Search Results (82)

Accurate estimation of actual evapotranspiration (ET) is critical for understanding hydrothermal cycles and ecosystem functioning in arid regions, where water scarcity governs ecological resilience. To address persistent gaps in ET quantification, this study integrates multi-source remote sensing data, energy balance modeling, and ground-based validation that significantly enhances spatiotemporal ET accuracy in the vulnerable desert steppe ecosystems. The study utilized meteorological data from several national stations and Landsat-8 imagery to process monthly remote sensing images in 2019. The Surface Energy Balance System (SEBS) model, chosen for its ability to estimate ET over large areas, was applied to derive modeled daily ET values, which were validated by a large-weighted lysimeter. It was shown that ET varied seasonally, peaking in July at 6.40 mm/day, and reaching a minimum value in winter with 1.83 mm/day in December. ET was significantly higher in southern regions compared to central and northern areas. SEBS-derived ET showed strong agreement with lysimeter measurements, with a mean relative error of 4.30%, which also consistently outperformed MOD16A2 ET products in accuracy. This spatial heterogeneity was driven by greater vegetation coverage and enhanced precipitation in the southeast. The steppe ET showed a strong positive correlation with surface temperatures and vegetation density. Moreover, the precipitation gradients and land use were primary controllers of spatial ET patterns. The process-based SEBS frameworks demonstrate dual functionality as resource-optimized computational platforms while enabling multi-scale quantification of ET spatiotemporal heterogeneity; it was therefore a reliable tool for ecohydrological assessments in an arid steppe, providing critical insights for water resource management and drought monitoring. Full article

The Tabu River Basin (TRB) is one of the most ecologically fragile areas in the arid regions of northern China; it is a key component of the desert steppe north of the Yinshan Mountains. The fractional vegetation coverage (FVC) represents a vital indicator of ecological health in the TRB. In this study, we explored the impacts of climate change and human activities on vegetation growth and utilized Landsat data (30 m) from the Google Earth Engine to generate a long-term FVC dataset (1986–2023) in the TRB. Furthermore, we established a framework for quantitatively identifying the effects of climate change and anthropogenic activities on the FVC in desert steppe regions. The results revealed that: (1) the FVC exhibits considerable spatial heterogeneity, with higher values observed in the southeastern and southwestern areas and lower values in the northern part; (2) over the past 38 years, the annual average FVC has shown fluctuations, with a slight declining trend, while the Hurst exponent indicates a reverse persistence pattern in the FVC across the TRB; and (3) the correlation between the FVC and the temperature is marginally stronger than that with precipitation, and the influence of climate change on promoting the FVC outweighs the role of human activities. These results offer valuable insights for ecological restoration and sustainable development efforts and provide scientific support for monitoring vegetation in the region. Full article

The shifting irrigation reduction in the Hetao Irrigation District and the inability to effectively discharge salts from the system have led to significant changes in salt migration patterns. Based on the integration of long-term field observations (2017–2023) with soil hydrodynamics and solute transport models, this study explored the impact of irrigation reduction on water and salt migration in a cropland–wasteland–lake system. The results indicated that before and after the reduction in irrigation and decline in groundwater levels, the migration rates of groundwater from croplands to wastelands and from wastelands to lakes remained relatively stable, averaging 78% and 40%. During the crop growth period, after irrigation reduction and groundwater level decline, the volume of groundwater recharging lakes from wastelands decreased by 80–120 mm, causing a water deficit in the lakes of 679–789 mm. After irrigation reduction and groundwater level decline, during the crop growth period, 1402 kg/ha of salt remained in the wasteland groundwater, and 597–861 kg/ha of salt accumulated in the cropland groundwater, exceeding previous levels, leading to salinization in the cropland and wasteland groundwater. This study provides insights relevant to managing groundwater and soil salinity in irrigation areas. Full article

The ecological environment of Inner Mongolia constitutes a critical component of China’s ecological civilization construction. To comprehensively assess and monitor ecological quality dynamics in this region, this study employed MODIS remote sensing data products (2000–2020) and derived four key indicators, —vegetation index (NDVI), wetness index (WET), build-up and soil index (NDBSI), and land surface temperature (LST)—via the Google Earth Engine (GEE) platform. A Remote Sensing-based Ecological Index (RSEI) was constructed using principal component analysis (PCA) to establish an annual long-term time series, thereby eliminating subjective bias from artificial weight assignment. Integrated methodologies—including Theil–Sen Median and Mann–Kendall trend analysis, Hurst exponent, and geographical detector—were applied to investigate the spatiotemporal evolution of ecological quality in Inner Mongolia and its responses to climatic and anthropogenic drivers. This study proposes a novel framework for large-scale ecological quality assessment using remote sensing. Key findings include the following: The mean RSEI value of 0.41 (2000–2020) indicates an overall improving trend in ecological quality. Areas with ecological improvement and degradation accounted for 76.06% and 23.84% of the region, respectively, exhibiting a spatial pattern of “northwestern improvement versus southeastern degradation.” Pronounced regional disparities were observed: optimal ecological conditions prevailed in the Greater Khingan Range (northeast), while the Alxa League (southwest) exhibited the poorest conditions. Northwestern improvement was primarily driven by increased precipitation, rising temperatures, and conservation policies, whereas southeastern degradation correlated with rapid urbanization and intensified socioeconomic activities. Our results demonstrate that MODIS-derived RSEI effectively enables large-scale ecological monitoring, providing a scientific basis for regional green development strategies. Full article

This study examined the short-term effects of nitrogen (N) and phosphorus (P) addition on soil N₂O flux and organic carbon content in the lakeshore zone of an arid inland lake, Daihai. Treatments included control (N0P0), N addition (N1P0), P addition (N0P1), and NP co-addition (N1P1). Using the static chamber method and lab analyses, we measured soil N₂O flux and organic carbon content at different growth stages. Results showed that, in the early growing season, short-term N and P addition had no significant effect on soil N₂O flux, with all treatments acting as N₂O sources. However, N and NP treatments significantly increased soil organic carbon (SOC) storage, improving carbon sequestration benefits by 72.7% to 98.1%. During the peak growing season, N and NP treatments significantly enhanced soil N₂O emissions, while NP treatment further increased SOC storage, the carbon sequestration benefits of all treatments ranging from 49.0% to 56.5%. At the late growing season, N and P addition had no significant impact on soil N₂O flux or organic carbon storage, with all sites acting as N₂O sinks and SOC storage showing no significant change across treatments (carbon sequestration benefits ranged from 0.3% to 38.5%). The study highlights that the response of soil N₂O flux to short-term N and P addition varies at different growth stages, while overall, N and P addition promotes soil carbon sequestration throughout the growing season in the lakeshore zone. Full article

Maowusu sandy land is characterized by a fragile ecological environment and extreme sensitivity to external disturbances such as climate change and human activities. Identifying and zoning ecological spaces in this region are crucial for maintaining eco-environmental safety and promoting sustainable regional development. With Maowusu sandy land as the study object, the temporal and spatial characteristics of land use and the driving forces were explored via spatial analysis technology—the geographic information system. Then, a 2D relation judgment matrix was constructed by evaluating the importance of ecosystem service functions and ecological sensitivity. Next, restoration zoning of natural ecological space was performed, and relevant restoration suggestions were put forward accordingly. Results show that the land use in Maowusu sandy land has significantly changed in the past 30 years, with construction land and forest continuously expanding, cropland and grassland being squeezed, and some areas of unutilized land being transformed into other land use types. Ecosystem service functions tend to weaken from southwest to northeast, whereas the ecologically sensitive zones are mainly distributed in the middle of Maowusu sandy land. The high-importance and high-sensitivity zones of natural ecological space account for 3.60% of the total area of natural ecological space, mainly distributed near Ejin Horo Banner. A comprehensive restoration project of soil and water conservation should be conducted in this zone to alleviate soil erosion and maintain the management and restoration of ecological protection red lines. Moderately important sensitive zones account for the largest proportion (80.42%) of the total area of natural ecological space, being widely distributed. In such zones, water resources should be taken as constraints, with emphasis on ecological protection and improvement measures. Low-importance and low-sensitivity zones account for the smallest proportion, in which ecosystem protection, near-natural restoration, and moderate development and utilization should be carried out. This study aims to provide a scientific basis for reasonably protecting natural ecological resources and promoting the healthy and ordered development of natural ecosystems. Full article

The prediction of soil moisture conditions using multispectral data from unmanned aerial vehicles (UAVs) has advantages over ground measurements in terms of costs and monitoring range. However, the prediction accuracy for moisture conditions using spectral data alone is low. In this study, relationships between water deficits and phenotypic characteristics in oats were evaluated and used to develop a UAV multispectral-based water prediction model. The vegetation indices NDRE (Normalized Difference Red Edge), CIG (Chlorophyll Index), and MCARI (Modified Chlorophyll Absorption in Reflectance Index) were highly correlated with oat yield. Based on a multipath analysis in the structural equation modeling framework, irrigation (p < 0.01), leaf area index (LAI) (p < 0.001), and SPAD (p < 0.001) had direct positive effects on NDRE. Three distinct machine learning approaches—linear regression (LR), random forest (RF), and artificial neural network (ANN) were employed to establish predictive models between the Normalized Difference Red Edge Index (NDRE) and soil water content (SWC). The linear regression model showed moderate correlation (R² = 0.533). Machine learning approaches demonstrated markedly superior performance (RF: R² = 0.828; ANN: R² = 0.810). Nonlinear machine learning algorithms (RF and ANN) significantly outperform conventional linear regression in estimating SWC from spectral vegetation indices. Full article

Vegetation productivity, as an essential global carbon sink, directly influences the variety and stability of ecosystems. Precise vegetation productivity monitoring and forecasting are crucial for the global carbon cycle. Traditional machine learning algorithms frequently experience overfitting when processing high-dimensional time-series data or substantial numbers of outliers, impeding the accurate prediction of various vegetation metrics. We propose a multimodal regression prediction model utilizing the TCLA framework—comprising the Transient Trigonometric Harris Hawks Optimizer (TTHHO), Convolutional Neural Networks (CNN), Least Squares Support Vector Machine (LSSVM), and Adaptive Bandwidth Kernel Density Estimation (ABKDE)—with the Hetao Irrigation District, a vast irrigation basin in China, serving as the study area. This model employs TTHHO to effectively navigate the search space and adaptively optimize network node positions, integrates CNN-LSSVM for feature extraction and regression analysis, and incorporates ABKDE for probability density function estimation and outlier detection, resulting in accurate interval probability prediction and enhanced model resilience to interference. Experimental data indicate that the TCLA model improves prediction accuracy by 10.57–26.47% compared to conventional models (Long Short-Term Memory (LSTM), Transformer). In the presence of 5–15% outliers, the fusion of multimodal data results in a substantial drop in RMSE (p < 0.05), with a reduction of 45.18–69.66%, yielding values between 0.079 and 0.137, thereby demonstrating the model’s high robustness and resistance to interference in predicting the next three years. This work introduces a scientific approach for precisely forecasting alterations in regional vegetation productivity using the proposed multimodal TCLA model, significantly enhancing global vegetation resource management and ecological conservation techniques. Full article

Pinus tabuliformis is a well-recognized woody mycorrhizae host plant growing in North China. EM fungi contribute to the host health and the stability of the forest ecosystem. However, ectomycorrhiae (EM) fungal community associated with this species is less documented. In this study, we examined EM fungal diversity and composition of P. tabuliformis from three sites in Inner Mongolia, China by using Illumina MiSeq sequencing on the rDNA ITS2 region. Our results showed that a total of 105 EM fungal operational taxonomic units (OTUs) were identified from 15 composite root samples, and the dominant lineages were /suillus-rhizopogon, /tomentella-thelephora, /tricholoma, /amphinema-tylospora, /wilcoxina, /inocybe, and /Sebacina. A high proportion of unique EM fungal OTUs (33, 31.4% of total OTUs) were detected, and some abundant OTUs preferred to exist in specific sites. The composition of EM fungal communities was significantly different among the sites, with soil, climatic, and spatial variables being related to the community variations. The EM fungal community assembly was mainly driven by environmental factors in deterministic processes. These findings suggest that this endemic Pinaceae species in China also harbored a rich and distinctive EM fungal community and deterministic processes played more important roles than stochastic in shaping the symbiotic fungal community. Our study improves our understanding of EM fungal diversity and community structure from the perspective of a single host plant that has not been investigated exclusively before. Full article

Isoflavones are important secondary metabolites in leguminous plants with significant physiological functions and economic value. However, the genetic variation, transcriptional regulation, and metabolic pathways governing isoflavone biosynthesis in Trifolium pratense remain largely unexplored. In this study, we systematically analyzed 500 accessions of T. pratense for isoflavone content and performed RNA-seq-based transcriptomic profiling to investigate the molecular mechanisms underlying isoflavone biosynthesis. Cluster analysis revealed significant genetic variation, with distinct transcriptional profiles between high- (H1, H2, H3) and low-isoflavone (L1, L2, L3) groups. GO and KEGG pathway enrichment analyses identified key metabolic pathways, including phenylpropanoid metabolism, flavonoid biosynthesis, carbohydrate metabolism, and hormone signaling, which play crucial roles in isoflavone regulation. Weighted gene co-expression network analysis (WGCNA) identified three key gene modules—MEblue, MEturquoise, and MEyellow—strongly correlated with isoflavone content. The MEturquoise and MEyellow modules were upregulated in high-isoflavone groups and were enriched in phenylpropanoid biosynthesis, lipid metabolism, and transcriptional regulation, suggesting that these pathways actively promote isoflavone accumulation. Conversely, the MEblue module, highly expressed in low-isoflavone groups, was enriched in sugar metabolism and MAPK signaling, indicating a potential metabolic flux shift away from secondary metabolism. Moreover, key rate-limiting enzymes (PAL, C4H, 4CL, CHS, and IFS) exhibited higher expression in high-isoflavone groups, highlighting their importance in precursor supply and enzymatic catalysis. Additionally, transcription factors such as MYB, WRKY, and NAC were identified as potential regulators of isoflavone biosynthesis, indicating a complex interplay between hormonal, circadian, and environmental signals. This study provides a comprehensive molecular framework for understanding isoflavone biosynthesis in T. pratense and identifies key regulatory genes and metabolic pathways that could be targeted for genetic improvement, metabolic engineering, and molecular breeding. The findings offer valuable insights into enhancing isoflavone production in legumes for agricultural, nutritional, and pharmaceutical applications. Full article

Factors such as climate change, fire, and overgrazing have been commonly considered the main causes of the global expansion of shrub invasion in grasslands over the past 160 years. Nevertheless, the influence of soil substrates on the progression of shrub encroachment has been insufficiently examined. This study examines the fundamental characteristics of the shrub-encroached desert steppe communities of Caragana tibetica in the Mongolian Plateau. Combining field surveys (field surveys and drone aerial photography) and laboratory experiments, using Spearman’s rank correlation analysis and structural equation modeling (SEM), this research systematically explores the impact of varying degrees of soil sandification on the survival of shrubs and herbaceous plants within these grassland communities. The findings indicate the following: (1) In the eight shrub-encroached grassland plots, the soil exhibited a significantly higher sand content compared to silt and clay, with the sand content generally exceeding 64%. (2) The coverage of shrub species is predominantly influenced by soil factors, particularly the soil sand content. (The path coefficient is 0.56, with p < 0.01). In contrast, herbaceous plants are more strongly influenced by climatic factors. (The path coefficient is 0.83, with p < 0.001). This study examines the response patterns of Caragana tibetica communities to edaphic and climatic factors, highlighting the pivotal role of soil sandification in the initiation and succession of shrub encroachment. The findings furnish a theoretical framework for forecasting future trends in grassland shrub encroachment and provide empirical evidence for the conservation and sustainable management of shrub-encroached grasslands. Full article

The desert steppe ecosystem at the Northern Foothills of the Yinshan Mountains (NFYS) is characterized by its fragility and heightened sensitivity to global climate change. Understanding the response and lag effects of Gross Primary Productivity (GPP) to climate change is imperative for advancing ecological management and fostering sustainable development. The spatiotemporal dynamics of chlorophyll fluorescence-based GPP data and its responses to precipitation, temperature, and extreme climate from 2001 to 2023 were analyzed. The random forest model and the partial least squares regression model were employed to further elucidate the response mechanisms of GPP to extreme climate, with a specific focus on the lag effect. The findings revealed that the GPP in the NFYS exhibited distinct regional characteristics, demonstrating a predominantly increasing trend over the past 23 years. The region has experienced a warming and drying trend, marked by a decrease in the intensity and frequency of extreme precipitation events, and an increase in extremely high temperatures and consecutive hot days, except a slight, albeit insignificant, increase in precipitation in the northeastern part. GPP exhibits varying degrees of lag, ranging from one to three months, in response to both normal and extreme climatic conditions, with a more immediate response to extreme temperatures than to precipitation. The influence of different climatic conditions on the lag effects of GPP can amplify the negative effects of extreme temperatures and the positive impact of extreme precipitation. The anticipated trend towards a warmer and more humid climate is projected to foster an increase in GPP. This research is of great theoretical and practical significance for deeply understanding the adaptation mechanisms of ecosystems under the context of climate change, optimizing desertification control strategies, and enhancing regional ecological resilience. Full article

Biochar is an effective soil amendment for improving soil function; however, the effects of biochar produced at different pyrolysis temperatures on soil microbial community structure and enzyme activities remain insufficiently studied. A field experiment was conducted from 2023 to 2024 in the arid and semi-arid region of Northern China to investigate the effects of biochar produced at different pyrolysis temperatures (T1: 300 °C; T2: 500 °C; T3: 700 °C) and application rates (C1: 10 t ha⁻¹; C2: 20 t ha⁻¹; C3: 30 t ha⁻¹) on soil chemical properties, microbial community structure, enzyme activity, and potato nutrient use efficiency. The results indicated that the C2T2 treatment was most effective in enhancing soil fungal and actinomycete populations, increasing total microbial biomass, significantly improving soil enzyme activities, and ultimately promoting crop yield. Structural equation modeling indicated that biochar regulates soil nutrient supply, drives microbial community succession toward functional specialization, prolongs microbial regeneration cycles, and ultimately enhances potato nutrient use efficiency. The results of this research provide scientific evidence to support the sustainable development of potato farming in the North China region. Full article

The carbon cycle in terrestrial ecosystems is a crucial component of the global carbon cycle, and drought is increasingly recognized as a significant stressor impacting their carbon sink function. Net ecosystem productivity (NEP), which is a key indicator of carbon sink capacity, is closely related to vegetation Net Primary Productivity (NPP), derived using the Carnegie-Ames-Stanford Approach (CASA) model. However, there is limited research on desert grassland ecosystems, which offer unique insights due to their long-term data series. The relationship between NEP and drought is complex and can vary depending on the intensity, duration, and frequency of drought events. NEP is an indicator of carbon exchange between ecosystems and the atmosphere, and it is closely related to vegetation productivity and soil respiration. Drought is known to negatively affect vegetation growth, reducing its ability to sequester carbon, thus decreasing NEP. Prolonged drought conditions can lead to a decrease in vegetation NPP, which in turn affects the overall carbon balance of ecosystems. This study employs the improved CASA model, using remote sensing, climate, and land use data to estimate vegetation NPP in desert grasslands and then calculate NEP. The Standardized Precipitation Evapotranspiration Index (SPEI), based on precipitation and evapotranspiration data, was used to assess the wetness and dryness of the desert grassland ecosystem, allowing for an investigation of the relationship between vegetation productivity and drought. The results show that (1) from 1982 to 2022, the distribution pattern of NEP in the Inner Mongolia desert grassland ecosystem showed a gradual increase from southwest to northeast, with a multi-year average value of 29.41 gCm⁻². The carbon sink area (NEP > 0) accounted for 67.99%, and the overall regional growth rate was 0.2364 gcm⁻²yr⁻¹, In addition, the area with increasing NEP accounted for 35.40% of the total area (p < 0.05); (2) using the SPEI to characterize drought changes in the Inner Mongolia desert grassland ecosystems, the region as a whole was mainly affected by light drought. Spatially, the cumulative effect was primarily driven by short-term drought (1–2 months), covering 54.5% of the total area, with a relatively fast response rate; (3) analyzing the driving factors of NEP using the Geographical detector, the results showed that annual average precipitation had the greatest influence on NEP in the Inner Mongolian desert grassland ecosystem. Interaction analysis revealed that the combined effect of most factors was stronger than the effect of a single factor, and the interaction of two factors had a higher explanatory power for NEP. This study demonstrates that NEP in the desert grassland ecosystem has increased significantly from 1982 to 2022, and that drought, as characterized by the SPEI, has a clear influence on vegetation productivity, particularly in areas experiencing short-term drought. Future research could focus on extending this analysis to other desert ecosystems and incorporating additional environmental variables to further refine the understanding of carbon dynamics under drought conditions. This research is significant for improving our understanding of carbon cycling in desert grasslands, which are sensitive to climate variability and drought. The insights gained can help inform strategies for mitigating climate change and enhancing carbon sequestration in arid regions. Full article

Soil moisture plays a critical role in the water and energy cycle within the soil–vegetation–atmosphere system and is a primary limiting factor in dryland ecosystems. Given the ongoing vegetation restoration in drylands, understanding the impact of vegetation changes on soil moisture is crucial for maintaining ecosystem stability and ensuring the sustainability of restoration efforts. This study combined long-term satellite data with eco-hydrological modeling to investigate the interannual and seasonal responses of soil moisture to vegetation changes in the Yinshanbeilu region during 1982–2018. The results indicated that vegetation in the region predominantly exhibited a greening trend, with 60.43% of the area experiencing significant increases in LAI. In areas with vegetation greening, soil moisture declined, with the effect being more pronounced at deeper soil profiles. Furthermore, the soil moisture trends shifted from wetting to drying, or, in more cases, from drying to intensified drying. The influence of vegetation greening on soil moisture exhibited seasonal variations, with more significant effects found in summer and autumn. This study highlights the complex responses of soil moisture to vegetation changes in grassland ecosystems in northern China’s drylands and provides a scientific guidance for ecological restoration and water management in these regions. Full article