Gross primary production
algorithm development
and validation
K.Muramatsu（Nara Women’s Univ.）
S. Furumi (Nara Saho College )
M. Daigo (Doshisha Univ.)
Yukiko Mineshita ( NWU M2 student)
Yuri Hattori (NWU B4 student)
14年1月14日火曜日
Framework of GPP estimation
Characteristics of the algorithm
Correspond to photosynthesis process ● Photosynthesis velocity = Capacity x depression
● Use light response curves ● Estimate directly GPP, not use LAI
Photosynthesis process:
only the light exposure area
14年1月14日火曜日
stomatal
opening and
closing
photosynthesis process
light
Light reaction
Carbon fixation
Carbon fixation
chlorophyll
Chemical
energy
∼ amount of absorbed light
chlorophyll contents,
light intensity
plant physiological parameter
related to color
fcapacity (PAR, chlorophyll)
14年1月14日火曜日
Calvin cycle
stomata
CO2
∼ amount of absorbed CO2
stomatal opening and closing,
Weather, soil moisture
plant physiological actions
gas exchange is controlled
fstomata (xxx, xxx, •••)
GPP capacity estimation framework
photosynthesis
velocity with low stress
initial slope
Pmax_capacity
slope x Pmax_capacity x PAR
1+slope xPAR
PAR
From the plant physiological study
For a leaf
Initial slope: efficiency of light conversion to carbon
related to chlorophyll contents
Pmax_capacity: volume of chloroplasts [Ono et. al, 1995, Oguchi 2003]
Pmax_capacity related to chlorophyll contents
CIgreen=G/NIR -1.
[Gitellson et. al, 2006]
14年1月14日火曜日
Characteristics of CIgreen(=NIR/G-1 )
Results from Previous study
i) High sensitivity to Chlorophyll contents：Green＞Red
ii) Linear relationship with Chlorophyll contents of a leaf
iii) Linear relationship with Pmax_capacity_2000
Photosynthesis
velocity with low
stress
initial slope
2000
14年1月14日火曜日
Pmax_capacity
PAR
(μmol)
Pmax_capacity_2000
slope x Pmax_capacity x PAR
1+slope xPAR
Previous study
Pmax_capacity2000 vs. CIgreen
for each vegetation functional types
J.Thanyapraneedkul. et.al, 2012,
Remote Sensing, 4, 3689-3720
14年1月14日火曜日
GPP capacity estimation ：
Parameters determined mainly
using Japan FLUX
EF
DNF
DBF
DBF
Grass
PF
14年1月14日火曜日
???
Use Grass parameters
Open Shrub ?
Closed Shrub ?
Evergreen Broad Leaf forests ?
Amazon
Study site , this year
US-Los: ハンノキ,湿地
IGBP: Closed shrub bland
CA-Let
IGBP: Grass
US-Ses: Desert shrub land
IGBP: Open shrub land
US-Wjs: Savanna（EGF）
IGBP: Open shrub land
US-Whs: Grazing area
IGBP: Open shrub land
Km67: natural Forest
IGBP: EBF
Km83: Logged Forest
IGBP: EBF
14年1月14日火曜日
Open shrub and GRASS
Pmaxcapacity2000[mgCO2/m2/s]
CI-2000
2
CA-Let
US-Wjs
US-Ses
US-Whs
0.42*x-0.33
1.5
Previous study (Grass)
added data (Open Shrub)
1
0.5
0
0
2
4
6
8 10
CI=NIR/Green-1
12
Grass (CA-Let) and Open shrubs (US-Wjs, US-Ses, US-Whs)
On the same line
14年1月14日火曜日
Closed shrub and Amazonia Forest (EBF)
Pmaxcapacity2000[mgCO2/m2/s]
CI-2000
2
JP-TMK
JP-FJY
TH-SKR
US-Los
km67
km83
JP-TKY
0.14*x+0.21
0.17*x-0.35
1.5
1
NDF
NEF
Previous study
BEF
closed shrub
Added
BEF( Amazonia F.)
data
BDF (Previous study)
0.5
0
0
2
4
6
8 10
CI=NIR/Green-1
12
Amazonian Forest: Pmax_capacity_2000 has almost constant value
Except for Amazonian Forest
On the same line
US-Los(Closed shrub), JP-FJY(ENF), JP-TMK (DNF), TH-SKR(EBF)
Another line
JP-TKY
Slopes of them are the almost same value
14年1月14日火曜日
The relationship between CI vs. Pmax_capacity_2000
Three vegetation groups
1) Grass and Shrubs
2) Woody plant except for Amazonian Forest
3) Amazonian Forest
In the same group:
The slope are almost same values
If there is the determination rule of the intercept,
determination of parameters can be generalized.
14年1月14日火曜日
CI seasonal changes of Shrubs
2.5
2.5
CI
US-Wjs
2
CI
US-Whs
2
1.5
CI
CI
1.5
1
1
0.5
0.5
0
0
1
2
3
4
5
6
7
MONTH
8
9
10
11
12
1
2
3
4
5
6
7
MONTH
8
9
10
11
12
Not growing season’s CI value is
this start point
Pmaxcapacity2000[mgCO2/m2/s]
CI-2000
2.5
CA-Let
US-Wjs
US-Ses
US-Whs
0.42*x-0.33
2
1.5
1
0.5
0
0
For Woody plant group: considering
14年1月14日火曜日
2
4
6
CI=NIR/Green-1
8
10
Stomata opening closing
http://www.jspp.org/17hiroba/kaisetsu/kinoshita.html
Stomatal Conductance ( mol H20 m-2 s-1 )
Daily change of stomatal
conductance
0.4
Observation with LI-6400
0.35
0.3
0.25
0.2
0.15
0.1
0.05
0
08:
09:
10:
11:
12:
13:
14:
15:
Time of Day
Gas exchange is controlled by stomata opening and closing.
When absorbing CO2, H2O is evapolated
→ Leaf temperature rising is suppressed
14年1月14日火曜日
16:
17:
18:
At Takayama (Gifu, Pref.) and Ymashiro (Kyoto Pref.) site
For three vegetation species: ミズナラ•ダケカンバ•コナラ
Quercus crispula, Betula ermanii, Quercus serrata
Leaf : Measurements
Photosynthesis
Leaf Temperature :
Thermal imager
Brightness thermometer
14年1月14日火曜日
Canopy: Measurements
Canopy temperature:
Thermal imager
Daily change of leaf temperature
Quercus crispula
ミズナラ
Betula ermanii
ダケカンバ
The relationship among conductance, leaf
temperature, weather conditions (air temperature,
humidity, solar radiation) Calculate LUT using the moel[Baldocchi,1994]
14年1月14日火曜日
LUT
Leaf T
VPD(0~4)
VPD(5~9)
VPD:Vapor pressure deficit
(kPa)
VPD(25~29)
VPD(35~39)
VPD(15~19)
VPD(40~44)
VPD(20~24)
Leaf temperature
Thermal imager
Brightness
thermometer
VPD(30~34)
VPD(10~14)
quercus serrata コナラ
Thermocouple
(LI6400)
Estimation results of
stomatal conductance
VPD(45~)
Thermal imager
PAR
stomata conductance
(mol H20m-2s-1)
14年1月14日火曜日
PAR
Brightness
thermometer
Measurements
(LI6400)
Pattern : O.K.
Absolute
values:
Calibration
This year:
Continue the field measurements
for scaling up from leaf to canopy
at Takayama and Yamashiro site
MODIS data analysis
for scaling up from canopy to satellite
Using MODIS11C3 products
(monthly averaged daily LST data )
14年1月14日火曜日
Example of field measurements at Yamashiro site
44. (℃)
36. (℃)
5 branches:
daily change of photosynthesis measurement
14年1月14日火曜日
Stomatal conductance
0.4
0.3
0.25
b1
b2
b3
b4
b5
(μmol/m2/s)
Photosynthesis
Conductance
20
b1
b2
b3
b4
b5
0.35
(mol/m2/s)
photosynthesis
0.2
0.15
0.1
15
10
5
0.05
0
8
12
14
Time
16
18
0
20
6
8
10
12
14
Time
16
18
20
Brightness Temperature (℃)
b1
40
Brightness Temperature
b1
35
30
25
6
8
10
12 14
Time
16
18
20
(℃)
45
b3
40
b3
35
30
25
6
8
10
12
14
Time
14年1月14日火曜日
16
18
20
45
40
LUT is checked
using these data.
b2
b2
35
30
25
6
8
45
10
12 14
Time
18
20
(℃)
b4
b4
40
16
Brightness Temperature
(℃)
45
(℃)
Brightness Temperature
10
Brightness Temperature
Brightness Temperature
(℃)
6
35
30
25
6
8
10
12 14
Time
16
18
20
45
b5
b5
40
35
30
25
6
8
10
12 14
Time
16
18
20
MODIS data analysis
study the daily variation range of surface
temperature for evergreen and deciduous types
14年1月14日火曜日
MODIS daily variation of LST(max.-min. ) vs. CI
Jan.
Jan.
desert
Evergreen
Jul.
0.1CI
•¥
Jul.
0.1CI
0.1CI
desert
Evergreen
0.1CI
14年1月14日火曜日
Deciduous
Deciduous
0.1CI
Study Plan
• GPP capacity estimation
• Another vegetation types : FLUX site
• consider the generalized rule for the
estimation formula
• Stomatal opening and closing
• Consider scaling up method ( LUT for
global study )
14年1月14日火曜日
Preparation for Validation
• Collect the validation data
• FLUX data sets
• find the available ( we can use ) data
• Calculate typical value of the site
• Ground observation data sets
• Nara prefecture forest, Yatsugatake site,,,
• Ecological study sites : Nasahara-san G
14年1月14日火曜日
Thank you!
14年1月14日火曜日