Component-I(A) - Personal Details

Component-I (B) - Description of Module

Role Name Affiliation

Principal Investigator Prof.MasoodAhsanSiddiqui Department of Geography,

JamiaMilliaIslamia, New Delhi

Paper Coordinator, if any Dr. M P Punia Head, Department of Remote

Sensing, Birla Institute of

Scientific Research, Jaipur

Content Writer/Author (CW) Kaushal Panwar Senior Research Fellow, Birla

Institute of Scientific Research,

Jaipur

Content Reviewer (CR) Dr. M P Punia Head, Department of Remote

Sensing, Birla Institute of

Scientific Research, Jaipur

Language Editor (LE)

Items Description of Module

Subject Name Geography

Paper Name Remote Sensing, GIS, GPS

Module Name/Title Remote Sensing Applications in Agriculture

Module Id RS/GIS 15

Pre-requisites

Objectives Student will get to know RS analysis helps in agriculture

field.

Student will acquire skill how to study data and its

algorithms in agriculture sector.

Student will be equipped with knowledge to study further

about the applications.

Remote Sensing Applications in Agriculture

Outline

Introduction about role of remote sensing in agriculture and impact on

Indian economy.

Remote Sensing application in agriculture.

Crop production forecasting.

Assessment of crop damage and crop progress.

Horticulture, Cropping Systems Analysis.

Environmental Impact Assessment of Agricultural System.

1.

Keywords

Non-point source pollution.

Impact of Climate Change on Agricultural System.

Biophysical parameter retrieval and process modeling.

Demonstration of technique for forewarning Pests and Diseases.

National Mission

National production forecast (FASAL).

Crop Identification Technology Assessment for Remote

Sensing (CITARS).

Conclusion.

Introduction

Earth observations (EO), comprising satellite, aerial, and in situ systems, are

increasingly recognized as critical tool for studying and monitoring of natural

resources and unfolding the intricate behavior of the complex earth’s dynamic

processes. The Remote Sensing research has evolved as multidisciplinary theme

dedicated to developing the applications of remote sensing technology of Land

Ocean and atmosphere addressing geologic, botanic, and hydrologic issues at

national, regional, and site-specific scales.

Agriculture plays dominant role in economy of almost every nation. Whether

agriculture represents a substantial trading industry for an economically strong

country or simply sustenance for a hungry, overpopulated one, it plays a significant

role in almost every nation. The production of food is important to everyone and

producing food in a cost-effective manner is the goal of every farmer, large-scale

farm manager and regional agricultural agency. A farmer needs to be informed to

be efficient, and that includes having the knowledge and information products to

forge a viable strategy for farming operations. These tools will help him

understand the health of his crop, extent of infestation or stress damage, or

potential yield and soil conditions. Commodity brokers are also very interested in

how well farms are producing, as yield (both quantity and quality) estimates for all

products control price and worldwide trading. The policy makers are interested in

harnessing the best available tools for optimizing the resource use, minimizing the

damage/losses and ensuring the societal benefit. Most important component of

such decisions is the agricultural and allied information at best possible resolution

of spatial and temporal scales. Remote sensing techniques come handy for

achieving such an objective. The diverse agro climatic conditions give rise to

cultivations of large number of crops. The information on long term sustainability

vis-à-vis environmental impacts demands seasonal and yearly information of

variant as well as invariant resources. The diverse crop growing conditions coupled

with uncertainties of climate situation demands much more finer crop information

needs on temporal and spatial scale.

During the last two decades remote sensing and GIS techniques are applied to

explore agricultural applications such as crop identification, area estimation, crop

condition assessment, soil moisture estimation, yield estimation, agriculture water

management, agro meteorological and agro advisories. The application of remote

sensing in agriculture, i.e. in crops and soils is extremely complex because of

highly dynamic and inherent complexity of biological materials and soils (Myers,

1983). However, remote-sensing technology provides many advantages over the

traditional methods in agricultural resources survey. The advantages include,

i) capability of synoptic view

ii) potential for fast survey

iii) capability of repetitive coverage to detect the changes

iv) low cost involvement

v) higher accuracy

vi) Use of multispectral data for increased information, and so on.

Now almost all part of the electromagnetic spectrum is utilized for agricultural

applications at various scales. Crop growth and associated factors change

dynamically and need continuous monitoring. The different sub themes of

agriculture are shown in fig. 1 Remote sensing (RS) technology has potential to

estimate crop area and forecast productivity at district and regional level due to its

multispectral, large area and repetitive coverage. The following sections explain

the developments taken place during the last three decades with special reference

to agricultural remote sensing in India.

Fig. 1: Remote Sensing Applications in Agriculture

Source: https://nrsc.gov.in/Agriculture

Remote Sensing Application in Agriculture

Crop Production Forecasting

Fig.2: Crop Production Forecasting Graph

Source: http://apal.org.au/2015-pome-fruit-crop-forecast/

Agricultural crop identification and area estimation has been the focus ever since

the inception of civilian RS program in the U.S. in the early 1960s. Some of the

early studies conducted were experiments such as Crop Identification Technology

Assessment for Remote Sensing (CITARS) and Large Area Crop Inventory

Experiment (LACIE). Exploring the use of remote sensing for agricultural

application in India started with the use of multi band and colour infra red (CIR)

aerial photographs as early as 1974-75. Further knowledge on crop signature was

gathered through scientifically designed field experiments using multi band

radiometer under the Indian Remote sensing Satellite-Utilisation Programme (IRS-

UP) on:

i) Crop production forecasting

ii) Crop stress detection, and

iii) Crop yield modelling. Many of these studies have led to the operationalisation

of the methodology and conduct of national-level projects.

Launch of the Indian Remote Sensing Satellites (IRS-1A, IB & 1C) carrying linear

imaging self-scanning sensors (LISS I,II &III) provided a much-required impetus

to agricultural applications. IRS-1C carried onboard a unique combination of three

sensors viz., (i) Wide Field Sensor (WiFS) with 188m spatial, two spectral bands –

red and near infrared, 810km swath and a repetivity of 5 days, (ii) Linear Imaging

Self scanning Sensor (LISS-III) with 23.5m spatial resolution in the green red and

near infrared region, and 70.5 m in the middle infrared region, and 140 km swath,

and (iii) Panchromatic (PAN) camera with 5.8m spatial resolution, 70km swath

and stereo capability. The launch of RISAT has filled another dimension to the

agricultural remote sensing as all weather capability of data is now reality from

Indian satellite.

Crop production forecasting comprises identification of crops, acreage estimation

and forecasting their yield. Crop identification and discrimination is based upon

the fact that each crop has a unique reflectance pattern in different parts of the

electromagnetic spectrum which is termed as spectral signature. The general

spectral response of a crop canopy in the visible and NIR region is characterised by

absorption in the 0.35 to 0.5 um and 0.6 to 0.7 um regions (due to chlorophyll

pigments), high reflectance in the green region (around 0.54 um), a steep increase

in the reflection in the 0.7 to 0.74 um and high reflectance in 0.74 to 1.3 um region

due to internal cellular structure of the leaves. The absorption at 1.45, 1.95 and 2.6

um spectral bands is due to leaf water content. The varying response of the crops

stems from the fact that various factors such as type of crop, stage of the crop,

canopy architecture, per cent ground cover, differences in cultural practices, crop

stress conditions, background soil/water etc., contribute to the composite response.

Each crop has its own architecture, growing period, etc. thus enabling

discrimination through remote sensing data. If there are two crops with similar

spectral signatures on a given date (confusion crops), multidate data are used to

discriminate them Vigour of the crop is manifested in the absorption in the red and

reflectance in the near infrared spectral regions. It has been observed that the ratio

of near infrared to red radiance is a good indicator of the vigour of the crop. Some

of these properties are utilised in crop identification, crop condition assessment and

yield forecasting. The broad procedure used for crop acreage estimation is shown

in illustration below. The data is analysed applying Maximum Likelihood

Supervised classification technique (other classifiers are also used), where limited

field information called ‘Ground truth’ is used to generate the training signature. In

case of complete enumeration data for analysis was selected by overlaying the

boundary mask of the area over the remote sensing images. When it was extended

to large area stratified sampling technique was developed where area was first

divided with a grid representing sampling frame size (5 X 5 km) and then data

belonging to selected sample segments is extracted (20%) are analysed. The

samples are randomly drawn proportionate to size of each stratum. Area estimate is

made from the proportion of crop present in the sample.

Fig. 3: Procedure For Acreage Estimation using Sampling Techniques

Source: self

Since the space technology has advanced and variety of sensors of different spatial,

spectral and temporal resolutions is available and there is a continual need for crop

information throughout the growing period (Fig. 3), frequent monitoring is feasible

at various scales. Realizing the importance of multiple source information like

weather, econometric and field survey towards a robust approach for multiple

forecasts of a number of crops, a new concept was formulated: FASAL

(Forecasting Agricultural output using Space, Agro- meteorology and Land based

observations) (fig. 4). Implementation of FASAL was initiated in 2007-08.

Fig. 4: Information need and sources in frequent crop monitoring

Source: https://www.crcpress.com/GIS-Applications-in-Agriculture/Pierce-

Clay/p/book/9780849375262

Assessment of crop damage and crop progress

Damage to crop due to moisture stress is a common occurrence in rain fed rice

growing region. The characteristic backscatter profile of rice using temporal SAR

data is useful in characterising the crop condition as normal and sub-normal. Flood

is a common phenomenon in many rice-growing regions, particularly in monsoon

season. Temporal SAR data is found not only to map flood affected rice fields, but

also to model duration and degree of submergence. Complete submergence of rice

at any given period of growth lower the backscatter. The degree of submergence

was modelled with reference to crop height and its deviation from the reference

normal growth profile. The model thus can detect the completely submerged fields

as well as partially submerged fields. It is well established that sowing dates have a

significant effect on crop biomass and yield. Temporal SAR data is used to

categorise the fields as normal, late and very late sown which is additional

component of crop assessment need that enables identifying the reasons for delay.

Similar efforts have been made which for deriving the regional variation of rice

growth profile using optical data (fig. 6)

Fig: 5a.crop growth pattern in feb 2016

Fig: 5b. Crop growth pattern in April 2016

Fig: 5c.crop growth pattern in oct 2016

Fig. 6: Rice spectral profile of different regions in India derived using multidate

optical data

Source: Remote Sensing of the Environment, John R. Jensen

O – OBSERVEDM - MODELLED

J&k

TNAPPUNJAB

Crop yield models

Fig.7: Crop yield model

Source: https://www.slideshare.net/grssieee/luoigarss20112385ppt

Out of the two constituents of crop production, namely crop acreage and crop

yield, the assessment of the latter is most complex because of the high variability

involved. The information on crop yield is an important input for production

estimation shown in fig(7). Every crop genotype has certain yield potential, which

can be achieved (to an approximation) in experimental field with optimal

conditions. However, in the real world, the crop yield is conditioned by various

parameters like soil, weather and cultivation practices, like date of sowing,

irrigation and fertiliser. Crop yield is also influenced by biotic stresses like disease

and pest. While the variability of the weather explain most of the annual variability

over a short period of time, the cultivation practices and new varieties explain most

of the variability over a period of 10 to 20 years. For longer periods of time,

climatic changes or soil improvements or degradation are the main factors

influencing the crop yield However, the fact that all these factors are

interdependent makes the yield assessment a more complex task. Hence, one way

of forecasting of the yield is understanding the variability in the above parameters

and defining their relationship with the final crop yield. Satellite based remote

sensing provides a suitable alternative for crop condition and yield assessment/

forecasting, as it gives a timely, accurate, synoptic and objective estimation of

various crop parameters. The time series based trend and arima models were

developed in the beginning of CAPE project based on district-wise yield data of

DES which were used to compute production by multiplying with the RS derived

area estimates. Further agro meteorology model, spectral models and combination

of these models were tested and used for a variety of conditions, crops and regions.

The agro meteorology inputs were predominantly significant rainfall at fortnightly

intervals, minimum and maximum temperatures etc that would form part of

correlation weighted regression model. The RS input, NDVI (single date or derived

from profiles integration) in such models were used for model development. The

rice biomass has high correlation with SAR backscatter and therefore the yield.

The crop vigour is an indication of crop yield. The vigour of crop can be assessed

using vegetation indices derived from different parts of the spectrum. The

normalized vegetation index is one such index which represents the green biomass

of the plant. The NDVI can be directly correlated to the yield of the crop and this

relation can be used for estimating yield. A variety of models involving

combination of factors of weather and spectral parameters have been developed

and used in conjugation with remote sensing derived acreage for providing

production estimates. Plant growth simulation models have been used for

monitoring crop growth, health shown in fig(8) and predicting yield .However,

their use in large areas has been limited because most plant growth models were

developed at the field scales and the performance of the models is not so

satisfactory when they are extended from field to regional scales.

Fig.8: Crop Stress Detection

Source: http://www.cornandsoybeandigest.com/soybeans/detect-crop-stress-

thermal-maps

Remote Sensing Applications - Horticulture

India is bestowed with varied agro-climate which is highly favourable for growing

a large number of horticultural crops such as fruits, vegetables, root tuber,

ornamental, aromatic plants, medicinal, spices and plantation crops like coconut,

areca nut, cashew and cocoa. India is the largest producer of fruits (49.36 MT) and

second largest producer of vegetables (93 MT) in the world. Horticulture occupies

about 12 per cent of the total cultivated area in the country, and contributes about

25 per cent of the total agricultural export. Remote sensing technology helps in

generation of crop Inventory of major horticultural crops, site suitability analysis

for expansion/introduction, infra-structure planning for post harvest requirement,

disease detection and precision planning for horticulture. The general approach

involves the use of high resolution/high temporal data (LISS-III) for identifying the

crop of interest and relevant collateral information (i.e soil, water, climatic,

infrastructure etc) and processing for logical clustering for decision-making.

Feasibility studies demonstrating the remote sensing technology in horticultural

sector have been carried out. Inventory of orchards like apple, grape, mango,

coconut, banana and vegetables like potato, onion has been carried out in different

agro climatic regions of the country.

The post-harvest infrastructure planning and optimization of cold store facility for

post-harvest management of potato has been demonstrated. Apart from these early

trends in national winter potato production from the country is regularly brought

out to infer about National/state production prospects and identifying areas with

significant change.

Cropping Systems Analysis

A cropping system is defined as the cropping pattern and its management to derive

benefits from a given resource base under a specific environmental condition. This

requires identification of crops and areas where changes in cropping patterns are

desirable. This calls for an initial step of creating an updated database of the

present cropping systems of the country and simulate the long-term effects, taking

into consideration the resource base and agro climatic condition. Satellite remote

sensing (RS) and Geographical Information System have a crucial role to play in

this direction. The multidate satellite data is helpful in deriving seasonal cropping

pattern, sowing pattern, crop rotation, efficiency indicators and other related

parameters.

Environmental Impact Assessment of Agricultural System

Fig. 9:Concept of Environmental Impact Assessment of Agricultural System

Source: Environmental Impact Review.v.28

The agriculture has transformed from simple sustenance objective to intensive-

commercial form thus depleting and degrading the environmental resources.

Pressure on high production has led to intensification of agriculture. Intensive

Agriculture, long term sustainability and quality of natural resources, thus is matter

of compromise and concern. Agricultural is a major reservoir and transformer in

global cycles of carbon, nitrogen and water. Intensive agriculture leads to erosion

of soil resources, loss of biodiversity, alienation of ecological niches, temporary

imbalance in soil microbial functioning, associated long-term effects on microbial

processes and changes in biogeochemical Cycles. Agricultural activities contribute

about 70% of all anthropogenic N2O emissions and about 65% of all anthropogenic

CH4 emissions. Nutrient leakage from agriculture is a prime cause of degradation

of groundwater, surface waters and estuarine and coastal marine systems, and via

the atmosphere affects other terrestrial systems. Nitrate contamination of

groundwater is common in agricultural areas around the world. Some of the

specific components include fertilizer and pesticide residual toxicity, plant/soil

metabolic exudates such as methane/nitrous oxide in the immediate micro/macro

environment. Methane and nitrous oxide form important components of such an

interface. The GHG pattern which is also available using sensors on board

satellites is also being studied in detail which is clearly shown in fig(8).

The methodology was developed to generate total annual methane emission map

from the rice areas of India and its temporal pattern taking into consideration the

diverse conditions under which the rice is grown. The methodology was developed

for the variety components on use of RS data for stratification, spatial and temporal

sampling strategy, development of indigenous method of sampling and up-scaling

of methane. Results showed that the major stratum emerged as the rain fed drought

prone with 42.8 per cent of total rice lands (wet season) and found in many states.

Livestock constitutes an integral component of Indian agriculture. India possesses

the world’s largest total livestock population of 485 million, which accounts for

about 57% and 16% of the world’s buffalo and cattle populations, respectively. A

detailed state/ district-level methane emission inventory for different livestock

categories was made using the country-specific and Indian feed standard based

methane emission coefficients, which are based on IPCC guidelines, and the latest

available livestock census. The total methane emission including enteric

fermentation and manure management has been estimated as 11.75 Tg for the year

2003 (Chhabra et al. 2008, 2009). Enteric fermentation accounts for ~92% or 10.65

Tg of the total, while manure management contributes only 8% or 1.09 Tg.

Non-point source pollution

Agriculture has been identified as the largest contributor of non-point source (NPS)

pollution of surface and ground water systems globally (Thorburn et al., 2003).

Fertilizers, which are used as important inputs in agriculture to supply essential

nutrients like nitrogen (N), phosphorus (P), and potassium (K) also, serve as a

major non-point source pollutant. An integrated methodology was developed for

quantification of different forms of nitrogen losses from rice crop using remote

sensing derived inputs, field data of fertilizer application, collateral data of soil and

rainfall and nitrogen loss coefficients derived from published nitrogen dynamics of

kharif and rabi seasons. The nitrogen losses through leaching in form of urea-N,

ammonium-N (NH4-N) and nitrate-N (NO3-N) are dominant over ammonia

volatilization loss. The study results indicate that nitrogen loss through leaching in

kharif and rabi rice is of the order of 34.9% and 39.8% of the applied nitrogenous

fertilizer in the Indo-Gangetic plain region. This study provides a significant

insight to the role of nitrogenous fertilizer as a major non-point source pollutant

from agriculture.

Impact of Climate Change on Agricultural System

Fig.10: Impact of Climate Change on Agriculture

Source: http://www.mdpi.com/2071-1050/7/7/8437/htm

Climate change is one of the most discussed topics of the last two decades shown

in the fig(10). It impact on agricultural systems with inputs from multiple sources

were studies with simulation models. The CropSyst and water balance models

along with the climate forecasts using GCMs, RCM and statistical downscaled

model (under different scenario) for understanding the impact of climate changes

on agricultural systems were studied. The impact of climate change as projected by

the RCM-PRECIS (A1B scenario) on the rice-rice system of West Bengal showed

yield reduction ranging from 7.88 to 18.84 %.The adaptation study in rice-rice

system was early sowing by a period of 12 days to increase the yield in 2020 and

compensate the yield reduction in 2050. Analysis of the climatic extreme events

under climate change scenario of HadCM3 through different temperature and

precipitation indices was carried out.

Biophysical parameter retrieval and process modeling

The satellite derived biophysical products is one of the key developments taken

place during last two decades. The investigations on deriving these products were

carried out in India using Indian and other sensors. The NDVI, fAPAR, insolation,

LAI, LST etc are some of them on which R & D were carried out. The Leaf Area

Index (LAI) is a key biophysical variable used by plant physiologists and

modellers’ for estimating foliage cover and plant growth and biomass. The

regional modeling of growth processes such as evapotranspiration (ET) and net

CO2 assimilation require retrieval of some core variables such as land surface

temperature (LST), leaf area index (LAI), albedo and soil moisture etc.. Field-scale

(local) and regional-scale (agro-climatic zone) non-linear empirical models are

developed for wheat leaf area index (LAI) based on normalized difference

vegetation index (NDVI) using IRS 1D LISS-III.

Demonstration of technique for forewarning Pests and Diseases

The remote sensing pests and diseases has remained a challenge due to the

complexity of occurrence, overlapping with other factors, varying magnitudes and

subtle manifestation. The mustard aphid (Lypaphis erysimi) infestation models

have been developed from near-surface air temperature and relative humidity from

sounder data (e.g. TOVS), and sowing dates (Bhattacharya et al, 2007) and

validated through a collaborative study with National Research Centre on

Rapeseeds and Mustard (NRCRM), Bharatpur, Rajasthan. These models were later

up scaled and extrapolated using SPOT-VGT to map aphid onset dates (Dutta et al,

2008, Fig.).A new methodology of multi-stage tracking of Sclerotinia rot

(Sclerotinia sclerotiorum) disease in a large mustard growing region over in

Bharatpur district has been conceptualized and demonstrated.

National Missions:

FASAL: National production forecast

Fig.11: Concept of FASAL

Source:https://nrsc.gov.in/Forecasting_Agricultural_Output_using_Space_Agro-

meteorology_Land_based_Observations

Multiple in-season wheat forecasts using multi-date IRS WiFS data and weekly

weather variables at meteorological sub-divisions in major wheat producing states

of India are being made at national level shown in fig(11). Seven major wheat-

producing states, Uttar Pradesh, Punjab, Madhya Pradesh, Haryana, Rajasthan,

Gujarat and Bihar form the study area. These states account for more than 90% of

LandObservations

MULTIPLE IN-SEASON FORECAST

Pre-Season

Early-Season

Mid-SeasonState

Pre-HarvestState

Pre-HarvestDistrict

Cropped area Crop condition

Crop acreage

Crop yield

Revised Assessing Damage

Crop area &

Production

Crop area &

Production

the wheat production. A three level stratification of the area based on agro-climatic

zones, an agricultural area and crop proportion is made. A grid of 5X5 km is

overlaid on the satellite images and fifteen per cent random sample (each of size

5*5 km) is selected within a state. The sample segments are classified using in-

season ground truth and a hierarchical (decision rule based) classifier. The state

level acreage estimates are then statistically aggregated to arrive at national level

wheat acreage estimates. The methodology for state level yield forecasting is

multiple regression models based on temperature, using a correlation weighted

regression approach. The National level acreage and yield estimates are then

combined to provide National Production Forecast.

Investigations using space borne SAR data started with limited use of data from

JERS, SIR C sensors. However, the possibility of examining space borne radar

data for large area agricultural application was realised with the successful launch

of ERS-1 Synthetic Aperture Radar (SAR) (SAC, 1997). Due to the problem of

cloudy weather during rainy season, Kharif rice crop production estimates in the

major rice growing states are being generated using multi-date Synthetic Aperture

Radar data (fig. 11a). Rice growing environment or management practices ensures

that there is standing water beneath the canopy at least for a short duration during

crop season. This information is used to characterize the rice crop on a temporal

domain (fig. 11b). The rate of change and direction of change of SAR response

aids in building decision rule for classification of rice pixels. A stratified random

sampling approach for each state is adopted for acreage estimation with a sample

size of 5*5 km. A fifteen per cent sampling fraction and in-season ground truth

information of the selected sample segments are used. The segments are classified

using a decision rule classifier followed by statistical aggregation of state level

acreages to national level rice acreage estimate. The statistical relationship between

yield and rainfall during the cropping season is used for yield forecasts. The

district level models are combined with acreage and production forecasts for the

country is made.

Fig. 12a: Three date colour

composite of Scan SAR data (

R:G:B: Date1:Date2: Date 3) over

the study district showing distinct

signature of rice (b,c,d,e,f) in

different growth stage and other

land cover classes (a: water, h:

forest and i: urban).

Fig. 12b: Temporal pattern of rice crop

Source:https://www.researchgate.net/publication/301310407_Special_Issue_Extre

me_Weather_Events_and_Indian_Agricultu.

Crop Identification Technology Assessment for Remote Sensing (CITARS)

CITRAS (Crop Identification Technology Assessment for Remote Sensing) was an

experiment to quantify the crop identification performance achievable with several

automatic data processing classification technique .It was conducted from April

1973 to April 1975.The five specific objectives of CITARS were:

a

b c d

e

f

g

h

i

-25

-20

-15

-10

-5

0

5

-24 0 24 48 72 96 120 144 168

Days ( July 1=1)

Ba

ck

scatt

er

co

-eff

icie

nt

(dB

)

Rice Water Urban Homestead Other Crops

Temporal backscattering profile of Rice & Non-Rice Area

1

2

3

1 45 6 7

2 3 4 5 6 7

Field-preparation Puddling Transplanted Vegetative Peak-vegetative Heading Maturity

-25

-20

-15

-10

-5

0

5

-24 0 24 48 72 96 120 144 168

Days ( July 1=1)

Ba

ck

scatt

er

co

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icie

nt

(dB

)

Rice Water Urban Homestead Other Crops

-25

-20

-15

-10

-5

0

5

-24 0 24 48 72 96 120 144 168

Days ( July 1=1)

Ba

ck

scatt

er

co

-eff

icie

nt

(dB

)

Rice Water Urban Homestead Other Crops

Temporal backscattering profile of Rice & Non-Rice Area

1

2

3

1 45 6 7

2 3 4 5 6 7

Field-preparation Puddling Transplanted Vegetative Peak-vegetative Heading Maturity

1) To assess the effects of Landsat data acquisition during the corn and soybean

growing season on crop-identification performance.

2) To access the effect of differing geographical locations having differing soil,

weather, management practice, crop distributions, and field sizes on crop-

identification performance.

3) To quantify the variation in crop-identification performance using the differing

automatic data processing (ADP) classification procedure.

4) To test the ability to extend training signatures, selected within the test area, to

train the classifiers in other areas.

5) To access crop identification benefits to be derived from classifying with

multiple Landsat data acquire multi-temporally during the crop growing season.

Technical Approach

Six major tasks were completed during CITARS they were:

i) Design of sampling scheme in Illinois and India for corn and soybeans

using Landsat MSS data.

ii) Acquisition and preparation of a Landsat-1 data set with ancillary data

sufficient to support the experimental objectives and design.

iii) Computer added processing of this data set with the selected

classification algorithms and procedures.

iv) Quantification of the crop-identification performances to evaluate the

ability of these procedures to satisfy agricultural applications

requirements.

v) Statistical analysis to quantitatively evaluate the impact of major factors

known to affect crop identification performance.

vi) Interpretation of the results to ascertain the underlying factors responsible

for the result and to drawn inference as to the status of the technology as

it relates to agricultural applications.

Conclusions

Remote sensing applications of agriculture expanded into different domains and

further many of them grew to higher levels of maturity during last twenty-five

years. The crop production forecasting for example started from experimental

stage and moved up to operational stage. The newer applications of agriculture

and its environment assessment were also explored. With availability of SAR

sensors, monitoring of crop during kharif season became a reality. India is a

global leader in agricultural applications of remote sensing and has carried out

capacity building on not only Indian scientist, but also of scientists of other

countries in India as well as outside. The specific tools and techniques have

been developed to cater to above needs and operationalisation. Almost all

sensors spanning the entire range of EMS used in RS application have been

studied and host of them have been showcased. The emphasis in future should

be on products and services sector encompassing more decipherable and ready

to used knowledge based RS products. The agro ecosystems analysis and

climate change impacts would be the focal theme in which variety of

components can come from RS data. The horticulture and site specific

management would demand much more complex algorithms and service

oriented products. The RS and communications technologies would fuse in the

future to deliver the near-real time service to all stakeholders of agriculture.