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遥感技术与应用  2020, Vol. 35 Issue (2): 435-447    DOI: 10.11873/j.issn.1004-0323.2020.2.0435
遥感应用     
基于遥感和站点观测数据的生态系统呼吸模型比较
沈倩(),周艳莲(),单良
南京大学地理与海洋科学学院,江苏 南京 210046
Comparison of Ecosystem Respiration Models based on Remote Sensing Data
Qian Shen(),Yanlian Zhou(),Liang Shan
School of Geography and Ocean Science, Nanjing university, Nanjing 210046, China
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摘要:

生态系统呼吸(Ecosystem respiration,Re)是陆地生态系统碳收支的重要组成部分,准确模拟Re对研究碳循环具有重要意义。利用3种典型的遥感模型,C-flux(The carbon flux model)、ReRSM (Ecosystem respiration Remote Sensing Model)和TPGPP(Temperature Precipitation Gross Primary Production)模型,基于不同时间尺度(1 d和8 d尺度)的通量观测和遥感数据,对包含5种植被类型(农作物CROP、落叶阔叶林DBF、常绿针叶林ENF、草地GRASS和混交林MF)的24个站点(52个站年)的Re进行了模拟。结果表明:不同模型模拟结果的差异较大,C-Flux模型模拟结果R2和RMSE的范围为0.72~0.96 gCm-2d-1和0.30~3.47 gCm-2d-1,ReRSM模型R2与RMSE的范围为0.70~0.98 gCm-2d-1和0.45~6.07 gCm-2d-1,TPGPP模型R2与RMSE的范围为0.76~0.97 gCm-2d-1和0.41~2.45 gCm-2d-1;1 d和8 d尺度,TPGPP模型模拟效果最好,分别73%和67%的站年的TPGPP模型模拟结果的R2高于其他两种模型,65%和50%的站年的TPGPP模型模拟结果的RMSE低于另两种模型。大部分站年(分别为75%和77%)ReRSM模型模拟的Re与观测Re之间的R2明显高于C-flux模型,然而大部分站年(79%和77%)的RMSE高于C-flux模型,这表明ReRSM模型结构合理,能较好地模拟Re的季节变化趋势但模型参数有待改进。ReRSM模型中,年均生长季平均LSWI(Mean annual growing season of Land surface water indexLSWIsm)与其他站年相比过低,会导致模拟的Re高估,反之则低估。

关键词: 生态系统呼吸C?Flux模型ReRSM模型TPGPP模型    
Abstract:

Ecosystem respiration (Re) is an important component of terrestrial ecosystem carbon budget, and it was important to simulate Re accurately. In this study, Re was simulated at daily and 8-day time scales at 24 flux sites (52 site years) including 5 vegetation types by using three typical ecological models established based on remote sensing data, C-flux (the carbon flux model), ReRSM (Ecosystem respiration Remote Sensing Model) and TPGPP (Temperature Precipitation Gross Primary Production) model. Results showed that the three models had different performances. At 52 site years, the ranges of R2 and RMSE were 0.72~0.96 and 0.30~3.47 gCm-2d-1 for the C-flux model, 0.70~0.98 and 0.45~6.07 gCm-2d-1 for the ReRSM model, and 0.76~0.97 and 0.41~2.45 gCm-2d-1 for the TPGPP model. The TPGPP performed best compared with the other two models. R2 simulated with the TPGPP model was higher than the other two models at most site years with proportions of 73% and 67% at daily and 8-day scale, respectively. At daily and 8-day scale, R2 simulated with the ReRSM model was higher than that with the C-flux model at most site years with proportions of 75% and 77%, while RMSE with ReRSM model was higher than that with the C-flux model at most site years with proportions of 79% and 76%, respectively. Results indicated that the ReRSM model could simulate the trends of seasonal variations of Re while model parameters had some uncertainties. One important parameter in the ReRSM model, LSWIsm (Mean annual growing season of land surface water index), which was much lower would result in overestimation of Re, and higher LSWIsm would result in Re underestimation.

Key words: Ecosystem respiration    C-flux model    ReRSM model    TPGPP model
收稿日期: 2018-11-07 出版日期: 2020-07-10
ZTFLH:  TP79  
基金资助: 国家重点研发计划项目(2016YFA0600202);国家自然科学基金项目(41671343)
通讯作者: 周艳莲     E-mail: 1344611702@qq.com;zhouyl@nju.edu.cn
作者简介: 沈 倩(1994-),女,江苏盐城人,硕士研究生,主要从事遥感数据的碳通量模拟研究。E?mail:1344611702@qq.com
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引用本文:

沈倩,周艳莲,单良. 基于遥感和站点观测数据的生态系统呼吸模型比较[J]. 遥感技术与应用, 2020, 35(2): 435-447.

Qian Shen,Yanlian Zhou,Liang Shan. Comparison of Ecosystem Respiration Models based on Remote Sensing Data. Remote Sensing Technology and Application, 2020, 35(2): 435-447.

链接本文:

http://www.rsta.ac.cn/CN/10.11873/j.issn.1004-0323.2020.2.0435        http://www.rsta.ac.cn/CN/Y2020/V35/I2/435

站点ID国家纬度/°经度/°植被类型年份
BE-LonBelgium50.64.7CROP2005~2006
DE-KliGermany50.913.5CROP2006
CA-TP4Canada42.7-80.4ENF2003~2005
FI-HyyFinland61.824.3ENF2004、 2006
NL-LooItaly42.411.9ENF2003~2005
US-Me2US44.5-121.6ENF2004~2005
US-NR1US40.0-105.5ENF2002~2003
AT-NeuAustria47.111.3GRASS2004~2006
CN-CngChina44.6123.5GRASS2008~2010
CN-Du2China42.0116.3GRASS2008
CN-Ha2China37.6101.3GRASS2003~2005
CN-HaMChina37.6101.3GRASS2003
RU-Ha1Russia54.790.0GRASS2002~2004
US-ArcUS35.5-98.0GRASS2005
US-IB2US41.8-88.2GRASS2006~2007
IT-ColItaly41.813.6DBF2005~2006
US-MMSUS39.3-86.4DBF2003~2004
US-WCrUS45.8-90.1DBF2004~2006
US-Wi8US46.7-91.3DBF2002
BE-VieBelgium50.36.0MF2004~2006
CA-GroCanada48.2-82.2MF2004~2005
CA-OasCanada53.6-106.2MF2003~2005
CA-ObsCanada54.0-105.1MF2003~2005
CN-ChaChina42.4128.1MF2003
表1  研究站点信息
PFTRLAI=0aLAIk2E0/KaK/mm
CROP0.250.400.244129.4980.9340.035
ENF1.020.420.478124.8330.6040.222
GRASS0.411.140.578101.1810.6700.765
DBF1.270.340.24787.6550.7960.184
MF0.780.440.391176.5420.7032.831
表2  TPGPP模型在不同植被类型中的参数[24]
图1  1 d和8 d时间尺度C-Flux、ReRSM和TPGPP模型模拟结果图
图2  3种模型在不同站年的R2与RMSE柱状图续图2
图2  3种模型在不同站年的R2与RMSE柱状图
图3  5种植被类型在3种模型中Re模拟值与观测值的散点图图a1~a15为1 d尺度、图b1~b15为8 d尺度
图4  C-Flux、ReRSM和TPGPP模型模拟结果的R2和RMSE箱形图
图5  各站年的a值柱状图
R2(1 d)RMSE (1 d) (g C m-2 d-1R2(8 d)RMSE(8 d)(g C m-2 d-1
统计值C-FluxReRSMTPGPPC-FluxReRSMTPGPPC-FluxReRSMTPGPPC-FluxReRSMTPGPP
最大值0.910.940.953.596.292.420.960.980.973.476.072.45
最小值0.630.610.690.380.490.420.720.700.760.300.450.41
中位数0.800.820.831.191.721.080.870.880.891.121.541.04
平均值0.800.800.831.331.841.200.860.870.881.211.701.14
表3  1 d和8 d尺度各模型模拟结果统计表
图6  所有站年的LSWIsm与Tn_am散点图
图7  1 d和8 d尺度模型间R2和RMSE差值图
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