气候暖化和人类活动的加剧使得中国干旱问题日益突出，干旱作为影响当前陆地生态系统碳汇功能的最主要胁迫因子之一，其不仅会显著减弱生态系统碳汇功能，甚至会使之变成碳源。净生态系统生产力（Net Ecosystem Productivity, NEP）是植被净初级生产力（Net Primary Productivity, NPP）扣除土壤异养呼吸所消耗的光合产物之后的部分，是表征生态系统碳收支的重要指标，可定量描述陆地生态系统碳源/汇功能。因此，研究干旱影响下陆地生态系统NEP的变化特征，对于深入理解生态系统碳循环过程、区域碳汇管理及水资源合理分配具有重大现实意义。西南地区是中国的重要生态屏障以及农业主产区，近年来，在人类活动和气候变化影响下，该区域面临的水土流失、植被退化等生态问题日益突出，且干旱频发。因此，本文基于标准化降水蒸散发指数（Standardized Precipitation Evapotranspiration Index, SPEI）分析了2000-2018年西南地区干旱事件的发生频率、强度、影响范围以及周期等特征，对于日光诱导叶绿素荧光（Solar-induced Chlorophyll Fluorescence, SIF）及植被绿度指数（Normalized Difference Vegetation Index, NDVI）在区域干旱生态胁迫监测中的有效性也做了进一步研究。同时基于光能利用率模型（Carnegie–Ames–Stanford Approach, CASA）和土壤异养呼吸模型估算了净生态系统生产力（NEP），将其作为研究生态系统碳源/汇的指标。在此基础之上，定量分析了西南地区不同植被生态系统碳循环对两类干旱事件（长期/短期）的响应，进一步评估了干旱事件对不同气候分区下典型植被生态系统碳汇的影响以及植被旱后恢复力差异。主要结论如下：

（1）2000-2018年西南地区不同时间尺度的SPEI指数均呈微弱增加趋势；季节上，春季和夏季的干旱化趋势明显，秋季和冬季的湿润化趋势明显；空间上，云南省东部及四川省中部干旱发生的频率高且强度大；近19a研究区的干旱影响范围呈缓慢减小趋势，下降趋势率为-0.004/10a；西南地区SPEI指数主要存在4~5a左右、2~3a的2个时间尺度的周期变化；相比较NDVI，干旱引发的SIF负异常更为突出。

（2）2000-2018年西南地区植被NEP整体呈波动增加趋势；NPP、R_H、NEP年内变化均近似“单峰型”曲线；空间上，广西壮族自治区部分地区的碳汇增加趋势明显；季节上，夏季（105.35 g C·m^-2）>秋季（39.61 g C·m^-2）>春季（24.62 g C·m^-2）>冬季（-3.10 g C·m^-2）；不同植被类型NEP从高到低依次为：阔叶林>农田>草甸和草本沼泽>草原和稀树灌木草原>针叶林>灌丛和萌生矮林；当区域海拔小于200m时，生态系统的碳汇能力最高；月尺度上，西南地区的森林、农田NEP值受气温影响较大，草地生态系统NEP则受降水的影响较多；年尺度上，区域植被NEP与太阳辐射主要呈正相关，气温和降水的相关性空间分布差异较大。

（3）西南地区大部分地区的SPEI指数与植被NEP呈正相关；SIF、NDVI与植被NEP均呈现显著线性相关（P<0.01），除针叶林、草甸和草本沼泽生态系统外，其余植被生态系统的NEP与SIF线性拟合的R²均高于NDVI；灌丛和萌生矮林、农田生态系统的恢复期最长（6个月），草甸和草本沼泽的恢复期最短（3个月）；草原和稀树灌木草原的碳汇功能主要受到长期干旱胁迫的影响，而草甸和草本沼泽则易受到短期干旱的影响；高原气候下的针叶林、灌丛和萌生矮林、草原和稀树灌木草原具有较强的碳汇能力，但干旱会导致亚热带气候区的针叶林以及灌丛和萌生矮林的生长期出现2个月左右的延迟。

Drought disaster has become increasingly prominent in China under the effect of climate warming and human activities. Drought, as one of the most important stress factors affecting the carbon sink function of terrestrial ecosystems, will not only weaken the carbon sink function of terrestrial ecosystems at regional and continental scales, but also make it become carbon source. As an important indicator of carbon revenue and expenditure of ecosystem, net ecosystem productivity (NEP) can quantitatively describe the carbon source/sink function of terrestrial ecosystem. It can be calculated by net primary productivity deducting soil heterotrophic respiration. Therefore, it is of great practical significance to study the variation characteristics of NEP in terrestrial ecosystems under the influence of drought. Which is helpful for in-depth understanding of ecosystem carbon cycle process, regional carbon sink management, rational allocation of water resources. As an important ecological barrier and main agricultural production area in China, the Southwest china has suffered serious ecological problems such as soil erosion, land desertification and vegetation degradation, and drought events occurred frequently in recent years under the influence of human activities and climate change. Therefore, based on the SPEI index, the frequency, intensity, influence range and period of drought events in the study area were analyzed. The effectiveness of solar-induced chlorophyll fluorescence (SIF) and normalized difference vegetation index (NDVI) in the ecological stress monitoring of regional drought has also been further studied. Then, The net ecosystem productivity (NEP) were calculated based on Carnegie–Ames–Stanford Approach (CASA) terrestrial ecosystem model and soil heterotrophic respiration model, and it was used as the index to study the carbon cycle of terrestrial ecosystems. On this basis, the response of carbon cycle of different vegetation ecosystems to two types of drought events ( long-term / short-term ) in southwest China was quantitatively analyzed, and the impact of drought events on carbon sinks of typical vegetation ecosystems under different climatic zones and the difference of vegetation resilience after drought were further evaluated. The main conclusions are as follows :

(1) The SPEI indexes at different time scales in Southwest China showed a slight increase trend from 2000 to 2018; Seasonal, drought trend of spring and summer is obvious, autumn and winter showed a trend of wetting; Spatially, the frequency and intensity of drought in eastern Yunnan and central Sichuan are more serious; The influence range of drought in the study area showed a slow downward trend in the past 19 years (the downward trend rate was -0.004/10a); The SPEI index in southwest China has two periodic changes in time scales of 4 ~ 5 a and 2 ~ 3 a; Compared with NDVI, the negative SIF anomaly caused by drought is more significant.

(2) From 2000 to 2018, the overall vegetation NEP in Southwest China showed a fluctuating increase trend; The annual variation of NPP, R_H and NEP roughly showed a mono-peak curve; Spatially, the carbon sink in some regions of Guangxi Zhuang Autonomous Region has an obvious increase trend; Seasonally, the average NEP values ranked as summer (105.35 g C·m^-2) > autumn (39.61 g C·m^-2) > spring (24.62 g C·m^-2) > winter (-3.10 g C·m^-2); The averaged NEP values of different vegetation types ranked as broadleaf forest > Croplands > meadow > grassland > Needleleaf forest > Shrublands; The carbon sink capacity of the ecosystem is the highest when the regional altitude is less than 200 m; On the monthly scale, the NEP values of forests and farmlands in Southwest China are greatly affected by temperature, and precipitation has a great influence on the NEP of grassland ecosystem; At the annual scale, the NEP of regional vegetation mainly positively correlated with solar radiation, and the spatial distribution of the correlation between temperature and precipitation has strong heterogeneity.

(3) The SPEI index was positively correlated with vegetation NEP in most areas of southwest China; SIF, NDVI and vegetation NEP showed a significant linear correlation (P<0.01). The linear fitting R² of NEP and SIF in other vegetation ecosystems were higher than NDVI except needleleaf forest and meadow ecosystems; Shrublands, croplands ecosystems have the longest recovery period (6 months) and meadow have the shortest recovery period (3 months); The carbon sink function of grassland is mainly affected by long-term drought stress, while meadow are susceptible to short-term drought; The needleleaf forest, shrublands, grassland in the plateau climate have strong carbon sink capacity. however, drought will lead to the delay of growth period of needleleaf forest and shrublands in subtropical climate area for about 2 months.

[1] 段居琦, 徐新武, 高清竹. IPCC 第五次评估报告关于适应气候变化与可持续发展的新认知[J]. 气候变化研究进展, 2014, 10(3):197-202.

[2] Wang W, Liao Y, Wen X, et al. Dynamics of CO2 fluxes and environmental responses in the rain-fed winter wheat ecosystem of the Loess Plateau, China[J]. Science of the total environment, 2013, 461: 10-18.

[3] 方精云, 朱江玲, 石岳. 生态系统对全球变暖的响应[J]. 科学通报, 2018, 63(2):136-140.

[4] Mansanet-Bataller M, Pardo Á. What you should know about carbon markets[J]. Energies, 2008, 1(3): 120-153.

[5] Raich J W , Schlesinger W H . The global carbon dioxide flux in soil respiration and its relationship to vegetation and climate[J]. Tellus. Series B: Chemical and Physical Meteorology, 1992, 44(2):81-99.

[6] 张梅, 黄贤金, 揣小伟, 等. 中国净生态系统生产力空间分布及变化趋势研究[J]. 地理与地理信息科学, 2020, 36(2):69-74.

[7] 朴世龙, 张新平, 陈安平, 等. 极端气候事件对陆地生态系统碳循环的影响[J]. 中国科学:地球科学, 2019, 49(9):1321-1334.

[8] Ciais P , Reichstein M , Viovy N , et al. Europe-wide reduction in primary productivity caused by the heat and drought in 2003[J]. Nature, 2005, 437(7058): 529-533.

[9] Zscheischler J, Mahecha M D, Von Buttlar J, et al. A few extreme events dominate global interannual variability in gross primary production[J]. Environmental Research Letters, 2014, 9(3): 035001.

[10] Esquivel‐Muelbert A, Baker T R, Dexter K G, et al. Compositional response of Amazon forests to climate change[J]. Global change biology, 2019, 25(1): 39-56.

[11] 马明国, 汤旭光, 韩旭军, 等. 西南岩溶地区碳循环观测与模拟研究进展和展望[J]. 地理科学进展, 2019, 38(8): 1196-1205.

[12] 王霖娇, 盛茂银, 杜家颖, 等. 西南喀斯特石漠化生态系统土壤有机碳分布特征及其影响因素[J]. 生态学报, 2017, 37(4):1358-1365.

[13] 宋冰, 牛书丽. 全球变化与陆地生态系统碳循环研究进展[J]. 西南民族大学学报(自然科学版), 2016, 42(1):14-23.

[14] 于贵瑞, 温学发, 王秋凤, 等. 全球变化与陆地生态系统碳循环和碳积蓄[M]. 北京:气象出版社, 2003.

[15] Zheng J, Mao F, Du H, et al. Spatiotemporal simulation of net ecosystem productivity and its response to climate change in subtropical forests[J]. Forests, 2019, 10(8): 708.

[16] Sitch S, Smith B, Prentice I C, et al. Evaluation of ecosystem dynamics, plant geography and terrestrial

carbon cycling in the LPJ dynamic global vegetation model[J]. Global change biology, 2003, 9(2): 161-185.

[17] Zhang L, Guo H, Jia G, et al. Net ecosystem productivity of temperate grasslands in northern China: An upscaling study[J]. Agricultural and Forest Meteorology, 2014, 184: 71-81.

[18] 梁思琦, 彭守璋, 陈云明. 陕西省典型天然次生林和人工林生产力对气候变化的响应[J]. 应用生态学报, 2019, 30(9):2892-2902.

[19] 张新中, 李育, 张成琦, 等. 2000-2014年石羊河流域净生态系统生产力变化分析[J]. 兰州大学学报(自然科学版), 2020, 56(4):486-492.

[20] Pathak, K, Malhi Y, Sileshi, G W, et al. Net ecosystem productivity and carbon dynamics of the traditionally managed Imperata grasslands of North East India[J]. Science of the Total Environment, 2018, 635: 1124–1131.

[21] 戴尔阜, 黄宇, 吴卓, 等. 内蒙古草地生态系统碳源/汇时空格局及其与气候因子的关系[J]. 地理学报, 2016, 71(1): 21-34.

[22] 巩杰, 张影, 钱彩云. 甘肃白龙江流域净生态系统生产力时空变化[J]. 生态学报, 2017, 37(15):5121-5128.

[23] 周夏飞, 於方, 曹国志, 等. 2001—2015年青藏高原草地碳源/汇时空变化及其与气候因子的关系[J]. 水土保持研究, 2019, 26(1):76-81.

[24] 韩其飞, 陆研, 李超凡. 气候变化对中亚草地生态系统碳循环的影响研究[J]. 干旱区地理, 2018, 41(6): 1351-1357.

[25] Cui E Q, Bian C Y, Luo Y Q, et al. Spatial variations in terrestrial net ecosystem productivity and its local indicators[J]. Biogeosciences, 2020, 17(23):6237-6246.

[26] 杨静, 黄秉光, 黄玫, 等. 近55 a新疆净生态系统生产力对气候变化的响应[J]. 干旱区地理, 2017, 40(5):1054-1060.

[27] Xu Q, Yang R, Dong Y X, et al. The influence of rapid urbanization and land use changes on terrestrial carbon sources/sinks in Guangzhou, China[J]. Ecological indicators, 2016, 70: 304-316.

[28] 徐英明, 李昊, 李鑫, 等. 全球森林管理的趋势及对碳储量的影响[J]. 林业与环境科学, 2018, 34(1):123-131.

[29] Tang Y K, Jia C, Wang L N, et al. Solar energy dominates and soil water modulates net ecosystem productivity and evapotranspiration across multiple timescales in a subtropical coniferous plantation[J]. Agricultural and Forest Meteorology, 2021, 300:108310.

[30] IPCC. Global warming of 1.5℃ //IPCC. Special report on the impacts of global warming of 1.5℃ above pre-industrial levels and related global greenhouse gas emission path ways, in the context of strengthening the global response to the threat of climate change, sustainable development, and efforts to eradicate poverty. Katowice, Poland, 2018.

[31] IPCC. Summary for policymakers //IPCC. Climate Change 2013: The Physical Science Basis. Cambridge, UK: Cambridge University Press, 2013.

[32] 王蔚丹, 孙丽, 裴志远, 等. 典型旱年农业干旱遥感监测指标在东北地区生长季的表现[J]. 中国农业气象, 2021, 42(4):307-317.

[33] 刘永佳, 黄生志, 方伟, 等. 不同季节气象干旱向水文干旱的传播及其动态变化[J]. 水利学报, 2021, 52(1):93-102.

[34] 李忆平, 李耀辉. 气象干旱指数在中国的适应性研究进展[J]. 干旱气象, 2017, 35(5): 709-723.

[35] Svoboda, M, Fuchs, B. Handbook of drought indicators and indices[M]. Drought Mitigation Center Faculty Publications, 2016, 117.

[36] Ogunrinde A T, Oguntunde P G, Olasehinde D A, et al. Drought spatiotemporal characterization using self-calibrating Palmer Drought Severity Index in the northern region of Nigeria[J]. Results in Engineering, 2020, 5: 100088.

[37] 黄梦杰, 贺新光, 卢希安, 等. 长江流域的非平稳SPI干旱时空特征分析[J]. 长江流域资源与环境, 2020, 29(7):1597-1611.

[38] Vicente-Serrano S M, Beguería S, López-Moreno J I. A multiscalar drought index sensitive to global warming: the standardized precipitation evapotranspiration index[J]. Journal of climate, 2010, 23(7): 1696-1718.

[39] 史尚渝, 王飞, 金凯, 等. 基于SPEI的1981—2017年中国北方地区干旱时空分布特征[J]. 干旱地区农业研究, 2019, 37(4):215-222.

[40] 侯青青, 裴婷婷, 陈英, 等. 1986—2019年黄土高原干旱变化特征及趋势[J]. 应用生态学报, 2021, 32(2):649-660.

[41] 姚蕊, 夏敏, 孙鹏, 等. 淮河流域干旱时空演变特征及成因[J]. 生态学报, 2021, 41(1):333-347.

[42] 史晓亮, 吴梦月, 丁皓. SPEI和植被遥感信息监测西南地区干旱差异分析[J]. 农业机械学报, 2020, 51(12):184-192.

[43] 刘雷震, 武建军, 周洪奎, 等. 叶绿素荧光及其在水分胁迫监测中的研究进展[J]. 光谱学与光谱分析, 2017, 37(9): 2780-2787.

[44] Frankenberg C, Fisher J B, Worden J, et al. New global observations of the terrestrial carbon cycle from GOSAT: Patterns of plant fluorescence with gross primary productivity[J]. Geophysical Research Letters, 2011, 38(17).

[45] Lee J E, Frankenberg C, van der Tol C, et al. Forest productivity and water stress in Amazonia: Observations from GOSAT chlorophyll fluorescence[J]. Proceedings of the Royal Society B: Biological Sciences, 2013, 280(1761): 20130171.

[46] Sun Y, Fu R, Dickinson R, et al. Drought onset mechanisms revealed by satellite solar‐induced chlorophyll fluorescence: Insights from two contrasting extreme events[J]. Journal of Geophysical Research: Biogeosciences, 2015, 120(11): 2427-2440.

[47] Yoshida Y, Joiner J, Tucker C, et al. The 2010 Russian drought impact on satellite measurements of solar-induced chlorophyll fluorescence: Insights from modeling and comparisons with parameters derived from satellite reflectances[J]. Remote Sensing of Environment, 2015, 166: 163-177.

[48] Trenberth K E, Dai A, Van Der Schrier G, et al. Global warming and changes in drought[J]. Nature Climate Change, 2014, 4(1): 17-22.

[49] Golnaraghi M, Etienne C, Guha-Sapir D, et al. Atlas of Mortality and Economic Losses from Weather, Climate, and Water Extremes (1970—2012)[M]. World Meteorological Organization, 2014.

[50] 贾艳青, 张勃, 马彬, 等. 1960-2015年中国西南地区持续性干旱事件时空演变特征[J]. 干旱区资源与环境, 2018, 32(5):171-176.

[51] 周贵尧, 周灵燕, 邵钧炯, 等. 极端干旱对陆地生态系统的影响:进展与展望[J]. 植物生态学报, 2020, 44(5):515-525.

[52] 雷添杰. 干旱对草地生产力影响的定量评估研究[J]. 测绘学报, 2017, 46(1): 134-134.

[53] Phillips O L, Aragão L E O C, Lewis S L, et al. Drought sensitivity of the Amazon rainforest[J]. Science, 2009, 323(5919): 1344-1347.

[54] Kaewthongrach R, Chidthaisong A, Charuchittipan D, et al. Impacts of a strong El Nino event on leaf phenology and carbon dioxide exchange in a secondary dry dipterocarp forest[J]. Agricultural and Forest Meteorology, 2020, 287: 107945.

[55] 胡中民, 于贵瑞, 樊江文, 等. 干旱对陆地生态系统水碳过程的影响研究进展[J]. 地理科学进展, 2006, (6):12-20.

[56] 孙晓敏, 温学发, 于贵瑞, 等. 中亚热带季节性干旱对千烟洲人工林生态系统碳吸收的影响[J]. 中国科学 D 辑: 地球科学, 2006, 36(S1): 103.

[57] 姜海梅, 张德广, 王若静, 等. 不同生态系统呼吸模型在半干旱草原生长季碳循环研究中的比较及应用[J]. 北京大学学报（自然科学版）, 2018, 287(3):138-149.

[58] Doughty C E, Metcalfe D B, Girardin C A J, et al. Drought impact on forest carbon dynamics and fluxes in Amazonia[J]. Nature, 2015, 519(7541): 78-82.

[59] Sun Y L, Qu F Y, Zhu X J, et al. Non-linear responses of net ecosystem productivity to gradient warming in a paddy field in Northeast China[J]. PeerJ, 2020, 8:e9327.

[60] He B, Liu J, Guo L, et al. Recovery of ecosystem carbon and energy fluxes from the 2003 drought in Europe and the 2012 drought in the United States[J]. Geophysical Research Letters, 2018, 45(10): 4879-4888.

[61] Rdenbeck C, Zaehle S, Keeling R, et al. The European carbon cycle response to heat and drought as seen from atmospheric CO2 data for 1999-2018[J]. Philosophical Transactions of The Royal Society B Biological Sciences, 2020, 375(1810):20190506.

[62] Li X, Li Y, Chen A, et al. The impact of the 2009/2010 drought on vegetation growth and terrestrial carbon balance in Southwest China[J]. Agricultural and forest meteorology, 2019, 269: 239-248.

[63] Hammond W M, Adams H D. Dying on time: traits influencing the dynamics of tree mortality risk from drought[J]. Tree physiology, 2019, 39(6): 906-909.

[64] 庞勇, 荚文, 覃先林, 等. 机载光学全谱段遥感林火监测[J]. 遥感学报, 2020, 24(10):1280-1292.

[65] Seidl R, Klonner G, Rammer W, et al. Invasive alien pests threaten the carbon stored in Europe’s forests[J]. Nature communications, 2018, 9(1): 1-10.

[66] 周广胜, 张新时. 全球变化的中国气候一植被分类研究[J]. 植物学报, 1996, 38(1) : 8-17.

[67] 缑倩倩, 屈建军, 韩致文, 等. 西北干旱半干旱区生态系统碳循环研究进展[J]. 中国农学通报, 2013, 29(35):205-210.

[68] 杨智杰, 郑裕雄, 陈仕东, 等. 应用小波多尺度分析亚热带森林土壤异养呼吸特征[J]. 生态学报, 2018, 38(14):5078-5086.

[69] Pei Z Y, Ouyang H, Zhou C P, et al. Carbon Balance in an Alpine Steppe in the Qinghai-Tibet Plateau[J]. Journal of Integrative Plant Biology, 2009, 51(5):521-526.

[70] 杨星星, 杨云川, 邓思敏, 等. 基于SPEI的广西干旱综合特征及农业旱灾风险研究[J]. 水土保持研究, 2020, 27(4):113-121.

[71] 罗纲, 阮甜, 陈财, 等. 农业干旱与气象干旱关联性——以淮河蚌埠闸以上地区为例[J]. 自然资源学报, 2020, 35(4):977-991.

[72] 周帅, 王义民, 畅建霞, 等. 黄河流域干旱时空演变的空间格局研究[J]. 水利学报, 2019, 50(10):1231-1241.

[73] 刘小刚, 冷险险, 孙光照, 等. 基于1961—2100年SPI和SPEI的云南省干旱特征评估[J]. 农业机械学报, 2018, 49(12):236-245.

[74] 于家瑞, 艾萍, 袁定波, 等. 基于SPI的黑龙江省干旱时空特征分析[J]. 干旱区地理, 2019, 42(5):1059-1068.

[75] 郭梦, 张奇莹, 钱会, 等. 基于SPEI干旱指数的陕西省干旱时空分布特征分析[J].水资源与水工程学报, 2019, 30(3):127-132.

[76] 王景才, 郭佳香, 徐蛟, 等. 近55年淮河上中游流域气候要素多时间尺度演变特征及关联性分析[J]. 地理科学, 2017, 37(4):611-619.

[77] 贺敏, 宋立生, 王展鹏, 等. 基于多源数据的干旱监测指数对比研究——以西南地区为例[J]. 自然资源学报, 2018, 33(7):1257-1269.

[78] Zhang L, Xiao J, Li J, et al. The 2010 spring drought reduced primary productivity in southwestern China[J]. Environmental Research Letters, 2012, 7(4):045706.

[79] 王东, 张勃, 安美玲, 等. 基于SPEI的西南地区近53a干旱时空特征分析[J]. 自然资源学报, 2014, 29(6):1003-1016.

[80] 王亚林, 丁忆, 胡艳, 等. 中国灌木生态系统的干旱化趋势及其对植被生长的影响[J]. 生态学报, 2019, 39(6):2054-2062.

[81] 高云建, 陈宁生, 胡桂胜, 等. 西南山区泥石流灾害与厄尔尼诺-拉尼娜事件时空耦合关系分析[J]. 长江科学院院报, 2019, 36(4):43-48.

[82] 王伟, 冯爽. 西南地区夏季旱涝与厄尔尼诺(拉尼娜)的关系分析[J]. 成都信息工程学院学报, 2012, 27(4):412-418.

[83] Li X, Xiao J F, Kimball J S, et al. Synergistic use of SMAP and OCO-2 data in assessing the responses of ecosystem productivity to the 2018 U.S. drought[J]. Remote Sensing of Environment, 2020, 251:112062.

[84] Gu Y X, Wylie B K, Howard D M, et al. NDVI saturation adjustment: A new approach for improving cropland performance estimates in the Greater Platte River Basin, USA[J]. Ecological Indicators, 2013, 30: 1-6.

[85] Awuma K S, Stuth J W, Kaitho R, et al. Application of normalized differential Vegetation Index and geostatistical techniques in cattle diet quality mapping in Ghana[J]. Outlook on Agriculture, 2007, 36(3): 205-213.

[86] 邱文君. 干旱对西南地区植被净初级生产力的影响研究[D]. 济南:山东师范大学, 2013.

[87] 张璐, 王静, 施润和. 2000—2010年东北三省碳源汇时空动态遥感研究[J]. 华东师范大学学报(自然科学版), 2015(4):164-173.

[88] 潘竟虎, 文岩. 中国西北干旱区植被碳汇估算及其时空格局[J]. 生态学报, 2015, 35(23):7718-7728.

[89] Wang X, Zhu B, Li C, et al. Dissecting soil CO2 fluxes from a subtropical forest in China by integrating field measurements with a modeling approach[J]. Geoderma, 2011, 161(1-2): 88-94.

[90] 王小国, 朱波, 高美荣, 等. 川中丘陵区桤柏混交林地土壤CO2释放与Forest-DNDC模型模拟[J]. 北京林业大学学报, 2008, (2):27-32.

[91] 田祥宇, 涂利华, 胡庭兴, 等. 华西雨屏区苦竹人工林土壤呼吸各组分特征及其温度敏感性[J]. 应用生态学报, 2012, 23(2):293-300.

[92] 卢华正, 沙丽清, 王君, 等. 西双版纳热带季节雨林与橡胶林土壤呼吸的季节变化[J]. 应用生态学报, 2009, 20(10):2315-2322.

[93] Nayak R K, Patel N R, Dadhwal V K . Spatio-temporal variability of net ecosystem productivity over India and its relationship to climatic variables[J]. Environmental Earth Sciences, 2015, 74(2):1743-1753.

[94] 陈迪. 基于遥感技术的甘南州陆地生态系统NEP研究[D]. 兰州:兰州大学, 2016.

[95] Gnanamoorthy P, Selvam V, Burman P K D, et al. Seasonal variations of net ecosystem (CO2) exchange in the Indian tropical mangrove forest of Pichavaram[J]. Estuarine, Coastal and Shelf Science, 2020, 243: 106828.

[96] Koju U A, Zhang J, Maharjan S, et al. Analysis of spatiotemporal dynamics of forest Net Primary Productivity of Nepal during 2000–2015[J]. International Journal of Remote Sensing, 2020, 41(11): 4336-4364.

[97] 何敏, 王鹤松, 孙建新. 基于植被生产力的西南地区生态系统脆弱性特征[J]. 应用生态学报, 2019, 30(2):73-82.

[98] Li P, Sayer E J, Jia Z, et al. Deepened winter snow cover enhances net ecosystem exchange and stabilizes plant community composition and productivity in a temperate grassland[J]. Global change biology, 2020, 26(5): 3015-3027.

[99] 李亚婷, 宜树华, 侯扶江, 等. 高寒草甸生长盛期CO2交换特征对欧拉羊短期放牧的响应[J]. 草业学报, 2017, 26(4):24-32.