Articles | Volume 18, issue 22
https://doi.org/10.5194/bg-18-6077-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/bg-18-6077-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
25 Nov 2021
Research article |
| 25 Nov 2021
| 25 Nov 2021
Unveiling spatial and temporal heterogeneity of a tropical forest canopy using high-resolution NIRv, FCVI, and NIRvrad from UAS observations
Remote Sensing Division, Naval Research Laboratory, 4555 Overlook
Ave. SW, Washington, DC 20375, USA
Stephanie Pau
Department of Geography, Florida State University, 113 Collegiate
Loop, Tallahassee, FL 32306, USA
Matteo Detto
Smithsonian Tropical Research Institute, Apartado 0843–03092,
Balboa, Ancón, Panama
Department of Ecology and Evolutionary Biology, Princeton University,
Princeton, NJ 08544, USA
Eben N. Broadbent
Spatial Ecology and Conservation Lab, School of Forest, Fisheries, and
Geomatics Sciences, University of Florida, Gainesville, FL 32608, USA
Stephanie A. Bohlman
Smithsonian Tropical Research Institute, Apartado 0843–03092,
Balboa, Ancón, Panama
School of Forest, Fisheries, and Geomatics Sciences, University of
Florida, Gainesville, FL 32608, USA
Christopher J. Still
Department of Forest Ecosystems and Society, Oregon State University,
Corvallis, OR 97331, USA
Angelica M. Almeyda Zambrano
Spatial Ecology and Conservation Lab, Center for Latin American
Studies, University of Florida, Gainesville, FL 32608, USA
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Cited articles
Alonso, L., Moreno, J., Moya, I., and Miller, J. R.: A comparison of different techniques for passive measurement of vegetation photosynthetic activity: solar-induced fluorescence, red-edge reflectance structure and photochemical reflectance indices, in: IGARSS 2003, 2003 IEEE International Geoscience and Remote Sensing Symposium, Proceedings IEEE, 1, 604–606, 2003.
Alonso, L., Gómez-Chova, L., Vila-Francés, J., Amorós-López,
J., Guanter, L., Calpe, J., and Moreno, J.: Sensitivity analysis of the
Fraunhofer Line Discrimination method for the measurement of chlorophyll
fluorescence using a field spectroradiometer, in: 2007 IEEE International Geoscience and Remote Sensing Symposium, IEEE, 3756–3759, 2007.
Alonso, L., Gómez-Chova, L., Vila-Francés, J., Amorós-López,
J., Guanter, L., Calpe, J., and Moreno, J.: Improved Fraunhofer Line
Discrimination Method for Vegetation Fluorescence Quantification, IEEE
Geosci. Remote Sens. Lett., 5, 620–624, 2008.
Badgley, G., Field, C. B., and Berry, J. A.: Canopy near-infrared
reflectance and terrestrial photosynthesis, Sci. Adv., 3, e1602244,
https://doi.org/10.1126/sciadv.1602244, 2017.
Badgley, G., Anderegg, L. D. L., Berry, J. A., and Field, C. B.: Terrestrial
gross primary production: Using NIRV to scale from site to globe, Glob. Change
Biol., 25, 3731–3740, https://doi.org/10.1111/gcb.14729, 2019.
Baldocchi, D. D., Ryu, Y., Dechant, B., Eichelmann, E., Hemes, K., Ma, S.,
Rey Sanchez, C., Shortt, R., Szutu, D., Valach, A., Verfaillie, J., Badgley,
G., Zeng, Y., and Berry, J. A.: Outgoing Near Infrared Radiation from
Vegetation Scales with Canopy Photosynthesis Across a Spectrum of Function,
Structure, Physiological Capacity and Weather, J. Geophys.
Res.-Biogeo., 125, e2019JG005534, https://doi.org/10.1029/2019jg005534, 2020.
Berry, Z. C. and Goldsmith, G. R.: Diffuse light and wetting
differentially affect tropical tree leaf photosynthesis, New
Phytol., 225, 143–153, https://doi.org/10.1111/nph.16121, 2020.
Bohlman, S.: Hyperspectral remote sensing of exposed wood and
deciduous trees in seasonal tropical forests, in: Hyperspectral remote sensing of tropical and subtropical forests, CRC Press,
edited by: Kalacska, M. and Sanchez-Azofeifa, G. A., 177–192, 2008.
Bohlman, S. and O'Brien, S.: Allometry, adult stature and regeneration
requirement of 65 tree species on Barro Colorado Island, Panama, J.
Trop. Ecol., 22, 123–136, https://doi.org/10.1017/s0266467405003019, 2006.
Bohlman, S. and Pacala, S.: A forest structure model that determines crown
layers and partitions growth and mortality rates for landscape-scale
applications of tropical forests, J. Ecol., 100, 508–518,
https://doi.org/10.1111/j.1365-2745.2011.01935.x, 2012.
Castro, A. O., Chen, J., Zang, C. S., Shekhar, A., Jimenez, J. C.,
Bhattacharjee, S., Kindu, M., Morales, V. H., and Rammig, A.: OCO-2
Solar-Induced Chlorophyll Fluorescence Variability across Ecoregions of the
Amazon Basin and the Extreme Drought Effects of El Niño (2015–2016),
Remote Sens., 12, 1202, https://doi.org/10.3390/rs12071202, 2020.
Clark, D. B., Olivas, P. C., Oberbauer, S. F., Clark, D. A., and Ryan, M.
G.: First direct landscape-scale measurement of tropical rain forest Leaf
Area Index, a key driver of global primary productivity, Ecol. Lett., 11,
163–172, https://doi.org/10.1111/j.1461-0248.2007.01134.x, 2008.
Clark, D. A., Asao, S., Fisher, R., Reed, S., Reich, P. B., Ryan, M. G.,
Wood, T. E., and Yang, X.: Reviews and syntheses: Field data to benchmark
the carbon cycle models for tropical forests, Biogeosciences, 14, 4663–4690,
https://doi.org/10.5194/bg-14-4663-2017, 2017.
Cogliati, S., Verhoef, W., Kraft, S., Sabater, N., Alonso, L., Vicent, J.,
Moreno, J., Drusch, M., and Colombo, R.: Retrieval of sun-induced
fluorescence using advanced spectral fitting methods, Remote Sens.
Environ., 169, 344–357, https://doi.org/10.1016/j.rse.2015.08.022, 2015.
Condit, R. S., Watts, K., Bohlman, S., Perez, R., Foster, R. B., and
Hubbell, S. P.: Quantifying the deciduousness of tropical forest canopies
under varying climates, J. Veg. Sci., 11, 649–658, 2000.
Dechant, B., Ryu, Y., Badgley, G., Zeng, Y., Berry, J. A., Zhang, Y.,
Goulas, Y., Li, Z., Zhang, Q., Kang, M., Li, J., and Moya, I.: Canopy
structure explains the relationship between photosynthesis and sun-induced
chlorophyll fluorescence in crops, Remote Sens. Environ., 241, 111733,
https://doi.org/10.1016/j.rse.2020.111733, 2020.
Detto, M., Baldocchi, D., and Katul, G. G.: Scaling Properties of
Biologically Active Scalar Concentration Fluctuations in the Atmospheric
Surface Layer over a Managed Peatland, Bound.-Lay. Meteorol., 136,
407–430, https://doi.org/10.1007/s10546-010-9514-z, 2010.
Detto, M., Wright, S. J., Calderon, O., and Muller-Landau, H. C.: Resource
acquisition and reproductive strategies of tropical forest in response to
the El Nino-Southern Oscillation, Nat. Commun., 9, 1–8,
https://doi.org/10.1038/s41467-018-03306-9, 2018.
Frankenberg, C., Fisher, J. B., Worden, J. R., Badgley, G., Saatchi, S. S.,
Lee, J. E., Toon, G. C., Butz, A., Jung, M., Kuze, A., and Yokota, T.: New
global observations of the terrestrial carbon cycle from GOSAT: Patterns of
plant fluorescence with gross primary productivity, Geophys. Res.
Lett., 38, 17, https://doi.org/10.1029/2011gl048738, 2011.
Gamon, J. A., Kovalchuck, O., Wong, C. Y. S., Harris, A., and Garrity, S.
R.: Monitoring seasonal and diurnal changes in photosynthetic pigments with
automated PRI and NDVI sensors, Biogeosciences, 12, 4149–4159,
https://doi.org/10.5194/bg-12-4149-2015, 2015.
Gao, W., Kim, Y., Ustin, S. L., Huete, A. R., Jiang, Z., and Miura, T.:
Multisensor reflectance and vegetation index comparisons of Amazon tropical
forest phenology with hyperspectral Hyperion data, Remote Sens.
Model. Ecosyst. Sustain. IV, https://doi.org/10.1117/12.734974, 2007.
Gelybó, G., Barcza, Z., Kern, A., and Kljun, N.: Effect of spatial
heterogeneity on the validation of remote sensing based GPP estimations,
Agr. Forest Meteorol., 174/175, 43–53,
https://doi.org/10.1016/j.agrformet.2013.02.003, 2013.
Glenn, E. P., Huete, A. R., Nagler, P. L., and Nelson, S. G.: Relationship
Between Remotely-sensed Vegetation Indices, Canopy Attributes and Plant
Physiological Processes: What Vegetation Indices Can and Cannot Tell Us
About the Landscape, Sensors, 8, 2136–2160, 2008.
Guan, K., Pan, M., Li, H., Wolf, A., Wu, J., Medvigy, D., Caylor, K. K.,
Sheffield, J., Wood, E. F., Malhi, Y., Liang, M., Kimball, J. S., Saleska,
Scott R., Berry, J., Joiner, J., and Lyapustin, A. I.: Photosynthetic
seasonality of global tropical forests constrained by hydroclimate, Nat.
Geosci., 8, 284–289, https://doi.org/10.1038/ngeo2382, 2015.
Guanter, L., Frankenberg, C., Dudhia, A., Lewis, P. E., Gómez-Dans, J.,
Kuze, A., Suto, H., and Grainger, R. G.: Retrieval and global assessment of
terrestrial chlorophyll fluorescence from GOSAT space measurements, Remote
Sens. Environ., 121, 236–251, https://doi.org/10.1016/j.rse.2012.02.006, 2012.
Guanter, L., Zhang, Y., Jung, M., Joiner, J., Voigt, M., Berry, J. A.,
Frankenberg, C., Huete, A. R., Zarco-Tejada, P., Lee, J. E., Moran, M. S.,
Ponce-Campos, G., Beer, C., Camps-Valls, G., Buchmann, N., Gianelle, D.,
Klumpp, K., Cescatti, A., Baker, J. M., and Griffis, T. J.: Global and
time-resolved monitoring of crop photosynthesis with chlorophyll
fluorescence, P. Natl. Acad. Sci. USA, 111, E1327–E1333, https://doi.org/10.1073/pnas.1320008111, 2014.
Hao, D., Asrar, G. R., Zeng, Y., Yang, X., Li, X., Xiao, J., Guan, K., Wen,
J., Xiao, Q., Berry, J. A., and Chen, M.: Potential of hotspot solar-induced
chlorophyll fluorescence for better tracking terrestrial photosynthesis,
Glob. Change Biol., 27, 2144–2158, https://doi.org/10.1111/gcb.15554, 2021.
Heinsch, F. A., Maosheng, Z., Running, S. W., Kimball, J. S., Nemani, R. R.,
Davis, K. J., Bolstad, P. V., Cook, B. D., Desai, A. R., Ricciuto, D. M.,
Law, B. E., Oechel, W. C., Hyojung, K., Hongyan, L., Wofsy, S. C., Dunn, A.
L., Munger, J. W., Baldocchi, D. D., Liukang, X., Hollinger, D. Y.,
Richardson, A. D., Stoy, P. C., Siqueira, M. B. S., Monson, R. K., Burns, S.
P., and Flanagan, L. B.: Evaluation of remote sensing based terrestrial
productivity from MODIS using regional tower eddy flux network observations,
IEEE Trans. Geosci. Remote Sens., 44, 1908–1925,
https://doi.org/10.1109/tgrs.2005.853936, 2006.
Huete, A., Didan, K., Miura, T., Rodriguez, E. P., Gao, X., and Ferreira, L.
G.: Overview of the radiometric and biophysical performanceof the MODIS
vegetation indices, Remote Sens. Environ., 83, 195–213, 2002.
Huete, A. R., Restrepo-Coupe, N., Ratana, P., Didan, K., Saleska, S. R.,
Ichii, K., Panuthai, S., and Gamo, M.: Multiple site tower flux and remote
sensing comparisons of tropical forest dynamics in Monsoon Asia,
Agr. Forest Meteorol., 148, 748–760,
https://doi.org/10.1016/j.agrformet.2008.01.012, 2008.
Jiang, Z., Huete, A., Didan, K., and Miura, T.: Development of a two-band
enhanced vegetation index without a blue band, Remote Sens.
Environ., 112, 3833–3845, https://doi.org/10.1016/j.rse.2008.06.006, 2008.
Joiner, J., Yoshida, Y., Vasilkov, A. P., Yoshida, Y., Corp, L. A., and
Middleton, E. M.: First observations of global and seasonal terrestrial
chlorophyll fluorescence from space, Biogeosciences, 8, 637–651,
https://doi.org/10.5194/bg-8-637-2011, 2011.
Julitta, T.: Optical proximal sensing for vegetation monitoring, PhD
Dissertation, Department of Earth and Environmental Sciences, University of
Milano-Bicocca, 136 pp., 2015.
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.: Global patterns of land-atmosphere fluxes of
carbon dioxide, latent heat, and sensible heat derived from eddy covariance,
satellite, and meteorological observations, J. Geophys. Res.,
116, G3, https://doi.org/10.1029/2010jg001566, 2011.
Köhler, P., Guanter, L., Kobayashi, H., Walther, S., and Yang, W.:
Assessing the potential of sun-induced fluorescence and the canopy
scattering coefficient to track large-scale vegetation dynamics in Amazon
forests, Remote Sens. Environ., 204, 769–785, https://doi.org/10.1016/j.rse.2017.09.025,
2017.
Lasslop, G., Reichstein, M., Detto, M., Richardson, A. D., and Baldocchi, D.
D.: Comment on Vickers et al.: Self-correlation between assimilation and
respiration resulting from flux partitioning of eddy-covariance CO2 fluxes,
Agr. Forest Meteorol., 150, 312–314,
https://doi.org/10.1016/j.agrformet.2009.11.003, 2010.
Lee, J. E., Frankenberg, C., van der Tol, C., Berry, J. A., Guanter, L.,
Boyce, C. K., Fisher, J. B., Morrow, E., Worden, J. R., Asefi, S., Badgley,
G., and Saatchi, S.: Forest productivity and water stress in Amazonia:
observations from GOSAT chlorophyll fluorescence, Proc. Biol.
Sci., 280, 20130171, https://doi.org/10.1098/rspb.2013.0171, 2013.
Lewis, S. L., Lloyd, J., Sitch, S., Mitchard, E. T. A., and Laurance, W. F.:
Changing Ecology of Tropical Forests: Evidence and Drivers, Ann. Rev.
Ecol. Evol. Syst., 40, 529–549,
https://doi.org/10.1146/annurev.ecolsys.39.110707.173345, 2009.
Liu, L., Yang, X., Gong, F., Su, Y., Huang, G., and Chen, X.: The Novel
Microwave Temperature Vegetation Drought Index (MTVDI) Captures Canopy
Seasonality across Amazonian Tropical Evergreen Forests, Remote Sens., 13, 339,
https://doi.org/10.3390/rs13030339, 2021.
Logan, B. A., Adams, W. W., and Demmig-Adams, B.: Viewpoint:Avoiding common
pitfalls of chlorophyll fluorescence analysis under field conditions,
Funct. Plant Biol., 34, 853–859, https://doi.org/10.1071/fp07113, 2007.
Magney, T. S., Frankenberg, C., Fisher, J. B., Sun, Y., North, G. B., Davis,
T. S., Kornfeld, A., and Siebke, K.: Connecting active to passive
fluorescence with photosynthesis: a method for evaluating remote sensing
measurements of Chl fluorescence, New Phytol., 215, 1594–1608,
https://doi.org/10.1111/nph.14662, 2017.
Malenovsky, Z., Mishra, K. B., Zemek, F., Rascher, U., and Nedbal, L.:
Scientific and technical challenges in remote sensing of plant canopy
reflectance and fluorescence, J. Exp. Bot., 60, 2987–3004,
https://doi.org/10.1093/jxb/erp156, 2009.
Malhi, Y.: The productivity, metabolism and carbon cycle of tropical forest
vegetation, J. Ecol., 100, 65–75,
https://doi.org/10.1111/j.1365-2745.2011.01916.x, 2012.
Medlyn, B. E.: Physiological basis of the light use efficiency model, Tree
Physiol., 18, 167–176, https://doi.org/10.1093/treephys/18.3.167, 1998.
Meroni, M., Rossini, M., Guanter, L., Alonso, L., Rascher, U., Colombo, R.,
and Moreno, J.: Remote sensing of solar-induced chlorophyll fluorescence:
Review of methods and applications, Remote Sens. Environ., 113,
2037–2051, https://doi.org/10.1016/j.rse.2009.05.003, 2009.
Merrick, T. and Broadbent, E. B.: Barro Colorado Data, available at: http://www.gatoreye.org, last access: 6 June 2021.
Merrick, T., Pau, S., Jorge, M. L. S. P., Silva, T. S. F., and Bennartz, R.: Spatiotemporal Patterns and
Phenology of Tropical Vegetation Solar-Induced Chlorophyll Fluorescence
across Brazilian Biomes Using Satellite Observations, Remote Sens., 11, 1746,
https://doi.org/10.3390/rs11151746, 2019.
Merrick, T., Jorge, M. L. S. P., Silva, T. S. F., Pau, S., Rausch, J.,
Broadbent, E. N., and Bennartz, R.: Characterization of chlorophyll
fluorescence, absorbed photosynthetically active radiation, and
reflectance-based vegetation index spectroradiometer measurements,
Int. J. Remote Sens., 41, 6755–6782,
https://doi.org/10.1080/01431161.2020.1750731, 2020.
Mitchard, E. T. A.: The tropical forest carbon cycle and climate change,
Nature, 559, 527–534, https://doi.org/10.1038/s41586-018-0300-2, 2018.
Monteith, J. L.: Climate and the efficiency of crop production in Britain,
Philos. T. R. Soc. Land., 281, 277–294, 1977.
Morton, D. C., Rubio, J., Cook, B. D., Gastellu-Etchegorry, J.-P., Longo, M., Choi, H., Hunter, M., and Keller, M.: Amazon forest structure generates diurnal and seasonal variability in light utilization, Biogeosciences, 13, 2195–2206, https://doi.org/10.5194/bg-13-2195-2016, 2016.
Morton, D. C., Nagol, J., Carabajal, C. C., Rosette, J., Palace, M., Cook,
B. D., Vermote, E. F., Harding, D. J., and North, P. R.: Amazon forests
maintain consistent canopy structure and greenness during the dry season,
Nature, 506, 221–224, https://doi.org/10.1038/nature13006, 2014.
Moya, I., Camenen, L., Evain, S., Goulas, Y., Cerovic, Z. G., Latouche, G.,
Flexas, J., and Ounis, A.: A new instrument for passive remote sensing1.
Measurements of sunlight-induced chlorophyll fluorescence, Remote Sens.
Environ., 91, 186–197, https://doi.org/10.1016/j.rse.2004.02.012, 2004.
Plascyk, J. A.: The MK II Fraunhofer Line Discriminator (FLD-II) for
Airborne and Orbital Remote Sensing of Solar-Stimulated Luminescense,
Opt. Eng., 14, 144339, https://doi.org/10.1117/12.7971842, 1975.
Porcar-Castell, A., Tyystjarvi, E., Atherton, J., van der Tol, C., Flexas,
J., Pfundel, E. E., Moreno, J., Frankenberg, C., and Berry, J. A.: Linking
chlorophyll a fluorescence to photosynthesis for remote sensing
applications: mechanisms and challenges, J. Exp. Bot., 65,
4065–4095, https://doi.org/10.1093/jxb/eru191, 2014.
R Development Core Team: R: A language and environment for statistical
computing, R Foundation for Statistical Computing [code], available at: https://www.r-project.org/ (last access: 6 June 2021),
2010.
Rocha, A. V., Appel, R., Bret-Harte, M. S., Euskirchen, E. S., Salmon, V.,
and Shaver, G.: Solar position confounds the relationship between ecosystem
function and vegetation indices derived from solar and photosynthetically
active radiation fluxes, Agr. Forest Meteorol., 298, 108291,
https://doi.org/10.1016/j.agrformet.2020.108291, 2021.
Rouse Jr., J. W., Haas, R. H., Schell, J. A., and Deering, D. W.: Paper A 20,
hird Earth Resources Technology Satellite-1 Symposium: The Proceedings of a
Symposium Goddard Space Flight Center at Washington, DC 309, 1974.
Running, S. W., Nemani, R. R., Heinsch, F. A., Zhao, M., Reeves, M., and
Hashimoto, H.: A Continuous Satellite-Derived Measure of Global Terrestrial
Primary Production, BioScience, 54, 547–551, 2004.
Ryu, Y., Jiang, C., Kobayashi, H., and Detto, M.: MODIS-derived global land
products of shortwave radiation and diffuse and total photosynthetically
active radiation at 5 km resolution from 2000, Remote Sens.
Environ., 204, 812–825, https://doi.org/10.1016/j.rse.2017.09.021, 2018.
Saatchi, S. S., Harris, N. L., Brown, S., Lefsky, M., A., E. T., Mitchare,
W. S., Zutta, B. R., Buerman, W., Lewis, S. L., Hagen, S., Petrova, S.,
White, L., Silman, M., and Morel, A.: Benchmark map of forest carbon stocks
in tropical regions across three continents, P. Natl.
Acad. Sci. USA, 108, 9899–9905, 2010.
Saatchi, S. S., Harris, N. L., Brown, S., Lefsky, M., Mitchard, E. T.,
Salas, W., Zutta, B. R., Buermann, W., Lewis, S. L., Hagen, S., Petrova, S.,
White, L., Silman, M., and Morel, A.: Benchmark map of forest carbon stocks
in tropical regions across three continents, P. Natl.
Acad. Sci. USA, 108, 9899–9904,
https://doi.org/10.1073/pnas.1019576108, 2011.
Samanta, A., Ganguly, S., and Myneni, R.: MODIS Enhanced Vegetation Index
data do not show greening of Amazon forests during the 2005 drought, New
Phytol., 189, 11–15, 2010.
Schickling, A., Matveeva, M., Damm, A., Schween, J., Wahner, A., Graf, A.,
Crewell, S., and Rascher, U.: Combining Sun-Induced Chlorophyll Fluorescence
and Photochemical Reflectance Index Improves Diurnal Modeling of Gross
Primary Productivity, Remote Sens., 8, 574, https://doi.org/10.3390/rs8070574, 2016.
Sims, D., Rahman, A., Cordova, V., Elmasri, B., Baldocchi, D., Bolstad, P.,
Flanagan, L., Goldstein, A., Hollinger, D., and Misson, L.: 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
Sens. Environ., 112, 1633–1646, https://doi.org/10.1016/j.rse.2007.08.004, 2008.
Springer, K., Wang, R., and Gamon, J. A.: Parallel Seasonal Patterns of
Photosynthesis, Fluorescence, and Reflectance Indices in Boreal Trees,
Remote Sens., 9, 1–18, https://doi.org/10.3390/rs9070691, 2017.
Sun, Y., Frankenberg, C., Wood, J. D., Schimel, D. S., Jung, M., Guanter,
L., Drewry, D. T., Verma, M., Porcar-Castell, A., Griffis, T. J., Gu, L.,
Magney, T. S., Kohler, P., Evans, B., and Yuen, K.: OCO-2 advances
photosynthesis observation from space via solar-induced chlorophyll
fluorescence, Science, 358, eaam5747, https://doi.org/10.1126/science.aam5747, 2017.
Torrence, C. and Compo, G. P.: A Practical Guide to Wavelet Analysis,
Bull. Am. Meteorol. Soc., 79, 61–79, 1998.
Tucker, C.: Red and photographic infrared linear combinations for vegetation
monitoring, Remote Sens. Environ., 8, 127–150, 1979.
Turner, D. P., Ritts, W. D., Cohen, W. B., Gower, S. T., Zhao, M., Running,
S. W., Wofsy, S. C., Urbanski, S., Dunn, A. L., and Munger, J. W.: Scaling
Gross Primary Production (GPP) over boreal and deciduous forest landscapes
in support of MODIS GPP product validation, Remote Sens. Environ.,
88, 256–270, https://doi.org/10.1016/j.rse.2003.06.005, 2003.
Van Wittenberghe, S., Alonso, L., Verrelst, J., Moreno, J., and Samson, R.:
Bidirectional sun-induced chlorophyll fluorescence emission is influenced by
leaf structure and light scattering properties – A bottom-up approach,
Remote Sens. Environ., 158, 169–179, https://doi.org/10.1016/j.rse.2014.11.012,
2015.
Van Wittenberghe, S., Alonso, L., Verrelst, J., Hermans, I., Delegido, J.,
Veroustraete, F., Valcke, R., Moreno, J., and Samson, R.: Upward and
downward solar-induced chlorophyll fluorescence yield indices of four tree
species as indicators of traffic pollution in Valencia, Environ.
Pollut., 173, 29–37, https://doi.org/10.1016/j.envpol.2012.10.003, 2013.
Wang, C., Beringer, J., Hutley, L. B., Cleverly, J., Li, J., Liu, Q., and
Sun, Y.: Phenology Dynamics of Dryland Ecosystems Along the North Australian
Tropical Transect Revealed by Satellite Solar-Induced Chlorophyll
Fluorescence, Geophys. Res. Lett., 46, 5294–5302,
https://doi.org/10.1029/2019gl082716, 2019.
Wang, S., Zhang, Y., Ju, W., Qiu, B., and Zhang, Z.: Tracking the seasonal
and inter-annual variations of global gross primary production during last
four decades using satellite near-infrared reflectance data, Sci.
Total Environ., 755, 142569, https://doi.org/10.1016/j.scitotenv.2020.142569, 2020.
Wickham, H.: ggplot2: Elegant Graphics for Data Analysis, Springer-Verlag
[code], available at: https://cran.r-project.org/web/packages/ggplot2/index.html
(last access: 5 June 2021), 2016.
Wickham, H.: tidyverse: Easily Install and Load the “Tidyverse” (R package
version 1.2.1) [code], available at: https://cran.r-project.org/web/packages/tidyverse/index.html (last access: 5 June 2021), 2017.
Wickham, H., François, R., Henry, L., and Müller, K.: dplyr: A
Grammar of Data Manipulation (R package version 0.7.8) [code], available at: https://cran.r-project.org/web/packages/dplyr/index.html (last access: 5 June 2021), 2018.
Wright, S. J.: The future of tropical forests, Ann. N. Y. Acad. Sci., 1195, 1–27,
https://doi.org/10.1111/j.1749-6632.2010.05455.x, 2010.
Wu, G., Guan, K., Jiang, C., Peng, B., Kimm, H., Chen, M., Yang, X., Wang,
S., Suyker, A. E., Bernacchi, C. J., Moore, C. E., Zeng, Y., Berry, J. A.,
and Cendrero-Mateo, M. P.: Radiance-based NIRv as a proxy for GPP of corn
and soybean, Environ. Res. Lett., 15, 034009, https://doi.org/10.1088/1748-9326/ab65cc,
2020.
Xu, L., Saatchi, S. S., Yang, Y., Myneni, R. B., Frankenberg, C., Chowdhury,
D., and Bi, J.: Satellite observation of tropical forest seasonality:
spatial patterns of carbon exchange in Amazonia, Environ. Res.
Lett., 10, 084005, https://doi.org/10.1088/1748-9326/10/8/084005, 2015.
Yang, H., Yang, X., Zhang, Y., Heskel, M. A., Lu, X., Munger, J. W., Sun,
S., and Tang, J.: Chlorophyll fluorescence tracks seasonal variations of
photosynthesis from leaf to canopy in a temperate forest, Glob. Change Biol.,
23, 2874–2886, https://doi.org/10.1111/gcb.13590, 2017.
Yang, J., Tian, H., Pan, S., Chen, G., Zhang, B., and Dangal, S.: Amazon
droughts and forest responses: Largely reduced forest photosynthesis but
slightly increased canopy greenness during the extreme drought of 2015/2016,
Glob. Change Biol., 24, 1919–1934, https://doi.org/10.1111/gcb.14056, 2018a.
Yang, K., Ryu, Y., Dechant, B., Berry, J. A., Hwang, Y., Jiang, C., Kang,
M., Kim, J., Kimm, H., Kornfeld, A., and Yang, X.: Sun-induced chlorophyll
fluorescence is more strongly related to absorbed light than to
photosynthesis at half-hourly resolution in a rice paddy, Remote Sens.
Environ., 216, 658–673, https://doi.org/10.1016/j.rse.2018.07.008, 2018b.
Yang, P., van der Tol, C., Campbell, P. K. E., and Middleton, E. M.:
Fluorescence Correction Vegetation Index (FCVI): A physically based
reflectance index to separate physiological and non-physiological
information in far-red sun-induced chlorophyll fluorescence, Remote Sens. Environ., 240, 111676, https://doi.org/10.1016/j.rse.2020.111676, 2020.
Yuan, W., Cai, W., Xia, J., Chen, J., Liu, S., Dong, W., Merbold, L., Law,
B., Arain, A., Beringer, J., Bernhofer, C., Black, A., Blanken, P. D.,
Cescatti, A., Chen, Y., Francois, L., Gianelle, D., Janssens, I. A., Jung,
M., Kato, T., Kiely, G., Liu, D., Marcolla, B., Montagnani, L., Raschi, A.,
Roupsard, O., Varlagin, A., and Wohlfahrt, G.: Global comparison of light
use efficiency models for simulating terrestrial vegetation gross primary
production based on the LaThuile database, Agr. Forest
Meteorol., 192/193, 108–120,
https://doi.org/10.1016/j.agrformet.2014.03.007, 2014.
Zarco-Tejada, P. J., González-Dugo, V., and Berni, J. A. J.:
Fluorescence, temperature and narrow-band indices acquired from a UAV
platform for water stress detection using a micro-hyperspectral imager and a
thermal camera, Remote Sens. Environ., 117, 322–337,
https://doi.org/10.1016/j.rse.2011.10.007, 2012.
Zarco-Tejada, P. J., Morales, A., Testi, L., and Villalobos, F. J.:
Spatio-temporal patterns of chlorophyll fluorescence and physiological and
structural indices acquired from hyperspectral imagery as compared with
carbon fluxes measured with eddy covariance, Remote Sens. Environ.,
133, 102–115, https://doi.org/10.1016/j.rse.2013.02.003, 2013.
Zarco-Tejada, P. J., Miller, J. R., Mohammed, G. H., Noland, T. L., and
Sampson, P. H.: Estimation of chlorophyll fluorescence under natural
illumination from hyperspectral data, Int. J. Appl. Earth
Obs, 3, 321–327, 2001.
Zeng, Y., Badgley, G., Dechant, B., Ryu, Y., Chen, M., and Berry, J. A.: A
practical approach for estimating the escape ratio of near-infrared
solar-induced chlorophyll fluorescence, Remote Sens. Environ., 232, 111209,
https://doi.org/10.1016/j.rse.2019.05.028, 2019.
Zhang, Z., Zhang, Y., Zhang, Q., Chen, J. M., Porcar-Castell, A., Guanter,
L., Wu, Y., Zhang, X., Wang, H., Ding, D., and Li, Z.: Assessing
bi-directional effects on the diurnal cycle of measured solar-induced
chlorophyll fluorescence in crop canopies, Agr. Forest
Meteorol., 295, 108147, https://doi.org/10.1016/j.agrformet.2020.108147, 2020.
Zhang, Z., Zhang, Y., Zhang, Y., Gobron, N., Frankenberg, C., Wang, S., and
Li, Z.: The potential of satellite FPAR product for GPP estimation: An
indirect evaluation using solar-induced chlorophyll fluorescence, Remote
Sens. Environ., 240, 111686, https://doi.org/10.1016/j.rse.2020.111686, 2020.
Zhao, M., Running, S., Heinsch, F. A., and Nemani, R.: MODIS-Derived
Terrestrial Primary Production, in: Land Remote Sensing and Global Environmental Change, Springer, New York, NY, 11, 635–660,
https://doi.org/10.1007/978-1-4419-6749-7_28, 2010.
Zhu, X. and Liu, D.: Improving forest aboveground biomass estimation using
seasonal Landsat NDVI time-series, ISPRS J. Photogramm.
Remote Sens., 102, 222–231, https://doi.org/10.1016/j.isprsjprs.2014.08.014, 2015.
Short summary
Remote sensing measurements of forest structure promise to improve monitoring of tropical forest health. We investigated drone-based vegetation measurements' abilities to capture different structural and functional elements of a tropical forest. We found that emerging vegetation indices captured greater variability than traditional indices and one new index trends with daily change in carbon flux. These new tools can help improve understanding of tropical forest structure and function.
Remote sensing measurements of forest structure promise to improve monitoring of tropical forest...
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