利用遥感数据优化物候模型时样本选择的新方法

doi:10.17521/cjpe.2015.0026

摘要/Abstract

摘要：

植被物候模型是生态系统模型的重要组成部分, 其精度对准确地模拟陆面和大气之间的能量和物质交换具有重要意义。利用遥感获取空间物候信息并与气候数据进行耦合分析是在中亚干旱区等地面物候观测数据缺乏的地区构建物候模型的重要方法。为减小混合植被像元和气候数据资料的内在误差及二者在空间尺度的不匹配对物候模型构建产生的影响, 该研究提出一种在气象站点周围选取满足规定规则集的“代表植被类型像元”作为样本点的选择方法, 以代表植被类型像元的遥感物候数据和气象站点数据为基础, 结合经典物候模型和改进物候模型, 在粒子群优化算法支持下, 分别以独立的拟合与评价样本数据, 完成了荒漠草原植被与落叶阔叶林的模型拟合与评价。研究发现中亚干旱区荒漠草原植被的最优模型为温度-降水修正模型, 落叶阔叶林的最优模型为替代模型。通过此方法模型总体精度在8-10 d左右。结果表明此方法在气候数据和植物物候空间匹配方面有改进, 有助于提高物候模型精度。

关键词: 干旱区, 物候模型, 遥感, 中亚

Abstract: Aims

Phenology model is considered as the most efficient tool to assess the phonological responses of plants to future climate change. Furthermore, as an important component in dynamic ecological models, the performance of phenology model is of significance for the precision in simulating mass and energy exchanges between land and atmosphere. Combining long time series remote sensing data and climate data to construct regional phenology model may be the only way to solve the problem of deficiency in lacking in-situ observational data on phenology and species-specific phenology models. The objective of this study was to develop a new method of sample selections for constructing phenology in the arid zone of Central Asia where only sparse observational data are available.

Methods

Based the phenology data retrieved from 250 m-resolution MODIS images for the period 2000-2010, a new method is developed for constructing the vegetation phenology model in arid zone. A set of rules were built to select the representative pixels of PFTs (plant function types) surrounding the climate station, making sure that the climate of the representative pixels of PFTs could be represented by the observed meteorological data at the station, and then the phenology data on the representative pixels of PFTs were extracted from the MODIS images and the corresponding climatic data were set as the sample for model fitting and assessment. Forty-six representative pixels of PFTs for desert grassland vegetation and broadleaved deciduous forests were selected under the rules. The phenology model parameters were estimated using data from odd-numbered years and the simulation accuracy was assessed with the independent even-year data. Particle swarm optimization algorithm was used to parameterize the pre-selected model. The root-mean-square error and coefficient of determination were used to examine the performance of model with independent data.

Important findings

The best model for desert steppe vegetation was the modified temperature-precipitation model and the best model for deciduous broadleaved forest was the alternative model. Compared with other documented findings, the new method was proven feasible, and the results also suggested that this new method may improve the spatial match of climatic data and vegetation phenology data, and therefore contribute to improvement of the phenology model accuracy.

Key words: arid zone, phenology model, remote sensing, Central Asia

马勇刚, 张弛, 陈曦. 利用遥感数据优化物候模型时样本选择的新方法. 植物生态学报, 2015, 39(3): 264-274. DOI: 10.17521/cjpe.2015.0026

MA Yong-Gang,ZHANG Chi,CHEN Xi. A new method of sample selections for optimizing phenology model based remote sensing data. Chinese Journal of Plant Ecology, 2015, 39(3): 264-274. DOI: 10.17521/cjpe.2015.0026

导出引用管理器 EndNote|Ris|BibTeX

链接本文: https://www.plant-ecology.com/CN/10.17521/cjpe.2015.0026

https://www.plant-ecology.com/CN/Y2015/V39/I3/264

图/表 6

参考文献 42

1	Ahl DE, Gower ST, Burrows SN, Shabanov NV, Myneni RB, Knyazikhin Y (2006). Monitoring spring canopy phenology of a deciduous broadleaf forest using MODIS.Remote Sensing of Environment, 104, 88-95.
2	Archibald S, Scholes RJ (2007). Leaf green-up in a semi-arid African savanna-separating tree and grass responses to environmental cues.Journal of Vegetation Science, 18, 583-594.
3	Augustine DJ (2010). Spatial versus temporal variation in precipitation in a semiarid ecosystem.Landscape Ecology, 25, 913-925.
4	Botta A, Viovy N, Ciais P, Friedlingstein P, Monfray P (2000). A global prognostic scheme of leaf onset using satellite data.Global Change Biology, 6, 709-725.
5	Cannell MGR, Smith RI (1983). Thermal time, chill days and prediction of budburst in Picea sitchensis.Journal of Applied Ecology, 20, 951-963.
6	Chuine I (2000). A unified model for budburst of trees.Journal of Theoretical Biology, 207, 337-347.
7	Chuine I, Cour P, Rousseau DD (1998). Fitting models predicting dates of flowering of temperate-zone trees using simulated annealing.Plant, Cell & Environment, 21, 455-466.
8	Cleland EE, Chuine I, Menzel A, Mooney HA, Schwartz MD (2007). Shifting plant phenology in response to global change.Trends in Ecology & Evolution, 22, 357-365.
9	de Beurs KM, Henebry GM (2004). Land surface phenology, climatic variation, and institutional change: Analyzing agricultural land cover change in Kazakhstan.Remote Sensing of Environment, 89, 497-509.
10	Delbart N, Kergoat L, le Toan T, Lhermitte J, Picard G (2005). Determination of phenological dates in boreal regions using normalized difference water index.Remote Sensing of Environment, 97, 26-38.
11	Foley JA, Prentice IC, Ramankutty N, Levis S, Pollard D, Sitch S, Haxeltine A (1996). An integrated biosphere model of land surface processes, terrestrial carbon balance, and vegetation dynamics.Global Biogeochemical Cycles, 10, 603-628.
12	Friend AD, Stevens AK, Knox RG, Cannell MGR (1997). A process-based, terrestrial biosphere model of ecosystem dynamics (Hybrid v3.0).Ecological Modelling, 95, 249-287.
13	Hänninen H (1990). Modelling bud dormancy release in trees from cool and temperate regions.Acta Forestalia Fennica, 213, 1-47.
14	Haxeltine A, Prentice IC (1996). BIOME3: An equilibrium terrestrial biosphere model based on ecophysiological constraints, resource availability, and competition among plant functional types.Global Biogeochemical Cycles, 10, 693-709.
15	Howell SEL, Brown LC, Kang KK, Duguay CR (2009). Variability in ice phenology on Great Bear Lake and Great Slave Lake, Northwest Territories, Canada, from SeaWinds/QuikSCAT: 2000-2006.Remote Sensing of Environment, 113, 816-834.
16	Hu ZY, Zhang C, Hu Q, Tian HQ (2014). Temperature changes in Central Asia from 1979-2011 based on multiple datasets.Journal of Climate, 27, 1143-1167.
17	Hudson Dunn A, de Beurs KM (2011). Land surface phenology of North American mountain environments using moderate resolution imaging spectroradiometer data.Remote Sensing of Environment, 115, 1220-1233.
18	Hufkens K, Friedl M, Sonnentag O, Braswell BH, Milliman T, Richardson AD (2012). Linking near-surface and satellite remote sensing measurements of deciduous broadleaf forest phenology.Remote Sensing of Environment, 117, 307-321.
19	Jiang Z, Huete A, Didan K, Miura T (2008). Development of a two-band enhanced vegetation index without a blue band.Remote Sensing of Environment, 112, 3833-3845.
20	Kariyeva J, van Leeuwen WJD, Woodhouse CA (2012). Impacts of climate gradients on the vegetation phenology of major land use types in Central Asia (1981-2008).Frontiers of Earth Science, 6, 206-225.
21	Koltunov A, Ustin SL, Asner GP, Fung I (2009). Selective logging changes forest phenology in the Brazilian Amazon: Evidence from MODIS image time series analysis.Remote Sensing of Environment, 113, 2431-2440.
22	Kramer K (1994). Selecting a model to predict the onset of growth of Fagus sylvatica.Journal of Applied Ecology, 31, 172-181.
23	Krinner G, Viovy N, de Noblet-Ducoudré N, Ogée J, Polcher J, Friedlingstein P, Ciais P, Sitch S, Prentice CI (2005). A dynamic global vegetation model for studies of the coupled atmosphere-biosphere system. Global Biogeochemical Cycles, 19, GB1015.
24	Levis S, Bonan G, Vertenstein M, Oleson K (2004). The Community Land Model’s Dynamic Global Vegetation Model (CLM-DGVM): Technical Description and User’s Guide. NCAR Technical Note. . Cited: May 2014.
25	Li RP, Zhou GS (2012). A temperature-precipitation based leafing model and its application in Northeast China.PLoS ONE, 7, e33192.
26	Linkosalo T, Lappalainen HK, Hari P (2008). A comparison of phenological models of leaf bud burst and flowering of boreal trees using independent observations.Tree Physiology, 28, 1873-1882.
27	Ma YG, Zhang C, Tiyip T (2014). Spatial-temporal change of vegetation phenology in arid zone of Central Asia and Xinjiang, China.Advances in Climate Change Research, 10, 95-102. (in Chinese with English abstract)
	[马勇刚, 张弛, 塔西甫拉提·特依拜 (2004). 中亚及中国新疆干旱区植被物候时空变化. 气候变化研究进展, 10, 95-102.]
28	Randerson JT, Hoffman FM, Thornton PE, Mahowald NM, Lindsay K, Lee YH, Nevison CD, Doney SC, Bonan G, Stöckli R, Covey C, Running SW, Fung IY (2009). Systematic assessment of terrestrial biogeochemistry in coupled climate-carbon models.Global Change Biology, 15, 2462-2484.
29	Richardson AD, Anderson RS, Arain MA, Barr AG, Bohrer G, Chen GS, Chen JM, Ciais P, Davis KJ, Desai AR, Dietze MC, Dragoni D, Garrity SR, Gough CM, Grant R, Hollinger DY, Margolis HA, McCaughey H, Migliavacca M, Monson RK, Munger JW, Poulter B, Raczka BM, Ricciuto DM, Sahoo AK, Schaefer K, Tian H, Vargas R, Verbeeck H, Xiao JF, Xue YK (2012). Terrestrial biosphere models need better representation of vegetation phenology: Results from the North American Carbon Program Site Synthesis.Global Change Biology, 18, 566-584.
30	Rocha AV, Shaver GR (2009). Advantages of a two band EVI calculated from solar and photosynthetically active radiation fluxes.Agricultural and Forest Meteorology, 149, 1560-1563.
31	Sakamoto T, Yokozawa M, Toritani H, Shibayama M, Ishitsuka N, Ohno H (2005). A crop phenology detection method using time-series MODIS data.Remote Sensing of Environment, 96, 366-374.
32	Sarvas R (1972). Investigations on the annual cycle of development of forest trees: active period.Communicationes Instituti Forestalls Fenniae, 76, 1-110.
33	Sato H, Itoh A, Kohyama T (2007). SEIB-DGVM: A new Dynamic Global Vegetation Model using a spatially explicit individual-based approach.Ecological Modelling, 200, 279-307.
34	Shu SJ, Wang Y, Chu HY (2009). Spatial distribution of temperature in China: Geographic and orographic influences. Journal of Nanjing University (Natural Science), 45, 334-342. (in Chinese with English abstract)
	[舒守娟, 王元, 储惠云 (2009). 地理和地形影响下我国区域的气温空间分布. 南京大学学报(自然科学版), 45, 334-342.]
35	Sitch S, Smith B, Prentice IC, Arneth A, Bondeau A, Cramer W, Kaplan JO, Levis S, Lucht W, Sykes MT, Thonicke K, Venevsky S (2003). Evaluation of ecosystem dynamics, plant geography and terrestrial carbon cycling in the LPJ dynamic global vegetation model.Global Change Biology, 9, 161-185.
36	White MA, Thornton PE, Running SW (1997). A continental phenology model for monitoring vegetation responses to interannual climatic variability.Global Biogeochemical Cycles, 11, 217-234.
37	Wright CK, de Beurs KM, Akhmadieva ZK, Groisman PY, Henebry GM (2009). Reanalysis data underestimate significant changes in growing season weather in Kazakhstan.Environmental Research Letters, 4, doi:10.1088/1748-9326/4/4/045020.
38	Wu YF, Li MS, Song JQ (2008). Advance in vegetation phenology monitoring based on remote sensing.Journal of Meteorology and Environment, 24(3), 51-58. (in Chinese with English abstract)
	[武永峰, 李茂松, 宋吉青 (2008). 植物物候遥感监测研究进展. 气象与环境学报, 24(3), 51-58.]
39	Yang S, Sun GJ, He WY, Zhao H, Li FM, Xiong YC (2011). Correlation of topographic factors with precipitation and surface temperature in arid and cold region of Northwest China: A case study in Gansu Province.Acta Ecologica Sinica, 31, 2414-2420. (in Chinese with English abstract)
	[杨森, 孙国钧, 何文莹, 赵鸿, 李凤民, 熊友才 (2011). 西北旱寒区地理、地形因素与降雨量及平均温度的相关性——以甘肃省为例. 生态学报, 31, 2414-2420.]
40	Yang X, Mustard JF, Tang JW, Xu H (2012). Regional-scale phenology modeling based on meteorological records and remote sensing observations.Journal of Geophysical Research, 117, doi: 10.1029/2012JG001977.
41	Zhang XY, Friedl MA, Schaaf CB (2009). Sensitivity of vegetation phenology detection to the temporal resolution of satellite data.International Journal of Remote Sensing, 30, 2061-2074.
42	Zhao H, Yang ZW, Di LP, Pei ZY (2012). Evaluation of temporal resolution effect in remote sensing based crop phenology detection studies. In: Li D, Chen Y eds. Computer and Computing Technologies in Agriculture V. Springer, Berlin. 135-150.

模型 Model	参数T₀Parameter T₀	拟合程度 Fitting result			精度评价 Accuracy assessment
模型 Model	固定型/游动型 Fixed or moved (mobile)	样本个数 Sample size	均方根误差 RMSE	决定系数 R²	样本个数 Sample size	均方根误差 RMSE	决定系数 R²
Chuine模型 Chuine model	游动 Moved	70	11.18	0.55	79	15.42	0.10
Chuine模型 Chuine model	固定 Fixed		11.94	0.51		18.25	0.15
SW模型 SW model	游动 Moved		10.75	0.57		9.68	0.55
SW模型 SW model	固定 Fixed		13.24	0.42		16.27	0.61
Seq模型 Seq model	游动 Moved		14.01	0.37		18.31	0.12
Seq模型 Seq model	固定 Fixed		14.02	0.37		12.06	0.69
Par模型 Par model	游动 Moved		9.38	0.61		10.72	0.69
Par模型 Par model	固定 Fixed		19.36	-		-	-
Al模型 Al model	游动 Moved		9.20	0.74		19.6	0.37
Al模型 Al model	固定 Fixed		-	0.20		-	-
Al-P模型 Al-P model	游动 Moved		6.60	0.68		16.57	0.51
Al-P模型 Al-P model	固定 Fixed		12.40	0.37		-	0.12
T-P修正模型 Modified T-P model	游动 Moved		7.70	0.83		9.87	0.70
T-P修正模型 Modified T-P model	固定 Fixed		10.25	0.77		14.19	0.64
Chuine-P模型 Chuine-P model	游动 Moved		12.32	0.39		14.13	0.27
Chuine-P模型 Chuine-P model	固定 Fixed		15.25	0.33		16.93	-
T-D模型 T-D model	游动 Moved		9.79	0.67		11.77	0.66
T-D模型 T-D model	固定 Fixed		10.64	0.68		15.18	0.68
T-P-D1模型 T-P-D1 model	游动 Moved		7.78	0.69		10.01	0.48
T-P-D1模型 T-P-D1 model	固定 Fixed		8.60	0.77		11.34	0.71
T-P-D2模型 T-P-D2 model	游动 Moved		8.00	0.47		17.21	0.47
T-P-D2模型 T-P-D2 model	固定 Fixed		10.54	0.64		14.50	0.41

模型 Model	参数T₀Parameter T₀	拟合程度 Fitting result			精度评价 Accuracy assessment
模型 Model	固定型/游动型 Fixed or moved (mobile)	样本个数 Sample size	均方根误差 RMSE	决定系数 R²	样本个数 Sample size	均方根误差 RMSE	决定系数 R²
Chuine模型 Chuine model	游动 Moved	70	11.18	0.55	79	15.42	0.10
Chuine模型 Chuine model	固定 Fixed		11.94	0.51		18.25	0.15
SW模型 SW model	游动 Moved		10.75	0.57		9.68	0.55
SW模型 SW model	固定 Fixed		13.24	0.42		16.27	0.61
Seq模型 Seq model	游动 Moved		14.01	0.37		18.31	0.12
Seq模型 Seq model	固定 Fixed		14.02	0.37		12.06	0.69
Par模型 Par model	游动 Moved		9.38	0.61		10.72	0.69
Par模型 Par model	固定 Fixed		19.36	-		-	-
Al模型 Al model	游动 Moved		9.20	0.74		19.6	0.37
Al模型 Al model	固定 Fixed		-	0.20		-	-
Al-P模型 Al-P model	游动 Moved		6.60	0.68		16.57	0.51
Al-P模型 Al-P model	固定 Fixed		12.40	0.37		-	0.12
T-P修正模型 Modified T-P model	游动 Moved		7.70	0.83		9.87	0.70
T-P修正模型 Modified T-P model	固定 Fixed		10.25	0.77		14.19	0.64
Chuine-P模型 Chuine-P model	游动 Moved		12.32	0.39		14.13	0.27
Chuine-P模型 Chuine-P model	固定 Fixed		15.25	0.33		16.93	-
T-D模型 T-D model	游动 Moved		9.79	0.67		11.77	0.66
T-D模型 T-D model	固定 Fixed		10.64	0.68		15.18	0.68
T-P-D1模型 T-P-D1 model	游动 Moved		7.78	0.69		10.01	0.48
T-P-D1模型 T-P-D1 model	固定 Fixed		8.60	0.77		11.34	0.71
T-P-D2模型 T-P-D2 model	游动 Moved		8.00	0.47		17.21	0.47
T-P-D2模型 T-P-D2 model	固定 Fixed		10.54	0.64		14.50	0.41

模型名称 Model name	参数 T₀ Parameter T₀	拟合程度 Fitting result			精度评价 Accuracy assessment
模型名称 Model name	固定型/游动型 Fixed or moved	样本个数 Sample size	均方根误差 RMSE	决定系数 R²	样本个数 Sample size	均方根误差 RMSE	决定系数 R²
Chuine模型 Chuine model	游动 Moved	15	9.70	0.71	18	18.30	0.46
Chuine模型 Chuine model	固定 Fixed		11.70	0.42		16.20	0.41
SW模型 SW model	游动 Moved		12.80	0.22		13.95	0.56
SW模型 SW model	固定 Fixed		13.25	0.13		13.73	0.51
Seq模型 Seq model	游动 Moved		12.60	0.31		17.50	0.21
Seq模型 Seq model	固定 Fixed		12.56	0.31		15.88	0.59
Par模型 Par model	游动 Moved		11.90	0.40		12.80	0.57
Par模型 Par model	固定 Fixed		11.90	0.44		12.63	0.58
Al模型 Al model	游动 Moved		8.00	0.82		12.10	0.63
Al模型 Al model	固定 Fixed		14.29	0.62		-	0.29
Al-P模型 Al-P model	游动 Moved		-	-
Al-P模型 Al-P model	固定 Fixed		-	-
T-P修正模型 Modified T-P model	游动 Moved		-	0.44		-	-
T-P修正模型 Modified T-P model	固定 Fixed		-	0.10		-	-
Chuine-P模型 Chuine-P model	游动 Moved		-	-
Chuine-P模型 Chuine-P model	固定 Fixed		-	-
T-D模型 T-D model	游动 Moved		12.66	0.34		17.75	0.33
T-D模型 T-D model	固定 Fixed		13.10	-		19.32	-
T-P-D1模型 T-P-D1 model	游动 Moved		-	0.44		-	-
T-P-D1模型 T-P-D1 model	固定 Fixed		-	-		-	-
T-P-D2模型 T-P-D2 model	游动 Moved		-	0.22		-	-
T-P-D2模型 T-P-D2 model	固定 Fixed		-			-	-

模型名称 Model name	参数 T₀ Parameter T₀	拟合程度 Fitting result			精度评价 Accuracy assessment
模型名称 Model name	固定型/游动型 Fixed or moved	样本个数 Sample size	均方根误差 RMSE	决定系数 R²	样本个数 Sample size	均方根误差 RMSE	决定系数 R²
Chuine模型 Chuine model	游动 Moved	15	9.70	0.71	18	18.30	0.46
Chuine模型 Chuine model	固定 Fixed		11.70	0.42		16.20	0.41
SW模型 SW model	游动 Moved		12.80	0.22		13.95	0.56
SW模型 SW model	固定 Fixed		13.25	0.13		13.73	0.51
Seq模型 Seq model	游动 Moved		12.60	0.31		17.50	0.21
Seq模型 Seq model	固定 Fixed		12.56	0.31		15.88	0.59
Par模型 Par model	游动 Moved		11.90	0.40		12.80	0.57
Par模型 Par model	固定 Fixed		11.90	0.44		12.63	0.58
Al模型 Al model	游动 Moved		8.00	0.82		12.10	0.63
Al模型 Al model	固定 Fixed		14.29	0.62		-	0.29
Al-P模型 Al-P model	游动 Moved		-	-
Al-P模型 Al-P model	固定 Fixed		-	-
T-P修正模型 Modified T-P model	游动 Moved		-	0.44		-	-
T-P修正模型 Modified T-P model	固定 Fixed		-	0.10		-	-
Chuine-P模型 Chuine-P model	游动 Moved		-	-
Chuine-P模型 Chuine-P model	固定 Fixed		-	-
T-D模型 T-D model	游动 Moved		12.66	0.34		17.75	0.33
T-D模型 T-D model	固定 Fixed		13.10	-		19.32	-
T-P-D1模型 T-P-D1 model	游动 Moved		-	0.44		-	-
T-P-D1模型 T-P-D1 model	固定 Fixed		-	-		-	-
T-P-D2模型 T-P-D2 model	游动 Moved		-	0.22		-	-
T-P-D2模型 T-P-D2 model	固定 Fixed		-			-	-

[1]	田地, 迟小龙, 石亮, 刘宵含, 赵常提, 吴梅, 张玉忠, 高永亮. 塞罕坝地区优势造林树种叶片化学计量特征及其环境驱动[J]. 植物生态学报, 2026, 50(2): 362-373.
[2]	梁天豪, 吴帆, 黄锦学, 景陈鸿, 傅贺菁, 杨智杰, 熊德成. 增温对中亚热带格氏栲天然林细根生长量及形态特征的影响[J]. 植物生态学报, 2026, 50(1): 94-106.
[3]	段俊丞, 王志勇, 高维聪, 张成凯, 高长宏, 刘晓彤, 李振今. 黄河三角洲湿地入侵物种互花米草时空演化与景观格局分析[J]. 植物生态学报, 2025, 49(6): 922-938.
[4]	马富龙, 王雨晴, 郝瑜, 段继超, 刘霏霏, 席琳乔, 韩路. 海拔梯度对昆仑山北坡中部草原植物与土壤微生物群落结构与多样性的影响[J]. 植物生态学报, 2025, 49(5): 732-747.
[5]	王秀媛, 申磊, 刘婷婷, 尉雯雯, 张帅, 张伟. ‘塞外红’苹果-大豆复合系统根系时空分布与种间竞争策略[J]. 植物生态学报, 2025, 49(5): 748-759.
[6]	赵洪贤, 刘鹏, 史曼英, 徐铭泽, 贾昕, 田赟, 查天山. 毛乌素沙地典型固沙植物黑沙蒿和赖草叶片氮分配对最大净光合速率的影响[J]. 植物生态学报, 2025, 49(3): 460-474.
[7]	马煦晗, 黄菊莹, 余海龙, 韩翠, 李冰. 降水量变化及氮添加下荒漠草原土壤有机碳及其易分解组分研究[J]. 植物生态学报, 2024, 48(8): 1065-1077.
[8]	郑亚纹, 樊海东, 刘立斌, 倪健. 基于高精度遥感影像和精细植被踏查的金华北山植被制图[J]. 植物生态学报, 2024, 48(11): 1471-1485.
[9]	吴瀚, 白洁, 李均力, 古丽•加帕尔, 包安明. 新疆地区植被覆盖度时空变化及其影响因素分析[J]. 植物生态学报, 2024, 48(1): 41-55.
[10]	韩路, 冯宇, 李沅楷, 王雨晴, 王海珍. 地下水埋深对灰胡杨叶片与土壤养分生态化学计量特征及其内稳态的影响[J]. 植物生态学报, 2024, 48(1): 92-102.
[11]	王晓悦, 许艺馨, 李春环, 余海龙, 黄菊莹. 长期降水量变化下荒漠草原植物生物量、多样性的变化及其影响因素[J]. 植物生态学报, 2023, 47(4): 479-490.
[12]	郝晴, 黄昌. 森林地上生物量遥感估算研究综述[J]. 植物生态学报, 2023, 47(10): 1356-1374.
[13]	张志山, 韩高玲, 霍建强, 黄日辉, 薛书文. 固沙灌木柠条锦鸡儿和中间锦鸡儿木质部导水与叶片光合能力对土壤水分的响应[J]. 植物生态学报, 2023, 47(10): 1422-1431.
[14]	丛楠, 张扬建, 朱军涛. 北半球中高纬度地区近30年植被春季物候温度敏感性[J]. 植物生态学报, 2022, 46(2): 125-135.
[15]	姜艳, 陈兴芳, 杨旭杰. 基于Landsat影像的武汉东湖30年来水生植物动态变化[J]. 植物生态学报, 2022, 46(12): 1551-1561.