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Using the first-principles calculation, two doping two-dimensional (2D) BN (boron nitride) polymorphs are constructed in this work. The two doping 2D BN polymorphs B5N6Al and B5N6C sheets are thermally stable under 500 K. All the B6N6, B5N6Al, and B5N6C sheets are semiconductor materials with indirect band gaps on the basis of a hybrid functional. The anisotropic calculation results indicate that Young's modulus (E) and Poisson's ratio (v) of the B6N6, B5N6Al, and B5N6C sheets are anisotropic in the xy plane. In addition, the magnetic properties of the B6N6, B5N6Al, and B5N6C sheets have also been investigated. According to the calculation of the magnetic properties, B6N6 sheet does not exhibit magnetism, while it shows weak magnetism after doping carbon atom to the BN sheet. This paper explores the influence mechanism of doping different atoms on the basic physical properties of two-dimensional BN sheets. It not only constructs a relationship between structure and performance but also provides theoretical support for the performance regulation of BN materials. [ABSTRACT FROM AUTHOR]

Optical remote sensing of aquatic vegetation in shallow water is an essential aid to ecosystem protection, but it is difficult because the spectral characteristics of the vegetation are sensitive to external features such as water background effects, atmospheric effects, and the structural properties of the canopy. A global sensitivity analysis of an aquatic vegetation radiative transfer model provides invaluable background for algorithm development for use in optical remote sensing. Here, we use the extended Fourier Amplitude Sensitivity Test (EFAST) method for the modelling. Four different cases were identified by subdividing the ranges of water depth and leaf area index (LAI) involved. The results indicate that the reflectance of emergent vegetation is affected mainly by the concentrations of chlorophyll a + b in leaves (Cab), leaf inclination distribution function parameter (LIDFa) and LAI. The parameter LAI is influential in sparse vegetation cases whereas Cab and LIDFa are influential in dense vegetation cases. Canopy reflectance for submerged vegetation is dominated by water parameters. Relatively, LAI and Cab are highly sensitive vegetation parameters. The analysis is extended to vegetation index as well, which takes the Sentinel-2A as the reference sensor. It shows that NDAVI (Normalized Difference Aquatic Vegetation Index) is suitable for retrieving LAI in all cases except deep-sparse for emergent vegetation, whereas NDVI (Normalized Difference Vegetation Index) would be better in the deep-sparse case. NDVI, NDAVI and WAVI (Water Adjusted Vegetation Index), respectively, are suitable for retrieving Cab, Car and LIDFa in dense cases. For submerged vegetation, the sensitivity of LAI to NDAVI is relatively high only in the shallow-sparse case. The adjustment factor L in SAVI and WAVI fails to suppress the sensitivity to water constituent parameters. The sensitivity of LAI and Cab to NDVI in deep cases is relatively higher than that to the other indices, which may provide clues for the construction of inversion algorithms in macrophyte remote sensing in the aquatic environment using spectral signatures in the visible and near infrared regions. [ABSTRACT FROM AUTHOR]