Grasslands are critical to the global carbon cycle and support livestock production, yet long-term, high-resolution global datasets of grassland aboveground biomass (AGB) remain scarce, since existing products are often spatially coarse, temporally discontinuous, or regionally limited. Here, we present a global grassland AGB dataset spanning 2000-2022 at 0.005° spatial resolution. The dataset was generated by employing an optimized random forest model, which was trained on 11,265 spatially aggregated samples with global distribution. The multi-source predictors included Normalized Difference Vegetation Index (NDVI), climate variables, atmospheric CO2 concentration, and topographical factors. At the global scale, the model achieved an R2 of 0.60 for both validation and independent testing, with RMSEs of 85.97 and 93.69 g m-2, respectively. When evaluated across all samples, the model exhibited an R2 of 0.93 and an RMSE of 51.85 g m-2. Comparison with other datasets showed its comparable value distributions and interannual trajectories, and closer agreement with field observations. This dataset provides a foundation for advancing research on global grassland carbon dynamics and management practices.

Reservoirs provide essential ecosystem services to their surrounding areas. Evaluating the value of Ecosystem Services in Reservoir ecosystems (ESRs) is essential for promoting environmental sustainability. However, unclear definitions of reservoir ecosystems and ESRs have limited the development of a unified framework for large-scale ecosystem service assessments, leading to controversies in ESR research. To address these issues, this paper proposes a unified framework for assessing ESR, based on a clear definition of reservoir ecosystems and a comprehensive description of both general and specialized ESR. The framework is then applied to a large-scale assessment, monetizing the value of ESRs in 400 representative Chinese reservoirs at a 30-m spatial resolution using multi-source remote sensing and statistical datasets. The main findings of this work are as follows: 1) The proposed framework for assessing ESR proved to be practically feasible for large-scale assessments of Chinese reservoirs. The 400 Chinese reservoirs provided a total of 2.36 trillion CNY in ecosystem services for the year 2020, with specialized services contributing more than general services. The nationwide primary ESR was identified as food supply; 2) Generally, the ESRs showed higher values in central and southwestern China and lower values in northwestern China. Approximately 63.6% of provinces had higher contributions from specialized ESR compared to general ESR. Yunnan, Guangxi, and Hubei were the top three provinces with the highest ESR contributions. This study contributes to both conceptual understanding and practical application of national-scale ESRs assessment, providing a framework to inform integrated water and environmental management.

The establishment of constructed grasslands is a widely used method for restoring severely degraded alpine grasslands in the Three-River Headwaters Region (TRHR). The response of soil net nitrogen mineralization (SNNM) to grassland construction for ecological restoration and how this response changes with recovery ages remain unclear. One-way ANOVA, the Tukey’s HSD test, ordinary linear regression and PLS regression were employed to analyze the differences in SNNM rate between constructed and black-soil-type grasslands, their relationships with soil properties, and their key influencing factors. The SNNM rate in constructed grasslands exhibited a hyperbolic trend of initial surge– decrease–low-level stabilization, whereas those in black-soil-type grasslands indicated no clear temporal trend. Soil net nitrification dominated the SNNM processes in both grassland types, accounting for 94.3

Existing fresh snow density parameterization schemes underestimate fresh snow density and overestimate snow depth (SD) on the Qinghai-Tibetan Plateau (QTP). This study proposed an improved fresh snow density parameterization scheme that considers ground surface temperature for model time steps (STFSDM), optimized by minimizing simulated SD bias by incorporating a terrestrial snow modeling system. A case study was conducted using data from 33 observation stations on the QTP during September 1, 2001-July 31, 2005. Compared to existing parameterization schemes, STFSDM increased the simulated average fresh snow density by 43.72-102.77 kg/m3 and decreased the root mean square error of simulated SD by 15.82-174.58%, and achieved the highest simulated SD accuracy over all seasons and in most climatic zones. STFSDM also decreased the root mean square error of simulated SD by 2.82% and 15.82%, compared to two local parameter optimization experiments that only utilized air temperature or ground surface temperature. These results showed that STFSDM increased estimated fresh snow density and thus reduced SD overestimation by considering ground surface temperature, and achieved more accurate SD simulation because it incorporated two distinct temperature-driven effects on fresh snow density: (1) the influence of air temperature on ice crystal formation as ice crystals fall, and (2) the impact of surface temperature on snow compaction upon landing. Thus, STFSDM reduces simulated SD overestimation under the observational conditions of QTP meteorological stations. This scheme is most applicable to SD simulations in areas similar to the QTP with short-duration seasonal snow cover.

The homogeneous turbid medium assumption inherent to the Beer-Lambert’s law can lead to a reduction in the shading effect between leaves when non-green vegetation canopies are present, resulting in an overestimation of the fraction of absorbed photosynthetically active radiation (FAPAR). This paper proposed a method to improve the FAPAR estimation (FAPARFVC) based on Beer-Lambert’s law by incorporating fractional vegetation coverage (FVC). Initially, the canopy-scale leaf area index (LAI) of the green canopy distribution area within the pixel (sample site) was determined based on the FVC. Subsequently, the canopy-scale FAPAR was calculated within the green canopy distribution area, adhering to the assumption of a homogeneous turbid medium in the Beer-Lambert’s law. Finally, the average FAPAR across the pixel (sample site) was calculated based on the FVC. This paper conducted a case study using measured data from the BigFoot Project and grass savanna in Senegal, West Africa, as well as Moderate Resolution Imaging Spectroradiometer (MODIS) LAI/FPAR products. The results indicated that the FAPARFVC approach demonstrated superior accuracy compared to the FAPAR determined by MODIS LAI, according to the Beer-Lambert’s law (FAPARLAI) and MODIS FPAR products (FAPARMOD). The mean absolute percentage error of FAPARFVC was 48.2%, which is 25.6% and 52.1% lower than that of FAPARLAI and FAPARMOD, respectively. The mean percentage error of FAPARFVC was 16.8%, which was 71.6% and 73.4% lower than that of FAPARLAI and FAPARMOD, respectively. The improvements in accuracy and the decrease in overestimation for FAPARFVC became more pronounced with increasing FVC compared to FAPARLAI. The findings suggested that the FAPARFVC method enhanced the accuracy of FAPAR estimation under the presence of non-green vegetation canopies. The method can be extended to regional scale FAPAR and gross primary production (GPP) estimations, thereby providing more accurate inputs for understanding its tempo-spatial patterns and drivers.

Phenological models are valuable tools for predicting vegetation phenology and investigating the relationships between vegetation dynamics and climate. However, compared to temperate and boreal ecosystems, phenological modeling in alpine regions has received limited attention. In this study, we developed a semi-mechanistic phenological model, the Alpine Growing Season Index (AGSI), which incorporates the differential impacts of daily maximum and minimum air temperatures, as well as the constraints of precipitation and photoperiod, to predict foliar phenology in alpine grasslands on the Qinghai-Tibetan Plateau (QTP). The AGSI model is driven by daily minimum temperature (Tmin), daily maximum temperature (Tmax), precipitation averaged over the previous month (PA), and daily photoperiod (Photo). Based on the AGSI model, we further assessed the impacts of Tmin, Tmax, PA, and Photo on modeling accuracy, and identified the predominant climatic controls over foliar phenology across the entire QTP. Results showed that the AGSI model had higher accuracy than other GSI models. The total root mean square error (RMSE) of predicted leaf onset and offset dates, when evaluated using ground observations, was 12.9 +/- 5.7 days, representing a reduction of 10.9%-54.1% compared to other models. The inclusion of Tmax and PA in the AGSI model improved the total modeling accuracy of leaf onset and offset dates by 20.2%. Overall, PA and Tmin showed more critical and extensive constraints on foliar phenology in alpine grasslands. The limiting effect of Tmax was also considerable, particularly during July-November. This study provides a simple and effective tool for predicting foliar phenology in alpine grasslands and evaluating the climatic effects on vegetation phenological development in alpine regions. (sic)(sic) (sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic) (sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic).(sic)(sic),(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic),(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic).(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(GSI),(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic),(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)--(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(AGSI).(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(T-min),(sic)(sic)(sic)(sic)(sic)(T-max),(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(PA)(sic)(sic)(sic)(sic)(Photo)4(sic)(sic)(sic)(sic)(sic).(sic)(sic)AGSI(sic)(sic),(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)4(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic),(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic).(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic),(sic)(sic)(sic)(sic)AGSI(sic)(sic)(sic)(sic)(sic)(sic)GSI(sic)(sic)(sic)(sic)(sic)(sic),AGSI(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(RMSE)(sic)12.9 +/- 5.7(sic),(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)10.9%-54.1%, (sic)(sic)T-max(sic)PA(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)20.2%.(sic)(sic)AGSI(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic),4(sic)(sic)(sic)(sic)PA(sic)T-min(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic), (sic)T-max(sic)7(sic)11(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic).(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic).

Alpine grasslands in the Three River Headwater (TRH) region have suffered serious degradation owing to global climate change and human activity. Fencing is a major countermeasure implemented in the TRH region by the Ecological Protection and Restoration Program (EPRP) launched by the Chinese government. Fencing from the EPRP was to guarantee prohibited grazing during the growing season and rotation grazing during the cold season in the cold-season pasture. However, fencing excluded grazing over the entire growing season in previous studies, which was quite different from the EPRP. Thus, the protective effect of fencing from the EPRP in the TRH region cannot be confirmed based on previous studies. This study presents trends in vegetative and reproductive branch heights, vegetation cover, and aboveground biomass from 2005 to 2017, using ordinary least squares regression based on field observation data from 39 fenced sites from the EPRP in the TRH region. The results indicated that vegetative branch height, reproductive branch height, and vegetation cover decreased significantly by 34.8%, 38.2%, and 5.4%, respectively, over the study period ( P < 0.05). The biomass proportion of Gramineae and Cyperaceae decreased by 48.2% and 23.9%, respectively ( P < 0.05), whereas those of poisonous weeds and edible forbs increased by 170.3% and 42.0% ( P < 0.10), respectively. This indicated a decrease in grassland quality at the fenced sites from the EPRP. The decrease in grassland quality may have been mainly caused by severe livestock overloading during the cold season. A competitive edge from forbs and poisonous weeds under fencing in degraded alpine grasslands may have further exacerbated grassland degradation. These results suggest that fencing in cold-season pasture from the EPRP did not achieve the objective of restoring grasslands under severe livestock overloading in the TRH region over the study period. These findings provide a significant basis for improving ecological protection and restoration policies in the TRH region. (c) 2025 The Authors. Published by Elsevier Inc. on behalf of The Society for Range Management. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)

Temporal changes in autumn leaf senescence dates and their drivers across meadows, steppes, and cultivated vegetation on the Qinghai–Tibetan Plateau (QTP) remain poorly understood. Using long-term ground observation data from 12 sites, we analyzed interannual change trends in leaf senescence date and quantified the influences of climatic factors (i.e., temperature, precipitation, and sunshine duration), soil moisture, and spring phenology on its shifts. Meadows showed a significant delaying trend (0.891 days/year), mainly driven by substantial increases in air temperature and precipitation during the growing season, particularly in the late season. Steppes exhibited an overall insignificant delaying trend (0.214 days/year), where the delaying effects of increased precipitation and reduced sunshine duration were partly offset by the late-season soil moisture decline. In cultivated vegetation, a slight advance in leaf senescence date (−0.108 days/year) was primarily caused by earlier spring green-up date, supplemented by increased temperature and precipitation during the growing season, which led plants to grow earlier and complete their life cycles sooner. Climatic influences were stronger in the late season for meadows but greater in the early season for steppes. Compared with meadows and steppes, spring phenology had the strongest effect on the timing of leaf senescence in cultivated vegetation. These findings advance our understanding of vegetation phenological shifts on the QTP and highlight the necessity of incorporating the notable differences across vegetation types into both phenological models and ecosystem management practices.

Plant phenological shifts on the Qinghai-Tibetan Plateau (QTP) have gained considerable attention over the last few decades. However, temporal changes in plant autumn phenology and the main driving factors remain un-certain. Most previous studies used satellite-derived phenological transition dates and climatic statistics during the preseason, which have relatively large uncertainties and may mask some important climate change char-acteristics at the intra-annual scale, thus affecting exploration of the underlying phenological change causes. This study collected 1685 phenological records at 27 ground stations on the QTP during 1983-2017. Temporal change trends and break points in leaf senescence date (LSD) of 23 herbaceous species were assessed using least squares regression, a meta-analysis procedure, and the Pettitt test. The main drivers and causes were investigated through correlation analysis and contribution calculation based on LSD observations and monthly climatic data. Results showed that, LSD of QTP herbaceous plants was significantly delayed at a rate of 4.45 days/decade during 1983-2017. Break points were concentrated during 1999-2003, with an overall mean in 2001. After 2001, the delay trend in LSD decreased, falling from 5.26 days/decade to 2.54 days/decade. Air temperature and precipitation were the most important climatic factors that showed closer and more extensive correlations with LSD and greater contributions to the inter-annual variations in LSD. August and September were the most critical period during which climatic factors had higher contributions to the LSD shifts. However, August was drier, with precipitation significantly decreasing and temperature increasing, and September was colder after 2001. Therefore, the declining trend in LSD may be attributed to the drier August and colder September. This study has not only provided reliable field evidence on temporal changes in autumn phenology on the QTP, but has also provided valuable insights into autumn phenological modelling and regional carbon cycling in alpine regions.

Mapping vegetation formation types in large areas is crucial for ecological and environmental studies. However, this is still challenging to distinguish similar vegetation formation types using existing predictive vegetation mapping methods, based on commonly used environmental variables and remote sensing spectral data, especially when there are not enough training samples. To solve this issue, we proposed a predictive vegetation mapping method by integrating an advanced machine learning algorithm and knowledge in an early coarse-scale vegetation map (VMK). First, we implemented classification using the random forest algorithm by integrating the early vegetation map as an auxiliary feature (VMF). Then, we determined the rationality of classified vegetation types and distinguished the confusing types, respectively, based on the knowledge of the spatial distributions and hierarchies of vegetation. Finally, we replaced each recognized unreasonable vegetation type with its corresponding reasonable vegetation type. We implemented the new method in upstream of the Yellow River based on GaoFen-1 satellite images and other environmental variables (i.e., topographical and climate variables). Results showed that the overall accuracy using the VMK method ranged from 67.7 % to 76.8 %, which was 10.9 % to 13.4 % and 3.2 % to 6.6 %, respectively, higher than that of the method without the early vegetation map (NVM) and the VMF method, based on cross-validation with 20 % to 60 % random training samples. The spatial details of the vegetation map using the VMK method were also more reasonable compared to the NVM and VMF methods. These results indicated that the VMK method can distinctly improve the mapping accuracy at the vegetation formation level by integrating knowledge of existing vegetation maps. The proposed method can largely reduce the requirements on the number of field samples, which is especially important for alpine mountains and arctic region, where collecting training samples is more difficult due to the harsh natural environment.

Water quality in the Three-River Headwaters Region (TRHR) is important for downstream water security. The soil net nitrogen mineralization (SNNM) rate directly affects nitrogen losses in the soil and river inputs. In this research, in situ nitrogen mineralization control experiments were performed on one- to six-year-old artificial grasslands from restoration projects and the surrounding black-soil-type grasslands from July to August 2022 to determine the temporal variation in the SNNM rate and major impact factors. The results showed that the SNNM rates were 0.847 ± 0.547 and 0.235 ± 0.114 mg·kg-1·d-1 in one- and two-year-old artificial grasslands, respectively, and changed from 0.089 to 0.070 mg·kg-1·d-1 in three- to six-year-old artificial grasslands, indicating a hyperbolic function. However, the SNNM rates in the black-soil-type grasslands ranged from 0.047 to 0.334 mg·kg-1·d-1 and exhibited no clear trend. The soil net nitrification rate was the dominant form, with an average of 94.3% of SNNM in artificial grasslands and 93.1% in black-soil-type grasslands, and exhibited a temporal pattern mirroring that of the SNNM rate. Changes in soil chemical properties related to soil carbon (P <0.05), nitrogen (P <0.05), and pH (P <0.10) significantly affected the SNNM rate differences between artificial grasslands and black soil grasslands; soil nitrogen had the greatest effect (28.6%), followed by soil carbon (27.1%). Alterations in soil physical attributes, including bulk density, water content, and temperature, did not significantly affect SNNM rate changes. The results suggest that artificial grassland plantations from black-soil-type grasslands for ecological restoration might induce rapid soil nitrogen loss from fertilization in the TRHR. The findings are significant for understanding the mechanisms behind the recent rapid increase in total nitrogen concentration in TRHR rivers and for policymaking to balance the conflict between artificial grassland plantations and aquatic environment protection in this area.

Understanding of the influences of soil moisture changes on plant phenological shifts on the Qinghai–Tibetan Plateau (QTP) is insufficient mainly because previous studies focused on the climatic factors. We explored the role of soil moisture in regulating plant autumn phenology on the QTP. Based on long-term ground observations of soil moisture, plant phenology, and meteorology, temporal and spatial changes in soil moisture and leaf senescence dates (LSD) were analyzed using ordinary least squares regression and a meta-analysis procedure. Influences of soil moisture changes on the LSD shifts were assessed through correlation analysis and support vector machine, and also compared with those of air temperature and precipitation. Nonsignificant interannual changes in soil moisture were observed, and LSD significantly delayed at a rate of 2.7 days/decade. Spatial changes of LSD were more correlated with site elevation and air temperature, and soil moisture and precipitation showed insignificant negative impacts. However, correlations between annual LSD and average soil moisture were mainly positive. Soil moisture and precipitation showed greater importance in regulating the LSD of sedges and grasses, whereas temperature exerted a larger influence on the LSD of forbs. Precipitation showed higher importance in regulating the interannual shifts in LSD, while temperature played a more important role in determining the spatial variations. Soil moisture had divergent influences on the temporal and spatial shifts in LSD of different plant functional groups on the QTP. Overall, soil moisture was outweighed by temperature and precipitation in regulating autumn phenological shifts. However, soil moisture may become increasingly important in the future and forbs are expected to be more competitive if the QTP becomes warmer and drier, which will bring challenges in grassland management and utilization on the QTP.

Economic development has historically led to environmental challenges, notably in China where fine particulate matter with an aerodynamic diameter no greater than 2.5 mu m (PM2.5), has significantly influenced human health and social issues. However, the scarcity and uneven distribution of ground-based PM2.5 observation sites hinder studies about air pollution impacts at regional and national scales. Although PM2.5 datasets based on remote sensing retrieval algorithms have provided long-term and high-resolution gridded near surface PM2.5 concentration data recently, comparisons on accuracy between datasets were not conducted by previous studies. This study evaluated eight publicly accessible PM2.5 datasets (i.e., CHAP, GHAP, GWRPM25, HQQPM25, LGHAP v1, LGHAP v2, MuAP, and TAP) across China using independent records at 1020 monitoring sites from 2017 to 2022 at monthly and annual granularities. Mean Absolute Errors (MAEs) showed a seasonal trend, with higher errors in winter and lower in summer. Datasets exhibited a bias towards overestimation or underestimation based on concentration levels. CHAP, GWRPM25, and HQQPM25 had better estimation control. Additionally, the incorporation of spatiotemporal features into original machine learning based algorithms was likely credited to the outperformance compared to conventional PM2.5 simulation methods. Overall, this study contributed to comprehensive references for PM2.5 concentration dataset users and potential explanations to the variations within and among datasets.

The in-situ leaf area index is important for remote sensing and ecological process model validation. A few studies have adopted the digital hemispherical photograph method to calculate the in-situ leaf area index in grasslands with a camera oriented toward the sky or downward. However, few studies have validated this method for grasslands with short and dense canopies. An experiment was conducted to measure the leaf area index of short and dense alpine grasslands in the Three-River Headwaters region (China). The results showed that the accuracy of the estimated leaf area index was low, greatly overestimating the actual values. The mean absolute percent error and root mean square error of “true” leaf area index estimated using the digital hemispheric photography method were 373.9–452.7% and 3.0–4.6, respectively, as compared to leaf area index directly measured through harvesting. Almost all plots were on the upper side of the 1:1 regression line, with a mean percent error ranging from -442.52 to -281.03%. The root square error and absolute percentage error increased from 0.61 to 6.20 and from 94.15% to 658.68%, respectively, as the vegetation cover fraction increased from 4.9% to 99.7%. The low accuracy and high overestimation may be related to the failure to accurately capture the gap fraction due to the method’s limited image resolution. These results indicate that the digital hemispheric photography method is unsuitable for the measurement of in-situ leaf area index in grasslands with short, dense canopies. Leaf area index estimation using image methods is also not recommended when the vegetation canopy is dense but not short, considering the limitations of image resolution. Future work should instead consider analyzing the biochemical processes of leaves from an ecological perspective to estimate the in-situ leaf area index of grasslands with short and dense canopies.

Mapping the spatial distribution of artificial grassland for ecological restoration is of great significance for evaluating its secondary degradation and negative consequences, such as nonpoint source pollution of water bodies in the Three-River Headwaters (TRH) region. Because of the numerous challenges faced in obtaining ground training samples caused by adverse natural conditions, inclement cloudy weather and spectral similarity between natural and artificial grassland, commonly used classification or temporal-profile extraction methods have proven ineffective in identifying artificial grasslands. To overcome these challenges, we present a novel artificial grassland detection index for mapping their distribution using optical images with a resolution of 10 ∼ 30 m, along with their corresponding quality control data based on the Google Earth Engine cloud computing platform. The index is calculated using the ratio of the normalized difference vegetation index during the sowing and emergence period and the growth peak period of artificial grassland. A case study was conducted in Maqin County in the TRH region covering an area of 1.35 × 104 km2 from 2017 to 2021. Our proposed method demonstrated high accuracy and achieved a favorable balance between commission errors and omission errors. Over the study period, the average overall accuracy and Cohen's kappa were 96.2% and 0.91, respectively; with average precision, recall, and F1-score of artificial grassland being 89.6%, 99.2%, and 94.0%, respectively. The proposed method exhibited excellent robustness for the critical threshold used, with the average overall accuracy, F1-score, precision, and recall of artificial grassland between 2017 and 2021 consistently exceeding 90% for threshold values ranging from 1.5 to 2.0 throughout the study period. These findings suggest that our proposed method is capable of efficiently and accurately obtaining the detailed spatiotemporal distribution of artificial grassland in the TRH region. Moreover, the method also meets the pressing requirement for the rapid acquisition of detailed spatiotemporal distribution of artificial grassland across the Qinghai-Tibet Plateau.

The pathway, direction, and potential drivers of the evolution in global arid ecosystems are of importance for maintaining the stability and sustainability of the global ecosystem. Based on the Climate Change Initiative Land Cover dataset (CCILC), in this study, four indicators of land cover change (LCC) were calculated, i.e., regional change intensity (RCI), rate of change in land cover (CR), evolutionary direction index (EDI), and artificial change percentage (ACP), to progressively derive the intensity, rate, evolutionary direction, and anthropogenic interferences of global arid ecosystems. The LCC from 1992 to 2020 and from 28 consecutive pair-years was observed at the global, continental, and country scales to examine spatiotemporal evolution in the Earth’s arid ecosystems. The following main results were obtained: (1) Global arid ecosystems experienced positive evolution despite complex LCCs and anthropogenic interferences. Cautious steps to avoid potential issues caused by rapid urbanization and farmland expansion are necessary. (2) The arid ecosystems in Australia, Central Asia, and southeastern Africa generally improved, as indicated by EDI values, but those in North America were degraded, with 41.1% of LCCs associated with urbanization or farming. The arid ecosystems in South America also deteriorated, but 83.4% of LCCs were in natural land covers. The arid ecosystems in Europe slightly improved with overall equivalent changes in natural and artificial land covers. (3) Global arid ecosystems experienced three phases of change based on RCI values: ‘intense’ (1992–1998), ‘stable’ (1998–2014), and ‘intense’ (2014–2020). In addition, two phases of evolution based on EDI values were observed: ‘deterioration’ (1992–2002) and ‘improvement’ (2002–2020). The ACP values indicated that urbanization and farming activities contributed increasingly less to global dryland change since 1992. These findings provide critical insights into the evolution of global arid ecosystems based on analyses of LCCs and will be beneficial for sustainable development of arid ecosystems worldwide within the context of ongoing climate change.

Plant autumn phenology affects ecosystem carbon and water cycles. However, autumn phenological shifts of herbaceous plants and their primary regulators on the Qinghai-Tibetan Plateau (QTP) remain uncertain because previous studies were mainly based on remote sensing data. Studies using ground autumn phenological data observed at many stations are very few. This study explored the primary drivers of temporal shifts in leaf senescence date (LSD) and assessed the relative importance of climatic factors and spring phenophases to LSD shifts using correlation analysis, general linear regression, and partial least squares regression based on 1685 phenological records at 27 stations on the QTP. Results showed that, preseason total precipitation (PRE) and minimum air temperature (Tmin) had higher correlation coefficients with LSD and displayed more extensive importance in predicting LSD than other factors. Despite larger influences, contributions of PRE and Tmin to LSD inter-annual variations were only 14.9% and 14.2%, respectively. In total, PRE and air temperatures (TEMs) just explained 38.0% of LSD temporal shifts. These results indicate that although PRE and TEMs are the major regulators of LSD, temporal shifts in LSD are controlled by multiple factors. Overall, climatic factors and spring phenology contributed 53.7% and 15.6% to LSD variations, respectively, which means that climate change obviously outweighed spring phenology in driving LSD shifts. Our study revealed a critical role of PRE in regulating LSD shifts directly or indirectly by enhancing the sensitivity of LSD to TEMs. Moreover, we found that the impacts of precipitation change were more prominent on LSD of sedges than those of grasses and forbs. Therefore, we suggest that accurate autumn phenological models for plants on the QTP should include multiple factors and incorporate the influences of precipitation. This study can also provide insight into QTP grassland management from the perspective of phenological responses to climate change.

The soil surface nitrogen balance (SSNB) method is commonly used to assess the nutrient use efficiency (NUE) of agricultural systems and any associated potential environmental impacts. However, the nitrogen flow of wide natural grasslands and other natural areas differ from that of artificial croplands and mown grasslands. In this study, we integrated root growth and the important nutrient resorption process into the SSNB model and used the improved model to clarify the nitrogen (N) flow and balance in the Three Rivers Headwater Region (TRHR)-an area dominated by alpine meadows-from 2012-2019. In the grassland system, the N surplus (Delta N) was 0.274 g m- 2 year -1, and root return (BLD) dominated the N input, accounting for 67% of the total input (3.924 g m- 2 year -1). N resorption was the main internal N flow in the grassland system (1.079 g m- 2 year -1), and 30% of grassland uptake (NUP- grass). The Delta N of the agricultural system was 1.097 g m- 2 year -1, which was four times that of the grassland, and chemical fertilizer was the largest input, accounting for 84% of the total input. The NUE in grassland was 93%, which suggests a risk of soil mining and degradation, while that of cropland was 76% and within an ideal range. The Delta N provides a robust measure of river N export, the TRHR was divided into three catchments, and the export coefficient was 16.14%-55.68%. The results of this study show that the improved SSNB model can be applied to a wide range of natural grasslands that have high root biomass and resorption characteristics.