海南岛橡胶林区域不同SIF产品的差异性分析及其对GPP估算的影响

安映荷; 张润卿; 刘文杰; 王鹏; 章杰; 乌兰; 孙仲益; 安映荷; 张润卿; 刘文杰; 王鹏; 章杰; 乌兰; 孙仲益

doi:10.15886/j.cnki.rdswxb.2023.04.009

[1]

MOHAMMED G H, COLOMBO R, MIDDLETON E M, et al. Remote sensing of solar-induced chlorophyll fluorescence (SIF) in vegetation: 50 years of progress [J]. Remote sensing of environment, 2019, 231: 111177. doi: 10.1016/j.rse.2019.04.030

[2]

GU L, HAN J, WOOD J D, et al. Sun-induced Chl fluorescence and its importance for biophysical modeling of photosynthesis based on light reactions [J]. New Phytologist, 2019, 223(3): 1179 − 1191. doi: 10.1111/nph.15796

[3]

GUANTER L, ALONSO L, GÓMEZ-CHOVA L, et al. Estimation of solar-induced vegetation fluorescence from space measurements [J]. Geophysical Research Letters, 2007, 34(8): L08401.

[4]

VERRELST J, RIVERA J P, VAN DER TOL C, et al. Global sensitivity analysis of the SCOPE model: What drives simulated canopy-leaving sun-induced fluorescence? [J]. Remote Sensing of Environment, 2015, 166: 8 − 21. doi: 10.1016/j.rse.2015.06.002

[5]

YANG X, TANG J, MUSTARD J F, et al. Solar-induced chlorophyll fluorescence that correlates with canopy photosynthesis on diurnal and seasonal scales in a temperate deciduous forest [J]. Geophysical Research Letters, 2015, 42(8): 2977 − 2987. doi: 10.1002/2015GL063201

[6]

LIU L, GUAN L, LIU X. Directly estimating diurnal changes in GPP for C3 and C4 crops using far-red sun-induced chlorophyll fluorescence [J]. Agricultural and Forest Meteorology, 2017, 232: 1 − 9. doi: 10.1016/j.agrformet.2016.06.014

[7]

VAN DER TOL C, VERHOEF W, ROSEMA A. A model for chlorophyll fluorescence and photosynthesis at leaf scale [J]. Agricultural and Forest Meteorology, 2009, 149(1): 96 − 105. doi: 10.1016/j.agrformet.2008.07.007

[8]

ZHANG Y, GUANTER L, BERRY J A, et al. Model-based analysis of the relationship between sun-induced chlorophyll fluorescence and gross primary production for remote sensing applications [J]. Remote Sensing of Environment, 2016, 187: 145 − 155. doi: 10.1016/j.rse.2016.10.016

[9]

DU S, LIU L, LIU X, et al. Response of canopy solar-induced chlorophyll fluorescence to the absorbed photosynthetically active radiation absorbed by chlorophyll [J]. Remote Sensing, 2017, 9(9): 911. doi: 10.3390/rs9090911

[10]

YANG P, VAN DER TOL C, CAMPBELL P K E, et al. Unraveling the physical and physiological basis for the solar- induced chlorophyll fluorescence and photosynthesis relationship using continuous leaf and canopy measurements of a corn crop [J]. Biogeosciences, 2021, 18(2): 441 − 465. doi: 10.5194/bg-18-441-2021

[11]

CHENG Y-B, MIDDLETON E, ZHANG Q, et al. Integrating solar induced fluorescence and the photochemical reflectance index for estimating gross primary production in a cornfield [J]. Remote Sensing, 2013, 5(12): 6857 − 6879. doi: 10.3390/rs5126857

[12]

CASTRO A O, CHEN J, ZANG C S, et al. OCO-2 solar-induced chlorophyll fluorescence variability across ecoregions of the Amazon basin and the extreme drought effects of El Niño (2015–2016) [J]. Remote Sensing, 2020, 12(7): 1202. doi: 10.3390/rs12071202

[13]

JIAO W, CHANG Q, WANG L. The Sensitivity of satellite solar‐induced chlorophyll fluorescence to meteorological drought [J]. Earth's Future, 2019, 7(5): 558 − 573. doi: 10.1029/2018EF001087

[14]

PAGÁN B, MAES W, GENTINE P, et al. Exploring the potential of satellite solar-induced fluorescence to constrain global transpiration estimates [J]. Remote Sensing, 2019, 11(4): 413. doi: 10.3390/rs11040413

[15]

SHEN Q, LIU L, ZHAO W, et al. Relationship of surface soil moisture with solar-induced chlorophyll fluorescence and normalized difference vegetation index in different phenological stages: a case study of Northeast China [J]. Environmental Research Letters, 2021, 16(2): 024039. doi: 10.1088/1748-9326/abd2f1

[16]

MERRICK, PAU, JORGE, et al. Spatiotemporal patterns and phenology of tropical vegetation solar-induced chlorophyll fluorescence across Brazilian biomes using satellite observations [J]. Remote Sensing, 2019, 11(15): 1746. doi: 10.3390/rs11151746

[17]

PARAZOO N C, BOWMAN K, FRANKENBERG C, et al. Interpreting seasonal changes in the carbon balance of southern Amazonia using measurements of XCO₂ and chlorophyll fluorescence from GOSAT [J]. Geophysical Research Letters, 2013, 40(11): 2829 − 2833. doi: 10.1002/grl.50452

[18]

QUIROS-VARGAS J, SIEGMANN B, DAMM A, et al. Spatial dependency of Solar-induced Chlorophyll Fluorescence (SIF)-emitting objects in the footprint of a FLuorescence EXplorer (FLEX) pixel: a SIF-downscaling perspective[C]. EGU General Assembly Conference Abstracts, 2022: EGU22 − 12671.

[19]

JOINER J, YOSHIDA Y, VASILKOV A, et al. The seasonal cycle of satellite chlorophyll fluorescence observations and its relationship to vegetation phenology and ecosystem atmosphere carbon exchange [J]. Remote Sensing of Environment, 2014, 152: 375 − 391. doi: 10.1016/j.rse.2014.06.022

[20]

FRANKENBERG C, FISHER J B, WORDEN J, et al. New global observations of the terrestrial carbon cycle from GOSAT: Patterns of plant fluorescence with gross primary productivity [J]. Geophysical Research Letters, 2011, 38(17): L17706.

[21]

JOINER J, GUANTER L, LINDSTROT R, et al. Global monitoring of terrestrial chlorophyll fluorescence from moderate-spectral-resolution near-infrared satellite measurements: methodology, simulations, and application to GOME-2 [J]. Atmospheric Measurement Techniques, 2013, 6(10): 2803 − 2823. doi: 10.5194/amt-6-2803-2013

[22]

KÖHLER P, GUANTER L, JOINER J. A linear method for the retrieval of sun-induced chlorophyll fluorescence from GOME-2 and SCIAMACHY data [J]. Atmospheric Measurement Techniques, 2015, 8(6): 2589 − 2608. doi: 10.5194/amt-8-2589-2015

[23]

FRANKENBERG C, O'DELL C, BERRY J, et al. Prospects for chlorophyll fluorescence remote sensing from the Orbiting Carbon Observatory-2 [J]. Remote Sensing of Environment, 2014, 147: 1 − 12. doi: 10.1016/j.rse.2014.02.007

[24]

DU S, LIU L, LIU X, et al. Retrieval of global terrestrial solar-induced chlorophyll fluorescence from TanSat satellite [J]. Science Bulletin, 2018, 63(22): 1502 − 1512. doi: 10.1016/j.scib.2018.10.003

[25]

ZHANG Y, JOINER J, ALEMOHAMMAD S H, et al. A global spatially contiguous solar-induced fluorescence (CSIF) dataset using neural networks [J]. Biogeosciences, 2018, 15(19): 5779 − 5800. doi: 10.5194/bg-15-5779-2018

[26]

LI X, XIAO J. A global, 0.05-degree product of solar-induced chlorophyll fluorescence derived from OCO-2, MODIS, and reanalysis data [J]. Remote Sensing, 2019, 11(5): 517. doi: 10.3390/rs11050517

[27]

YU L, WEN J, CHANG C Y, et al. High‐Resolution global contiguous SIF of OCO‐2 [J]. Geophysical Research Letters, 2019, 46(3): 1449 − 1458. doi: 10.1029/2018GL081109

[28]

WEN J, KöHLER P, DUVEILLER G, et al. A framework for harmonizing multiple satellite instruments to generate a long-term global high spatial-resolution solar-induced chlorophyll fluorescence (SIF) [J]. Remote Sensing of Environment, 2020, 239: 111644. doi: 10.1016/j.rse.2020.111644

[29]

DUVEILLER G, FILIPPONI F, WALTHER S, et al. A spatially downscaled sun-induced fluorescence global product for enhanced monitoring of vegetation productivity [J]. Earth System Science Data, 2020, 12(2): 1101 − 1116. doi: 10.5194/essd-12-1101-2020

[30]

JOINER J, YOSHIDA Y, VASILKOV A P, et al. Filling-in of far-red and near-Infrared solar lines by terrestrial and atmospheric effects: simulations and space-based observations from SCIAMACHY and GOSAT [J]. Atmospheric Measurement Techniques Discussions, 2012, 5(1): 163 − 210.

[31]

WEI J, TANG X, GU Q, et al. Using solar-induced chlorophyll fluorescence observed by OCO-2 to predict autumn crop production in China [J]. Remote Sensing, 2019, 11(14): 1715. doi: 10.3390/rs11141715

[32]

杨凤珠, 王震山, 张乾, 等. 多源日光诱导叶绿素荧光产品在中国地区的一致性研究[J]. 遥感技术与应用, 2022, 37(1): 125 − 136.

[33]

MALHI Y. The productivity, metabolism and carbon cycle of tropical forest vegetation [J]. Journal of Ecology, 2012, 100(1): 65 − 75. doi: 10.1111/j.1365-2745.2011.01916.x

[34]

JATOI M T, LAN G, WU Z, et al. Comparison of soil microbial composition and diversity between mixed and monoculture rubber plantations in Hainan Province, China [J]. Tropical Conservation Science, 2019(12): 1 − 9.

[35]

LAN G, LI Y, JATOI M T, et al. Change in soil microbial community compositions and diversity following the conversion of tropical forest to rubber plantations in Xishuangbanan, Southwest China [J]. Tropical Conservation Science, 2017, 10: e33230.

[36]

LAN G, WU Z, CHEN B, et al. Species Diversity in a Naturally Managed Rubber Plantation in Hainan Island, South China [J]. Tropical Conservation Science, 2017, 10: e12427.

[37]

SODHI N S, KOH L P, BROOK B W, et al. Southeast Asian biodiversity: an impending disaster [J]. Trends in Ecology & Evolution, 2004, 19(12): 654 − 660.

[38]

祁栋灵, 兰国玉, 陈帮乾, 等. 橡胶林生态系统生态功能述评[J]. 生物学杂志, 2021, 38(1): 102 − 105. doi: 10.3969/j.issn.2095-1736.2021.01.102

[39]

吴梅花, 王利堂, 林之盼, 等. 儋州林场公益林生态系统服务功能价值评估[J]. 热带林业, 2021, 49(4): 57 − 60. doi: 10.3969/j.issn.1672-0938.2021.04.014

[40]

侯元兆. 中国热带森林的分布、类型和特点[J]. 世界林业研究, 2003(3): 47 − 51. doi: 10.3969/j.issn.1001-4241.2003.03.010

[41]

耿思文, 吴志祥, 杨川. 海南儋州地区橡胶林生态系统水汽通量变化特征及其对环境因子的响应[J]. 西北林学院学报, 2021, 36(1): 77 − 85. doi: 10.3969/j.issn.1001-7461.2021.01.11

[42]

LI X, LIANG S, YU G, et al. Estimation of gross primary production over the terrestrial ecosystems in China [J]. Ecological Modelling, 2013, 261-262: 80 − 92. doi: 10.1016/j.ecolmodel.2013.03.024

[43]

YUAN W, LIU S, ZHOU G, et al. Deriving a light use efficiency model from eddy covariance flux data for predicting daily gross primary production across biomes [J]. Agricultural and Forest Meteorology, 2007, 143(3/4): 189 − 207. doi: 10.1016/j.agrformet.2006.12.001

[44]

CUI W, XIONG Q, ZHENG Y, et al. A study on the vulnerability of the gross primary production of rubber plantations to regional short-term flash drought over Hainan Island [J]. Forests, 2022, 13(6): 893. doi: 10.3390/f13060893

[45]

WOOD J D, GRIFFIS T J, BAKER J M, et al. Multiscale analyses of solar-induced florescence and gross primary production [J]. Geophysical Research Letters, 2017, 44(1): 533 − 541. doi: 10.1002/2016GL070775

[46]

黎玉芳, 李志鸿. 桂林地区气温与降水量的时间序列预测模型[J]. 广西科学, 2013, 20(2): 107 − 110. doi: 10.3969/j.issn.1005-9164.2013.02.008

[47]

李广洋, 寇卫利, 吴志祥, 等. 近30年海南岛橡胶林时空变化分析[J]. 南京林业大学学报(自然科学版), 2022, 47(1): 189 − 198.

[48]

GENTINE P, ALEMOHAMMAD S H. Reconstructed solar-induced fluorescence: A machine learning vegetation product based on MODIS surface reflectance to reproduce GOME-2 solar-induced fluorescence [J]. Geophysical research letters, 2018, 45(7): 3136 − 3146. doi: 10.1002/2017GL076294

[49]

SAMANTA A, GANGULY S, HASHIMOTO H, et al. Amazon forests did not green-up during the 2005 drought [J]. Geophysical Research Letters, 2010, 37(5): 1 − 5.

[50]

DUVEILLER G, CESCATTI A. Spatially downscaling sun-induced chlorophyll fluorescence leads to an improved temporal correlation with gross primary productivity [J]. Remote Sensing of Environment, 2016, 182: 72 − 89. doi: 10.1016/j.rse.2016.04.027

[51]

孙忠秋, 高显连, 杜珊珊, 等. 全球日光诱导叶绿素荧光卫星遥感产品研究进展与展望[J]. 遥感技术与应用, 2021, 36(5): 1044 − 1056.