Separate antennas for 23.8/31.5

GHz and the 50-60 GHz band have been the

final choice for a scanning system to achieve

uniform beam-width and yet not compromise

the beam-efficiency. The dual-reflector

Gregorian antenna has been adopted to

achieve ~95% beam efficiency, and better

than -23dB cross-polarization performance

within reasonably compact dimensions. Each

antenna system consists of a scanning main

reflector, a stationary secondary reflector, a

profile-corrugated feed-horn and an otho-

mode transducer for polarization separation.

The scanning reflectors of both antenna

systems will be placed back-to-back so that

their rotation can be synchronised by a

common scan-mechanism.

The atmosphere-leaving radiance

captured and polarization-separated by the

antenna will be fed to a wideband (50-60

GHz) low-noise amplifier and down-converted

at dc to 5.5 GHz using a sub-harmonic mixer

at the front-end. At the backend, the IF energy

undergoes power-division using 1:10 and

1:5 Wilkinson power dividers, separation of

channels using 17 precise band-pass filters,

detection using backward tunnel diode and

digitization using 14 bit data acquisition

system. An automatic gain control loop is in

place to adjust the power level at the input to

the detector so that the diode can be made to

operate in linear power-to-voltage conversion

regime and the dynamic range of the sensor is

captured within the ENOB. An array of platinum

resistance thermometers will be deployed for

sensing the physical temperature of hot-load

as well as reflectors with a precision of 0.1K. A

payload controller will generate all timing and

control signals for the sub-systems, interface

Fig. 17: (a) Weighting functions of TSU channel (b) TSU channel centre frequencies amidst the 50-60 GHz oxyen absorption spectrum

Fig. 18: (a) A schematic view of TSU aboard Scatsat- (b) A schematic view of TSU payload configuration

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3with the spacecraft BMU and the scan control

electronics, format the data grabbed over a

complete scan and transfer the same to BDH.

Radar Based Imaging Sensors

ISRO mastered space based imaging

capability two decades ago with the launch

of IRS-1A on 17th March, 1988. Progressively,

optical camera based imaging capability is in

the path of continuous improvement both in

terms of resolution and swath coverage till

this date. In a programmatic approach, the

scope of ISROs remote sensing capability was

extended to microwave bands after attaining

successful grip over imaging technology in

optical bands.

Synthetic Aperture Radar (SAR) was

logical choice for Imaging Radars in microwave

bands, considering its all weather and day-

night capability. Further, radar response to

geometric shape and distribution of features,

was realized to be complementary and

supplementary to spectral reflectance based

feature characterization available in optical

imaging process. However, development of

SAR needed mastering of both sophisticated

radar technology and complex SAR processing

algorithm. Since space borne SAR was

highly cost intensive, airborne version of

SAR was embarked upon as cost effective

route of mastering SAR technology in

all its nuances. Over the period, both airborne

and space borne versions of SAR technology

development have taken vibrant shape in ISRO

towards harnessing their utilization potential.

Airborne Imaging Radar Development

X-Band Side Looking Airborne Radar

Imaging Radar development was initiated modestly with development of Side Looking

Airborne Radar (SLAR) in X-band (Table-4). This SLAR was basically used for development

Fig. 18: (c) DVM of TSU under test

Fig. 19: A schematic view of TSU cross-track scan configuration

Fig. 20: SLAR system mounted in dakota and a sample of imagery obtained by SLAR

43

of radar technology and it was in flying condition from 1985-1989. The view of the SLAR system and a typical

scene imaged by this system, is presented in Figure 20. SLAR provided imaging over 2-5 km swath with

50 m -100 m resolution. Compared to todays technology, its capability was primitive; but it acted as a test bed to test

various new technologies like the radar itself, data recording system and on-board Quick Look Display (QLD) system.

Further, the experience gained by experimenting with this system, acted as foundation for subsequent development of

complex SAR technology.

Airborne Synthetic Aperture Radar (ASAR)

ASAR (Airborne SAR) was the first proud foray of ISRO in SAR development . The aim of this development was

not only technology attainment but also demonstration of application potential. Consequently this system was configured

in C-band providing 6 m resolution over 25 km swath. Detailed specifications of the ASAR system is presented in Table-4.

The ASAR system was used for experimentation since 1997 and for the first time, a modest narrow swath (2.5 km) Real

Time Processor was demonstrated in this system.

The high point of this project was development of a sophisticated SAR processor involving motion sensing and

compensation. In fact it had a novel two track motion compensation processor which added to the robustness of the

system. This algorithm was implemented on a parallel processing system consisting of 8 Xeon processors. A sample

image of generated by ASAR along with photograph of the system can be seen in Figure 21.

SLAR (X Band)

ASAR(CBand)

DMSAR(C Band)

DMSAR(X Band)

Frequency 9.6 GHz 5.3 GHz 5.35 GHz 9.65 GHz

Polarisation HH VV/HH VV/HH HH

Platform/ Altitude Dakota (2-3 km) Beechcraft (8 km) Beechcraft (8 km) Beechcraft (8 km)

Pulsewidth/ Chirp Bandwidth

80/300 ns 20 sec25 MHz

20 sec 225,75,37.5,18.75 MHz

20 sec450,225,75MHz

Resolution/Swath 50-100 m/5 km

6-8 m/25 km

1 m/6 km3 m/ 25 km5 m/50 km10 m/70 km

0.5 m/4.5 km1 m/ 7.7 km3 m/7.7 km

PRF 850 Hz 425-525 Hz 452 hz 360-720 Hz

Peak Power 25 kW 2 kW 8 kW 6 kW

Antenna Length/ Pattern

2 m/ Cosine Weighted 1.3 m/ Cosec2 1.3 m/ Cosec2 0.8 m/ Cosec2

Table 4: Summary specifications of airborne imaging radars developed in ISRO

ASAR has been used to demonstrate flood mapping and monitoring operation in 2003-2004 (Figure 22). ASAR

experience provided the impetus for development of an improved version of C-band airborne SAR, DMSAR (SAR for

Disaster Management), exclusively for flood monitoring activity. ASAR experience has demonstrated that for quick reaction

to flood related emergency, airborne SAR system is more effective than its space borne counterpart. Space SARs, with

their fixed orbital configuration, cannot ensure quick reaction to flood situation.

DMSAR Airborne SAR for Disaster Management

DMSAR in C-band was developed with operational requirement in view. To aid flood monitoring and assessment

of damage to infrastructure, four resolution modes were provided (Table-4). DMSAR was test flown for the first time

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3in Nov. 2005. It was calibrated completely

and its effectiveness in flood assessment was

evaluated in 2006. From the year 2007, it has

been pressed into trial operational service.

To support its high resolution and

wide swath capability, the ASAR processing

algorithm was significantly modified. A new

algorithm called Track Steering algorithm

was developed (under process of patenting)

to process in high resolution modes. The

DMSAR processor is now available on three

platforms:

a. A parallel machine with 32/64 Itanium

processors

b. In house developed Near Real Time

Processor (NRTP) built around 96 TS-

101 processors . Th is i s a portab le

system, which can be carried in the Beechcraft

itself and taken to base camp for processing

and dissemination of DMSAR data.

c. GARUDA computing grid provided by

CDAC. Using this grid, DMSAR data can

be processed simultaneously on an array of

computing nodes available in academic and

research institutions across India.

Figure 23 shows the photograph of

DMSAR in operation and also a sample of

high resolution imagery obtained by it. Figure

24 depicts a typical flood region, mapped by

DMSAR, over Dharbhanga, Bihar in 2007. Figure 25 shows the devastating breach of Koshi river in

Napal in Sept. 2008 as captured by DMSAR. This breach resulted in change of Koshi river track and

resulted in devastating flood of Bihar plains. Presently DMSAR in X-band is under test. While C-band

DMSAR operates in high incidence angle of 65 - 85 for wide swath coverage, X-band DMSAR is

being designed for high resolution operation at low incidence angle of 25 - 55 .

RISAT-1 Radar Imaging Satellite, ISROs First SAR Satellite

Radar Imaging Satellite-1 (RISAT-1), carrying Indias first indigenously developed space-borne,

C-Band Synthetic Aperture Radar (SAR) payload was launched on 26th April, 2012 by PSLV-C19 flight.

Fig. 21: ASAR system mounted in beechcraft and a sample of imagery obtained by ASAR

Fig. 22: Preflood and during flood image over Maligaon, Assam, imaged by ASAR in 2004

45

RISAT-1, after its positioning at 536 km sun-

synchronous dawn-dusk circular orbit, was

operated on May 1, 2012 and the SAR images

of good quality, have been received.

Success of mission marks entry of

ISRO into a select group of space agencies

operating C band SAR. At present only

Canadian RADARSAT -2 mission is providing

images in this frequency band. However,

a number of agencies have been providing

SAR images in X band (TerraSAR-X from

Germany, Cosmos Skymed from Italy and

RISAT-2 from India).

RISAT-1 Mission and SAR Payload

Configurations

RISAT-1 mission is designed to

provide SAR images covering the country

with a repetivity period of 25 days, at 6 am

- 6 pm equator crossing time. It is possible to

operate RISAT-1 for up to 10 minutes duration

in each of its 14 orbits per day around the

earth. It is possible to send the image data

collected by the payload to ground station

in real time or use the onboard solid state

recorder and down link later to the ground

station. This recorder capability enables

imaging of any portion of the globe.

The spacecraft design features a very

high transmission data rate of 640 Mbits/sec

in X-band. This is achieved using frequency

reuse of X band with right hand circular and

left hand circular polarisations.

RISAT payload is an active remote

sensing system as it carries its own source of

illumination. The payload transmits a series

of electromagnetic pulses of radiation in C

band using an active antenna array of 576

Fig. 23: DMSAR system mounted in beechcraft and a sample of high resolution imagery obtained by DMSAR

Fig. 24: Extent of flood from preflood image obtained on 23/06/07 and flood image ob-tained on 03/08/07 over Dharbhanga, BiharBlue Colour: flooded regionBlack Colour: perennialy water logged region

Fig. 25: Koshi river breach imaged by DMSAR in 2008

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3transmit- receive modules mounted on panel of ~6 meters by ~2 meters. The electromagnetic pulses

strikes the earth surface and the back scattered signal is received by the receive modules mounted on

the antenna and by time correlated processing of this signal, information about the earth surface is

deciphered. The large number of transmit receive modules on the antenna panel enables generation

of a beam of electro-magnetic pulses in C band. By controlling the phase and number of modules

energized, it is possible to change the beam direction as well as beam-width. This is known as

electronic beam steering. Beam steering capability enables operation of the payload in a mode called

ScanSAR mode.

The system is also designed to send and receive the signals in different linear and circular

polarisations. These capabilities make RISAT 1 payload a unique SAR, currently in operation from

space. This payload is unique because of its ability to provide a large swath( ~200 kms) images in multi

polarization modes. These modes cover transmit in Horizontal polarisation and receive in horizontal

polarisation (HH mode). Similarly one can transmit in Horizontal and receive both in Horizontal and

vertical polarizations. (HH+HV modes). In

similar fashion one can obtain VV or VV+VH.

One can even obtain Quad polarization data

(VV+HH+HV+VH) by transmitting the signal

in H and V in periodic bursts and receiving

the data in both H and V polarization

simultaneously. RISAT SAR has unique

Hybrid polarimetry mode, where signal

is transmitted in circular polarization and

signal is simultaneously received in H and V

polarization.

Figure 26 illustrates overall satellite configuration and Table-5 gives the salient features.

RISAT-1 has imaging capabilities in Strip-map and ScanSAR modes with resolution from

1-50 m and swath coverage from 25 km to 223 km, with multi-polarization capabilities. Imaging mode

configurations are illustrated in Figure 27 and salient imaging specifications are shown in Table-6. The

basic imaging modes, identified for RISAT-1 SAR payload, are as follows:

Fine Resolution Strip-map Mode-1 (FRS-1): It is based on conventional mode

of SAR Strip-map imaging. The orientation of the antenna beam is fixed with

respect to flight path so that constant swath (25 km) is illuminated along the flight direction.

The intended resolution is 3 m for FRS-1 mode.

Coarse Resolution ScanSAR Mode (CRS): This mode allows for a multifold increase of the swath.

This is achieved by periodically stepping the antenna beam to the neighboring sub-swaths

(in the range direction using 12 beams). Swath in CRS mode would be 223 km with a resolution

of 50 m.

Fig. 26: RISAT spacecraft configuration

47

Medium Resolution ScanSAR Mode

(MRS): This is a 6-beam scanSAR mode, similar

to the CRS mode, providing a resolution of

25 m over a swath of 115 km.

Fine Resolution Strip-map Mode-2 (FRS-2):

This mode has quad polarization capability.

Conceptually, this mode is a hybrid of

strip-map and scanSAR, where polarization

combinations are switched periodically

instead of beams.

High Resolution Spotlight Mode (HRS):

In the spotlight mode, the antenna beam

is oriented continuously to illuminate a

particular 10 km x 10 km spot on the ground

which can be imaged with 1 m resolution. The spot size can be increased up to 10 km x 100 km.

Circular Polarimetric Modes (C-HRS, C-FRS-1, C-FRS-2, CMRS, C-CRS): All the modes mentioned above can be

operated in hybrid-circular polarimetric configuration.

RISAT-1 has the capability to cover both sides of the sub-satellite track by roll-tilting the satellite. The antenna

is mounted on satellite with antenna normal coinciding with satellite yaw axis. Before start of the imaging, the satellite

will be roll-tilted by 36 to enable left/right imaging. With this position of the satellite, electronic beam steering will

be used to cover the ground range distance of 107 km to 659 km off-nadir covering an incidence angle of 12 to 55.

Orbit Circular Polar Sun Synchronous

Orbit Altitude 536 km

Orbit Inclination 97.552O

Orbit Period 95.49 minutes

No. of orbits per day 14

Equator Crossing 6.00 AM/6.00 PM

Spacecraft Height 3.85 m

Mass 1858 kg

Power Solar Array Generating 2200W and one 70 AH Ni-H2 Battery

Max power handling capacity 4.3 KW

Data rate 2x160 Mbps (Total 640 Mbps in 2 chains)

SSR 240 Gbits (EOL)

TT&C S-band

Payload down link X-band

Power 70V bus /42V bus

Pointing accuracy 0.05O

Drift rate 5.0 x 10-5 O/sec

Attitude knowledge 0.02 O

Table 5: Salient RISAT-1 spacecraft parameters

Fig. 27: Illustration of imaging Modes of RISAT-1

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To provide near-constant swath, elevation beam width is varied from 2.48 to 1.67 by varying the

electrical width of the antenna by switching-off certain rows of TR-Modules, when imaging closer

to the satellite track and switching-on all the rows when imaging farther off-nadir regions. Total 122

antenna-beams (61 for left- and 61 for right-imaging) have been defined to cover the entire given

range. Out of these, any 12 beams can be used to define a particular imaging session. During imaging,

satellite will be steered in both yaw and pitch to align the beam to zero Doppler line to ease SAR

processing by reducing Range Cell Migration (RCM) to minimum.

The earth viewing part of the antenna is a printed micro-strip patch array. 24 dual polarised

linear arrays are printed in each of 12 tiles. Each of these linear arrays, consisting of 20 patch arrays,

is fed by one pair of TR modules, each of which is dedicated for one polarisation. The active-antenna

configuration will also help tolerate random failure of up to 10% TR-Modules, with only graceful

degradation of antenna pattern. Four Tiles make one panel. RISAT-1 antenna has total 3 panels, of

which one is fixed and adjacent two are deployable. The radiating surface of the antenna is covered

with glass wool blanket to prevent heating

by earths radiation.

Both of the TR pair receives DC

power from a miniaturised EPC called Power

Control and Processing Unit (PCPU). Each

such TR pair is controlled for synchronous

operation by an ASIC based TR Control

Computer (TRC). All the 24 TR module pairs

Swath Coverage Selectable within 107 659 km off-nadir distance on either side

Incidence Angle Coverage 120 550

Image Quality

Mode Polarisation

Single PolHH/HV/VV/VH

Dual PolHH+HV/VV+VH

Circular (Hybrid) PolarimetryTX: CPRx: V and H(Experimental)

Quad PolHH+HV+VV+VH

HRS 1 m (Azimuth) x 0.67 m (Range) resolution,10 x 10 Km (10 x100 km Experimental) Spot Min o= -16 dB

FRS-1 3 m (Azimuth) x 2 m (Range)resolution 25 km swath

Min o= -17 dB

FRS-2 3 m (Azimuth) x 4 m (Range) resolution25 km swathMin o= -20 dB

9 m (Azimuth) x 4 m (Range) resolution 25 km swatho= -19dB

MRS 21-23 m (Azimuth) x 8 m (Range) resolution,115 km swathMin o= -17 dB

CRS 41-55 m (Azimuth) x 8 m (Range) resolution223 km swathMin o= -17 dB

Table 6: RISAT-1 image quality parameters

Fig. 28: Photographs of a representative tile and panel49

in a Tile are managed by a Tile Control Unit

(TCU). TR modules, each with maximum

pulsed power of 10 watts, along with other

Tile Electronics (TRC, TCU, PCPU, RF power

dividers & harness) are mounted on the back

side of antenna. Typical configuration of a Tile

and Panel is shown in Figure 28.

The deck elements constitute

conventional Pulse Doppler Radar, consisting

of two chains of receivers for simultaneous

reception of V and H signals, one frequency

generator, one Feeder SSPA and two sets of

Data Acquisition and Compression units. The

complete payload management is done by

radar payload controller. Block diagram and

photograph of deck elements are presented

in Figure 29. Printed side of patch antenna

side of the deployed antenna is shown in

Figure 30. Figure 31 depicts RISAT SAR

payload at different stages of testing and

integration with spacecraft.

What is new in RISAT-1 SAR ?

RISAT-1, when conceived in way

back 2002, was an ambitious project where

many new technologies were required to be

mastered. The access to these technologies

was restricted because of international

technology embargo, imposed on ISRO.

The number of subsystems needed for this

system was prohibitively large (precisely 1393

subsystems of them 312 are 8 bit computers).

To qualify each of these subsystems to space

grade, it required almost 3 weeks testing of

each of these subsystems. ISROs experience

with satellites never exceeded dealing with

more than 100 subsystems. ISROs resources

were inadequate to carry out fabrication and

testing of such large number of elements.

Indian industry was not equipped to handle

Fig. 29: Block diagram and photographs of deck elements

Fig. 30: Patch antenna side of the deployed antenna

Fig. 31: Clockwise from top left: RISAT SAR in testing; RISAT-1 in deployed condition backside and frontside; R ISAT-1 being lowered in thermovac chamber; RISAT-1 mounted a top PSLV

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3space quality fabrication, let alone handle such large numbers with zero defect production and testing

approach. A number of industries were needed to be hand-held, with training and expertise impartment

to carry out mass productions based on final blueprints which were designed and perfected in-house.

The many firsts of RISAT-1 are:

a. SAR Feature wise

Only C-band SAR capable of giving 1 m resolution over 10 km x100 km spot. It is to be

mentioned that 1 meter SAR is presently possible with X-band only, that too over an area of

5 - 10 km spot.

It is the first SAR with Hybrid polarimetry. Linear polarimetric modes are available in some

space-borne SAR systems, where data rate gets doubled and imaging is restricted below

35 incidence angle. This limitation is not imposed on Hybrid polarimetry and it is available

seamlessly for all imaging modes. Further, this polarimetry mode can be self-calibrated using

normal imaging data.

It has a very low incidence angle modes operating at incidence angle as low as 12. It will usher

in new application in soil moisture, glacier studies and better imaging in hilly regions.

Apart from SAR, RISAT carries Indias first space-borne Active antenna array. The SAR payload, along

with this antenna, is a complex array of electronics consisting of 1393 subsystems including 312 units

of 8 bit computers.

b. Payload Technology

Completely indigenous

Space qualified MMICs were fabricated in India

Miniaturised TR modules

Miniaturised EPCs with planar transformer technology where transformer windings are achieved

by printing them on 16 layer PCB

Programmable digital Chirp generators

High speed digitization

High data rate communication all the way upto 1.5 Gbits

Down-linking data in two orthogonal circular polarizations with speed of 640 Mbits

On-board recorder capability of 300 Gbits (240 Gbits at End of Life, EOL)

Designed for truly Global operation

Industry Partnership

The highlight of RISAT-1 programme was development of hardware elements through

industry partnership. Various new technology elements like different types of MMICs, miniaturized

C-band TR-module and pulsed power supplies, dual polarized printed antenna, integration block and

power distribution network, high speed digitizers, micro-controller and FPGA based central distributed

control systems, etc. have been realized with the active participation and collaboration of public and 51

private sector industries like GAETEC, ASTRA,

CENTUM, CMC, SHAHJANAND LASER etc.

Indigenous MMIC fabrication line has

been qualified at GAETEC foundry. Design,

development and qualification of an

indigenous Onboard Controller ASIC for

Tile Electronics have also been accomplished

in collaboration with private industries like

CG COREL/AEROFLEX. The participating

industries had to go through a learning

curve with issues like space grade circuit

fabrication, quality control, test facility

development and testing methodology.

Their contribution in mass production is

illustrated in Figure 32. This programme

has also added to industrial capacity building

within the country. Thus, the challenge

of microwave SAR payload realization has

been addressed and wi th indust ry

participation in these activities, a new

beginning has been made.

RISAT-1 Imaging Operations

Since 1st May, 2012, RISAT is being

operated over India as well as over foreign

countries. All the imaging modes except

HRS have been operated successfully. Since

October, 2012, calibrated images are made

available operationally. Initial results indicate

that system behavior is as per prediction

and RISAT-1 system is expected to meet

users expectations during its lifetime. A set

of hybrid polarimetry images obtained in

FRS-1 and MRS modes are presented in

Figures 33 and 34. It is to be mentioned that,

RISAT-1 is the first SAR, capable of providing

polarimetric images in scanSAR mode.

Dual_Frequency SAR Onboard

Chandrayaan-2

Indias second endeavour to moon,

Fig. 32: Contributions of Indian industry in realisation of RISAT-1 SAR

Fig. 33: Sample hybrid polarimetry images obtained by RISAT-1 in FRS-1 mode

Fig. 34: Sample hybrid polarimetry image obtained by RISAT-1 in MRS mode (Worlds first polarimetry in scanSAR mode)

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3Chandrayaan-2, is planned as a two-module

configuration comprising Orbiter and Lander,

to be launched in 2014. L & S-band Synthetic

Aperture Radar (Dual-frequency SAR) is an

important sensor in the instrument-suite

planned for the orbiter.

Dual-frequency (L & S-band) SAR

has been envisaged to provide continuity

to ongoing studies of S-band MiniSAR

(onboard Chandrayaan-1) data and to

cater to further advanced scientific studies.

The salient features of the L & S-band SAR

are (Table 7):

a. Configured for circular polarimetric mode and full polarimetric mode

b. L-band SAR for greater depth of penetration in lunar regolith (which is about 5-10 m, depending

on FeO+TiO2 content of lunar regolith)

c. Wide range of resolution options (2 m 75 m slant range)

d. Wide range of incidence angle coverage (9.5 - 35). (Figure 35)

e. L-band and S-band SAR operation in stand-alone or simultaneous mode of operation

f. Miniaturized hardware to meet 14.3 kg mass constraint

The basic configuration of Chandrayaan-2 SAR payload is shown in Figure 36a. DVM

development of the complete SAR system is already completed and is shown in Figure 36b. FM

development of the payload is targeted to be over by Dec-2013.

Frequency 1.25GHz (L-band), 2.5GHz (S-band)

Antenna Microstrip patch antenna of 1.35 m x 1.1 m of gain 22 dBi (L-band) and 25 dBi(S-band)

Polarization Circular and Full-Polarimetry Peak power 45W (L-band), 40W (S-band)

Bandwidth 75MHz to 2MHz selectable Duty Cycle 24% (max)

Swath 10 km Data Rate 160 Mbps (max, combined H&V channels)

Resolution 2 m 75 m selectable Raw Bus Power 100W (max)

Sigma-Naught @2m resolution

-26 dB (L-band-Circular)-22 dB (S-band-Circular)

Table 7: Specifications of Chandrayan-2 SAR

Fig. 35: Imaging geometry of Chandrayaan-2 SAR

L-Band SAR Satellite

Presently plans are afoot to build an L-band SAR as successor of RISAT-1. Presently the payload is

in drawing board. This SAR will have scanSAR modes matching with RISAT-1. It will have strip-map mode

of resolution 5 m and swath of 60 m. The interesting addition will be electronically steered spotlight 53

mode providing 2.5 m resolution over 40

km x 40 km spot. This SAR will also provide

moving target detection through Along Track

Interferometry (ATI).Compared to RISAT-1,

it will have additional linear polarimetry

mode apart from Hybrid polarimetry.

Basic imaging modes are shown in Figure

37 and the performance specifications are

presented in Table-8.

Like RISAT-1, the antenna will be

based on active array technology to provide

electronic steering in elevation and limited

electronic steering in azimuth to support

spotlight mode. The antenna size will be

10 m x 3 m, weighing 600 kg. and the satellite

is being planned for launch by PSLV.

L-band SAR pay load will also carry a

demonstration model of real time processor,

for the first time, for processing the SAR raw

data on the fly. The mechanical configuration

of the proposed L-band SAR system is shown

in Figure 38.

Development of Building

Blocks for Microwave Remote

Sensors

Significant development of range

of microwave remote sensors in ISRO, was

possible because of gradual acquisition of key

technologies in antennas, RF receivers, power

Table 8: Salient Specifications of L-band SAR

1 Frequency L-BAND (1.25 GHz)

2 Polarization Single, Dual, Circular (Hybrid) Polarization and Full-polarimetry

3 Swath 50 km To 200 km

4 Range Coverage Between Off-nadir Distance Of 150 km and 500 km

5 Incidence Angles ~15deg To ~45deg

6 Spatial Resolution 5 m, 10 m & 25 m

7 Minimum Sigma Naught -20/-23 dB (multilook) Or Better

8 Radiometric Resolution 2-3 dB

Fig. 36: (a) Overall L/S-band SAR system configuration (b) DVM of L/S-band SAR system

Fig. 37: Imaging Configuration of L-band SAR

Fig. 38: Mechanical configuration of L-band SAR with satellite structure

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3amplifiers, EPCs and digital subsystems.

Whether passive or active sensors, very

low noise receivers are must component

of microwave sensors. Presently expertise

exists in ISRO for development of receiver

all the way from UHF to G band (183 GHz)

(Figure 39a). This expertise development called

for large investment in different design, test

and high precision fabrication facilities. Off late,

emphasis has been given to acquire expertise

in Monolithic Microwave Integrated Circuit

(MMIC) technology (Figure 39b). Presently

design capability exists in designing MMICs

upto V band. Microwave Remote Sensors call

for large number of complex RF receivers. This

capability is not only important from the point

of view of miniaturizing the receivers but also

important from strategic aspects.

For active sensors, Solid State Power

Amplifier (SSPA) technology is gradually

replacing costly Travelling Wave Tube Amplifier

(TWTA) technology. Consequently a strategic

initiative has been taken to master SSPA

designs, from a few Watts to a few hundred

Watts. Also graduation has been done from low

efficiency GaAs technology to high efficiency

GaN technology. Similarly SSPA circuit

topology has migrated from classical class AB

to very sophisticated class F. Presently SSPA

design and development capability exists from

L-Ku band and efforts are on to migrate to

higher bands like Ka band and beyond (Figure 39c).

EPCs are key to building successful systems with optimum performance and high longevity.

The array of expertise in EPC technology, under MRS activity, ranges from low power-high voltage

to low voltage-high power to pulsed operation. Gradually our EPC designers migrated from Printed

Circuit Boards (PCBs) to Hybrid Microwave Circuit (HMCs), LDOs to POLs, coiled transformers to

planar transformers (Figure 39d). The key drivers are miniaturization and seamless integration within

targeted Radio Frequency (RF) or digital packages to reduce Electro Magnetic Interference (EMI) /

Electro Magnetic Compatability (EMC) effects.

Fig. 39: (a) Range of receiver expertise acquired under MRS activity (b) Range of MMIC expertise acquired under MRS activity

Fig. 39: (c) Range of SSPA expertise acquired under MRS activity (d) Range of EPC expertise acquired under MRS activity

Fig. 39: Imaging geometry of Chandrayaan-2 SAR

55

As far as digital sub-systems are

concerned, discrete devices are passive. A

strong expertise have been developed in

designing programmable and reconfigurable

digital systems using high end fault tolerant

Field Programmable Gate Array (FPGAs).

Already migration from FPGAs to Application

Specific Integrated Circuit (ASICs) has happened

and present effort is there for designing and

manufacturing a large range of ASICs as key to

miniaturization of technology. Similarly already

we have expertise for handling data digitization

and modulated signal generation up-to

700 MHz and efforts are on to stretch the

limit beyond 1 GHz within a short future

(Figure 39e). All the efforts have now resulted

in much miniaturized and low power digital

subsystems.

ConclusionLast two decades have been invested in

understanding the nuances of Microwave

Remote Sensing technology, both in terms

of system development and algorithm. It is

demonstrated in successful journey from SAMIR to RISAT. However, there are three formidable challenges:

(i) Ability to master a diverse array of technologies which function with precision. The range of frequencies encompassed

today ranges from 1 GHz to 183 GHz. Complete spectrum of devices change drastically over these bands. Each

of the bands provide different challenges in terms of design, simulation, fabrication and testing requirements.

Consequently wide investments are to be made in the all the above fields.

Coupled with it, they pose complexities in system designs to varying degrees. As an example TSU, where filters are

to be achieved are to the order of 10 MHz at 50-60 GHz bandwidth. The estimate of emission is to be made to the

accuracy of 0.5 K. This calls for high degree of machining of RF fabrication requirements. The testing system has

to be evolved as conventional test systems will not meet these requirements.

Take another example of RISAT consisting of close to 1400 sub systems, which includes 312 computers. The complexity

is equivalent to fabricating 5 INSAT satellites. The sheer number of subsystems forced us to look towards harnessing

Indian industries on partnership basis. But Indian industry scenario is not geared to high quality fabrication required

for space systems. The strategy included (a) convincing industries that they can do it (b) they had to be hand-held

during their initiation with tough fabrication methodology and processes (c) resorting to partnership with industry

in place of treating them as any other vendor.

(ii) A great emphasis is to be provided to algorithm development for testing or processing or simulation. In fact all the

designers need to be trained in simulation and algorithm development. In DMSAR, a completely new SAR processing

Fig. 39: (e) Improvement in the performance of digital subsystems under MRS activity

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3algorithm had to be developed for severe motion errors and lack of stabilization system. Similarly,

the sheer requirement of measuring active antenna pattern at different stages of integration

of RISAT-1, led to the development of a time domain near field antenna pattern measurement

facility in the clean room itself. The need for calibrating the active antenna led to development

of a single scan calibration methodology. Fear of accidental damage of RISAT-1 payload by

external AC supply, led to the development of a concept of a payload measuring itself. Before

introduction of Hybrid polarimetry, as a new concept in RISAT-1, tremendous simulation activity

had to be undertaken to explore whether there is any lacunae in the concept.

(iii) The microwave signatures do not conform remotely to human experience with vision. Interpretation

of signatures to physical reality needs considerable understanding of the physics of interaction

of microwave signals to the physical objects. A great deal of analytical and experimental studies

are required to finally bring the fruits of microwave remote sensing technology to the last mile

post : the users.

AcknowledgementDevelopment of Microwave Remote Sensing technology, in all its gamut, would not have been possible

without active contributions and participations from a large number of engineers and scientists who

worked as a team. Author would like to take this opportunity, to acknowledge and salute the individual

team members who made this journey successful.

57

agriculTural applicaTions: eVoluTion during lasT 25 years parihar Js and manjunath Krspace applications centre, (isro), ahmedabad 380 115, indiaemail: jsparihar@sac.isro.gov.in

IntroductionIndia has a geographical area of 329 mha with a wide diversity of its natural resources spread

over 15 agro-climatic zones. Out of which, the agricultural area is 142 mha with 37 per cent of the

area under irrigation and about 215 crops are grown in India. The diverse crop growing conditions

coupled with uncertainties of climate situation demands crop information need on temporal and

spatial scale. Though India receives 420 mha m of rain and snowfall every year, still is prone to floods

and droughts due to uneven distribution. Apart from this, it is prone to several other natural disasters.

In view of these, the country needs accurate and timely information on the agricultural resources

at regular intervals. Realizing the significance of orbital remote sensing, country initiated, evolved

and developed its own space programme for earth observations. The agricultural remote sensing

during the last twenty five years has reached high level of maturity in various domains.

Consistent efforts have been made on exploring the Remote Sensing (RS) applications, technique

development, user interaction and institutionalisation. The following sections explain the

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

remote sensing in India.

Crop Production ForecastingRemote sensing (RS) technology has potential in estimation of crop area and forecast

productivity at district and regional level due to its multispectral, large area and repetitive coverage.

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 (Sahai et al., 1977). 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).

Since then a large number of experiments have been carried out for developing techniques for

extracting agriculture-related information from airborne and space borne data. Many of these studies

have led to the operationalisation of the methodology and conduct national-level projects.

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 m regions (due to chlorophyll

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

in the 0.7 to 0.74 m and high reflectance in 0.74 to 1.3 m region due to internal cellular structure

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3of the leaves. The absorption at 1.45, 1.95 and 2.6 m 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.

Launch of the Indian Remote Sensing Satellites (IRS-1A & 1B) carrying Linear Imaging Self-

scanning Sensors (LISS I & II) in 1988 and 1991 provided a much-required impetus to agricultural

applications. Further boost was provided by the state-of-the-art satellite IRS-1C in December 1995

and IRS-1D in 1997. These satellites carried onboard a unique combination of three sensors viz.,

(i) Wide Field Sensor (WiFS) with 188m spatial, two spectral bands red and near infrared, 810 km

swath and a repetivity of 5 days, (ii) Linear Imaging Self scanning Sensor (LISS-III) with 23.5 m 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 5.8 m spatial resolution, 70 km swath and

stereo capability. IRS-P3 MOS provided data in many narrow spectral bands, although at coarse spatial

resolution facilitating the study of crop stress and crop senescence. 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.

Use of multi band and Colour Infra Red (CIR) aerial photographs was explored since inception

of remote sensing applications programme in the country. Projects on I) Crop production forecasting

ii) Crop stress detection, and iii) Crop yield modeling were taken up in 1983. Early success in these, led

to launching of Large Area Crop Acreage (LACA) estimation experiment in 1986. Regional level wheat

acreage estimation using Landsat MSS data was demonstrated (Sahai and Dadhwal, 1990, Navalgund

et al., 1991). Results of LACA were appreciated by the Ministry of Agriculture in the country and at its

behest Crop Acreage and Production Estimation (CAPE) project was launched in 1988 (SAC 1990). This

project was sponsored by the Ministry of Agriculture, was executed jointly by Department of Space, State

Remote Sensing Centres, State Departments of Agriculture and Agricultural Universities. Pre-harvest

production estimates carried out are of two types one: small scale (district-level, single forecast) and

two: regional scale (National level, multiple forecasts). Pre-harvesting production forecast of wheat,

rice, cotton, rapeseed/mustard, groundnut and winter sorghum is made under the project.

Remote sensing data of moderate spatial resolution (Landsat MSS & TM, IRS LISS I, LISS II &

LISS III) acquired at optimum bio-window was used for crop area estimation. The data was analysed

applying Maximum Likelihood Supervised classification technique, 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 59

proportionate to size of each stratum.

Area estimate is made from the proportion

of crop present in the sample. The CAPE

project was further followed by CAPE-II,

second phase. A semi-automated procedure

called CAPEWORKS was developed for

this purpose.

The scope of the CAPE project

progressively increased since 1988 to

include more crops as well as multiple

forecasts as the crop season progresses.

Since the space technology has advanced and

variety of sensors of different spatial, spectral

and temporal resolutions are available and

there is a continual need for crop information

throughout the growing period (Figure 1),

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 Forecasting Agricultural

output using Space, Agro- meteorology

and Land based observations (FASAL), was

formulated by Parihar and Dadhwal (2002)

(Figure 2). Implementation of FASAL was

initiated in 2007-08 and SAC was entrusted

with the responsibility of implementing

Space Technology based production forecast

of crops and upgradation of the procedure

with new data availability. Thus, the work

components encompass two aspects:

operational forecast of production and

developmental aspects.

During the 11th Five Year Plan

(2007-8 to 2011-12) procedures for multiple

forecasts of 6 crops viz: jute, kharif rice,

winter potato, rapeseed/mustard, wheat and

rabi rice was developed and regular estimates

were made available. Realising the need to

integrate this advanced technique in the routine crop statistics gathering, Department of Agriculture and Cooperation

(DAC), Ministry of Agriculture, Govt. of India initiated steps to set up a centre for this purpose. Accordingly, the centre

Fig. 1: Information need and sources in frequent crop monitoring

Fig. 1: Concept of FASAL

Fig. 3: Time line of activities leading to the formation of MNCFC

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3Mahalanobis National Crop Forecast Centre (MNCFC) has been set up at PUSA, Rajendranagr, New

Delhi on April 23rd, 2012. Key features of FASAL are (a) Institutionalising the operational use of RS data

for diverse applications in agriculture, (b) Developing a system for multiple in-season crop assessment

and forecasting in near real-time, and (c) Integrated use of tools such as GIS, large databases, modelling

and networking. The concept of FASAL thus strengthens the current capabilities of early season crop

estimation from econometric and weather-based techniques by adding the tools of remote sensing in

a major way. A timeline of activities leading to the formation of MNCFC is shown in Figure 3.

Currently MNCFC covers National forecast of kharif Rice: 13 states (90%area), rabi rice:4

states (95% of area), wheat: 7 states (95% of area), jute: 3 states (99% of area), mustard: 5 states

(84% of area) and winter potato: 4 states (88% of area). Customized software called FASALSOFT was

developed at Space Applications Centre, ISRO, Ahmedabad to cater to these applications.

FASAL: National Wheat Production Forecast National-level wheat acreage estimation using multi-date WiFS data from IRS-1C was initiated

in 1995-96 season. 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.

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 the wheat production. 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 (Oza, et al., 2002). The wheat

c rop unde r r a in fed and i r r i ga ted

regions of MP and inter-annual changes

between 2011 and 2012, showing

variation of wheat crop is shown in Figures 4a & b, respectively.

FASAL: National Rice Production Forecasting 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

Fig. 4a: The rainfed (left region) and irrigated (lower right) wheat in Madhya Pradesh

Fig. 4b: Inter-annual changes- Increase in wheat crop (Kota, Rajasthan

61

estimates in the major rice growing states are

being generated using multi-date Synthetic

Aperture Radar data (Figure 5a, Patel et

al., 2004). 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 (Figure 5b). 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 x 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.

This is being regularly carried out since 1998. The inter-annual changes are shown in Figure 5c.

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. Crop planting

depends on many factors. There is a latitudinal

variation in the rice crop calendar. This

gives rise to a wide period in crop planting

with in any country/region. Spatial crop

calendars can be of great advantage to study

such processes. Specific advantage of SAR

sensitivity to target properties were used to

categorise the rice area based on the sowing

period. 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

Fig. 5: (a) Three date color composite of ScanSAR 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). (b) Temporal pattern of rice crop

Fig. 5c: Multidate SAR data showing decrease in kharif rice crop (Begusarai, Samastipur, Bihar) in 2011 in comparison to 2012

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

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3crop assessment need that enables identifying

the reasons for delay. Efforts have been made

for deriving the regional variation of rice

growth profile using optical data (Figure 6;

Manjunath et al., 2006)

Crop Yield Models Crop yield assessment is complex

because of the high variability involved in the

growth processes. 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 abiotic (soil, weather and cultivation

practices, like date of sowing, irrigation and

fertilizer) and biotic stresses (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. All these factors are interdependent and make the yield assessment a more complex task.

Hence, one way of forecasting the yield is accounting 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, and

synoptic 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 (Agarwal et al., 1983).

Further agrometeolorlogical model, spectral models and combination of these models were tested

and used for a variety of conditions, crops and regions. The rice biomass has high correlation with

SAR backscatter (Figure 7) and therefore the yield. Hence, SAR backscatter estimation using radiative

transfer and estimation of rice biomass and yield using its inverse is now standardized.

The vigour of crop, an indication of yield is being 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 and was directly correlated to the yield of the crop and this relation

was used for estimating yield ( Manjunath et al., 2002). 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 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 due to the

unavailability of crop information at regional scale. The main advantage of remotely sensed information

is that it provides a quantification of actual state of the plant on a regional scale and shows the spatial

variability. While models provide a continuous description of plant growth over the period of interest,

remotely sensed observations are discrete time events. Hence integrating the inputs from remote

sensing to the crop models is useful technique to predict crop yield at large scale. Leaf Area Index (LAI)

is most important in explaining the ability of the crop to intercept solar energy and in understanding the

Fig. 7: Relationship between rice biomass and SAR backscatter

63

impact of crop management practices. Hence

spatial distribution of LAI was derived using

NDVI data and wheat yield forecasting at

spatial scale was tried through forcing the LAI

in a crop simulation model WOFOST. As the

model is point based, its spatial application

required spatial database generation

(both agro-meteorological and biophysical)

and linking of the spatial database to the

WOFOST application program. The broad

procedure is shown in Figure 8. Using this

procedure in-season wheat yield forecasting

at 5 km 5 km has been demonstrated

(Tripathy et al., 2013).

With the availability of inputs

like insolation from Kalpana satellite, the

refinement of this approach would now be

feasible (Figure 9).

A lot of experience has been

gained for yield model development using

space-borne RS data directly (spectral yield

model) and their use in pre-harvest yield

forecasting for wheat, rice, cotton, mustard

and groundnut. The approach followed has

been direct regression of spectral indices

(NIR/R of NDVI) to district level yields. The

crop yield data used is in two format (i)

site specific yields where the Crop Cutting

Experiments (CCE) are specifically carried

out (ii) those collected by State Department

of Agriculture as a part of yield estimation

programme. These are used to derive district

average yield, which are then related to RS

based indices.

Kharif Progress Assessment The technique of early assessment

of crop prospects based on soil moisture

modeling using Satellite-based daily spatial

rainfall has been developed during last five

years. Early assessment of All-India kharif rice

crop acreage (progress of sowing) for the

year 2012 was carried out at 7-day interval

starting from July 15, with final assessment on

Fig. 8: The crop simulation modeling procedure using RS data

Fig. 9: The yield variation pattern obtained using interpolated insolation and Kalpana derived insolation

Fig. 10a: Kharif rice area sown based on Available Soil Moisture (ASM)

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3September 30, 2012 (Figure 10a). Along-with statistics of rice acreage, various all-India colour-coded

maps showing various parameters like available soil moisture, average irrigation requirement, aridity

index for the current year, last year (2011) and last 5-year average (2007-2011), aridity anomaly and

current year soil-moisture rank were generated. The colour-coded maps show, at a glance, agricultural

prospects at 7-day interval as the kharif season progresses.

Rabi Progress Assessment using INSAT 3A CCD

Vegetation mapping using satellite

data has became a regular practice in recent

year. The advancement in satellite technology

and improvement in the resolutions of data

for monitoring the vegetation growth is

carried out all over the world using high

resolution satellite data. This attempt to

monitor the progress of rabi crop using the

Indian Geostationary Satellite is first of its

kind. The rabi season NDVI profiles were

extracted and decision rule were made by

applying proper NDVI threshold. For each decade, decision tree has been updated according to NDVI

value. Following this methodology, rabi progress has been assessed at each decade during the year

2011-12 and 2012-13 (Figure 10b, Vyas et al., 2011)

Remote Sensing Applications in Horticulture India is bestowed with varied agro-climate which is highly favorable 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 (Figure 11), grape, mango, coconut, banana and

vegetables like potato, onion has been carried

out in different agroclimatic regions of the

country (Panigrahy and Manjunath, 2009).

The post-harvest infrastructure

planning and optimization of cold store

facility for post-harvest management of

potato has been demonstrated (Panigrahy

Fig. 10b: Rabi crop progression derived using INSAT CCD

Fig. 11: Apple orchards as seen in LISS-III FCC and classified image65

and Manjunath, 2009). 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. As a part of Technology Mission for

Integrated Mission of Horticulture in North

Eastern states including Sikkim, remote

sensing based studies in the following states

has been carried out (Arunachal Pradesh:

Apple; Meghalaya: Cashewnut; Tripura:

Pineapple; Mizoram: Passion fruit cultivation

in zoom area; Manipur: Pineapple; Sikkim:

Mandarin; Himachal Pradesh: Apple). The

latest addition is onion crop estimation, which

uses a combination of LISS-III and LISS-IV

(Figure 12) and methodology was transferred

to NHRDF, Nashik which makes periodical

estimation of late kharif and rabi onion with

technical support from SAC.

Agro-Ecosystems Assessment and Modelling

During the last two decades

techniques of deriving and assessing the agro-

ecosystems components were developed

which include cropping systems analysis,

methane emission inventory, parameter

retrieval and process modelling, energy and

mass exchange in vegetative systems etc.

Some of them are described below:

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

agroclimatic condition. Satellite remote

sensing (RS) and Geographical Information

Fig. 12: The LISS-IV FCC showing onion crop fields

Fig. 13: The cropping pattern and crop rotation maps derived using RS data

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3System (GIS) 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. The pilot studies on cropping systems was initiated by SAC in Punjab and West Bengal

states, which represent high potential and high productivity and high potential and low productivity

conditions, respectively. The multidate SAR data was used deriving the kharif season cropping pattern

and multidate WiFS/AWiFS data was used for deriving rabi and summer season pattern. These were

logically combined to derive the crop rotation maps (Panigrahy et al., 2010). Encouraged by the

findings, the study was extended to the entire Indo-gangetic plains (Figure 13).

The outputs generated in the study were seasonal cropping pattern, crop rotation, kharif fallow

area maps as well as crop intensification

and diversity maps. The cropping systems

performance indicators were also generated.

The Multiple Cropping Index (MCI), Diversity

Index (DI), and Cultivated Land Utilisation

Index (CLUI) have been deduced using

remote sensing data (Manjunath et al., 2011).

An illustration of West Bengal is shown

in Figure 14.

The crop divers if icat ion aids

in efficient management of resources,

maintaining the soil fertility and reduced pests/

disease incidence. As part of pilot study the

spatial map of crop diversification of Punjab

state was prepared (Panigrahy et al., 2010a).

Similarly the techniques of crop intensification

based on cropping pattern, rotation, fallow

area/duration and invariant resources was

developed in the above study.

E n v i r o n m e n t a l I m p a c t Assessment of Agricultural System

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

Fig. 14: Cropping system performance indicators

67

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 issue related to methane emission from rice fields and livestock from India is addressed in detail in the

project entitled Environmental Impact Assessment in Agriculture System. The GHG pattern which is also available using

sensors on board satellites is being studied in detail (Panigrahy et al., 2010). Apart from this attempt has been made to

grossly quantify the nitrous oxide emissions from live stock. The environmental impacts need spatio-temporal assessment

with best possible scientific tools and techniques. Use of Remote sensing and GIS along with field and collateral data

helps in deriving and assessing the important parameters required for environmental impact assessment. According to

IPCC guidelines, the rice ecosystem need to be categorized into four strata for methane emission study. Ancillary data

on rainfall, elevation, soil, command area/ irrigation statistics were used with remote sensing derived rice maps in GIS

to categorize the rice lands into four strata as: irrigated, rain fed flood prone and rain fed drought prone and others

(Manjunath et al., 2006). In-situ weekly measurements of methane emission from the representative ecosystems was

collected and analysed using gas chromatography following the IPCC standards for three consecutive years; 2003, 2004 and

2005. 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

(Manjunath et al., 2011). 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.

Livestock constitutes an integral

component of Indian agriculture, which is

another major source of methane emission

mostly from enteric fermentation by ruminants.

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).

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. The non-point Fig. 15: An illustration of non-point source pollution studies using remote sensing and field data.

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3source pollution of nitrogenous fertilizers in the Indo-gangetic plains vis--vis fertilizer consumption

was also studied using remote sensing derived inputs in GIS environment (Chhabra et al., 2010).

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 (Figure 15) 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 SystemClimate change is one of the most discussed topics of the last two decades. Its impact on

major agricultural systems of India with inputs from multiple sources was studied using simulation

model CropSyst (Tripathy et al., 2011). The climate forecasts from different GCMs, RCM and statistical

downscaled model (under different scenario) were used for understanding the impact of climate

changes on agricultural systems. In addition to the impact of mean seasonal climate change, index

based analysis was carried out for the past weather extremes. Trends of extreme weather parameters

under future climate change scenarios were derived and their impact on crop productivity was studied.

The study also identified optimum planting date as one of the adaptation strategies for the agriculture

sector to cope with the projected impacts in the study regions and analyzed the uncertainty due to

both climate model and impact model.

Energy and Mass Exchange in Agro-ecosystemsAn internalized calibrated model and evaporative fraction-based model were used to estimate

large-area sensible and latent heat fluxes over semi-arid climate of Gujarat. The validation with collocated

measurements over rice-wheat system from INSAT-linked Agro-Met Station (AMS) and Large Aperture

Scintillometer (LAS) showed improvement in validation accuracy when area-averaged measurements

from LAS were used as compared to AMS measurements monthly sensible heat fluxes were found to

have generalized, inverse and strong power fit relation with rice and wheat grain yield.

Regional climatology of evapo-transpiration was generated using 30 years (1981 - 2010)

satellite based long term optical and thermal band observations, upscaling functions for energy balance

components from measurements of INSAT-linked AMS network. Mann-Kendal test statistics showed

Evapo Transpiration (ET) change hotspots over India. Dekadal ET-change during 1991-2000 and 2001-

2010 showed opposite change (+ve to ve or vice versa) behaviour. However, majority part of India

including central, north-west, part of eastern and northern India showed a declining ET trend during

2001-2010 which resemble with trend in rainfall and surface soil moisture in majority patches. The

climatology from 30 years showed a range of annual evapo-transpiration 100 to 1300 mm.

Surface insolation product using Indian geostationary satellite (Kalpana-1) was

made operational. Relative insolation was used to determine monthly and annual frequency

of clear days and assured solar energy exposure (Figure 16) over India on monthly and 69

annual scale using three years of datasets

(2009 to 2011). It showed well differentiated

zones of low (500 MJm-2) to high (4000

MJm-2) solar energy zones.

B i o p h y s i c a l P a r a m e t e r R e t r i e v a l a n d P r o c e s s 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 modelers 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

(Chourasia et al., 2006) and IRS-P6 AWiFS (Chourasia et al., 2011) data. The modeled wheat LAI were validated with

independent ground measurements through systematic campaigns and the range of errors was found to be 0.7-1.2

with higher errors at LAI more than 3.0 due to saturation of NDVI. The atmosphere-corrected reflectance of AWiFS was

generated using SMAC (Simple model for Atmospheric Correction) using real time MODIS atmospheric products of

aerosol, ozone and water vapour. These atmospheric corrected AWiFS reflectance were used to invert the ProSail model

through look up table to get desired biophysical parameter, LAI. The daily LAI from INSAT CCD data was retrieved using

radiative transfer simulation. By applying wheat mask we can segregate the wheat LAI from the agriculture LAI. From

daily wheat LAI of Indo-gangetic plane, maximum wheat LAI of the season was estimated using the LAI from January

to March (Figure 17)

The retrieval of LST was first carried out using NOAA AVHRR LAC (Local Area Coverage) data at 1km x 1km and

validated over LASPEX sites in Gujarat by comparison with tower-based air and surface soil temperatures (Bhattacharya

and Dadhwal, 2003). Later, mono-window method was used to retrieve LST from Kalpana-1 VHRR thermal data and

compared with MODIS LST within 3C (Bhattacharya et al., 2009).

Surface soil moisture in cropped soil was estimated using a soil wetness index (SWI) based on LST-NDVI

2D scatter at field-scale (~90 m) and landscape-scale using ASTER and MODIS (~ 1km) products. The validation

Fig. 16: Assured solar energy potential over India from Kalpana-1 insolation product

Fig. 17: Spatial distribution of maximum Leaf Area Index for wheat crop from daily INSAT 3A CCD data for year 2011-12

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3with in situ measurements showed errors of 0.039 and 0.033, respectively. The comparison with

regional-scale microwave-radiometer (C-band) soil moisture product from AMSR-E showed a good

match (correlation coefficient = 0.75, RMSE = 0.027) for fractional vegetation cover upto 0.5

(Mallick et al., 2009).

Satellite-based simplified evaporative fraction based surface energy balance was used to

estimate clear-sky daily actual and relative evapo-transpiration over agricultural landscapes (~ 1km)

and regional-scale (~ 8 km) agricultural land uses using MODIS (Mallick et al., 2009) and Kalpana-1

VHRR (Bhattacharya et al., 2010) optical and thermal data, respectively. Two-step validation from

ground-to-MODIS-to-Kalpana produced an error of 0.8 mmd-1

Demonstration of Technique for Forewarning Pests and Diseases

Forewarning of disease is the need

of the hour. Weather based assessment of

disease conduciveness was evaluated. The

remote sensing of 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 upscaled

and extrapolated using SPOT-VGT to map aphid onset dates (Dutta et al., 2008, Figure 18).

A new methodology of multi-stage tracking of Sclerotinia rot (Sclerotinia sclerotiorum) disease

in a large mustard growing region over Bharatpur district has been conceptualized and demonstrated

that showed the spatial variability of conducive temperature and humidity level for pest infestation

in wheat growing regions.

Crop Assessment: Beyond India In-season rice area estimation using C-band Synthetic Aperture Radar (SAR) data is being done

in India for more than a decade. Since the rice crop growing environment in India is a diverse one in the

world having all the rice cultural types, the rice backscatter is quite exhaustive. A long term backscatter

signature bank of rice fields has been developed. The well calibrated backscatter signature bank thus

has the potential to act as seed for extending signature to classify rice lands of other Asian countries.

The signature of the rice crop in Southern Bangladesh matched well with that of Gangetic West

Bengal and the signature of rice crop in the Sub-Himalayan West Bengal particularly that of Dinajpur

and Maldah districts matched well with that of the northern area of Bangladesh. A sample segment

Fig. 18: Regional mustard aphid onset over north-west India

71

approach was used to estimate the country

level acreage. A 20 x 20 km grid was used and

10 % of grid was selected randomly (Figure

19). Acreage estimation was done using the

standard random sampling aggregation using

crop statistics of each selected segments. In

total, 27 sample segments were analysed.

The country level acreage estimated using

statistical aggregation of random sampling

resulted in acreage of 5.42 mha which is 5.0

per cent of the reported rice acreage for the

same year. The acreage estimation of other

crops in different parts of the world is also

being explored.

Institutionalisation and Human Resources Development

The institutionalisation of remote

sensing in agriculture was given deep thought

right from the beginning. Initially educational,

academia and research institutes were roped

in to aid in research and feed to further

development of programmes. The need for

state remote sensing centres and regional

remote sensing service centres was then

visualised and brought into implementation.

Apart from this, state agricultural universities and agricultural departments were also involved in these projects in

groundtruth collection and image analysis. These interactions led to human resource development in RS applications of

agriculture. The periodic interaction with collaborating and user agencies and keeping them informed about their need

and technological strength/advances resulted in formation of MNCFC, New Delhi and geoinformatics lab in NHRDF,

Nashik.

ConclusionsRemote 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 upto operational stage. The 70s was a experimental phase while the 80s was formulation of RS applications projects.

In the 90s these were tested on large area and operationalised. Programme like FASAL was transferred to MNCFC under

Ministry of Agriculture. 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 not only in India, but also in other countries. 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 agroecosystems 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

Fig. 19: Multidate SAR FCC overlaid with 20 X 20 km sample frame overlaid

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3more complex algorithms and service oriented products. The RS and communications technologies

would fuse in the future to deliver the near-realtime service to all stakeholders of agriculture.

AcknowledgementsAuthors are thankful to scientists of SAC and NRSC, officials of Ministry of Agriculture, scientists of

collaborating institutes for valuable contributions to the project. The encouragement, technical guidance

and support of Shri. A. S. Kiran Kumar, director, SAC and Dr. R. R. Navalgund, former director, SAC

is highly acknowledged.

ReferencesAgrawal, R., Jain, R.C. and Jha, M.P. (1983). Joint effects of weather variables on rice yields. Mausam,

vol. 34, pp 189-194.

Bhattacharya, B. K. and Dadhwal, V. K. (2003). Retrieval and validation of land surface temperature

(LST) from NOAA AVHRR thermal images of Gujarat, India. International Journal of Remote Sensing.

24(6), 1197-1206

Bhattacharya, B. K., Dutta, S