Application and comparison of remote sensing GPP models with multi-site data in China

doi:10.17521/cjpe.2016.0182

Abstract

Abstract:

Aims Estimation of gross primary productivity (GPP) of vegetation at the global and regional scales is important for understanding the carbon cycle of terrestrial ecosystems. Due to the heterogeneous nature of land surface, measurements at the site level cannot be directly up-scaled to the regional scale. Remote sensing has been widely used as a tool for up-saling GPP by integrating the land surface observations with spatial vegetation patterns. Although there have been many models based on light use efficiency and remote sensing data for simulating terrestrial ecosystem GPP, those models depend much on meteorological data; use of different sources of meteorological datasets often results in divergent outputs, leading to uncertainties in the simulation results. In this study, we examines the feasibility of using two GPP models driven by remote sensing data for estimating regional GPP across different vegetation types. Methods Two GPP models were tested in this study, including the Temperature and Greenness Model (TG) and the Vegetation Index Model (VI), based on remote sensing data and flux data from the China flux network (ChinaFLUX) for different vegatation types for the period 2003-2005. The study sites consist of eight ecological stations located in Xilingol (grassland), Changbaishan (mixed broadleaf-conifer forest), Haibei (shrubland), Yucheng (cropland), Damxung (alpine meadow), Qianyanzhou (evergreen needle-leaved forest), Dinghushan (evergreen broad-leaved forest), and Xishuangbanna (evergreen broad-leaved forest), respectively. Important findings All the remote sensing parameters employed by the TG and VI models had good relationships with the observed GPP, with the values of coefficient of determination, R², exceeding 0.67 for majority of the study sites. However, the root mean square errors (RMSEs) varied greatly among the study sites: the RMSE of TG ranged from 0.29 to 6.40 g·m^-2·d^-1, and that of VI ranged from 0.31 to 7.09 g·m^-2·d^-1, respectively. The photosynthetic conversion coefficients m and a can be up-scaled to a regional scale based on their relationships with the annual average nighttime land surface temperature (LST), with 79% variations in m and 58% of variations in a being explainable in the up-scaling. The correlations between the simulated outputs of both TG and VI and the measured values were mostly high, with the values of correlation coefficient, r, ranging from 0.06 in the TG model and 0.13 in the VI model at the Xishuangbanna site, to 0.94 in the TG model and 0.89 in the VI model at the Haibei site. In general, the TG model performed better than the VI model, especially at sites with high elevation and that are mainly limited by temperature. Both models had potential to be applied at a regional scale in China.

http://jtp.cnki.net/bilingual/detail/html/ZWSB201703006

Key words: gross primary productivity, remote sensing, enhanced vegetation index, land surface temperature, eddy covariance

Ke-Qing WANG, He-Song WANG, Osbert Jianxin SUN. Application and comparison of remote sensing GPP models with multi-site data in China[J]. Chin J Plant Ecol, 2017, 41(3): 337-347.

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URL: https://www.plant-ecology.com/EN/10.17521/cjpe.2016.0182

https://www.plant-ecology.com/EN/Y2017/V41/I3/337

Figures/Tables 8

Table 1 Basic information of the study sites

植被类型 Vegetation type	站点名称 Site name	地理位置 Geo-location	数据时段 Data period
灌丛 Shrubland	海北高寒草甸生态系统通量观测站 Haibei Alpine Meadow Ecosystem Flux Observation Site	37.67° N, 101.33° E	2003-2005
常绿阔叶林 Evergreen broad-leaved forest	鼎湖山南亚热带常绿阔叶林通量观测站 Dinghushan South Subtropical Evergreen Broad-leaved Forest Flux Observation Site	23.17° N, 112.54° E	2003-2005
温带草原 Temperate steppe	锡林郭勒温性典型草原通量观测站 Xilingol Temperate Grassland Flux Observation Site	43.53° N, 116.67° E	2004-2005
常绿针叶林 Evergreen needle-leaved forest	千烟洲人工林通量观测站 Qianyanzhou Planted Forest Flux Observation Site	26.74° N, 115.06° E	2003-2005
农田 Cropland	禹城暖温带半湿润旱作农田通量观测站 Yucheng Warmer Temperate Dry Farming Cropland Flux Observation Site	36.83° N, 116.57° E	2003-2005
高寒草甸 Alpine meadow	当雄高寒草甸碳通量观测站 Damxung Alpine Meadow Flux Observation Site	30.83° N, 91.12° E	2004-2005
针阔混交林 Mixed broadleaf-conifer forest	长白山温带红松阔叶林通量观测站 Changbaishan Temperate Broad-leaved Korean Pine Forest Flux Observation Site	42.40° N, 128.07° E	2003-2005
热带雨林 Tropical rain forest	西双版纳热带雨林通量观测站 Xishuangbanna Tropical Rainforest Flux Observation Site	21.93° N, 101.20° E	2003-2005

Fig. 1 Relationships of the photosynthetic conversion coefficient m and a with annual mean nighttime land surface temperature (LSTan).

Fig. 2 Relationships between the eddy covariance gross primary production (EC-GPP) and the product of the scaled land surface temperature (LSTScaled) multiplied by the scaled enhance vegetation index (EVIScaled) for different study sites. CBS, Changbaishan Temperate Broad-leaved Korean Pine Forest Flux Observation Site; DHS, Dinghushan South Subtropical Evergreen Broadleaved Forest Flux Observation Site; DX, Damxung Alpine Meadow Flux Observation Site; HB, Haibei Alpine Meadow Ecosystem Flux Observation Site; QYZ, Qianyanzhou Planted Forest Flux Observation Site; XLGL, Xilingol Temperate Grassland Flux Observation Site; XSBN, Xishuangbanna Tropical Rainforest Flux Observation Site; YC, Yucheng Warmer Temperate Dry Farming Cropland Flux Observation Site.

Fig. 3 Relationships between the eddy covariance gross primary production (EC-GPP) and the product of the enhanced vegetation index (EVI) multiplied by photosynthetic active radiation (PAR) for different study sites. CBS, Changbaishan Temperate Broad-leaved Korean Pine Forest Flux Observation Site; DHS, Dinghushan South Subtropical Evergreen Broadleaved Forest Flux Observation Site; DX, Damxung Alpine Meadow Flux Observation Site; HB, Haibei Alpine Meadow Ecosystem Flux Observation Site; QYZ, Qianyanzhou Planted Forest Flux Observation Site; XLGL, Xilingol Temperate Grassland Flux Observation Site; XSBN, Xishuangbanna Tropical Rainforest Flux Observation Site; YC, Yucheng Warmer Temperate Dry Farming Cropland Flux Observation Site.

Fig. 4 Relationships between the simulated gross primary production in 2005 by the TG model (TG-GPP) and the eddy covariance gross primary production (EC-GPP) for the corresponding time period for different study sites. CBS, Changbaishan Temperate Broad-leaved Korean Pine Forest Flux Observation Site; DHS, Dinghushan South Subtropical Evergreen Broadleaved Forest Flux Observation Site; DX, Damxung Alpine Meadow Flux Observation Site; HB, Haibei Alpine Meadow Ecosystem Flux Observation Site; QYZ, Qianyanzhou Planted Forest Flux Observation Site; XLGL, Xilingol Temperate Grassland Flux Observation Site; XSBN, Xishuangbanna Tropical Rainforest Flux Observation Site; YC, Yucheng Warmer Temperate Dry Farming Cropland Flux Observation Site.

Fig. 5 Relationships between the simulated gross primary production (VI-GPP) in 2005 by the VI model and the eddy covariance gross primary production (EC-GPP) for different study sites. CBS, Changbaishan Temperate Broad-leaved Korean Pine Forest Flux Observation Site; DHS, Dinghushan South Subtropical Evergreen Broadleaved Forest Flux Observation Site; DX, Damxung Alpine Meadow Flux Observation Site; HB, Haibei Alpine Meadow Ecosystem Flux Observation Site; QYZ, Qianyanzhou Planted Forest Flux Observation Site; XLGL, Xilingol Temperate Grassland Flux Observation Site; XSBN, Xishuangbanna Tropical Rainforest Flux Observation Site; YC, Yucheng Warmer Temperate Dry Farming Cropland Flux Observation Site.

Table 2 Performance of the TG and VI models in simulating the gross primary production in 2005 for different study sites

站点 Site	决定系数 Coefficient of determination (R²)		相对误差 Relative error (RE) (%)		均方根误差 Root mean square error (RMSE) (g·m^-2·d^-1)
站点 Site	TG	VI	TG	VI	TG	VI
锡林郭勒温性典型草原通量观测站 Xilingol Temperate Grassland Flux Observation Site	0.67	0.75	126.79	196.62	0.62	0.77
长白山温带红松阔叶林通量观测站 Changbaishan Temperate Broad-Leaved Korean Pine Forest Flux Observation Site	0.87	0.85	-9.94	-13.79	1.39	1.46
海北高寒草甸生态系统通量观测站 Haibei Alpine Meadow Ecosystem Flux Observation Site	0.94	0.89	-3.10	3.32	0.55	0.75
禹城暖温带半湿润旱作农田通量观测站 Yucheng Warmer Temperate Dry Farming Cropland Flux Observation Site	0.67	0.83	-55.41	-39.42	4.30	3.15
当雄高寒草甸碳通量观测站 Damxung Alpine Meadow Flux Observation Site	0.80	0.82	10.90	33.16	0.29	0.31
千烟洲人工林通量观测站 Qianyanzhou Planted Forest Flux Observation Site	0.73	0.72	-29.23	-56.02	1.77	2.75
鼎湖山南亚热带常绿阔叶林通量观测站 Dinghushan South Subtropical Evergreen Broadleaved Forest Flux Observation Site	0.64	0.55	-60.08	-79.09	2.19	2.84
西双版纳热带雨林通量观测站 Xishuangbanna Tropical Rainforest Flux Observation Site	0.06	0.13	-67.71	-77.52	6.40	7.09

Fig. 6 Time series of the eddy covariance gross primary production (EC-GPP) and the simulated gross primary production by the TG and VI models (TG-GPP and VI-GPP) for different study sites. CBS, Changbaishan Temperate Broad-leaved Korean Pine Forest Flux Observation Site; DHS, Dinghushan South Subtropical Evergreen Broadleaved Forest Flux Observation Site; DX, Damxung Alpine Meadow Flux Observation Site; HB, Haibei Alpine Meadow Ecosystem Flux Observation Site; QYZ, Qianyanzhou Planted Forest Flux Observation Site; XLGL, Xilingol Temperate Grassland Flux Observation Site; XSBN, Xishuangbanna Tropical Rainforest Flux Observation Site; YC, Yucheng Warmer Temperate Dry Farming Cropland Flux Observation Site.

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