Spatial representativeness of flux footprint at pixel scales over mountainous ecosystem

Wu Changlin; Xie Xinyao; Li Ainong

doi:10.11834/jrs.20232509

Views : 0 下载量: 20 CSCD: 0 更多指标

R-PDF
PDF
Export
Share
Collection
Album

Spatial representativeness of flux footprint at pixel scales over mountainous ecosystem
Pages: 1-21(2023)
Published Online： 01 December 2023 ，
DOI： 10.11834/jrs.20232509

扫描看全文

邬昌林，谢馨瑶，李爱农.XXXX.山地生态系统通量足迹遥感像元尺度空间代表性分析.遥感学报，XX（XX）： 1-21

Wu Changlin，Xie Xinyao，Li Ainong. XXXX. Spatial representativeness of flux footprint at pixel scales over mountainous ecosystem. National Remote Sensing Bulletin， XX（XX）：1-21
邬昌林，谢馨瑶，李爱农.XXXX.山地生态系统通量足迹遥感像元尺度空间代表性分析.遥感学报，XX（XX）： 1-21 DOI： 10.11834/jrs.20232509.

Wu Changlin，Xie Xinyao，Li Ainong. XXXX. Spatial representativeness of flux footprint at pixel scales over mountainous ecosystem. National Remote Sensing Bulletin， XX（XX）：1-21 DOI： 10.11834/jrs.20232509.

摘要

遥感技术是大尺度上估算地表碳、水和能量等通量的重要信息来源，全球涡度通量观测数据集已广泛用于遥感通量数据产品的生产和评价，评估遥感像元尺度上通量足迹的空间代表性尤为重要。本文选择我国西南典型山地生态系统-王朗山地遥感四川省野外观测研究站（简称王朗站）区域为例，使用二维参数化足迹模型刻画了通量观测足迹的时空变化特征，同时解析了通量观测足迹在多个遥感像元尺度（30 m、60 m、120 m、250 m、500 m、1000 m、1500 m和2000 m）上的空间代表性。结果表明，在通量足迹的空间变化上，王朗站内不同观测塔通量足迹范围跨度较大（10 ~ 10

m）且对称性较低（通常在40%以下），因此在山地生态系统进行遥感模型及产品验证时需要更加关注通量足迹的空间代表性差异。在通量足迹的时序变化上，王朗站内日尺度上的足迹重叠性差异明显（0% ~ 88%），结合时序变化的足迹特征可进一步提升高时间分辨率下的模型验证和产品精度。王朗研究区内落叶阔叶灌丛站点、落叶阔叶林站点和常绿针叶林站点等三个观测塔的高度为10 m、30 m和75 m，其分别在30 m、60 m和1000 m像元尺度取得对通量足迹的最佳空间代表性。由于山区通量观测的高空间代表性局限于高空间分辨率（观测高度较低时）和中低空间分辨率（观测高度较高时）的遥感像元，认知通量足迹在像元尺度上的空间代表性，结合多尺度遥感观测数据和时空尺度扩展方法，可促进山区生态系统参量估算和通量研究。本文可为站点观测尺度扩展、山地生态系统遥感数据产品生产和地球系统模型验证提供参考。

Abstract

Objective With the availability of remote sensing images since the 1970s

it is feasible to obtain the spatial-temporal continuum observations of the land surface at the global scale. In this way

remote sensing is an important information source for large-scale estimation of land surface carbon

water

and energy fluxes. Global eddy covariance flux datasets are widely used to evaluate and produce remote sensing flux products. As the tower-based fluxes can only represent the small areas around the tower

there is usually a mismatch between the tower-based fluxes and multi-scale pixels of remote sensing. Thus

it is crucial to evaluate the spatial representativeness of flux footprint at multi-scale pixels.Method In this paper

we choose Wanglang Mountain Remote Sensing Field Observation and Research Station of Sichuan Province - a typical mountainous ecosystem of southwest China as the study area. This study used a two-dimensional parametric footprint model (Flux Footprint Prediction

FFP) to characterize the spatiotemporal variations and analyze the spatial representativeness of flux footprint at multi-scale pixels (i.e.

30 m

60 m

120 m

250 m

500 m

1000 m

1500 m and 2000 m). In this work

the land cover types and Normalized Difference Vegetation Index (NDVI) were used to characterize the spatial representativeness of footprint among vegetation types and vegetation density at multi-scale pixels

respectively. At the same time

two site-level simple representativeness indices for land cover type and vegetation density were proposed to evaluate the footprint-to-pixel representativeness across flux towers at Wanglang station.Result Results showed that the footprint fetch varied across flux towers at Wanglang station (10 ~ 10

and the footprint at multiple temporal resolutions had a lower symmetry (usually less than 40%). For the temporal variations of footprint

there were more obvious changes for the overlap of footprint at daily scale (0% ~ 88%)

and the variations were smaller at monthly scale (usually larger than 83%). As for the three flux towers around Wanglang station

results showed that the station of deciduous broadleaf shrub (with observed height at 10 m)

deciduous broadleaf forest (with observed height at 30 m)

and evergreen needleleaf forest (with observed height at 75 m) had a best spatial representativeness at the pixel scale of 30 m

60 m

and 1000 m

respectively. Moreover

compared to vegetation density

the discrepancies of spatial representativeness were more obvious for vegetation cover.Conclusion It should be more attention to the spatial representativeness differences of footprint while validating remote sensing models and producing flux datasets around mountainous ecosystems. Moreover

it was necessarily to combine the corresponding footprints with tower-based observations to characterize the temporal variations of fluxes when modeling and producing flux products at high temporal resolution (e.g

daily scale). Due to the high spatial representativeness of footprint was limited to the pixels at high (a lower tower) and medium-low (a higher tower) spatial resolution

the estimation of ecosystem parameters and fluxes research over mountainous area could be promoted by cognizing the spatial representativeness of footprint at pixel scales and combing the multi-scale remote sensing observations with the spatial and temporal scaling method.

关键词

遥感像元空间代表性涡动技术通量足迹模型山地生态系统

Keywords

pixelspatial representativenesseddy covarianceflux footprint modelmountainous ecosystem

references

Baldocchi D D. 2003. Assessing the eddy covariance technique for evaluating carbon dioxide exchange rates of ecosystems: past, present and future. Global Change Biology, 9(4): 479-92. [DOI: 10.1046/j.1365-2486.2003.00629.xhttp://dx.doi.org/10.1046/j.1365-2486.2003.00629.x]

Barman R, Jain A K and Liang M L. 2014. Climate-driven uncertainties in modeling terrestrial gross primary production: a site level to global-scale analysis. Global Change Biology, 20(5): 1394-411. [DOI: 10.1111/gcb.12474http://dx.doi.org/10.1111/gcb.12474]

Beziat P, Ceschia E, Facon G, Scholz Y, Sanz M J, Pérez-Landa G, Schrems O, Macatangay R, Elbers J A, Hutjes R W A, Neininger B, Ciais P, Ramonet M, Glademard P, Körner S, Gerbig C, Esposito M, Magliulo V, Gioli B, Miglietta F, Ronda R, Tolk L, Lamaud E, Loustau D, Brunet Y, Jarosz N, Butet A, Piguet B, Brut A, Sarrat C, Durand P, Noilhan J and Dolman A J. 2006. The CarboEurope Regional Experiment Strategy. Bulletin of the American Meteorological Society, 87(10): 1367-1380. [DOI: 10.1175/bams-87-10-1367http://dx.doi.org/10.1175/bams-87-10-1367]

Brunsell N A, Ham J M and Owensby C E. 2008. Assessing the multi-resolution information content of remotely sensed variables and elevation for evapotranspiration in a tall-grass prairie environment. Remote Sensing of Environment, 112(6): 2977-87. [DOI: 10.1016/j.rse.2008.02.002http://dx.doi.org/10.1016/j.rse.2008.02.002]

Burba G and Anderson D. 2010. A Brief Practical Guide to Eddy Covariance Flux Measurements: Principles and Workflow Examples for Scientific and Industrial Applications. (Lincoln: Li-Cor Biosciences)

Chen B Z, Coops N C, Fu D, Margolis H A, Amiro B D, Black T A, Arain M A, Barr A G, Bourque C P A, Flanagan L B, Lafleur P M, McCaughey J H and Wofsy S C. 2012. Characterizing spatial representativeness of flux tower eddy-covariance measurements across the Canadian Carbon Program Network using remote sensing and footprint analysis. Remote Sensing of Environment, 124: 742-755. [DOI: 10.1016/j.rse.2012.06.007http://dx.doi.org/10.1016/j.rse.2012.06.007]

Chen B Z, Coops N C, Fu D J, Margolis H A, Amiro B D, Barr A G, Black T A, Arain M A, Bourque C P A, Flanagan L B, Lafleur P M, McCaughey J H and Wofsy S C. 2011. Assessing eddy-covariance flux tower location bias across the Fluxnet-Canada Research Network based on remote sensing and footprint modelling. Agricultural and Forest Meteorology, 151(1): 87-100. [DOI: 10.1016/j.agrformet.2010.09.005http://dx.doi.org/10.1016/j.agrformet.2010.09.005]

Chen B Z, Black T A, Coops N C, Hilker T, Trofymow J A and Morgenstern K. 2009. Assessing Tower Flux Footprint Climatology and Scaling Between Remotely Sensed and Eddy Covariance Measurements. Boundary-Layer Meteorology, 130(2): 137-67. [DOI: 10.1007/s10546-008-9339-1http://dx.doi.org/10.1007/s10546-008-9339-1]

Chu H S, Luo X Z, Ouyang Z T, Chan W S, Dengel S, Biraud S C, Torn M S, Metzger S, Kumar J, Arain M A, Arkebauer T J, Baldocchi D, Bernacchi C, Billesbach D, Black T A, Blanken P D, Bohrer G, Bracho R, Brown S, Brunsell N A, Chen J Q, Chen X Y, Clark K, Desai A R, Duman T, Durden D, Fares S, Forbrich I, Gamon J A , Gough C M, Griffis T, Helbig M, Hollinger D, Humphreys E, Ikawa H, Iwata H Ju Y, Knowles J F, Knox S H, Kobayashi H, Kolb T, Law B, Lee X H, Litvak M, Liu H P, Munger J W, Noormets A, Novick K, Oberbauer S F, Oechel W, Oikawa P, Papuga S A, Pendall E, Prajapati P, Prueger J, Quinton W L, Richardson A D, Russell E S, Scott R L, Starr G, Staebler R, Stoy P C, Stuart-Haentjens E, Sonnentag O, Sullivan R C, Suyker A, Ueyama M, Vargas R, Wood J D and Zona D. 2021. Representativeness of Eddy-Covariance flux footprints for areas surrounding AmeriFlux sites. Agricultural and Forest Meteorology, 301: 15. [DOI: 10.1016/j.agrformet.2021.108350http://dx.doi.org/10.1016/j.agrformet.2021.108350]

Durden D J, Metzger S, Chu H, Collier N, Davis K J, Desai A R, Kumar J, Wieder W R, Xu M and Hoffman F M. 2020. Automated Integration of Continental-Scale Observations in Near-Real Time for Simulation and Analysis of Biosphere–Atmosphere Interactions. Driving Scientific and Engineering Discoveries Through the Convergence of HPC, Big Data and AI (pp. 204-225). Cham: Springer International Publishing

Gockede M, Foken T, Aubinet M, Aurela M, Banza J, Bernhofer C, Bonnefond J M, Brunet Y, Carrara A, Clement R, Dellwik E, Elbers J, Eugster W, Fuhrer J, Granier A, Grunwald T, Heinesch B, Janssens I A, Knohl A, Koeble R, Laurila T, Longdoz B, Manca G, Marek M, Markkanen T, Mateus J, Matteucci G, Mauder M, Migliavacca M, Minerbi S, Moncrieff J, Montagnani L, Moors E, Ourcival J M, Papale D, Pereira J, Pilegaard K, Pita G, Rambal S, Rebmann C, Rodrigues A, Rotenberg E, Sanz M J, Sedlak P, Seufert G, Siebicke L, Soussana J F, Valentini R, Vesala T, Verbeeck H and Yakir D. 2008. Quality control of CarboEurope flux data - Part 1: Coupling footprint analyses with flux data quality assessment to evaluate sites in forest ecosystems. Biogeosciences, 5(2): 433-450. [DOI: 10.5194/bg-5-433-2008http://dx.doi.org/10.5194/bg-5-433-2008]

Hargrove W W, Hoffman F M and Law B E. 2003. New analysis reveals representativeness of the AmeriFlux network. Eos, Transactions American Geophysical Union, 84(48): 529-35. [DOI: 10.1029/2003eo480001http://dx.doi.org/10.1029/2003eo480001]

Heidbach K, Schmid H P and Mauder M. 2017. Experimental evaluation of flux footprint models. Agricultural and Forest Meteorology, 246: 142-53. [DOI: 10.1016/j.agrformet.2017.06.008http://dx.doi.org/10.1016/j.agrformet.2017.06.008]

Hsieh C, Katul G and Chi T. 2000. An approximate analytical model for footprint estimation of scalar fluxes in thermally stratified atmospheric flows. Advances in Water Resources, 23: 765-72. [DOI: 10.1016/S0309-1708(99)00042-1http://dx.doi.org/10.1016/S0309-1708(99)00042-1]

Jung M, Reichstein M, Margolis H A, Cescatti A, Richardson A D, Arain M A, Arneth A, Bernhofer C, Bonal D, Chen J, Gianelle D, Gobron N, Kiely G, Kutsch W, Lasslop G, Law B E, Lindroth A, Merbold L, Montagnani L, Moors E J, Papale D, Sottocornola M, Vaccari F and Williams C. 2011. Global patterns of land-atmosphere fluxes of carbon dioxide, latent heat, and sensible heat derived from eddy covariance, satellite, and meteorological observations. Journal of Geophysical Research-Biogeosciences, 116: 16. [DOI: 10.1029/2010jg001566http://dx.doi.org/10.1029/2010jg001566]

Jung M, Schwalm C, Migliavacca M, Walther S, Camps-Valls G, Koirala S, Anthoni P, Besnard S, Bodesheim P, Carvalhais N, Chevallier F, Gans F, Goll D S, Haverd V, Kohler P, Ichii K, Jain A K, Liu J Z, Lombardozzi D, Nabel J E M S, Nelson J A, O'Sullivan M, Pallandt M, Papale D, Peters W, Pongratz J, Rodenbeck C, Sitch S, Tramontana G, Walker A, Weber U and Reichstein M. 2020. Scaling carbon fluxes from eddy covariance sites to globe: synthesis and evaluation of the FLUXCOM approach. Biogeosciences, 17(5): 1343-65. [DOI: 10.5194/bg-17-1343-2020http://dx.doi.org/10.5194/bg-17-1343-2020]

Kim J, Hwang T, Schaaf C L, Kljun N and Munger J W. 2018. Seasonal variation of source contributions to eddy-covariance CO2 measurements in a mixed hardwood-conifer forest. Agricultural and Forest Meteorology, 253: 71-83. [DOI: 10.1016/j.agrformet.2018.02.004http://dx.doi.org/10.1016/j.agrformet.2018.02.004]

Kljun N, Calanca P, Rotach M W and Schmid H P. 2015. A simple two-dimensional parameterisation for Flux Footprint Prediction (FFP). Geoscientific Model Development, 8(11): 3695-713. [DOI: 10.5194/gmd-8-3695-2015http://dx.doi.org/10.5194/gmd-8-3695-2015]

Kong J, Ryu Y, Liu J G, Dechant B, Rey-Sanchez C, Shortt R, Szutu D, Verfaillie J, Houborg R and Baldocchi D D. 2022. Matching high resolution satellite data and flux tower footprints improves their agreement in photosynthesis estimates. Agricultural and Forest Meteorology, 316: 25. [DOI: 10.1016/j.agrformet.2022.108878http://dx.doi.org/10.1016/j.agrformet.2022.108878]

Kormann R and Meixner F. 2001. An Analytical Footprint Model For Non-Neutral Stratification. Boundary-Layer Meteorology, 99: 207-224. [DOI: 10.1023/A:1018991015119http://dx.doi.org/10.1023/A:1018991015119]

Li A N, Bian J H, Zhang Z J, Zhao W and Yin G F. 2016. Progresses, opportunities, and challenges of mountain remote sensing research. Journal of Remote Sensing, 20(5): 1199–1215

李爱农, 边金虎, 张正健, 赵伟, 尹高飞. 2016a. 山地遥感主要研究进展、发展机遇与挑战. 遥感学报, 20(5): 1199-1215 [DOI:10.11834/jrs.20166227http://dx.doi.org/10.11834/jrs.20166227]

Li A N, Yin G F, Jin H A, Bian J H and Zhao W. 2016. Principles and Methods for the Retrieval of Biophysical Variables in Mountainous Areas. Remote Sensing Technology and Applications, 31(1): 1-11

李爱农, 尹高飞, 靳华安, 边金虎, 赵伟. 2016b. 山地地表生态参量遥感反演的理论、方法与问题. 遥感技术与应用, 31(1): 1-11 [DOI: 10.11873/j.issn.1004-0323.2016.1.0001http://dx.doi.org/10.11873/j.issn.1004-0323.2016.1.0001]

Li A N, Yin G F, Zhang Z J, Tan J B, Nan X, Ma K P and Guo Q H. 2018. Space-air-field integrated biodiversity monitoring based on experimental station. Biodiversity Science, 26(8): 819-827

李爱农, 尹高飞, 张正健, 谭剑波, 南希, 马克平, 郭庆华. 2018. 基于站点的生物多样性星空地一体化遥感监测. 生物多样性, 26(8): 819-827 [DOI: 10.17520/biods.2018052http://dx.doi.org/10.17520/biods.2018052]

Li D R. 2012. On Space-Air-Ground Integrated Earth Observation Network. 2012. Journal of Geo-information Science, 14(4): 419-425

李德仁. 2012. 论空天地一体化对地观测网络. 地球信息科学学报, 14(4): 419-425 [DOI: 10.3724/SP.J.1047.2012.00419http://dx.doi.org/10.3724/SP.J.1047.2012.00419]

Li F Q, Kustas W P, Anderson M C, Prueger J H and Scott R L. 2008. Effect of remote sensing spatial resolution on interpreting tower-based flux observations. Remote Sensing of Environment, 112(2): 337-349. [DOI: 10.1016/j.rse.2006.11.032http://dx.doi.org/10.1016/j.rse.2006.11.032]

Li X W. 2015. Retrospect, Prospect and Innovation in Quantitative Remote Sensing. Journal of Henan University (Nature Science), 35(4): 49-56

李小文. 2005. 定量遥感的发展与创新. 河南大学学报(自然科学版), 35(4): 49-56 [DOI:10.15991/j.cnki.411100.2005.04.012http://dx.doi.org/10.15991/j.cnki.411100.2005.04.012]

Li X W and Wang W T. 2013. Prospects on future developments of quantitative remote sensing. Acta Geographica Sinica, 68(9): 1163-1169

李小文, 王祎婷. 2013. 定量遥感尺度效应刍议. 地理学报, 68(9): 1163-1169 [DOI: 10.11821/dlxb201309001http://dx.doi.org/10.11821/dlxb201309001]

Liang S, Zhang X, Xiao Z, Cheng J, Liu Q and Zhao X. 2014. Introduction. Global LAnd Surface Satellite (GLASS) Products: Algorithms, Validation and Analysis (pp. 1-2). Cham: Springer International Publishing

Liu J, Chen J M, Cihlar J and Park W M. 1997. A process-based boreal ecosystem productivity simulator using remote sensing inputs. Remote Sensing of Environment, 62(2): 158-75. [DOI: 10.1016/s0034-4257(97)00089-8http://dx.doi.org/10.1016/s0034-4257(97)00089-8]

Metzger S. 2018. Surface-atmosphere exchange in a box: Making the control volume a suitable representation for in-situ observations. Agricultural and Forest Meteorology, 255: 68-80. [DOI: 10.1016/j.agrformet.2017.08.037http://dx.doi.org/10.1016/j.agrformet.2017.08.037]

Mizoguchi Y, Miyata A, Ohtani Y, Hirata R and Yuta S. 2009. A review of tower flux observation sites in Asia. Journal of Forest Research, 14(1): 1-9. [DOI: 10.1007/s10310-008-0101-9http://dx.doi.org/10.1007/s10310-008-0101-9]

Mueller B, Seneviratne S I, Jimenez C, Corti T, Hirschi M, Balsamo G, Ciais P, Dirmeyer P, Fisher J B, Guo Z, Jung M, Maignan F, McCabe M F, Reichle R, Reichstein M, Rodell M, Sheffield J, Teuling A J, Wang K, Wood E F and Zhang Y. 2011. Evaluation of global observations-based evapotranspiration datasets and IPCC AR4 simulations. Geophysical Research Letters, 38: 7. [DOI: 10.1029/2010gl046230http://dx.doi.org/10.1029/2010gl046230]

Nappo C J, Caneill J Y, Furman R W, Gifford F A, Kaimal J C, Kramer M L, Lockhart T J, Pendergast M M, Pielke R A, Randerson D, Shreffler J H and Wyngaard J C. 1982. THE WORKSHOP ON THE REPRESENTATIVENESS OF METEOROLOGICAL-OBSERVATIONS, JUNE 1981, BOULDER, COLO. Bulletin of the American Meteorological Society, 63(7): 761-4.

Novick K A, Biederman J A, Desai A R, Litvak M E, Moore D J P, Scott R L and Torn M S. 2018. The AmeriFlux network: A coalition of the willing. Agricultural and Forest Meteorology, 249: 444-56. [DOI: 10.1016/j.agrformet.2017.10.009http://dx.doi.org/10.1016/j.agrformet.2017.10.009]

Piao S, Fang J, Ciais P, Peylin P, Huang Y, Sitch S and Wang T. 2009. The carbon balance of terrestrial ecosystems in China. Nature, 458(7241): 1009-U82. [DOI: 10.1038/nature07944http://dx.doi.org/10.1038/nature07944]

Ran Y H, Li X, Sun R, Kljun N, Zhang L, Wang X F and Zhu G F. 2016. Spatial representativeness and uncertainty of eddy covariance carbon flux measurements for upscaling net ecosystem productivity to the grid scale. Agricultural and Forest Meteorology, 230: 114-27. [DOI: 10.1016/j.agrformet.2016.05.008http://dx.doi.org/10.1016/j.agrformet.2016.05.008]

Ricciuto D, Sargsyan K and Thornton P. 2018. The Impact of Parametric Uncertainties on Biogeochemistry in the E3SM Land Model. Journal of Advances in Modeling Earth Systems, 10(2): 297-319. [DOI: 10.1002/2017ms000962http://dx.doi.org/10.1002/2017ms000962]

Running S W. 1993. Generalization of a forest ecosystem process model for other biomes, BIOME-BGC, and an application for global-scale models. Scaling Physiological Processes Leaf to Globe.

Schmid H P. 1997. Experimental design for flux measurements: matching scales of observations and fluxes. Agricultural and Forest Meteorology, 87(2-3): 179-200. [DOI: 10.1016/s0168-1923(97)00011-7http://dx.doi.org/10.1016/s0168-1923(97)00011-7]

Schmid H P. 2002. Footprint modeling for vegetation atmosphere exchange studies: a review and perspective. Agricultural and Forest Meteorology, 113(1-4): 159-83. [DOI: 10.1016/S0168-1923(02)00107-7http://dx.doi.org/10.1016/S0168-1923(02)00107-7]

Shuang X, Liu S M, Xu Z W and Wang W Z. 2009. Investigation of Spatial Representativeness for Surface Flux Measurements in the Heihe River Basin. Advances in Earth Science, 24(7): 724-733

双喜, 刘绍民, 徐自为, 王维真. 2009. 黑河流域观测通量的空间代表性研究. 地球科学进展, 24(7): 724-733

Sims D A, Rahman A F, Cordova V D, El-Masri B Z, Baldocchi D D, Bolstad P V, Flanagan L B, Goldstein A H, Hollinger D Y, Misson L, Monson R K, Oechel W C, Schmid H P, Wofsy S C and Xu L. 2008. A new model of gross primary productivity for North American ecosystems based solely on the enhanced vegetation index and land surface temperature from MODIS. Remote Sensing of Environment, 112(4): 1633-46. [DOI: 10.1016/j.rse.2007.08.004http://dx.doi.org/10.1016/j.rse.2007.08.004]

Sulkava M, Luyssaert S, Zaehle S and Papale D. 2011. Assessing and improving the representativeness of monitoring networks: The European flux tower network example. Journal of Geophysical Research-Biogeosciences, 116: 14. [DOI: 10.1029/2010jg001562http://dx.doi.org/10.1029/2010jg001562]

Sun S Y, Wang W Z and Xu F N. 2021. Comparison of Footprint Models of Surface Heat and Water Vapor Fluxes in the Middle and Upper Reaches of Heihe River Basin. Remote Sensing Technology and Application, 36(4): 887-897

孙赛钰, 王维真, 徐菲楠. 2021. 黑河流域中上游水热通量足迹模型的对比分析. 遥感技术与应用, 36(4): 887-897 [DOI: 10.11873/j.issn.1004-0323.2021.4.0887http://dx.doi.org/10.11873/j.issn.1004-0323.2021.4.0887]

Verma M, Friedl M A, Law B E, Bonal D, Kiely G, Black T A, Wohlfahrt G, Moors E J, Montagnani L, Marcolla B, Toscano P, Varlagin A, Roupsard O, Cescatti A, Arain M A and D'Odorico P. 2015. Improving the performance of remote sensing models for capturing intra- and inter-annual variations in daily GPP: An analysis using global FLUXNET tower data. Agricultural and Forest Meteorology, 214: 416-29. [DOI: 10.1016/j.agrformet.2015.09.005http://dx.doi.org/10.1016/j.agrformet.2015.09.005]

Villarreal S and Vargas R. 2021. Representativeness of FLUXNET Sites Across Latin America. Journal of Geophysical Research-Biogeosciences 126(3): 18. [DOI: 10.1029/2020jg006090http://dx.doi.org/10.1029/2020jg006090]

Wang S Q, Chen D C, Zhou L, He H L, Shi H, Yan H M and Su W. 2013. Assessing the spatial representativeness of eddy covariance flux observation stations of terrestrial ecosystems in China. Acta Ecologica Sinica, 33(24): 7715-7728

王绍强, 陈蝶聪, 周蕾, 何洪林, 石浩, 闫慧敏, 苏文. 2013. 中国陆地生态系统通量观测站点空间代表性. 生态学报, 33(24): 7715-7728 [DOI: 10.5846 /stxb201208291219http://dx.doi.org/10.5846/stxb201208291219]

Wang W Z, Xu Z W, Liu S M, Li X, Ma M G and Wang J M. 2009. The characteristics of heat and water vapor fluxes over different surfaces in the heihe river basin. Advances in Earth Science, 24(7): 714-723

王维真, 徐自为, 刘绍民, 李新, 马明国, 王介民. 2009. 黑河流域不同下垫面水热通量特征分析. 地球科学进展, 24(7): 714-723

Wu C Y, Chen J M and Huang N. 2011. Predicting gross primary production from the enhanced vegetation index and photosynthetically active radiation: Evaluation and calibration. Remote Sensing of Environment, 115(12): 3424-35. [DOI: 10.1016/j.rse.2011.08.006http://dx.doi.org/10.1016/j.rse.2011.08.006]

Wu X D, Wen J G, Xiao Q, Li X, Liu Q, Tang Y, Dou B C, Peng J J, You D Q and Li X W. 2015. Advances in validation methods for remote sensing products of land surface parameters. Journal of Remote Sensing, 19(1): 75-92

吴小丹, 闻建光, 肖青, 李新, 刘强, 唐勇, 窦宝成, 彭菁菁, 游冬琴, 李小文. 2015. 关键陆表参数遥感产品真实性检验方法研究进展. 遥感学报, 19(1): 75-92 [DOI: 10.11834/jrs.20154009http://dx.doi.org/10.11834/jrs.20154009]

Wu X J, He H L, Zhang L, Feng L L, Ren X L, Liu W H, Zou C X and Lin N F. 2022. Spatial sampling design optimization of monitoring network for terrestrial ecosystem in China. Science of the Total Environment, 847: 157397. [DOI: 10.2139/ssrn.4009684http://dx.doi.org/10.2139/ssrn.4009684]

Xie X Y and Li A N. 2020. Development of a topographic-corrected temperature and greenness model (TG) for improving GPP estimation over mountainous areas. Agricultural and Forest Meteorology, 295: 13. [DOI: 10.1016/j.agrformet.2020.108193http://dx.doi.org/10.1016/j.agrformet.2020.108193]

Xie X Y, Li A N, Jin H, Bian J H, Zhang Z J and Nan X. 2021. Comparing Three Remotely Sensed Approaches for Simulating Gross Primary Productivity over Mountainous Watersheds: A Case Study in the Wanglang National Nature Reserve, China. Remote Sensing, 13(18): 25. [DOI: 10.3390/rs13183567http://dx.doi.org/10.3390/rs13183567]

Xu B D. 2018. Leaf Area Index Retrieval and Product Validation over Heterogeneous Land Surfaces

徐保东. 2018. 非均质地表叶面积指数反演及产品真实性检验. 北京: 中国科学院大学(中国科学院遥感与数字地球研究所)

Yu G R, Fu Y L, Sun X M, Wen X F and Zhang L M. 2006. Research Progress and Development Ideas of China Terrestrial Ecosystem Flux Observation and Research Network (ChinaFLUX). Science in China Ser. D Earth Sciences, (S1): 1-21

于贵瑞, 伏玉玲, 孙晓敏, 温学发, 张雷明. 2006. 中国陆地生态系统通量观测研究网络(ChinaFLUX)的研究进展及其发展思路. 中国科学. D辑:地球科学, (S1): 1-21

Yu G R, Zhang L M, Sun X M, Li Z Q and Fu Y L. 2004. Research Progress on Terrestrial Ecosystem Carbon Fluxes in Asia. Science in China Ser. D Earth Sciences, (S2): 15-29

于贵瑞, 张雷明, 孙晓敏, 李正泉, 伏玉玲. 2004. 亚洲区域陆地生态系统碳通量观测研究进展. 中国科学(D辑:地球科学), 15-29

Yu W T. 2021. Spatial-temporal Continuous Vegetation Parameter Inversion Methodology over Complex Surface Based on Remote Sensing

于文涛. 2021. 复杂地表时空连续植被参数遥感反演方法研究. 北京: 中国科学院大学(中国科学院空天信息创新研究院) [DOI: 10.44231/d.cnki.gktxc.2021.000010http://dx.doi.org/10.44231/d.cnki.gktxc.2021.000010]

Zhang Y, Jia Z Z, Liu S M, Xu Z W, Xu T R, Yao Y J, Ma Y F, Song L S, Li X, Hu X, Wang Z Y, Guo Z X and Zhou J. 2020. Advances in validation of remotely sensed land surface evapotranspiration. Journal of Remote Sensing(Chinese), 24(8): 975-999

张圆, 贾贞贞, 刘绍民, 徐自为, 徐同仁, 姚云军, 马燕飞, 宋立生, 李相, 胡骁, 王泽宇, 郭枝虾, 周纪. 2020. 遥感估算地表蒸散发真实性检验研究进展. 遥感学报, 24(8): 975-999 [DOI: 10.11834/jrs.20209099http://dx.doi.org/10.11834/jrs.20209099]

Zheng Y, Shen R Q, Wang Y W, Li X Q, Liu S G, Liang S L, Chen J M, Ju W M, Zhang L and Yuan W P. 2020. Improved estimate of global gross primary production for reproducing its long-term variation, 1982-2017. Earth System Science Data, 12(4): 2725-46. [DOI: 10.5194/essd-12-2725-2020http://dx.doi.org/10.5194/essd-12-2725-2020]

Zheng Y, Zhang L, Xiao J F, Yuan W P, Yan M, Li T and Zhang Z Q. 2018. Sources of uncertainty in gross primary productivity simulated by light use efficiency models: Model structure, parameters, input data, and spatial resolution. Agricultural and Forest Meteorology, 263: 242-57. [DOI: 10.1016/j.agrformet.2018.08.003http://dx.doi.org/10.1016/j.agrformet.2018.08.003]

Zhou Y L, Wu X C, Ju W M, Chen J M, Wang S Q, Wang H M, Yuan W P, Black T A, Jassal R, Ibrom A, Han S J, Yan J H, Margolis H, Roupsard O, Li Y N, Zhao F H, Kiely G, Starr G, Pavelka M, Montagnani L, Wohlfahrt G, D'Odorico P, Cook D, Arain M A, Bonal D, Beringer J, Blanken P D, Loubet B, Leclerc M Y, Matteucci G, Nagy Z, Olejnik J U K T P and Varlagin A. 2016. Global parameterization and validation of a two-leaf light use efficiency model for predicting gross primary production across FLUXNET sites. Journal of Geophysical Research-Biogeosciences, 121(4): 1045-72. [DOI: 10.1002/2014jg002876http://dx.doi.org/10.1002/2014jg002876

Alert me when the article has been cited

提交

暂无数据