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Structural complexity biases vegetation greenness measures

Nature Ecology & Evolution volume 7, pages 1790–1798 (2023)Cite this article

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Abstract

Vegetation ‘greenness’ characterized by spectral vegetation indices (VIs) is an integrative measure of vegetation leaf abundance, biochemical properties and pigment composition. Surprisingly, satellite observations reveal that several major VIs over the US Corn Belt are higher than those over the Amazon rainforest, despite the forests having a greater leaf area. This contradicting pattern underscores the pressing need to understand the underlying drivers and their impacts to prevent misinterpretations. Here we show that macroscale shadows cast by complex forest structures result in lower greenness measures compared with those cast by structurally simple and homogeneous crops. The shadow-induced contradictory pattern of VIs is inevitable because most Earth-observing satellites do not view the Earth in the solar direction and thus view shadows due to the sun–sensor geometry. The shadow impacts have important implications for the interpretation of VIs and solar-induced chlorophyll fluorescence as measures of global vegetation changes. For instance, a land-conversion process from forests to crops over the Amazon shows notable increases in VIs despite a decrease in leaf area. Our findings highlight the importance of considering shadow impacts to accurately interpret remotely sensed VIs and solar-induced chlorophyll fluorescence for assessing global vegetation and its changes.

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Fig. 1: Comparisons of VIs, LAI and fesc between the US Midwest Corn Belt (43° N and 94° W, 824,871 km2) and the Amazon rainforest (5° S and 62° W, 7,755,160 km2) in August in 2001–2019.
Fig. 2: POLDER (July–August 2008), MISR (July–August 2017) and 3D ray-tracing model simulated multi-angular observations in summer.
Fig. 3: An example of the canopy spectrum in summer over the Amazon rainforest and the Corn Belt acquired by the Earth Observing-1 Hyperion sensor.
Fig. 4: The opposite trend of LAI and EVI time series in land-cover transition.

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The data are available through figshare56 at https://doi.org/10.6084/m9.figshare.23677407.v1.

Code availability

The source code is available through figshare57 at https://doi.org/10.6084/m9.figshare.23677260.v1.

References

  1. Tucker, C. J. Red and photographic infrared linear combinations for monitoring vegetation. Remote Sens. Environ. 8, 127–150 (1979).

    Article  Google Scholar 

  2. Myneni, R. B., Keeling, C., Tucker, C. J., Asrar, G. & Nemani, R. R. Increased plant growth in the northern high latitudes from 1981 to 1991. Nature 386, 698–702 (1997).

    Article  CAS  Google Scholar 

  3. Zeng, Y. et al. Optical vegetation indices for monitoring terrestrial ecosystems globally. Nat. Rev. Earth Environ. 3, 477–493 (2022).

  4. Piao, S. et al. Characteristics, drivers and feedbacks of global greening. Nat. Rev. Earth Environ. 1, 14–27 (2020).

    Article  Google Scholar 

  5. Chen, J. M. et al. Vegetation structural change since 1981 significantly enhanced the terrestrial carbon sink. Nat. Commun. 10, 4259 (2019).

    Article  PubMed  PubMed Central  Google Scholar 

  6. Zhang, H. et al. A novel red-edge spectral index for retrieving the leaf chlorophyll content. Methods Ecol. Evol. 13, 2771–2787 (2022).

    Article  Google Scholar 

  7. Huete, A. et al. Overview of the radiometric and biophysical performance of the MODIS vegetation indices. Remote Sens. Environ. 83, 195–213 (2002).

    Article  Google Scholar 

  8. Badgley, G., Field, C. B. & Berry, J. A. Canopy near-infrared reflectance and terrestrial photosynthesis. Sci. Adv. 3, e1602244 (2017).

    Article  PubMed  PubMed Central  Google Scholar 

  9. Kimm, H. et al. Deriving high-spatiotemporal-resolution leaf area index for agroecosystems in the US Corn Belt using Planet Labs CubeSat and STAIR fusion data. Remote Sens. Environ. 239, 111615 (2020).

    Article  Google Scholar 

  10. Maeda, E. E. et al. Large-scale commodity agriculture exacerbates the climatic impacts of Amazonian deforestation. Proc. Natl Acad. Sci. USA 118, e2023787118 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  11. Bi, J. et al. Sunlight mediated seasonality in canopy structure and photosynthetic activity of Amazonian rainforests. Environ. Res. Lett. 10, 064014 (2015).

    Article  Google Scholar 

  12. Wu, J. et al. Biological processes dominate seasonality of remotely sensed canopy greenness in an Amazon evergreen forest. New Phytol. 217, 1507–1520 (2018).

    Article  PubMed  Google Scholar 

  13. Hashimoto, H. et al. New generation geostationary satellite observations support seasonality in greenness of the Amazon evergreen forests. Nat. Commun. 12, 684 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  14. Zhang, Y. et al. A global moderate resolution dataset of gross primary production of vegetation for 2000–2016. Sci. Data 4, 170165 (2017).

  15. Park, T. et al. Changes in growing season duration and productivity of northern vegetation inferred from long-term remote sensing data. Environ. Res. Lett. 11, 084001 (2016).

    Article  Google Scholar 

  16. Schaaf, C. B. et al. First operational BRDF, albedo nadir reflectance products from MODIS. Remote Sens. Environ. 83, 135–148 (2002).

    Article  Google Scholar 

  17. Morton, D. C. et al. Amazon forests maintain consistent canopy structure and greenness during the dry season. Nature 506, 221–224 (2014).

    Article  CAS  PubMed  Google Scholar 

  18. Saleska, S. R. et al. Dry-season greening of Amazon forests. Nature 531, E4–E5 (2016).

    Article  CAS  PubMed  Google Scholar 

  19. Zeng, Y. et al. A practical approach for estimating the escape ratio of near-infrared solar-induced chlorophyll fluorescence. Remote Sens. Environ. 232, 111209 (2019).

    Article  Google Scholar 

  20. Myers-Smith, I. H. et al. Complexity revealed in the greening of the Arctic. Nat. Clim. Change 10, 106–117 (2020).

    Article  Google Scholar 

  21. Rautiainen, M. & Stenberg, P. Application of photon recollision probability in coniferous canopy reflectance simulations. Remote Sens. Environ. 96, 98–107 (2005).

    Article  Google Scholar 

  22. Chen, C. et al. China and India lead in greening of the world through land-use management. Nat. Sustain. 2, 122–129 (2019).

    Article  PubMed  PubMed Central  Google Scholar 

  23. Parker, G. G., Fitzjarrald, D. R. & Sampaio, I. C. G. Consequences of environmental heterogeneity for the photosynthetic light environment of a tropical forest. Agric. For. Meteorol. 278, 107661 (2019).

    Article  Google Scholar 

  24. Camps-Valls, G. et al. A unified vegetation index for quantifying the terrestrial biosphere. Sci. Adv. 7, eabc7447 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  25. Yin, G., Verger, A., Descals, A., Filella, I. & Peñuelas, J. A broadband green–red vegetation index for monitoring gross primary production phenology. J. Remote Sens. 2022, 9764982 (2022).

    Article  Google Scholar 

  26. Breon, F.-M. & Maignan, F. A BRDF–BPDF database for the analysis of Earth target reflectances. Earth Syst. Sci. Data 9, 31–45 (2017).

    Article  Google Scholar 

  27. Diner, D. J. et al. Multi-angle Imaging SpectroRadiometer (MISR) instrument description and experiment overview. IEEE Trans. Geosci. Remote Sens. 36, 1072–1087 (1998).

    Article  Google Scholar 

  28. Kaufmann, R. K. et al. Effect of orbital drift and sensor changes on the time series of AVHRR vegetation index data. IEEE Trans. Geosci. Remote Sens. 38, 2584–2597 (2000).

    Article  Google Scholar 

  29. Qi, J. et al. LESS: LargE-Scale remote sensing data and image simulation framework over heterogeneous 3D scenes. Remote Sens. Environ. 221, 695–706 (2019).

    Article  Google Scholar 

  30. Dos-Santos, M., Keller, M. & Morton, D. LiDAR Surveys Over Selected Forest Research Sites, Brazilian Amazon, 2008–2018 (ORNL DAAC, 2019); https://doi.org/10.3334/ORNLDAAC/1644

  31. Köhler, P., Guanter, L., Kobayashi, H., Walther, S. & 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 (2018).

    Article  Google Scholar 

  32. Vancutsem, C. et al. Long-term (1990–2019) monitoring of forest cover changes in the humid tropics. Sci. Adv. 7, eabe1603 (2021).

    Article  PubMed  PubMed Central  Google Scholar 

  33. Abera, T. A., Heiskanen, J., Pellikka, P., Rautiainen, M. & Maeda, E. E. Clarifying the role of radiative mechanisms in the spatio-temporal changes of land surface temperature across the Horn of Africa. Remote Sens. Environ. 221, 210–224 (2019).

    Article  Google Scholar 

  34. Zheng, L. et al. Spatial, temporal, and spectral variations in albedo due to vegetation changes in China’s grasslands. ISPRS J. Photogramm. Remote Sens. 152, 1–12 (2019).

    Article  Google Scholar 

  35. Alibakhshi, S., Naimi, B., Hovi, A., Crowther, T. W. & Rautiainen, M. Quantitative analysis of the links between forest structure and land surface albedo on a global scale. Remote Sens. Environ. 246, 111854 (2020).

    Article  Google Scholar 

  36. Yan, H. et al. Forest greening increases land surface albedo during the main growing period between 2002 and 2019 in China. J. Geophys. Res. Atmos. 126, e2020JD033582 (2021).

    Article  Google Scholar 

  37. Ollinger, S. V. et al. Canopy nitrogen, carbon assimilation, and albedo in temperate and boreal forests: functional relations and potential climate feedbacks. Proc. Natl Acad. Sci. USA 105, 19336–19341 (2008).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  38. Zhang, Y. et al. Spatio-temporal convergence of maximum daily light-use efficiency based on radiation absorption by canopy chlorophyll. Geophys. Res. Lett. 45, 3508–3519 (2018).

    Article  Google Scholar 

  39. Lin, S. et al. Multi-site assessment of the potential of fine resolution red-edge vegetation indices for estimating gross primary production. Int. J. Appl. Earth Obs. Geoinf. 113, 102978 (2022).

    Google Scholar 

  40. Xiao, X. et al. Satellite-based modeling of gross primary production in an evergreen needleleaf forest. Remote Sens. Environ. 89, 519–534 (2004).

    Article  Google Scholar 

  41. Joiner, J. et al. Estimation of terrestrial global gross primary production (GPP) with satellite data-driven models and eddy covariance flux data. Remote Sens. 10, 1346 (2018).

    Article  Google Scholar 

  42. Badgley, G., Anderegg, L. D., Berry, J. A. & Field, C. B. Terrestrial gross primary production: using NIRV to scale from site to globe. Glob. Change Biol. 25, 3731–3740 (2019).

    Article  Google Scholar 

  43. Neale, C. M., Gonzalez-Dugo, M. P., Serrano-Perez, A., Campos, I. & Mateos, L. Cotton canopy reflectance under variable solar zenith angles: implications of use in evapotranspiration models. Hydrol. Process. 35, e14162 (2021).

    Article  Google Scholar 

  44. Yebra, M., Van Dijk, A., Leuning, R., Huete, A. & Guerschman, J. P. Evaluation of optical remote sensing to estimate actual evapotranspiration and canopy conductance. Remote Sens. Environ. 129, 250–261 (2013).

    Article  Google Scholar 

  45. Chen, J. M. & Liu, J. Evolution of evapotranspiration models using thermal and shortwave remote sensing data. Remote Sens. Environ. 237, 111594 (2020).

    Article  Google Scholar 

  46. Chen, M. & Zhuang, Q. Evaluating aerosol direct radiative effects on global terrestrial ecosystem carbon dynamics from 2003 to 2010. Tellus B 66, 21808 (2014).

    Article  Google Scholar 

  47. Marshak, A. et al. Earth observations from DSCOVR/EPIC instrument. Bull. Am. Meteorol. Soc. 99, 1829–1850 (2018).

    Article  PubMed  PubMed Central  Google Scholar 

  48. Myneni, R. et al. Global products of vegetation leaf area and fraction absorbed PAR from year one of MODIS data. Remote Sens. Environ. 83, 214–231 (2002).

    Article  Google Scholar 

  49. Friedl, M. A. et al. Global land cover mapping from MODIS: algorithms and early results. Remote Sens. Environ. 83, 287–302 (2002).

    Article  Google Scholar 

  50. Gorelick, N. et al. Google Earth Engine: planetary-scale geospatial analysis for everyone. Remote Sens. Environ. 202, 18–27 (2017).

    Article  Google Scholar 

  51. Köhler, P. et al. Global retrievals of solar-induced chlorophyll fluorescence with TROPOMI: first results and intersensor comparison to OCO-2. Geophys. Res. Lett. 45, 10,456–10,463 (2018).

    Article  Google Scholar 

  52. Hao, D. et al. Estimating hourly land surface downward shortwave and photosynthetically active radiation from DSCOVR/EPIC observations. Remote Sens. Environ. 232, 111320 (2019).

    Article  Google Scholar 

  53. Hao, D. et al. DSCOVR/EPIC-derived global hourly and daily downward shortwave and photosynthetically active radiation data at 0.1°× 0.1° resolution. Earth Syst. Sci. Data 12, 2209–2221 (2020).

    Article  Google Scholar 

  54. Ni, X. et al. Vegetation angular signatures of equatorial forests from DSCOVR EPIC and Terra MISR observations. Front. Remote Sens. 2, 766805 (2021).

  55. Aneece, I. P. et al. in Fundamentals, Sensor Systems, Spectral Libraries, and Data Mining for Vegetation (eds Thenkabail P. S. et al.) 251–272 (CRC Press, 2018).

  56. Zeng, Y. The data for the NEE paper: structural complexity biases vegetation greenness measures. figshare https://doi.org/10.6084/m9.figshare.23677407.v1 (2023).

  57. Zeng, Y. The code for the NEE paper: structural complexity biases vegetation greenness measures. figshare https://doi.org/10.6084/m9.figshare.23677260.v1 (2023).

  58. Seyednasrollah, B. et al. Tracking vegetation phenology across diverse biomes using Version 2.0 of the PhenoCam Dataset. Sci. Data https://doi.org/10.1038/s41597-019-0229-9 (2019).

    Article  PubMed  PubMed Central  Google Scholar 

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Acknowledgements

Support for this research was provided by the Office of the Vice Chancellor for Research and Graduate Education, University of Wisconsin-Madison, with funding from the Wisconsin Alumni Research Foundation. D.H. acknowledges support from the Earth System Model Development programme area, Office of Biological and Environmental Research, Office of Science, US Department of Energy, as part of the Climate Process Team projects. T.P. acknowledges support from the Earth Science Division of NASA. Y.G. acknowledges support from Universities Scientific Fund (15053347). M.C. acknowledges support from a McIntire–Stennis grant (1027576) from the National Institute of Food and Agriculture, US Department of Agriculture. We acknowledge PhenoCam for providing the site imagery at Tapajos and Mead1; the latter was a contribution of the Long-Term Agroecosystem Research network, supported by the US Department of Agriculture. We also thank G. Badgley for fruitful discussions on SIF.

Author information

Author notes

  1. These authors contributed equally: Yelu Zeng, Dalei Hao, Taejin Park.

Authors and Affiliations

  1. College of Land Science and Technology, China Agricultural University, Beijing, China

    Yelu Zeng, Jianxi Huang, Yongyuan Gao & Baoguo Li

  2. Department of Forest and Wildlife Ecology, University of Wisconsin–Madison, Madison, WI, USA

    Yelu Zeng, Fa Li, Fujiang Ji & Min Chen

  3. Atmospheric Sciences and Global Change Division, Pacific Northwest National Laboratory, Richland, WA, USA

    Dalei Hao

  4. NASA Ames Research Center, Moffett Field, CA, USA

    Taejin Park & Ramakrishna R. Nemani

  5. Bay Area Environmental Research Institute, Moffett Field, CA, USA

    Taejin Park & Ramakrishna R. Nemani

  6. Department of Geography and Institute for Climate and Carbon Neutrality, The University of Hong Kong, Hong Kong SAR, China

    Peng Zhu

  7. Faculty of Science, University of Technology Sydney, Sydney, New South Wales, Australia

    Alfredo Huete

  8. Department of Earth and Environment, Boston University, Boston, MA, USA

    Ranga Myneni & Yuri Knyazikhin

  9. Centre d’Etudes Spatiales de la Biosphere, Toulouse, France

    Jianbo Qi

  10. EUMETSAT, Darmstadt, Germany

    Philipp Köhler

  11. Jet Propulsion Laboratory, California Institute of Technology, Pasadena, CA, USA

    Christian Frankenberg

  12. Department of Global Ecology, Carnegie Institution for Science, Stanford, CA, USA

    Joseph A. Berry

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Contributions

Y.Z., M.C., D.H. and T.P. conceptualized the study, designed the research and methodology, and wrote the initial draft of the paper. P.Z., A.H., R.M., Y.K., R.N., P.K., C.F., J.B., F.L., Y.G. and F.J. contributed to the data collection and result interpretation. J.Q., J.H., B.L. and F.J. contributed to the 3D ray-tracing model simulation and sensitivity analysis. F.L., Y.G. and F.J. drew and polished the figures. All authors reviewed and edited the paper and made substantial contributions to the improvement of the paper.

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Correspondence to Yelu Zeng, Dalei Hao or Min Chen.

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Nature Ecology & Evolution thanks Anping Chen, Mathias Disney and Shangrong Lin for their contribution to the peer review of this work.

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Extended data

Extended Data Fig. 1 The contrasting patterns between the Corn Belt (upper circle) and Amazon rainforest (lower circle) in August during 2001–2019 at global maps.

The same as in Fig. 1, the MODIS EVI, NIRv and fesc were smaller over the Amazon rainforest than over the Corn Belt, while the MODIS LAI shows the opposite pattern. This effect is also found in the cropland in Northeast Asia, the rainforest in Southeast Asia, and the eastern forest region in North America near the Corn Belt with the same season and similar latitude.

Extended Data Fig. 2

Comparison of VIs, FPAR and SIF between the Corn Belt (upper panels) and Amazon rainforest (lower panels) in August during 2001–2019.

Extended Data Fig. 3 The relationship among LAI, FPAR, SIF and VIs over the Corn Belt and Amazon rainforest.

(a) The relationship between LAI or FPAR with EVI, NIRv, SIF/PAR and NDVI over the Corn Belt (in red) and Amazon rainforest (in blue) in August during 2001–2019. The linear slopes over the Corn Belt were at least twice as high as those over the Amazon rainforest. In each panel, the degree of point transparency represents the point density. (b) The correlation coefficient among LAI, FPAR, SIF and VIs over the Corn Belt and Amazon rainforest in August during 2001–2019. The relationship among VIs and SIF were much higher than they were compared to FPAR and LAI, especially over the Corn Belt.

Extended Data Fig. 4

Comparisons of VIs, LAI and fesc between the Corn Belt and Amazon rainforest in August of each year during 2001–2019.

Extended Data Fig. 5 The comparison of the Corn Belt (upper panels) and Amazon rainforest (lower panels) in August during the period of 2001–2019.

Additional global VI and LAI products, including MOD13 NDVI/EVI, GEOV LAI and GLASS LAI, were used for the analysis in addition to the MCD43 NDVI/EVI and MODIS LAI shown in Fig. 1. Similar to Fig. 1, the EVI values are lower over the Amazon rainforest compared to the Corn Belt, while the LAI shows the opposite pattern, with larger values observed in the Amazon rainforest.

Extended Data Fig. 6

Comparison of VIs, LAI and fesc between the Corn Belt and Amazon rainforest in August during 2001–2019 at different levels of GRVI, which was used as a proxy of canopy pigment pools.

Extended Data Fig. 7 Comparisons of VIs, LAI and fesc between the US Corn Belt (upper panels in a) and a nearby forest region in the Northeastern North America (NA, lower panels in a) in August during 2001–2019.

(b) The MODIS EVI, NIRv and fesc were lower over the forest region compared to the Corn Belt, due to the stronger shadows in view. In contrast, the MODIS LAI was higher in the forest region than in the Corn Belt. The corresponding violin plots display the quartile and mean of the target variables, and as well as their distributions. (c) Comparison of VIs, LAI and fesc between the Corn Belt and the nearby forest region in North America in August during 2001–2019 at different levels of GRVI, which was used as a proxy of canopy pigment pools. (d) Comparison of EVI between the Corn Belt and the nearby forest region in North America in August during 2001–2019 at different levels of GRVI and LAI.

Extended Data Fig. 8 The canopy structure reconstructed by the 3D ray-tracing model over the Amazon rainforest (a, 100 m × 100 m) and the corn field (b,50 m × 50 m).

The Amazon rainforest structure was extracted by the LiDAR data acquired over a site located at (E 54.99, S 3.37) in 201822. Detailed input soil-leaf-canopy parameters were as in Supplementary Table 1. Multi-angular EVI, NIRv and NDVI simulations by the 3D ray-tracing model over the Corn Belt and Amazon rainforest (c) had close values at the hotspot direction (phase angle = 0 when the solar and sensor directions coincide), while EVI and NIRv were always much higher over the Corn Belt than the Amazon rainforest at the off-hotspot directions (phase angle > 0, for example, at nadir view but the solar zenith angle is 45° for the standard MAIAC surface reflectance product). The phase angle in (c) is the angle between the solar and sensor directions. The ‘Spectrum Replaced’ represents the simulation of the corn field while the soil-leaf spectrum was replaced by the spectrum of the Amazon rainforest (c).

Extended Data Fig. 9 Sensitivity analysis by the LESS 3D ray-tracing model simulations of forest and crops with varied parameters in Supplementary Table 2, especially under the control of the same LAI.

(a) The separate simulated VIs of forest and crops. (b) The differences of simulated VIs between forest and crops. The same as the contrast in Extended Data Fig. 7d, the EVI and NIRv under the control of similar LAI (2.5 or 5) were smaller over forest than crops, especially when the LAI was relatively large at 5.

Extended Data Fig. 10

The eight deforestation locations in Fig. 4a with the land cover change from forest to crops within 2005–2015 over Amazon rainforest.

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Zeng, Y., Hao, D., Park, T. et al. Structural complexity biases vegetation greenness measures. Nat Ecol Evol 7, 1790–1798 (2023). https://doi.org/10.1038/s41559-023-02187-6

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