Dr. Sujay Dutta

Crop Inventory & Modelling DivisionABHG/EPSASpace Applications CentreISROAhmedabad – 380 015

sujaydutta@sac.isro.gov.in

Monitoring wheat rust affected areas through remote sensing – a case study for Punjab

IRS P6 AWiFS Mar.10, 2011

Green and red band is suitable to detect symptom related to change in pigments Near infra red band is suitable for tissue damage detectionShortwave infrared is sensitive to water/ content/drying detectionThermal band is suitable for change in canopy temperature

Rational of disease detection using RS data

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

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

Methodology

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 LSWI imageMukerian Block , Hoshiarpur

Mukerian Block , Hoshiarpur

Kharar, Rupnangar

Rust affected wheat in Banga block

LSWI alone LSWI & NDVI

District Diseased crop area (ha)

Percent of area affected

Percent of wheat area affected

Diseased crop area ( ha )

Gurdaspur 28368 15.32 2.38 4413

Hoshiarpur 32667 28.69 4.38 499

Jalandhar 28252 22.25 4.61 586

Kapurthala 1920 21.45 3.86 346

Nawanshahar 1002 16.96 4.13 245

NDVI, Banga Block,NawansaharMaximum air Temperature on feb. 15, 2011

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

100Image DifferencePercent

LSWI(i)

LSWI(i)LSWI(j)

where LSWI(j) - LSWI value of 10 March,2011 LSWI (i) - LSWI value of 9 Feb., 2011

Land Surface Water Index(LSWI) SWIRNIR

SWIRNIR LSWI

where ρNIR = reflectance in near infrared band

ρ SWIR = reflectance in short wave infrared band

….. Thankyou