photosynthetic seasonality of global tropical supplementary … · “photosynthetic seasonality of...

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Supplementary Information for

“Photosynthetic seasonality of global tropical forests constrained by hydroclimate”

Kaiyu Guan*, Ming Pan, Haibin Li, Adam Wolf, Jin Wu, David Medvigy, Kelly K.

Caylor, Justin Sheffield, Eric F. Wood, Yadvinder Malhi, Miaoling Liang, John S.

Kimball, Scott Saleska, Joe Berry, Joanna Joiner, and Alexei I. Lyapustin

*Correspondence to: Kaiyu Guan

Tel: +1-609-647-1368

Email: [email protected]

1. Datasets:

Enhanced Vegetation Index (EVI): EVI is an index of landscape-integrated

vegetation greenness [Huete et al., 2006] and photosynthetic capacity [Sellers et

al., 1992], which is related to photosynthetic potentials under ideal environmental

conditions, and thus EVI reflects an inherent vegetation photosynthetic property.

EVI has been found to correlate with independent ground-based measures of

photosynthesis from eddy flux towers across multiple biomes [Rahman, 2005;

Huete et al., 2006, 2008; Sims et al., 2006], though the direct linkage between

EVI and photosynthesis rate (or gross primary production, GPP) in tropical

evergreen forests is more complicated and is an ongoing research topic, as leaves

in tropical rainforests are present throughout the year. Here we use the EVI as a

proxy for canopy photosynthetic capacity in tropical forest regions. We use the

MODIS MOD13C1 Collection 5 EVI product [Didan and Huete, 2006]. This

product contains cloud-free spatial composites of the gridded 16-day 1-km

MOD13A2, and cloud-free global coverage is achieved by replacing clouds with

the historical MODIS time series climatology for that specific time step. This 16-

day composite EVI has 0.05 degree resolution, extending from 2000 to present.

Data from 2002 to 2012 are used in this work. EVI has improved performance

over the traditionally used Normalized Difference of Vegetation Index (NDVI),

by addressing atmospheric contamination effects and having improved sensitivity

Photosynthetic seasonality of global tropical forests constrained by hydroclimate

SUPPLEMENTARY INFORMATIONDOI: 10.1038/NGEO2382

NATURE GEOSCIENCE | www.nature.com/naturegeoscience 1

© 2015 Macmillan Publishers Limited. All rights reserved

http://dx.doi.org/10.1038/ngeo2382

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to higher canopy leaf biomass levels [Huete et al., 2002]. The MODIS Collection

5 EVI has also been refined in its correction and filtering of atmosphere aerosol

and cloud contamination [Didan and Huete, 2006], and its quality has been

largely improved as compared with the Collection 4 EVI product [Samanta et al.,

2012]. Only “good-quality” EVI retrievals identified from the first two bits of the

MOD13C1 product Quality flag were used in this study for each pixel. It is worth

noting that the standard MODIS EVI product used here is not specifically

corrected for sun-sensor geometry (or Bi-directional reflectance distribution

function, BRDF), which can relatively exaggerate (when the sensor is in the

direction of solar backscatter ) or minimize (when the sensor is in the direction of

solar forward-scatter) surface reflectances [Jupp and Strahler, 1991]. The

standard 16-day EVI product composites individual observations that are taken

from a mix of back- and forward-scatter directions, and thus in principal could

lead to seasonal patterns that are artifacts if composites are made from

systematically biased observations [Morton et al., 2014]. We tested whether such

artifacts were present, and whether they were sufficient to influence our results

by comparing the mean seasonal cycle of the standard MODIS EVI product with

another EVI version from the Multi-Angle Implementation of Atmospheric

Correction algorithm (MAIAC) [Lyapustin et al., 2012]. The MAIAC EVI

incorporates both rigorous BRDF correction and stricter cloud, atmosphere and

aerosol corrections [Lyapustin et al., 2012]. We found that MAIAC EVI

normalized to the fixed sun-view geometry using derived BRDF model has

smaller absolute changes in seasonal variation than the standard MODIS EVI,

indicating that the artifact was present; but both corrected and uncorrected data

records have very consistent relative seasonal patterns (Figure S1, also see details

in section 2 below). These results demonstrate that, though present at low levels,

the seasonal artifact is not the cause of the signal of dry season green-up in the

central Amazon (Figure S1, S2). Additional details are provided in section 2

below. We conclude that the seasonal variation in the standard MODIS EVI

product is dominated by a true vegetation signal and is thus appropriate for

understanding the co-variation of photosynthetic capacity with climate.


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Solar-induced Fluorescence (SIF): SIF is an electromagnetic emission in the 650-

800nm range originating from plant photosynthetic machinery, and it is

theoretically linearly correlated with the electron transport rate of photosynthetic

activity [Zhang et al., 2014]. Theoretical and experimental studies [van der Tol et

al., 2009; Zarco-Tejada et al., 2013] demonstrate that under normal temperature

range and normal light conditions (e.g. most satellite overpass times), SIF is

linearly correlated with GPP and co-varies with GPP under environmental

stresses. At canopy and ecosystem scales, SIF is found to be better correlated

with and more closely tracks the seasonality of GPP observations determined

from tower eddy covariance flux measurements than other available GPP

products and reflectance-based vegetation indices [Guanter et al., 2014; Joiner et

al., 2014]. The SIF data used here are retrieved near the λ=740nm far-red

fluorescence emission peak from the Global Ozone Monitoring Experiment-2

(GOME-2) instrument onboard Eumetsat’s MetOp-A satellite. GOME-2 is in a

sun-synchronous orbit with overpass at about 09:30AM local time, near the

window of peak daily photosynthesis at these latitudes. The SIF algorithm

[Joiner et al., 2013] disentangles three spectral components near the peak of the

far-red chlorophyll fluorescence emission feature: atmospheric absorption (due to

water vapor), surface reflectance, and fluorescence radiance. The derived

GOME-2 SIF record (version 25, level 3) extends from 2007-present, and the

data from 2007 to 2012 has been used in this study. The SIF data has been cloud

filtered, and the data with the effective cloud fraction (fc)>0.4 have been

eliminated, where fc was computed from the black-sky 16 day gridded filled land

surface albedo product from Aqua MODIS (MOD43B3) at 656 nm [Schaaf et al.,

2002]. Using more or less stringent criteria on cloud filtering within a moderate

range did not substantially alter the derived spatial and temporal patterns of SIF

[Joiner et al., 2013]. All SIF data with SZA > 70° were also eliminated. The

current version of SIF has not incorporated any sun-sensor geometry correction,

but we find that its multi-year mean seasonal cycle is highly consistent with that

of the standard MODIS EVI and the BRDF-corrected MAIAC EVI (Figure S1),

which provides confidence of using these data in our analysis. The SIF data have


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been gridded to 0.5° spatial resolution and monthly time resolution, with

estimated errors of 0.1-0.2 mW/m2/nm/sr [Joiner et al., 2013].

Precipitation: The precipitation dataset is from TRMM Multi-satellite

Precipitation Analysis (TMPA) 3b42V6 [Huffman et al., 2007], which has 0.25

degree resolution and 3-hourly time step, covering period from 1998 to near

present. Data from 2002 to 2012 are used in this analysis. The TMPA is merged

from microwave and infrared satellite data, and adjusted to match local gauge

data at the monthly scale.

Potential evapotranspiration (PET): PET was calculated using the Penman-

Monteith equation forced by various climate records and remote sensing data

[Vinukollu et al., 2011] at 0.5 degree resolution from 2002 to 2012; we also

calculated PET using a revised Priestley-Taylor approach [Fisher et al., 2009;

Vinukollu et al., 2011] with the same forcing records, and found that two PET

algorithms have less than 5% difference in the mean annual range of PET, and

thus using either of them does not affect our conclusion.

Terrestrial Water Storage (TWS) Anomaly: The Gravity Recovery and Climate

Experiment (GRACE) monthly TWS Anomaly data used in this study were

obtained from the NASA data archive at the Jet Propulsion Laboratory (JPL)

[Swenson and Wahr, 2006]. We used the recommended ensemble mean

[Sakumura et al., 2014] of three independent versions of the data record from

three difference research units: GFZ (GeoforschungsZentrum Potsdam), CSR

(Center for Space Research at University of Texas, Austin), and JPL in our study.

The GRACE data are available in a global grid with 1 degree spatial resolution

and have been processed using a consistent 300 km wide Gaussian filter. GRACE

monthly TWS data from 2002 to 2012 are used in this analysis. The GRACE data

were downloaded from ftp://podaac-

ftp.jpl.nasa.gov/allData/tellus/L3/land_mass/RL05/netcdf/. The data details can

be found in http://grace.jpl.nasa.gov/data/gracemonthlymassgridsland/.

Land Cover: the Global Land Cover 2000 database (Global Land Cover 2000

database, European Commission, Joint Research Centre, 2003

(http://bioval.jrc.ec.europa.eu/products/glc2000/glc2000.php) was used.


ftp://podaac-ftp.jpl.nasa.gov/allData/tellus/L3/land_mass/RL05/netcdf/

ftp://podaac-ftp.jpl.nasa.gov/allData/tellus/L3/land_mass/RL05/netcdf/

http://grace.jpl.nasa.gov/data/gracemonthlymassgridsland/

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GLC2000 uses 14 months of pre-processed daily global data acquired by the

VEGETATION instrument on board the SPOT 4 satellite. The data was acquired

from http://bioval.jrc.ec.europa.eu/products/glc2000/glc2000.php. In this study,

we focus only on intact tropical evergreen forests and try to exclude all other

human impacted area and land cover types.

2. Comparison between MODIS MOD13C EVI and MAIAC EVI over Amazonia:

Figure S1 and S2

A recent study [Morton et al., 2014] suggested that the effects of sun-sensor geometry on

reflectance of vegetation introduced seasonal artifacts in Amazon forests. They claimed

that this artifact was sufficiently large to eliminate the previously reported dry-season

“green-up” in the EVI signal that had been attributed to vegetation dynamics [Huete et al.,

2006; Myneni et al., 2007]. To investigate whether sun-sensor geometry has a significant

impact on the mean seasonality pattern of EVI reported here, we compared the mean

seasonal cycles of two EVI data products for Amazon evergreen forest regions from 2002

to 2012: (1) the standard MODIS (MOD13C1, Collection 5) EVI product used in this

study, which is processed without BRDF correction; and (2) a new EVI dataset from the

MAIAC algorithm which also includes BRDF adjusted to a fixed sun angle and nadir

view as described below.

MAIAC is an advanced algorithm that uses time series analysis and a combination of

pixel- and image-based processing to improve accuracy of cloud detection, aerosol

retrievals, and atmospheric correction based on MODIS calibrated and geolocated (L1B)

measurements [Lyapustin et al., 2011a, 2011b, 2012]. MAIAC provides a suite of

gridded 1km resolution atmospheric and surface products including bidirectional

reflectance factors (BRF, also called "surface reflectance"), albedo, and Ross-Thick Li-

Sparse (RTLS) [Lucht et al., 2000] BRDF model parameters in 7 spectral land bands. The

data were downloaded from ftp://ladsweb.nascom.nasa.gov/MAIAC/. We conducted the

regional EVI comparison for Amazon tropical evergreen forests because MAIAC data

were only available for the Amazon and U.S. region at the time of this study.


http://bioval.jrc.ec.europa.eu/products/glc2000/glc2000.php

ftp://ladsweb.nascom.nasa.gov/MAIAC/

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We calculated both MAIAC EVI and BRDF-corrected EVI (called “EVIn” below). The

BRDF correction normalizes BRF from a given view geometry to the fixed geometry of

nadir view and 45 sun zenith angle using the retrieved BRDF model, and then

normalized EVIn is computed from these values.

We compared the mean seasonal cycle based on bi-monthly data among MODIS EVI,

MAIAC EVI and MAIAC EVIn for Amazon tropical evergreen forest pixels (Figure S1).

We found that the three EVI versions are highly correlated in their mean seasonal cycle.

80% of the pixels show statistically significant positive correlations (p-value<0.1) of

mean seasonality between MODIS EVI and MAIAC EVIn, and 65% of the pixels have

Spearman correlation coefficients above 0.5. Similar results were found for the

correlations of mean seasonality between MODIS EVI and MAIAC EVI (78% of all the

pixels are statistically significantly positive correlations, with 59% having correlation

coefficient of more than 0.5). The strong correlation of mean seasonality between

MAIAC EVI and MAIAC EVIn (98% of all the pixels have statistically significantly

positive correlations, and 95% have correlation coefficient of more than 0.5) demonstrate

that the added BRDF correction brings little change to the mean seasonal cycle of EVI.

Thus the difference between MODIS EVI and MAIAC EVI/EVIn can presumably be

attributed primarily to the improved mitigation of cloud, aerosol and atmospheric effects

by the MAIAC algorithm.

We also analyzed changes in the EVI mean seasonality during the dry-season (June to

October) for the Amazon tropical evergreen forest regions in Figure S2, following the

similar analysis of Morton et al. (2014). Our results show that the great majority of pixels

show positive EVI change from June to October from the different data sources,

including MODIS EVI (89%), MAIAC EVI (90%), and MAIAC EVIn (82%). The

MOD13C1 and MAIAC records show very similar ranges of dry-season EVI change,

with an average increase of 8.0±6.4% of the maximum EVI range at per-grid level from

June to October from MOD13C1 product, and 9.1±7.1% of the maximal EVI range from

the MAIAC product; the MAIAC EVIn shows a smaller dry-season EVI increase

(4.8±6.0%), but the dry season signal increase is still well above noise (1%~2% for


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MAIAC EVI) [see also Hilker et al., 2012]. We thus conclude that the Amazon dry-

season green-up is a robust signal from the different MODIS EVI data sources, and that

the BRDF correction can decrease the absolute range of seasonality, but does not change

the relative seasonal variation. This analysis adds confidence to our analysis and findings

based on the seasonality of EVI.

3. Relationship between SIF and EVI: Figure S3 and S4

We compared the multi-year mean seasonality of SIF and EVI (both from the standard

MODIS EVI product and the MAIAC EVIn) at the monthly scale for tropical evergreen

forests in the Amazon, by calculating their Spearman correlation coefficient at the

gridded resolution (Figure S3). We find that SIF seasonality is highly correspondent with

that of EVI across the Amazon, with 96.3% of all valid grid cells having positive

correlation (67.4% of all the valid grids are statistically significant at p<0.1), and with

92.3% having positive correlation with the MAIAC EVIn (55.3% are statistically

significant).

We also compared the ΔEVI(wet-dry) and ΔSIF(wet-dry) for all pan-tropical evergreen forest

regions (Figure S4, only using the standard MODIS EVI-derived ΔEVI(wet-dry)). We find

that these two derived variables are highly correlated, with Spearman correlation

coefficient of 0.86, and the fitted Lowess curve has a very small y-axis intercept (0.02).

The above results demonstrate that SIF and EVI have a consistent seasonal pattern across

the broad area spanning the entire pan-tropical evergreen forest domain.

4. Validation of ΔEVI(wet-dry) and ΔSIF(wet-dry) with flux tower observations in

Amazon: Figure S5

We applied the same approach of calculating wet- and dry-season differences normalized

by their annual mean values to GPP observed at the eight flux tower sites located across

the Amazon basin. These sites were chosen based on data availability and relative land-

cover homogeneity (i.e. 90% of the land cover type in the 0.5 degree grid should be the

same type that the flux tower site has). All the flux tower information can be found in


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Restrepo-Coupe et al. [2013]. The same large-scale precipitation and PET records at 0.5

degree resolution used in our study were also used at the flux-tower sites to derive wet

and dry season, and the derived dry season length is consistent with the site-level

measurements and analysis [Restrepo-Coupe et al., 2013]. ΔEVI(wet-dry) and ΔSIF(wet-dry)

were extracted at the finest resolution of the products (MODIS EVI: 0.05 degree; MAIAC

EVIn: 1km; SIF: 0.5 degree) at each corresponding flux tower location.

Our results (Figure S5) show that GPP at the flux towers shows a consistent pattern in

relation to those of EVI and SIF. Almost all the data show a significantly positive

difference for the wet-season value minus dry-season value at the four sites (i.e. PDG,

JAV, FNS and RJA), where their MAP values are well below the determined rainfall

threshold (2000 mm/year). The other four sites (i.e. K67, K83, K34 and CAX), with

MAP close to or above the determined threshold, have wet-dry season differences that are

either negative or insignificant (close to zero). These flux tower GPP estimates thus

support our findings which are based on the continental scale analysis.

The authors would like to thank all the PIs and the staff of each tower site for their

technical, logistical and extensive fieldwork.

5. t-test distribution for ΔEVI(wet-dry) and ΔSIF(wet-dry) patterns: Figure S6

We employed the t-test to determine the statistical significance of ΔEVI(wet-dry) and

ΔSIF(wet-dry) patterns for each grid, with the null hypothesis that ΔEVI(wet-dry) (or ΔSIF(wet-

dry)) is equal to zero (paired t-test, n=11 years per grid for EVI, and n=6 for SIF). Figure

S6 shows the spatial pattern for the t-test distribution, with blue indicating significantly

negative values, and red indicating significantly positive values. These spatial patterns

largely mirror those in Figure 1 d-i. For ΔEVI(wet-dry), the Amazon has 26.1% of tropical

evergreen forest grids as significantly positive (p<0.05), while 33.7% are significantly

negative; SE Asia has 16.3% (11.8%) significantly positive (negative); Africa has 75.4%

(0.5%) significantly positive (negative); the remaining areas are insignificant. For

ΔSIF(wet-dry), the Amazon has 25.0% of all the valid grids as significantly positive, while


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20.0% are significantly negative; SE Asia has 14.7% (1.3%) significantly positive

(negative); Africa has 78.4% (0.1%) significantly positive (negative).

6. Hydrological characteristics in the pan-tropical forest regions

6.1 Mean annual precipitation and dry season length: Figure S7

6.2 Spatial pattern of “maximum release of water storage”, “dry season total deficit” and

their difference: Figure S8

6.3 Impacts of dry-season length on “maximum release of water storage”, “dry season

total deficit” in tropical rainforests: Figure S9

6.4 Different rainfall regimes in supply-demand space: Figure S10

The scatterplot of “Maximum release of water storage” (supply) and “dry season total

deficit” (demand) shows the supply-demand relationship for dry season plant water use.

MAP of each grid are color shaded in this scatterplot (Figure S10) showing how total

annual rainfall progresses in supply-demand space.

6.5 Dry-season supply-demand relationships for different continents: Figure S11

7. Basin-scale quantification of water budget: Figure S12 and S13

A basin-scale analysis involving a complete quantification of the water budget (only for

Amazon and Congo basins due to data availability, Pan et al., 2012) further corroborates

our findings that 39.8% of the total dry-season water supply in the Amazon basin is from

the previous wet-season water storage, compared with only 14.4% in the Congo basin

(Figure S13). The seasonal carry-over of subsurface water storage plays a much more

important role in tropical forests of Amazonia than Africa. These continental differences

in the water budget mostly arise from longer dry seasons in Africa, as well as more

excess precipitation inputs and thus relatively larger subsurface water storage during wet

seasons in Amazonia (Figure S7).

We used a multi-source optimized basin-scale water budget record [Pan et al., 2012]. A

systematic method is used to optimally combine estimates of the terrestrial water budget

from different data sources and to enforce the water balance constraint using data

assimilation techniques. Specifically, a conventional analysis of the errors and biases in


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different data sources is conducted based on existing validation/error studies and other

information such as sensor network density, model physics, and calibration procedures.

Then, the data merging process combines different estimates so that biases and errors

from different data sources can be compensated to the greatest extent and the merged

estimates have the best possible confidence. Finally, water balance errors are resolved

using the constrained Kalman filter technique. Ten global datasets have been included

including: in-situ observations, remote sensing retrievals, land surface model simulations,

and global reanalyses (Table 1 in [Pan et al., 2012]). The procedure is applied to 32

globally distributed major basins for the 1984–2006 record. We use Amazon and Congo

basin data in this study to represent the water budget in Amazonian and African tropical

forest regions (Figure S13).

We find that the Amazonian tropical forest region has much wetter and longer wet

seasons than those in Africa, though both regions have similarly low monthly

precipitation during the dry seasons at 99.6 mm/month and 95.1 mm/month, respectively

(Figure S13). We find that the surplus of precipitation during the long wet season in

Amazonia (~9 months at the basin scale) leads to a significant storage of water, primarily

as soil moisture and ground water [Crowley and Mitrovica, 2006; Pokhrel et al., 2013].

This reserve is subsequently used by tropical forests during the short dry seasons of ~3

months duration, with a depletion rate of approximately 65.5 mm/month, through the

mechanism of deep plant roots and facilitated hydraulic redistribution [Nepstad et al.,

1994; Meir et al., 2009; Betts and Silva Dias, 2010]. In contrast, African tropical forests

do not gain substantial water storage during their relatively shorter wet seasons of ~5

months, which limits water consumption during dry seasons to a depletion rate of only

15.7mm/month. When taking into account carry-over from water storage, Amazonian

tropical forest regions have approximately 165.3mm/month total water supply during the

dry seasons, which is 49% larger than tropical forests in Africa (111.1mm/month). Thus

water surplus across the wet and dry seasons generally satisfies the plant water need in

Amazonian tropical forest regions but not in Africa, leading to contrasting sensitivity of

photosynthetic seasonality to seasonal drought. The basin-scale analysis is thus consistent

with what we discovered at the grid level.


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8. The relationship between groundwater table depth and rooting depths in tropical

forest regions: Figure S14 and S15

A global dataset for groundwater table depth [Fan et al., 2013] is used here to assess

whether characteristic rooting depth of tropical forest is sufficient to enable access to

groundwater. The spatial pattern of groundwater table depths for global tropical regions

are shown in Figure S14, which has a native spatial resolution of 30 arc-sec (~1km), and

was aggregated to 0.25 degree here. Elevations above 500 meters are filtered out because

of biased deeper water table depths in mountain regions. The elevation data is from the

GLDAS at 0.25 degree resolution (http://ldas.gsfc.nasa.gov/gldas/GLDASelev.php). As

the coarse resolution of recharge input (1 degree) to the model of Fan et al. [2013] could

not provide reliable details particularly for the Asian Islands (i.e. many small patchy

islands), the data provider suggested focusing the analysis only on continental Amazon

and Africa.

Figure S15 shows that groundwater table depth decreases with increasing MAP for the

tropical forest regions. The result confirms that in very humid tropical regions, deep roots

are not necessary because the groundwater table is shallow (~ 2m) from which

groundwater could be easily accessed via shallow roots. The water table depth starts to

increase sharply with MAP decreasing below 2500 mm/year, indicating that deeper

rooting systems are necessary for accessing ground water especially in drier regions with

a deeper water table, e.g. southeast Amazon. It has been reported that tropical forests in

eastern and southern Amazon have deep roots of 10-15 meters [Nepstad et al., 1994,

2004; Davidson et al., 2011]. We find that the groundwater table depths in tropical forest

regions are around 10±3 meters at the MAP of 2000 mm/year, which allows deep-rooted

forests in these regions to tap the groundwater resources.

References:

Betts, A. K., and M. A. F. Silva Dias (2010), Progress in understanding land-surface-

atmosphere coupling from LBA research, J. Adv. Model. Earth Syst., 2, 6,

doi:10.3894/JAMES.2010.2.6.


12

Crowley, J., and J. Mitrovica (2006), Land water storage within the Congo Basin inferred

from GRACE satellite gravity data, Geophys. Res. Lett., 33, 2–5,

doi:10.1029/2006GL027070.

Davidson, E., P. A. Lefebvre, P. M. Brando, D. M. Ray, S. E. Trumbore, L. A. Solorzano,

J. N. Ferreira, M. M. C. Bustamante, and D. C. Nepstad (2011), Carbon Inputs and

Water Uptake in Deep Soils of an Eastern Amazon Forest, For. Sci., 57(1), 51–58.

Didan, K., and A. Huete (2006), MODIS Vegetation Index Product Series Collection 5

Change Summary

(http://landweb.nascom.nasa.gov/QA_WWW/forPage/MOD13_VI_C5_Changes_Do

cument_06_28_06.pdf).

Fan, Y., H. Li, and G. Miguez-Macho (2013), Global patterns of groundwater table

depth., Science, 339(6122), 940–3, doi:10.1126/science.1229881.

Fisher, J. B. et al. (2009), The land-atmosphere water flux in the tropics, Glob. Chang.

Biol., 15(11), 2694–2714, doi:10.1111/j.1365-2486.2008.01813.x.

Guanter, L. et al. (2014), Global and time-resolved monitoring of crop photosynthesis

with chlorophyll fluorescence, Proc. Natl. Acad. Sci. U. S. A., 111(14), E1327–33,

doi:10.1073/pnas.1320008111.

Hilker, T., A. I. Lyapustin, C. J. Tucker, P. J. Sellers, F. G. Hall, and Y. Wang (2012),

Remote sensing of tropical ecosystems: Atmospheric correction and cloud masking

matter, Remote Sens. Environ., 127, 370–384, doi:10.1016/j.rse.2012.08.035.

Huete, A., K. Didan, T. Miura, E. P. Rodriguez, X. Gao, and L. G. Ferreira (2002),

Overview of the radiometric and biophysical performance of the MODIS vegetation

indices, Remote Sens. Environ., 83, 195–213.

Huete, A. R., K. Didan, Y. E. Shimabukuro, P. Ratana, S. R. Saleska, L. R. Hutyra, W.

Yang, R. R. Nemani, and R. Myneni (2006), Amazon rainforests green-up with

sunlight in dry season, Geophys. Res. Lett., 33(6), L06405,

doi:10.1029/2005GL025583.

Huete, A. R., N. Restrepo-Coupe, P. Ratana, K. Didan, S. R. Saleska, K. Ichii, S.

Panuthai, and M. Gamo (2008), Multiple site tower flux and remote sensing

comparisons of tropical forest dynamics in Monsoon Asia, Agric. For. Meteorol.,

148(5), 748–760, doi:10.1016/j.agrformet.2008.01.012.

Huffman, G. J., D. T. Bolvin, E. J. Nelkin, D. B. Wolff, R. F. Adler, G. Gu, Y. Hong, K.

P. Bowman, and E. F. Stocker (2007), The TRMM Multisatellite Precipitation

Analysis (TMPA): Quasi-Global, Multiyear, Combined-Sensor Precipitation

Estimates at Fine Scales, J. Hydrometeorol., 8(1), 38–55, doi:10.1175/JHM560.1.


13

Joiner, J., L. Guanter, R. Lindstrot, M. Voigt, a. P. Vasilkov, E. M. Middleton, K. F.

Huemmrich, Y. Yoshida, and C. Frankenberg (2013), Global monitoring of

terrestrial chlorophyll fluorescence from moderate spectral resolution near-infrared

satellite measurements: methodology, simulations, and application to GOME-2,

Atmos. Meas. Tech., 6, 2803–2823, doi:10.5194/amtd-6-3883-2013.

Joiner, J. et al. (2014), The seasonal cycle of satellite chlorophyll fluorescence

observations and its relationship to vegetation phenology and ecosystem atmosphere

carbon exchange, Remote Sens. Environ., 152, 375–391,

doi:10.1016/j.rse.2014.06.022.

Jupp, D. L. B., and A. H. Strahler (1991), A hotspot model for leaf canopies, Remote

Sens. Environ., 38(3), 193–210, doi:10.1016/0034-4257(91)90089-O.

Lucht, W., C. B. Schaaf, and A. H. Strahler (2000), An algorithm for the retrieval of

albedo from space using semiempirical BRDF models, IEEE Geosci. Remote.

Remote, 38(2), 977–998, doi:10.1109/36.841980.

Lyapustin, a., Y. Wang, I. Laszlo, R. Kahn, S. Korkin, L. Remer, R. Levy, and J. S. Reid

(2011a), Multiangle implementation of atmospheric correction (MAIAC): 2. Aerosol

algorithm, J. Geophys. Res., 116(D3), D03211, doi:10.1029/2010JD014986.

Lyapustin, A., J. Martonchik, Y. Wang, I. Laszlo, and S. Korkin (2011b), Multiangle

implementation of atmospheric correction (MAIAC): 1. Radiative transfer basis and

look-up tables, J. Geophys. Res., 116(D3), D03210, doi:10.1029/2010JD014985.

Lyapustin, A. I., Y. Wang, I. Laszlo, T. Hilker, F. G.Hall, P. J. Sellers, C. J. Tucker, and

S. V. Korkin (2012), Multi-angle implementation of atmospheric correction for

MODIS (MAIAC): 3. Atmospheric correction, Remote Sens. Environ., 127, 385–

393, doi:10.1016/j.rse.2012.09.002.

Meir, P. et al. (2009), The Effects of Drought on Amazonian Rain Forests, Amaz. Glob.

Chang. (eds M. Keller, M. Bustamante, J. Gash P. Silva Dias), Am. Geophys. Union,

Washington, D. C., 429–449.

Morton, D. C., J. Nagol, C. C. Carabajal, J. Rosette, M. Palace, B. D. Cook, E. F.

Vermote, D. J. Harding, and P. R. J. North (2014), Amazon forests maintain

consistent canopy structure and greenness during the dry season., Nature, 506(7487),

221–4, doi:10.1038/nature13006.

Myneni, R. B. et al. (2007), Large seasonal swings in leaf area of Amazon rainforests.,

Proc. Natl. Acad. Sci. U. S. A., 104(12), 4820–3, doi:10.1073/pnas.0611338104.

Nepstad, D., C. de Carvalho, E. Davidson, P. H. Jipp, P. A. Lefebvre, G. H. Negreiros, E.

D. da Silva, T. A. Stone, S. E. Trumbore, and S. Vieira (1994), The role of deep


14

roots in the hydrological and carbon cycles of Amazonian forests and pastures,

Nature, 3, 666–669.

Nepstad, D., P. Lefebvre, U. Lopes da Silva, J. Tomasella, P. Schlesinger, L. Solorzano,

P. Moutinho, D. Ray, and J. Guerreira Benito (2004), Amazon drought and its

implications for forest flammability and tree growth: a basin-wide analysis, Glob.

Chang. Biol., 10(5), 704–717, doi:10.1111/j.1529-8817.2003.00772.x.

Pan, M., A. K. Sahoo, T. J. Troy, R. K. Vinukollu, J. Sheffield, and E. F. Wood (2012),

Multisource Estimation of Long-Term Terrestrial Water Budget for Major Global

River Basins, J. Clim., 25(9), 3191–3206, doi:10.1175/JCLI-D-11-00300.1.

Pokhrel, Y. N., Y. Fan, G. Miguez-macho, S. Han, P. Sciences, N. Brunswick, and N. P.

Group (2013), The Role of Groundwater in the Amazon Water Cycle : 3 . Influence

on Terrestrial Water Storage and Comparison with GRACE, J. Geophys. Res.

Atmos., 118(8), 3233–3244.

Rahman, a. F. (2005), Potential of MODIS EVI and surface temperature for directly

estimating per-pixel ecosystem C fluxes, Geophys. Res. Lett., 32(19), L19404,

doi:10.1029/2005GL024127.

Restrepo-Coupe, N. et al. (2013), What drives the seasonality of photosynthesis across

the Amazon basin? A cross-site analysis of eddy flux tower measurements from the

Brasil flux network, Agric. For. Meteorol., 182-183, 128–144,

doi:10.1016/j.agrformet.2013.04.031.

Sakumura, C., S. Bettadpur, and S. Bruinsma (2014), Ensemble prediction and

intercomparison analysis of GRACE time-variable gravity field models, Geophys.

Res. Lett., 1389–1397, doi:10.1002/2013GL058632.1.

Samanta, A., S. Ganguly, E. Vermote, R. R. Nemani, and R. B. Myneni (2012),

Interpretation of variations in MODIS-measured greenness levels of Amazon forests

during 2000 to 2009, Environ. Res. Lett., 7(2), 024018, doi:10.1088/1748-

9326/7/2/024018.

Schaaf, C. B. et al. (2002), First operational BRDF , albedo nadir reflectance products

from MODIS, Remote Sens. Environ., 83, 135–148.

Sellers, P. J., J. A. Berry, G. J. Collatz, C. B. Field, and E. G. Hall (1992), Canopy

Reflectance , Photosynthesis , and Transpiration . III . A Reanalysis Using Improved

Leaf Models and a New Canopy Integration Scheme ., Remote Sens. Environ., 42,

187–216.

Sims, D. a. et al. (2006), On the use of MODIS EVI to assess gross primary productivity

of North American ecosystems, J. Geophys. Res., 111(G4), G04015,

doi:10.1029/2006JG000162.


15

Swenson, S., and J. Wahr (2006), Post-processing removal of correlated errors in

GRACE data, Geophys. Res. Lett., 33(8), L08402, doi:10.1029/2005GL025285.

Van der Tol, C., W. Verhoef, and a. Rosema (2009), A model for chlorophyll

fluorescence and photosynthesis at leaf scale, Agric. For. Meteorol., 149(1), 96–105,

doi:10.1016/j.agrformet.2008.07.007.

Vinukollu, R. K., E. F. Wood, C. R. Ferguson, and J. B. Fisher (2011), Global estimates

of evapotranspiration for climate studies using multi-sensor remote sensing data:

Evaluation of three process-based approaches, Remote Sens. Environ., 115(3), 801–

823, doi:10.1016/j.rse.2010.11.006.

Zarco-Tejada, P. J., a. Morales, L. Testi, and F. J. Villalobos (2013), 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,

doi:10.1016/j.rse.2013.02.003.

Zhang, Y., L. Guanter, J. a Berry, J. Joiner, C. van der Tol, A. Huete, A. Gitelson, M.

Voigt, and P. Köhler (2014), Estimation of vegetation photosynthetic capacity from

space-based measurements of chlorophyll fluorescence for terrestrial biosphere

models., Glob. Chang. Biol., doi:10.1111/gcb.12664.


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Figure S1 | Spearman correlations of the mean EVI seasonal cycles among three different

EVI data sources: MOD13C1 (MODIS EVI), MAIAC without BRDF correction

(MAIAC EVI), and MAIAC with BRDF correction (MAIAC EVIn). The left column

shows the spatial patterns of correlation coefficients, and the right column shows the

corresponding histograms of the correlation coefficients. The red regions in the

histograms indicate the proportion of grids with statistically significant correlation at the

p-value of 0.1, while the black regions are statistically insignificant at the same p-value

level.


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Figure S2 | EVI changes from October to June normalized by the maximum EVI range at

per grid level for Amazon tropical evergreen forest regions, based on the mean EVI

seasonality of three data sources: MODIS MOD13C1 (a, b), MAIAC without BRDF

correction (c, d), and MAIAC with BRDF correction (e, f). The left column shows the

spatial patterns of normalized EVI changes, and the right column shows the

corresponding histograms of the EVI changes, with the error bars showing the mean and

standard deviation of the whole distribution.


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Figure S3 | Spearman ccorrelations of the mean seasonal cycles between SIF and two

EVI data sources: MOD13C1 (MODIS EVI) and MAIAC with BRDF correction

(MAIAC EVIn). The left column shows the spatial patterns of correlations, and the right

column shows the corresponding histograms of the correlations. The red regions in the

histograms indicate the proportion of grids with statistically significant correlation at the

p-value of 0.1, while the black regions are statistically insignificant at the same p-value

level.


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Figure S4 | Density scatterplot between multiple-year mean ΔEVI(wet-dry) and ΔSIF(wet-dry)

for the global tropical forests areas studied here. The red dashed line is the robust Lowess

fit for the data with a span of 2.


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Figure S5 | Inter-comparison of satellite-based photosynthetic properties and flux-tower-

based GPP estimates for wet-/dry-season difference at eight flux tower sites in the

Amazon. The wet- and dry-season differences are normalized by their annual mean

values. All flux tower information can be found in [Restrepo-Coupe et al., 2013].


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Figure S6 | Statistical significance of ΔEVI(wet-dry) and ΔSIF(wet-dry) pattern, quantified as

percentiles of t-test distribution under the null hypothesis that ΔEVI(wet-dry) (or ΔSIF(wet-

dry)) is equal to zero (paired t-test, n=11 years per grid for EVI, and n=6 for SIF). Red

color indicates that the grid has significantly positive ΔEVI(wet-dry) (or ΔSIF(wet-dry)), and

blue color indicates significantly negative ΔEVI(wet-dry) (or ΔSIF(wet-dry)).


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Figure S7 | Dry season length and mean annual precipitation (MAP) for the study areas.

MAP values beyond 3000mm/year are not differentiated.


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Figure S8 | Spatial patterns of maximum release from water storage (first column), dry

season total water deficit (second column) and their difference (third column) for tropical

forest regions in Amazonia, Insular Southeast Asia and Africa.


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Figure S9 | The impacts of MAP and dry season length on the dry season water deficit

(demand) and maximum withdraw from storage (supply) over the tropical forest regions.


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Figure S10 | Scatterplots between maximum release from water storage and dry season

total water deficit for all tropical evergreen forest regions, color shaded with mean annual

precipitation, with different panels showing the correspondence between various ranges

in MAP and the 1:1 line in the scatterplot. The 1:1 line indicates whether maximum

release from water storage can offset dry season total deficit and thus satisfy plant water

needs during the dry season. Circle, triangle and square symbols represent respective

grids from Amazonia, Insular SE Asia and Africa tropical rainforests. In each panel, all

grids within specific MAP ranges are black-edged, and the red dashed line is the linear

regression of these points; (a) 1250-1550mm/year; (b) 1550-1850mm/year; (c) 1850-

2150mm/year; (d) 2150-2450mm/year; (e) 2450-2750mm/year; (f) 2750-3050mm/year.


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Figure S11 | Scatterplots between the maximum release from water storage and dry

season total water deficit for the different tropical evergreen forest regions; color shading

refers to mean annual precipitation. The black-edged circles refer to grids with MAP

range between 1850 to 2150 mm/year; a-Amazonia; b-Insular Southeast Asia; c-Africa.


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Figure S12 | Geographic locations of Amazon and African Congo basins used for basin-

scale water budget analysis (revised from Pan et al., 2012).


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Figure S13 | Left column: mean monthly water budget for Amazonia and Congo basins.

The water budget terms (P-precipitation, ET and Q-runoff) are from a global long-term

record of the terrestrial water budget by merging a number of global datasets using data

assimilation techniques [Pan et al., 2012]. The PET data is derived based on the Penman-

Monteith equation forced with various remote sensing products [Vinukollu et al., 2011].

Right column: water budgets during wet and dry seasons in Amazonia and Congo basins.

Wet/dry seasons are defined as the months where PET is smaller/larger than P. This

water budget result shows that 39.8% of the total dry-season water supply in the Amazon

basin is from the previous wet-season water storage, compared with only 14.4% in the

Congo basin.


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Figure S14 | Simulated groundwater table depths from Fan et al. (2013) in tropical forest

regions: Amazon (A), Asian Islands (B) and Africa (C). Regions with elevations higher

than 500 meters were excluded from the analysis.

Figure S15 | Median (50% quantile) of the simulated water table depths (excluding

regions with elevation higher than 500 meters and SE Asian Islands due to large data

uncertainties) as a function of MAP for the global tropical forest regions, with the error

bar representing the 75% and 25% quantile values.


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