气象要素对华北地区夏季植被覆盖度的影响

白慧敏; 龚志强; 孙桂全; 李莉; 周莉

doi:10.3878/j.issn.1006-9895.2102.20233

气象要素对华北地区夏季植被覆盖度的影响

doi: 10.3878/j.issn.1006-9895.2102.20233

白慧敏^1,,
龚志强^2, ,,
孙桂全^{1, 3},
李莉⁴,
周莉⁵

1.
山西大学复杂系统研究所，太原030006
2.
中国气象局国家气候中心开放实验室，北京100081
3.
中北大学理学院，太原030051
4.
山西大学计算机与信息技术学院，太原030006
5.
中国气象科学研究院，北京100081

基金项目: 国家重点研发计划项目2018YFA0606301、2018YFE0109600，国家自然科学基金项目41875100、42075057、42075029

详细信息

作者简介:
白慧敏，女，1996年出生，硕士研究生，主要从事气候变化对华北生态影响研究。E-mail: bhm0828@126.com

通讯作者:
龚志强，E-mail: gzq0929@126.com

中图分类号: P467
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出版历程
- 收稿日期: 2020-11-24
- 录用日期: 2021-07-16
- 网络出版日期: 2021-08-27
- 刊出日期: 2022-01-18

Influence of Meteorological Elements on Summer Vegetation Coverage in North China

Huimin BAI^1
,,
Zhiqiang GONG^{2
, ,},
Guiquan SUN^{1, 3},
Li LI⁴,
Li ZHOU⁵

1.
Complex Systems Research Center, Shanxi University, Taiyuan 030006
2.
Laboratory for Climate Studies, National Climate Center, China Meteorological Administration, Beijing 100081
3.
Department of Mathematics, North University of China, Taiyuan 030051
4.
School of Computer and Information Technology, Shanxi University, Taiyuan 030006
5.
Chinese Academy of Meteorological Sciences, Beijing 100081

Funds: National Key Research and Development Program of China (Grants 2018YFA0606301, 2018YFE0109600), National Natural Science Foundation of China (Grants 41875100, 42075057, 42075029)

摘要

摘要: 植被覆盖对气候变化极其敏感，华北区地处我国半干旱—半湿润过渡地区，气象因子对该地区植被覆盖有重要的影响，但缺少有效的数理模型定量刻画气象要素对植被的影响。因此，本文基于华北区2000～2018年中分辨率成像光谱仪的植被覆盖数据和主要气象要素数据，开展了多气象要素影响植被覆盖度的研究，初步建立了华北夏季植被覆盖度与气象要素的关系。研究表明：（1）华北存在向暖干转变的趋势，且夏季植被覆盖度与降水、相对湿度呈正相关，与气温、日照时数和地温呈负相关。（2）影响华北地区植被覆盖度最重要的气象要素是相对湿度，体现了温度和降水的共同作用。（3）基于多元回归法和最小二乘法可以定量描述气象要素变化对植被覆盖度的可能影响。其中，五变量影响模型对植被覆盖度模拟的相关系数略偏高，因此，基于五变量气象要素可以更好的模拟植被覆盖度的变化。研究结果有利于了解气象要素如何影响植被生态系统，进而为国家生态文明建设提供理论参考依据。
- 华北 /
- 植被覆盖度 /
- 气象要素 /
- 评估模型
Abstract: Vegetation coverage is extremely sensitive to climate change. North China is located in the semiarid and subhumid transitional region of China. Meteorological factors have an important impact on the vegetation coverage in this area. However, effective mathematical models to quantitatively describe the influences of meteorological elements on vegetation are lacking. Therefore, based on the vegetation coverage data of the Moderate Resolution Imaging Spectroradiometer and main meteorological element data in North China from 2000 to 2018, the authors analyzed the influence of multiple meteorological elements on vegetation coverage and initially established the relationship between the summer vegetation coverage and meteorological elements in North China. The main study conclusions are summarized as follows: (1) A trend toward warm and dry climate conditions in North China is observed, and summer vegetation coverage is positively correlated with precipitation and relative humidity and negatively correlated with temperature, sunshine hours, and ground temperature. (2) The most important meteorological element affecting the vegetation coverage in North China is relative humidity, which reflects the combined effect of temperature and precipitation. (3) The multiple regression and least squares methods can quantitatively describe the possible impact of the changes in meteorological elements on vegetation coverage. Therefore, the five-variable combination of meteorological elements can better simulate the change in vegetation coverage. Results elucidate how meteorological elements affect vegetation ecosystem and provide a theoretical reference for constructing national ecological civilization.
- North China /
- Vegetation coverage /
- Meteorological elements /
- Evaluation model

HTML全文

图 1 2000～2018年各月份华北地区的植被覆盖度平均值的空间分布

Figure 1. Spatial distributions of the average vegetation coverage in North China for each month from 2000 to 2018

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图 2 2000～2018年华北地区夏季（a1–a3）平均植被覆盖度、（b1–b3）平均气温、（c1–c3）累积降水、（d1–d3）平均相对湿度、（e1–e3）平均日照时数、（f1–f3）平均地温的年变化：（a1–f1）原始变化曲线；（a2–f2）去线性趋势后的变化曲线；（a3–f3）累积距平曲线。p＜0.01表示通过99%信度水平的显著性检验，p＞0.05表示未通过95%信度水平的显著性检验

Figure 2. Annual variations of (a1–a3) mean vegetation coverage (C_V), (b1–b3) mean temperature (T), (c1–c3) cumulative precipitation (P), (d1–d3) mean relative humidity (RH), (e1–e3) mean sunshine duration (SD), (f1–f3) mean ground surface temperature (GST) in summer in North China from 2000 to 2018: (a1–f1) original change curves; (a2–f2) change curves after de-linear trend regression; (a3–f3) cumulative anomalies curves. p＜0.01 indicates passing the test at 99% confidence level, and p＞0.05 indicates failing the test at 95% confidence level

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图 3 2000～2018年华北夏季植被覆盖度（a）平均值、（b）线性趋势系数、（c）线性趋势显著性水平、（d）变异系数的空间分布

Figure 3. Spatial distributions of (a) mean value, (b) linear trend coefficients, (c) linear trend significance level, and (d) variation coefficient for summer vegetation coverage in North China from 2000 to 2018

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图 4 2000～2018年华北夏季（a）温度（单位：°C）、（b）降水（单位：mm）、（c）相对湿度、（d）日照时数（单位：h）以及（e）地温（单位：°C）的多年气候态空间分布

Figure 4. Climatological spatial distributions of (a) temperature (units: °C), (b) precipitation (units: mm), (c) relative humidity, (d) sunshine duration (units: h), and (e) ground surface temperature (units: °C) in summer in North China from 2000 to 2018

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图 5 2000～2018年华北夏季（6～8月）植被覆盖度与（a1–e1）夏季（6～8月）、（a2–e2）夏季超前一个月（5～7月）的气温、降水、相对湿度、日照时数和地温的相关系数。NC（Negative correlation）表示负相关，PC（Positive correlation）表示正相关，p＜0.1表示通过90%信度水平的显著性检验，p＞0.1表示未通过90%信度水平的显著性检验

Figure 5. Correlations between vegetation coverage in summer (June–August) and temperature, precipitation, relative humidity, sunshine duration, and ground surface temperature (a1–e1) in summer (June–August), (a2–e2) in May–July in North China from 2000 to 2018. NC denotes negative correlation; PC denotes positive correlation; p＜0.1 indicates passing the test at 90% confidence level; and p＞0.1 indicates failing the test at 90% confidence level

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图 6 2000～2018年华北地区去除线性趋势后的植被覆盖度的观测值、8个模型拟合值。2000～2015年为历史拟合值，2016～2018年为独立样本模拟值

Figure 6. Observed and fitting values of eight models of VCR (Vegetation Coverage Residual) in North China from 2000 to 2018. 2000–2015: historical fitted values; 2016–2018: independent sample simulated values

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图 7 2016～2018年华北夏季植被覆盖度的（a1–c1）观测值、（a2–c2）多元线性回归（MLR）方法的模拟值、（a3–c3）偏最小二乘回归（PLS）方法的模拟值

Figure 7. (a1–c1) Observed values, (a2–c2) fitted values for MLR (Multiple Linear Regression), (a3–c3) fitted values for PLS (Partial Least Squares Regression) of VCR in North China from 2016 to 2018

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表 1 2000～2018年华北夏季（6～8月）植被覆盖度与不同时间段（6～8月、5～7月、4～6月、3～5月）的各气象要素的相关系数

Table 1. Correlation coefficients between vegetation coverage in summer (June–August) and meteorological elements in different periods (June–August, May–July, April–June, March–May) in North China from 2000 to 2018

	6～8月植被覆盖度与不同时间段气象要素的相关系数
	6～8月	5～7月	4～6月	3～5月
T	−0.39	−0.58*	−0.33	−0.35
P	0.47^*	0.45	0.26	0.42
RH	0.68^**	0.57^*	0.23	0.23
SD	−0.75^***	−0.72^***	−0.23	−0.28
GST	−0.61^**	−0.68^**	−0.40	−0.41
注：^、^、^**分别表示通过95%、99%、99.9%信度水平的显著性检验。

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表 2 气象要素对植被覆盖度影响的定量化模型的统计数据

Table 2. Statistical data of quantitative model for the impact of meteorological elements on vegetation coverage

	自变量	多元线性回归			偏最小二乘回归
	自变量	复相关系数	均方根误差	符号一致率	相关系数	均方根误差	符号一致率
五变量模型	T、P、RH、SD、GST	0.76^*	0.0133	81%	0.85^**	0.0116	88%
四变量模型	T、P、SD、GST	0.76^*	0.0127	81%	0.84^**	0.0117	88%
	T、RH、SD、GST	0.75^*	0.0130	81%	0.83^**	0.0119	88%
	P、RH、SD、GST	0.73^*	0.0136	88%	0.85^**	0.0114	88%
	T、P、RH、GST	0.70^*	0.0142	75%	0.82^**	0.0124	75%
	T、P、RH、SD	0.69^*	0.0144	94%	0.83^**	0.0120	88%
三变量模型	T、SD、GST	0.74^**	0.0126	81%	0.80^**	0.0130	94%
	P、SD、GST	0.72^*	0.0131	88%	0.85^**	0.0114	88%
	RH、SD、GST	0.70^*	0.0136	88%	0.84^**	0.0118	88%
	P、RH、GST	0.69^*	0.0139	75%	0.83^**	0.0122	75%
	T、RH、GST	0.68^*	0.0140	75%	0.81^**	0.0127	81%
	T、P、GST	0.68^*	0.0140	75%	0.80^**	0.0130	75%
	T、RH、SD	0.68^*	0.0141	88%	0.82^**	0.0123	88%
	T、P、SD	0.67^*	0.0142	94%	0.82^**	0.0123	94%
	T、P、RH	0.66^*	0.0145	81%	0.81^**	0.0128	81%
	P、RH、SD	0.66^*	0.0146	94%	0.81^**	0.0127	94%
二变量模型	RH、GST	0.67^**	0.0138	81%	0.82^**	0.0126	75%
	P、GST	0.66^**	0.0139	81%	0.81^**	0.0127	75%
	RH、SD	0.65^*	0.0141	94%	0.81^**	0.0128	94%
	SD、GST	0.64^*	0.0143	88%	0.80^**	0.0129	81%
	P、RH	0.62^*	0.0148	88%	0.75^**	0.0146	81%
	P、SD	0.62^*	0.0148	94%	0.79^**	0.0133	94%
	T、P	0.59^*	0.0152	81%	0.77^**	0.0138	81%
	T、GST	0.59^*	0.0152	75%	0.77^**	0.0138	75%
	T、RH	0.58^*	0.0154	81%	0.80^**	0.0131	81%
	T、SD	0.58^*	0.0154	88%	0.76^**	0.0139	88%
注：^表示通过99%信度水平的显著性检验，^*表示通过99.9%信度水平的显著性检验。

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表 3 四组气象要素影响植被模型中的最优模型及其统计数据

Table 3. Best model and statistical data of the four groups of meteorological factors affecting vegetation coverage

方法	自变量	模型方程	相关系数	RMSE	符号一致率
方法	自变量	模型方程	相关系数	RMSE	2000～2015	2016～2018
多元线性回归	T、P、RH、SD、GST	y=0.0007+0.0383T+0.0001P−0.0002RH−0.0197SD−0.0365GST	0.76^*	0.0133	81%	100%
	T、P、SD、GST	y=0.0008+0.0375T+0.0001P−0.0193SD−0.0358GST	0.76^*	0.0127	81%	100%
	T、SD、GST	y=0.0002+0.0500T−0.0235SD−0.0431GST	0.74^**	0.0126	81%	100%
	RH、GST	y=0.0019+0.0057RH−0.0119GST	0.67^**	0.0138	81%	67%
偏最小二乘回归	T、P、RH、SD、GST	y=−00047T+0.0001P+0.0028RH−0.0127SD−0.0062GST	0.84^**	0.0116	88%	100%
	P、RH、SD、GST	y=0.0001P+0.0027RH−0.0145SD−0.0096GST	0.85^**	0.0114	88%	100%
	P、SD、GST	y=0.0001P−0.0165SD−0.0126GST	0.85^**	0.0114	88%	100%
	RH、GST	y=0.0057RH−0.0119GST	0.82^**	0.0126	75%	67%
注：^表示通过99%信度水平的显著性检验，^*表示通过99.9%信度水平的显著性检验。

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表 4 五变量模型模拟植被覆盖度的统计量

Table 4. Statistical value of the five-variable (temperature, precipitation, relative humidity, sunshine duration, ground surface temperature) model simulating vegetation coverage

方法	相关系数	均方根误差	符号一致率
方法	2000～2015	2000～2015	2000～2015	2016	2017	2018	2016～2018
多元线性回归	0.46	0.044	68%	49%	59%	56%	55%
偏最小二乘回归	0.61	0.039	71%	51%	59%	62%	57%
注：2000～2015年为建模拟合值，2016～2018年为独立样本模拟值。

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参考文献(41)

[1]	A D, Zhao W J, Qu X Y, et al. 2016. Spatio-temporal variation of vegetation coverage and its response to climate change in North China plain in the last 33 years [J]. International Journal of Applied Earth Observation and Geoinformation, 53: 103−117. doi: 10.1016/j.jag.2016.08.008
[2]	阿多, 赵文吉, 宫兆宁, 等. 2017. 198l~2013华北平原气候时空变化及其对植被覆盖度的影响 [J]. 生态学报, 37(2): 576−592. doi: 10.5846/stxb201507301600 A Duo, Zhao Wenji, Gong Zhaoning, et al. 2017. Temporal analysis of climate change and its relationship with vegetation cover on the North China plain from 1981 to 2013 [J]. Acta Ecologica Sinica (in Chinese), 37(2): 576−592. doi: 10.5846/stxb201507301600
[3]	曹芸. 2011. 日照时数3S集成模型研究 [D]. 南京信息工程大学硕士学位论文. Cao Yun. 2011. 3S integration model of sunshine duration [D]. M. S. thesis (in Chinese), Nanjing University of Information Science and Technology. doi: 10.7666/d.y1891485
[4]	Chen C, He B, Guo L L, et al. 2018. Identifying critical climate periods for vegetation growth in the Northern Hemisphere [J]. J. Geophys. Res. :Biogeosci., 123(8): 2541−2552. doi: 10.1029/2018JG004443
[5]	Fensholt R, Langanke T, Rasmussen K, et al. 2012. Greenness in semi-arid areas across the globe 1981–2007—An Earth observing satellite based analysis of trends and drivers [J]. Remote Sensing of Environment, 121: 144−158. doi: 10.1016/j.rse.2012.01.017
[6]	高春娟, 夏晓剑, 师恺, 等. 2012. 植物气孔对全球环境变化的响应及其调控防御机制 [J]. 植物生理学报, 48(1): 19−28. Gao Chunjuan, Xia Xiaojian, Shi Kai, et al. 2012. Response of stomata to global climate changes and the underlying regulation mechanism of stress responses [J]. Plant Physiology Journal (in Chinese), 48(1): 19−28.
[7]	高海东, 庞国伟, 李占斌, 等. 2017. 黄土高原植被恢复潜力研究 [J]. 地理学报, 72(5): 863−874. doi: 10.11821/dlxb201705008 Gao Haidong, Pang Guowei, Li Zhanbin, et al. 2017. Evaluating the potential of vegetation restoration in the Loess Plateau [J]. Acta Geographica Sinica (in Chinese), 72(5): 863−874. doi: 10.11821/dlxb201705008
[8]	Gong Z N, Zhao S Y, Gu J Z. 2017. Correlation analysis between vegetation coverage and climate drought conditions in North China during 2001–2013 [J]. Journal of Geographical Sciences, 27(2): 143−160. doi: 10.1007/s11442-017-1369-5
[9]	侯美亭, 赵海燕, 王筝, 等. 2013. 基于卫星遥感的植被NDVI对气候变化响应的研究进展 [J]. 气候与环境研究, 18(3): 353−364. doi: 10.3878/j.issn.1006-9585.2012.11137 Hou Meiting, Zhao Haiyan, Wang Zheng, et al. 2013. Vegetation responses to climate change by using the satellite-derived normalized difference vegetation index: A review [J]. Climatic and Environmental Research (in Chinese), 18(3): 353−364. doi: 10.3878/j.issn.1006-9585.2012.11137
[10]	侯美亭, 胡伟, 乔海龙, 等. 2015. 偏最小二乘(PLS)回归方法在中国东部植被变化归因研究中的应用 [J]. 自然资源学报, 30(3): 409−422. doi: 10.11849/zrzyxb.2015.03.005 Hou Meiting, Hu Wei, Qiao Hailong, et al. 2015. Application of Partial Least Squares (PLS) regression method in attribution of vegetation change in eastern China [J]. Journal of Natural Resources (in Chinese), 30(3): 409−422. doi: 10.11849/zrzyxb.2015.03.005
[11]	Hu P, Wang M H, Yang L, et al. 2018. Water vapor transport related to the interdecadal shift of summer precipitation over northern East Asia in the late 1990s [J]. J. Meteor. Res., 32(5): 781−793. doi: 10.1007/s13351-018-8021-x
[12]	黄辉, 于贵瑞, 孙晓敏, 等. 2007. 华北平原冬小麦冠层导度的环境响应及模拟 [J]. 生态学报, 27(12): 5209−5221. doi: 10.3321/j.issn:1000-0933.2007.12.031 Huang Hui, Yu Guirui, Sun Xiaomin, et al. 2007. The environmental responses and simulation of canopy conductance in a winter wheat field of North China plain [J]. Acta Ecologica Sinica (in Chinese), 27(12): 5209−5221. doi: 10.3321/j.issn:1000-0933.2007.12.031
[13]	黄荣辉, 徐予红, 周连童. 1999. 我国夏季降水的年代际变化及华北干旱化趋势 [J]. 高原气象, 18(4): 465−476. doi: 10.3321/j.issn:1000-0534.1999.04.001 Huang Ronghui, Xu Yuhong, Zhou Liantong. 1999. The interdecadal variation of summer precipitations in China and the drought trend in North China [J]. Plateau Meteorology (in Chinese), 18(4): 465−476. doi: 10.3321/j.issn:1000-0534.1999.04.001
[14]	Ji Y H, Zhou G S, Wang S D, et al. 2020. Prominent vegetation greening and its correlation with climatic variables in northern China [J]. Environmental Monitoring and Assessment, 192(10): 636. doi: 10.1007/s10661-020-08593-8
[15]	Jiang L L, Guli·Jiapaer N, Bao A M, et al. 2017. Vegetation dynamics and responses to climate change and human activities in central Asia [J]. Science of the Total Environment, 599–600: 967–980. doi: 10.1016/j.scitotenv.2017.05.012
[16]	Kawabata A, Ichii K, Yamaguchi Y. 2001. Global monitoring of interannual changes in vegetation activities using NDVI and its relationships to temperature and precipitation [J]. Int. J. Remote Sens., 22(7): 1377−1382. doi: 10.1080/01431160119381
[17]	Kong D D, Zhang Q, Singh V P, et al. 2017. Seasonal vegetation response to climate change in the Northern Hemisphere (1982–2013) [J]. Global and Planetary Change, 148: 1−8. doi: 10.1016/j.gloplacha.2016.10.020
[18]	李明星, 马柱国. 2012. 中国气候干湿变化及气候带边界演变: 以集成土壤湿度为指标 [J]. 科学通报, 57(28–29): 2740–2754. Li Mingxing, Ma Zhuguo. 2013. Soil moisture-based study of the variability of dry-wet climate and climate zones in China [J]. Chinese Science Bulletin, 58(4): 531–544. doi: 10.1007/s11434-012-5428-0
[19]	Li P L, Hu Z M, Liu Y W. 2020. Shift in the trend of browning in southwestern Tibetan Plateau in the past two decades [J]. Agricultural and Forest Meteorology, 287: 107950. doi: 10.1016/j.agrformet.2020.107950
[20]	李彦, 陈祖森, 张保, 等. 2005. 参考作物蒸发蒸腾量的多元线性回归模型研究 [J]. 新疆农业大学学报, 28(1): 70−72. doi: 10.3969/j.issn.1007-8614.2005.01.017 Li Yan, Chen Zusen, Zhang Bao, et al. 2005. Study on the method of reference crop evapo-transpiration by dependence analysis [J]. Journal of Xinjiang Agricultural University (in Chinese), 28(1): 70−72. doi: 10.3969/j.issn.1007-8614.2005.01.017
[21]	Liu Z S, Yang W H, Yu X L. 2019. A new predictive model for plants photosynthesis influenced by major climatic conditions [J]. IOP Conference Series:Earth and Environmental Science, 291: 012016. doi: 10.1088/1755-1315/291/1/012016
[22]	Nemani R R, Keeling C D, Hashimoto H, et al. 2003. Climate-driven increases in global terrestrial net primary production from 1982 to 1999 [J]. Science, 300(5625): 1560−1563. doi: 10.1126/science.1082750
[23]	Piao S, Nan H J, Huntingford C, et al. 2014. Evidence for a weakening relationship between interannual temperature variability and northern vegetation activity [J]. Nature Communications, 5(1): 5018. doi: 10.1038/ncomms6018
[24]	Potter C S, Brooks V. 1998. Global analysis of empirical relations between annual climate and seasonality of NDVI [J]. Int. J. Remote Sens., 19(15): 2921−2948. doi: 10.1080/014311698214352
[25]	Prescott J. 1940. Evaporation from a water surface in relation to solar radiation [J]. Transactions of the Royal Society of South Australia, 64: 114−118.
[26]	Qu B, Zhu W B, Jia S F, et al. 2015. Spatio-temporal changes in vegetation activity and its driving factors during the growing season in China from 1982 to 2011 [J]. Remote Sensing, 7(10): 13729−13752. doi: 10.3390/rs71013729
[27]	Shen M G, Piao S, Jeong S J, et al. 2015. Evaporative cooling over the Tibetan Plateau induced by vegetation growth [J]. Proceedings of the National Academy of Sciences of the United States of America, 112(30): 9299−9304. doi: 10.1073/pnas.1504418112
[28]	Shi W J, Tao F L, Zhang Z. 2013. A review on statistical models for identifying climate contributions to crop yields [J]. Journal of Geographical Sciences, 23(3): 567−576. doi: 10.1007/s11442-013-1029-3
[29]	孙康慧, 曾晓东, 李芳. 2019. 1980~2014年中国生态脆弱区气候变化特征分析 [J]. 气候与环境研究, 24(4): 455−468. doi: 10.3878/j.issn.1006-9585.2018.18058 Sun Kanghui, Zeng Xiaodong, Li Fang. 2019. Climate change characteristics in ecological fragile zones in China during 1980–2014 [J]. Climatic and Environmental Research (in Chinese), 24(4): 455−468. doi: 10.3878/j.issn.1006-9585.2018.18058
[30]	Tian H Q, Lu C Q, Yang J, et al. 2015. Global patterns and controls of soil organic carbon dynamics as simulated by multiple terrestrial biosphere models: Current status and future directions [J]. Global Biogeochemical Cycles, 29(6): 775−792. doi: 10.1002/2014GB005021
[31]	王长燕, 赵景波, 李小燕. 2006. 华北地区气候暖干化的农业适应性对策研究 [J]. 干旱区地理, 29(5): 646−652. doi: 10.3321/j.issn:1000-6060.2006.05.005 Wang Changyan, Zhao Jingbo, Li Xiaoyan. 2006. Study on agricultural adaptation to warming and drying climate in North China [J]. Arid Land Geography (in Chinese), 29(5): 646−652. doi: 10.3321/j.issn:1000-6060.2006.05.005
[32]	王惠文. 1999. 偏最小二乘回归方法及其应用[M]. 北京: 国防工业出版社, 200–210 Wang Huiwen. 1999. Partial Least Squares Regression Method and Applications (in Chinese) [M]. Beijing: National Defense Industry Press, 200–210.
[33]	魏凤英. 2007. 现代气候统计诊断与预测技术[M]. 2版. 北京: 气象出版社, 27–59, 188–194 Wei Fengying. 2007. Modern Climate Statistical Analysis and Prediction Techniques (in Chinese) [M]. 2nd ed. Beijing: China Meteorological Press, 27–59, 188–194.
[34]	Xiao J, Moody A. 2005. Geographical distribution of global greening trends and their climatic correlates: 1982–1998 [J]. Int. J. Remote Sens., 26(11): 2371−2390. doi: 10.1080/01431160500033682
[35]	杨思遥, 孟丹, 李小娟, 等. 2018. 华北地区2001~2014年植被变化对SPEI气象干旱指数多尺度的响应 [J]. 生态学报, 38(3): 1028−1039. doi: 10.5846/stxb201611242398 Yang Siyao, Meng Dan, Li Xiaojuan, et al. 2018. Multi-scale responses of vegetation changes relative to the SPEI meteorological drought index in North China in 2001–2014 [J]. Acta Ecologica Sinica (in Chinese), 38(3): 1028−1039. doi: 10.5846/stxb201611242398
[36]	张茂省, 卢娜. 2013. 植被生态对气候变化和人类活动的响应——以陕西省榆林能源化工基地为例 [J]. 地质论评, 59(5): 909−918. doi: 10.16509/j.georeview.2013.05.003 Zhang Maosheng, Lu Na. 2013. Responses of vegetation ecology to the climate changes and human activities—A case study at Yulin energy & chemical industry base [J]. Geological Review (in Chinese), 59(5): 909−918. doi: 10.16509/j.georeview.2013.05.003
[37]	Zhao L, Dai A G, Dong B. 2018. Changes in global vegetation activity and its driving factors during 1982–2013 [J]. Agricultural and Forest Meteorology, 249: 198−209. doi: 10.1016/j.agrformet.2017.11.013
[38]	Zhou C, Shi R H, Zhang C, et al. 2014. Spatio-temporal distribution of NDVI and its correlation with climatic factors in eastern China during 1998–2008 [C]//Proceedings Volume 9221, Remote Sensing and Modeling of Ecosystems for Sustainability XI. San Diego, California, United States: SPIE. doi: 10.1117/12.2060768
[39]	周丹, 罗静, 郑玲, 等. 2019. 基于格点数据的华北地区气象干旱特征及成因分析 [J]. 水土保持研究, 26(4): 195−202. doi: 10.13869/j.cnki.rswc.2019.04.030 Zhou Dan, Luo Jing, Zheng Ling, et al. 2019. Characteristics and causes of meteorological drought in North China based on grid data [J]. Research of Soil and Water Conservation (in Chinese), 26(4): 195−202. doi: 10.13869/j.cnki.rswc.2019.04.030
[40]	周广胜, 王玉辉, 白莉萍, 等. 2004. 陆地生态系统与全球变化相互作用的研究进展 [J]. 气象学报, 62(5): 692−707. doi: 10.3321/j.issn:0577-6619.2004.05.014 Zhou Guangsheng, Wang Yuhui, Bai Liping, et al. 2004. Study on the interaction between terrestrial ecosystems and global change [J]. Acta Meteorologica Sinica (in Chinese), 62(5): 692−707. doi: 10.3321/j.issn:0577-6619.2004.05.014
[41]	祝聪, 彭文甫, 张丽芳, 等. 2019. 2006~2016年岷江上游植被覆盖度时空变化及驱动力 [J]. 生态学报, 39(5): 1583−1594. doi: 10.5846/stxb201805040993 Zhu Cong, Peng Wenfu, Zhang Lifang, et al. 2019. Study of temporal and spatial variation and driving force of fractional vegetation cover in upper reaches of Minjiang River from 2006 to 2016 [J]. Acta Ecologica Sinica (in Chinese), 39(5): 1583−1594. doi: 10.5846/stxb201805040993