Accurate detection of cadmium (Cd) and lead (Pb)-induced cross-stress on crops is essential for agricultural, ecological environment, and food security. The feasibility to diagnose and predict Cd–Pb cross-stress in agricultural soil was explored by measuring the visible and near-infrared reflectance of rice leaves. In this study, two models were developed—namely a diagnostic model and a prediction model. The diagnostic model was established based on visible and near-infrared reflectance spectroscopy (VNIRS) datasets with Support Vector Machine (SVM), followed by leave-one-out cross-validation (LOOCV). A partial least-squares (PLS) regression, as the prediction model was employed to predict the foliar concentration of Cd and Pb contents. To accurately calibrate the two models, a rigorous greenhouse experiment was designed and implemented, with 4 levels of treatments on each of the Cd and Pb stress on rice. Results show that with the appropriate pre-processing, the diagnostic model can identify 79% of Cd and 85% of Pb stress of any levels. The significant bands that have been used mainly distributed between 681–776 nm and 1224–1349 nm for Cd stress and 712–784 nm for Pb stress. The prediction model can estimate Cd with coefficient of determination of 0.7, but failed to predict Pb accurately. The results illustrated the feasibility to diagnose Cd stress accurately by measuring the visible and near-infrared reflectance of rice canopy in a cross-contamination soil environment. This study serves as one step forward to heavy metal pollutant detection in a farmland environment.

This paper proposed an optimal spectral resolution for diagnosing cadmium-lead (Cd-Pb) cross contamination with different pollution levels based on the hyperspectral reflectance of rice canopy. Feature bands were sequentially selected by two-way analysis of variance (ANOVA2) and random forests from the high-dimensional hyperspectral data after preprocessing. Then Support Vector Machine (SVM) was applied to diagnose the pollution levels using different feature bands combination with different spectral resolutions and cross validation was conducted to evaluate the distinguishing accuracies. Finally, the optimal spectral resolution could be determined by comparing the diagnosing accuracies of the optimal feature bands combination in each spectral resolution. In the experiments, the hyperspectral reflectance data of rice canopy with ten different spectral resolutions was captured, covering 16 pretreatments of Cd and Pb pollution. The experimental results showed the optimal spectral resolution was 9 nm with the highest average accuracy of 0.71 and relatively standard deviation of 0.07 for diagnosing the categories and levels of Cd-Pb cross contamination. The useful exploration provided an evidence for optimal spectral resolution selection to reduce the cost of heavy metal pollution diagnose.

The accurate estimation of grassland carrying capacity (GCC) in the alpine grasslands of the Changjiang River source region is crucial for managing livestock loads and ensuring ecological security on the Qinghai-Tibetan Plateau. Previous remote sensing methods have predominantly focused on yield indicators, often neglecting quality indicators, which hampers precise GCC estimation. Here, we collected 25 samples from the Dangqu basin, analyzing various grass parameters including yield, crude protein (CP), neutral detergent fiber (NDF), and acid detergent fiber (ADF). Then, we developed models to optimize GCC using quality indicators derived from GF5B images, assessing performance through Pearson correlation coefficient (R²), root mean square error (RMSE), and relative root mean square error (rRMSE). Results were found to show an average yield of 61.26 g/m², with CP, ADF, and NDF ranging from 5.81% to 18.75%, 45.47% to 58.80%, and 27.50% to 31.81%, respectively. Spectra in the near-infrared range, such as 1918 nm, and spectral indices improved the accuracy of the hyperspectral inversion of grass parameters. The GCC increased from 0.51 SU·hm⁻² to 0.63 SU·hm⁻² post-optimization, showing an increasing trend from northwest to southeast. This study enhances GCC estimation accuracy, aiding in reasonable livestock management and effective ecological preservation.

Hyperspectral remote sensing images offer a unique opportunity to quickly monitor water depth, but how to utilize the enriched spectral information and improve its spatial resolution remains a challenge. We proposed a water depth estimation framework to improve spatial resolution using deep learning and four inversion methods and verified the effectiveness of different super resolution and inversion methods in three waterbodies based on HJ-2 hyperspectral images. Results indicated that it was feasible to use HJ-2 hyperspectral images with a higher spatial resolution via super resolution methods to estimate water depth. Deep learning improves the spatial resolution of hyperspectral images from 48 m to 24 m and shows less information loss with peak signal-to-noise ratio (PSNR), structural similarity (SSIM), and spectral angle mapper (SAM) values of approximately 37, 0.92, and 2.42, respectively. Among four inversion methods, the multilayer perceptron demonstrates superior performance for the water reservoir, achieving the mean absolute error (MAE) and the mean absolute percentage error (MAPE) of 1.292 m and 22.188%, respectively. For two rivers, the random forest model proves to be the best model, with an MAE of 0.750 m and an MAPE of 10.806%. The proposed method can be used for water depth estimation of different water bodies and can improve the spatial resolution of water depth mapping, providing refined technical support for water environment management and protection.

The in-situ tensile test of the field emission scanning electron microscope was used to study the deformation mechanism of the TC4 titanium alloy manufactured by laser additive manufacturing along the L direction (grain growth direction) and T direction (perpendicular to the grain growth direction). The test results show that during the tensile process, as deposited specimen mainly cracks along the interfaces of α and β which is 45 ° to the tensile direction. With the increase of load, the cracking increases. The surface of the sample fluctuates until breaks. There is no obvious change in the prior β grain boundary in L direction. The microcracks along grain boundaries were observed in T direction, which is consistent with the result that the plasticity in the T direction is less than that in L direction. After heat treatment, due to the increase of α lath width compared with the as-deposition, slip bands were observed in α phase. However, a large number of slip bands appear on the equiaxed α phase of the forged specimen. 利用扫描电镜的原位拉伸台研究了激光增材制造TC4钛合金沿晶粒生长方向（L）和垂直于晶粒生长方向（T）拉伸过程中的变形机理。结果表明沉积态拉伸过程中显微组织主要沿与拉伸方向呈约45°的α，β两相界面开裂；随着流动应力的增加，微裂纹数量增多，试样表面在载荷作用下出现起伏错动，直至破坏。L方向原始β晶界上没有明显的变化，T方向观察到沿晶界开裂的微裂纹，这与T方向的塑性比L方向差的结果相一致。热处理后由于α板条变宽，α相内出现滑移带，锻造态则是在等轴α相上出现了大量的滑移带。