j02 swe beny friend project report

8/14/2019 j02 Swe Beny Friend Project Report

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CROP YIELD ESTIMATION OF WHEAT BY INTEGRATING

REMOTE SENSING, LAND AND MANAGEMENT FACTORS

A case study of Saharanpur District, Uttar Pradesh, India

A Project Report Submitted in Partial Fulfillment of The Requirements for The Award

of Post Graduate Diploma in Remote Sensing and Geographical Information System

By :

THEIN SWE

Settlement and Land Records Department

Ministry of Agriculture and Irrigation ( Myanmar)


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June, 2005


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CENTRE FOR SPACE SCIENCE AND TECHNOLOGY EDUCATION

IN ASIA AND THE PACIFIC (CSSTE-AP)

(Affiliated to the United Nations)

CERTIFICATE

This is to certify that Mr. U Thein Swe has carried out Pilot Project study entitled

CROP YIELD ESTIMATION OF WHEAT BY INTEGRATING REMOTE

SENSING, LAND AND MANAGEMENT FACTORS for the fulfillment of


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Abstract

Many studies have revealed that there is correlation between remotely sensed

NDVI and yield. Few studies have applied remote sensing data at farmers field level to

estimate yield. At this scale agricultural production is a result of complex environmental

stresses including farmers management. This study, therefore, propose to investigate the

relationship between space-borne Satellite based NDVI and wheat yield at field level, and

combining NDVI with land and management factors for yield prediction at field level.

The study was carried out in Saharanpur district ( 29 34 19 to 30 23 58 N

latitude and 77 07 24 to 77 57 10 E longitude), Uttar Pradesh, India. High-resolution

LISS-III on board IRS-P6 satellite data for of IRS-P6, has been used for crop

discrimination and area estimation. Data was collected through interviewing farmers on

the management practices and farmers yield for rabi season (2004 2005). Crop yield

i f ti l th d b t l h t t d l l t d tti


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Acknowledgements

I wish to express my appreciation to Mr Cihat H.Basocak, GIS Officer of

UNESCAP, Bangkok for greeting me a scholarship to pursue a course of study at 9 th Post

graduate course of CSSTE-AP, India Institute of Remote Sensing (IIRS), Director

General of Settlement and Land Records Department,Ministry of Agriculture and

Irrigation,Myanmar for allowing me to make use of the opportunity.

I am very thankful to Dr. V.K Dadhwal, Dean, IIRS, for his unrelenting

encouragement and effort towards providing all necessary facilities during the training

course.

My sincere and special thanks to Dr.N.R Patel, Agriculture and Soil Division,

IIRS, for his valuable guidance, encouragement advices and constructive criticism

throughout this paper. I wish to extend my sincere thanks to Dr. Suresh Kumar,

Agriculture and Soil Division, IIRS for his valuable comments, suggestions, help,

id d t d i th fi ld t d


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Table of Contents

Abstract II

Acknowledgement III

Table of Contents IV

List of figures VII

List of tables IX

1 Introduction 1

1.1 The need for crop yield forecasting 2

2 Review of literature 4


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4.1.3 Physiographic soil map 10

4.1.4 Land Management factors 10

4.1.5 Software used 10

4.1.6 Hardware used 11

4.2 Methods 11

4.2.1 Atmospheric and radiometric correction 11

4.2.2 Rectification 13

4.2.3 Digital image classification 13

4.2.4 Crop discrimination using high resolution data 13

4.2.5 Post classification 14


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5..3.1 Spectral characteristics and spectral separability 23

5.3.2 Crop acreage estimation and accuracy assessment 25

5.4 Crop Yield Modeling 29

5.4.1 Spectral vegetation indices based yield estimation 29

5.4.1.1 NDVI Extraction 29

5.4.1.2 Land and management factors 30

5.5 Distribution of yield data 31

5.5.1 Yield prediction using NDVI 32

5.5.2 The effect of land parameters on yield and NDVI 35

5.5.2.1 Relationship between yield , NDVI and soil sub-group 35


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List of figures

Figure 3.1 Location map of Saharanpur district 7

Figure 4.1 Crop cutting experiment in field 15

Figure 4.2 Flow diagram of crop acreage estimation 19

Figure 4.3 Schematic diagram for crop yield model development 20

Figure 5.1 Atmospheric correction of satellite data 21

Figure .2 Spectral reflectance of healthy vegetation 22

Figure 5.3 Spectral response curve IRS-P6-LISS-III 25

Fi 5 4 L d /L d f S h di t i t i 2004 05 26


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Figure 5.14 Correlation of soil type, NDVI and yield 38

Figure 5.15 Effect of fertilizer application on yield 39

Figure 5.16 Relationship between irrigation frequency and yield 40

Figure 5.17 Correlation of Yield, NDVI, IRRI, LPI, SYS 40

Figure 5.18 a) Land use /land cover map b) Wheat mask map

c) wheat mask NDVI map d) Yield map 44

Figure 5.19 Correlation of farmers expected yield and observed yield 45


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List of tables

Table 4.1 Satellite data product 12

Table 5.1 Seperrability of different land use / land cover

classes of LISS-III data(Rabi) 24

Table 5.2 Land use/ land cover statistics of Saharanpur district

in 2004-2005 (Rabi) 26

Table 5.3 Error matrix showing the digital classification accuracy

f d th l d ( R bi) 27


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1. Introduction

India underwent a series of successful agricultural revolutions, starting with the

"green" revolution in wheat and rice in the 1970s, the "white" revolution in milk and, in

the 1980s, the "yellow" revolution in oil seeds. Despite these major transformations, the

agricultural sector continues to be dominated by a large number of small landholders (70

% of rural people and 8 % of urban household depend on agriculture). The country is alsomarked by large fluctuations in agricultural output, though to a declining extent with the

development of irrigation facilities, adoption of new technologies and changes in

cropping patterns (FAO, 2000a). The traditional approach of crop estimation in India

involves complete enumeration (except in a few states where sample surveys are

employed) for estimating crop acreage and sample surveys based on crop cuttingexperiments (CCE) for estimating crop yield. The crop acreage and corresponding yield

estimate data are used to obtain production estimates.

These yield surveys are extensive; plot yield data being collected under complex

scientifically designed sampling design that is based on a stratified multistage random


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1.1 The need of remote sensing for crop yield forecasting

Forecasting crop yield well before harvest is crucial especially in regions

characterised by climatic uncertainties. This enables planners and decision makers to

predict how much to import in case of shortfall or optionally, to export in case of surplus.

It also enables governments to put in place strategic contingency plans for redistribution

of food during times of famine. Therefore, monitoring of crop development, crop growth,

and early yield prediction are generally important.

Crop yield estimation in many countries are based on conventional techniques of

data collection for crop and yield estimation based on ground-based field visits and

reports. Such reports are often subjective, costly, time consuming and are prone to large

errors due to incomplete ground observations, leading to poor crop yield assessment and

crop area estimations (Reynolds et al.2000). In most countries the data become available

too late for appropriate actions to be taken to avert food shortage. In some countries

weather data are also used (de Wit & Boogaard 2001, Liu & Kogan 2002) and models

based on weather parameters have been developed. This approach is associated with a

number of problems including the spatial distribution of the weather station, incomplete

d/ il bl ti l th d t d th b ti th t d t d t l


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To achieve timely and accurate information on the status of crops and crop yield,

there is need to have an up-to-date crop monitoring system that provides accurateinformation on yield estimates way before the harvesting period. The earlier and more

reliable information the greater the value (Hamar et al.1996, Reynolds et al. 2000).

Remote sensing data has the potential and the capacity to achieve this.

Keeping in view the potential of satellite remote sensing to quantitatively describe

actual crop conditions on remote wide area,non-destructive and /or real-time basis, the

present study was undertaken in Saharanpur district,(India) with following objectives :

To discriminate crop types and wheat acreage using IRS-P6-LISS-III(3rd

March,2005) data during Rabi season.

To investigate the relationship between NDVI and field level crop yield in

wheat.

To investigate the relationship between wheat yield and NDVI combining with

land and management factors for yield prediction at field level


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2 . Review of Literature

Various scientists in different part of world have demonstrated the use of remotely

sensed data for agricultural crop investigation. Agriculture is a major user of data from

satellite remote sensing. For more than a decade, in 1986 a project on Crop Acreage and

Production Estimation (CAPE) have been addressed on crop production estimates using

satellite observation in India which aimed at estimating production of crops viz, wheat,

rice, sorghum, cotton, groundnut and mustard in their major growing areas ( Navalgund et

al., 1991)

Recently, a FASAL (Forecasting Agricultural outputs using Space Agro-

meteorology and Land based Observations) is under operation which strengthen the

current capabilities from econometric and weather based techniques with remote sensing

application (Parihar, 1999)

U f lli i d f l d / l d i d


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Recently, multi-spectral satellite data based indices along with agro-

meteorological indices were used for yield prediction for rabi paddy crop area of Nellore

district of Andhra Pradesh by using IRS-1A LISS I data. Of the various spectral, agro-

meteorological yield models developed, they concluded that paddy yield estimation can

be improved by combining agro-meteorological indices like growing degree day ( GDD),

potential evapotranspiration (PET) with NDVI. (Saha and Jona, 1994)

Digital supervised classification of LANDSAT MSS data was used for

identification and district level acreage estimation of Kharif paddy ( Kalubrame, 1986).

The two stage stratified sampling approach and supervised digital classification of

LANDSAT MSS and TM and IRS-IA and IB, LISS I data gave better estimates of

paddy crop acreage in larger areas such as a group of district or a state ( Parihar et.

al.,1987, Sharma et. al., 1990, Panigraphy et. al., 1991)

Krishna Rao et al., (1997) evaluated the feasibility of IRS IC LISS-III data in

discriminating and estimation acreage of crops grown under multiple cropping situation

in 2mandals of Guntur district, Andhra Pradesh concluded that the data sat under

i i i h d i l f lfill h bj i i l i l i i i


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have high correlation with yield, therefore,they can best be used for yield prediction. The

findings of Muthy et al. (1994) agrees with the findings of this study.

However, it is difficult to have a single date image representing one phenological

stage at field level because of the differences in planting dates and the varieties used,

resulting in wide differences in crop phenological stages. To improve the predictability of

yield, Muthy et al. (1994) and Gat et al. (2000) proposed the use of time composited

multi-date images for yield prediction covering panicle initiation and heading stages and

considering maximum NDVI which normally occurs at heading stage. It is difficult,

especially in most tropical environments, to get a series of images due to clouds or other

logistical problems. In this case a single date image, as demonstrated, still provides good

information to predict end-of-season yield as long as it is within the time when there is

maximum vegetation (between panicle initiation and heading stage) and other production

factors are taken into account.


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3. Description of the study area

The geographic setting of the study area, materials used during investigation and

methodology adopted to find out the desired objectives are briefly described below.

3.1 Geographic Setting

Location and extent, climate, geology, agricultural land use are delt in this section.3.1.1 Location and Extent

The study area of Saharanpur district, Uttar Pradesh State is surround by

Dehradun district in the north, Yamuna river forms its boundary in the west which

separates it from Haryana district, in the east Haridwar district and in the south lies the

district of Muzaffarnagar. Saharanpur district is situated in north 29 34 19 to 30 23

58latitude and east 77 07 24 to 77 57 10longitude.The area stretchs between

53G/1, 53G/2, 53G/5, 53G/6, 53G/9, 53G/10, 53G/13, 53G/14, 53F/8,53F/11, 53F/12,

53F/15 and 53F/16 topographical maps of 1: 50,000 scale which are prepared by Survey

of India, the study area is around 368,000 ha.


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3.1.2 : Climate

The climate of study area is the average climate of Uttar Pradesh in general but its

northern position and its proximity to the hills give its on peculiarity. Though the region

lies well outside the tropic yet its climate like that of the rest of north India is essentially

tropical because of Himalayan chain. It belongs to the uppermost part of the upper Ganga

plain which is a sub-humid region between the dry Punjab plain and the humid middleGanga plain within the monsoon region of the great plains and naturally partakes the

characteristics of the to adjoining regions.

The average temperature recorded is 23.3 degree centigrade June being the hottest

month while January is the coldest one. The highest percentage of humidity i.e. 72 to 85

% is found during the rainy season at the lower range of humidity between 29 to 51.5 %

is recorded in the summers. The eastern part of the region is more humid than the western

part and relative humidity tends to increase in the winters season. Pressure of the region is

inversely related to the temperature July recording the lowest while December recording

the highest pressure. The average pressure of the district is found to be around 979 lbs.


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(2) The Bhabar land

(3) Bangar land

(4) Khadar land ( Yamuna, Hindon)

3.1.4 Agriculture and Present Land use

Saharanpur is primarily agricultural district. Roughly 70% of the land is used for

agriculture. Agriculture plays an important role in the economy of the district. Hence,

major agricultural systems viz, paddy, wheat, sugarcane and orchards are practiced in the

district. The developed and fertility alluvial plain of Saharanpur district is contributed by

the network of eastern Yamuna canal and its distributaries of many channels. The easternYamuna canal runs through the center of district from north to south. One significant

feature is that even thought the agricultural land for food crops has reduced in recent

years the food production has increased considerably. The significance of commercial

crops have increased manifold as a consequence of sugarcane production. In study area,

h i l d i f i dd h h d d


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4. Materials and Methods

4.1 Materials

Data used for the study and software specification are delt in this section;

4.1.1 Remote Sensing Data

Satellite Sensor Product Path/Row Date of Acquisition Source

IRS-P6 LISS-III Hard Copy 96 /49 3-03-05 NRSA

4.1.2 Ancillary Data

Survey of India Toposheet

Toposheet Nos : 53G/1, 53G/2, 53G/5, 53G/6, 53G/9, 53G/10, 53G/13,

53G/14, 53F/8, 53F/11, 53F/12, 53F/15, 53F/16

Scale : 1 : 50,000


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2) Data generation

a) ERDAS imaging 8.7b) ARC GIS

3) For GIS analysis

a) ARC VIEW

b) ARC GIS

c) ILWIS 3.2

4) For Calculation and report writing

a) MS Office

b) MS Excel

4.1.6 Hardware Used :

Pentium III,128 Mhz memory,

4.2 Methods

The methods used during the investigation is briefed in the following section.


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Distance, sun elevation angle and minimum DN values are the other required inputs. For

each band, the theoretical radiance of a dark object is assumed to have a reflectance of

one per cent (Moran et al. 1992 and Chavez, 1996) and calculated using the following

equation.

L , 1% = 0.01 * d2 * cos2 / ( * ESUN )Where, ESUN = mean solar exo-atmospheric spectral irradiance (table 4.1)

d is the sun-earth distance and is the solar zenith angle (90-solar elevation angle).

Haze correction is computed from the dark object values (Chavez 1996):

L ,haze = L ,min - L ,1%

The radiance image is then converted into reflectance by the fundamental radiance to

reflectance (rho) equation:

2 2


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* and # Pandya et al., 2003 and 2004, respectively.

4.2.2 Rectification

A full scene (path/row:96/49) of high-resolution satellite data from LISS III

sensor onboard IRS 1D and IRS P6 were georeferenced in UTM projection using ground

control points (GCPs) from the Survey of India topographical maps at 1:50,000 scale.

These georeferenced images were then resample to 23.5m pixel size using nearest

neighbour technique and the images were clipped using the study area boundary mask.

4.2.3 Digital Image Classification

Before final classification of satellite data spectral seperability between

crop and other land use/ land cover classes were evaluated multi band scatter diagram of

training classes. The crops discrimination of study area was generated by digital


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Accuracy assessment of classified pixels was done using independent reference

sites of the study area, collected using GPS. Overall accuracy was defined as the

percentage of total independent reference pixels that were correctly classified by the

MXL algorithm. Producers accuracy was calculated by dividing the number of pixels

correctly classified for each crop by the total number of independent reference pixels for

that crop, while users accuracy was the number of correctly classified pixels divided by

the total number of classified pixels for that crop. Kappa coefficientwas calculated to

measure the significance of classification results relative to chance agreement. A kappa

value of zero indicates that the classification is no better than random assignment of

pixels, while a value of one indicates perfect agreement between training pixels and their

prescribed classes (Lillesand and Kiefer, 2000).

4.2.5 Post Classification sorting

After classification with MXL, some classification errors could be already detected

during a visual examination of classified image. Reclassification was done by merging

relevant classes and generation or smoothing of classified image was done by using


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yield and biomass was separated and oven dried to obtain final grain yield for different

sample sites.

Fig 4.1 Crop Cutting Experiments in Field

4.2.8 Land Productivity Index ( LPI)

LPI is based on general characteristics of the soil profile, texture of the surface

soil, soil of the land, climate and other physical factors affecting use of land. It is a


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Land productivity classes

4.2.9 SYS method of land evaluation

Classes Ranges

Excellent (Class I) 80 100

Good (Class II ) 60 80

Fairly Good (Class III) 40 60

Average ( Class IV ) 20- 40

Poor ( Class V ) 10 20Very Poor ( Class VI ) < 10


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Sys Index = A * B/100 * C/100* .

( A, B and C are ratings of soil and land characteristics)

The successful application of the system applies the respect of the following rules:

1. The number of land characteristics to consider has to be reduced to a

strict minimum to avoid repetition of related characteristics in the

formula, leading to depression of the land index.

2. An important characteristics is rated in a wide scale ( 100 25), a less

important characteristics in a narrower scale ( 100 60). This

introduces the concept of weighting factor.

3. The depth to which the land index has to be calculated must be defined

for each land utilization type.

The depth to be considered should coincide with the normal depth of

the root system in a deep soil.


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classified into four suitability classes of S1 ( >60), S2 ( 40 60 ), S3 (2040), and N ( 1900, the two classes can be well separated. Between 1700 and 1900, it


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TABLE - 5.1 Seperability of different land cover classes of LISS III data(Rabi)

*Best average seperability over all pair wise combination of signature: 1944

Class_

Name Wheat Sugarcane Orchard Fallowland Forest RiverineForest PlantationForestSettlement RiverBed WaterBody

Wheat 0 1999 2000 2000 2000 2000 2000 2000 2000 2000

Sugarcane 2000 0 1967 1992 1999 1995 1908 1985 2000 1985

Orchard 2000 1967 0 1591 1283 1653 1995 1978 2000 2000

Fallow_land 2000 1992 1591 0 1533 1991 2000 2000 2000 2000

Forest 2000 1999 1283 1533 0 1964 2000 2000 2000 1999

Riverine Forest 2000 1995 1653 1991 1964 0 1995 1895 2000 1999

Plantatio Forest 2000 1908 1995 2000 2000 1995 0 1837 2000 2000

Settlement 2000 1999 1978 2000 2000 1895 1837 0 1918 1997

River Bed 2000 2000 2000 2000 2000 2000 2000 1918 0 2000

Water Body 2000 1985 2000 2000 1999 1999 2000 1997 2000 0

25


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Spectral Response Curve (LISS_ III)

0

20

40

60

80

100

120

140

160

Ban

d_

1

Ban

d_

2

Ban

d_

3

Ban

d_

4

(DN

Value)

Wheat Sugarcane Orchard Fallow_land

forest Settlement water_body

Fig - 5.3 Spectral response curve IRS-P-6 LISS-III


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The overall accuracy for all the crops and other land use classes is more than 80%

and Kappa coefficient is 0.91.The accuracy achieved is much above the acceptable

accuracy (80%) for any kind of thematic map. The wheat crop in the study area shown

more than 99% accuracy.

Land Use Area (ha) Area %

Wheat 187298.00 50.89 %

Sugarcane 22200.10 6.03 %

Orchard 50411.40 13.70%

Fallow land 34057.80 9.25%

Forest 28772.70 7.82%

Riverine forest 2635.83 0.72%

Plantation forest 2051.39 0.56%

Settlement 28602.60 7.77%

River bed 9479.76 2.58%

Water body 2508.82 0.68%

Total Area (ha) 368018.40


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TABLE 5. 3 Error matrix showing the digital classification accuracy of crops and other land use (IRS P6-LISS_III)data (Rabi)

Class_

Name Wheat

Sugar-

cane OrchardFallow

landForest

Riverine

Forest

P;antation

Forest

Settle-

ment

River-

Bed

Water

BodyTotal

Accuracy

%

Wheat 2047 0 0 8 0 0 0 0 0 1 2056 99.56

Sugarcane 19 254 1 0 0 1 12 9 0 3 299 85.62

Orchard 0 0 198 6 16 12 0 9 0 3 244 81.15

Fallow_land 0 0 6 651 36 0 0 0 0 0 693 93.94

Forest 0 0 13 7 793 0 0 0 0 0 813 97.54

Riverine_Forest 0 0 3 1 0 792 0 41 0 4 841 94.17

Plantatio Forest 0 7 0 0 0 0 493 43 0 1 544 90.63

Settlement 0 0 1 6 0 18 7 1628 66 0 1726 94.32

River_Bed 0 0 0 0 0 0 0 31 758 0 789 96.07

Water_Body 0 0 0 0 0 1 0 0 0 429 430 99.77

Total 2066 261 222 679 845 824 512 1761 824 441 8435 93.28

Accuracy% 99.08 97.32 89.19 95.88 93.85 96.12 96.29 92.45 91.99 97.28 94.94 95.49

Confusion Matrix

Average User Accuracy = 93.28 %

Average Producer Accuracy = 94.94 %

Overall Accuracy = 95.49%

Kappa Statistics = 0.91

28


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( a ) ( b )

Fig 5. 5 Crop Acreage Estimation (a ) False Color Composite ( IRS-P-6 ,LISS-III -Rabi) (b) Digitally Classified Image

29


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5.4 Crop Yield Modeling

Crop yield prediction models are necessary for assessing the production of

particular crop in region. Most of these models use either agronomic variables or

meteorological variable or combination of both and they become highly location specific.

In this study, spectral data is an integrated with land and management factors.

5.4.1 Spectral vegetation indices based yield estimation

Crop yield is key element for rural development and an indicator of global food

security. As global food demand continues to grow, crop yield assessments on a regional

scale will be increasingly important. In the present study empirical models which directly

relate single-spectral satellite data or derived parameters (Vegetation Indices, VIs) to crop

yield was used in yield estimation of wheat. In this approach, NDVI at particular growth

stage (normally, maximum vegetation growth) is related to final crop yield through

regression techniques and pre-harvest crop yield is predicted with the assumption that


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Data on land and management practices and respective yield data were collected

from farmers through interviews. Data units as reported by farmers were converted into

standard metric (S.I.) units. The IRS satellite image of 3rd Mar 2005 provided the field

level NDVI data. The total sample size for this study consisted of 44 valid fields.

Parametric statistical analysis techniques require data to be distributed normally.

Means and standard deviations are useful to describe data but become poor when the data

are not normally distributed. Histograms, stem-and-leaf plots and box plots can also be

used to visualize data. They help to show their distribution characteristics.

( a ) ( b )


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manifestation of all important factors affecting the agricultural crop and cumulative

environmental impacts on crop growth (Liu & Kogan 2002, Singh et al. 2002), therefore

remotely sensed data could be used to monitor crop condition through NDVI.

Management practices in the production system and how land is utilized will have

an effect on the overall productivity. In this respect, crop growth and crop yield is a

response to the type of management and the quality of the land unit.

Based on the above, hypothesis adopted in this study are as follows:

1. There is significant relationship between NDVI, and yield at field level.

Yield = ( NDVI )

2. There is significant relationship between NDVI, field level management and land.

NDVI = (Land, Management)


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techniques can be employed for further analysis without fulfilling any

transformation requirement.

YIELD(Q/ha)

504540353025201510

Frequen

cy

10

8

6

4

2

0

Fig 5. 7 Histogram fitted with a normal probability curve

Sd = 10.187

Mean = 33.05

Kolmogorov_Smirnov Z test=0.815


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analysis for yield estimation is presented in figures5.10. This study established that there

is a significant positive relationship between remotely sensed NDVI and CCE based yield

(Adj. r2 = 0.521), where production is dependent on many factors acting upon crop

growth. This clearly shows the potential of using NDVI for regional yield prediction for

wheat.

0

10

20

30

40

50

60

0.2 0.4 0.6 0.8 1

yield(Q

/ha)

Yield (Q/ha) = 60.84*NDVI 9.895

(Adj. R2 = 0.521, SEE = 7.142 N = 44)


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Table 5.4 Yield statistics of Saharanpur district in 2004-2005 (Rabi)

5.5.2 The effect of land parameters on yield and NDVI

5.5.2.1 Relationship between yield, NDVI and soil sub-groups

Soil is a major role in crop production. It is a medium for water and nutrient

supply to crops. Its natural characteristics determine the availability and supply of these

resources to the crop. Fig 5.11. shows the distribution of yield by soil sub-group. The box

plots (figure-5.11 ) indicate that the highest yield in soil sub group AP-FL. Most of the

sample sites were in soil sub-group AP_ Fl, and the least samples in sub-group AP_FS.

This bias in sampling frequency relates to extent each subgroup occur.

In study area, the highest yield is found in AP_FL(Alluvial Plain, Fine Loamy)and

the lowest yield is found in UP_CL(Upper Piedmont, Coarse Loamy)of subsoil group.

Mostly, high vigor wheat crop and high NDVI value are also found in AP_FL soil

subgroup.


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Fig- 5.11 Relationship between soil type and yield

The box plots, figure 5.11, show the distribution of yield in different soil sub-

groups. The box plots suggest more variation in soil sub-group AP_FS and least in sub-

group AFP_SS. Testing for differences in mean yield by soils suggested that at least one

soil sub-group is significantly different from other soil sub-groups ( p = 0.001).

A step-wise forward regression analysis with all soil sub-groups showed that

yields from soil UP-CL are significantly different yields from other soil sub-groups.

These results suggest that soil has a significant impact on growth and condition of wheat,

which can be measured through remotely sensed NDVI.


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5.5.3.1 Urea fertilizer applications.

Figure 5.15 : a box plot showing the effect of urea fertilizer application frequency

on yield. The box plot suggests more yield if fertilizer was applied more. Analysis is used

data on collected from field of 14 samples. Fertilizer application in analysis is from(n=4)

180 Kg to (n=5) 360 Kg /ha. Mean fertilizer usage in wheat crop is 276 kg/ha. Testing to

find if there is a significant difference in yield, Study suggested that the number of

fertilizer applications relates to the yield variability between fields.

Yield(Q/ha)

50

40

30


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was found mini (1) and maxi(4)times. The correlation of irrigation frequency and wheat

yield was high significant in determination (0.716). Fig 5.16 shows the mean yield and

irrigation frequency for the water regimes as expressed by farmers. Box plots show the

frequency of water (n=4)was high distribution on yield than less frequency (n). Analysis

found irrigation frequency are highly related to yield.

0.00

10.00

20.00

30.00

40.00

50.00

yield(Q/ha)

n= 3

n= 11

n= 10

n= 4

Yield = 11.85 + 8.13*Irrigation applied

R2 = 0.51

Error bars (95% CL of Mean)


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Fig5.17 Correlation of

NDVI,LPI,Irri, Sys and Yield

Fig : 5.17 - is shown the correlation of all parameters. In this study, found NDVI

and yield are influence in correlation. NDVI and IRRI, LPI, SYS are not influence in

correlation, those are found scatters in correlation. The correlation of yield and other

806040200806040321.8.6.4

60

40

200

40200

80

60

40

3

2

1

.8

.6

.4

60

40

20

0

YIELD

NDVI

IRRI

LPI

YIELD

SYS

NDVI IRRI LPI SYS


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its correlation (r2) was 0.661. Likewise, NDVI and LPI (n= 18)was significant high

coefficient of determination( 0.596) low standard error (7.049) and NDVI and Sys

(n=18)was high correlated in determination (0.561) low standard error (7.340). In RS,

Land and management factor input, combination of NDVI, LPI and irrigation

frequency(n=18)was had high coefficient of determination(0.722) low standard error

(6.049). While NDVI, Sys and Irrigation Frequency model had experienced relativity less

variability of wheat yield with a coefficient of determination (0.653).

The result in this study was found using RS, land and management factors model

is better than NDVI alone model in wheat crop yield estimation. The correlation of NDVI

alone and yield was coefficient of determination (0.532).NDVI, land and management

factors were coefficient of determination(more than 0.532).

The study found single date images can provide useful information of the crops

and yield status. But the timing of the image to be used for yield estimation is important.

Though Gielen et al. (2001) explained that there is good correlation between NDVI and

yield but using NDVI as an end-of season yield estimator gives unsatisfactory results


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Variable Count Min Max Mean SD

Pearson

Correlation

Remote Sensing

NDVI 44 0.44 0.88 0.719 0.118 0.729**

Land factors

L P I 18 40.4 95.0 76.7 19.9 0.609**

SYS Index 18 15.0 79.0 61.0 17.19 0.661**

Management input

Urea applied

(Kg/ha)14 180 360 276 74.5 0.446

Irrigation

frequency 28 1 4 2.57 0.87 0.176**

** Significant Et 0.01% level (2 failed)


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frequency LPI+5.694*Irri

NDVI, SYS, Irrigation

Input18

-0.787+13.534*NDVI-0.178*SYS

index+5.116*Irrigation0.653 0.579 6.758 0.002

( a ) ( b)


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Fig 5.19 Correlation of observed yield and Farmers expected yield

The result in this study, predicted yield and observed yield were also high

correlated. In Fig -19: showed independent CCE site (n=2) and farmers expected yield

were high significant in correlation. In this method, CCE sites and farmers expected yield


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The use of land and management parameters alone has shown that the yield

variability can be explained. The combination of NDVI, land and management factors

together are improved the model. This shows that use of NDVI alone, as done

in many studies, can be improved if land and management factors are

also considered, especially at field level where parameters vary from

field to field, as opposed to regional or national level, where these

factors are generalised.

All these findings indicate that there is correlation between remotely sensed

spectral data and yield. The differences in the correlations and explaining ability of yield

variability is due to the level of application and the quality of data being used to

investigate the relationships and to derive models. Muthy et al. (1994) used yield

estimates from CCE, which are fairly accurate and used time composite NDVI. Mohd et

al. (1994) used yield from highly controlled research plots. This study used data collected

through interviews. From the results of this study, it is a significant effect on the degree of

the relationships between remotely sensed NDVI and yield.


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6. Conclusion

The Saharanpur district of Uttar Pradesh ha been taken as the study area for this

project with an objective of Crop Yield Estimation of Wheat by Integrating RS, Land and

Management Factors. The result of this study shown that wheat crop is highly separable

and can be discriminated with more than 95% accuracy using high resolution multi-

spectral LISS-III on board IRS-P6 satellite data. A strong linear and non-linear empirical

relation of NDVI and land, management factors has shown possibility of using satellite

NDVI for retrieving yield model for regional productivity analysis.

High-resolution multi-spectral LISS-III satellite data is good for discriminating

crop in the study area. The combination of satellite NDVI, land and management factors


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REFERENCES

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T., and Parihar, J. S., (1991). Wheat acreage estimation for Haryana using

satellite data. Journal of Indian Society of Remote Sensing, 9 : 295-301 .

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Kalubarme, M. H., Vyas, S. P., Manjunath, K.R., Bhagia, N., Sharma, r., Zadoo, s.,

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data. In Proceedings of the National Symposium of Remote Sensing forSustainable Development, 17-19 Nov. 1992, pp : 349-353


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Grasslands, Colorado. Eighth International symposium on remote Sensing of

Environment, ( Ann Arbor : University of Michigan), 1357-1381

Saha, S.K., and Jonna, s., 1994. Paddy acreage and yield dstimation and irrigated crop

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Field Data Collection Photos

j02 swe beny friend project report

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