Assessment of Net Primary Productivity over
India Using Indian Geostationary Satellite
(INSAT-3A) Data
Sheshakumar Goroshi, Raghavendra P. Singh, Rohit Pradhan
and Jai Singh Parihar
Presented by:
Sheshakumar Goroshi
PhD Research Scholar
Environment and Hydrology Division, EPSA
Space Applications Centre, ISRO
Ahmedabad – 380 015
Email: [email protected]
ISPRS TCVIII Mid-Term Symposium on Operational Remote Sensing Applications, December 09-12 , 2014, Hyderabad, India
INTRODUCTION
(NPP background, Regional, Global Knowledge and Gap area)
•
NPP is an important component of the global carbon cycle...
•
Accurate estimation of NPP is required to understand the carbon dynamics within the
atmosphere– vegetation–soil continuum and the response of terrestrial ecosystem to
predict future climate change.
•
Field based NPP measurements have been conventionally carried out using long term
ecological monitoring of biomass in selected ecological site.
•
In-situ NPP measurements are limited by its spatial distribution, which are difficult to
upscale for understanding the global carbon balance.
•
Polar orbiting satellites (MODIS and SPOT) have been commonly used to measure
terrestrial NPP at regional/global scale.
•
Charge Coupled Devices (CCD) instrument on geostationary INSAT-3A platform
provides a unique opportunity for continuous monitoring of ecosystem pattern and
process study.
ISPRS TCVIII Mid-Term Symposium on Operational Remote Sensing Applications, December 09-12 , 2014, Hyderabad, India
INTRODUCTION
(Ecosystem Models: Regional, Global knowledge and Gap area)
Model
TEM
CASA
Global NPP (Pg C yr-1)
53.2
48
Source
Melillo et al. 1993
Potter et al. 1993; Field et al. 1995
CASA-VGPM
56.4
Field et al. 1998
GLO-PEM
69.7
Cao et al., 2004
TURC
64
Lafont et al., 2002
reduced-form model ‘NNN’
54.4
Moldenhauer and Ludeke 2000
Chikugo model
55.4
Awaya et al. 2004
MOD17
MOD-Sim-CYCLE,
Sim-CYCLE
MOD17
56
Heinsch et al., 2006
59.6, 62.7
Hazarika et al. 2005
56
Heinsch et al., 2006
Regional studies (India)
GLO-PEM
3.4
Singh et al., 2011
CASA
1.42
Nayak et al., 2003 & 2013
CASA
0.83
Bala et al., 2013
ISPRS TCVIII Mid-Term Symposium on Operational Remote Sensing Applications, December 09-12 , 2014, Hyderabad, India
OBJECTIVES
• Estimation of NPP using an Indian Geo-stationery
satellite (INSAT-3A CCD) data and Improved CASA
ecosystem model
• Comparison of an INSAT derived NPP values with
• Global NPP products (MODIS)
• Earlier estimates in India (Literature)
STUDY AREA
Total geographical area of 329 million
hectares situated in the tropics between
7°N and 40°N and between 68°E and
100°E.
Vegetation: Diverse vegetation
ecosystems and biodiversity (Figure)
Seasons: Southwest summer monsoon
(June-August), northeast winter monsoon
(December to February), spring or premonsoon (March-May) and autumn postmonsoon (September-November)
ENF:EVERGREEN NEEDLE LEAF FOREST
DBF: DECIDUOUS BROAD LEAF FOREST
CSH: CLOSED SHRUBLAND
WSA: WOODY SAVANNAS
WET: WETLAND
CRO/NAT. MOSAIC: CROPLAND NAURAL MOSAIC
BARREN AND SPARSE VEGETATION
EBF: EVERGREEN BROAD LEAF FOREST
MF: MIXED FOREST
OSH: OPEN SHRUBLAND
SA: GRASSLANDS
CRO: CROPLAND
SNOW AND ICE
WATER BODIES
ISPRS TCVIII Mid-Term Symposium on Operational Remote Sensing Applications, December 09-12 , 2014, Hyderabad, India
DATA USED
Data
Temporal Frequency
NDVI (INSAT-3A CCD)
10 day
0.072°x0.072°
Land Cover Ver. 2.0
Solar
Radiation
(MEERA)
Ancillary data (NDVI,
fPAR)
Precipitation &
Temperature (CRU)
Biome specific
Maximum Light Use
Efficiency values
Spatial Resolution
0.0089°x0.0089°
Monthly
0.5°x0.6°
Monthly
0.072°x0.072°
Monthly
0.5°x0.5°
2009
Point
Source
ftp://mosdac.gov.in
http://edc2.usgs.gov/
glcc/glcc.php
http://gmao.gsfc.nasa.
gov/merra
ftp://ftp.glcf.umd.edu/g
lcf
Yu et al., (2009)
IMPROVEMENT
Data
TF
NDVI
10 day
Land cover (INSAT)
SR
0.072°x0.072°
Source
0.0089°x0.0089°
SAC, IIRS (Agrawal et al., 2003)
ftp://mosdac.gov.in
Insolation (Kalpana)
Daily
0.072°x0.072°
ftp://mosdac.gov.in
Rainfall and Temperature
Daily
0.25°x0.25°
ftp: imd.gov.in
In-situ NPP
Annual
Point
SAC
METHODOLOGY
ISPRS TCVIII Mid-Term Symposium on Operational Remote Sensing Applications, December 09-12 , 2014, Hyderabad, India
NPP x, t   APAR x, t  εx, t 
(1)
APAR x, t   F  fPAR x, t   Sx, t 
fPAR NDVI x, t  
fPAR SR x, t  
(2)
NDVI x, t   NDVI i , min  fPAR max  fPAR min   fPAR
NDVI i, max - NDVI i, min
SR x, t   SR i ,min  fPAR max  fPAR min   fPAR
SR i, max - SR i, min
min
(4)
min
fPAR x, t   α  fPAR NDVI x, t   1 - α   fPAR SR x, t 
(5)
εx, t   W x, t   Tε1x, t   Tε 2 x, t   ε max
(6)
W x, t   0.5  0.5  ET x, t  PET x, t 
T1x, t   0.8  0.02Topt x   0.0005Topt x 


(3)
(7)
2
T2 x, t   1.1814 / 1  e 0.2 Topt x 10T x, t  1  e 0.3-Topt x 10T x, t 
(8)

(9)
RESULT AND DISCUSSION
Jan
Feb
Mar
Apr
May
June
Jul
Aug
Sept
Oct
Nov
Dec
Mean NPP of the country: 49.15±10.46 gCm -2 month-1
Net Primary Productivity (g C m-2 month-1)
Two distinct seasons can apparently see from the figures. The winter (January to March) and
monsoon (July to October) seasons.
Low NPP in Jan: Dry winter spell (12% < Mean monthly NPP of the country)
February month mean NPP Increases by 6% due to coincidence crops in Punjab and IndoGangetic plains
ISPRS TCVIII Mid-Term Symposium on Operational Remote Sensing Applications, December 09-12 , 2014, Hyderabad, India
As the crops achieved maturity and senescence stage during the end of the
winter season (March). NPP reduced by 13% from mean country’s NPP
During April, NPP reduced by 20% from mean country’s NPP (Lowest NPP in
the study year)
Growth Profile
From May, starts increasing and achieved peak in September.
Total NPP=1.18 PgC
INSAT NPP
MODIS NPP
Net Primary Productivity (g C m-2 year-1)
Table 2. Agreement analysis between MODIS NPP and INSAT NPP
EBF
DBF
MF
CS
OS
GRA
CRO
CRO/NVM
1007
1039
1320
471
625
377
841
7103
INSAT NPP (T)
1001.25
867.23
976.33
680.47
358.14
523.82
640.87
594.48
MODIS NPP (C)
702.62
613.60
989.69
615.08
241.12
520.06
675.51
513.17
Difference (C-T)
298.62
253.63
13.36
65.39
117.02
3.76
34.63
81.30
0.96
0.63
0.61
0.68
0.87
0.92
0.99
0.78
339.42
346
290
158.4
136.3
113.2
114.7
133.95
N
d
RMSE (gC m-2 month-1)
n
d  1-
 O
i 1
 O
n
i 1
i
 Pi 
2
i
 O  Pi - O

2
Oi:MODIS NPP AND Pi: INSAT NPP
CONCLUSIONS and FUTURE SCOPE
 INSAT CCD derived NPP followed the characteristic growth profile of most of
the vegetation types in the country.
 NPP attained maximum during August and September, while minimum in
April. Annual NPP for different vegetation types varied from 1104.55 gC
m-2 year-1 (evergreen broadleaf forest) to 231.9 gC m -2 year-1 (grassland)
with an average NPP of 590 gC m-2 year-1.
 We estimated 1.9 PgC of net carbon fixation over Indian landmass as
compared to 1.18 PgC from MODIS NPP in 2009.
 Biome level comparison between INSAT derived NPP and MODIS NPP
indicated a good agreement with the Willmott’s index of agreement (d)
ranging from 0.61 (Mixed forest) to 0.99 (Open Shrubland).
 NPP can be improved using high spatial and temporal input
data sets.
 Validation will be done using In-situ NPP measurements
ISPRS TCVIII Mid-Term Symposium on Operational Remote Sensing Applications, December 09-12 , 2014, Hyderabad, India
ACKNOWLEDGEMENTS
• Authors gratefully acknowledge Shri. A. S. Kiran Kumar,
Director, SAC, Dr. Pradeep Pal, Deputy Director EPSA
and Dr. Prakash Chauhan, Group Head, BPSG/SAC for
their suggestions and encouragement.
• One of the authors (Sheshakumar Goroshi) wishes to
acknowledge the grant of fellowship by SAC.
• Sheshakumar Goroshi also wishes to acknowledge the
grant of participation fee by ISPRS
• The authors thank NOAA, MODIS, CRU and MERRA for
providing data sets for analysis.