Introduction
Vegetation growth in semi-arid regions is an important sink for fossil fuel
emissions. Mean carbon dioxide (CO2) uptake by terrestrial ecosystems
is dominated by highly productive lands, mainly tropical forests, whereas
semi-arid regions are the main biome driving its inter-annual variability (Ahlström et al., 2015; Poulter et al., 2014). Semi-arid regions
contribute to 60 % of the long-term trend in the global terrestrial C sink
(Ahlström et al., 2015). It is thus important to understand
long-term variability of vegetation growth in semi-arid areas and the
response of vegetation to environmental conditions to better quantify and
forecast effects of climate change.
The Sahel is a semi-arid transition zone between the dry Sahara desert in
the north and the humid Sudanian savanna in the south. The region has
experienced numerous severe droughts over the last decades, which resulted
in region-wide famines in 1972–1973 and 1984–1985 and localized food
shortages across the region in 1990, 2002, 2004, 2011 and 2012 (Abdi
et al., 2014; United Nations, 2013). Vegetation production is thereby an
important ecosystem service for livelihoods in the Sahel, but it is under
threat. The region is experiencing strong population growth, increasing the
demand on ecosystem services due to cropland expansion, increased pasture
stocking rates and fuelwood extraction (Abdi et al., 2014).
At the same time as we have reports of declining vegetation production, we
have contradicting reports of the greening of the Sahel based on earth
observation (EO) data (Dardel et al., 2014; Fensholt et al., 2013). The
greening of the Sahel has mainly been attributed to alleviated drought
stress conditions due to increased precipitation since the mid-1990s
(Hickler et al., 2005). Climate is thus another important
factor regulating vegetation production. Semi-arid regions, such as the
Sahel, are particularly vulnerable to climate fluctuations due to their
dependency on moisture.
Estimation of gross primary production (GPP), i.e. uptake of atmospheric
CO2 by vegetation, is still a major challenge for the remote sensing of
ecosystem services. Gross primary production is a main driver of ecosystem
services such as climate regulation, carbon (C) sequestration, C storage,
food production and livestock grassland production. Within EO, spatial
quantification of GPP generally involves light use efficiency (LUE), defined
as the conversion efficiency of absorbed solar light into CO2 uptake
(Monteith, 1972, 1977). It has been shown that LUE varies in
space and time due to factors such as plant functional type, drought and
temperature, nutrient levels, and physiological limitations of photosynthesis
(Garbulsky et al., 2010; Paruelo et al., 2004; Kergoat et al., 2008). The
LUE concept has been applied through various methods, either by using a
biome-specific LUE constant (Ruimy et al., 1994) or by modifying
a maximum LUE using meteorological variables (Running et
al., 2004).
An example of a LUE-based model is the standard GPP product from the
Moderate Resolution Imaging Spectroradiometer (MODIS) sensor (MOD17A2).
Within the model, absorbed photosynthetically active radiation (PAR) is
estimated as a product of the fraction of PAR absorbed by green vegetation
(FPAR from MOD15A2) multiplied with daily PAR from the meteorological data
of the Global Modeling and Assimilation Office (GMAO). A set of maximum LUE
parameters specified for each biome are extracted from a Biome Properties
Look-Up Table (BPLUT). Then maximum LUE is modified depending on air
temperature (Tair) and vapour pressure deficit (VPD; Running et al., 2004).
Sjöström et al. (2013)
evaluated the MOD17A2 product (collection 5.1) for Africa and showed that it
underestimated GPP for semi-arid savannas in the Sahel. Explanations for
this underestimation were that the assigned maximum LUE from BPLUT was set
too low and that there were uncertainties in the FPAR product (MOD15A2).
Recently, a new collection of MOD17A2 at a 500 m spatial resolution was
released (MOD17A2H, collection 6) with an updated BPLUT, updated GMAO
meteorological data, improved quality control and gap-filling of the FPAR
data from MOD15A2 (Running and Zhao, 2015).
It has been shown that the LUE method does not perform well in arid
conditions and at agricultural sites (Turner et al.,
2005). Additionally, the linearity assumed by the LUE model is not usually
found as the response of GPP to incoming light follows more of an asymptotic
curve (Cannell and Thornley, 1998). Investigating other methods for
remotely determining GPP is thus of great importance, especially for
semi-arid environments. Therefore, instead of LUE, we focus on the light
response function of GPP at the canopy scale, and spatial and temporal
variation of its two main parameters: maximum GPP under light saturation
(canopy-scale photosynthetic capacity, Fopt) and the initial slope of
the light response function (canopy-scale quantum efficiency, α;
Falge et al., 2001; Tagesson et al., 2015a). Photosynthetic capacity is
a measure of the maximum rate at which the canopy can fix CO2 during
photosynthesis (µmol CO2 m-2 s-1), whereas α is
the amount of CO2 fixed per incoming PAR (µmol CO2 µmol PAR-1). To clarify the difference in LUE and α in this
study, LUE (µmol CO2 µmol APAR-1) is the slope of a
linear fit between CO2 uptake and absorbed PAR, whereas α (µmol CO2 µmol PAR-1) is the initial slope of an asymptotic
curve against incoming PAR.
It has been proven that Fopt and α are closely related to
chlorophyll abundance due to their coupling with the electron transport rate
(Ide et al., 2010). Additionally, in semi-arid ecosystems, water
availability is generally considered to be the main limiting factor
affecting intra-annual dynamics of vegetation growth (Fensholt et al.,
2013; Hickler et al., 2005; Tagesson et al., 2015b). Several remote sensing
studies have established relationships between remotely sensed vegetation
indices and ecosystem properties such as chlorophyll abundance and
equivalent water thickness (Yoder and Pettigrew-Crosby, 1995; Fensholt
and Sandholt, 2003). In this study, we will analyse whether EO vegetation
indices can be used to upscale Fopt and α and investigate
whether this could offer a promising way to map GPP in semi-arid areas. This
potential will be analysed by the use of detailed ground observations from
six eddy covariance (EC) flux tower sites across the Sahel.
The three aims of this study are
to investigate whether the recently released MOD17A2H GPP (collection 6)
product is better at capturing GPP for the Sahel than collection 5.1. We
hypothesize that the MOD17A2H GPP (collection 6) product will estimate GPP
well for the six Sahelian EC sites because of major changes made in
comparison to collection 5.1 (Running and Zhao, 2015);
to characterize the relationships between spatial and temporal variability
in Fopt and α and remotely sensed vegetation indices. We
hypothesise that EO vegetation indices that are closely related to
chlorophyll abundance will be most strongly coupled with spatial and
inter-annual dynamics in Fopt and α, whereas vegetation indices
closely related to equivalent water thickness will be most strongly coupled
with intra-annual dynamics in Fopt and α across the
Sahel;
to evaluate the applicability of a GPP model based on the light response
function using EO vegetation indices and incoming PAR as input data.
Description of the six measurement sites, including location, soil
type, ecosystem type and dominant species.
Measurement site
Coordinates
Soil type
Ecosystem
Dominant species
Agoufoua
15.34∘ N,
Sandy ferruginous
Open woody
Trees: Acacia spp.,
(ML-AgG, Mali)
1.48∘ W
Arenosol
savanna (4 %
Balanites aegyptiaca,
tree cover)
Combretum glutinosum
Herbs: Zornia glochidiata
Cenchrus biflorus, Aristida
mutabilis, Tragus berteronianus
Dahrab
15.40∘ N,
Sandy luvic
Grassland and/or shrubland
Trees: Acacia spp.,
(SN-Dah, Senegal)
15.43∘ W
Arenosol
Savanna (3 %
Balanites aegyptiaca
tree cover)
Herbs: Zornia latifolia,
Aristida adscensionis,
Cenchrus biflorus
Demokeyac
13.28∘ N,
Cambic Arenosol
Sparse acacia
Trees: Acacia spp.,
(SD-Dem, Sudan)
30.48∘ E
savannah (7 %
Herbs: Aristida pallida,
tree cover)
Eragrostis tremula,
Cenchrus biflorus
Kelmaa
15.22∘ N,
Clay soil
Open acacia forest
Trees: Acacia seyal,
(ML-Kem, Mali)
1.57∘ W
depression
(90 % tree cover)
Acacia nilotica, Balanites aegyptiaca,
Herbs: Sporobolus hevolvus
Echinochloa colona, Aeschynomene
sensitive
Wankama Fallowd
13.65∘ N,
Sandy ferruginous
Fallow bush
Guiera senegalensis
(NE-WaF, Niger)
2.63∘ E
Arenosol
Wankama Millete
13.64∘ N,
Sandy ferruginous
Millet crop
Pennisetum glaucum
(NE-WaM, Niger)
2.63∘ E
Arenosol
a Timouk et al. (2009).
b Tagesson et al. (2015b).
c Sjöström et al. (2009).
d Velluet et al. (2014).
e Boulain et al. (2009).
Land cover classes for the Sahel and the location of the six
measurement sites of this study. The land cover classes are based on
multi-sensor satellite observations (Mayaux et al.,
2003). The sites are Agoufou (ML-AgG), Dahra (SN-Dah), Demokeya (SD-Dem),
Kelma (ML-Kem), Wankama Fallow (NE-WaF) and Wankama Millet (NE-WaM). The
thick black line delineates borders of the Sahel based on annual 150 and 700 mm isohyets (Prince et al., 1995).
Results
Evaluation of the MODIS GPP product
There was a strong linear relationship between the MODIS GPP product
(MOD17A2H; collection 6) and independent GPP (slope = 0.17; intercept = 0.11 g C m-2 d-1;
R2= 0.69; n= 598). However, MOD17A2H strongly
underestimated independent GPP (Fig. 2), resulting in a high RMSE (2.69 g C m-2 d-1). It can be seen that some points for the Kelma site were
quite low for MOD17A2H, whereas they were relatively high for the
independent GPP (Fig. 2). Kelma is an inundated Acacia forest located in a
clay soil depression. These differentiated values were found in the
beginning of the dry season, when the depression was still inundated,
whereas the larger area was turning dry.
Correlation between intra-annual dynamics in photosynthetic
capacity (Fopt; Fopt_frac for all sites), quantum
efficiency (α; α_frac for all sites) and
the different vegetation indices for the six measurement sites (Fig. 1).
Values are averages ±1 SD generated from 200
bootstrapping runs. The bold values are the indices with the strongest
correlation. EVI is the enhanced vegetation index, NDVI is the normalized
difference vegetation index, RDVI is the renormalized difference vegetation
index and SIWSI is the shortwave infrared water stress index. SIWSI12
is based on the MODIS NBAR bands 2 and 5, whereas SIWSI16 is based on
MODIS NBAR bands 2 and 6.
Fopt
α
Measurement site
EVI
NDVI
RDVI
SIWSI12
SIWSI16
EVI
NDVI
RDVI
SIWSI12
SIWSI16
ML-AgG
0.89 ± 0.02
0.87 ± 0.02
0.95 ± 0.01
-0.95 ± 0.01
-0.93 ± 0.02
0.92 ± 0.02
0.91 ± 0.01
0.96 ± 0.01
-0.94 ± 0.01
-0.88 ± 0.02
SN-Dah
0.92 ± 0.005
0.91 ± 0.01
0.96 ± 0.003
-0.96 ± 0.004
-0.93 ± 0.01
0.89 ± 0.01
0.90 ± 0.01
0.93 ± 0.01
-0.92 ± 0.01
-0.87 ± 0.01
SD-Dem
0.81 ± 0.01
0.78 ± 0.01
0.91 ± 0.01
-0.93 ± 0.01
-0.90 ± 0.01
0.76 ± 0.02
0.73 ± 0.02
0.86 ± 0.01
-0.82 ± 0.02
-0.79 ± 0.02
MA-Kem
0.77 ± 0.02
0.83 ± 0.02
0.95 ± 0.01
-0.95 ± 0.01
-0.90 ± 0.02
0.69 ± 0.05
0.73 ± 0.04
0.80 ± 0.03
-0.77 ± 0.03
-0.76 ± 0.03
NE-WaF
0.87 ± 0.02
0.81 ± 0.02
0.78 ± 0.02
-0.90 ± 0.01
-0.80 ± 0.02
0.89 ± 0.01
0.84 ± 0.01
0.85 ± 0.01
-0.88 ± 0.01
-0.79 ± 0.01
NE-WaM
0.41 ± 0.05
0.50 ± 0.04
0.72 ± 0.03
-0.55 ± 0.04
-0.43 ± 0.05
0.72 ± 0.02
0.76 ± 0.02
0.81 ± 0.01
-0.75 ± 0.01
-0.72 ± 0.01
All sites
0.86 ± 0.0
0.79 ± 0.0
0.90 ± 0.0
0.75 ± 0.0
0.70 ± 0.0
0.83 ± 0.01
0.80 ± 0.01
0.86 ± 0.01
0.62 ± 0.01
0.54 ± 0.01
Intra-annual dynamics in photosynthetic capacity and quantum
efficiency
Intra-annual dynamics in Fopt and α differed in amplitude, but
were otherwise similar across the measurement sites in the Sahel (Fig. 3).
There was no green ground vegetation during the dry season, and the low
photosynthetic activity was due to few evergreen trees. This resulted in low
values for both Fopt and α during the dry season. The
vegetation responded strongly to rainfall, and both Fopt and α
increased during the early phase of the rainy season. Generally, Fopt peaked slightly earlier than α (average ±1 SD: 7 ± 10 days; Fig. 3).
Time series of photosynthetic capacity (Fopt) and quantum
efficiency (α) for the six measurement sites. Also included are time
series of the vegetation indices with highest correlation with Fopt (VIFopt) and quantum efficiency (VIα; Table 2). The sites
are (a) Agoufou (ML-AgG), (b) Dahra (SN-Dah), (c) Demokeya (SD-Dem), (d) Kelma
(ML-Kem), (e) Wankama Fallow (NE-WaF) and (f) Wankama Millet (NE-WaM).
All vegetation indices described intra-annual dynamics in Fopt
reasonably well at all sites (Table 2). The vegetation index SIWSI12
had the highest correlation for all sites except Wankama Millet, where it
was RDVI. When all sites were combined, all indices described
seasonality in Fopt well, but RDVI had the strongest correlation (Table 2).
Intra-annual dynamics in α were also closely coupled to intra-annual
dynamics in the vegetation indices for all sites (Table 2). For α,
RDVI was the strongest index describing intra-annual dynamics, except for
Wankama Fallow, where it was EVI. When all sites were combined, all indices
described intra-annual dynamics in α well, but RDVI was still the
index with the strongest relationship (Table 2).
Statistics for the regression tree analysis. Regression tree
analysis was used to study relationships between intra-annual dynamics in
photosynthetic capacity (Fopt; Fopt_frac for all
sites) and quantum efficiency (α; α_frac
for all sites) and explanatory variables. The pruning level is the number of
splits of the regression tree and an indication of complexity of the system.
Measurement site
Explanatory variables
Pruning
R2
level
Fopt
1
2
3
4
5
ML-AgG
SIWSI12
Tair
PAR
SWC
16
0.98
SN-Dah
SIWSI12
SWC
VPD
Tair
PAR
84
0.98
SD-Dem
SIWSI12
VPD
SWC
Tair
PAR
33
0.97
ML-Kem
SIWSI12
PAR
Tair
VPD
22
0.98
NE-WaF
SIWSI12
SWC
VPD
Tair
14
0.92
NE-WaM
RDVI
SWC
VPD
Tair
18
0.75
All sites
RDVI
SWC
Tair
VPD
16
0.87
α
ML-AgG
RDVI
3
0.95
SN-Dah
RDVI
VPD
SWC
Tair
PAR
21
0.93
SD-Dem
RDVI
SWC
PAR
Tair
16
0.93
ML-Kem
RDVI
Tair
4
0.75
NE-WaF
EVI
SWC
VPD
10
0.90
NE-WaM
RDVI
SWC
VPD
Tair
15
0.86
All sites
RDVI
SWC
VPD
Tair
16
0.84
The regression trees used for gap-filling explained the intra-annual
dynamics in Fopt and α well for all sites (Table 3; Fig. S2). The regression trees explained intra-annual
dynamics in Fopt better than in α, and multi-year sites were
better predicted than single-year sites (Fig. S2). The main explanatory
variables coupled to intra-annual dynamics in Fopt for all sites across
the Sahel were on the order of RDVI, SWC, VPD, Tair and PAR. For
α, they were RDVI, SWC, VPD and Tair (Table 3). The strong
relationship to SWC and VPD indicates drought stress during periods of low
rainfall. For all sites across the Sahel, incorporating hydrometeorological
variables increased the ability to determine intra-annual dynamics in
Fopt and α compared to the ordinary least square linear
regressions against vegetation indices (Table 2, data given as r; Table 3; Figs. 3 and S2). For all sites, incorporation of these
variables increased R2 from 0.81 to 0.87 and from 0.74 to 0.84 for
Fopt and α, respectively.
Annual peak values of quantum efficiency (αpeak; µmol CO2 µmol PAR-1) and photosynthetic capacity
(Fopt_peak; µmol CO2 m-2 s-1) for
the six measurement sites (Fig. 1). The peak values are the 2-week running
mean with highest annual value.
Measurement site
Year
αpeak
Fopt_peak
ML-AgG
2007
0.0396
24.5
SN-Dah
2010
0.0638
50.0
2011
0.0507
42.3
2012
0.0480
39.2
2013
0.0549
40.0
SD-Dem
2007
0.0257
16.5
2008
0.0327
21.0
2009
0.0368
16.5
ML-Kem
2007
0.0526
33.5
NE-WaF
2005
0.0273
18.2
2006
0.0413
21.0
NE-WaM
2005
0.0252
10.6
2006
0.0200
10.1
Average
0.0399
26.4
Spatial and inter-annual dynamics in photosynthetic capacity and quantum
efficiency
Large spatial and inter-annual variability in Fopt_peak
and αpeak were found across the six measurement sites;
Fopt_peak ranged between 10.1 (Wankama Millet 2005) and
50.0 µmol CO2 m-2 s-1 (Dahra 2010), and αpeak
ranged between 0.020 (Demokeya 2007)
and 0.064 µmol CO2 µmol PAR-1 (Dahra 2010; Table 4).
The average 2-week running mean peak values of Fopt and α for
all sites were 26.4 µmol CO2 m-2 s-1 and 0.040 µmol CO2 µmol PAR-1, respectively.
All vegetation indices
determined spatial and inter-annual dynamics well in both
Fopt_peak and αpeak (Table 5);
Fopt_peak was most closely coupled with
NDVIpeak,
whereas αpeak was more closely coupled with RDVIpeak (Fig. 4). Fopt_peak also correlated well with peak dry weight
biomass, C content in the soil and RH, whereas αpeak also
correlated with peak dry weight biomass and C content in the soil (Table 5).
Scatter plots of annual peak values for the six measurement sites
(Fig. 1) of (a) photosynthetic capacity (Fopt_peak) and
(b) quantum efficiency (αpeak) against peak values of normalized
difference vegetation index (NDVIpeak) and renormalized difference
vegetation index (RDVIpeak), respectively. The annual peak values were
estimated by taking the annual maximum of a 2-week running mean.
Correlation matrix between annual peak values of photosynthetic
capacity (Fopt_peak) and quantum efficiency (αpeak) and measured environmental variables:
annual rainfall (P);
yearly averages of air temperature at 2 m height (Tair), soil water
content measured at 0.1 m depth (SWC; % volumetric water content),
relative humidity (Rh), vapour pressure deficit (VPD) and incoming global
radiation (Rg); soil nitrogen (N) and carbon (C) contents; and annual peak
values of the normalized difference vegetation index (NDVIpeak), the
enhanced vegetation index (EVIpeak), the renormalized difference
vegetation index (RDVIpeak), the shortwave infrared water stress index
based on MODIS NBAR bands 2 and 5 (SIWSI12peak), and the SIWSI based on
MODIS NBAR bands 2 and 6 (SIWSI16peak). Sample size was 13 for all
except the marked explanatory variables.
Explanatory variable
Fopt_peak
αpeak
Meteorological data
P (mm)
0.24 ± 0.26
0.13 ± 0.27
Tair (∘C)
-0.07 ± 0.25
-0.01 ± 0.25
SWC (%)a
0.33 ± 0.25
0.16 ± 0.27
Rh (%)
0.73 ± 0.16*
0.60 ± 0.19
VPD (hPa)
0.20 ± 0.26
0.15 ± 0.30
Rg (W m-2)
-0.48 ± 0.21
-0.41 ± 0.24
Biomass and edaphic data
Biomass (g DW m-2)a
0.77 ± 0.15*
0.74 ± 0.14*
C3 / C4 ratio
-0.05 ± 0.26
0.06 ± 0.30
N cont. ( %)b
0.22 ± 0.11
0.35 ± 0.14
C cont. ( %)b
0.89 ± 0.06**
0.87 ± 0.07**
Earth observation data
NDVI peak
0.94 ± 0.05**
0.87 ± 0.07**
EVIpeak
0.93 ± 0.04**
0.87 ± 0.07**
RDVIpeak
0.93 ± 0.04**
0.89 ± 0.07**
SIWSI12peak
0.85 ± 0.08**
0.84 ± 0.08**
SIWSI16peak
0.67 ± 0.12*
0.65 ± 0.15*
Photosynthetic variables
Fopt
–
0.94 ± 0.03**
a Sample size equals 11.
b Sample size equals 9.
* Significant at 0.05 level.
** Significant at 0.01 level.
Spatially extrapolated photosynthetic capacity, quantum efficiency and
gross primary production across the Sahel and evaluation of the GPP model
The spatially extrapolated Fopt, α and GPP averaged over the
Sahel for 2001–2014 were 22.5 ± 1.7 µmol CO2 m-2 s-1, 0.030 ± 0.002 µmol CO2 µmol PAR-1
and 736 ± 39 g C m-2 yr-1, respectively. At a
regional scale, it can be seen that Fopt, α and GPP decreased
substantially with latitude (Fig. 5). The highest values were found in
south-eastern Senegal, western Mali, in parts of southern Sudan and on the
border between Sudan and South Sudan. The lowest values were found along the
northernmost parts of the Sahel on the border to the Sahara in Mauritania,
in northern Mali and in northern Niger.
Maps of (a) peak values of photosynthetic capacity
(Fopt_peak) averaged for 2001–2014, (b) peak values of
quantum efficiency (αpeak) averaged for 2001–2014 and
(c) annual budgets of GPP averaged for 2001–2014.
Modelled GPP was similar to independent GPP on average, and there was a
strong linear relationship between modelled GPP and independent GPP for all
sites (Fig. 6; Table 6). However, when separating the evaluation between
measurement sites, it can be seen that the model reproduced some sites
better than others (Fig. 7; Table 6). Wankama Millet was generally
overestimated, whereas on average the model worked well for Demokeya but
underestimated high values (Fig. 7; Table 6). Variability of independent GPP
at the other sites was reproduced by the model reasonably well (Fig. 7;
Table 6). The final parameters of the GPP model (Eq. 13) are shown in Table 7.
Statistics regarding the evaluation of the gross primary production
(GPP) model for the six measurement sites (Fig. 1). In situ and modelled GPP
are averages ±1 SD. RMSE is the root mean square
error, and slope, intercept and R2 are from the fitted ordinary least
square linear regressions.
Measurement
In situ GPP
Modelled GPP
RMSE
Slope
Intercept
R2
site
(µmol CO2 m-2 s-1)
(µmol CO2 m-2 s-1)
(µmol CO2 m-2 s-1)
(µmol CO2 m-2 s-1)
ML-AgG
5.35 ± 6.38
5.97 ± 5.80
2.48 ± 0.10
0.84 ± 0.003
1.46 ± 0.01
0.86 ± 0.002
SN-Dah
9.39 ± 10.17
8.87 ± 9.67
3.99 ± 1.34
0.88 ± 0.002
0.62 ± 0.01
0.85 ± 0.001
SD-Dem
4.26 ± 4.55
3.98 ± 3.90
3.15 ± 1.06
0.63 ± 0.003
1.31 ± 0.007
0.54 ± 0.02
ML-Kem
11.16 ± 8.02
10.52 ± 9.22
4.35 ± 1.23
1.02 ± 0.003
-0.82 ± 0.03
0.78 ± 0.002
NE-WaF
5.77 ± 4.17
6.63 ± 3.53
2.47 ± 1.05
0.70 ± 0.005
2.58 ± 0.02
0.69 ± 0.003
NE-WaM
3.04 ± 1.93
6.35 ± 3.47
4.12 ± 0.99
1.31 ± 0.004
2.37 ± 0.02
0.53 ± 0.003
Average
6.73 ± 7.72
7.02 ± 7.39
3.68 ± 0.55
0.83 ± 0.07
1.34 ± 0.82
0.84 ± 0.07
The parameters for Eq. (13) that were used in the final gross primary
production (GPP) model. RMSE is the root mean square error, and R2 is
the coefficient of determination for the regression models predicting the
different variables.
Parameter
Value
RMSE
R2
kFopt
79.6 ± 6.3
5.1 ± 1.3
0.89 ± 0.05
mFopt
-7.3 ± 3.2
lFopt
3.51 ± 0.19
0.15 ± 0.02
0.88 ± 0.06
nFopt
0.03 ± 0.006
α
0.16 ± 0.02
0.0069 ± 0.0021
0.81 ± 0.10
mFopt
-0.014 ± 0.007
lFopt
3.75 ± 0.27
0.20 ± 0.02
0.80 ± 0.10
nFopt
0.02 ± 0.007
Discussion
Our hypothesis that vegetation indices closely related to SIWSI would be most strongly coupled with intra-annual dynamics
in Fopt and α was not rejected for Fopt since this was
the case for all sites except for Wankama Millet (Table 2). However, our
hypothesis was rejected for α, since it was more closely related to
vegetation indices of chlorophyll abundance (RDVI and EVI). In the Sahel,
soil moisture conditions in the early rainy season are important for
vegetation growth, and during this phase vegetation is especially vulnerable
to drought conditions (Rockström and de Rouw, 1997; Tagesson et al.,
2016a; Mbow et al., 2013). Photosynthetic capacity (Fopt) peaked
earlier than α did in the rainy season (Fig. 3), thereby explaining
the close relationship of Fopt to SIWSI. Leaf area index increased over
the growing season and leaf area index is closely coupled with vegetation
indices related to chlorophyll abundance (Tagesson et al., 2009).
The increase in leaf area index increased canopy level quantum efficiency
(α), thereby explaining the closer relationship of α with
RDVI.
Our hypothesis that vegetation indices closely related to chlorophyll
abundance would be most strongly coupled with spatial and inter-annual
dynamics in Fopt and α was not rejected for either Fopt or
α; NDVI, EVI and RDVI all correlated with spatial and inter-annual
dynamics in Fopt and α (Table 5). However, it was surprising
that NDVIpeak had the strongest correlation with spatial and
inter-annual variability in Fopt (Table 5). Both EVI and RDVI should
be less sensitive to saturation effects than NDVI
(Huete et al., 2002; Roujean and Breon, 1995), and
based on this it can be assumed that peak values of these indices should
have stronger relationships with peak values of Fopt and α.
However, vegetation indices with a high sensitivity to changes in green
biomass at high biomass loads become less sensitive to green biomass changes
at low biomass loads (Huete et al., 2002). The peak leaf area
index for ecosystems across the Sahel is generally ∼ 2 m2 m-2 or less, whereas the saturation issue of NDVI generally starts at a
leaf area index of about 2–5 m2 m-2 (Haboudane
et al., 2004).
The Fopt_peak estimates from Agoufou, Demokeya and the
Wankama sites were similar, whereas Dahra and Kelma values were high in
relation to previously reported canopy-scale Fopt_peak
from the Sahel (∼ -8 to -23 µmol m-2 s-1; Hanan et al., 1998; Merbold et al., 2009; Moncrieff
et al., 1997; Boulain et al., 2009; Levy et al., 1997; Monteny et al.,
1997). These previous studies reported much lower Fopt at canopy scale
than at leaf scale (e.g. Levy et al., 1997: 10 vs.
44 µmol m-2 s-1; Boulain
et al., 2009: 8 vs. 50 µmol m-2 s-1). The leaf area index
at Dahra and Kelma peaked at 2.1 and 2.7, respectively (Timouk et al.,
2009; Tagesson et al., 2015a), and it was substantially higher than at the
above-mentioned sites. A possible explanation for high Fopt estimates
at Dahra and Kelma could therefore be the higher leaf area index.
Tagesson et al. (2016b) performed a quality check of the EC
data due to the high net CO2 exchange measured at the Dahra field site
and explained the high values by a combination of moderately dense
herbaceous C4 ground vegetation, high soil nutrient availability and a
grazing pressure resulting in compensatory growth and fertilization effects.
Another possible explanation could be that the West African monsoon brings a
humid layer of surface air from the Atlantic, possibly increasing vegetation
production for the most western part of the Sahel (Tagesson
et al., 2016a).
Evaluation of the modelled gross primary production (GPP; Eq. 13)
against in situ GPP from all six measurement sites. The thick grey line
shows the one-to-one ratio, whereas the thin dotted grey line is the fitted
ordinary least square regression.
Evaluation of the modelled gross primary production (GPP; Eq. 13)
against in situ GPP for the six sites across the Sahel (Fig. 1). The thick black
lines show the one-to-one ratios, whereas the dotted thin grey lines are the
fitted ordinary least square regressions. The sites are (a) Agoufou
(ML-AgG), (b) Dahra (SN-Dah), (c) Demokeya (SD-Dem), (d) Kelma (ML-Kem),
(e) Wankama Fallow (NE-WaF) and (f) Wankama Millet (NE-WaM).
Our model substantially overestimated GPP for Wankama Millet (Fig. 7f).
Being a crop field, this site differed from the other sites in its species
composition and ecosystem structure, as well as land and vegetation
management. Crop fields in southwestern Niger are generally characterized by
rather low production, resulting from decreased fertility and soil loss
caused by intensive land use (Cappelaere
et al., 2009). These specifics of the Wankama Millet site may cause
the model, parameterized with observations from the other study sites
without this strong anthropogenic influence, to overestimate GPP at this
site. Similar results were found by Boulain
et al. (2009) when applying an upscaling model using leaf area index for
Wankama Millet and Wankama Fallow. It worked well for Wankama fallow,
whereas it was less conclusive for Wankama Millet. The main explanation for
this difference was low leaf area index in millet fields because of a low
density of millet stands due to agricultural practice. There is extensive
savanna clearing for food production in the Sahel (Leblanc et al., 2008;
Boulain et al., 2009; Cappelaere et al., 2009). To further understand
impacts of this land cover change on vegetation production and
land–atmosphere exchange processes, there is an urgent need for more study
sites covering cropped areas in this region.
In Demokeya, GPP was slightly underestimated for 2008 (Fig. 7c) because
modelled Fopt was much lower than the actual measured value in 2008
(the thick black line in Fig. 4). An improvement of the model could be to
incorporate some parameters that constrain or enhance Fopt depending on
environmental stress. Indeed, the regression tree analysis indicated that
incorporating hydrometeorological variables increased the ability to predict
both Fopt and α. Conversely, for spatial upscaling
purposes, it has been shown that including modelled hydrometeorological
constraints on LUE decreases the ability to predict vegetation production
due to the incorporated uncertainty in these modelled variables (Fensholt
et al., 2006; Ma et al., 2014). For spatial upscaling to regional scales, it
is therefore better to simply use relationships with EO data. This is
particularly the case for the Sahel, one of the largest dryland areas in the
world, which includes only a few sites of hydrometeorological observations.
The pattern seen in the spatially explicit GPP budgets (Fig. 5c) may be
influenced by a range of biophysical and anthropogenic factors. The clear
north–south gradient is expected given the strong north–south rainfall
gradient in the Sahel. The West African monsoon mentioned above could also
be an explanation of high GPP values in the western part of the Sahel, where
values were relatively high in relation to GPP at similar latitudes in the
central and eastern Sahel (Fig. 5c). The areas with the highest GPP are sparsely
populated woodlands or shrubby savanna with a relatively dense tree cover
(Brandt et al., 2016). However, the maps produced here
should be used with caution as they are based on upscaling of data collected
at only six EC sites available in the region, especially given the issues
related to the cropped fields discussed earlier. Still, the average GPP
budget for the entire Sahel 2001–2014 was close to an average annual GPP
budget estimated at these six sites
(692 ± 89 g C m-2 yr-1;
Tagesson et al., 2016a). The range of GPP budgets in Fig. 5c is also similar to previous annual GPP budgets reported from other
savannas across the world (Veenendaal et al., 2004; Chen et al., 2003, 2006;
Kanniah et al., 2010).
Although MOD17A2 GPP has previously been shown to capture GPP in several
ecosystems types well (Turner et al., 2006, 2005; Heinsch
et al., 2006; Sims et al., 2006; Kanniah et al., 2009), it has been shown to
underestimate it in others (Coops et al., 2007; Gebremichael and Barros,
2006; Sjöström et al., 2013). Gross primary production of Sahelian
drylands have not been captured well by MOD17A2 (Sjöström et al.,
2013; Fensholt et al., 2006), and as we have shown, this underestimation
persists in the latest MOD17A2H GPP (collection 6) product (Fig. 2). The
main reason for this pronounced underestimation is that maximum LUE is set
to 0.84 g C MJ-1 (open shrubland, Demokeya) and 0.86 g C MJ-1
(grassland; Agoufou, Dahra, Kelma; Wankama Millet and Wankama Fallow) in the
BPLUT, i.e. much lower than maximum LUE measured at the Sahelian
measurement sites of this study (average: 2.47 g C MJ-1; range:
1.58–3.50 g C MJ-1; Sjöström et al., 2013; Tagesson et al.,
2015a), a global estimate of ∼ 1.5 g C MJ-1
(Garbulsky et al., 2010) and a savanna site in
Australia (1.26 g C MJ-1; Kanniah et al.,
2009).
Several dynamic global vegetation models have been used for decades to
quantify GPP at different spatial and temporal scales (Dickinson, 1983;
Sellers et al., 1997). These models are generally based on the
photosynthesis model of Farquhar et al. (1980), a model
particularly sensitive to uncertainty in photosynthetic capacity
(Zhang et al., 2014). This and several previous studies have
shown that both photosynthetic capacity and efficiency (both α and
LUE) can vary considerably between seasons as well as spatially vary, and both
within and between vegetation types (Eamus et al., 2013; Garbulsky et
al., 2010; Ma et al., 2014; Tagesson et al., 2015a). This variability is
difficult to estimate using broad values based on land cover classes, yet
most models apply a constant value, which can cause substantial inaccuracies
in the estimates of seasonal and spatial variability in GPP. This is
particularly a problem in savannas that consist of several plant functional
types (C3 and C4 species, and a large variability in tree and/or herbaceous
vegetation fractions; Scholes and Archer, 1997). This study
indicates the applicability of EO as a tool for parameterizing spatially
explicit estimates of plant physiological variables, which could improve our
ability to simulate GPP. Spatially explicit estimates of GPP at a high
temporal and spatial resolution are essential for environmental change
studies in the Sahel and can contribute to increased knowledge regarding
changes in GPP, its relationship to climatic change and anthropogenic
forcing, and simulations of ecosystem processes and hydro-biochemical
cycles.