Missing Gaps in the Estimation of the Carbon Gains Service from Light Use Efficiency Models

Abstract

The Monteith´s model offers an attractive alternative to describe the service that regulates the carbon gains by ecosystems at regional and global scales, clearly an important question in assessing Global Change. This approach estimates net primary productivity (NPP) from photosynthetically active solar radiation (PAR), the fraction of PAR absorbed by vegetation (fPAR), and the light use efficiency (LUE) for species or ecosystems. PAR may be measured using radiometers and fPAR derived from spectral vegetation indices provided by remote sensors. LUE constitutes the most difficult term to estimate. In this chapter, we reviewed the approaches used to estimate LUE, the land cover types and levels of organization (i.e. from individuals to ecosystems) best represented by LUE estimates, and the effect of time interval of estimation in LUE. We found 125 articles on LUE estimation but only 97 provided quantitative LUE data representing different land cover types or levels of organization. LUE values were mostly determined using the Monteith´s model through remote sensing data. A small percentage of LUE estimations were based on biomass harvest estimates. LUE values were distinct according to the land cover types and the organizational level. In addition, we detected significant differences related to the time interval of LUE estimation (i.e. annual, seasonal or daily). A more thorough study of main LUE gaps will facilitate a global estimate of the service that regulates the carbon gains by ecosystems.

Full text

105
6
Missing Gaps in the Estimation
of the Carbon Gains Service from
Light Use Efficiency Models
A. J. Castro Martínez
University of Oklahoma, Oklahoma, USA; University of Almería, Spain
J. M. Paruelo
University of Buenos Aires, Argentine
D. Alcaraz-Segura
University of Granada, Spain; University of Almería, Spain
J. Cabello
University of Almería, Spain
M. Oyarzabal
University of Buenos Aires, Argentine
E. López-Carrique
University of Almería, Spain
CONTENTS
6.1 Introduction ................................................................................................ 106
6.2 Material and Methods ............................................................................... 107
6.3 Results ......................................................................................................... 108
6.3.1 Estimation Methods and LUE Units ........................................... 108
6.3.2 LUE Estimates across Organizational Levels and
Land Cover Types .......................................................................... 110
6.3.3 Time Interval of LUE Estimates ................................................... 112
6.4 Conclusions ................................................................................................. 115
K14591_C006.indd 105 28/08/13 9:11 PM

106 Earth Observation of Ecosystem Services
6.1 Introduction
The scientic community is being urged to invest more time and economic
resources to improve current estimates of global and regional carbon bud-
gets (Scurlock etal. 1999). Carbon gains are considered either as an interme-
diate service (Fisher etal. 2009) or as supports of provision and regulating
services (MA 2005). In addition, net primary production (NPP), an esti-
mate of ecosystem carbon gains, is often considered the most integrative
descriptor of ecosystem function (McNaughton etal. 1989). NPP estimates
are derived from biomass harvesting, ux tower measurements, remote
sensing, and model simulation (Ruimy etal. 1995; Sala etal. 2000; Stilletal.
2004). Biomass harvesting is expensive and not exempt from errors and
methodological problems. These methods are limited in their spatial and
temporal coverage. Given the linear relationship between the fraction of
solar radiation absorbed by vegetation and spectral vegetation indices
(Sellers etal. 1992), Monteith’s model (Monteith 1972) offers the possibil-
ity of estimating seasonal variation in carbon gains from remote sensing
data (Potter 1993). Monteith’s model states that carbon gains (Equation 6.1)
of vegetation cover are a function of the quantity of incoming photosyn-
thetically active radiation (PAR), the fraction of this radiation intercepted
by vegetation (fPAR), and the light use efciency (LUE; Still et al. 2004).
The ux estimated using the Monteith’s model included net and gross pri-
mary production and net ecosystem exchange (NEE) (Ruimy etal. 1999; see
Equations6.2 and 6.3).
NPP = PAR*fPAR*LUE (6.1)
GPP = PAR*f PAR*LU E (6 .2)
NEE = PAR*fPAR*LUE (6.3)
PAR can be directly measured using radiometers; fPAR can be esti-
matedfrom spectral indices such as the Normalized Difference Vegetation
Index(NDVI; Asrar etal. 1984) or the Enhanced Vegetation Index (EVI). The
relationship of fPAR-spectral indices may vary between land cover types,
but several authors have proposed different empirical relationships: (a) lin-
ear (Choudhury 1987); (b) nonlinear (Potter 1993; Sellers etal. 1994); and
Acknowledgments .............................................................................................. 115
Appendix 6.1 (Articles Reviewed from 1972 to 2007) ................................... 115
References .............................................................................................................121
K14591_C006.indd 106 28/08/13 9:11 PM

107Missing Gaps in the Estimation of the Carbon Gains Service
(c)a combination of both (Los etal. 2000). The LUE termhas a maximum
value comparable to the photosynthetic efciency or quantum yield at leaf
level under optimum conditions (Gower etal. 1999). However, low tempera-
tures and water and nutritional stress reduce LUE value (Field etal. 1995;
Gamon etal. 1995). Field etal. (1995) reported LUE differences from0.27gC/
MJ APAR for deserts to 0.70 g C/MJ APAR for tropical forests.
LUE was rst dened at the species level and mainly for crop species
(Andrade etal. 1993; Kiniry etal. 1998). The use of Monteith’s model as the
conceptual framework for remotely sensed estimates of aboveground net pri-
mary productivity (ANPP) (see Chapter5) requires a denition of LUE at the
ecosystem level (Ruimy etal. 1999; Sala etal. 2000; Fensholt etal. 2006). Often
a single xed value of approximately 1 g C/MJ APAR is used for a wide range
of spatiotemporal situations (Maselli etal. 2009). Several authors showed that
LUE varied in space (Field etal. 1995; Paruelo etal. 2004; Tongetal. 2008;
Garbulsky etal. 2010) and time (Nouvellon etal. 2000; Piñeiro et al. 2006)
and that the use of a single value may lead to substantial errors in regional
(Hilker etal. 2008) and global (Turner etal. 2002, 2003, 2005; Tong etal. 2008)
estimates of carbon gains.
Many factors affect the spatiotemporal patterns of LUE variation. Species
composition, plant structure and physiology, including leaf form and
RUBISCO (Ribulose-1,5-bisphosphate carboxylase oxygenase) content (Zhao
etal. 2007), and environmental factors (i.e., water stress, CO2 concentration,
temperature) modify LUE at the ecosystem level. Measuring LUE is not a
simple task. LUE can be estimated at different levels of organization (e.g.,
from individuals to ecosystems), using leaf-level estimates for single indi-
viduals or eddy covariance towers to derive LUE values at the ecosystem
level (Garbulsky etal. 2010).
LUE is the more uncertain parameter of the Monteith’s model since it is
not possible to measure it directly, and it depends on estimates of GPP/NPP/
NEE and absorbed radiation (Gower etal. 1999; Ruimy etal. 1999). This chap-
ter reviews the reported estimates of LUE, and the effect of the time inter-
val in LUE estimation at different organizational levels, from individuals
to ecosystems. We sought to answer the following questions: (1) How was
LUE estimated? (2) How did LUE differ across land cover types and levels
of organization? (3) How variable are LUE estimates according to the time
interval of estimation?
6.2 Material and Methods
We reviewed 125 articles from 1972 to 2007 containing the terms “light use
efciency” and “radiation use efciency,” but only 101 provided quantita-
tive LUE data (Appendix 6.1). The review included 65 different journals
K14591_C006.indd 107 28/08/13 9:11 PM

108 Earth Observation of Ecosystem Services
primarily in the eld of ecology (72% of total studies) and remote sensing
(22% of total studies). From the published studies, we developed a data-
base that included the organizational level, the ux estimated, LUE esti-
mates, and the geographical coordinates of the study site (Table6.1). We
characterized LUE values at three organizational levels: (1) “Individual”
referred to local-scale studies that estimated LUE based on individuals
of a single species; (2)“Single-species-dominated ecosystems” referred to
when the study focused on plots with one dominant species (e.g., NEE
of agroecosystem in eddy covariance ux tower with a footprint of 100
m2); and (3)“Multispecies-dominated ecosystems.” Assuming that 50% of
the dry biomass corresponds to carbon, LUE values were transformed to
the most common unit system: grams of carbon xed per megajoules of
absorbed PAR (g C/MJ APAR). To analyze the variability in LUE data, we
assigned each data to one category of Archibold’s (1995) classication of
terrestrial land cover types. We calculated the mean, maximum, and mini-
mum LUE values, as well as the deviation for each organizational level and
land cover type. Kruskal–Wallis tests were applied to detect signicant
differences in LUE estimates (n = 185) across organizational levels and
land cover types.
6.3 Results
6.3.1 Estimation Methods and LUE Units
LUE values were estimated using two main approaches. In the rst approach
(82% of total studies), LUE values were calculated at a local scale using the
Monteith’s equation and based on previous eld estimates of a carbon ux
(i.e., NPP or GPP). Here, the 20% of carbon ux estimates were derived from
CO2 ux between atmosphere and vegetation observations using eddyco-
variance techniques (Ruimy etal. 1995; Zhao etal. 2007); fPAR data were
calculated as a lineal function of satellite-derived NDVI in 44% of the total
data. The remaining studies used fPAR data reported from other studies or
compiled from direct measurements of the canopy. In most of the studies,
PAR was calculated by radiometers.
In the second approach (18% of total studies), LUE was estimated based
on correlative models with other variables such as leaf area index (LAI) or
the photochemical reectance index (PRI; Gu etal. 2002; Filella etal. 2004;
Graceet al. 2007) (see Chapter3). Here, LUE was also derived as a ratio
between the harvested biomass at the plot scale and the incoming APAR
throughout an entire year or a growing season.
Most of the articles reviewed (77% of total studies) offered quantitative
estimates of LUE. From these studies, we obtained 185 LUE values that
K14591_C006.indd 108 28/08/13 9:11 PM

109Missing Gaps in the Estimation of the Carbon Gains Service
TABLE6.1
Summary Sample of Light Use Efciency Data Reviewed for 1972–2007
Land
Cover
Types
Number
of Studies Locations LUE Units
Carbon Flux Model
(expressed as
percentage of total
studies within biome)
PHM 1 Alaska g C/MJ APAR (100%) Other*
CF 49 Durham, Canada,
Wisconsin,
Sweden
g C/MJ APAR Mol
C/mol photons
Moles CO2/
molPAR
(8%) GPP = APAR*LUE
(16%) NEE = APAR*LUE
(32%) NPP = APAR*LUE
(44%) Other*
TW 12 Canada, Europe,
EEUU
g C/MJ APAR
Mol C/mol
photons
Moles CO2/
molPAR
(8%) ANPP = APAR*LUE
(29%) NPP = APAR*LUE
(29%) Other*
Cr 38 Ireland, EEUU,
China, Italy,
Australia, United
Kingdom, South
Africa, India
g C/MJ
Kg (CO2/ha·h)/
(J/m2·sg)
(30%) NPP = APAR*LUE
(70%) Other*
TFE 65 Europe, EEUU,
Japan, China,
New Zealand
g C/MJ APAR
Mol CO2/
molAPAR
mmol CO2/
mmolphotons
(8%) GPP = APAR*LUE
(32%) NEE = APAR*LUE
(27%) NPP = APAR*LUE
(33%) Other*
ME 12 Spain, Italy, India,
EEUU
g C/MJ APAR
Mol C/mol APAR
(20%) NEE = APAR*LUE
(70%) NPP = APAR*LUE
(10%) Other*
TG 16 Canada, EEUU,
Argentina
g C/MJ APAR
g DM/MJ
(13%) NEE = APAR*LUE
(40%) NPP = APAR*LUE
(47%) Other*
TpF 4 Panama,
Colombia, EEUU
g C/MJ APAR
Kg(CO2/ha·h)/
(J/m2·sg)
(67%) GPP = APAR*LUE
(33%) NEE = APAR*LUE
TpS 9 Senegal, Argentina g C/MJ APAR
Mol C/mol APAR
(100%) Other*
AR 8 Sahara, Southern
Australia, Mali,
Mexico
g C/MJ APAR
gr DM/MJ
(17%) GPP = APAR*LUE
(50%) NPP = APAR*LUE
(33%) Other*
Note: Other* expresses (a) when the study did not specify the carbon ux model for LUE esti-
mation, (b) modications of Monteith’s model such as NASA-CASA model (i.e.,Carnegie-
Ames-Stanford Approach) simulates net primary productivity and the soil heterotrophic
respiration at regional to global scales or the TURC model for the estimation of the conti-
nental gross primary productivity and net primary productivity, or derived models by
Montetih’s approach based on the inclusion of other physiological parameters, and (c) a
constant LUE value. Archibold’s land cover type classication: PHM = polar and high
mountain tundra, CF = coniferous forests, TW = terrestrial wetlands, Cr = crops,
TFE= temperate forest ecosystems, ME = Mediterranean ecosystems, TG = temperate
grasslands, TpF = tropical forests, TpS = tropical savannas, and AR = arid regions.
K14591_C006.indd 109 28/08/13 9:11 PM

110 Earth Observation of Ecosystem Services
were originally expressed in four different units, including g C/MJ APAR
(65%), mol CO2/mol APAR (14%), g of dry matter/MJ APAR (9%), and mol
C/mol absorbed photons per minute (3%). After converting to g C/MJ
APAR, the average LUE value was 0.99 g C/MJ APAR (SD = 1.09), with
an absolute maximum of 8.2 g C/MJ APAR and an absolute minimum of
0.05gC/MJ APAR.
6.3.2 LUE Estimates across Organizational Levels and Land Cover Types
The number of studies varied among organizational levels and land cover
types (Figure 6.1). The multispecies-dominated ecosystems level was the
most commonly studied (62%) followed by single-species-dominated eco-
systems (19%) and individuals (19%). Temperate forests, coniferous forests,
and croplands were the most highly represented land cover types in the lit-
erature reviewed, and the least represented were polar and high mountain
tundra, tropical forests, and arid regions (Figure6.1). Croplands and temper-
ate forest ecosystems were the unique land cover types studied at all levels
of organization.
We found that the average LUE values at the individual level
(1.7gC/MJAPAR; SD = 1.6) were signicantly higher than at the multispe-
cies-dominated (0.8gC/MJ APAR; SD = 0.9) and single-species-dominated
Frequency (%)
40
30
20
10
0
PHM CF TW Cr TFE ME TG TpF TpS AR
1% 5%
0.5%
0.5%
2%
3%
17%
11%
9%
3%
5%
3%
2%
8%
5%
31%
17%
22%
6%
2%
20%
1%
Land cover types
Individuals (19%)
Single-species-dominated
ecosystems (19%)
Multispecies-dominated
ecosystems (62%)
FIGURE 6.1
Frequency of articles for land cover types at individual, single-species-dominated ecosystems,
and multispecies-dominated ecosystems levels. Total LUE values = 185. Archibold’s land cover
type classication: PHM = polar and high mountain tundra, CF = coniferous forests, TW =
terrestrial wetlands, Cr=crops, TFE = temperate forest ecosystems, ME = Mediterranean eco-
systems, TG= temperate grasslands, TpF = tropical forests, TpS = tropical savannas, and AR =
arid regions.
K14591_C006.indd 110 28/08/13 9:11 PM

111Missing Gaps in the Estimation of the Carbon Gains Service
(0.7g C/MJ APAR; SD = 0.4) ecosystems levels (Figure 6.2). Average LUE
values by land cover type exhibited signicant differences (Figure6.3). The
average LUE values varied between 2.20 g C/MJ APAR (SD = 1.67) in crop-
lands and 0.55gC/MJ APAR (SD = 0.23) in terrestrial wetlands. The maxi-
mum LUE was found in crops (8.20 g C/MJ APAR) and temperate grasslands
(5.20gC/MJ APAR), whereas the minimum LUE value was observed in trop-
ical savannas (0.05gC/MJ APAR) and temperate grasslands (0.06 g C/MJ
APAR)(Figure6.3).
At the individual level, signicant differences in LUE were observed
between tropical savannas, tropical forests, and crops (Figure 6.4). At the
single-species-dominated ecosystems level, coniferous forests and crops
were signicantly different in LUE values (Figure6.4). At the multispecies-
dominated ecosystems level, crops were signicantly different from conif-
erous forests, terrestrial wetlands, tropical forests, and Mediterranean
ecosystems. Temperate grasslands and tropical forest ecosystems were signif-
icantly different from coniferous forest, terrestrial wetlands, and temperate
forest ecosystems (Figure6.4).
Although LUE estimates were not available in the literature for all
organizational levels, we observed signicant differences within land
6
5
(116) (34) (35)
4
3
2
1
LUE (g C/MJ APAR)
0
Multispecies-
dominated ecosystems
Single-species-
dominated ecosystems Individuals
FIGURE 6.2
Box plot of light use efciency (LUE) values at multispecies-dominated ecosystems, single-
species- dominated ecosystems, and individual levels. The graphic explains t he minimum, rst
quartile (25%), median, mean, and third quartile (75%) of LUE values. The mean is displayed
with a +, and a black line corresponds to the median. The maximum LUE value at the indi-
vidual level was 8.2g C/MJ APAR; at the single-species-dominated ecosystems level, it was
2gC/MJ APAR; and at multispecies-dominated ecosystem level, it was 5.7 g C/MJ APAR. The
horizontal dotted line represents the total average of LUE values. The total LUE values per box
plot appear in brackets.
K14591_C006.indd 111 28/08/13 9:11 PM

112 Earth Observation of Ecosystem Services
cover types. Coniferous forests showed signicant differences between
single-species-dominated and multispecies-dominated ecosystems levels. In
temperate forest ecosystems, we observed signicant differences between
the individual and single- species-dominated ecosystems and multispecies-
dominated ecosystems levels (Figure6.4). In Mediterranean ecosystems, dif-
ferences were observed between the individual and multispecies-dominated
ecosystems levels. Crops, represented at all levels of organization, were the
only land cover type showing no signicant differences. Tropical forest eco-
systems with values for individual and multispecies-dominated ecosystems
levels did not exhibit signicant differences (Figure6.4).
6.3.3 Time Interval of LUE Estimates
LUE values signicantly differed according to the time interval of the
estimation (i.e., sampled within a day, season, or year). Annual and sea-
sonal estimates were obtained for all organizational levels. Daily LUE
estimates were only found in the literature at the multispecies-domi-
nated ecosystems level. Signicant differences between measurement
time intervals were detected at the single-species-dominated ecosystems
level between annual and seasonal LUE estimates. Kruskal–Wallis tests
did not reveal signicant differences in annual and seasonal estimates
at the individual level and for annual, seasonal, and daily estimates at
LUE (g C/MJ APAR)
(1)
0
PHM CF TW Cr TFE ME TG TpF TpS
AR
1
2
3
4
5
6
(41) (12) (32) (58) (10) (15) (2) (9) (5)
FIGURE 6.3
Box plot of light use efciency (LUE) values by land cover types. The graphic explains the
minimum, rst quartile (25%), median, mean, and third quartile (75%) of LUE values. The
mean is displayed with a +, and a black line corresponds to the median. Total LUE values =
185. Archibold’s land cover type classication: PHM = polar and high mountain tundra, CF =
coniferous forests, TW = terrestrial wetlands, Cr=crops, TFE = temperate forest ecosystems,
ME = Mediterranean ecosystems, TG = temperate grasslands, TpF = tropical forests, TpS =
tropical savannas, and AR = arid regions.
K14591_C006.indd 112 28/08/13 9:11 PM

113Missing Gaps in the Estimation of the Carbon Gains Service
the multispecies-dominated ecosystems level. However, we observed
signicant differences between annual and seasonal estimates at the
single- species-dominated ecosystems level. A comparison between the
time interval of the estimation and the organizational levels (Figure6.5)
revealed signicant differences between LUE obtained in annual and sea-
sonal observations. Annual and seasonal estimates were not signicantly
different at the multispecies-dominated and single-species-dominated
ecosystems levels, but we did detect signicant differences with the results
obtained at the individual level.
LUE (g C/MJ APAR) LUE (g C/MJ APAR) LUE (g C/MJ APAR)
6C
CA
cd
BCDH J
efg
g
gf
e
e
g
ef
ef
PHM
(1)
CF
(41)
TW
(12)
Cr
(32)
TFE
(58)
ME
(10)
TG
(15)
TpF
(2)
TpS
(9)
AR
(5)
c
dc
D
b
ab ab
a
a
FG J
4
2
0
6
4
2
0
6
4
2
0
IndividualsSingle-species-
dominated
ecosystems
Multispecies-
dominated
ecosystems
FIGURE 6.4
Box plot of light use efciency (LUE) values comparing values between organizational lev-
els and land cover types. The graphic explains the minimum, maximum, rst quartile (25%),
median, mean, and third quartile (75%) of LUE values. The mean is displayed with a +, and a
black line corresponds to the median. Letters indicate signicantly different groups (Kruskal–
Wallis test, p < 0.05). Lowercase letters indicate signicantly different groups within each
organizational level and between land cover types. Uppercase letters indicate signicantly
different groups between organizational levels and per each land cover type. Total LUE values
= 185. Archibold’s land cover type classication: PHM = polar and high mountain tundra, CF
= coniferous forests, TW = terrestrial wetlands, Cr=crops, TFE = temperate forest ecosystems,
ME = Mediterranean ecosystems, TG = temperate grasslands, TpF = tropical forests, TpS =
tropical savannas; and AR = arid regions.
K14591_C006.indd 113 28/08/13 9:11 PM

114 Earth Observation of Ecosystem Services
LUE (g C/MJ APAR)LUE (g C/MJ APAR)LUE (g C/MJ APAR)
6
4
2
0
6
4
2
0
6
4
2
0
Annual
(85)
Seasonal
(23)
Daily
(27)
IndividualsSingle-species-
dominated ecosystems
Multispecies-
dominated ecosystems
BD
a
A
b
ddd
c
C
AD
a
FIGURE 6.5
Box plot of temporal variation of average light use efciency (LUE) values in each organi-
zational level and the estimate periods. The graphic explains the minimum, maximum, rst
quartile (25%), median, mean, and third quartile (75%) of LUE values. The mean is displayed
with a +, and a black line corresponds to the median. Different letters indicate signicant
differences (Kruskal–Wallis test, p < 0.05). Lower case letters indicate signicantly different
groups between time interval of estimation and within each organizational level. Capital let-
ters indicate signicantly different groups between organizational levels and the time interval
of estimation. Total LUE values = 185. Archibold’s land cover type classication: PHM = polar
and high mountain tundra, CF = coniferous forests, TW = terrestrial wetlands, Cr = crops,
TFE= temperate forest ecosystems, ME = Mediterranean ecosystems, TG = temperate grass-
lands, TpF = tropical forests, TpS = tropical savannas, and AR = arid regions.
K14591_C006.indd 114 28/08/13 9:11 PM

115Missing Gaps in the Estimation of the Carbon Gains Service
6.4 Conclusions
Monteith’s model, based on LUE and remotely sensed estimates of fPAR,
constitutes the most widely used approach for mapping the terrestrial carbon
cycle (Jenkins etal. 2007; Pereira etal. 2007), though it is not free of uncertain-
ties. The inherent spatiotemporal variability found among different meth-
odologies may explain the variability of LUE estimates found in this study.
The time interval of the estimates and the level of organization are two clear
sources of such variation. In such a way, annual estimates of NPP at regional
scale should not be used for LUE estimation at individual level and derive
for short-term (e.g., days) measurements. The variability of LUE estimates
related to environmental and physiological factors (such as leaf form, ribu-
lose diphosphate carboxylase content, temperature, and/or moisture) (Ito and
Oikawa 2007; Tong etal. 2008) can result in large errors if these values are
extrapolated to global or regional scales (Nouvellon etal 2000; Piñeiro etal.
2006). Our results indicated that high temporal variation in LUE estimates
at the individual and multispecies-dominated ecosystems levels across land
cover types (see also Grace etal. 2007; Cook etal. 2008; Hilkeretal. 2008) do
not account for regional and global NPP estimates, which typically apply a
constant LUE value (Drolet etal. 2008; Maselli etal. 2009).
Acknowledgments
The authors gratefully acknowledge the staff of the Andalusian Government’s
Department of the Environment for providing the facilities required to obtain
the necessary assistance. They also thank Gervasio Piñeiro, Dolores Arocena,
Carlos Di Bella, Piedad Cristiano, and two anonymous reviewers for their usefu l
comments. Financial support was provided by the ERDF (FEDER), Andalusian
Regional Government (Junta de Andalucía GLOCHARID & SEGALERT
Projects, P09–RNM-5048), and the Ministry of Science and Innovation (Project
CGL2010-22314). Support for A. J. C. was also provided by the Centro Andaluz
para la Evaluación y Seguimiento del Cambio Global (CAESCG) and the
Oklahoma Biological Survey (OBS) at the University of Oklahoma.
Appendix 6.1 (Articles Reviewed from 1972 to 2007)
Aalto, T., P. Ciais, A. Chevillard, and C. Moulin. 2004. Optimal determination of the
parameters controlling biospheric CO2 uxes over Europe using eddy covari-
ance uxes and satellite NDVI measurements. Tellus B 56:93–104.
K14591_C006.indd 115 28/08/13 9:11 PM

116 Earth Observation of Ecosystem Services
Ahl, D. E., S. T. Gower, D. S. Mackay, S. N. Burrows, J. M. Norman, and G. R. Diak.
2004. Heterogeneity of light use efciency in a northern Wisconsin forest:
Implications for modeling net primary production with remote sensing. Remote
Sensing of Environment 93:168–178.
Ahl, D. E., S. T. Gower, D. S. Mackay, S. N. Burrows, J. M. Norman, and G. R. Diak.
2005. The effects of aggregated land cover data on estimating NPP in northern
Wisconsin. Remote Sensing of Environment 97:1–14.
Anderson, M. C., W. P. Kustas, and J. M. Norman. 2007. Upscaling ux observations
from local to continental scales using thermal remote sensing. Agronomy Journal
99:240–254.
Asner, G. P., K. M. Carlson, and R. E. Martin. 2005. Substrate age and precipitation
effects on Hawaiian forest canopies from spaceborne imaging spectroscopy.
Remote Sensing of Environment 98:457–467.
Asrar, G., M. Fuchs, E. T. Kanemasu, and J. L. Hateld. 1984. Estimating absorbed
photosynthetic radiation and leaf-area index from spectral reectance in wheat.
Agronomy Journal 76:300–306.
Baldocchi, D. D. 2003. Assessing the eddy covariance technique for evaluating carbon
dioxide exchange rates of ecosystems: Past, present and future. Global Change
Biology 9:479–492.
Black, T. A., D. Gaumont-Guay, R. S. Jassal, etal. 2005. Measurement of carbon dioxide
exchange between the boreal forest and the atmosphere. In Carbon balance of
forest biomes, eds. H. Grifths and P. G. Jarvis, 151–185. Oxfordshire, UK: BIOS
Scientic Publishers.
Boschetti, L., P. A. Brivio, H. D. Eva, J. Gallego, A. Baraldi, and J. M. Gregoire. 2006.
A sampling method for the retrospective validation of global burned area prod-
ucts. IEEE—Transactions on Geoscience and Remote Sensing 44:1765−1773.
Bradford, J. B., J. A. Hickec, and W. K. Lauenroth. The relative importance of light-use
efciency modications from environmental conditions and cultivation for esti-
mation of large-scale net primary productivity. Remote Sensing of Environment
96:246–255.
Cannell, M. G. R., R. Milne, L. J. Sheppard, and M. H. Unsworth. 1987. Radiation
interception and productivity of willow. Journal of Applied Ecology 24:261–278.
Christensen, S., and J. Goudriaan. 1993. Deriving light interception and biomass from
spectral reectance ratio. Remote Sensing of the Environment 43:87–95.
D’Antuono, L. F., and F. Rossini. 2006. Yield potential and ecophysiological traits of
the Altamurano linseed (Linum usitatissimum L.), a landrace of southern Italy.
Genetic Resources and Crop Evolution 53:65–75.
Drolet, G. G., K. F. Huemmrich, F. G. Hall, etal. 2005. A MODIS-derived photochemical
reectance index to detect inter-annual variations in the photosynthetic light-use
efciency of a boreal deciduous forest. Remote Sensing of Environment 98:212–224.
Dungan, R. J., and D. Whitehead. 2006. Modelling environmental limits to light use
efciency for a canopy of two broad-leaved tree species with contrasting leaf
habit. New Zealand Journal of Ecology 30:251–259.
Fang, S., X. Xizeng, X. Xiang, and L. Zhengcai. 2005. Poplar in wetland agroforestry:
A case study of ecological benets, site productivity, and economics. Wetlands
Ecology and Management 13:93–104.
Fensholt, R., I. Sandholt, and M. S. Rasmussen. 2004. Evaluation of MODIS LAI,
fAPAR and the relation between fAPAR and NDVI in a semi-arid environment
using in situ measurements. Remote Sensing of Environment 91:490−507.
K14591_C006.indd 116 28/08/13 9:11 PM

117Missing Gaps in the Estimation of the Carbon Gains Service
Fensholt, R., I. Sandholt, M. S. Rasmussen, S. Stisen, and A. Diouf. 2006. Evaluation
of satellite based primary production modeling in the semi-arid Sahel. Remote
Sensing of Environment 105:173–188.
Fernández, M. E., J. E. Gyenge, and T. M. Schlichter. 2006. Growth of the grass Festuca
pallescens in silvopastoral systems in a semi-arid environment, Part1: Positive
balance between competition and facilitation. Agroforestry Systems 66:259–269.
Field, C. B., J. T. Randerson, and C. M. Malmstrom. 1995. Global net primary production:
Combining ecology and remote sensing. Remote Sensing of Environment 51:74–88.
Fleisher, D. H., D. J. Timlin, and V. R. Reddy. 2006. Temperature inuence on potato
leaf and branch distribution and on canopy photosynthetic rate. Agronomy
Journal 98:1442–1452.
Fuentes, D., J. A. Gamon, Y. Cheng, etal. 2006. Mapping carbon and water ux in
a chaparral ecosystem using vegetation indices derived from AVIRIS. Remote
Sensing of Environment 103:312−323.
Gamon, J. A., K. Kitajima, S. S. Mulkey, L. Serrano, and S. J. Wright. 2005. Diverse
optical and photosynthetic properties in a neotropical dry forest during the
dry season: Implications for remote estimation of photosynthesis. Biotropica
37:547–560.
Gebremichael, M., and A. P. Barros. 2006. Evaluation of MODIS gross primary
productivity (GPP) in tropical monsoon regions. Remote Sensing of Environment
100:150–166.
Goetz, S. J., and S. D. Prince. 1999. Modelling terrestrial carbon exchange and stor-
age: Evidence and implications of functional convergence in light-use efciency.
Advances in Ecological Research 28:57–92.
Goetz, S. J., S. D. Prince, S. N. Goward, M. M. Thawle, J. Small, and A. Johnston. 1999.
Mapping net primary production and related biophysical variables with remote
sensing: Application to the Boreas region. Journal of Geophysical Research—
Atmospheres 104:27719–27734.
Goulden, M. L., J. W. Munge, S. M. Fan, B. C. Daube, and S. C. Wofsy. 1996. Exchange
of carbon dioxide by a deciduous forest: Response to interanual climate vari-
ability. Science 271:1576–1578.
Goward, S. N., and D. G. Dye. 1987. Evaluating North American net primary produc-
tivity with satellite observations. Advances in Space Research 7:165–174.
Goward, S. N., and K. F. Huemmrich. 1992. Vegetation canopy PAR absorbance and
the normalized difference vegetation index: An assessment using the SAIL
model. Remote Sensing of the Environment 39:119–140.
Guo, J., and C. M. Trotter. 2004. Estimating photosynthetic light-use efciency using
the photochemical reectance index: Variations among species. Functional Plant
Biology 31:255–265.
Hill, M., A. A. Held, R. Leuning, etal. 2006. MODIS spectral signals at a ux tower site:
Relationships with high-resolution data, and CO2 ux and light use efciency
measurements. Remote Sensing of Environment 103:351–368.
Inoue, Y., and J. Peñuelas. 2006. Relationships between light use efciency and
photochemical reectance index in soybean leaves as affected by soil water con-
tent. International Journal of Remote Sensing 27:5109–5114.
Ito, A., and T. Oikawa. 2004. Global mapping of terrestrial primary productivity and
light-use efciency with a process-based model. In Global environmental change
in the ocean and on land, eds. M. Shiyomi, H. Kawahata, H. Koizumi, A. Tsuda,
and Y. Awaya, 343–358. Tokyo: Terrapub.
K14591_C006.indd 117 28/08/13 9:11 PM

118 Earth Observation of Ecosystem Services
Kato, T., Y. Tang, S. Gu, etal. 2006. Temperature and biomass inuences on interannual
changes in CO2 exchange in an alpine meadow on the Qinghai-Tibetan Plateau.
Global Change Biology 12:1285–1298.
Kinirya, J. R., C. E. Simpsonb, A. M. Schubertc, and J. D. Reed. Peanut leaf area index,
light interception, radiation use efciency, and harvest index at three sites in
Texas. Field Crops Research 91:297–306.
Krishnan, P., T. A. Black, N. J. Grant, etal. 2006. Carbon dioxide and water vapour
exchange in a boreal aspen forest during and following severe drought.
Agricultural and Forest Meteorology 139:208–223.
Lagergren, F. 2005. Net primary production and light use efciency in a mixed conif-
erous forest in Sweden. Plant, Cell & Environment 28:412–423.
Leuning, R., H. A. Cleugh, S. J. Zegelin, and D. Hughes. 2005. Carbon and water
uxes over a temperate Eucalyptus forest and a tropical wet/dry savanna in
Australia: Measurements and comparison with MODIS remote sensing esti-
mates. Agricultural and Forest Meteorology 129:151–173.
Li, S. G., J. Asanuma, W. Eugster, etal. 2005. Net ecosystem carbon dioxide exchange
over grazed steppe in central Mongolia. Global Change Biology 11:1941–1955.
Li, S. G., W. Eugster, J. Asanuma, et al. 2006. Energy partitioning and its biophysi-
cal controls above a grazing steppe in central Mongolia. Agricultural and Forest
Meteorology 137:89–106.
Monteith, J. L. 1972. Solar-radiation and productivity in tropical ecosystems. Journal
of Applied Ecology 9:747–766.
Monteith, J. L. 1977. Climate and the efciency of crop production in Britain.
Philosophical Transactions of the Royal Society of London B 281:277–297.
Monteith, J. L. 1994. Validity of the correlation between intercepted radiation and
biomass. Agricultural and Forest Meteorology 68:213–220.
Myneni, R. B., S. Hoffman, Y. Knyazikhin, etal. 2002. Global products of vegetation
leaf area and fraction absorbed PAR from year one of MODIS data. Remote
Sensing of Environment 83:214–231.
Myneni, R. B., R. R. Nemani, and S. W. Running. 1997. Estimation of global leaf área
index and absorbed PAR using radiative transfer models. IEEE Transactions on
Geoscience and Remote Sensing 35:1380–1393.
Myneni, R. B., and D. L. Williams. 1994. On the relationship between fAPAR and
NDVI. Remote Sensing of Environment 49:200–11.
Nakaji, T., H. Oguma, and Y. Fujinuma. 2006. Seasonal changes in the relation-
ship between photochemical reectance index and photosynthetic light use
efciency of Japanese larch needles. International Journal of Remote Sensing
27:493–509.
Nakaji, T., T. Takeda, Y. Fujinuma, and H. Oguma. 2005. Effect of autumn senescence
on the relationship between the PRI and LUE of young Japanese larch trees.
Phyton 45:535–542.
Nemani, R. R., and S. W. Running. 1989. Estimation of regional surface-resistance to
evapotranspiration from NDVI and thermal-IR AVHRR data. Journal of Applied
Meteorology 28:276–284.
Nichol, C. J., K. F. Huemmrich, T. A. Black, etal. 2002. Sensing of photosynthetic-light-
use efciency of boreal forest. Agricultural and Forest Meteorology 101:131–142.
Nichol, C. J., J. Lloyd, O. Shibistova, etal. 2002. Remote sensing of photosynthetic-
light-use efciency of a Siberian boreal forest. Tellus B 54:677–687.
K14591_C006.indd 118 28/08/13 9:11 PM

119Missing Gaps in the Estimation of the Carbon Gains Service
Niinemets, U., A. Cescatti, and R. Christian. 2004. Constraints on light interception
efciency due to shoot architecture in broad-leaved Nothofagus species. Tree
Physiology 24:617–630.
Niinemets, U., and L. Sack. 2006. Structural determinants of leaf light-harvesting
capacity and photosynthetic potentials. Progress in Botany 67:385–419.
Norby, R. J., J. Ledford, C. D. Reilly, etal. 2004. Fine-root production dominates
response of a deciduous forest to atmospheric CO2 enrichment. Proceedings of
the National Academy of Sciences of the United States of America 101:9689–9693.
Nouvellon, Y., D. Lo Seen, S. Rambal, et al. 2000. Time course of radiation use
efciency in a shortgrass ecosystem: Consequences for remotely sensed estima-
tion of primary production. Remote Sensing of Environment 71:43–55.
Paruelo, J. M., H. E. Epstein, W. K. Lauenroth, and I. C. Burke. 1997. ANPP estimates
from NDVI for the central grassland region of the US. Ecology 78:953–958.
Paruelo, J. M., M. Oesterheld, C. M. Di Bella, etal. 2000. Estimation of primary pro-
duction of subhumid rangelands from remote sensing data. Applied Vegetation
Science 3:189–195.
Patel, N. R. 2006. Investigating relations between satellite derived land surface param-
eters and meteorological variables Geocarto International 21:47–53.
Pereira, J. S., J. A. Mateus, L. M. Aires, etal. 2007. Net ecosystem carbon exchange in
three contrasting Mediterranean ecosystems: The effect of drought. Biogeosciences
4:791–802.
Piñéiro, G., M. Oesterheld, and J. M. Paruelo. 2006. Seasonal variation in aboveg-
round production and radiation use efciency of temperate rangelands esti-
mated through remote sensing. Ecosystems 9:357–373.
Pitman, J. I. 2000. Absorption of photosynthetically active radiation, radiation use
efciency and spectral reectance of bracken [Pteridium aquilinum (L.) Kuhnl]
canopies. Annals of Botany 85:101–111.
Potter, C., S. Klooster, A. Huete, and V. Genovese. 2007. Terrestrial carbon sinks for
the United States predicted from MODIS satellite data and ecosystem modeling.
Earth Interactions 11:1–21.
Potter, C. S., J. T. Randerson, C. B. Field, etal. 1993. Terrestrial ecosystem production—
a process model-based on global satellite and surface data. Global Biogeochemical
Cycles 7:811–841.
Prince, S. D., S. J., Goetz, and S. N. Goward. 1995. Monitoring primary production
from Earth observing satellites. Water, Air and Soil Pollution 82:509–522.
Ruimy, A., B. Saugier, and G. Dedieu. 1994. Methodology for the estimation of terres-
trial net primary production from remotely sensed data. Journal of Geophysical
Research 99:5263–5283.
Running, S. W., D. D. Baldocchi, D. P. Turner, S. T. Gower, P. S. Bakwin, and
K.A.Hibbard. 1999. A global terrestrial monitoring network integrating tower
uxes, ask sampling, ecosystem modeling and EOS satellite data. Remote
Sensing of Environment 70:108–127.
Running, S. W., and R. R. Nemani. 1988. Relating seasonal patterns of the AVHRR
vegetation index to simulated photosynthesis and transpiration of forests in
different climates. Remote Sensing of Environment 24:347–367.
Running, S. W., R. R. Nemani, F. A. Heinsch, M. S. Zhao, M. Reeves, and H. Hashimoto.
2004. A continuous satellite-derived measure of global terrestrial primary
production. BioScience 54:547–560.
K14591_C006.indd 119 28/08/13 9:11 PM

120 Earth Observation of Ecosystem Services
Running, S. W., L. Queen, and M. Thornton. 2000. The Earth observing system and
forest management. Journal of Forestry 98:29–31.
Russell, G., P. G. Jarvis, and J. L. Monteith. 1989. Absorption of radiation by cano-
pies and stand growth. In Plant canopies: Their growth, form and function, eds.
G. Russell, B. Marshall, and P. G. Jarvis, 21–39. Cambridge, UK: Cambridge
University Press.
Sakai, T. 2005. Microsite variation in light availability and photosynthesis in a cool-
temperate deciduous broadleaf forest in central Japan. Ecological Research
20:537–545.
Salazar, M. R., B. Chaves, J. W. Jones, and A. Cooman. 2006. A simple potential
production model of Cape gooseberry (Physalis peruviana L.). Acta Hortic
718:105–112.
Schwalm, C. R., T. A. Black, B. D. Amiro, etal. 2006. Photosynthetic light use ef-
ciency of three biomes across an east–west continental-scale transect in Canada.
Agricultural and Forest Meteorology 140:269–286.
Seaquist, J., L. Olsson, and J. Ardö. 2003. A remote sensing-based primary production
model for grassland biomes. Ecological Modelling 169:131–155.
Seaquist, J. W., L. Olsson, J. Ardo, and L. Eklundh. 2006. Broad-scale increase in
NPP quantied for the African Sahel, 1982–1999. International Journal of Remote
Sensing 27:5115–5122.
Sims, D., and J. Gamon. 2002. Relationships between leaf pigment content and spec-
tral reectance across a wide range of species, leaf structures and developmental
stages. Remote Sensing of Environment 81:337−354.
Sims, D., H. Luo, S. Hastings, W. Oechel, A. Rahman, and J. Gamon. 2006. Parallel
adjustments in vegetation greenness and ecosystem CO2 exchange in response
to drought in a southern California chaparral ecosystem. Remote Sensing of
Environment 103:289−303.
Sims, D. A., A. F. Rahman, V. D. Cordova, et al. 2005. Midday values of gross
CO2ux and light use efciency during satellite overpasses can be used to
directly estimate eight-day mean ux. Agricultural and Forest Meteorology
131:1–12.
Still, C. J., J. Randerson, and I. Fung. 2004. Large-scale plant light-use efciency
inferred from the seasonal cycle of atmospheric CO2. Global Change Biology
10:1240–1252.
Storkey, J. 2006. A functional group approach to the management of UK arable weeds
to support biological diversity. Weed Research 46:513–522.
Tracol, Y., E. Mougin, P. Hiernaux, and L. Jarlan. 2006. Testing a Sahelian grassland
functioning model against herbage mass measurements. Ecological Modelling
193:437–446.
Tsubo, M., S. Walker, and H. O. Ogindo. 2005. A simulation model of cereal–legume
intercropping systems for semi-arid regions I. Model development. Field Crops
Research 93:10–22.
Tucker, C. J., I. Y. Fung, C. D. Keeling, and R. H. Gammon. 1986. Relationship between
atmospheric CO2 variations and a satellite-derived vegetation index. Nature
319:195–199.
Tucker, C. J., W. H. Jones, W. A. Kley, and G. J. Sundstrom. A 3-band hand-held radi-
ometer for eld use. Science 211:281–283.
Tucker, C. J., C. O. Justice, and S. D. Prince. 1986. Monitoring the grasslands of the
Sahel 1984–1985. International Journal of Remote Sensing 7:1571–1581.
K14591_C006.indd 120 28/08/13 9:11 PM

121Missing Gaps in the Estimation of the Carbon Gains Service
Tucker, C. J., and P. J. Sellers. 1986. Satellite remote-sensing of primary production.
International Journal of Remote Sensing 7:1395–1416.
Turner, D. P., W. D. Ritts, W. B. Cohen, etal. 2003. Scaling gross primary production
(GPP) over boreal and deciduous forest landscapes in support of MODIS GPP
product validation. Remote Sensing of Environment 88:256–270.
Turner, D. P., W. D. Ritts, W. B. Cohen, T. K. Maeirsperger, S. T. Gower, and
A.Kirschbaum. 2005. Site-level evaluation of satellite-based global terrestrial
gross primary production and net primary production monitoring. Global
Change Biology 11:666–684.
Turner, D. P., W. D. Ritts, W. B. Cohen, etal. 2006. Evaluation of MODIS NPP
and GPP products across multiple biomes. Remote Sensing of Environment
102:282–292.
Turner, D. P., S. Urbanski, D. Bremer, etal. 2003. Cross-biome comparison of daily light
use efciency for gross primary production. Global Change Biology 9:383–395.
Ueyama, M., Y. Harazono, E. Ohtaki, and A. Miyata. 2006. Controlling factors on the
inter-annual CO2 budget at a sub-arctic black spruce forest in interior Alaska.
Tellus B 58:491–450.
Veroustraete, F., H. Sabbe, and H. Eerens. 2002. Estimation of carbon mass uxes over
Europe using the C-Fix model and Euroux data. Remote Sensing of Environment
83:376–399.
Walcroft, A. S., K. J. Brown, W. S. F. Schuster, etal. 2005. Radiative transfer and carbon
assimilation in relation to canopy architecture, foliage area distribution and
clumping in a mature temperate rainforest canopy in New Zealand. Agricultural
and Forest Meteorology 135:326–339.
Xiao, X. M. 2006. Light absorption by leaf chlorophyll and maximum light use ef-
ciency. IEEE Transactions on Geoscience and Remote Sensing 44:1933–1935.
Xiao, X. M., Q. Y. Zhang, B. Braswell, etal. 2004. Modeling gross primary production
of temperate deciduous broadleaf forest using satellite images and climate data.
Remote Sensing of Environment 91:256–270.
Zhang, Q. Y., X. M. Xiao, B. Braswell, E. Linder, F. Baret, and B. Moore. 2005. Estimating
light absorption by chlorophyll, leaf and canopy in a deciduous broadleaf forest
using MODIS data and a radiative transfer model. Remote Sensing of Environment
99:357–371.
Zhao, M., F. A. Heinsch, R. R. Nemani, and S. W. Running. 2005. Improvements of
the MODIS terrestrial gross and net primary production global data set. Remote
Sensing of Environment 95:164–175.
References
Andrade, F. H., S. A. Uhart, and A. Cirilo. 1993. Temperature affects radiation use
efciency in maize. Field Crops Research 32:17–25.
Archibold, O. W. 1995. Ecology of world vegetation. London: Chapman & Hall.
Asrar, G., M. Fuchs, E. T. Kanemasu, and J. L. Hateld. 1984. Estimating absorbed
photosynthetic radiation and leaf area index from spectral reectance in wheat.
Agronomy Journal 76:300–306.
K14591_C006.indd 121 28/08/13 9:11 PM

122 Earth Observation of Ecosystem Services
Choudhury, B. J. 1987. Relationships between vegetation indices, radiation absorption,
and net photosynthesis evaluated by a sensitivity analysis. Remote Sensing of
Environment 22:209–233.
Cook, B. D., P. V. Bolstad, J. G. Martin, etal. 2008. Using light-use and production ef-
ciency models to predict photosynthesis and net carbon exchange during forest
canopy disturbance. Ecosystems 11:26–44.
Drolet, G. G., E. M. Middleton, K. F. Huemmrich, F. G. Hall, and H. A. Margolis. 2008.
Regional mapping of gross light-use efciency using MODIS spectral indices.
Remote Sensing of Environment 112:3064–3078.
Fensholt, R., I. Sandholt, M. S. Rasmussen, S. Stisen, and A. Diouf. 2006. Evaluation
of satellite based primary production modelling in the semi-arid Sahel. Remote
Sensing of Environment 105:173–188.
Field, C. B., R. B. Jackson, and H. A. Mooney. 1995. Stomatal responses to increased
CO2—Implications from the plant to the global scale. Plant Cell and Environment
18:1214–1225.
Filella, I., J. Peñuelas, L. Llorens, and M. Estiarte. 2004. Reectance assessment of
seasonal and annual changes in biomass and CO2 uptake of a Mediterranean
shrubland submitted to experimental warming and drought. Remote Sensing of
Environment 90:308–318.
Fisher, B., R. K. Turner, and P. Morling. 2009. Dening and classifying ecosystem ser-
vices for decision making. Ecological Economics 3:643–653.
Gamon, J. A., C. B. Field, M. L. Goulden, etal.1995. Relationships between NDVI, can-
opy structure, and photosynthesis in 3 Californian vegetation types. Ecological
Applications 5:28–41.
Garbulsky, M. F., J. Peñuelas, D. Papale, etal. 2010. Patterns and controls of the vari-
ability of radiation use efciency and primary productivity across terrestrial
ecosystems. Global Ecology and Biogeography 19:253–267.
Gower, S. T., C. J. Kucharik, and J. M. Norman. 1999. Direct and indirect estimation of
leaf area index, f(APAR), and net primary production of terrestrial ecosystems.
Remote Sensing of Environment 70:29–51.
Grace, J., C. Nichol, M. Disney, L. T. Quaife, and P. Bowyer. 2007. Can we measure ter-
restrial photosynthesis from space directly, using spectral reectance and uo-
rescence? Global Change Biology 13:1484–1497.
Gu, L., D. Baldocchi, S. B. Verma, etal. 2002. Advantages of diffuse radiation for terres-
trial ecosystem productivity. Journal of Geophysical Research: Atmospheres 107:2–23.
Hilker, T., N. C. Coops, M. A. Wulder, T. A. Black, and R. G. Guy. 2008. The use of
remote sensing in light use efciency based models of gross primary produc-
tion: A review of current status and future requirements. Science of the Total
Environment 404:411–423.
Ito, A., and T. Oikawa. 2007. Absorption of photosynthetically active radiation, dry-
matter production, and light-use efciency of terrestrial vegetation: A global
model simulation. Elsevier Oceanography Series 73:335–359; 503–505.
Jenkins, J. P., A. D. Richardson, B. H. Braswell, S. V. Ollinger, D. Y. Hollinger, and
M.L. Smith. 2007. Rening light-use efciency calculations for a deciduous for-
est canopy using simultaneous tower-based carbon ux and radiometric mea-
surements. Agricultural and Forest Meteorology 143:64–79.
Kiniry, J. R., J. A. Landivar, M. Witt, T. J. Gerik, J. Cavero, and L. J. Wade. 1998.
Radiation-use efciency response to vapor pressure decit for maize and sor-
ghum. Field Crops Research 56:265–270.
K14591_C006.indd 122 28/08/13 9:11 PM

123Missing Gaps in the Estimation of the Carbon Gains Service
Los, S. O., G. J. Collatz, P. J. Sellers, etal. 2000. A global 9-yr biophysical land surface
dataset from NOAA AVHRR data. Journal of Hydrometeorology 1:183–199.
MA (Millennium Ecosystem Assessment). 2005. Ecosystems and human well-being: The
assessment series (four volumes and summary). Washington, DC: Island Press.
Maselli, F., M. Chiesi, M. Moriondo, L. Fibbi, M. Bindi, and S. W. Running. 2009.
Modelling the forest carbon budget of a Mediterranean region through the inte-
gration of ground and satellite data. Ecological Modelling 220:330–342.
McNaughton, S. J., M. Oesterheld, D. A. Frank, and K. J. Williams. 1989. Ecosystem-
level patterns of primary productivity and herbivory in terrestrial habitats.
Nature 341:142–144.
Monteith, J. L. 1972. Solar radiation and productivity in tropical ecosystems. Journal
of Applied Ecology 9:747–766.
Nouvellon, Y., D. L. Seen, S. Rambal, et al. 2000. Time course of radiation use
efciency in a shortgrass ecosystem: Consequences for remotely sensed esti-
mation of primary production. Remote Sensing of Environment 71:43–55.
Paruelo, J. M., M. F. Garbulsky, J. P. Guerschman, and E. G. Jobbágy. 2004. Two
decades of Normalized Difference Vegetation Index changes in South America:
Identifying the imprint of global change. International Journal of Remote Sensing
25:2793–2806.
Pereira, J. S., J. A. Mateus, L. Aires, etal. 2007. Net ecosystem carbon exchange in three
contrasting Mediterranean ecosystems—The effect of drought. Biogeosciences
4:791–802.
Piñeiro, G., M. Oesterheld, and J. M. Paruelo. 2006. Seasonal variation in aboveg-
round production and radiation-use efciency of temperate rangelands esti-
mated through remote sensing. Ecosystems 9:357–373.
Potter, C. S. 1993. Terrestrial ecosystem production: A process model based on global
satellite and surface data. Global Biogeochemical Cycles 7:811–841.
Ruimy, A., P. Jarvis, D. D. Baldocchi, and B. Saugier. 1995. CO2 uxes over plant cano-
pies and solar radiation: A review. Advances in Ecological Research 26:1–68.
Ruimy, A., L. Kergoat, A. Bondeau, etal. 1999. Comparing global NPP models of
terrestrial net primary productivity (NPP): Analysis of differences in light
absorption and light-use efciency. Global Change Biology 5:56–64.
Sala, O. E., R. B. Jackson, H. A. Mooney, and R. W. Howarth. 2000. Methods in ecosys-
tem science: Progress, tradeoffs, and limitations. In Methods in ecosystem science,
eds. O. E. Sala, R. B. Jackson, H. A. Mooney, and R. W. Howarth, 1–3. New York:
Springer-Verlag.
Scurlock, J. M. O., W. Cramer, R. J. Olson, W. J. Parton, and S. D. Prince. 1999.
Terrestrial NPP: Toward a consistent data set for global model evaluation.
Ecological Applications 9:913–919.
Sellers, P. J., J. A. Berry, G. J. Collatz, C. B. Field, and E. G. Hall. 1992. Canopy reectance,
photosynthesis, and transpiration. III. A reanalysis using improved leaf models
and a new canopy integration scheme. Remote Sensing of Environment 42:187–216.
Sellers, P. J., C. J. Tucker, G. J. Collatz, et al. 1994. A global 1-degrees by 1 degrees
NDVI data set for climate studies. The generation of global elds of terrestrial
biophysical parameters from the NDVI. International Journal of Remote Sensing
15:3519–3545.
Still, C. J., J. T. Randerson, and I. Y. Fung. 2004. Large-scale plant light-use efciency
inferred from the seasonal cycle of atmospheric CO2. Global Change Biology
10:1240–1252.
K14591_C006.indd 123 28/08/13 9:11 PM

124 Earth Observation of Ecosystem Services
Tong, X. J., J. Li, and L. Wang. 2008. A review on radiation use efciency of the
cropland. Chinese Journal of Ecology 27:1021–1028.
Turner, D. P., S. T. Gower, W. B. Cohen, M. Gregory, and T. K. Maiersperger. 2002.
Effects of spatial variability in light use efciency on satellite-based NPP moni-
toring. Remote Sensing of Environment 80:397–405.
Turner, D. P., W. D. Ritts, W. B. Cohen, etal. 2005. Site-level evaluation of satellite-
based global terrestrial gross primary production and net primary production
monitoring. Global Change Biology 11:666–684.
Turner, D. P., S. Urbanski, D. Bremer, et al. 2003. A cross-biome comparison of
daily light use efciency for gross primary production. Global Change Biology
9:383–395.
Zhao, Y. M., S. K. Niu, J. B. Wang, H. T. Li, and G. C. Li. 2007. Light use efciency of
vegetation: A review. Chinese Journal of Ecology 26:1471–1477.
K14591_C006.indd 124 28/08/13 9:11 PM