Forest stock volume (FSV) is a major indicator of forest ecosystem health and it also plays an important part in understanding the worldwide carbon cycle. A precise comprehension of the distribution patterns and variations of FSV is crucial in the assessment of the sequestration potential of forest carbon and optimization of the management programs of the forest carbon sink. In this study, a novel vegetation index based on Sentinel-2 data for modeling FSV with the random forest (RF) algorithm in Helan Mountains, China has been developed. Among all the other variables and with a correlation coefficient of r = 0.778, the novel vegetation index (NDVI_RE) developed based on the red-edge bands of the Sentinel-2 data was the most significant. Meanwhile, the model that combined bands and vegetation indices (bands + VIs-based model, BVBM) performed best in the training phase (R² = 0.93, RMSE = 10.82 m³ha⁻¹) and testing phase (R² = 0.60, RMSE = 27.05 m³ha⁻¹). Using the best training model, the FSV of the Helan Mountains was first mapped and an accuracy of 80.46% was obtained. The novel vegetation index developed based on the red-edge bands of the Sentinel-2 data and RF algorithm is thus the most effective method to assess the FSV. In addition, this method can provide a new method to estimate the FSV in other areas, especially in the management of forest carbon sequestration.

Since systematic direct measurements of refractive index structure constant ( $C_n^2$) for many climates and seasons are not available, an indirect approach is developed in which $C_n^2$ is estimated from the mesoscale atmospheric model outputs. In previous work, we have presented an approach that a state-of-the-art mesoscale atmospheric model called Weather Research and Forecasting (WRF) model coupled with Monin-Obukhov Similarity (MOS) theory which can be used to estimate surface layer $C_n^2$ over the ocean. Here this paper is focused on surface layer $C_n^2$ over snow and sea ice, which is the extending of estimating surface layer $C_n^2$ utilizing WRF model for ground-based optical application requirements. This powerful approach is validated against the corresponding 9-day $C_n^2$ data from a field campaign of the 30th Chinese National Antarctic Research Expedition (CHINARE). We employ several statistical operators to assess how this approach performs. Besides, we present an independent analysis of this approach performance using the contingency tables. Such a method permits us to provide supplementary key information with respect to statistical operators. These methods make our analysis more robust and permit us to confirm the excellent performances of this approach. The reasonably good agreement in trend and magnitude is found between estimated values and measurements overall, and the estimated $C_n^2$ values are even better than the ones obtained by this approach over the ocean surface layer. The encouraging performance of this approach has a concrete practical implementation of ground-based optical applications over snow and sea ice.

Soil extracellular ecoenzymatic activities (EEA) are major players in the biogeochemical cycles and are closely related to the metabolic demand and nutrient supply in microbes. However, their effects on biogeochemistry along the elevation gradient on mountain ecosystems in arid regions remain unclear. To address this, we investigated the variations of soil microbial resource limitation and the relative contributing factors along the elevation gradient of the Helan Mountains, northwest China. The results showed that the relative abundance of total microbial, bacterial, fungal, actinomycetes, and N-acquiring enzymatic activities (N-Acetyl-β-D-glucosaminidase and Leucine- α-aminopeptidase, that are NAG and LAP, respectively) in the soil first increased and then decreased with an increase in elevation. This variation pattern could be due to the changes in soil temperature and moisture along the elevation gradient. Soil enzyme stoichiometry and resource allocation further revealed that the microbial metabolism activity in the Helan Mountains was limited by carbon (C) and phosphorus (P). Furthermore, the two limited elements were significantly higher at the mid and high altitudes (2,139–2,438 m) than at low altitudes (1,380–1,650 m). Additionally, redundancy analysis revealed that the soil water content and bulk density played a crucial role in microbial community structures, while the soil pH had the most influence on soil EEA and ecoenzymatic stoichiometry. Our findings revealed the patterns of soil microbial community structure, extracellular enzyme activities, and microbial metabolism at various elevations, which will help in understanding the microbial resource limitation and nutrient cycling in mountain ecosystems in arid regions.

As an important barrier against desert invasion in Northwest China, Helan Mountains (HL), Luoshan Mountains (LS) and their natural forests have an extremely important ecological status. It is of great significance to study the relationship between forest growth and climate in this region under the background of global change. At present, relevant research mostly focuses on the Chinese pine (Pinus tabulaeformis Carr.), and little is known about how Qinghai spruce (Picea crassifolia Kom.) responds to climate change. To investigate the potential relationships between radial growth of P. crassifolia and climatic conditions in Ningxia, China, we collected tree-ring samples from P. crassifolia growing in the HL and LS and then established the standard tree-ring width chronologies for the two sites. Correlation analysis together with multivariate linear regression and relative contribution analyses were used, and results showed that radial growth in the HL was determined by the precipitation in the previous September, by the standardized evapotranspiration index (SPEI) in the current March and June, and by the maximum air temperature in the current September. The maximum air temperature in the current September contributed the most (0.348) to the radial growth in the HL. In the LS, radial growth was determined by the precipitation in the previous September and in the current March and by the minimum air temperature in the current July. The factor that made the most contribution was the precipitation in the current March (0.489). Our results suggested that in the wetting and warming future, growth of P. crassifolia in the HL will increase while that in the LS needs further investigation. Our results also provide a basis for predicting how P. crassifolia in northwest China will grow under the background of future climate change and provide a reference for formulating relevant management measures to achieve ecological protection and sustainable development policies.