基于遥感指数的中国南亚热带常绿林光合作用季节动态变化研究

doi:10.5814/j.issn.1674-764X.2019.02.002

资源与生态学报 ›› 2019, Vol. 10 ›› Issue (2): 112-126.DOI: 10.5814/j.issn.1674-764X.2019.02.002

基于遥感指数的中国南亚热带常绿林光合作用季节动态变化研究

孙雷刚^1,^2,³, 王绍强^1,^2,⁴(), A.MICKLER Rober⁵, 陈敬华^1,², 于泉洲⁶, 钱钊晖^1,², 周国逸⁷, 孟泽⁷

1. 中国科学院地理科学与资源研究所,生态系统网络观测与模拟重点实验室,北京 100101
2. 中国科学院大学,北京 100049
3. 河北省科学院地理科学研究所,河北省地理信息开发应用工程技术研究中心,石家庄 050011
4. 中国地质大学（武汉）地理与信息工程学院,武汉 430074
5. 北卡罗莱纳州立大学森林与环境资源系,洛利 27601,美国
6. 聊城大学环境与规划学院,山东聊城 252059
7. 中国科学院华南植物园,广州 510650

收稿日期:2018-10-11 接受日期:2018-11-12 出版日期:2019-03-30 发布日期:2019-03-30

Remote Sensing Indices to Measure the Seasonal Dynamics of Photosynthesis in a Southern China Subtropical Evergreen Forest

SUN Leigang^1,^2,³, WANG Shaoqiang^1,^2,^4,^*(), Robert A. MICKLER⁵, CHEN Jinghua^1,², YU Quanzhou⁶, QIAN Zhaohui^1,², ZHOU Guoyi⁷, MENG Ze⁷

1. Key Laboratory of Ecosystem Network Observation and Modeling, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China
2. University of Chinese Academy of Sciences, Beijing 100049, China
3. Hebei Engineering Research Center for Geographic Information Application, Institute of Geographical Sciences, Hebei Academy of Sciences, Shijiazhuang 050011, China
4. School of Geography and Information Engineering, China University of Geosciences, Wuhan 430074, China
5. Department of Forestry and Environmental Resources, North Carolina State University, Raleigh, North Carolina, USA
6. School of Environment and Planning, Liaocheng University, Liaocheng, Shandong 252059, China
7. South China Botanical Garden, Chinese Academy of Sciences, Guangzhou 510650, China

Received:2018-10-11 Accepted:2018-11-12 Online:2019-03-30 Published:2019-03-30
Contact: WANG Shaoqiang
Supported by:
National Key Research and Development Program of China (2017YFC0503803);National Natural Science Foundation of China (41571192);Natural Science Foundation of Hebei, China (D2016302002);Science and Technology Planning Project of Hebei, China (17390313D).

摘要/Abstract

摘要：

准确监测中国南亚热带常绿林生态系统光合作用动态变化对全球陆地生态系统碳吸收估计及其对气候变化的响应至关重要。涡动协方差技术一直被认为是评估生态系统碳通量最直接的方法,虽然具有较高的时间分辨率,但在空间上有其自身的局限性。近10年,光谱观测和卫星遥感技术在植被生产力监测方面的应用大大提高了对碳通量的时空评估能力。本研究基于长时间序列光谱观测数据,提取叶绿素荧光指数（FRI）和光化学植被指数（PRI）,进而评价两个生理遥感指数跟踪亚热带常绿林光合作用季节动态变化的能力。结果表明,传统NDVI指数受光照条件影响较大（R²=0.88,p<0.001）,并呈现出饱和现象,而FRI和PRI指数则能较好地跟踪植物光和功能季节性变化,且在季节尺度上两者受光照条件的影响相对较弱（FRI指数R²=0.13;PRI指数R²=0.51）;相比PRI指数与光能利用效率（LUE）在午间具有较强的相关性,FRI指数与GPP的相关性则在早上优于午间时段;而这两种相关关系均在植被衰退季优于植被生长季。另外,通过考虑光合有效辐射因子,基于FRI指数监测GPP的能力得到显著提高,R²从0.22提高到0.69,呈显著正相关关系（p<0.001）;同时,在植被衰退季也呈现出更强的相关性（R²=0.79,p<0.001）。研究成果表明,FRI和PRI两个生理遥感指数能够准确地监测亚热带常绿林光合作用季节动态变化,建议把其引入碳循环模型中以改进区域碳收支估计。

关键词: 荧光指数, 光化学植被指数, 光合作用, 总初级生产力, 光能利用率, 亚热带常绿林

Abstract:

The accurate measurement of the dynamics of photosynthesis in China’s subtropical evergreen forest ecosystems is an important contribution to carbon (C) sink estimates in global terrestrial ecosystems and their responses to climate change. Eddy covariance has historically been the only direct method to assess C flux of whole ecosystems with high temporal resolution, but it suffers from limited spatial resolution. During the last decade, continuous global monitoring of plant primary productivity from spectroradiometer sensors on flux towers and satellites has extended the temporal and spatial coverage of C flux observations. In this study, we evaluated the performance of two physiological remote sensing indices, fluorescence reflectance index (FRI) and photochemical reflectance index (PRI), to measure the seasonal variations of photosynthesis in a subtropical evergreen forest ecosystem using continuous canopy spectral and flux measurements in the Dinghushan Nature Reserve in southern China. The more commonly used NDVI has been shown to be saturated and mainly affected by illumination (R²=0.88, p < 0.001), but FRI and PRI could better track the seasonal dynamics of plant photosynthetic functioning by comparison and are less affected by illumination (R²=0.13 and R²=0.51, respectively) at the seasonal scale. FRI correlated better with daily gross primary production (GPP) in the morning hours than in the afternoon hours, in contrast to PRI which correlated better with light-use efficiency (LUE) in the afternoon hours. Both FRI and PRI could show greater correlations with GPP and LUE respectively in the senescence season than in the recovery-growth season. When incident PAR was taken into account, the relationship between GPP and FRI was improved and the correlation coefficient increased from 0.22 to 0.69 (p < 0.001). The strength of the correlation increased significantly in the senescence season (R²=0.79, p < 0.001). Our results demonstrate the application of FRI and PRI as physiological indices for the accurate measurement of the seasonal dynamics of plant community photosynthesis in a subtropical evergreen forest, and suggest these indices may be applied to carbon cycle models to improve the estimation of regional carbon budgets.

Key words: fluorescence reflectance index (FRI), photochemical reflectance index (PRI), photosynthesis, gross primary productivity (GPP), light-use efficiency (LUE), subtropical evergreen forest

孙雷刚, 王绍强, A.MICKLER Rober, 陈敬华, 于泉洲, 钱钊晖, 周国逸, 孟泽. 基于遥感指数的中国南亚热带常绿林光合作用季节动态变化研究[J]. 资源与生态学报, 2019, 10(2): 112-126.

SUN Leigang,WANG Shaoqiang,Robert A. MICKLER,CHEN Jinghua,YU Quanzhou,QIAN Zhaohui,ZHOU Guoyi,MENG Ze. Remote Sensing Indices to Measure the Seasonal Dynamics of Photosynthesis in a Southern China Subtropical Evergreen Forest[J]. Journal of Resources and Ecology, 2019, 10(2): 112-126.

图/表 11

Fig. 1 Seasonal variation of climatic factors, GPP, LUE and remote sensing indicesNote: The data observation period was from 2014 (DOY 152, 1 June) to 2015 (DOY 273, 30 September). Daily average Ta, VPD, SM, LUE, FRI, PRI and NDVI were calculated using data observed from 8:30 a.m. to 17:00 p.m. each day (local solar time). PAR and GPP are the sums of values between 8:30 a.m. to 17:00 p.m. The solid lines indicate moving averages of 30 days. The red rectangle indicates the senescence season from early October 2014 (DOY 274) to early March 2015 (DOY 70).

Fig. 2 Relationships of daily climatic factors (PAR, Ta and VPD) with GPP (a-c) and LUE (d-f) from 1 June 2014 to 30 September 2015

Fig. 3 Relationships of PAR with FRI (a), PRI (b), and NDVI (c) from 1 June 2014 to 30 September 2015

Fig. 4 Seasonal variation of GPP and various FRI Note: Daily average FRI were calculated using data observed from 8:30 a.m. to 17:00 p.m. each day (local solar time). GPP are the sum values between 8:30 a.m. to 17:00 p.m. The average FRI_am were calculated using data observed from 8:30 a.m. to 9:30 a.m. The average FRI_mid were calculated using data observed from 11:30 a.m. to 13:30 p.m. △FRI were the differences between FRI_mid and FRI_am. -FRI, -FRI_am and -FRI_mid indicate the negative values of FRI, FRI_am and FRI_mid, respectively. The solid lines indicate moving averages of 30 days.

Fig. 5 Relationships of daily GPP with FRI (a), FRI_am (b), FRI_mid (c), and ΔFRI (d)

Fig. 6 Relationships of daily GPP (gC m-2 d-1) with Fyield (a), Fam-yield (b) and Fmid-yield (c). Fyield were calculated by dividing FRI by PAR from 8:30 a.m. to 17:00 p.m. Fam-yield were calculated by dividing FRI_am by PAR from 8:30 a.m. to 9:30 p.m. Fmid-yield were calculated by dividing FRI_mid by PAR from 11:30 a.m. to 13:30 p.m.

Fig. 7 Seasonal variations of GPP (gC m-2 d-1, black dots and lines), Fyield (red dots and lines), Fam-yield (blue dots and lines) and FRI_mid (green dots and lines) from 2014 (DOY 152, 1 June) to 2015 (DOY 273, 30 September). The solid lines indicate moving averages of 30 days.

Table 1 Coefficients of correlation (R2) between GPP and various FRI and Fyield during different seasons

Index	FRI	FRI_am	FRI_mid	Fyield	Fam-yield	Fmid-yield
All seasons	0.2243	0.3248	0.1332	0.6879	0.5533	0.6102
Recovery-growth season	0.1573	0.3021	0.0936	0.5974	0.4773	0.5163
Senescence season	0.5373	0.4876	0.4434	0.7924	0.6522	0.7353

Fig. 8 Seasonal variation of LUE (gC MJ-1, black dots and lines), PRI (red dots and lines), PRI_am (blue dots and lines), PRI_mid (light blue dots and lines) and ΔPRI (green dots and lines) from 2014 (DOY 152, 1 June) to 2015 (DOY 273, 30 September). The solid lines indicate moving averages of 30 days. Daily average LUE and PRI were calculated using data observed from 8:30 a.m. to 17:00 p.m. each day. The average PRI_am were calculated using data observed from 8:30 a.m. to 9:30 a.m. The average PRI_mid were calculated using data observed from 11:30 a.m. to 13:30 p.m. ΔFRI were the differences between FRI_mid and FRI_am. ΔPRI were the differences between PRI_mid and PRI_am.

Fig. 9 Relationships of daily average LUE with PRI (a), PRI_am (b), PRI_mid (c) and ΔPRI (d)

Table 2 Coefficients of correlation (R2) between LUE and PRI, PRI_am, and PRI_mid. during different seasons.

Index	PRI	PRI_am	PRI_mid
All seasons	0.5514	0.4317	0.5639
Recovery-growth season	0.5370	0.3883	0.5181
Senescence season	0.8034	0.7001	0.7995

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基于遥感指数的中国南亚热带常绿林光合作用季节动态变化研究

Remote Sensing Indices to Measure the Seasonal Dynamics of Photosynthesis in a Southern China Subtropical Evergreen Forest

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