and threaten marine ecological environment. Hyperspectral remote sensing images(HRSI) technology can quickly and accurately identify oil film of different thickness on marine surface. In order to overcome the traditional spectrum analysis method and space extraction method of long time sampling, calculation, analysis and other shortcomings. On account of the advantages of the spectral and spatial information in the field of HRSI classification, a spectral-spatial features extraction(SSFE) method based convolutional neural networks(CNNs) was proposed to analyse oil spills. In this way, one and two dimensional models based on convolutional neural networks(1D-CNN,2D-CNN) have been introduced as the spectral and spatial features extractor. When extracting spatial features, double-two convolution layers are connected to increasing nonlinearity and reduce the number of parameters. Furthermore, in order to address overfitting and imbalance samples, L2 regularization, class_weight and drouput is added to classes data modeling. More importantly, principal component analysis (PCA) is applied to data dimension reduction, 1D-CNN and 2D-CNN is combined into a unified model further extract the joint spatial-spectral features. To evaluate the effectiveness of the proposed approach, three hyperspectral datasets were utilized, including: University of Pavia dataset, oil spill area 1, oil spill area 2. Experimental results reveal that the proposed method have a very satisfactory performance and better distinguish oil spills.

In the design of two-degree-of-freedom (2-DOF) fast steering mirror (FSM) system, in order to improve the control bandwidth of the system, the low-order natural frequency in the working direction should be reduced as much as possible, and the high-order natural frequency in the non-working direction should be increased. In this paper, a deep-cut flexure hinge mirror system was studied. Firstly, the motion direction of the first to third order natural vibration mode of the system was analyzed. Next, the working stiffness in the third vibration mode direction was deduced to solve the problem that the traditional stiffness calculation method is not suitable for the third vibration mode direction. Then, the energy method and the Castigliano's second theorem were used to analyze the working stiffness of the deep-cut flexure hinge. Next, the formula for calculating the relationship between the thickness of the mirror and the moment of inertia in the direction of the vibration mode was derived. Combined with the calculation formula of working stiffness in the vibration mode direction, the formula of the first to third order natural frequencies was derived, and the finite element verification and sensitivity analysis of structural parameters were carried out. Finally, by using NSGA-II, a multi-objective optimization design was carried out on the first to third natural frequencies of the system with hinge structure parameters and reflector thickness as independent variables, and the optimization results were analyzed by theoretical calculation and finite element simulation verification. The results show that the first and second-order natural frequencies of fast steering mirror system are reduced by 7.8% and 7.11%, and the third-order natural frequencies are increased by 139.8%. It proves that the optimized structure is much better than the original structure, and the optimal calculation can effectively increase the control bandwidth of the system.

Rapid and accurate monitoring of soil organic matter (SOM) content is of great significance for precision fertilization of farmland. However, the SOM retrieval models are mainly established by statistical methods, which have limited application scope and incomplete theoretical foundation. Moreover, the accuracy of the SOM retrieval models remains raised. In this paper, for the first time, a semi-empirical SOM content retrieval model is constructed, which has certain theoretical basis, strong applicability and higher accuracy than before. Based on the Kubelka–Munk (KM) theory, the SOM retrieval model with the absorption coefficient k and scattering coefficient s related to SOM (r=k/s) is derived. The validity and reliability of the model are confirmed with validation set (n=26) including three sorts of soils. Results show that the model can estimate SOM content in different sorts of soils with high prediction accuracy and good prediction ability (root mean square errors of prediction (RMSEP), coefficients of determination (R2) and relative percentage deviation (RPD) values of 0.18%, 89.9% and 3.2, respectively ) in the range of 552-950nm. The model provides an innovative method for predicting SOM content.