Monitoring wheat rust affected areas through
remote sensing – a case study for Punjab
Dr.
Sujay Dutta
Crop Inventory &
Modelling Division
ABHG/EPSA
Space Applications Centre
ISRO
Ahmedabad – 380 015
sujaydutta@sac.isro.gov.in
IRS P6 AWiFS
Mar.10, 2011
Rational of disease detection using RS data
Green and red band is suitable to detect symptom related to
change in pigments
Near infra red band is suitable for tissue damage detection
Shortwave infrared is sensitive to water/ content/drying detection
Thermal band is suitable for change in canopy temperature
Basic studies have already shown the requirement of narrow
bands for better discrimination of disease levels. Thus, ideally
hyper spectral sensor is most suitable.
Data Used
IRS P6 AWiFS sensor
Date of satellite pass acquired: December 14, 2010,
January 16, 2011,
February 09, 2011
March 10 & 25, 2011
Methodology
The wheat crop area was generated under the national project “FASAL” for the same season
The mask of wheat classified pixels were used to derive the NDVI and LSWI profiles of
wheat crops for the region of Punjab
 Two crop sowing pattern was observed in the study area. Major percent of wheat crop was
early sown, where the peak growth stage was observed by mid February
To study the weather situation during the disease occurance, meteorological
parameters obtained from Weather Research and Forecasting (WRF; Skamarock et al.,
2008, Model version 3.1) was used. WRF Model is integrated for 72 hr with a horizontal
resolution of 45 km for the All India grid points. The meteorological parameters used
were: daily maximum and minimum air temperature and relative humidity
Study of the spectral profiles of wheat pixels showed sudden fall in NDVI and LSWI
profiles from February 9, 2011 to March 10, 2011 in the sub mountainous region of
Punjab state compared to other wheat areas in the plains. The observations made by
field teams (provided by MOA) in March 8, matches with this.
A logical combination of the rules based on NDVI and LSWI resulted 2-4 per cent of
wheat crop was damaged by the disease.
The difference of NDVI and LSWI values between Feb. and March 2011 data were used
to derive the percent of negative deviation from Feb. to march. A percent difference
image was created to located the infested areas.
Study area:
Punjab state
wheat crop
pixels
Banga block,
Nawan Shahar
Narote Jaimal
Singh block
Pathankote
taluka
NDVI image
Mukerian Block , Hoshiarpur
LSWI image
Mukerian Block , Hoshiarpur
Kharar, Rupnangar
0.8
0.7
Tasoli disease
Tasoli healthy
0.6
NDVI
0.5
0.4
0.3
0.2
0.1
14-Dec
11-Feb
10-Mar
25-Mar
Date
0.4
Tasoli disease
0.3
Tasoli healthy
LSWI
0.2
0.1
0
14-Dec
11-Feb
10-Mar
-0.1
-0.2
Date
25-Mar
LSWI alone
District
Gurdaspur
Hoshiarpur
Jalandhar
Kapurthala
Nawanshahar
Diseased
crop area
(ha)
LSWI & NDVI
Percent of
Percent of
area affected wheat area
affected
Diseased
crop area
( ha )
28368
32667
28252
1920
15.32
28.69
22.25
21.45
2.38
4.38
4.61
3.86
4413
499
586
346
1002
16.96
4.13
245
Rust affected wheat in Banga block
Maximum air Temperature on feb. 15, 2011
NDVI, Banga Block,Nawansahar
Diurnal difference in maximum and minimum temperature and relative
humidity during February at sites with disease incidence.
Diurnal difference in maximum and minimum temperature and relative
humidity during February at sites away from disease incidence.
limitations of realizing the full potential for crop
disease detection
Disease cycle being very short, one requires very high
temporal resolution data for early detection.
Thus, ideally, very high spatial resolution hyper spectral
remote sensing data with high temporal resolution is
essential for disease detection.
… Thank you
LSWI(j)  LSWI(i)
PercentDifferenceImage 
 100
LSWI(i)
where
LSWI(j) - LSWI value of 10 March,2011
LSWI (i) - LSWI value of 9 Feb., 2011
Land Surface Water Index(LSWI)
LSWI

r NIR  r SWIR
r NIR + r SWIR
where
ρNIR = reflectance in near infrared band
ρ SWIR = reflectance in short wave infrared band
….. Thankyou