Chapter 16
Remote Sensing of Terrestrial Primary
Production and Carbon Cycle
Maosheng Zhao and Steven W. Running
Abstract The objective of this chapter is to review the historical development of and
the recent advances in the application of satellite remote sensing data for estimating
terrestrial gross and net primary production (GPP and NPP), while also monitoring
carbon cycle related ecosystem dynamics and changes. We achieve this objective by
separating the topic into five sections:
1. A review of the history of using satellite data to study the carbon cycle, concentrating on using the Normalized Difference Vegetation Index (NDVI) and its
derived Fraction of Photosynthetically Active Radiation (FPAR) and Leaf Area
Index (LAI) for biomass and NPP estimations
2. A description of recent advances in the application of Moderate Resolution Imaging Spectroradiometer (MODIS) data to estimates of GPP and NPP, along with
related findings using MODIS Land Surface Temperature (LST) and the Enhanced Vegetation Index (EVI)
3. A discussion of the application of long-term satellite data to the study of terrestrial ecosystems, including phenology monitoring, changes in regional carbon
storage resulting from land use change, carbon flux changes induced by climate
change, disturbance detection, and validation of ecosystem models
4. A proposed general scheme for applying satellite data to terrestrial ecosystem
studies, highlighting the role of modeling
5. A summary that emphasizes the continuity of vegetation monitoring with satellites
The use of remote sensing information for studying terrestrial primary production
and the global carbon cycle is significant both for an increased understanding of the
earth system and improved management of land and natural resources.
Maosheng Zhao and Steven W. Running
Numerical Terradynamic Simulation Group, Department of Ecosystem and Conservation Science,
University of Montana, Missoula, USA
[email protected]
S. Liang (ed.), Advances in Land Remote Sensing, 423–444.
c Springer Science + Business Media B.V., 2008
423
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M. Zhao, S.W. Running
16.1 Introduction
Terrestrial Net Primary Production (NPP), the difference between Gross Primary
Production (GPP) and plant autotrophic respiration (Ra), is the net carbon fixed by
vegetation through photosynthesis. Quantification of NPP has socioeconomic significance, since NPP can directly measure the quantity of goods (e.g., food, fuel and
fiber) provided to human beings by ecosystems (Imhoff et al. 2004). NPP provides
the carbon required for maintenance of the structure and functions of an ecosystem.
The current climate change caused primarily by increasing anthropogenic greenhouse gas emissions, especially CO2 , is the largest global environmental issue facing
the world (IPCC, 2001), and there is now ample evidence of the ecological impacts
of recent climate change (Walther et al., 2002). Climate and terrestrial ecosystems
interact with and influence each other. On one hand, climate change and increasing
CO2 can cause changes in NPP and carbon storage in ecosystems (Nemani et al.,
2003a; Prentice et al., 2001), impacting the well-being of humans (Milesi et al.,
2005). On the other hand, terrestrial ecosystems can affect climate through carbon,
water, and energy exchange. For example, terrestrial ecosystems and oceans equally
absorbed nearly half of CO2 emission by human activities in the 1990s (Prentice
et al., 2001). Understanding the response of terrestrial NPP and the carbon cycle to
climate change is therefore critical for predicting future environmental change and
mitigating the impacts.
Satellite remote sensing data provide us with invaluable continuous temporal and
spatial information, which help us understand the processes, dynamics, and disturbances (such as land use change, wildfires, and insect outbreaks) in the biosphere,
and the impacts of environmental changes on terrestrial ecosystems. Since the application of AVHRR data to the study of vegetation in the early 1980s, great progress
has been made in the study of terrestrial carbon storage and fluxes, especially from
the NASA Earth Observing System (EOS) program. The NASA EOS program has
been planning and executing satellite-based earth monitoring for 15 years, and is the
heart of global change science for the USA. The central sensor on board the Terra
Satellite Platform is the Moderate Resolution Imaging Spectroradiometer (MODIS).
Terra was successfully launched on December 18, 1999, and the second MODISbased satellite, Aqua, was launched on May 4, 2002. For the first time in history, we
are able to obtain near-real time global vegetation growth status, including primary
production, at an 8-day time interval with 1 km spatial resolution (Justice et al.,
2002; Running et al., 2004).
This chapter concentrates on applying satellite data to terrestrial primary production and carbon cycle studies. We (1) review the history of using satellite data to
study the carbon cycle; (2) describe recent advances in the applications of MODIS
data; (3) discuss monitoring terrestrial ecosystems with the long-term satellite data
records; (4) propose a general scheme of applying the satellite data to terrestrial
ecosystem studies, highlighting the role of modeling; and (5) summarize the chapter by emphasizing the continuity of vegetation monitoring with satellites. We focus on the optical sensors on-board polar-orbiting satellites, especially on those
with medium to coarse resolution instruments. Radar and Lidar (light detection and
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425
ranging), which can also provide valuable vegetation information from microwaves,
are not covered in this chapter. In addition, land cover classification, an important
aspect of carbon relevant application, is detailed elsewhere (Chapter 13).
16.2 History of the Development of Using NDVI
16.2.1 Related NDVI to Green Biomass for Vegetation Monitoring
The basis for remote sensing of vegetation is the sharp contrast in reflectivity of
visible (0.4–0.7 µm) and near-infrared (0.7–1.3 µm, NIR) spectra caused by the optical properties of chlorophyll and the internal structure of green leaf cells. The
most widely used mathematic formula combining red and NIR reflectance (ρRED
and ρNIR ) is the Normalized Difference Vegetation Index (NDVI),
NDV I =
ρNIR − ρRED
ρNIR + ρRED
(16.1)
Tucker (1979) evaluated several proposed vegetation indices and found that NDVI
is strongly related to biomass, and in 1980, his lab experiments also revealed that
NDVI is most strongly related to the percentage of green/brown herbage, suggesting the potential of using satellite data to estimate dry green biomass (Tucker, 1980).
The first reported study for using NDVI from satellites (AVHRR / NOAA) to quantify grass production at the regional scale was published in 1983 by Tucker et al.
(1983). They first constructed the relationship between AVHRR NDVI and clipped
grass biomass on the ground for a limited number of sites, extrapolating the relationship to the study region using NDVI to estimate spatial patterns of biomass.
Today, similar studies still employ this method, sometimes using relatively higher
resolution satellite data (e.g., 30 m TM/ETM+, or 15 m ASTER) as an intermediate
to scale up from field observations to coarse resolution satellite data to enhance the
accuracy of estimations (e.g., Reeves et al., 2006).
The first use of multi-temporal AVHRR NDVI to monitor the dynamics of vegetation at the continental and global scales was in 1985. Tucker et al. (1985) found the
differences in temporal dynamic of vegetation reflected by NDVI are associated with
variations in climate and dependent on biome types, and that the integrated NDVI
over a given time interval is related to NPP. Justice et al. (1985) expanded this study
to the global scale, suggesting that AVHRR NDVI is effective for monitoring phenology of global vegetation. Townshend and Justice (1986) further analyzed NDVI
from different years in Africa and found inter-annual variation in NDVI can reveal
the response of vegetation to climate anomalies.
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16.2.2 Related NDVI to FPAR, LAI and NPP for Carbon
Cycle Study
Goward et al. (1985) first demonstrated the linear relationship between the growing
season integrated NDVI and ground-based observations of NPP for different biomes
over North America. Tucker et al. (1986) found a strong relationship between seasonal variations in atmospheric CO2 and NDVI. Both studies suggested that NDVI
can be used to estimate terrestrial photosynthesis, and therefore NPP. In the following year, Fung et al. (1987) was the first to relate NDVI to annual NPP at the
global scale to study atmosphere-biosphere exchange of CO2 . Fung et al.’s (1987)
study simply distributed annual NPP into monthly level based on the monotonic
function of monthly NDVI, without considering solar radiation or environmental
stresses. However, Running and Nemani (1988) examined the seasonal relationship
between photosynthesis estimated by a process-based model, FOREST-BGC, and
NDVI/AVHRR for seven sites in North America representing a wide range of annual climates, and found that NDVI can not reflect the drawdown of photosynthesis induced by summer drought for water stressed sites, implying that NDVI alone
could not fully represent seasonal photosynthesis.
Along with the above studies to directly relate NDVI to NPP and the carbon cycle, additional studies were conducted to develop the physiological linkage between
NDVI and NPP. Professor Monteith was the pioneering scientist who proposed the
concept of photosynthesis efficiency logic. Monteith (1972, 1977) found that crop
production under non-stressed conditions is linearly related to the amount of photosynthetically active radiation solar radiation (PAR) that is absorbed by green leaf
(APAR). Kumar and Monteith (1982) decomposed the linear model into independent parameters such as incoming solar radiation, radiation absorption efficiency,
and conversion efficiency of APAR, while also demonstrating how the fraction of
APAR (FPAR) is related to the ratio of red reflectance to NIR. Asrar et al. (1984)
found that NDVI can be simultaneously used to estimate both FPAR and leaf area
index (LAI), FPAR is a linear function of NDVI, and LAI is a curvilinear function of NDVI, while Sellers (1985, 1987) also found a linear relationship between
NDVI and FPAR. Running et al. (1989) first demonstrated a prototype for incorporation of the LAI derived from AVHRR NDVI into a process-based ecosystem model
to simulate regional forest evapotranspiration and photosynthesis. While numerous
empirical studies have found a strong relationship between NDVI and ground LAI
and FPAR, the first theoretical interpretation of this was provided by Myneni et al.
(1995), stating that vegetation indices are a measure of chlorophyll abundance and
energy absorption. Further, Myneni et al. (1997a) proposed using a canopy radiation
transfer model to derive FPAR and LAI.
Models calculating regional NPP based on Monteith’s photosynthesis efficiency
logic combined with FPAR estimated from remotely sensed NDVI were proposed
after 1990. Prince (1991) first proposed a model of regional primary production for
use with coarse resolution satellite data, accounting for the physiological costs of
maintenance and growth respirations, and the environmental constraints tending to
16 Remote Sensing of Terrestrial Primary Production and Carbon Cycle
427
reduce maximum light use efficiency (ε ). Prince and Goward (1995) later named
the model the Global Production Efficiency model (GLO-PEM), and simulate
global GPP and NPP at 8 km resolution. The first global NPP images estimated
using satellite NDVI were generated with the Carnegie–Ames–Standford approach
(CASA) model by Potter et al. (1993), which had a spatial resolution of one degree. Running and Hunt (1993) proposed a NDVI-based NPP model, also exploring the range and variability of ε using a mechanistic model. Ruimy et al. (1994,
1996) proposed similar models of NPP from AVHRR NDVI, but their models had
no constraints on potential maximum ε resulting from environmental stresses. In
addition, a forest stand growth model, 3-PG (Use of Physiological Principles in Prediction Growth) can use satellite FPAR as an input, and was developed by Landsberg
and Waring (1997). Though there are some differences among the different models,
Photosynthetic Efficiency Models (PEMs) can be expressed generally as,
P = ∑ PAR∗ FPAR∗ εm∗ f (T )∗ F(W )
(16.2)
t
where P is GPP or NPP over a given time interval t, FPAR is derived using vegetation indices, εm is the maximum ε , and f (T ) and f (W ) are the constraints resulting
from temperature and water stress. In general, water stress results from soil moisture
and air vapor pressure deficit (VPD).
16.3 Advances for the MODIS Sensor
16.3.1 MODIS GPP and NPP Products
MODIS may be so far the most complex instrument built and flown on a spacecraft
for civilian research purposes (Guenther et al., 2002). The MODIS sensor provides
higher quality data for monitoring terrestrial vegetation and other land processes
than previous AVHRR, not only because of its narrower spectral bands that enhance
the information derived from vegetation (Justice et al., 2002), onboard calibration
to guarantee the consistent time-series reflectance (Guenther et al., 2002), and orbit
and altitude satellite maneuvers to ensure sub-pixel geolocation accuracy (Wolfe
et al., 2002), but also because leading scientists are working as a team to improve the
accuracy of the data from low level reflectance data, to high level data, such as land
cover, fire, land surface temperature, vegetation indices (NDVI and EVI; EVI is the
enhanced vegetation index), FAPR / LAI and GPP / NPP (Justice et al., 2002). The
other important feature of MODIS data is that all of the land products have quality
flags, denoting any negative atmospheric impacts (e.g., cloudiness and aerosol) to
help users screen the data for their purposes (Justice et al., 2002). Combined with the
MODIS atmosphere and ocean products, MODIS vegetation data provide invaluable
information for earth system study.
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M. Zhao, S.W. Running
The algorithm for the MODIS 1 km 8-day GPP and annual NPP employs 1 km
land cover and 1 km 8-day FPAR/LAI in addition to daily spatial coarse resolution
meteorological data. The LAI is used to estimate the biomass of leaf, fine root and
live wood for plant maintenance respiration calculations in the algorithm (Running
et al., 2000; Heinsch et al., 2003; Running et al., 2004; Zhao et al., 2005). Because of
the near real-time processing of MODIS GPP/NPP products and the required suite
of large datasets, water stress is represented solely by air VPD rather than a full
water stress from both VPD and soil moisture, thereby avoiding extremely intensive
computation and the creation of the additional physical and biophysical datasets
required for water balance calculation. Our study has shown that VPD alone can
capture the inter-annual variability of the full water stress, although it may fail to
capture the seasonality of water stresses for dry areas dominated by strong monsoons (Mu et al., 2007). The quality flags in these MODIS land data products allow
us to fill the gaps in the time series of FPAR and LAI resulting from contamination by unfavorable atmospheric conditions, generating more accurate estimations
of GPP and NPP (Zhao et al., 2005).
For the first time, we have more than 6 years of consistent global 1 km GPP and
NPP data products estimated from MODIS. Figure 16.1 shows the averaged seasonality of MODIS GPP from 6-year (2000–2005) results, in which we have aggregated
8-day values to 3 month averages (Fig. 16.1a). Spatial seasonal variations clearly
Fig. 16.1 Spatial patterns of the seasonality of MODIS GPP (a), and the mean annual cycle of
GPP at an 8-day interval for four latitude bands and the globe (b). (From Zhao et al., 2006b.)
16 Remote Sensing of Terrestrial Primary Production and Carbon Cycle
429
demonstrate the expected peak GPP in June, July, and August and the low values in
December, January, and February over the mid- and high-latitudes of the northern
hemisphere (NH). The rainforests of the Amazon Basin have higher GPP during the
dry season from July to November than during the wet season, which agrees with
the studies by Huete et al. (2006) and Xiao et al. (2006). Monthly precipitation in
the Amazon Basin can reach approximately 100 mm in the dry season, making solar radiation, not water, the leading limiting factor in this region, limiting growth in
the wet season. Figure 16.1b shows the annual cycle of total GPP for four latitudinal bands and for the entire globe. Relatively strong seasonal signals occur for the
mid- and high-latitudes of the NH (i.e., north of 22.5 ◦ N). The areas south of 22.5 ◦ S
have the opposite seasonal profile relative to the mid- and high-latitudes of NH, and
the seasonality for the southern hemisphere is much weaker because there is significantly less land mass. For the entire tropical region (22.5 ◦ S–22.5 ◦ N), there is
almost no discernible seasonality, and total GPP is always the highest among the
four latitudinal bands. Therefore, at the global scale, the magnitudes of annual GPP
cycle can be mostly attributed to the tropical region, while the seasonality in the
global cycle is largely determined by areas north of 22.5 ◦ N.
Figure 16.2 reveals the spatial pattern of the 6-year mean annual total NPP. As
expected, high MODIS NPP occurs in areas covered by forests and woody savannas, especially in tropical regions. Low NPP occurs in areas dominated by harsh
environments, such as high latitudes with short growing seasons constrained by low
temperatures and daylength, and dry areas with limited water availability. At the
global scale, from 2000 to 2005, MODIS estimated a total terrestrial annual GPP
of 109.07 Pg C (std. 1.66), and an annual NPP of 52.03 Pg C (std. 1.17), ignoring
Fig. 16.2 Spatial patterns of global terrestrial MODIS NPP averaged over 6 years (2000–2005).
(From Zhao et al., 2006b.)
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M. Zhao, S.W. Running
Fig. 16.3 Interannual variations in global total C4.8 MOD17 NPP driven by NCEP and GMAO,
respectively, as compared with the inverted atmospheric CO2 growth rate. A Multivariate ENSO
Index (MEI) is shown in gray scale, where darker shades represent higher MEI values. (From Zhao
et al., 2006b.)
barren land cover as defined by the MODIS land cover product. For vegetated areas,
the mean annual GPP and NPP are 996.03 (std. 823.67) and 475.19 (std. 375.44)
g C m−2 year−1 , respectively.
Interannual anomalies in the MODIS global NPP record correlate well with
the inverted atmospheric CO2 growth rate, corresponding to results (correlation =
0.70, P < 0.001) found by Nemani et al. (2003a) for the AVHRR period of record
(1982–1999). Figure 16.3 shows the relationship between MODIS NPP anomalies
and CO2 growth rates. To account for the uncertainties from inputs from different
meteorological datasets, Zhao et al. (2006a) used both GMAO and NCEP meteorology to drive the MODIS GPP and NPP algorithm. For the 6-year MODIS record, the
correlations are 0.85 (P < 0.016) and 0.91 (P < 0.006) using GMAO and NCEP,
respectively (Zhao et al., 2006b), implying that NPP is the primary driver of the
atmospheric CO2 growth rate.
16.3.2 Recent Findings Using MODIS Vegetation Products
There were no standard global land surface products (temperature (LST), EVI, fire,
GPP and NPP) prior to 2000 when MODIS began providing data, but information
from these 1 km or sub 1 km data products help us understand the dynamics of terrestrial ecosystems and detect the underlying mechanisms at different levels ranging
from local to regional scales. MODIS LST and EVI data can provide information on
surface resistance and disturbance (Nemani and Running, 1989; Mildrexler et al.,
16 Remote Sensing of Terrestrial Primary Production and Carbon Cycle
431
2007), and EVI has been found to be strongly related to the GPP derived from
eddy covariance flux tower measurements (Xiao et al., 2004; Rahman et al., 2005;
Huete et al., 2006), indicating the potentially superior utility of EVI in comparison
to NDVI. However, Daniel A. Sims (personal communication on 3/2/06) has found
that EVI models break down for sites subjected to summer drought as indicated by
high summer VPD. This finding is consistent with the similar conclusion by Running and Nemani (1988) using NDVI, implying that neither EVI nor NDVI can
reflect the total reduction in carbon uptake resulting from water stress, especially
for evergreen ecosystems in dry areas. Hence, water stresses must be incorporated
into global remote sensing primary production models. For Amazon rainforests,
MODIS EVI has revealed enhanced vegetation growth during dry seasons (Huete
et al., 2006; Xiao et al., 2006), and the MODIS GPP, incorporating such stresses,
also shows this enhanced production during dry seasons (Fig. 16.1a).
16.4 Monitoring the Long-Term Dynamics of Ecosystems
and Carbon Cycle
The advantages of using satellites to monitor land are numerous. Not only can satellite data provide detailed spatial patterns and variations in ecosystem processes, but
they also provide information on the temporal changes. With the accumulated longterm satellite data from Landsat since 1972 and AVHRR/NOAA since 1981, there
are more than three decades of satellite data, enabling us to study dynamics of and
changes in terrestrial ecosystems. The following subsections present several important applications of long-term satellite data.
16.4.1 Long Term Phenology Monitoring
Vegetation activities at mid- and high-latitudes are largely controlled by short growing seasons resulting from temperature and daylength constraints. Recent warming
(1976–2000) has been greater over the continents of the northern hemisphere (NH)
than elsewhere (Folland et al., 2001), lengthening the growing season and stimulating vegetation growth. CO2 measurements have shown an advance of upto 7 days
in the timing of the drawdown of CO2 in spring and early summer since the 1960s
(Keeling et al., 1996). However, CO2 data alone can not depict detailed spatial information on the response of vegetation to the warming temperature. Though 3D
transfer models can retrieve some spatial information on carbon sinks and sources,
the spatial resolution is very coarse and the results contain relatively large uncertainties (Fan et al., 1998). Long-term AVHRR NDVI data allow us to detect vegetation
responses to the warming climate spatio-temporally. Myneni et al. (1997b) utilized
the AVHRR NDVI from 1981 to 1991 and found that biospheric activity increased
remarkably in the northern high latitudes as a result of warming. Zhou et al. (2001)
2.5
2
1.5
1
0.5
0
−0.5
−1
−1.5
−2
−2.5
M. Zhao, S.W. Running
NORTH AMERICA (40N-70N)
TEMPERATURE
1.5
1
0.5
0
−0.5
GREENNESS
EURASIA (40N-70N)
2
TEMPERATURE
GREENNESS
ANOMALY
ANOMALY
432
82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99
YEAR
−1
−1.5
−2
−2.5
82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99
YEAR
Fig. 16.4 Anomaly of standardized NDVI and air temperature from 1982 to 1999 for North
America and Eurasia (40◦ N–70◦ N). (Redrawn from Zhou et al., 2001.)
continued the study by using a longer term NDVI record from 1981 to 1999, comparing that data to anomalies in air temperature (Fig. 16.4) for North America and
Eurasia from 1982 to 1999. They found that Eurasia had larger increases in both the
magnitude and duration of the seasonal cycle of NDVI than did North America. The
length of active growing season increased by more than 2 weeks (18 ± 4 days) for
Eurasia and nearly 2 weeks for North America (12 ± 5 days). This lengthening of
the growing season has implications for many aspects of ecosystem processes, especially the carbon cycle. It may indicate an increase in NPP, but it may also enhance
processes that release carbon to the atmosphere, such as decomposition, wildfires
and insect outbreaks.
16.4.2 Estimation of Regional Carbon Storage Changes
from Land Use Change
Year-to-year variability in the growth rates of atmospheric CO2 concentrations is
large, and is principally induced by inter-annual variations in terrestrial metabolism
(Kindermann et al., 1996; Prentice et al., 2000; Keeling et al., 2001; McGuire et al.,
2001; Nemani et al., 2003a). Over a relatively long period (e.g., decades), the mean
carbon fluxes between atmosphere and land are controlled mainly by changes in
biomass density, soil carbon and land use (Prentice et al., 2001; Schimel et al.,
2001). Historical records and national inventories together with bookkeeping models have been used to estimate the contribution of carbon flux from land use change
(e.g., Houghton et al., 1983). There are, however, large uncertainties in quantifying
the role of changes in biomass density and land use in the contemporary global carbon cycle (Prentice et al., 2001; Schimel et al., 2001), largely because of varying
definitions of forest cover among countries and differing time intervals (Matthews,
2001). Satellite data offer the possibility of providing spatially and temporally consistent estimates of forest cover to complement national reports. Using AVHRR
data, for example, DeFries et al. (2002) estimated the changes in forest cover at the
sub-pixel level over tropical regions for the 1980s and 1990s, and found that for the
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1980s, the carbon emissions from land use change were 0.6 (0.3–0.8) Pg C year−1 ,
much less than 1.7 (0.6–2.5) Pg C year−1 , a value from IPCC report (Prentice et al.,
2001), which was estimated mainly based on the statistics from United Nation Food
and Agriculture Organization (FAO) and deforestation rates from the FAO Forest Resource Assessment (FRA). The measurements from satellite suggested less
“missing” carbon in the global carbon budget than the previous estimates. DeFries
et al. (2002) also found that, compared with the 1980s, clearing of tropical forests
increased by about 10% as revealed by satellite data, as opposed to the 11% reduction reported by FRA, implying that there are increasing carbon emissions from
changes in tropical land use. For forests in mid- and high-latitudes of NH, Myneni
et al. (2001) simply used the fitted empirical equations between growing season
cumulative NDVI and total biomass derived from inventory data of stem wood volume and long-term AVHRR NDVI records to discover an overall carbon sink for
these forests, especially for forests in Eurasia and east North America. Compared
with statistics and inventory data, satellite data can provide more detailed spatial
information, and the results are generally consistent and accurate.
16.4.3 Estimation of Regional Carbon Fluxes Changes
With a long-term AVHRR dataset and the PEMs mentioned previously, it is possible
to detect the trends and interannual variability in global NPP. At the regional level,
there have been several reports of increased NPP. For example, Randerson et al.
(1999) used the CASA model to study the relationships among NPP, Net Ecosystem Production (NEP) and seasonal cycle of atmospheric CO2 at high latitudes and
found that the enhanced photosynthesis activities in the early spring due to warming
temperature are responsible for the changing trend in the seasonal cycle of CO2 .
Also using the CASA model, Hicke et al. (2002a, b) reported an increase in NPP
over most of North America from 1982 to 1999; Fang et al. (2003) found increased
NPP from 1982 to 1999 in China.
Studies have also found increased NPP at the global scale. Ichii et al. (2001)
found that NPP increased during the 1980s with AVHRR NDVI. Cao et al. (2004),
using the GLO-PEM model, found that NPP increased from 1981 to 2000, and the
authors attribute the increased NPP to the effects of both increased CO2 fertilization effects and the favorable change in climate during the periods. Potter et al.
(2003a) used CASA and AVHRR data to calculate changes in NPP and NEP during
1982–1998, determining that NEP increased at the global scale, and further, that
Eurasia was a larger carbon sink than North America, contradicting the atmospheric
inversion study by Fan et al. (1998). These studies are concentrated, using only one
(solar radiation; Ichii et al., 2001) or two climate variables (temperature and precipitation; Potter et al., 2003a) or the combined effects of climate and CO2 (Cao et al.,
2004). Nemani et al. (2003a) used a PEM developed for the global MODIS GPP
and NPP products (Running et al., 2000) to study the effects of all three primary
climate controls (incoming solar radiation, temperature and water) on NPP for the
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M. Zhao, S.W. Running
Fig. 16.5 Spatial distribution of linear trends in estimated NPP (% change per year) using FPAR
and LAI derived from AVHRR data for 1982–1999. (From Nemani et al., 2003a.)
period 1982–1999, and found that global NPP increased 6%, with tropical regions,
especially Amazon rain forests (accounting for 42% of the global increase), being
the largest contributors (Fig. 16.5). The increased NPP in the Amazon is caused by
increased solar radiation resulting from reduced cloudiness during the dry season.
In addition, Nemani and colleagues found that climate alone was responsible for
more than 40% of the increase in the global NPP. Other factors, such as CO2 fertilization and nitrogen deposition may also played a role in enhancing NPP. Nemani
et al.’s (2003a) study emphasizes the role of radiation, generally a dominant limiting
climate factor in humid, highly productive rain forests covering large areas of the
tropics.
16.4.4 Monitoring Disturbances
Disturbances from fire, insect outbreaks, logging, etc., constitute the major components to ecosystem change and have great impact on the global carbon cycle, but
many terrestrial biogeochemistry models do not account for disturbances (Canadell
et al., 2000). The chief reason for omitting disturbances in these models is that there
is no reliable record of the timing, distribution, spatial extent, or severity of these
disturbances at the regional or the global scale. Only medium- to coarse-resolution
satellites can provide the timing of major disturbances at the global scale. Potter
et al. (2003b) retrieved major disturbances at the global scale with the AVHRR
FPAR data for the period 1982–1999 and combined it with the NASA-CASA model
to estimate the above-ground biomass carbon lost to the atmosphere during that
16 Remote Sensing of Terrestrial Primary Production and Carbon Cycle
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period. Van der Werf et al. (2004) used fire activity obtained from several satellites together with a biogeochemical model and an inverse analysis of atmospheric
CO anomalies to estimate the CH4 and CO2 emissions from fire during the 1997–
2001 El Niño/La Niña period. They found that CO2 released from fire accounts for
66 ± 24% of the observed atmospheric CO2 annual growth rates during El Niño.
Randerson et al. (2005) further separated the contributions C3 and C4 vegetation
to fire emissions and found that C3 vegetation is largely responsible for interannual
variations in global fire emissions.
MODIS provides real-time fire burned area products (Roy et al., 2005). However,
other disturbances besides fires, including insect outbreaks, flood, irrigation etc., are
also important contributors to the carbon cycle. With MODIS LST and EVI datasets,
Mildrexler et al. (2007) have proposed a powerful Disturbance Index (DI),
DI = (LSTmax /EV Imax )/(LSTmax /EV Imax )
(16.3)
to detect all disturbance, regardless of origin. In Eq. 16.3, LSTmax (EV Imax ) is the
annual maximum composite MODIS LST (EVI), and LSTmax /EV Imax is the multiyear mean of the ratio of LSTmax to EV Imax . Compared with the field data, the DI
can effectively detect fire scars and other disturbances at regional and global scales
on an annual basis (Fig. 16.6). As mentioned previously, MODIS NPP results show
that NPP anomalies can explain 72–83% of annual CO2 growth rate (Fig. 16.3),
while 66 ± 24% was found by Van der Werf et al. (2004) from fire emission. Incorporating DI into MODIS NPP algorithm will help to separate the roles of NPP and
disturbance in the global carbon cycle.
16.4.5 Validation of Process-Based Ecosystem Model
with Remote Sensing Data
Process-based ecosystem models are now playing an indispensable role in simulating the dynamics of ecosystems. These models all experience improvements
through validation and advances in knowledge. However, unlike General Circulation
Models (GCMs), which can be validated with observations from several thousand
weather stations covering nearly all climatic zones, there is a paucity of ecological
observations to validate ecosystem models. Remote sensing data, because of their
spatio-temporal consistency, can be used as independent observations to validate
such models. For example, Zhao et al. (2002) used AVHRR NDVI to test improvements in a biogeography model for simulation of leaf longevity (deciduous or evergreen) over China relative to the original method. Zhuang et al. (2003) compared
the trend in carbon storage simulated by a process-based ecosystem model with that
derived from NDVI data by Myneni et al. (2001) over the extra-tropical NH, determining that there is similar spatial pattern between the modeled and satellite data,
though the modeled results had a smaller rate of change than the satellite estimates.
436
M. Zhao, S.W. Running
Fig. 16.6 Correspondence between the DI results, the MODIS active fire-detection data (black
dots) and fire perimeter maps (black outlines) for (a, b) the 2003 wildfires near Missoula, Montana,
and (c, d) the 2003 southern California wildfires. The southern California fires occurred in savanna
and shrublands, vegetation types with the highest frequency of major disturbance at the global
scale. (From Mildrexler et al., 2006.)
More importantly, results from models and satellite observations can support
each other and strengthen our confidence in understanding the mechanisms behind
ecosystem processes. Lucht et al. (2002) provide an excellent example of this support in their study of LAI following the Mt. Pinatubo eruption. The trend in vegetation activities derived from the long-term AVHRR dataset has long been suspect
because of the need for data corrections resulting from instrument and navigational
drift, intercalibration of successive instruments, and consideration of aerosol effects
(Cihlar et al., 1998). In addition, there have been several different explanations for
the reduced atmospheric CO2 growth rates after the effects of Mt. Pinatubo have
been taken into account (Arora, 2003). Lucht et al. (2002) used a biogeochemical
model of vegetation and observed climate data to discover that there is a drawdown
of maximum LAI simulated by the model in the northern high-latitude growing seasons of 1992–1993, consistent with the reduced LAI in the same period derived from
16 Remote Sensing of Terrestrial Primary Production and Carbon Cycle
437
AVHRR. This consistency between two independent datasets lead to the discover
that the lower AVHRR-estimated LAI following the 1991 Mt. Pinatubo eruption
was not caused by contamination of the satellite record from high aerosol loading,
but by the volcanic-induced climate anomaly, especially low temperatures. Moreover, the model results demonstrated that the unbalanced effect of cooling on NPP
and heterotrophic respiration provides a much simpler explanation for the additional
high-latitude CO2 uptake than the proposed mechanism of increased NPP due to the
increased diffuse sky light by Gu et al. (2003). Krakauer and Randerson (2003) also
found reduced tree-ring width following Pinatubo eruption, confirming the reduced
NPP due to volcanic effects.
16.5 Towards an Integrated Study of the Terrestrial Carbon
Dynamics
Remote sensing data are able to provide spatio-temporal dynamics of vegetation at
regional and global scales. To quantify the role of terrestrial vegetation in the carbon
cycle, however, requires ecosystem models. These models are validated, calibrated
and refined based on the relationship between remote sensing data and the groundbased data related to carbon at local level (Heinsch et al., 2006; Turner et al., 2006;
Zhao et al., 2005). Remote sensing data are then used to scale up from the local to the
regional and the global scale both temporally and spatially (e.g., Zhao et al., 2005).
The ecosystem models mentioned here cover a large complexity, ranging from very
simple regression models such as the empirical relationship between NDVI and biomass or LAI to complex ecosystem process models. Figure 16.7 shows a scheme of
quantification of the terrestrial carbon cycle using remote sensing data in an integrated system. Ground data, such as these measured at the eddy flux towers, and
other observations, such as land cover, NPP, biomass, are the source for developing
linkages between field-based and remote sensing data. Then the ecosystem models
use remote sensing data to quantify the regional or global scale carbon exchange
(Running et al., 1999).
In reality, the interactions among ground data, remote sensing data, ecosystem
models and carbon cycle are more complex. As depicted by Fig. 16.7, remote sensing data sometimes can also detect some mechanisms not observable with field measurements and remote sensing data may even assist in selecting appropriate sites
for field observations. The results from ecosystem models may help us explain remote sensing data, and vice versa, while remote sensing data also can be used to
validate or drive ecosystems models. Ecosystem models can quantify carbon exchange between atmosphere and terrestrial ecosystems, but carbon cycle data can
also constrain and validate ecosystem models. Ecosystem models are the centerpiece
of terrestrial carbon cycle studies, because modeling is the only method for understanding ecosystem processes in an interactive manner and predicting the impacts of
biotic and abiotic factors on the carbon cycle, providing important information for
policy makers and stockholders. With the improved quality and increasing spatial
438
M. Zhao, S.W. Running
Fig. 16.7 An interactive scheme for studying the terrestrial carbon cycle using remote sensing data
coverage of ground-based data, improved quality of satellite data, and the increasing knowledge gained from these data, the performance of the ecosystem models
will continue to be enhanced, thereby enhancing our ability to study the earth as a
system.
16.6 Summary
Human beings have never before had such a great impact on the earth environment in
recorded history. Our activities have changed land cover, water and nutrient cycling,
the chemistry of the atmosphere, and therefore, climate systems and the structure
and function of ecosystems. In turn, the anthropogenically induced environmental
changes have influenced our well-being. More importantly, many current human-
16 Remote Sensing of Terrestrial Primary Production and Carbon Cycle
439
induced environment issues are not restricted to a given region or country, but are
having global impacts. Regular global measurements from satellites play a crucial
role in monitoring the earth, which can provide advanced warning to allow for favorable environmental change (Running et al., 2006). The atmospheric CO2 concentration measurements at Mauna Loa since 1958 (the famous “Keeling curve”),
has given us the advanced warning for global climate change, and led to the 1997
“Kyoto Protocol”, the international treaty on climate change, assigning mandatory
targets for the reduction of greenhouse gas emissions to signatory nations. Ironically, Keeling’s measurements have been nearly halted several times due to the reluctance of the US government to provide continued support (Keeling, 1998). With
the accumulated long-term time-series data, we gain more knowledge and find new
discoveries (Keeling, 1998). Likewise, EOS has provided valuable scientific knowledge, and we hope that NASA continues this mission in some EOS-like project.
The other lesson we learned from the use of satellite data to study the carbon
cycle is that it is vital to have basic standard datasets for use in land science. We
have made a lot of discoveries using only NDVI. However, the quality of NDVI
is very critical for the science research, and there should be some MODIS-like land
products generated continuously, with similar sensor spatial resolution, quality flags,
and easy access data formats. As discussed above, for example, without standard
LST and EVI datasets, it is impossible for the ecologists to effectively use satellite
data (e.g., Mildrexler et al., 2007); without FPAR and LAI derived from satellite
data, it is impossible for us to have a deeper understanding of the terrestrial carbon
cycle following the Mt. Pinatubo eruption (e.g., Lucht et al., 2002), and the longterm trend in global NPP (e.g., Nemani et al., 2003a).
With advancements in ecosystem modeling based on remotely sensing data, as
well as the enhancement of computer performance and Internet technology, it is now
becoming possible for land managers and policy makers to make decisions based on
near real-time satellite data (Running et al., 2004), or even to take preventative action based on the information from near future forecasting using the same satellite
data (Nemani et al., 2003b). Thus, the advancements in the study of terrestrial primary production and carbon cycle using satellite data are significant, not only for
understanding the global carbon cycle, but also for application of satellite data to
carbon-related natural resource management and land management.
Acknowledgements The work is funded by the NASA/EOS Natural Resource/Education Training
Center (grant NAG5-12540) and NASA MODIS Project (NNG04HZ19C). We thank Dr. Faith Ann
Heinsch for comments and Dr. Liming Zhou for providing Fig. 16.4 for the chapter.
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