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Amazon forests maintain consistent canopy structure and greenness during the dry season

Nature volume 506, pages 221–224 (2014)Cite this article

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Abstract

The seasonality of sunlight and rainfall regulates net primary production in tropical forests1. Previous studies have suggested that light is more limiting than water for tropical forest productivity2, consistent with greening of Amazon forests during the dry season in satellite data3,4,5,6,7. We evaluated four potential mechanisms for the seasonal green-up phenomenon, including increases in leaf area5,6,7 or leaf reflectance3,4,6, using a sophisticated radiative transfer model8 and independent satellite observations from lidar and optical sensors. Here we show that the apparent green up of Amazon forests in optical remote sensing data resulted from seasonal changes in near-infrared reflectance, an artefact of variations in sun-sensor geometry. Correcting this bidirectional reflectance effect eliminated seasonal changes in surface reflectance, consistent with independent lidar observations and model simulations with unchanging canopy properties. The stability of Amazon forest structure and reflectance over seasonal timescales challenges the paradigm of light-limited net primary production in Amazon forests and enhanced forest growth during drought conditions. Correcting optical remote sensing data for artefacts of sun-sensor geometry is essential to isolate the response of global vegetation to seasonal and interannual climate variability.

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Figure 1: Seasonal changes in sun-sensor geometry generate the appearance of a green up in MODIS observations of Amazon forest between June and October.
Figure 2: Independent GLAS lidar observations indicate consistent canopy properties between June and October across southern Amazonia.
Figure 3: Seasonal increases in MODIS near-infrared reflectance (NIR) and enhanced vegetation index (EVI) data were eliminated after normalizing sun-sensor geometry during the June to October dry season.

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Acknowledgements

Funding for this research was provided by NASA, the NASA Postdoctoral Program and the NERC National Centre for Earth Observation. We thank G. P. Asner for providing Amazon leaf reflectance data.

Author information

Authors and Affiliations

  1. NASA Goddard Space Flight Center, Greenbelt, 20771, Maryland, USA

    Douglas C. Morton, Claudia C. Carabajal, Jacqueline Rosette, Bruce D. Cook, Eric F. Vermote & David J. Harding

  2. Department of Geographical Sciences, University of Maryland, College Park, College Park, 20742, Maryland, USA

    Jyoteshwar Nagol & Jacqueline Rosette

  3. Global Land Cover Facility, College Park, 20740, Maryland, USA

    Jyoteshwar Nagol

  4. Sigma Space Corporation, Lantham, 20706, Maryland, USA

    Claudia C. Carabajal

  5. Department of Geography, Swansea University, Singleton Park, Swansea SA2 8PP, UK,

    Jacqueline Rosette & Peter R. J. North

  6. Earth System Research Center, University of New Hampshire, Durham, 03824, New Hampshire, USA

    Michael Palace

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Contributions

D.C.M. and J.N. designed the experiment. D.C.M., J.N. and E.F.V. analysed MODIS data. D.C.M., C.C.C., B.D.C. and D.J.H. analysed ICESat data. M.P. developed the synthetic forest model. D.C.M., J.N., J.R., B.D.C. and P.R.J.N. contributed to radiative transfer model simulations. D.C.M. wrote the manuscript, and all authors contributed material to the final version.

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Correspondence to Douglas C. Morton.

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The authors declare no competing financial interests.

Additional information

Data from NASA’s ICESat, Terra, and Aqua satellites are archived at http://www.nsidc.org and http://lpdaac.usgs.gov.

Extended data figures and tables

Extended Data Figure 1 Synthetic Amazon forest developed from field measurements of forest structure and reflectance properties.

a, Oblique view of the 1 km × 1 km synthetic forest. b, Reflectance (ρ) and transmittance (τ) properties of Amazon leaves (G. P. Asner, unpublished data) and litter41 at MODIS (red, 620–670 nm; NIR, 841–876 nm) and ICESat (NIR, 1064 nm) wavelengths. c, d, Parameter values for lidar and optical radiative transfer simulations. Field measurements of ρLitter differ slightly at optical (860 nm) and lidar (1064 nm) NIR wavelengths (see b). June conditions were used to estimate early dry season (baseline) lidar and optical remote sensing metrics with FLIGHT. e–g, Height-diameter relationships (e) and crown depth (f) and width (g) estimates for small trees (< 20-cm diameter at breast height) were derived from allometric relationships for larger trees (>20-cm diameter at breast height) using regression with a power-law function.

Extended Data Figure 2 FLIGHT model simulations of GLAS lidar waveforms.

a, Distribution of modelled waveform centroid relative height (WCRH) for 100 simulated GLAS lidar footprints located at the centre of each 100 × 100 m grid box of the synthetic Amazon forest. b, Nadir view of the waveform selected for FLIGHT model simulations (blue outline). The dashed vertical line in a indicates the June WCRH (0.519) for the footprint location in b.

Extended Data Figure 3 Seasonal variability in MODIS surface reflectance, vegetation indices, and sun-sensor geometry.

Seasonal distributions of NDVI, EVI and near-infrared (NIR) reflectance for uncorrected (a–c) and corrected (e–g) MODIS 1 km data from 2003–08, in which corrected MODIS data have been normalized to a consistent sun-sensor geometry. Lines denote the median (black) and upper and lower quartile median values (grey) for uncorrected and corrected MODIS observations. The decrease in solar zenith angle between day of year 150 and 300 (d) increases the frequency of observations near the principal plane (h, ϕ = 0°, ϕ = 180°).

Extended Data Figure 4 Impact of changing MODIS sun-sensor geometry on NDVI.

a–c, Seasonal changes in sun-sensor geometry decrease MODIS NDVI over Amazon forest between June and October. As for EVI (see Fig. 1), modelled directional anisotropy of the MODIS NDVI over southern Amazon forests is stronger in June (a) than October (b), but the realized bidirectional reflectance effect in MODIS observations is greater in October (b, c) when the Terra and Aqua MODIS instruments sample in the principal plane (ϕ = 0°, ϕ = 180°). For NDVI, the hot spot effect near θv = 20° increases red reflectance, thereby lowering NDVI values at these viewing angles. d, Seasonal profile of red reflectance for uncorrected (grey) and corrected (black) MODIS 1 km data in southern Amazonia. Values indicate the upper quartile median monthly red reflectance from all Terra and Aqua MODIS observations in 2003–08 (n = 197,651). e, Per-pixel changes in uncorrected (grey) and corrected (black) MODIS NDVI between October and June for all forested areas in southern Amazonia. Small seasonal decreases in MODIS NDVI data were eliminated after normalizing the sun-sensor geometry during the June to October dry season.

Extended Data Figure 5 Spatial pattern of seasonal changes in GLAS lidar metrics.

a–f, June, October, and difference maps (October minus June) of WCRH (a–c) and 1064 nm apparent reflectance (d–f) for 0.25° grid cells in southern Amazonia with ≥ 10 pairs of June and October GLAS footprints. White cells indicate non-forest areas or grid cells with <10 GLAS lidar footprints in one or both months.

Extended Data Figure 6 Correlation between GLAS lidar WCRH and mean annual precipitation.

Values indicate mean June WCRH and mean annual precipitation (September to August, 1997–2009) from the tropical rainfall measurement mission (TRMM) 3B43v6 product for 0.25° cells with ≥ 10 GLAS footprint pairs (n = 728, see Extended Data Fig. 5).

Extended Data Figure 7 Maps of seasonal amplitude in uncorrected and corrected MODIS EVI data.

a, Seasonal amplitude in uncorrected MODIS EVI data (October minus June) for two 10° × 10° spatial tiles in southern Amazonia (H11V09, H11V10). b, Seasonal amplitude in corrected EVI data (October minus June), using a single BRDF inversion model to normalize changes in sun-sensor geometry for all Amazon forest pixels per tile, per month. Instead of modelling BRDF on a per-pixel basis (see Fig. 3, Extended Data Figs 3, 4 and 8), this map reflects the reduction in the seasonal amplitude of EVI using generic BRDF models derived from all cloud-free MODIS data per tile, per month. c, The difference in the seasonal amplitude of MODIS EVI (uncorrected minus corrected) is positive, highlighting how changes in sun-sensor geometry generate the apparent green up phenomenon. White regions indicate non-forest cover types.

Extended Data Figure 8 Interannual variability in MODIS EVI for southern Amazon forests in 2003–08.

Values indicate the upper quartile median monthly EVI for uncorrected (grey) and corrected (black) MODIS data. Normalization of sun-sensor geometry reduced both seasonal and interannual variability in MODIS EVI over southern Amazon forests.

Extended Data Figure 9 Contribution of variability in MODIS view geometry to interannual differences in EVI over Amazon forests.

EVI and view zenith angle (θv) data from the MOD13A1 16-day composite product (data access: https://lpdaac.usgs.gov) are shown for spatial tile H11V09 when the Terra MODIS sensor was observing in the principal plane (October, day of year 273–288). The fraction of observations in the backscatter direction (θv > 0°) and mean EVI over forests were strongly correlated, highlighting the importance of sun-sensor geometry for interannual variability in uncorrected MODIS EVI data. The drought year of 2005 had the highest fraction of MODIS observations in the backscatter direction of any year during 2000–12.

Extended Data Table 1 Sensitivity of lidar WCRH and optical vegetation indices (EVI, NDVI) to modelled seasonal changes in forest structure and reflectance properties
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Morton, D., Nagol, J., Carabajal, C. et al. Amazon forests maintain consistent canopy structure and greenness during the dry season. Nature 506, 221–224 (2014). https://doi.org/10.1038/nature13006

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