Amidst climate change, managing water and vegetation adaptability is vital in ecology and agriculture. Salix psammophila is key in deserts, maintaining ecological balance and combating desertification. Understanding its surface condensation and response to weather is critical for survival. This study aims to investigate the formation patterns of surface condensation water under S. psammophila at different irrigation levels and the influence of precipitation, temperature, relative humidity, and wind speed. Conducted in China’s Kubuqi Desert from June to September 2022, the study employed micro-lysimeters with four irrigation test sets and a control group, conducting detailed observations at various locations. The results indicate that S. psammophila significantly influences surface condensation water by blocking solar radiation and reducing wind speed. The drip irrigation system also regulates surface condensation water on S. psammophila. Moreover, meteorological factors such as 24 h maximum temperature, relative humidity difference, wind speed, and air vapor pressure deficit show significant correlations with surface condensation water formation. In conclusion, this study enhances the understanding of desert ecosystem water balance, vegetation adaptability, and efficient water resource utilization. It provides valuable scientific guidance for the conservation and restoration of desert ecosystems.

The Chinese pearleaf crabapple tree, belonging to the Rosaceae family and with the scientific name Malus asiatica, bears fruits that serve as both food and medicine. The Horqin Right Wing Front Banner is a critical node for ecological security in the northern border regions of China, exerting significant influence on ecological health in China. Additionally, the resources in this region are limited, and residents have historically relied solely on maize cultivation for sustenance. Therefore, local measures combining ecological conservation and economic development have been implemented in vigorously promoting the Chinese pearleaf crabapple industry. Hence, it is necessary to study the ecological suitability of the local Chinese pearleaf crabapple. In this study, 15 evaluation indicators are employed for this purpose, with the analytic hierarchy process (AHP) used to calculate single-factor weights. Additionally, GIS is utilized to establish membership functions for achieving dimensionless evaluation indicators, as well as a weighted index and model used to compute comprehensive suitability scores. Using the natural breakpoint method, the overall scores were categorized into five groups: most suitable, more suitable, barely suitable, unsuitable, and not applicable. The validation of zoning results includes verification of existing planting locations and conducting principal component analysis of the characteristics and yield of Chinese pearleaf crabapple to ensure the accuracy of the zoning. It is found that the validation results are generally consistent with the zoning results. The results indicate that the most suitable, more suitable, suitable, barely suitable, and unsuitable represent 16.49%, 32.07%, 30.36%, 14.52%, and 6.55% of the total zoning area, respectively. These findings can be applied to land use planning and in expanding the planting area of Chinese pearleaf crabapple.

Salix psammophila, classified under the Salicaceae family, is a deciduous, densely branched, and erect shrub. As a leading pioneer tree species in windbreak and sand stabilization, it has played a crucial role in combating desertification in northwestern China. However, different genetic sources of Salix psammophila exhibit significant variations in their effectiveness for windbreak and sand stabilization. Therefore, it is essential to establish a rapid and reliable method for identifying different Salix psammophila varieties. Visible and near-infrared (Vis-NIR) spectroscopy is currently a reliable non-destructive solution for origin traceability. This study introduced a novel feature selection strategy, called qualitative percentile weighted sampling (QPWS), based on the principle of the long tail effect for Vis-NIR spectroscopy. The core idea of QPWS combines weighted sampling and percentage wavelength selection to identify key wavelengths. By employing a multi-threaded parallel execution of multiple QPWS instances, we aimed to search for the optimal feature bands to address the instability issues that can arise during the feature selection process. To address the problem of reduced prediction performance in one-dimensional convolutional neural network (1D-CNN) models after feature selection, we have introduced convolutional autoencoders (CAEs) to reduce the dimensions of wavelengths that are discarded during feature selection. Subsequently, these reduced dimensions are fused with the selected wavelengths, thereby enhancing the model’s performance. With our completed model, we selected outstanding models for model fusion and established a decision system for Salix psammophila. It is worth noting that all 1D-CNN models in this study were developed using Bayesian optimization methods. In comparison with principal component analysis (PCA) and full spectrum methods, QPWS exhibits superior predictive performance in the field of machine learning. In the realm of deep learning, the fusion of data combining QPWS with CAE demonstrated even greater potential with an improvement of average accuracy of approximately 2.13% when compared to QPWS alone and a 228% increase in operational speed compared to a model with full spectra. These results indicated that the combination of CAE with QPWS can be an effective tool for identifying the origin of Salix psammophila.