Terrestrial ecosystem consists of a series of spatiotemporal continuous, multiple scaled, and mutually connected processes. Since most of these ecological processes are regulated by temperature, climate warming will profoundly impact terrestrial ecosystems at the global scale. Recently, how key processes in terrestrial ecosystems respond and/or adapt to climate warming has become a fundamental question in global change ecology. Here, we reviewed the recent research progresses related to such question. This review focuses on those key ecosystem processes, such as plant ecophysiolgical processes, phenology, community dynamics, producitity and carbon allocation, decomposition of litter and soil organic carbon, nutrient cycling, and carbon-nitrogen coupling. Based on a literature review, we formed perspectives for future researches to tackle the fundamental question, such as the predictability of plant traits on ecosystem processes, coupling between biogochemical cycles, mechanisms driving ecosystem responses to extreme climate and asymmetric warming, and ecological forecasting with models. Based on these progresses, we finally suggest more research efforts on warming adaptation rather than response, on China’s specific ecosystems, and on the integration of experiments, observations and models for coordinating studies across scales.

陆地生态系统包含一系列时空连续、尺度多元且互相联系的生态学过程。由于大部分生态学过程都受到温度调控，因此气候变暖会对全球陆地生态系统产生深远的影响。近年来，全球变化生态学的基本科学问题之一是陆地生态系统的关键过程如何响应与适应全球气候变暖。围绕该问题，本文梳理了近年来的研究进展，重点关注植物生理生态过程、物候期、群落动态、生产力及其分配、凋落物与土壤有机质分解、养分循环等过程对温度升高的响应与适应机理。通过定量分析近二十年来发表于主流期刊的相关论文，展望了该领域的前沿方向，包括物种性状对生态系统过程的预测能力、生物地球化学循环的耦合过程、极端高温与低温事件的响应与适应机理、不对称气候变暖的影响机理和基于过程的生态系统模拟预测等。基于这些研究进展，本文建议进一步研究陆地生态系统如何适应气候变暖，更多关注我国的特色生态系统类型，并整合实验、观测或模型等研究手段开展跨尺度的合作研究。

Based on the observed daily temperature and precipitation of the land surface of 603 meteorological stations in China, the Global Inventory Modeling and Mapping Studies (GIMMS) Normalized Difference Vegetation Index (NDVI) 3rd generation dataset, the changing patterns of NDVI in China during 1982-2015 were investigated and the corresponding contributions of the main driving forces, climatic change and human activities, to these changes were distinguished using the methods of trend analysis and multiple regression residuals analysis. The results showed that vegetation recovered in whole China in research period significantly. Shanghai was the single case with a decrease in growing season NDVI in the selected 32 provincial-level administrative regions, while the growing season NDVI in Shanxi, Shaanxi, and Chongqing increased much faster compared with other regions. The climatic change and human activities drove the NDVI change jointly as main forces in China and induced both a rapid increasing trend on the whole and a huge spatial difference. The impacts of climatic change on NDVI change in the growing-season ranged from -0.01×10 -3 a -1 to 1.05×10 -3 a -1, while the impacts of human activities changed from -0.32×10 -3 a -1 to 1.77×10 -3 a -1. The contributions of climatic change and human activities accounted for 40% and 60%, respectively, to the increase of NDVI in China in the past 34 years. The regions where the contribution rates of human activities were more than 80% were mainly distributed in the central part of the Loess Plateau, the North China Plain, and the northeast and the southwest of China. There were 22 provincial-level regions where the contributions of human activities were more than 50%, and the shares of contribution induced by human activities in Shanghai, Heilongjiang, and Yunnan were much greater than those of any other regions. The results suggest that we should focus more on the role of human activities in vegetation restoration in the whole country.

基于中国603个气象站的地表气温和降水观测资料以及GIMMS NDVI3g数据,采用变化趋势分析和多元回归残差分析等方法研究了1982—2015年中国植被NDVI变化特征及其主要驱动因素（即气候变化和人类活动）的相应贡献。结果表明：① 1982—2015年中国植被恢复明显,在选择的32个省级行政区中,山西、陕西和重庆的生长季NDVI增加最快,仅上海生长季NDVI呈减小趋势。② 气候变化和人类活动的共同作用是中国植被NDVI呈现整体快速增加和巨大空间差异的主要原因,其中气候变化对各省生长季NDVI变化的影响在-0.01×10 ^-3~1.05×10 ^-3 a ^-1之间,而人类活动的影响在-0.32×10 ^-3~1.77×10 ^-3 a ^-1之间。③ 气候变化和人类活动分别对中国近34年来植被NDVI的增加贡献了40%和60%;人类活动贡献率超过80%的区域主要集中在黄土高原中部、华北平原以及中国东北和西南等地;人类活动贡献率大于50%的省份有22个,其中贡献率最大的3个地区为上海、黑龙江和云南。研究结果建议应更加重视人类活动在植被恢复中的作用。

As an important compartment of the Earth's surface, terrestrial ecosystems act as a vital harbor for human survival and development. Climate change significantly increased the frequency, intensity and duration of drought since the 21st century, which have marked impact on ecosystems, leading to serious restriction or even threat on the sustainable development of human beings. Therefore, developing integrative research on effects of drought on terrestrial ecosystems and assessing the associated ecological risk are impressive in global change field. This study reviewed the effects of drought on plant physiological and ecological processes, biogeochemical cycles, biodiversity, and ecosystem structure and functions in terrestrial ecosystems, and discussed current hotspot issues in this field as well as deeply analyzing the existing problems and the potential development direction. This study aims to provide some suggestions for the future observation, manipulative experiments, and modeling prediction on effects of drought on terrestrial ecosystems, and offer new insights to enhance risk assessment and management under drought.

作为地球表层重要的组成部分, 陆地生态系统是人类生存和发展的重要场所。21世纪以来, 气候变化导致干旱事件发生的强度、频度和持续时间显著增加, 对陆地生态系统带来深远的影响, 严重制约甚至威胁人类社会的可持续发展。因此, 开展极端干旱对陆地生态系统影响的研究并评估其生态风险效应, 是当前全球变化领域的重点问题。该文从植物生理生态过程、生物地球化学循环、生物多样性、以及生态系统结构和功能四个方面综述了极端干旱对陆地生态系统的影响, 并对当前的研究热点进行探讨, 深度剖析当前研究中存在的难点问题和未来可能的发展方向。该文以期为未来开展干旱对陆地生态系统影响的观测与预测研究提供参考依据, 同时为在未来干旱影响下加强陆地生态系统风险评估和管理提供新思路。

Arctic landscapes are changing rapidly in response to warming, but future predictions are hindered by difficulties in scaling ecological relationships from plots to biomes. Unmanned aerial systems (hereafter ‘drones’) are increasingly used to observe Arctic ecosystems over broader extents than can be measured using ground-based approaches and are facilitating the interpretation of coarse-grained remotely sensed data. However, more information is needed about how drone-acquired remote sensing observations correspond with ecosystem attributes such as aboveground biomass. Working across a willow shrub-dominated alluvial fan at a focal study site in the Canadian Arctic, we conducted peak growing season drone surveys with an RGB camera and a multispectral multi-camera array. We derived photogrammetric reconstructions of canopy height and normalised difference vegetation index (NDVI) maps along with in situ point-intercept measurements and aboveground vascular biomass harvests from 36, 0.25 m2 plots. We found high correspondence between canopy height measured using in situ point-intercept methods compared to drone-photogrammetry (concordance correlation coefficient = 0.808), although the photogrammetry heights were positively biased by 0.14 m relative to point-intercept heights. Canopy height was strongly and linearly related to aboveground biomass, with similar coefficients of determination for point-intercept (R\n 2 = 0.92) and drone-based methods (R\n 2 = 0.90). NDVI was positively related to aboveground biomass, phytomass and leaf biomass. However, NDVI only explained a small proportion of the variance in biomass (R\n 2 between 0.14 and 0.23 for logged total biomass) and we found moss cover influenced the NDVI-phytomass relationship. Vascular plant biomass is challenging to infer from drone-derived NDVI, particularly in ecosystems where bryophytes cover a large proportion of the land surface. Our findings suggest caution with broadly attributing change in fine-grained NDVI to biomass differences across biologically and topographically complex tundra landscapes. By comparing structural, spectral and on-the-ground ecological measurements, we can improve understanding of tundra vegetation change as inferred from remote sensing.

Vegetation is a key element in the energy, water and carbon balances over the land surfaces and is strongly impacted by climate change and anthropogenic effects. Remotely sensed observations are commonly used for the monitoring of vegetation dynamics and its temporal changes from regional to global scales. Among the different indices derived from Earth observation satellites to study the vegetation, the vegetation optical depth (VOD), which is related to the intensity of extinction effects within the vegetation canopy layer in the microwave domain and which can be derived from both passive and active microwave observations, is increasingly used for monitoring a wide range of ecological vegetation variables. Based on different frequency bands used to derive VOD, from L- to Ka-bands, these variables include, among others, the vegetation water content/status and the above ground biomass. In this review, the theoretical bases of VOD estimates for both the passive and active microwave domains are presented and the global long-term VOD products computed from various groups in the world are described. Then, major findings obtained using VOD are reviewed and the perspectives offered by methodological improvements and by new sensors onboard satellite missions recently launched or to be launched in a close future are presented.

. Since the late 1970s, space-borne microwave radiometers have been providing measurements of radiation emitted by the Earth’s surface. From these measurements it is possible to derive vegetation optical depth (VOD), a model-based indicator related to the density, biomass, and water content of vegetation. Because of its high temporal resolution and long availability, VOD can be used to monitor short- to long-term changes in vegetation. However, studying long-term VOD dynamics is generally hampered by the relatively short time span covered by the individual microwave sensors. This can potentially be overcome by merging multiple VOD products into a single climate data record. However, combining multiple sensors into a single product is challenging as systematic differences between input products like biases, different temporal and spatial resolutions, and coverage need to be overcome. Here, we present a new series of long-term VOD products, the VOD Climate Archive (VODCA). VODCA combines VOD retrievals that have been derived from multiple sensors (SSM/I, TMI, AMSR-E, WindSat, and AMSR2) using the Land Parameter Retrieval Model. We produce separate VOD products for microwave observations in different spectral bands, namely the Ku-band (period 1987–2017), X-band (1997–2018), and C-band (2002–2018). In this way, our multi-band VOD products preserve the unique characteristics of each frequency with respect to the structural elements of the canopy. Our merging approach builds on an existing approach that is used to merge satellite products of surface soil moisture: first, the data sets are co-calibrated via cumulative distribution function matching using AMSR-E as the scaling reference. To do so, we apply a new matching technique that scales outliers more robustly than ordinary piecewise linear interpolation. Second, we aggregate the data sets by taking the arithmetic mean between temporally overlapping observations of the scaled data. The characteristics of VODCA are assessed for self-consistency and against other products. Using an autocorrelation analysis, we show that the merging of the multiple data sets successfully reduces the random error compared to the input data sets. Spatio-temporal patterns and anomalies of the merged products show consistency between frequencies and with leaf area index observations from the MODIS instrument as well as with Vegetation Continuous Fields from the AVHRR instruments. Long-term trends in Ku-band VODCA show that since 1987 there has been a decline in VOD in the tropics and in large parts of east-central and north Asia, while a substantial increase is observed in India, large parts of Australia, southern Africa, southeastern China, and central North America. In summary, VODCA shows vast potential for monitoring spatial–temporal ecosystem changes as it is sensitive to vegetation water content and unaffected by cloud cover or high sun zenith angles. As such, it complements existing long-term optical indices of greenness and leaf area. The VODCA products (Moesinger et al., 2019) are open access and available under Attribution 4.0 International at https://doi.org/10.5281/zenodo.2575599.\n

China has increased its vegetation coverage and enhanced its terrestrial carbon sink through ecological restoration since the end of the 20th century. However, the temporal variation in vegetation carbon sequestration remains unclear, and the relative effects of climate change and ecological restoration efforts are under debate. By integrating remote sensing and machine learning with a modelling approach, we explored the biological and physical pathways by which both climate change and human activities (e.g., ecological restoration, cropland expansion, and urbanization) have altered Chinese terrestrial ecosystem structures and functions, including vegetation cover, surface heat fluxes, water flux and vegetation carbon sequestration (defined by gross and net primary production, GPP and NPP). Our study indicated that during 2001~2018, GPP in China increased significantly at a rate of 49.1~53.1 TgC/yr, and the climatic and anthropogenic contributions to GPP gains were comparable (48%~56% and 44%~52%, respectively). Spatially, afforestation was the dominant mechanism behind forest cover expansions in the farming-pastoral ecotone in northern China, on the Loess Plateau and in the southwest karst region, while climate change promoted vegetation cover in most parts of southeastern China. At the same time, the increasing trend in NPP (22.4~24.9 TgC/yr ) during 2001~2018 was highly attributed to human activities (71%~81%), particularly in southern, eastern and northeastern China. Both GPP and NPP showed accelerated increases after 2010 because the anthropogenic NPP gains during 2001~2010 were generally offset by the climate-induced NPP losses in southern China. However, after 2010, the climatic influence reversed, thus highlighting the vegetation carbon sequestration that occurs with ecological restoration.This article is protected by copyright. All rights reserved.

. Spaceborne microwave remote sensing is widely used to monitor global environmental changes for understanding hydrological, ecological, and climate processes. A new global land parameter data record (LPDR) was generated using similar calibrated, multifrequency brightness temperature (Tb) retrievals from the Advanced Microwave Scanning Radiometer for EOS (AMSR-E) and the Advanced Microwave Scanning Radiometer 2 (AMSR2). The resulting LPDR provides a long-term (June 2002–December 2015) global record of key environmental observations at a 25 km grid cell resolution, including surface fractional open water (FW) cover, atmosphere precipitable water vapor (PWV), daily maximum and minimum surface air temperatures (Tmx and Tmn), vegetation optical depth (VOD), and surface volumetric soil moisture (VSM). Global mapping of the land parameter climatology means and seasonal variability over the full-year records from AMSR-E (2003–2010) and AMSR2 (2013–2015) observation periods is consistent with characteristic global climate and vegetation patterns. Quantitative comparisons with independent observations indicated favorable LPDR performance for FW (R ≥ 0.75; RMSE  ≤  0.06), PWV (R ≥ 0.91; RMSE  ≤  4.94 mm), Tmx and Tmn (R ≥ 0.90; RMSE  ≤  3.48 °C), and VSM (0.63 ≤ R ≤ 0.84; bias-corrected RMSE  ≤  0.06 cm3 cm−3). The LPDR-derived global VOD record is also proportional to satellite-observed NDVI (GIMMS3g) seasonality (R ≥ 0.88) due to the synergy between canopy biomass structure and photosynthetic greenness. Statistical analysis shows overall LPDR consistency but with small biases between AMSR-E and AMSR2 retrievals that should be considered when evaluating long-term environmental trends. The resulting LPDR and potential updates from continuing AMSR2 operations provide for effective global monitoring of environmental parameters related to vegetation activity, terrestrial water storage, and mobility and are suitable for climate and ecosystem studies. The LPDR dataset is publicly available at http://files.ntsg.umt.edu/data/LPDR_v2/.\n

Climate change is a major driver of vegetation activity, and thus its complex processes become a frontier and difficulty in global change research. To understand the complex relationship between climate change and vegetation activity, the spatial distribution and dynamic characteristics of the response of NDVI to climate change from 1982 to 2013 in China were investigated by the geographically weighted regression (GWR) model. The GWR was run based on the combined datasets of satellite vegetation index (GIMMS NDVI) and climate observation (temperature and moisture) from meteorological stations nationwide. The results noted that the spatial non-stationary relationship between NDVI and surface temperature has appeared in China. The significant negative temperature-vegetation relationship was distributed in northeast, northwest and southeast parts of the country, while the positive correlation was more concentrated from southwest to northeast. And then, by comparing the normalized regression coefficients for different climate factors, regions with moisture dominants for NDVI were observed in North China and the Tibetan Plateau, and regions with temperature dominants for NDVI were distributed in the East, Central and Southwest China, where the annual mean maximum temperature accounts for the largest areas. In addition, regression coefficients between NDVI dynamics and climate variability indicated that the higher warming rate could result in the weakened vegetation activity through some mechanisms such as enhanced drought, while the moisture variability could mediate the hydrothermal conditions for the variation of vegetation activity. When the increasing rate of photosynthesis exceeded that of respiration, there was a positive correlation between vegetation dynamics and climate variability. However, the continuous and dynamic responding process of vegetation activity to climate change will be determined by spatially heterogeneous conditions in climate change and vegetation cover. Furthermore, the description of climate-induced vegetation activity from its rise to decline in different regions is expected to provide a scientific basis for initiating ecosystem-based adaptation strategies in response to global climate change.

为研究气候变化与植被活动之间的复杂关系,采用1982-2013年GIMMS NDVI与气象站点温度与水分的监测资料,应用基于像元的地理加权回归方法,探究了中国植被NDVI及其动态特征对气候变化响应的空间格局。中国植被NDVI与地表温度呈空间非平稳关系,在空间上的负相关关系主要集中在东北、西北及东南部分地区,空间正相关则更为集中和连片;针对不同气候指标的标准化系数对比可知,植被NDVI受水分控制作用较为显著的区域主要集中在北方地区以及青藏高原,温度的主导作用区域则分布在华东、华中及西南地区,其中年均最高气温对NDVI的主导区域范围最广;植被NDVI动态与气候变率的回归结果表明,增温速率的升高会通过加剧干旱等机制对植被活动产生抑制作用,水分变率对植被活动的强弱起到了重要的调节作用。

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Global estimation of Gross Primary Production (GPP) - the uptake of atmospheric carbon dioxide by plants through photosynthesis - is commonly based on optical satellite remote sensing data. This presents a source driven approach since it uses the amount of absorbed light, the main driver of photosynthesis, as a proxy for GPP. Vegetation Optical Depth (VOD) estimates obtained from microwave sensors provide an alternative and independent data source to estimate GPP on a global scale, which may complement existing GPP products. Recent studies have shown that VOD is related to aboveground biomass, and that both VOD and temporal changes in VOD relate to GPP. In this study, we build upon this concept and propose a model for estimating GPP from VOD. Since the model is driven by vegetation biomass, as observed through VOD, it presents a carbon sink driven approach to quantify GPP and, therefore, is conceptually different from common source-driven approaches. The model developed in this study uses single frequencies from active or passive microwave VOD retrievals from C-, X- and Ku-band (Advanced Scatterometer (ASCAT) and Advanced Microwave Scanning Radiometer for Earth Observation (AMSR-E)) to estimate GPP at the global scale. We assessed the ability for temporal and spatial extrapolation of the model using global GPP from FLUXCOM and in situ GPP from FLUXNET. We further performed upscaling of in situ GPP based on different VOD data sets and compared these estimates with the FLUXCOM and MODerate-resolution Imaging Spectroradiometer (MODIS) GPP products. Our results show that the model developed for individual grid cells using VOD and change in VOD as input performs well in predicting temporal patterns in GPP for all VOD data sets. For spatial extrapolation of the model, however, additional input variables are needed to represent the spatial variability of the VOD-GPP relationship due to differences in vegetation type. As additional input variable, we included the grid cell median VOD (as a proxy for vegetation cover), which increased the model performance during cross validation. Mean annual GPP obtained for AMSR-E X-band data tends to overestimate mean annual GPP for FLUXCOM and MODIS but shows comparable latitudinal patterns. Overall, our findings demonstrate the potential of VOD for estimating GPP. The sink-driven approach provides additional information about GPP independent of optical data, which may contribute to our knowledge about the carbon source-sink balance in different ecosystems.