聚集指数和最大羧化速率对基于遥感产品的植被生产力估算的影响

doi:10.11707/j.1001-7488.LYKX20230266

Abstract

Abstract:

Objective: This study aims to investigate the effect of clumping index (CI) and maximum carboxylation rate (V_cmax) from remote sensing products on estimation of vegetation productivity with the boreal ecosystem productivity simulator (BEPS) model. Method: The FLUXNET and ChinaFLUX data were used to analyze the sensitivities of CI and V_cmax in BEPS model, and compare the effects of CI and V_cmax on Gross Primary Productivity (GPP) estimation. On this basis, the vegetation productivity of terrestrial ecosystems in China in 2012 was estimated. By comparing with the estimated results of the default value, we determined the impact of the spatio-temporal changes of CI and V_cmax on the model performance. Result: 1) The results showed that CI and V_cmax had high sensitivities in the BEPS model. They were positively correlated with vegetation productivity, and the sensitivity of V_cmax was higher than that of CI in different vegetation types. 2) When CI and V_cmax remote sensing products (NDHD-CI and SIF-V_cmax) were used simultaneously, the simulation results had the smallest error and the highest accuracy. The root mean square error (RMSE) of GPP decreased from 665.60 g·m^?2a^?1 to 584.71 g·m^?2a^?1, and the mean absolute error (MAE) and mean relative error (MRE) were the lowest in the four simulation cases. 3) In 2012, the total GPP and Net Primary Productivity (NPP) of terrestrial ecosystems in China were 5.21 Pg·a^?1 and 2.49 Pg·a^?1, respectively. Affected by the spatio-temporal dynamics in the CI and V_cmax, the GPP and NPP estimates were 3.06% and 4.72% lower than the default results of the model, respectively. Conclusion: Our results have demonstrated that NDHD-CI and SIF-V_cmax can improve the accuracy of BEPS models in estimating vegetation productivity, and other high-sensitivity parameters and model mechanisms can be optimized and improved in the future. Affected by the temporal and spatial changes of CI and V_cmax, the estimation results of vegetation productivity are slightly lower than the default situation. The effect of V_cmax on vegetation productivity estimation is higher than that of CI.

Key words: vegetation productivity, BEPS model, clumping index, maximum carboxylation rate

CLC Number:

S718.52

Qi Li,Rui Sun,Jia Bai,Jingyu Zhang,Helin Zhang. Effects of Clumping Index and Maximum Carboxylation Rate on Vegetation Productivity Estimation Based on Remote Sensing Data[J]. Scientia Silvae Sinicae, 2024, 60(6): 25-36.

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站点 Site	经度 Longitude（E）	纬度 Latitude（N）	植被类型 Vegetation type	年份 Year
长白山Changbaishan (Cha)	128.10°	42.40°	MF	2003—2010
鼎湖山Dinghushan (Din)	112.54°	23.17°	EBF	2003—2006，2008—2010
千烟洲Qianyanzhou (Qia)	115.06°	26.74°	ENF	2003—2007, 2009
海北1 Haibei 1 (Ha2)	101.33°	37.61°	WET	2003—2005
长岭Changling (Cng)	123.51°	44.59°	GRA	2007—2010
当雄Dangxiong (Dan)	91.07°	30.50°	GRA	2004—2005，2007—2009
多伦Duolun (Du2)	116.28°	42.05°	GRA	2007—2008
海北2 Haibei 2 (HaM)	101.18°	37.37°	GRA	2003—2004
内蒙古Neimenggu (NMG)	116.18°	44.08°	GRA	2004—2005,2008
禹城Yucheng (YC)	116.57°	36.83°	CRO	2003—2010
西双版纳Xishuangbanna (XSBN)	101.25°	21.93°	EBF	2004

参数组合 Parameter combinations	CI	V_cmax/(μmol·m^?2s^?1)
组合1 Combinations one	模型缺省值BEPS model default	模型缺省值BEPS model default
组合2 Combination two	模型缺省值 BEPS model default	SIF-V_cmax遥感产品 SIF-V_cmax remote sensing data
组合3 Combination three	NDHD-CI遥感产品 NDHD-CI remote sensing data	模型缺省值 BEPS model default
组合4 Combination four	NDHD-CI遥感产品NDHD-CI remote sensing data	SIF-V_cmax遥感产品 SIF-V_cmax remote sensing data

序号 No.	参数 Parameter	含义 Definition
1	V_cmax (μmol·m^?2s^?1)	最大羧化速率(25 ℃) Maximum carboxylation rate at 25 ?C
2	CI	聚集指数 Clumping index
3	slope	羧化速率与叶片含氮量的关系斜率参数 The slope of the maximum carboxylation rate–the leaf nitrogen content curve
4	N (g·m^?2)	叶片氮含量 Leaf nitrogen content
5	LAI_max_o	冠层叶面积指数最大值 Maximum leaf area index for canopy
6	albedo_vis	冠层可见光反照率 Albedo of canopy in visible band
7	albedo_nir	冠层近红外反照率 Albedo of canopy in near infrared band
8	t₁	土壤水有效性因子参数1 Parameter 1 that determines the sensitivity of root water uptake to soil temperature
9	t₂	土壤水有效性因子参数2 Parameter 2 that determines the sensitivity of root water uptake to soil temperature
10	CO₂ (μmol·mol^?1)	大气CO₂浓度 Atmospheric CO₂ concentration

	参数组合1 Parameter combination 1	参数组合2 Parameter combination 2	参数组合3 Parameter combination 3	参数组合4 Parameter combination 4
MAE/ (g·m^?2a^?1)	534.69	465.06	523.43	454.47
MRE	0.78	0.71	0.76	0.70
RMSE/ (g·m^?2a^?1)	665.60	612.57	636.95	584.71

植被类型 Vegetation types	单位 Unit /(g·m^?2a^?1)	参数组合1 Parameter combination 1	参数组合2 Parameter combination 2	参数组合3 Parameter combination 3	参数组合4 Parameter combination 4
常绿针叶林 Evergreen needle leaf forest	GPP	572.75	636.86	574.14	638.12
常绿针叶林 Evergreen needle leaf forest	NPP	338.51	386.59	339.55	387.54
常绿阔叶林 Evergreen broadleaf forest	GPP	812.57	807.22	825.34	819.64
常绿阔叶林 Evergreen broadleaf forest	NPP	376.32	372.31	385.90	381.62
落叶针叶林 Deciduous needle leaf forest	GPP	628.90	691.50	627.06	688.89
落叶针叶林 Deciduous needle leaf forest	NPP	406.80	453.75	405.42	451.79
落叶阔叶林 Deciduous broadleaf forest	GPP	1 014.00	1 029.09	1 014.54	1 029.64
落叶阔叶林 Deciduous broadleaf forest	NPP	572.65	583.96	573.05	584.38
混交林 Mixed forest	GPP	818.94	836.76	831.85	850.52
混交林 Mixed forest	NPP	444.31	457.68	454.00	468.00
草地 Grassland	GPP	395.87	384.19	391.99	380.84
草地 Grassland	NPP	222.61	213.85	219.70	211.33
农田 Farmland	GPP	952.21	914.34	944.90	907.77
农田 Farmland	NPP	386.33	357.54	380.77	352.54

Effects of Clumping Index and Maximum Carboxylation Rate on Vegetation Productivity Estimation Based on Remote Sensing Data

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