Deep learning approaches have gained prominence for automatic glacier boundary extraction due to their localized nature of convolutional operations, potentially leading to incomplete or fragmented glacier pixel representations. Moreover, the accuracy of extracting glacier boundaries from a single remote sensing image (RSIs) is often influenced by seasonal snow, clouds, shadows, and frozen lakes. To overcome these challenges, we introduce a novel model for extracting the non-debris-covered areas of glaciers (NDCAG) from RSIs, termed GlacierSTR-UNet. This model enhances information flow and overall performance by embedding the Swin Transformer (ST) as an encoder into a U-shaped architecture and reduces training time and improves gradient handling by incorporating the ResNet block in the decoder. We deploy the GlacierSTR-UNet model on the Google Earth Engine (GEE) platform to efficiently generate multiple NDCAG results from RSIs taken at different periods. A pixel-by-pixel synthesis algorithm is then applied to aggregate the multiple NDCAG extraction results, producing the final NDCAG. Accuracy assessments indicate that GlacierSTR-UNet achieves an overall accuracy of 0.8817, and the relative deviation between automatically extracted and manually interpreted NDCAG remains within 2 %. Finally, we obtain the NDCAG datasets for the periods of 2015/2016 and 2022/2023 in HighMountain Asia, revealing a reduction of 4,185.12 +/- 7,870.96 km2 in NDCAG from 2015/2016 to 2022/2023. These findings demonstrate the effectiveness of our approach in efficiently and accurately extracting NDCAG, highlighting its potential for monitoring glacier changes and supporting glacier inventory efforts.

The Puruogangri Ice Field (PIF), classified as an ultra-continental glacier, is considered extremely stable. However, several glaciers in this area have recently experienced surge events with significant instability and information on surge-type glaciers (STGs) in this region remains scarce. In this study, we identified six STGs and reported the observed characteristics of their surging behavior in the region by mapping glacier boundaries, surface flow velocity information, and glacier surface elevation changes using recent Landsat satellite imagery and shuttle radar topography mission (SRTM), TanDEM, and ASTER digital elevation model (AST14DEM) data. These data provide valuable insights into recent glacial processes, flow instability, and rapid glacial movement. During the active phase of the glaciers, all exhibited frontal advances and changes in surface elevation. Owing to limitations in the satellite imagery, flow velocity profiles were only available for glaciers N1 (G089071E33998N), NE1(G089128E33943N), and SE3 (G089278E33913N) during the active phase. However, these results effectively reflect the velocity variations in both glaciers before, during, and after the surge. Based on the characteristics of the STG, scientific expeditions, and meteorological data, we believe that the surge in PIF was largely influenced by glacier meltwater and changes in subglacial drainage systems.

Quantifying the change of drought and their driving mechanisms is essential for monitoring land surface environmental changes and for understanding the land–atmosphere interaction in the arid region. Most researches are limited to average climate change, whereas the response analysis of extreme climate, the interaction of different climate factors and human activities to changes in drought are still lacking or not comprehensively considered. Firstly, we employed the temperature vegetation dryness index (TVDI), which associates land surface temperature with vegetation cover, to explore the spatiotemporal variation of drought across seasons in Qinghai province. Then, we specifically quantify the contribution of different climate factors and human activities to surface drought. Our findings revealed that drought exhibited upchange covering 73.71

Monitoring glacier velocity is crucial for revealing glacier dynamics and clarifying the responses of glaciers to climate change. The West Kunlun Mountain (WKM) is at the center of the Karakoram Anomaly; however, insufficient systematic studies have been conducted to determine glacier velocities in this region. This study reports annual and seasonal variations in glacier velocity and the characteristics of glacier surges in the WKM between 2013 and 2023 using multisource remote sensing data. Results show that the annual average velocity exhibits a slightly increasing trend (0.48 ± 0.16 m/yr), mainly owing to increasing glacier thickness. Larger glaciers flow faster than smaller ones, and glaciers with northern and southern slopes flow faster than those with slopes in other directions. Glacial velocities in 2016 and 2021 were 13.78 ± 3.54 and 13.65 ± 2.56 m/yr, respectively, which are higher than those in neighboring years. Velocities are approximately 4.3 m/yr higher during the warm season than in the cold season. Temperature and precipitation are the primary factors influencing annual and seasonal variations in glacier velocities. In total, 10 verified surging glaciers were observed during the period 2013–2023, with peak velocities ranging from 42.95 to 865.16 m/yr. The active, accelerating, and decelerating phases of b1 of and b2 of West Kunlun as well as Alakesayi, Zhongfeng, and G2 lasted approximately 3–6 years. The active phase of G11 may have lasted more than 23 years, whereas the active phases of Gongxing, Litian, G24, and G26 lasted no longer than 2 years.

The leaf area index (LAI) of grassland is critical for estimating the balance of livestock and livestock production, understanding the dynamics of climate change, and providing feedback for achieving sustainable development. The currently available LAI products have some uncertainties and need to be further improved. Previous studies proposed that integrating the physical model and machine-learning (ML) has great potential for the rapid and accurate retrieval of grassland LAI. However, there are few comparative studies on LAI forecast models for different grassland cover to assess the potential of the different hybrid models. Therefore, in this study, five hybrid models based on PROSAIL and ML including Deep Neural Network (DNN), Random Forest (RF), Gradient Boosting Regression Tree (GBRT), Support Vector Machine (SVR) and Artificial Neural Network (ANN) and five mixed models averaging are applied to compare the performance with different forecast models for grassland LAI estimation in Tianzhu County. According to the multiple training, validation and testing, the results demonstrate that five mixed models averaging and DNN model with a complex network structure are reliable and have higher accuracy and better performance than the estimates from the other four hybrid models, except for its computational efficiency. SVR achieves the best performance in computational efficiency, which it has great potentials to deliver near-real-time operational products for grassland LAI management. Our results show that the hybrid model based on machine learning algorithm coupled with physical process model has great application potential in grassland leaf area index inversion.

The spatiotemporal variations of water use efficiency(WUE)and its relationship with drought and land surface temperature(LST)on the Loess Plateau are crucial for assessing the maximum vegetation carrying ca-pacity in this region,known as the most severely eroded area globally and the largest greening area in China.This study employed Theil-Sen trend analysis and the first-order differencing relative contribution method to examine the spatiotemporal changes in WUE across different seasons on the Loess Plateau from 2001 to 2021 and to evalu-ate the contributions of drought and LST to these changes.The results reveal that:(1)The average WUE values in spring and autumn are below 2.0 g C·m-2·mm-1,while in summer,the average WUE exceeds 2.0 g C·m-2·mm-1.In spring and autumn,WUE is higher in cultivated land and forest areas compared to grassland areas,whereas in sum-mer,WUE is lowest in cultivated land,followed by forest areas,and highest in grassland.(2)WUE remains stable in spring and summer,displaying a spatial distribution of"reduction in the central part,stability in the western and eastern parts."The rate of decline in WUE is greater in grassland areas than in forest and cultivated land areas.In autumn,WUE shows an increasing trend,with a higher rate of increase observed in grassland areas than in forest and cultivated land areas,exhibiting a spatial pattern of"increase in the northwest,decrease in the southeast."(3)In spring and summer,LST positively contributes to WUE changes,with the most significant impacts in grass-land areas.In autumn,LST negatively affects WUE in grassland and forest areas but positively influences WUE in cultivated land areas.Drought positively contributes to WUE changes in spring and autumn,while it negatively affects WUE in summer.These findings enhance the understanding of the interactions between drought,LST,and water resources in the context of climate change and ecological restoration efforts on the Loess Plateau.

The middle reaches of the Yellow River basin (MYRB) are among the regions most severely affected by soil erosion globally. It has always held a pivotal role in soil and water conservation and ecological restoration efforts in China. Nonetheless, in the face of recurrent drought occurrences and growing human intervention, there have been notable alterations in the eco-environmental quality (EEQ) within the MYRB. However, the influences of drought and human intervention on the EEQ of MYRB remain unclear. In this study, the remote sensing ecological index (RSEI) was applied to quantify the spatiotemporal changes in EEQ and the contributions of drought and land use type transitions to EEQ in the MYRB from 1990 to 2022. The results showed that the EEQ in the MYRB fluctuated significantly and exhibited a weak overall improvement trend over the past 33 years. The proportion of good and excellent grades for EEQ significantly improved, while the proportion of poor and fair grades significantly decreased, especially in the northern regions. The trend of changes in EEQ follows a phased pattern. During the periods of 1990 - 2002 and 2011 - 2022, an improving trend is observed, while the period of 2003 - 2010 shows no significant change in EEQ. Drought had the strongest influence on the EEQ from 2003 to 2010, followed by that from 1990 to 2002, and had a lesser impact in the period from 2011 to 2022. The EEQ was primarily positively influenced by spring, autumn and winter droughts and negatively affected by summer droughts, especially in arid grassland and unused land areas. The EEQ in the MYRB improved primarily during the initial and final phases of the ecological projects, with a lesser impact from drought. The increase in EEQ during the initial phases of ecological project implementation was less noticeable, and drought significantly affected EEQ during this period.

研究我国草地植被生长季始期及末期对极端降水事件变化的响应是理解区域陆地生态系统生产力历史和未来变化机制的关键.本文利用草地、物候和极端降水数据,辅以Sen趋势分析和Pearson相关分析法研究了1986-2015年我国不同极端降水指标变化趋势及其对草地物候影响.结果表明,(1)我国极端降水事件的水量和强度都有显著增加的趋势,从空间分布来看,西部特别是西北地区有较明显增长.(2)草地植被生长季始期与长时序低温事件、持续干旱事件和极端强降水事件之间有显著相关性.(3)草地植被生长季末期与极端强降水,长时序极端干旱、湿润之间具有显著相关性.此研究对科学分析极端降水变化及其对植被物候的影响,促进植被生态恢复,制定行之有效的防灾减灾措施、构建稳定的生态屏障具有指导意义.

石羊河流域是河西典型的干旱内陆河流域,其生态本底极为敏感脆弱,探究该地区草地植被净初级生产力(NPP)数量和分布变化及其对气候的响应,对该地区草地管理等具有重要意义.本研究基于草原综合顺序分类系统(CSCS)改进的CASA模型模拟了 2000-2020年石羊河流域草地NPP,并辅以Sen's斜率、变异系数(CV)和Hurst指数探究了 NPP时空动态、变化趋势、变化稳定性、未来变化趋势,并通过偏相关分析方法分析了积温和降水与NPP之间的相关关系.结果表明:1)草地年均NPP为170.24g·(m2·a)-1,10年NPP增量为28.96 g·m-2,呈波动上升趋势,未来一段时间内NPP还会有所增加.2)草地类中,年均NPP最高的是山地草甸类(ⅡE30),年均548.74 g·(m2·a)-1;其次为山地草甸草原类(ⅡD23),年均为454.50 g·(m2·a)-1;年均NPP最低的是温带荒漠类(ⅣA4),年均91.65 g·(m2·a)-1.3)石羊河流域草地整体稳定,中波动草地占据主体地位,仅温带典型草原类(ⅢC17)存在较高波动.4)多数草地NPP增加的主导因素是降水,对降水响应敏感的区域占流域面积的24.93％,仅两类荒漠草地(ⅢA3和ⅣA4)对降水响应关系不明显;草甸类草地NPP对积温响应最为敏感,荒漠类草地与积温呈现一定的负相关关系.

使用机器学习算法快速、准确、大范围监测草地地上生物量(AGB)是目前研究热点,但不同机器学习算法因训练样本、超参数设置不同而存在较大差异.基于实测草地AGB和同期遥感数据、气象数据、地形数据,选择与草地AGB相关性较强的13个因子作为深度神经网络(DNN)、随机森林算法(RF)、梯度提升回归树(GBRT)、支持向量机(SVR)、人工神经网络(ANN)和高斯过程回归(GPR)算法的输入变量,建立草地AGB预测模型并从模型预测精度、稳定性、样本敏感性等方面综合评价6种模型应用潜力,分析2020年天祝藏族自治县生长季(4-9月)内草地AGB时空变化特征及其对气候的响应.结果表明:1)DNN估算草地AGB的综合性能最佳,但稳定性较差,对样本敏感性较高;GPR综合性能次于DNN,稳定性和精度均较好;GBRT、RF模拟精度较高,稳定性差;SVR和ANN精度相对其他模型较差,SVR稳定性较高,ANN稳定性较差.2)天祝藏族自治县草地AGB集中在50～250 g·m-2,不同月份草地AGB空间异质性较大,整体表现为从西北向东南呈下降趋势.3)山地草甸、高寒草甸和温性草原中的AGB变化与气温表现出较为明显的正相关关系.降水量对高寒草甸、温性草原和山地草甸的影响不明显,但对温性荒漠草原类的影响较大,AGB随降水量减少呈现减少态势.以上研究结果可为监测草地生物量的方法选择和参数设置提供一定技术支持和参考依据.

Studying grassland vegetation growing seasons’ spatial patterns and their environmental controls are crucial to promoting vegetation ecological restoration, formulating effective disaster prevention and reduction measures and building a stable ecological barrier. However, multi-grassland phenological factors are different, and this has not been well studied before. In this paper, the spatiotemporal patterns of the start of the growing season (SOS) and the end of growing season (EOS) in the grassland were investigated using the Normalized Difference Vegetation Index (NDVI) on the Qinghai–Tibetan Plateau (QTP) from 2000 to 2019. At the same time, we analyzed the environmental factors (including extreme, mean climate, drought, solar radiation, etc.) regulating grassland phenology under the ongoing conditions of climate change. The results show that the SOS appeared first in mountain meadow, shrub tussock, temperature steppe and desert, and then in alpine steppe and alpine meadow, showing a significant advancing tendency in all types. The EOS occurred first in the temperature steppe, alpine steppe and alpine meadow, and then in the mountain meadow, shrub-tussock and desert. Further analysis indicated that reductions in the annual lowest value of daily min temperature (TNN), annual highest value of daily min temperature (TNX) and temperature vegetation dryness index (TVDI), and increases in annual maximum consecutive 5‐day precipitation (RX5day) advanced the grassland spring phenology, whereas the increase in solar radiation (SR) delayed the grassland spring phenology. We also found that the decrease in TVDI and TNN and the increase in yearly mean value of temperature (MAT_MEAN), yearly mean value of daily maximum temperature (MAT_MAX) and yearly mean value of daily minimum temperature (MAT_MIN) advanced the autumn phenology. The EOS and its change rate advanced and increased with increasing altitude, respectively.

以C为驱动的WOFOST作物生长模型是基于作物生理生态过程,综合考虑了CO2、土壤、气候等因素对产量的胁迫作用,因此,对WOFOST模型参数进行本地化和优化便可实现时间连续且高精度的草地生物量监测.为探讨WOFOST参数敏感性分析结果在不同草地类型覆盖区表现出的不确定性问题,在天祝藏族自治县不同草地覆盖区选择了4个站点,利用气象数据、草地实测数据及土壤数据,基于扩展傅里叶幅度敏感性检验法(EFAST)研究潜在水平(指保证营养元素和水分为最佳供应,草地地上生物量仅由辐射、温度和作物特性决定)和水分限制水平(假设营养元素的供给仍然是最佳的,但需考虑土壤有效水分对蒸发和草地生物量的影响)下WOFOST模型在不同草地类型覆盖区的全局敏感性参数和优化模型模拟精度.结果表明潜在生产水平下草地地上生物量(AGB)的敏感参数有比叶面积(SLATB)、单叶片CO2的初始光能利用率(EFFTB)、最大光合速率(AMAXTB)、根相对维持呼吸速率(RMR)、总干物质占根和叶的比例(FRTB和FLTB),水分限制条件下的敏感参数有SLATB、AMAXTB、RMR和FLTB.不同生产水平下叶面积指数(LAI)的敏感参数一致,从出苗到出苗后60 d主要受到SLATB、FLTB和FRTB的影响,出苗后60～200 d的敏感性参数为FLTB、FRTB、SLATB和漫射可见光的消光系数(KDIFTB),LAI开始下降后受到KDIFTB的敏感性增强.其中,山地草甸AGB的模拟值与观测值模拟精度最高,R2=0.94、RMSE=11.71 g·m-2,高寒草甸模拟精度最低,R2=0.83、RMSE=32.68 g·m-2.温性荒漠草原LAI的模拟值与观测值模拟精度最高,R2=0.96、RMSE=0.02,温性草原模拟精度最低,R2=0.66、RMSE=0.38.敏感性分析方法在WOFOST模型中的应用减少了人为主观因素的影响,极大地缩短了调参时间,对获取时间连续的草地生长监测方法选择提供参考.

Drought indicators based on remote sensing include the temperature vegetation dryness index (TVDI) and the crop water stress index (CWSI). The data was processed using TVDI, which was calculated by parameterizing the MODIS EVI and LST data connection. For drought monitoring, we compared the efficiency of TVDI with that of CWSI, which is obtained from the MOD16A2 products. The study's findings revealed that drought conditions measured by TVDI and CWSI had a number of differences and similarities, which indicated that both CWSI and TVDI can be used for drought monitoring, although they had some discrepancies in the spatiotemporal characteristics of drought intensity in this region. High TVDI values were mainly concentrated on the northwestern Sichuan Plateau and mountainous areas of southwestern Sichuan, corresponding to extreme drought. The Panzhihua and the mountainous area of southwestern Sichuan had relatively high CWSI values. Spring had the highest TVDI values, followed by autumn and winter. TVDI and CWSI have different patterns, showing moderate and severe drought conditions in different areas. Overall, CWSI values showed a significant decreasing trend (P < 0.05) from 2001 to 2020. The overall trend change of TVDI was insignificant, mainly based on an insignificant increase and an insignificant decrease. An extremely significant decreasing trend, mainly concentrated in the eastern Sichuan basin plain, accounted for 15.54% of the entire province. Spring, summer, autumn, and winter account for 74.33%, 59.15%, 68.28%, and 64.87% of Sichuan Province, respectively, in total area change. The eastern Sichuan basin plain showed a significant increasing trend, accounting for 1.69% of the province in winter. TVDI correlates positively with Yearly maximum value of daily minimum temperature (TNx), Yearly maximum value of daily maximum temperature (TXx), and Yearly maximum consecutive one-day precipitation (PX1), and negatively with Yearly minimum value of daily maximum temperature (TXn), Yearly minimum value of daily minimum temperature (TNn), and Yearly mean temperature (YMT).

Revegetation is accelerating globally because of its benefits in terms of ecosystem restoration, desertification prevention, and warming mitigation. The Yellow River Basin (YRB), as an ecological barrier in northern China, has implemented revegetation projects (such as the 'Grain for Green' program) for over two decades. However, a consensus on whether a significant change in greenness has been achieved and to what extent have environmental factors contributed to this change, as well as their importance ranking, is lacking. Leaf area index (LAI) is a critical indicator for estimating global greenness and projecting the dynamics of climate change. Herein, we apply four methods (Geodetector, random forest, multiple linear regression, and structural equation models) to explore the contribution of different environmental factors to greenness using the LAI in the YRB. We found that greenness has been increasing (greening over 67.22% (p < 0.05; 47.7%) of the YRB) with great spatial heterogeneity in the entire basin since 2000. Specifically, the greening process differed with elevation and slope. Temperature vegetation dryness index (TVDI) and water-use efficiency (WUE) dominated the greening; however, the three subregions evaluated revealed differing performance. In the upstream region, LAI increased by 0.031 y-1. The primary positive factors of greening change were WUE and the annual highest value of daily minimum temperature; the negative factors were TVDI and the highest number of consecutive days when precipitation <1 mm. In the midstream region, LAI increased by 0.025 y-1; greenness was mainly affected by the negative interaction of TVDI and the positive interaction of WUE. Annual maximum consecutive 5-day precipitation and annual count when daily minimum temperature < 0 °C had a great indirect impact on greenness, mainly through TVDI and WUE. In the downstream region, LAI increased by 0.045 y-1, and the main driving factors were the annual lowest value of daily minimum temperature with a negative influence and the annual lowest value of daily maximum temperature with a positive influence. In addition, we found that the effect of the interaction of any two driving factors on greenness was greater than or equal to the single effect of a driving factor. This study concludes that drought and WUE are important predictors to evaluate the greenness in arid and semi-arid regions. We emphasise that the selection and assessment of greenness factors should follow a scientific and rigorous process rather than experience, and increased attention should be paid to the interaction of multiple factors. Furthermore, the perspective of system analysis will deepen our understanding of vegetation change in a vulnerable ecosystem.

根据青藏高原7个站点实测数据,计算站点地表层土壤热通量(G0)并分析站点的日、季变化特征;结合MODIS数据、中国西部1 km全天候地表温度数据集和中国区域地面气象要素驱动数据集,用Ma模型反演2003-2018年青藏高原地表土壤热通量,并且分析不同草地类型的G0变化.结果表明:1)站点地表层土壤热通量G0比不同深度的土壤热通量值大.G0的日变化曲线呈倒"U"形状,在夜晚相较于白天变化较为平缓.2)站点地表层土壤热通量G0的季节振幅变化呈现夏>春>秋>冬,春夏季G0均值整体为正值,秋冬季G0均值基本为负值.夏季高原西北地区的地表层土壤热通量相对于东南地区的较高,而冬季则相反.3)高原草地的土壤热通量值为40～80 W·m-2,16年各类草地G0平均值最高的是温性草原化荒漠类(76.557 W·m-2),最低的是高寒草甸类(46.118 W·m-2).4)高原草地的G0一年内呈现出先增后降的变化趋势.高原各类草地G0的季节变化呈现夏>春>秋>冬,夏春季G0最低的均为高寒草甸类,较高的分别是温性草原化荒漠类和温性草原类;秋冬季G0最高的均为暖性灌草丛类,最低的均为高寒荒漠类.以上结果可为高原草地地表能量平衡研究提供一定参考依据.

Revealing grassland growing season spatial patterns and their climatic controls is crucial for the understanding of the productivity change mechanism in regional terrestrial ecosystem. However, the multi-grassland phenological factors are different, which has not been well studied. In this paper, the spatio-temporal patterns of the grassland start of the growing season (SOS) and the end of growing season (EOS) were investigated using MODIS Normalized Difference Vegetation Index (NDVI) on the Qinghai-Tibetan Plateau (QTP) during 2000 to 2019. At the same time, we analyzed the factors (including extreme and mean climate, drought, solar radiation, etc.) regulating grassland phenology under ongoing climate change. The results showed that the SOS appeared first in mountain meadow, shrub-tussock, temperature steppe and desert, then in alpine steppe and alpine meadow, showed a significant advancing tendency in all types. The EOS appeared first in temperature steppe, alpine steppe and alpine meadow, then in mountain meadow, shrub-tussock and desert. Further analysis indicated that the decrease of yearly minimum value of daily minimum temperature (TNN), yearly maximum value of daily minimum temperature (TNX), Temperature vegetation dryness index (TVDI) and the increase of yearly maximum consecutive five-day precipitation (RX5day) advance the grassland spring phenology, whereas the increase of solar radiation (SR) delay the grassland spring phenology. Meanwhile, SOS and its change rate showed the trend of significant delay and decline with the increase of altitude, respectively. We also found that the decrease of TVDI, TNN and the increase of yearly mean value of temperature (MAT_MEAN), yearly mean value of daily maximum temperature (MAT_MAX) and yearly mean value of daily minimum temperature (MAT_MIN) advanced the autumn phenology. The EOS and its change rate advance and increase with increasing altitude, respectively.

Depending on the vegetation type, extreme climate and drought events have a greater impact on the end of the season (EOS) and start of the season (SOS). This study investigated the spatial and temporal distribution characteristics of grassland phenology and its responses to seasonal and extreme climate changes in Sichuan Province from 2001 to 2020. Based on the data from 38 meteorological stations in Sichuan Province, this study calculated the 15 extreme climate indices recommended by the Expert Team on Climate Change Detection and Indices (ETCCDI). The results showed that SOS was concentrated in mid-March to mid-May (80–140 d), and 61.83% of the area showed a significant advancing trend, with a rate of 0–1.5 d/a. The EOS was concentrated between 270–330 d, from late September to late November, and 71.32% showed a delayed trend. SOS was strongly influenced by the diurnal temperature range (DTR), yearly maximum consecutive five-day precipitation (RX5), and the temperature vegetation dryness index (TVDI), while EOS was most influenced by the yearly minimum daily temperature (TNN), yearly mean temperature (TEMP_MEAN), and TVDI. The RX5 day index showed an overall positive sensitivity coefficient for SOS. TNN index showed a positive sensitivity coefficient for EOS. TVDI showed positive and negative sensitivities for SOS and EOS, respectively. This suggests that extreme climate change, if it causes an increase in vegetation SOS, may also cause an increase in vegetation EOS. This research can provide a scientific basis for developing regional vegetation restoration and disaster prediction strategies in Sichuan Province.

受气候变化影响,全球范围内植被物候发生了显著变化,而目前针对不同植被分区类型下（荒漠草原区、典型草原区、森林草原区、落叶栎林区、落叶栎林亚区）植被物候变化及其对季节性气候变化响应的研究尚少。因此基于MODIS遥感归一化差值植被指数（MODIS NDVI:MOD13Q1）数据、中国植被区划数据及135个气象站点插值数据,利用Sen′s斜率估计、Hurst指数和高阶偏相关分析等方法,研究黄土高原2001—2018年植被物侯变化及其对季节性气候变化的响应。结果表明:（1）黄土高原植被生长季始期（SOS, Start of Growing Season）主要集中在第96—144天,子植被分区由西北向东南方向,逐渐呈现提前趋势,71.0%的像元植被SOS整体提前0—2 d/10a（α=0.05）,且在未来一段时间66%的像元植被SOS继续呈现提前趋势;植被生长季末期（EOS, End of Growing Season）主要集中在第288—304天,各子植被分区植被EOS变化基本保持一致,87.6%的像元植被EOS整体延迟0—3 d/10a（α=0.05）,且在未来一段时间有80%的像元植被EOS继续呈现推迟趋势。（2）黄土高原植被SOS主要受各季节温度的影响;当年春季降水导致植被SOS提前,主要分布在黄土高原中部;上年夏季和上年秋季降水增加会导致植被SOS推迟;当年春季、上年秋季和年初冬季的温度升高均会导致植被SOS提前;各子植被分区植被SOS对不同季节降水的响应存在差异,而对不同季节温度的响应具有一致性。（3）黄土高原植被EOS主要受各季节降水和秋季温度的影响;不同季节降水增加均会导致大部分植被EOS推迟;当年秋季温度导致整体区域植被EOS推迟,且各子植被区植被EOS对当年秋季温度响应具有一致性。该研究可为大尺度植被物候影响因素提供新的认识,也为植被适应未来气候变化提供借鉴。

Climate changes, especially increased temperatures, and precipitation changes, have significant impacts on vegetation phenology. However, the response of vegetation phenology to the extreme climate in the Loess Plateau in Northwest China remains poorly quantified. The research described here analyzed the spatial change in vegetation phenology and the response of vegetation phenology to climate change in the Loess Plateau from 2001 to 2018, using data from seven extreme climate indices based on the ridge regression method. The results showed that extreme climate indexes, TNn (yearly minimum value of the daily minimum temperature), TXx (yearly maximum value of the daily maximum temperature), and RX5day (yearly maximum consecutive five-day precipitation) progressively increased from 2001 to 2018 in the Loess Plateau region, but decrease trend was found in DRT (diurnal temperature range). The start of the growing season (SOS) of vegetation gradually advanced with precipitation from northwest to southeast, and the rate was +0.38 d/a. The overall vegetation end of the growing season (EOS) was delayed, and the trend was −2.83 d/a. The sensitivity of the different vegetation phenology to different extreme weather indices showed obvious spatial differences, the sensitivity coefficient of SOS being mainly positive in the region, whereas the sensitivity coefficient of EOS was negative generally. More sensitivity was found in the EOS to extreme climate indexes than in the SOS. Forest, shrubland and grassland have similar responses to DRT and TNn; namely, both SOS and EOS are advanced with the increase in DRT and delayed with the increase in TNn (the sensitivity coefficient is quite different) but have different responses to RX5day and TXx. These results reveal that extreme climate events have a greater impact on vegetation EOS than on vegetation SOS, with these effects varying with vegetation types. This research can provide a scientific basis for formulating a scientific basis for regional vegetation restoration strategies and disaster prediction on the Loess Plateau.

了解草地覆盖动态在生态环境保护和建设上有重要意义.基于GIMMS NDVI3g数据、气象数据和高程数据分析了1982—2015年中国北方草地NDVI时空动态及对气候变化的响应.结果表明:(1)1982—2015年中国北方草地NDVI以增加趋势为主(占76％),增速为0.002/10 a.其中,坡地草地的增加速率最大(增长速率为0.001/10 a),高山亚高山平原草原变化速率最小,其他4种草地类型速率为平原草地NDVI增长速率>高山亚高山草甸>荒漠草原>草甸;(2)NDVI变异系数均值为0.078,变化相对稳定(Cv<0.15);(3)Hurst指数均值为0.42,结合NDVI变化趋势结果发现未来草地NDVI变化趋势主要以下降为主(0<H<0.5,占79.8％);(4)降水是影响北方草地生长的主要气候因子.除昆仑山脉、青海高原东坡坡底及小兴安岭等海拔较高区域外,其他地区(占92.4％)的降水与NDVI呈显著相关关系.温度与草地NDVI主要以正相关为主(占62.7％),呈负相关关系的区域集中在内蒙古高原、黄土高原西南部、准噶尔盆地和塔里木盆地等较为干旱地区.以上研究结果可为草地资源管理、生态环境保护、荒漠化防治提供重要参考信息.