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Water resources are scarce, and secondary soil salinization is severe in the Hetao Irrigation District. Farmland shelterbelt systems (FSS) play a critical role in regulating soil water and salt dynamics within the irrigation district. However, the understanding of soil water and salt migration within FSS during the freeze–thaw period remains unclear due to the complex and multifaceted interactions between water and salt. This study focused on a typical FSS and conducted comprehensive monitoring of soil moisture, salinity, temperature, and meteorological parameters during the freeze–thaw period. The results revealed consistent trends in air temperature and soil temperature overall. Soil freezing durations exceeded thawing durations, and both decreased with an increasing soil depth. At the three critical freeze–thaw nodes, the soil moisture content at a 0–20 cm depth was significantly lower than at a 40–100 cm depth (p < 0.05). The soil water content increased with time and depth at varying distances from the shelterbelt, with an average increase of 7.63% after freezing and thawing. The surface water content at the forest edge (0.3H, 4H) was lower than inside the farmland (1H, 2H, 3H). Soil salt accumulation occurred during both freezing stable periods and melting–thawing periods in the 0–100 cm soil layer near the forest edge (0.3H, 4H), with the highest soil salinity reaching 0.62 g·kg−1. After the freeze–thaw period, the soil salt content in each layer increased by 11.41–47.26% compared to before the freeze–thaw period. Salt accumulation in farmland soil near the shelterbelt was stronger than in the far shelterbelt. The multivariate statistical model demonstrated goodness of fit for soil water and salt as 0.94 and 0.72, respectively, while the BP neural network model showed goodness of fit for soil water and salt as 0.82 and 0.85, respectively. Our results provide an efficient theoretical basis for FSS construction and agricultural water management practices.

The deterioration of farmland shelterbelts in the Ulan Buh desert oases could weaken their protective functions. Therefore, an accurate method is essential to assess tree decline degree in order to guide the rejuvenation and transformation of these shelterbelts. This study selected three typical farmland shelterbelts in the Ulan Buh desert oases as the objects. Terrestrial laser scanning (TLS) and airborne hyperspectral imagery (AHI) were used to acquire point cloud data and detailed spectral information of trees. Point cloud and spectral characteristics of trees with varying decline levels were analyzed. Six models were constructed to identify decline degree of shelterbelts, and model accuracy was evaluated. The coefficient of determination between the structural parameters of trees extracted by TLS and field measurements ranged from 0.76 to 0.94. Healthy trees outperformed declining trees in structural parameters, particularly in tridimensional green biomass and crown projection area. Spectral reflectance changes in the 740–950 nm band were evident among the three tree types with different decline levels, decreasing significantly with increased decline level. Among the TLS-derived feature parameters, the canopy relief ratio of tree points and point cloud density strongly correlated with the degree of tree decline. The plant senescence reflectance index and normalized difference vegetation index exhibited the closest correlation with tree decline in AHI data. The average accuracy of the models constructed based on the feature parameters of LiDAR, AHI, and the combination of both of them were 0.77, 0.61, and 0.81, respectively. The light gradient-boosting machine model utilizing TLS–AHI comprehensive feature parameters accurately determined tree decline. This study highlights the efficacy of employing feature parameters derived from TLS alone to accurately identify tree decline. Combining feature parameters from the TLS and AHI enhances the precision of tree decline identification. This approach offers guidance for decisions regarding the renewal and transformation of declining farmland shelterbelts.

Photosystem II (PSII) of grapevine leaves is easily damaged under heat stress, but no such injury is observed when the leaves are heated in low light. To elucidate the mechanisms, we compared the photosynthetic characteristics of grapevine seedlings under heat treatments (42 °C) for 4 h in the dark or low light (200 μmol m−2 s−1). At 42 °C in the dark, the PSII maximum quantum yield (Fv/Fm) decreased significantly with the increase in time but did not change much in low light. The JIP (chlorophyll a fluorescence rise kinetics) test results showed that low light significantly alleviated the damage to the oxygen evolving complexes (OECs; the K-step was less visible) by heat stress. Further, in the presence of de novo D1 protein synthesis inhibitor chloramphenicol, Fv/Fm did not differ significantly between dark and light treatments under heat stress. The 50% re-reduction (RR50) of P700+ on cessation of far-red illumination was faster after light treatment than that in the dark. After exposure to 25 °C in a low light for 15 min, Y(NO) (the constitutive non-regulatory non-photochemical quenching) treated by heat stress and darkness was higher than that by heat stress and light. Overall, our results suggested that enhanced CEFs around PSI in low light could assist PSII against heat damage by maintaining the rate of PSII repair and inhibiting the non-radiative charge recombination in PSII reaction centers.

Northwest China is significantly affected by sandstorm disasters. To mitigate the negative impacts of sandstorm events, it is critical to understand the spatio-temporal variations in typical sand and dust storms and their influencing factors. In this work, using ground-based measurements of particulate matter and remote sensing data such as MODIS, OMI, and CALIPSO data, the sources of aerosol pollution and aerosol optical properties of a typical sandstorm event that occurred in Northwest China in 2018 was studied. In addition, the HYSPLIT model was used to explore the air mass trajectories in order to analyze the sand and dust migration process during the sandstorm event. Furthermore, the wind erosion sensitivity of Northwest China was analyzed via single factor analysis and multi-factor superposition of wind field intensity, soil drought index, vegetation coverage, and relief amplitude. Finally, the region of the study area having a high comprehensive wind erosion sensitivity was identified. The results showed that the PM10 concentrations exceeded 400 µg/m3 and the PM2.5/PM10 ratio did not exceeded 0.6 during the sandstorm event, indicating that natural particulate matter was dominant in the ambient air. At the epicenter of pollution, the aerosol optical depth (AOD) at 550 nm was 0.75–1. By combining AOD data with wind speed and direction data from field observation stations, it was found that the sandstorm event in 2018 mainly occurred between 1 April and 3 April, and affected all of Northwest China on 2 April and 3 April. The absorbed aerosol index (AAI) ranged between 2.5 and 4, indicating that the Taklimakan Desert was the main source of sandstorm events in Northwest China. The CALIPSO total attenuated backscatter coefficient at 532 nm indicated that the main component of tropospheric aerosol in this region was distributed in the range of 0–12.5 km. The simulated airflow track showed that it had the same dust source regions as AAI index studies. Moreover, investigation of wind erosion sensitivity in the study areas indicated that the Taklimakan Desert and other desert regions were the main ecologically sensitive areas. These conclusions can provide references and suggestions for the mitigation of damage caused by sandstorm events, in addition to the enhancement of ecological governance.

The energy balance (EB), turbulent vapour transport (TVT), Penman-Monteith (PM) and Bowen ratio energy balance (BREB) equation were used to estimate dewfall based on meteorological data. Initially there were big disagreements between the estimates from these four equations. However, after multiplying the heat and vapour conductance terms by 0.33 the agreement was much better. This implies that the disagreements derived from improper conductance values. Initially we did not consider the effect of atmospheric stability on the conductances. With stability correction the conductances were on average 0.5 times the values without stability correction. To arrive at the aforementioned 0.33, the conductances with stability correction still need to be lower by a factor of 0.66. The value of the von Karman constant and the relationships for the zero plane displacement and the roughness length we used in our conductance computations are widely used, but not the only possible ones. With different values and relationships also suggested in the literature one can reach this factor. However, it is also possible that our wind speed data contributed to the fact that the conductances we computed were too high. Their computation for a given canopy—atmosphere system requires wind speeds from a wind profile in equilibrium with the vegetation. This in turn requires an adequate fetch around the investigated surface. The highly varied vegetation in and around the site where the study was conducted makes adequate fetch rather doubtful. To obtain valid conductance values the atmospheric stability conditions must be considered, the appropriate values for the von Karman constant, the zero plane displacement and roughness length must be used, and there must be adequate fetch. The BREB equation does not contain a conductance term and therefore does not suffer from the problems just stated. The other three equations do. However, the BREB, like the EB and TVT equations, need the surface temperature which is not routinely measured. This then leaves the PM equation from which this temperature has been eliminated as the only option. Hence, in a future study dewfall estimates from the PM equation should be compared with direct measurements with a high precision weighing lysimeter.