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TheInternationalArchivesofthePhotogrammetry,RemoteSensingandSpatialInformationSciences,VolumeXL-8,2014ISPRSTechnicalCommissionVIIISymposium,09–12December2014,Hyderabad,IndiaAN EXPERIMENTAL CASE STUDY TO ESTIMATE PRE-HARVEST WHEAT

ACREAGE/PRODUCTION IN HILLY AND PLAIN REGION OF UTTARAKHAND

STATE: CHALLENGES AND SOLUTIONS OF PROBLEMS BY USING SATELLITE

DATA

Divya Uniyal1, M.M. Kimothi2, Nita Bhagya2, Rajak 2, 2 and iyal1

1Uttarakhand Space Application Center, Dehradun

2 Space Application Center, Ahmedabad

E-mail: ***************************

KEY WORDS:

Acreage, Production, LISS-III, LISS-IV, NDVI, Geo-referencing, Hierarchical Decision rules

ABSTRACT:

Wheat is an economically important Rabi crop for the state, which is grown on around 26% of total available agriculture area in

the state. There is a variation in productivity of wheat crop in hilly and tarai region. The agricultural productivity is less in hilly

region in comparison of tarai region due to terrace cultivation, traditional system of agriculture, small land holdings, variation in

physiography, top soil erosion, lack of proper irrigation system etc.

Pre-harvest acreage/yield/production estimation of major crops is being done with the help of conventional crop cutting method,

which is biased, inaccurate and time consuming. Remote Sensing data with multi-temporal and multi-spectral capabilities has

shown new dimension in crop discrimination analysis and acreage/yield/production estimation in recent years.

In view of this, Uttarakhand Space Applications Centre (USAC), Dehradun with the collaboration of Space Applications Centre

(SAC), ISRO, Ahmedabad and Uttarakhand State Agriculture Department, have developed different techniques for the

discrimination of crops and estimation of pre-harvest wheat acreage/yield/production. In the 1st phase, five districts (Dehradun,

Almora, Udham Singh Nagar, Pauri Garhwal and Haridwar) with distinct hilly and plain regions, have been

selected for testing and verification of techniques using IRS (Indian Remote Sensing Satellites), LISS-III, LISS-IV satellite data

of Rabi season for the year 2008-09 and whole 13 districts of the Uttarakhand state from 2009-14 along with ground data were

used for detailed analysis. Five methods have been NDVI (Normalized Differential Vegetation Index), Supervised

classification, Spatial modeling, Masking out method and Programming on visual basics methods using multitemporal satellite

data of Rabi season along with the collateral and ground data. These methods were used for wheat discriminations and pre-harvest acreage estimations and subsequently results were compared with Bureau of Estimation Statistics (BES). Out of these

five different methods, wheat area that was estimated by spatial modeling and programming on visual basics has been found quite

near to Bureau of Estimation Statistics (BES). But for hilly region, maximum fields were going in shadow region, so it was

difficult to estimate accurate result, so frequency distribution curve method has been used and frequency range has been decided

to discriminate wheat pixels from other pixels in hilly region, digitized those regions and result shows good result. For yield

estimation, an algorithm has been developed by using soil texture, depth, drainage, temperature, rainfall and

historical yield data. To get the production estimation, estimated yield multiplied by acreage of crop per hectare. Result shows

deviation for acreage estimation from BES is around 3.28 %, 2.46%, 3.45%, 1.56%, 1.2% and 1.6% (estimation not declared till

now by state Agriculture dept. For the year 2013-14) estimation and deviation for production estimation is around 4.98 %, 3.66%

3.21% , 3.1% NA and 2.9% for the consecutive above mentioned 2008-09, 2009-10, 2010-11, 2011-12, 2012-13 and

2013-14. The estimated data has been provided to State Agriculture department for their use. To forecast production before

harvest facilitate the formulation of workable marketing strategies leading to better export/import of crop in the state, which will

help to lead better economic condition of the state. Yield estimation would help agriculture department in assessment of

productivity of land for specific crop. Pre-harvest wheat acreage/production estimation, is useful to facilitate the reliable and

timely estimates and enable the administrators and planners to take strategic decisions on import-export policy matters and trade

negotiations.

1. INTRODUCTION

Wheat is an economically important Rabi crop for the

Uttarakhand state, which is grown on around 26% of total

available agriculture area in the state. There is a variation in

productivity of wheat crop in hilly and tarai region. The

agricultural productivity is less in hilly region as comparison

of tarai region due to terrace cultivation, traditional system of

agriculture, small land holdings, variation in physiography,

top soil erosion, lack of proper irrigation system etc.

A timely forecast of any crop helps the government in farming

policies regarding its storage, distribution, export-import and

procurement of price. Various methods ranging from conventional

to Remote Sensing methods are used for crop production

forecasting. Crop production forecasts consist of two components

acreage and yield which are forecast separately. In this study an

attempt has been made to estimate acreage of wheat crop for

Uttarakhand state using LISS-III data.

Estimates for wheat crop is provided by Agriculture statistics

department, Government of India a project for making production

forecasts for wheat crop was taken by Department of Space(DOS)

as project FASAL (Forecasting Agricultural Output using Space,

This contribution has been peer-reviewed.

doi:10.5194/isprsarchives-XL-8-983-2014

983

TheInternationalArchivesofthePhotogrammetry,RemoteSensingandSpatialInformationSciences,VolumeXL-8,2014ISPRSTechnicalCommissionVIIISymposium,09–12December2014,Hyderabad,IndiaAgrometorological and Land based observations) USAC with

the collaboration of SAC (Ahmedabad). IRS LISS-III data of

January and February was used for acreage estimation. MXL

classifier was used to discriminate wheat from other crops.

Lack of satellite data due to cloud cover and mixing of other

Rabi crops affected the accuracy of estimation of Wheat crop.

In this study an attempt has been made to estimate acreage of

Wheat crop using LISS-III data.

The technique uses hierarchical decision rules to classify the

crop of interest. Initial zero fills, cloud, non agriculture (water,

sand, urban areas, etc.) and forest features were marked out.

Then the crop of interest was discriminated from competing

crops prevalent in that area.

Remote sensing technology has potential in estimating crop

acreage at district, regional and national level. Many crop

acreage estimation studies have been carried out using

remotely sensed data. As mentioned above Uttarakhand is a

hilly state so to estimating accurate crop acreage some

techniques must be developed. The study shows that use of

different techniques to estimate pre-harvest wheat acreage in

hilly areas using IRS LISS III data.

2. OBJECTIVES AND STUDY AREA







The main objectives of the study are to:

To Estimate pre-harvest wheat acreage.

Develop different techniques for the acreage estimation of

wheat crop in Uttarakhand state.

To Estimate pre-harvest wheat production.

Uttarakhand is located at 30 15 N latitude and 79 15 E

longitude, is a hilly state. Wheat is an economically important

Rabi crop for the state, which is grown on around 26% of total

available agriculture area in the state. All 13 districts of

Uttarakhand state were selected for wheat acreage/production

estimation. It is sown from last week of October to second

week of December. The crop reaches flowering stage around

mid-February and harvesting commences from April to first

week of May.

Figure 1: Satellite imagery of Uttarakhand state

3. DATA USED

3.1 Satellite data

IRS-P6-Resourcesat-2: LISS-III data.

3.2 Collateral data

 Wheat Acreage statistics from Agriculture department

 Historical district level wheat statistics from 2005 to 2008

for districts for all 13

 Shape file of the boundary of districts of Uttarakhand state

from SOI has been used.

 Topo-sheets and images of the study area were also used

for GT (Ground Truth).

 Ground truth sheet

 Rainfall

 Soil map

 Aspect

 Temperature

 DEM

4. METHODOLOGY

The major steps involved in the methodology that has been

formulated for remote sensing based crop acreage estimation are as

follows:

4.1 Acquisition of satellite data

4.2 Data processing

 Geo-referencing of Data IRS P6 LISS-III

 Mosaicing of different geo-referenced scenes

 Data subset (District wise Collection of Ground truth

information

 Collection of Ground truth information

Ground based information will be collected in the given format:

S. No. District

name

Year

of

observation

Crop

under

study

and

other

competing

crops

Crop

cover

on

ground

State/District/

Village

name

Free

hand

field

sketch

Date of

observation

Geog.

Latitude

Geog.

Longitude

Size of

crop

field

Crop

health

Field

photograph

(Overview)

Field

photograph

(Close

view)

Table 1: Ground truth sheet

4.3 Analysis of satellite data (For Acreage estimation)

 Modified NDVI image generation from geo-referenced

satellite data:

This contribution has been peer-reviewed.

doi:10.5194/isprsarchives-XL-8-983-2014

984

TheInternationalArchivesofthePhotogrammetry,RemoteSensingandSpatialInformationSciences,VolumeXL-8,2014ISPRSTechnicalCommissionVIIISymposium,09–12December2014,Hyderabad,IndiaWhere NDVI range scaled between 0 to 200.

 Masking out agriculture area from other classes

using spatial modelling.

 Image forward only Agriculture area from recoded

image using spatial modelling.

 Supervised classification of above forwarded image

with the help of above mentioned ground truth

4.4 This methodology is appropriate for plain area but in hilly

area some of the wheat pixels were going into shadow

portion so it is difficult to classify those pixels so a new

method has been adopted –Breakpoint editor in ERDAS

IMAGINE 9.3 is used to highlight those pixels which are

going to shadow portion in satellite image so digitized this

area and add to the wheat classified area.

By using this equation Maximum and Minimum yield have been

estimated. Production estimation Production has been estimated on

the basis of the following formula which gives relationship between

production, acreage and productivity:

Production of crop =Estimated yield*Acreage of crop

5. RESULTS

5.1 Geo-referencing of satellite data

Image-to-image registration was done with an accuracy level of

less than half a pixel.

5.2 Ground-truth Details

In Uttarakhand the ground truth period was January or February

when Wheat is in flowering stage.

5.3 The result of the study is shown below:

Variation in estimated records through satellite

data & BES data

Acreage (ha) in %

3.28

2.46

3.45

1.56

1.2

1.6

Production (m-t) in %

4.98

3.68

3.21

3.1

N.A.

2.9

Year

2008-09

2009-10

2010-2011

2011-2012

Figure 2: Methodology used to acreage estimation

4.5 For yield estimation: For yield estimation, a

mathematical equation has been drawn, in which Rainfall,

temperature; soil characteristics (Depth, Drainage and

Texture) and aspect factors have been considered. The

equation which has been used is as follows:

Productivity= Last year productivity + Productivity value

correction

Productivity value Correction =a*Temperature+ b*Rainfall+

c*Soil texture+ d*soil depth+ e*soil drainage+ + f*aspect,

Productivity value Correction =a*Temperature+ b*Rainfall+

g*value,

Where a, b, c, d, e, and f are the constants. For different

years, different physical parameters such as Temperature and

Rainfall are there so the values of constants a, b, c, d, e and f

have been calculated for this year. The values c, d, e, f will be

same but the values of a and b will be changed because these

features are dynamic in nature.

2012-2013

2013-2014

Table 2: Comparison of Wheat acreage/production estimation

from satellite data and data from Agriculture dept. for entire

Uttarakhand state during last 6 years

This contribution has been peer-reviewed.

doi:10.5194/isprsarchives-XL-8-983-2014

985

TheInternationalArchivesofthePhotogrammetry,RemoteSensingandSpatialInformationSciences,VolumeXL-8,2014ISPRSTechnicalCommissionVIIISymposium,09–12December2014,Hyderabad,India

Figure 3: Classified image overlaid on satellite imagery of

Uttarakhand state

For hilly area, the results which are coming from above

mentioned methodology, is quite similar to acreage estimation

of wheat crop from Agriculture department.

6. CONCLUSION:

Through remote sensing, GIS and land based observation, the

acreage and production estimation of Hilly and plains can be

estimated. This data has been handover to state Agriculture

dept. for their further use, this data is of great importance to

planners and policy makers for efficient and timely agricultural

development and making important decisions with respect to

procurement, storage, public distribution, export, import and

other related issues.

7. REFERENCES

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Assessment. In Natural Resources Management- a new

perspective, NNRMS Bangalore p267.

Bhgia, N.,Oza M.P.,Patel J.H., Dadhwal V.K., 1996. An

approach for all India wheat production forecasting using

remote sensing data. Scientific note: RSAM/SAC/CAPE-II/SN/53/96 April 1996,19p.

Bhagia,N., Oza, M.P., Rajak, D.R., Singh, R.P., Sehgal, V.K.,

Ravi, N.,Srivastava, , J.H., Ray, S.S. and Dabhwal,

V.K., attempt to make national wheat production

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NNRMS(B)-21,54-58.

Bhagia, N.,Rajak, D.R.,Oza, M.P.,Jaishankar R. and Dadhwal

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This contribution has been peer-reviewed.

doi:10.5194/isprsarchives-XL-8-983-2014

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