Remote Sensing Based Estimation of Gross
Primary Productivity (GPP)
Angela Tang Che Ing
GPHY 426 Remote Sensing
Fall 2014
1.0 Introduction
Gross primary productivity (GPP), photosynthetic assimilation of carbon by
vegetation, is a critical component in the global carbon cycle as it regulates the
carbon fluxes between the biosphere and atmosphere. The accurate
quantification of carbon uptake by terrestrial vegetation can yield a better
understanding of the feedback mechanisms between the terrestrial biosphere
and atmosphere which in turn can also improve the state of knowledge in climate
change model particularly in the aspect of predictions and uncertainties.
The eddy covariance technique which emerged in recent decades has become
the most useful tool for providing continuous and in-situ measurements of
ecosystem-scale exchange of carbon dioxide (CO2) spanning diurnal, seasonal
and interannual time scales. However, these measurements only represent the
fluxes at the scale of the tower footprint. More recently, Moderate Resolution
Imaging Spectroradiometer (MODIS) has provided new capabilities for
monitoring ecosystem-atmosphere CO2 flux at the global scale.
2.0
Fig. 1. MODIS on board the satellite.
(Source: http://aqua.nasa.gov/about/instrument_modis.php)
Methods and Materials
2.0.1 Instrument
NASA’s Moderate Resolution Imaging Spectroradiometer (MODIS):

Launched on board the Terra satellite in December
1999, and Aqua in May 2002

36 spectral bands; 21 within 0.4-3.0 µm; 15 within 314.5 µm

Measures visible and infrared radiation

Measures physical properties of the atmosphere,
and biological and physical properties
of the oceans and land



Provides multi-angular reflectance
reveal more insight into the threedimensional structure of vegetation
signals
that
Reflectance radiation is anisotropic
Uses its multi-angular detection system, coupled
with a three-dimensional canopy
radiative transfer model and advanced algorithms to
directly estimate leaf area index
(LAI) and absorbed photosynthetically active radiation
(aPAR)
2.0.2 MODIS Data
MODIS17 Algorithm:

Consists of two subproducts:

MOD17A2: 1km 8-day composite GPP, net photosynthesis (PsnNet)
and corresponding QC
GPP = εmax*PAR*FPAR*f(VPD)*f(Tmin)
where εmax is the biome-specific maximum light use efficiency (g
C/MJ), PAR is photosynthetically active radiation (MJ), FPAR is the
fraction of absorbed PAR, f(VPD) and f(Tmin) represent the biomespecific functions for vapor pressure deficit (VPD) and minimum
temperature (Tmin)
PsnNet = GPP – Rml – Rmr
where Rml and Rmr are maintenance respiration by leaves and fine
roots.

MOD167A3: annual NPP and QC
NPP   PsnNet   Rmo  Rg 
365
i 1
where Rmo is the maintenance respiration by all other living parts
except leaves and fine roots, and Rg is the growth respiration.
3.0
Results
3.0.1 Improvements to MOD17
3.0.1.1
Spatial interpolation of Data Assimilation Office (DAO)
3.0.1.2
Temporal filling of MOD15A2
Fig. 3. Two examples on how temporal filling unreliable 8-day FPAR and LAI, and th
located in Amazon basin where higher frequency and persistence of cloud cover e
cloud cover usually occurs in winter, and low frequency of cloud cover in summer. (S
3.0.2 MODIS GPP
Table 1. Three-year (2001types across the globe. (So


Fig. 4. Mean global GPP from 2000 to 2005.
(Source: http://secure.ntsg.umt.edu/projects/index.php/ID/ca2901a0/fuseaction/
projects.detail.htm)

MODIS GPP and
savannas, particu
Low GPP occurs
latitudes with sho
areas with limited
Generally, annual
Fig. 5. Interannual variability (2000-2005) of NPP at the global scale.
(Source: http://secure.ntsg.umt.edu/projects/index.php/ID/ca2901a0/fuseaction/projects.detail.htm)
Fig. 6. In
domain.
/projects.
3.0.3 Validation with tower-based estimates of GPP
(a) 15 AmeriFlux research sites in six different biome classes
Table 2. Comparison of tower estimates of GPP with GPP derived from the MODIS algorithm.
(Source: Heinsch et al., 2006)
(b) 21 FluxNet research sites for the years 2001-2008
Table 3. Average R2 and root mean square error (RMSE) in parenthesis between MODIS
products and flux tower GPP for each biome type. (Source: Hashimoto et. al., 2012)


4.0
MOD17 GPP was most highly correlated with short-term flux tower GPP, both for de
Low correlation found between the MOD17 GPP and flux tower GPP was in part
comparison to the temperate and boreal sites. More data for tropical forests are req
References
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