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Remote Sensing based indices for agricultural drought assessment
Dr. C.S. Murthy Head, Crop Monitoring & Assessment Division
Remote Sensing Applications Area National Remote Sensing Centre, Hyderabad
Understanding Drought and its management
• Complex nonlinear interactions • Slow process with multiple impact • No single index • Different states adopt different norms
Weather
Soil
Crop
Agricultural drought
Management
Information needs for Drought Management
Short term Management
Long term Management
Monitoring & Assessment
Prediction & Early warning
Agro-advisories Crop damage
assessment
Vulnerability maps
Risk maps Prioritization Impact
monitoring
Different indices • Meteorological • Soil moisture • Surface water • Ground water • Crop related • Socio-economic
Meteorological drought: reduced rainfall
Hydrological drought: reduced surface water
Agricultural drought: reduced soil moisture
Conceptualisation, development, operational services and institutionalisation of a remote sensing application project
2012+
• Institutional Arrangement at Ministry of Agriculture, GOI
• Creation of MNCFC
• Transfer of NADAMS project to MNCFC April 2012 • Enhanced end-use of NADAMS project
NADAMS Project A success story of
Institutionalization
• IRS WiFS based district / subdistrict level assessment • Participation of user departments 1998+
• Use of multiple indices • IRS AWiFS based sub-district level assessment • Decision rules for drought warning & declaration • Enhanced content & frequency of reporting • Institutional participation & Capacity building
2004+
• Conceptualization of NADAMS project at NRSC • Development of Methodology
1988
• Supplementation of WiFS with MODIS • Agricultural area monitoring • Increased number of indices
2002+
2009+
• Operational Services from NRSC • NOAA AVHRR • Regional/district level assessment • Prevalence, intensity and persistence of agricultural
1989 +
• New approach for sowing-period drought assessment • Geospatial products on soil mositure and rainfall • New Indices – Shortwave Angle Slope Index
Institutionalisation phase
Traj
ecto
ry o
f N
AD
AM
S p
roje
ct-
Dev
elo
pm
en
t an
d o
per
atio
nal
ser
vice
s
National Agricultural Drought Assessment and Monitoring Systems (NADAMS)
For NADAMS Drought reports kharif 2012 onwards visit: www.ncfc.gov.in
Wavelength range (µm)
0.62 – 0.67 (red) 0.841 – 0.876 (NIR) Spatial Resolution 250m Swath 2330 kms
IRS-WiFS IRS P6 AWiFS
Spatial resolution
56 metres
Wave lengths 4 bands
(green, red, NIR and MIR)
Swath : 700 kms.
Terra Modis NOAA AVHRR
Spatial resolution
188 metres
Wave lengths 3 bands
(green, red and NIR )
Swath : 700 kms.
Wavelength range (µm) 0.58 – 0.68 (red) 0.725 – 1.1 (NIR) 3.55 – 3.93 (MIR) 10.3 – 11.3 (TIR) 11.5 - 12.5 (TIR) Spatial Resolution 1.1 Km
Data from multiple satellites/sensors NATIONAL STATE DISTRICT
Cell
structure
Leaf
pigments
Chlorophyll
absorption
Water content
Water absorption R
E
F
L
E
C
T
A
N
C
E
(%)
WAVELENGTH (um)
0.4 0.6 0.8 1.0 1.2 1.4 1.6 1.8 2.0 2.2 2.4 2.6
80
70
60
50
40
30
20
10
0
Spectral response of vegetation
Dominant factor controlling leaf reflectance
NDVI
Ratio of difference and sum of surface reflectance in NIR and red spectral bands NIR can see roughly 8 layers Red – one layer Most successful indicator for describing vegetation Normalisation - reduces the effect of sensor degradation sensitive to changes in vegetation easy to compute and interpret available from most of the sensor systems
Spectral response of vegetation Red – more absorption due to chlorophyll Near Infra red – more reflection due to leaf structure
Normalized Difference Vegetation Index (NDVI)
NIR – Red / NIR+Red Reflected radiation in Near infrared and red bands. NDVI ranges from -1 to +1 Water – negative NDVI Clouds – zero NDVI Vegetation – positive NDVI represents density, vigor
Normalized Difference Vegetation Index
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
10
1-M
ay-1
10
9-M
ay-1
11
7-M
ay-1
12
5-M
ay-1
10
2-J
un
-11
10
-Ju
n-1
11
8-J
un
-11
26
-Ju
n-1
10
4-J
ul-
11
12
-Ju
l-1
12
0-J
ul-
11
28
-Ju
l-1
10
5-A
ug-
11
13
-Au
g-1
12
1-A
ug-
11
29
-Au
g-1
10
6-S
ep
-11
14
-Se
p-1
12
2-S
ep
-11
30
-Se
p-1
10
8-O
ct-1
11
6-O
ct-1
12
4-O
ct-1
10
1-N
ov-
11
09
-No
v-1
11
7-N
ov-
11
25
-No
v-1
10
3-D
ec-
11
11
-De
c-1
11
9-D
ec-
11
27
-De
c-1
10
4-J
an-1
21
2-J
an-1
22
0-J
an-1
22
8-J
an-1
20
5-F
eb
-12
13
-Fe
b-1
22
1-F
eb
-12
29
-Fe
b-1
20
8-M
ar-1
21
6-M
ar-1
22
4-M
ar-1
20
1-A
pr-
12
09
-Ap
r-1
21
7-A
pr-
12
25
-Ap
r-1
20
3-M
ay-1
21
1-M
ay-1
21
9-M
ay-1
2
ND
VI
NDVI profile over agricultural area
Satellite/ Sensor Indices Relevant Parameter
NOAA AVHRR (1km) NDVI Crop condition
Oceansat 2- OCM (360m) NDVI, ARVI Crop condition
Terra MODIS (500 m) SASI, NDWI Surface wetness/ sown area discrimination
Terra AMSRE (25 km) Soil moisture Surface wetness/ sown area discrimination
INSAT 3A CCD (1 km) NDVI Crop condition
Satellites Sensor Spatial resolution
Temporal resolution Swath
Resourcesat 1 & 2 AWiFS 56 m 5 days 750 km LISS III 23 m 26 days 140 km LISS IV 6 m 48 days 70 km LANDSAT 8 OLI 30 m 16 days 185 km
Some important VI data sets
Coarse resolution data
Moderate resolution data
.
-100
-50
0
50
100
150
200
250
300
.
12/6 19/6 6/6 3/710/7 17/7 24/7 31/7 7/8 14/8 21/8 28/8 4/9 11/9 18/9 25/9
% d
evi
atio
n
NDVI based agricultural drought assessment
0
0.1
0.2
0.3
0.4
0.5
0.6
June July Aug. Sept. Oct. Nov.
Month
ND
VI
Normal delayed season Drought
(1) relative deviation from normal, (2) vegetation Condition Index, (3) in season rate of transformation
Integration with ground data
.
0
10
20
30
40
50
60
70
80
90
100
5 J
un
12
Ju
n
19
Ju
n
26
Ju
n
3 J
ul
10
Ju
l
17
Ju
l
24
Ju
l
31
Ju
l
7 A
ug
14
Au
g
21
Au
g
31
Au
g
11
Se
p
18
Se
p
25
Se
p
30
Se
p
% o
f n
orm
al
Seasonal NDVI profiles for drought assessment
Weekly deviations of rainfall
Weekly progression of sown areas
NDWI/LSWI
Reflectance in 0.9 – 2.5 microns dominated by liquid water absorption
Sensitive to surface wetness/ vegetation moisture
Less affected by atmosphere
Reflectance 1.5-2.5 microns does not saturate till LAI reaches 8
SWIR availability – AWiFS, LISS-III, MODIS, INSAT 3A
MODIS – 3 SWIR bands – 1240 nm, 1640 nm, 2100 nm
LSWI – Land Surface Wetness Index – uses SWIR at 2100 nm – very sensitive to wetness NDWI/LSWI uses Agriculture – crop stress detection, crop yield, classification of succulent crops, surface moisture Forest – Monitoring, burnt area detection
Normalized Difference Wetness Index/ Land Surface Wetness Index
Most commonly adopted index – NDVI a) chlorophyll based index b) plant vigour and density c) easy to compute and interpret d) robust index e) Limitations – soil back ground, saturation, time lag etc.
Commonly used indices for drought assessment
Recently popularized index – NDWI/LSWI a) Plant moisture based index b) NIR and SWIR based c) No saturation issues d) Immediate response e) Sensitive to surface wetness during sowing period
Combination of NDVI and NDWI a) Overcomes limitations of either one b) amplifies anomalies and c) more responsive to ground situation
AWiFS NDVI - Odisha – June to November 2016
Jun 01-15, 2016 Jun 16-30, 2016 Jul 01-15, 2016 Jul 16-31, 2016
Aug 01-15, 2016 Aug 16-31, 2016 Sep 01-15, 2016 Sep 16-30, 2016
Oct 01-15, 2016 Oct 16-30, 2016 Nov 01-15, 2016 Nov 16-30, 2016
Increasing NDVI (greenness) Non-crop area
NDVI anomaly
% dev. from normal (actual NDVI-normal NDVI)/normal NDVI*100
Selection of normal year – average of recent past normal years NDVI is a conservative indicator and hence anomalies are not very high
Thumb rule: > 20% reduction in NDVI – drought conditions >30% reduction indicate moderate to severe drought conditions
Interpretation of NDVI changes to assess Agricultural drought
• Derivative of NDVI • Substitute to NDVI deviation
VCI = NDVI- NDVImin /(NDVImax –
NDVImin) *100 Range 0-100% 0-40 % drought 40-60 mild to moderate drought 60-100 good
Vegetation Condition Index (VCI)
Interpretation of NDVI changes to assess Agricultural drought
Vegetation Condition Index (VCI)
Computation
September
year NDVI VCI 2000 0.5 8
2001 0.55 50
2002 0.6 92
2004 0.52 25
2005 0.55 50 2006 0.51 17
2007 0.49 0 2008 0.59 83
2009 0.61 100 2010 0.54 42
2011 0.51 17
Mean 0.54
Min 0.49 Max 0.61
diff 0.12
Some critical issues Time series data base (at least 10-12 years) Differences due to cropping pattern, crop calendar to be normalised
VCI = NDVI- NDVImin /(NDVImax – NDVImin)
*100
Interpretation of NDVI changes for drought assesment
Reduction in NDVI is caused by; • Crop moisture stress • Flooding/excess rainfall • Crop and crop stage differences
between the two years under comparison
Despite these limitations, NDVI is still a successful indicator for agricultural drought assessment
Limitations of NDVI Chlorophyll based index – saturates with LAI (=3) Limited capability to detect vegetation water content Over-estimation when the veg. density is less Saturation at peak vegetative phases Conservative index Time lag
Block level crop condition – Anantpur district Comparison between normal year (2005) and drought year (2002)
Tadipatri mandal - Anantpur (Groundnut)
-0.100
-0.050
0.000
0.050
0.100
0.150
0.200
0.250
0.300
0.350
June July August Sep October
ND
VI
2005 2002
Pamidi mandal - Anantpur (Paddy)
-0.050
0.000
0.050
0.100
0.150
0.200
0.250
0.300
0.350
0.400
0.450
June July August Sep October
ND
VI
2005 2002
2002 drought conditions at mandal level in Anantpur district
Mudigubba mandal - Anantpur district
groundnut
-0.100
-0.050
0.000
0.050
0.100
0.150
0.200
0.250
0.300
0.350
0.400
0.450
June July August Sep October
ND
VI
2005 2002
Kanekal mandal - Anantpur district
groundnut + paddy
-0.100
-0.050
0.000
0.050
0.100
0.150
0.200
0.250
0.300
0.350
0.400
June July August Sep October
ND
VI
2005 2002
Crop area affected by drought in kharif 2015, West Bengal
5
17
40
29
8 1
0
10
20
30
40
50
< 10 % 10-20%
20-30%
30-40%
40-50%
< 50 %
Agr
iltu
ral a
rea
Aff
ecte
d
(%)
% of reduction in crop condition
Normal
Mild Moderate
Severe
Normal
Mild Moderate
Severe
10
26
47
15
2 0 0
10
20
30
40
50
< 10 % < 50 %
Agr
iltu
ral a
rea
Aff
ecte
d
(%)
% of reduction in crop condition
Normal
Mild Moderate
Severe
23
39 31
6 1 0
0
10
20
30
40
50
Agr
iltu
ral a
rea
Aff
ecte
d (
%)
% of reduction in crop condition
Purulia district Bankura district West Midnapur district
AWiFS derived crop condition anomalies showing agricultural drought situation in Andhra Pradesh, kharif 2011
Sep 2011 Sep 2010 NDVI anomaly Sep 2011
NDWI anomaly Oct 2011
0.00
0.10
0.20
0.30
0.40
0.50
FN1-SEP FN2-SEP FN1-OCT FN2-OCT Nov
ND
VI
2010 2011
0.00
0.05
0.10
0.15
0.20
0.25
0.30
0.35
F1SEP F2SEP F1OCT F2OCT F1NOV
ND
WI
2011 2010
NDWI - Roddam mandal, Anantpur NDVI - Roddam mandal, Anantpur
164 mandals 394 mandals
562 mandals
Assessment at sub-district level
Kharif 2010 – Agricultural drought assessment, Bihar
AWiFS NDVI Oct. 2010 (Drought year)
AWiFS NDVI Oct. 2008 (Normal year)
1. Pas. Champaran 2. Pur. Champaran 3. Sheohar 4. Sitamarhi 5. Madhubani 6. Supal 7. Ararai 8. Kishanganj 9. Purnia 10. Madhepura 11. Saharsa 12. Darbhanga 13. Muzaffarpur 14. Gopalganj 15. Siwan 16. Saran 17.Vaishali 18. Samastipur 19. Begusarai
20. Khagaria 21. Katihar 22. Bhagalpur 23. Banka 24. Munger 25. Lackeesarai 26. Sheikhpura 27. Nalanda 28. Patna 29. Bhojpur 30. Buxar 31. Bhabua 32. Rhotas 33. Aurangabad 34. Jahanabad 35. Gaya 36. Nawada 37. Jamui
-60
-40
-20
0
Jam
ui
Gay
a
Jahan
a…
Bu
xar
Aurang…
Samasti…
Bh
ojp
ur
Nal
and
a
Pat
na
Bhagalp…
% d
evia
tio
n f
rom
n
orm
al
Rainfall deficiency
0
50
100
Jam
ui
Gay
a
Jahan
a…
Bu
xar
Aurang…
Samasti…
Bh
ojp
ur
Nal
and
a
Pat
na
Bhagal…%
of
no
rmal
Crop sown area status
Crop areas affected by agricultural drought situation are showing lower NDVI compared to normal, in kharif 2010 in Bihar state.
Agricultural drought assessment
(based on multiple indices; NDVI, NDWI, SASI)
Moderate (103) Severe (55)
Moderate (103) Severe (55)
Satellite derived Area Favourable for Crop Sowing/Crop Sown Area (AFCS) , Lakh ha.
Kharif Nromal
Area
AFCS AFCS Unfavourable
Jul-10 Aug-10 area
37 19 23 14
Satellite based agricultural drought assessment
kharif 2010, West Bengal
Crop condition assessment Rajkot district, Gujarat Kharif 2016
+ 10% 10-20% 20-30% 30-40% 40-50% < 50%
Deviation
September October November
NDVI Anomaly – September 2016
Normal Normal Mild drought Mild – moderate Moderate to severe
Kerala 2016 Drought
PERCENTAGE OF AREA in different drought classes - SEPTEMBER
S.No Districts C1 C2 C3 C4 C5
1 Alappuzha 19.57 0.04 0.10 0.05 80.23
2 Ernakulam 36.42 0.16 0.10 0.01 63.31
3 Idukki 17.23 0.34 0.17 0.00 82.25
4 Kannur 86.78 0.07 0.05 0.01 13.09
5 Kasaragod 41.25 0.22 0.14 0.00 58.38
6 Kollam 55.96 0.05 0.10 0.01 43.87
7 Kottayam 16.33 0.04 0.06 0.03 83.54
8 Kozhikode 72.66 0.16 0.07 0.00 27.11
9 Malappuram 87.52 0.29 0.08 0.00 12.11
10 Palakkad 63.08 0.19 0.12 0.02 36.59
11 Pattanamthitta 47.75 0.12 0.07 0.01 52.06
12 Thiruvananthapuram 37.84 0.20 0.04 0.01 61.91
13 Thrissur 59.60 0.17 0.15 0.01 40.07
14 Wayanad 76.12 0.07 0.05 0.00 23.75
0
100
200
300
400
500
-0.20
-0.15
-0.10
-0.05
0.00
0.05
0.10
0.15
0.20
09
Jun
25
Jun
11
Jul
27
Jul
12
Au
g
28
Au
g
13
Se
p
29
Se
p
So
wn
are
a 0
00’
ha
SA
SI
Weeks
SASI Sown area
Response of SASI to crop sown area
Features SASI value
Dry vegetation low negative
Moist veg. high negative
Shortwave Angle Slope Index (SASI)
βSWIR1 = cos-1 [ (a2 + b2 - c2) /
(2*a*b) ]
Slope = (SWIR2 − NIR)
SASI = βSWIR1 * Slope (radians)
where a, b and c are Euclidian
distances between vertices NIR
and SWIR1, SWIR1 and SWIR2,
and NIR and SWIR2, respectively
Chronological synchronization between (a) Decrease in SASI (b) Increase in rainfall (c) Increase in sown area
NADAMS project Conceptually and computationally simple procedures to discriminate the crop sowing favorable areas at state level
Features SASI value
Dry soil highly positive
Wet soil low positive
-0.30
-0.25
-0.20
-0.15
-0.10
-0.05
0.00
0.05
0.10
0.15
0.20
09
Ju
ne
17
Ju
ne
25
Ju
ne
3 J
uly
11
Ju
ly
19
Ju
ly
27
Ju
ly
04
Au
g
12
Au
g
20
Au
g
28
Au
g
05
Se
p
13
Se
p
21
Se
p
29
Se
p
SASI
val
ue
s
2002 2006 2009
Seasonal dynamics of SASI
Before crop sowing
Crop growing and
maturity
Commencement of crop sowing
-1
-0.8
-0.6
-0.4
-0.2
0
0.2
0.40
1-M
ay-1
10
9-M
ay-1
11
7-M
ay-1
12
5-M
ay-1
10
2-J
un
-11
10
-Ju
n-1
11
8-J
un
-11
26
-Ju
n-1
10
4-J
ul-
11
12
-Ju
l-1
12
0-J
ul-
11
28
-Ju
l-1
10
5-A
ug-
11
13
-Au
g-1
12
1-A
ug-
11
29
-Au
g-1
10
6-S
ep-1
11
4-S
ep-1
12
2-S
ep-1
13
0-S
ep-1
10
8-O
ct-1
11
6-O
ct-1
12
4-O
ct-1
10
1-N
ov-
11
09
-No
v-1
11
7-N
ov-
11
25
-No
v-1
10
3-D
ec-1
11
1-D
ec-1
11
9-D
ec-1
12
7-D
ec-1
10
4-J
an-1
21
2-J
an-1
22
0-J
an-1
22
8-J
an-1
20
5-F
eb-1
21
3-F
eb-1
22
1-F
eb-1
22
9-F
eb-1
20
8-M
ar-1
21
6-M
ar-1
22
4-M
ar-1
20
1-A
pr-
12
09
-Ap
r-1
21
7-A
pr-
12
25
-Ap
r-1
20
3-M
ay-1
21
1-M
ay-1
21
9-M
ay-1
2
SASI
Seasonal SASI profile
Area Favourable for Crop Sowing/Crop Sowan Area (AFCS)
Geospatial product on Area Favourable for
Crop Sowing (AFCS) using multi-criteria approach
Soil Texture
Rice area mask
Kharif area mask
SASI Modelled soil moisture
Input datasets
September August
July June
State Kharif normal
AFCS M ha. Unfavorable area
state June July Aug Sep
Andhra Pradesh 7.8 2.0 6.8 6.9 7.3 0.4
Bihar 3.7 0.7 3.6 3.7 3.7 0
Chhattisgarh 4.8 3.2 4.8 4.8 4.8 0
Gujarat 8.7 1.3 5.0 5.8 8.1 0.6
Haryana 2.8 0.6 1.6 2.8 2.8 0
Jharkhand 2.5 0.3 2.4 2.5 2.5 0
Karnataka 7.5 3.5 6.0 6.0 7.0 0.5
Madhya Pradesh 10.4 0.7 9.7 10.3 10.4 0
Maharashtra 14.0 5.5 13.2 13.8 13.8 0.2
Odisha 6.3 3.9 6.1 6.2 6.3 0
Rajasthan 14.3 0.2 4.4 11.7 13.6 0.8
Tamil nadu 2.4 1.1 1.8 2.0 2.0 0.4
Uttar Pradesh 9.3 2.8 8.7 9.2 9.3 0
Sub-Total 94.5 25.8 74.2 85.7 91.7 2.9
All India 108.6 34.2 87.0 97.7 105.5 3.1
Area Favourable for Crop Sowing (AFCS) derived from SASI and water balance methodology, Kharif 2012
4_11 JUNE 12_18 JUNE 19_25 JUNE 26_2 JULY 3_9 JULY 10_16 JULY
Tracking the early season drought conditions of 2014 using LPRM Soil Moisture datasets
17_23 JULY 24_30 JULY 31_06 AUGUST 17_23 JULY
Soil moisture deviations from normal in 2014
0.000.050.100.150.200.250.300.350.40
01
jun
05
Ju
n
09
Ju
n
13
Ju
n
17
Ju
n
21
Ju
n
25
Ju
n
29
Ju
n
03
Ju
l
07
Ju
l
11
Ju
l
15
Ju
l
19
Ju
l
23
Ju
l
27
Ju
l
31
Ju
l
04
Au
g
08
Au
g
12
Au
g
Soil
mo
istu
re (
m3
/m3
)
LPRM_SM 2014 LPRM_SM 2013
Drought frequency in the sowing period
Field Data Collection using Geo-ICT
Observation
Transmission
Information
Decision
Action
Mobile app. from NRSC/ISRO
that allows users to share,
access and upload natural
resources information on a near
real time basis, with Bhuvan
serving as the platform
Crowd sourcing approach with
open source tools like Open
layers, PHP, Geoserver and
Mapserver, etc. for
visualization and uploading
Immense use for agricultural
information collection/analysis
Provision to upload the
information through internet or
customized mobile which will
be geo-tagged for
visualization through Bhuvan
Portal
Geo-tagged in-season field
data enables developing a
repository of agriculture/crop
related data
Successful and On-going applications of FDC • Crop mapping • Pest/disease surveillance • Crop Insurance • Crop damage assessment enumerations • Disaster • Drought impact enumeration.
Conclusions
Satellite indices have the potential to capture drought conditions
Satellite data – free access, analysis/computations simplified
Strengthen the drought monitoring and declaration systems
Selection of suitable satellite data - AWiFS is preferable
States – maintain NDVI, LSWI data, effective utilisation
State RS Centres - capability and expertise, support to State Depts.
National Centres – provide expertise, training etc.
Need for institutional mechanism Thank you