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.

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.

Due to the absence of rotational symmetry, off-axis astronomical telescopes with off-set pupil become subject to rotational misalignments. Rotational misalignments of large off-axis mirrors with reference to their geometric center can greatly degrade the imaging quality. This paper presents an in-depth discussion on the net aberration fields of off-axis astronomical telescopes induced by rotational misalignments. Aberration function of off-axis telescopes with rotational misalignments is derived based on the framework of nodal aberration theory. Expressions of several important aberrations are obtained under some approximations. Then the specific field characteristics of these aberrations are presented and explicated. Meanwhile, we demonstrate that rotational misalignments can be converted to a kind of surface decenters; on the other hand, the effects of rotational misalignments have their special features which are different from the effects of general surface decenters. Besides, some other insightful discussions are further presented. This work is also applicable to the rotational misalignments of the off-axis segments of primary mirror in segmented mirror astronomical telescopes.

Calibration of the channeled spectropolarimeter is significant for the quantitative application of this instrument. In current calibration methods for a channeled spectropolarimeter, an absolute angle between the coordinate system of an auxiliary polarizer and the global coordinate system of the instrument is usually indispensable. The effectiveness of calibration depends on the precision of the absolute angle, while it is usually difficult to achieve in a practical calibration process. This paper presents an easily implemented method to simultaneously calibrate the alignment and retardation errors of high-order retarders for a channeled spectropolarimeter. In the presented method, the requirement of an absolute angle between the coordinate system of an auxiliary polarizer and the global coordinate system of the instrument is replaced by only rotating a relative angle in the coordinate system of the auxiliary polarizer itself. First, we theoretically derive the modified reconstruction model considering the alignment errors of high-order retarders. By analyzing and summarizing the modified reconstruction model, the calibration and compensation models of the alignment and retardation errors are further proposed. Then, two linearly polarized beams with a relative angle of 45° between them are utilized to determine the alignment and retardation errors. Based on these results, the alignment and retardation errors can be compensated by a software correction algorithm without any precise mechanical adjustments. The effectiveness and feasibility of the presented method are verified by numerical simulations and experiments. The advantage of easy implementation makes this calibration method more suitable to apply in the laboratory and to be on track for correcting the channeled spectropolarimeter.