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Hyperspectral Remote Sensing Applications for Vegetation

(with special emphasis on Agriculture)

Shibendu S. Ray

Mahalanobis National Crop Forecast Centre, DAC, New Delhi – 110 012

Space Applications Centre, ISRO, Ahmedabad – 380 015

Email: shibendu.ncfc@nic.in

DST Sponsored Training Programme “Hyperspectral Remote Sensing for Agriculture”, February 18-27, 2013, IARI, New Delhi

Introduction

• Hyperspectral remote sensing deals with large number of

narrow spectral bands over a contiguous spectral range

• Because of its ability to detect narrow absorption features

hyperspectral data are related to specific vegetation

physico-chemical characteristics, ocean biological

constituents, soil physical and chemical properties, mineral

composition and snow characteristics

• Because of presence of large number of bands,

hyperspectral data needs different analysis approach

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Why Hyperspectral?

Absorption Spectra of Plant Pigments

Carotene Chlorophyll

Why Hyperspectral?

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425 725 1025 1325 1625 1925 2225

Wavelength(nm)

Re

fle

cta

nc

e

Crops

Habitation

Plantation

Soil

Water

0

0.1

0.2

0.3

0.4

0.5

425 925 1425 1925

Crops

Habitation

Plantation

Soil

Water

(As per IRS Bands)

Hyperion Data for Different Landcovers

~10 nm bandwidth Modipuram (U. P.)

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Data Sources

Hyperion Field Spectroradiometer

HySi/IMS-1

CHRIS/PROBA

350-1075 (2500) nm range

Resol. 3nm (350-1000nm)

10 nm (100-2500 nm)

224 bands

400-2500 nm range

Spectral Resol. 10/11 nm

Spatial Resol. 30 m

Swath 7.5 km

64 bands

400-950 nm range

Spectral separation 8 nm

Spatial Resol. 505.6 m

Swath 129.5 km

Various data set:

Land set has

18 bands

438-1035 nm range

6-10 nm bandwidth

Spatial Resol. 17m

Swath 14 km

Multi-angular (5 angles)

AIMS

Average Altitude: 6.473 km

Spatial Resolution: 4.4 m

Swath : ~1.6 km

Spectral Range: 456-889 nm

Number of Bands: 143

Band Width: 3.3-4.1 nm

AHYSI (Airborne Hyperspectral Imager)

Spatial Resolution : 3.5 m

Spectral Range : 420-950 nm

Number of Bands: 512

Spectral sampling interval: 1.2 nm

Causes of leaf spectral characteristics

(from Jensen, 2000)

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Wavelength [nm] Cause of absorption Chemicals

430 Electron transition Chlorophyll a

460 Electron transition Chlorophyll b

640 Electron transition Chlorophyll b

660 Electron transition Chlorophyll a

910 C-H stretch, 3rd overtone Protein

1020 N-H stretch Protein

1510 N-H stretch, 1st overtone Protein, Nitrogen

1690 C-H stretch, 1st overtone Lignin, Starch, Protein, Nitrogen

1940 O-H stretch, O-H deformation Water, Lignin, Protein, Nitrogen,

Starch, Cellulose

1980 N-H asymmetry Protein

2060 N-H bend, 2nd overtone /

N-H bend / N-H stretch

Protein, Nitrogen

2130 N-H stretch Protein

2180 N-H bend, 2nd overtone /

C-H stretch / C-O stretch

C-O stretch / C-N stretch

Protein, Nitrogen

2240 C-H stretch Protein

2300 N-H stretch / C-H stretch /

C-H bend, 2nd overtone

Protein, Nitrogen

2350 CH2 bend, 2nd overtone /

C-H deformation, 2nd overtone

Cellulose, Protein, Nitrogen

Absorption features in vegetation reflectance spectra

(Adapted from Curran, 1989; Lucas & Curran, 1999)

Canopy Spectral Profile

Spectral Profile of Vraious Kharif Season Crops

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0.700

300 600 900 1200 1500 1800 2100 2400

Wavelength (nm)

Refl

ec

tan

ce

Paddy

Sorghum

Maize

Finger millet

Cluster bean

Lady finger

Green gram

Horse gram

Cowpea

Dhaincha

Sugarcane

Cotton

Pigeon pea

Groundnut

Soybean

Sesamum

Sunflower

Castor

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Vegetation Indices

Vegetation Indices

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Red Edge

• The red edge position (REP), also known as the red edge inflection point (REIP), is defined as the wavelength around 720 nm at which the first derivative of the spectral reflectance curve reaches its maximum value.

• When a plant is healthy with high chlorophyll content and high leaf area index (LAI), the red edge position shifts towards longer wavelengths (red shifts) while the shift is towards shorter wavelengths (blue shift) when the plant suffers from disease or chlorosis and hence

low LAI.

show the spectral curves before and after this analysis is done using the software.

Figure 8.3 Spectral curve before and after continuum removal

Continuum Removal

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Hyperspectral Study of Pulse Crop

Spectral profiles of Rabi crops - Lalitpur, UP

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325 375 425 475 525 575 625 675 725 775 825 875 925 975 1025 1075

Wavelength (nm)

Refl

ecta

nce (

%)

LENTIL PEA-FLOWERING PEA-FLOW/POD CHICKPEA-POD CHICKPEA-BRANCHING

Objective: discrimination of pulse crop using hyperspectral data

Area: Patha village, Mahrouni taluk, Lalitpur-Jhansi, UP (IFGRI)

Chickpea Branching

Chickpea Pod

Lentil

Pea Flowering Pea Pod Formation Step Wavelength Wilk’s F-Value

1 800 0.305 45.6

2 750,800 0.014 144.6

3 750,800,960 0.002 149.1

4 750,800,940,960 0.001 149.9

5 450,750,800,940,960 0.000 162.7

6 450,490,750,800,940,960 0.000 187.1

7 450,490,670,750,800,940,960 0.000 188.3

Discriminant Analysis

Crop Stage Discrimination

• Data: Airborne HySI

• Crop: Sorghum 3 Stages

• Location: Visalpur Village near Ahmedabad

FCC Classified Simulated LISS IV

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Crop Stress Detection (Nitrogen)

• 7 levels of nitrogen applied to potato crop

• Lower level of nitrogen had low NIR reflectance and high red reflectance

0

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0.7

0.8

300 400 500 600 700 800 900 1000 1100

Wavelength (nm)

Re

fle

cta

nc

e

N0 N50 N100 N150 N200 N250 N300

Crop Stress Detection (Nitrogen)

• Best Bands were selected using Stepwise Discriminant Analysis, Principal

Component Analysis and Band-Band Correlation (560, 650, 730 and 760nm)

• Many Narrowband indices evaluated for discrimination

• Red edge ratio and SIPI((R800 - R445)/(R800 + R680)) best for discrimination

• Similar analysis for disease detection and water stress discrimination

1

1.1

1.2

1.3

1.4

1.5

1.6

0.65 0.7 0.75 0.8 0.85 0.9

SIPI

Re

d e

dg

e 7

40

/72

0

N0 N50 N100 N150 N200 N250 N300

1

1.1

1.2

1.3

1.4

1.5

1.6

0.65 0.7 0.75 0.8 0.85 0.9

SIPI

Re

d e

dg

e 7

40

/72

0

N0 N50 N100 N150 N200 N250 N300

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Crop Parameter Estimation (Development of Indices: Chlorophyll)

• Correlation curves generated for potato leaf chlorophyll with reflectance & derivatives

• Bands selected for ratio, using peaks and troughs of correlation curves

• Red-edge importance was shown in derivative based indices

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-0.1

0

0.1

0.2

300 500 700 900 1100

Wavelength (nm)

Co

rre

latio

n c

oe

ffic

ien

t

Chl a Chl b Total ChlorophyllIndices Chl a Chl b Total

Chl

Ratio based indices

R750/R700 0.63** 0.45* 0.53** R750/R610 0.51** 0.33 0.41* R750/R420 0.42* 0.25 0.33

First derivative based ratios

D740/D690 0.69** 0.50* 0.59**

Second derivative based ratios

Dd720/Dd680 0.63** 0.45* 0.53** Dd720/Dd750

-0.66**

-0.47*

-0.55**

400 500 600 700 800 900 1000

400

500

600

700

800

900

1000

-0.75

-0.7

-0.65

-0.6

0

0.6

0.65

0.7

0.75

Crop Parameter Estimation (Development of Indices)

• Correlation map of ratio based (all possible band combinations) indices with leaf nutrients

• Selection of ratios with high correlation

• Stepwise regression for best fit model (-0.373+2.634 r750/710; R2 = 0.551, F=31.9**)

Leaf Nitrogen

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LEAF

CHARACTERISTICS

N, Cab, Cw

PROSPECT

Leaf

Reflectance

Transmittance

SAIL

CANOPY

REFLECTANCE

Measurement

Characteristics

(View & Sun Angle)

Canopy

Structure

LAI, LAD

Soil

Reflectance

PROSPECT+SAIL

Canopy Reflectance Model

1. PROSPECT (leaf optical properties model) requires the leaf structure parameter N,

the chlorophyll a,b content Cab (g/cm2), the equivalent water thickness Cw (g/cm2),

and dry matter content (g/cm2) to simulate leaf reflectance and transmittance spectra

in the optical domain.

2. SAIL (Scattering by Arbitrary Inclined Leaves) is the canopy reflectance model, which

computes canopy reflectance coupled with PROSPECT and using leaf area index

(LAI) and leaf angle distribution (LAD), soil background reflectance.

PROSAIL Model Calibration & LAI Estimation

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PROSAIL Inversion Software (PRIS)

Hierarchical Calibration

Neural Network Training

Inversion using GA

Inversion using NN

HySi/IMS-1

64 bands; 400-950 nm range; Spectral separation 8 nm;

Spatial Resol. 505.6 m; Swath 129.5 km Specifications

Crop Classification using HySI Data

Soil Parameter RMSE R2 RPD

N 11.058 0.838 10.509

P 2.872 0.963 6.809

K 7.049 0.862 4.062

SOC 0.101 0.830 6.730

Sand 6.877 0.848 6.234

Silt 5.403 0.833 5.252

Clay 3.282 0.801 9.532

Soil Parameter Estimation using HySI Data, PLSR Model and Ground Observation

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Remote Sensing of Crop Residue

Soil

Residue

Straw

Matured Wheat

CAI

-2

-1

0

1

2

3

4

5

RESIDUE STRAW MATURED SOIL

LCA

0

1

2

3

4

5

6

7

RESIDUE STRAW MATURED SOIL

CAI: Cellulose Absorption Index

LCA =100[2*R2.2 − (R2.1 + R2.3)]

LCA: Lignin Cellulose Absorption Index

Forest Species Classification

Study Area: Shoolpaneswar Forest Data Used: Hyperion Data of October Approach: Band Selection using PLSR technique, Different Classifiers

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Discrimination of Different Forest Classes

Forest Research Institute (FRI)

Isodata: 6 classes FRI map SAM: 6 classes

Avicennia alba

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0.9

1

400 600 800 1000 1200 1400 1600 1800 2000 2200

Wavelength (nm)

Re

flec

tan

ce

Adaxial surface Abaxial surface

420

1130

1150

Avicennia alba

L = 0.001

Sonneratia caseolaris

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0.9

1

400 600 800 1000 1200 1400 1600 1800 2000 2200

Wanelength (nm)

Re

fle

cta

nc

e

Adaxial surface Abaxial surface

2050 1440

470

440

Sonneratia caseolaris

L = 0.044

Rhizophora mucronata

0

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0.9

1

400 600 800 1000 1200 1400 1600 1800 2000 2200

Wavelength (nm)

Re

flec

tan

ce

Adaxial surface Abaxial surface

400

920

1620

2070 2320

1640

Rhizophora mucronata

L = 0.000

Leaf reflectance spectra

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0.8

400 600 800 1000 1200 1400 1600 1800 2000 2200

Wavelength (nm)

Re

fle

cta

nc

e

Aa random leaves

Rm random leaves

Sc random leaves

0

0.005

0.01

0.015

0.02

0.025

0.03

0.035

0.04

0.045

0.05

400 600 800 1000 1200 1400 1600 1800 2000 22000

0.005

0.01

0.015

0.02

0.025

0.03

0.035

0.04

0.045

0.05

400 600 800 1000 1200 1400 1600 1800 2000 2200

p-v

alu

e

0

0.005

0.01

0.015

0.02

0.025

0.03

0.035

0.04

0.045

0.05

400 600 800 1000 1200 1400 1600 1800 2000 2200

p-v

alu

e

0

0.005

0.01

0.015

0.02

0.025

0.03

0.035

0.04

0.045

0.05

400 600 800 1000 1200 1400 1600 1800 2000 2200

p-v

alu

e

99% confidence level

95% confidence level

0

0.005

0.01

0.015

0.02

0.025

0.03

0.035

0.04

0.045

0.05

400 600 800 1000 1200 1400 1600 1800 2000 2200

p-v

alu

e

0 0

Random leaves

550, 720, 1630, 1750 nm

L = 0.003

Mangrove Study

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Canopies of Nypa fruticans and Phoenix paludosa

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400 600 800 1000 1200 1400 1600 1800 2000 2200

Wavelength (nm)

Re

fle

cta

nc

e

N. fruticans P. paludosa

Canopies of Sonneratia apetal vs Sonneratia caseolaris

0

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400 600 800 1000 1200 1400 1600 1800 2000 2200

Wavelength (nm)

Re

fle

cta

nc

e

S. apetala S. caseolaris

Canopies of Xylocarpus mekongensis vs Xylocarpus

granatum

0

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0.8

400 600 800 1000 1200 1400 1600 1800 2000 2200

Wavelength (nm)

Re

fle

cta

nc

e

X. mekongensis X. granatum

Canopies of Avicennia officinalis vs Avicennia alba

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0.1

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0.5

0.6

0.7

400 600 800 1000 1200 1400 1600 1800 2000 2200

Wavelength (nm)

Re

fle

cta

nc

e

A. officinalis A. alba

400 2310

L = 0.03

2080 580 1540

2150

550

400

430 690

960

1000

720

970

1000

L = 0.004

L = 0.011 L = 0.019

Avicennia officinalis vs. A. alba Sonneratia apetala vs. S. caseolaris

Nypa fruticans vs. Phoenix paludosa Xylocarpus mekingensis vs. X. granatum

Mangrove Study

a

b c

Class 1

Class 2

Class 3

Class 4

Class 5

Others

75.28%

К = 0.69 Overall Accuracy = 65.39%

Kappa Coefficient (К) = 0.59 97.97%

К = 0.97

FCC Minimum Distance Spectral Angle Mapper Support Vector Machine

Mangrove Classification: Bhitarkanika

Class 1 Pure/ dominant communities of

Heritiera fomes

Class 2 Mixed communities of H. fomes

(with Cynometra ramiflora,

Aegiceras corniculatum,

Rhizophora mucronata, etc.)

Class 3 Mixed communities of

Excoecaria agallocha (with

Avicennia sp.)

Class 4 Fringing stands of mixed

Sonneratia apetala (with other

landward species)

Class 5 Mixed communities of

mangrove associates & salt

tolerant grasses

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30

40

50

60

70

80

90

100

196 148 98 96 88 79 63 56 39 23 8

Number of bands

Ov

era

ll a

cc

ura

cy

(%

)

0

5

10

15

20

25

30

De

cre

as

e in

ac

cu

rac

y (%

)

Overall accuracy (%) Decrease in accuracy (%)

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In situ spectra of various wetland plant species

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325 375 425 475 525 575 625 675 725 775 825 875 925 975 1025 1075

Wavelength (nm)

Re

fle

cta

nc

e

Phragmites Ipomoea Typha Cheda Nelumbo Nympheae Chara

A

E

B

F

C

G

D

A: Phragmites karka

B: Typha angustata

C: Cheda (local name)

D: Ipomoea aquatica

E: Nelumbo nucifera

F: Nympheae stellata

G: Chara sp.

Wetland Vegetation Discrimination & Water Quality

AVERAGE SPECTRA OF WATER, CHILIKA LAGOON

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0.60

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0.80

300 400 500 600 700 800 900 1000 1100

WAVELENGTH (nm)

RE

FL

EC

TA

NC

E

Site-1

Site-2

Site-3

Site-4

Site-5

Site-6

Site-7

Site-8

Site-9

Site-10

Site-11

Best Reflectance Ratio

for Chlorophyll

Estimation

R713/R680

Correlation coefficient

for Chlorophyll-a (0.659)

and

Chlorophyll-a+

Pheophytine (0.677)

Site-1 Site-8

Site-10 Site-6

CV Indices

0

5

10

15

20

25

30

35

40

1 2 3

Stages Of Wheat

Perc

en

tag

e

NDVI

SR

NDVI705

mSR705

mNDVI705

SAVI

MSAVI2

OSAVI

MCARI

TCARI

NPCI

MCARI2

RdEdg

ZTM

PRI

SIPI

ARVI

EVI

RGRI

Angular effects on Vegetation Indices

Least anisotropy- SIPI and RGRI – CV <5%. They are ratios of bands

with similar angular effects and hence tend to cancel out the common

effects.

Maximum anisotropy for Red Edge, ARVI, MSAVI, MCARI etc

Other indices showing less anisotropy- ZTM,NPCI and mSR705

CV of Indices

Analysis of CHRIS/PROBA Data

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(a ) Earhead~Grain Formation

-20

0

20

40

60

80

100

120

140

160

180

-55.37 -36.77 +32.64 +53.31

View angle (°)

% C

ha

ng

e i

n d

no

rm

442.2 490 529.8 551 569.6 630.9

660.6 674.1 696.9 705.9 712 741.1

751.3 780.2 871.4 894.6 909 1018

(b ) Earhead~Milking Stage

-5

0

5

10

15

20

25

30

35

40

-55.37 -36.77 +32.64 +53.31

View angle (°)

% C

ha

ng

e i

n d

no

rm

442.2 490 529.8 551 569.6 630.9

660.6 674.1 696.9 705.9 712 741.1

751.3 780.2 871.4 894.6 909 1018

(c ) Grain Formation~ Milking Stage

-10

0

10

20

30

40

50

60

-55.37 -36.77 +32.64 +53.31

View angle (°)

% C

ha

ng

e i

n d

no

rm

442.2 490 529.8 551 569.6 630.9

660.6 674.1 696.9 705.9 712 741.1

751.3 780.2 871.4 894.6 909 1018

The Percent change in normalized distance from nadir between the reflectance of wheat crop in

different stages, for non- nadir view angles

Analysis of CHRIS/PROBA Data

LAI Vs VI

-0.6

-0.4

-0.2

0

0.2

0.4

0.6

0.8

ND

VI

SR

ND

VI7

05

mS

R7

05

mN

DV

I70

5

SA

VI

MS

AV

I2

OS

AV

I

MC

AR

I

TC

AR

I

NP

CI

MC

AR

I2

Rd

Ed

g

ZT

M

PR

I

SIP

I

EV

I

AR

VI

RG

RI

Vegetation Indices

R2

-55

-36

0

55

Graph showing correlation of LAI and vegetation indices computed using CHRIS/PROBA data.

Spectral Analysis Software

Spectral

Analysis

Continuum

Removal

Red Edge

Analysis

Data

Smoothing

Averaging

Derivative

Spectra

Vegetation

Indices

View Chart

Simple

Averaging

Broadband

Conversion

Chart

Chart

Chart

Chart

Chart

Predefined

Indices

Index

Calculator

Chart

An In-house developed

software to analyze spectral

profiles derived from

spectroradiometer

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Fig. 4. Graphic User Interface (GUI) and query modules of spectral Library

Send req. for

Plant List of

Plantation

Send req. for

Plant List of

Crops

Send req. for

Plant List of

Ornamental

User

Vegetation

Spectral Library

GUI

Natural

Vegetation

Plantation

Ornamental

Plants

SAC.mdb

Spectral

View

General

Info

Vegetatio

n AnalysisPlant

photograph

Observation

Details

Spectral

Details

Send Request

Send Request

Plant Info displayed on GUI on the basis of User’s Request

Plant List

displayed

Send request for Plant

List of Natural Veg.

Select

Plant

name

1

2

3

4

5

6

7

Development of Spectral Library

Optimum Band Selection

• Large Dataset

• Stepwise discriminant analysis

SOURCE OF VARIATION

WAVELENGTHS SELECTED WILKS’ LAMBDA

F VALUE

Rabi Season Crops (4)

400,450, 480, 550, 660, 680 nm 0.000 85.5

Kharif Season Crops (5)

400, 420, 450, 500, 550, 590, 600, 610, 670, 660, 710, 730, 740, 760, 830, 940 nm

0.001 86.3

Wheat Varieties (15) 370, 940, 770, 750, 1030 nm 6.32E-05

Mustard Stages (2) 400, 430, 480, 610 nm 0.065 53.9

Cotton- Dates of Sowing (2)

560,700 nm 0.001 1568.2

Rice: Nitrogen Treatments (5)

400, 440, 570, 710, 740, 760, 770, 800, 930, 970 nm

0.001 22.5

Rice: Phosphorus Treatments (5)

640, 680 nm 0.701 3.4

Potato: Irrigation Treatments (3)

540, 610, 630, 700,1000 nm 0.172 8.16

Soil Types (4) 420, 720, 770, 790, 850 nm 0.000 319.6

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Selected Narrowbands

• Integration of all outputs, Bands selected on the basis of frequency of occurrence

• 13 optimum bands in VNIR (400-1050 nm) region

• These included bands in violet (2), blue (2), green (1), red (3), red edge (2), NIR (2)

and moisture sensitive NIR (1) region.

Frequency of Bands

0

1

2

3

4

5

370

400

420

430

440

450

480

500

540

550

560

570

590

600

610

630

640

660

670

680

700

710

720

730

740

750

760

770

790

800

830

850

940

970

1000

1030

Wavelelength (nm)

Fre

qu

en

cy

First Derivative Spectra

-0.03

-0.02

-0.01

0

0.01

0.02

0.03

0.04

400 500 600 700 800 900 1000

Wavelength (nm)

Fir

st

deri

vati

ve (

off

set)

3-nm 5-nm

10nm 15nm

20nm 25nm

30nm

Optimum Bandwidth

1st Derivative of Reflectance at Different Bandwidth with offset

• Data from nitrogen treatment of rice crop

• Original spectral resolution 3 nm, resampled to 1 nm

• Comparison of reflectance and derivative

• Integrated to 3, 5, 10, 15, 20, 25, 30 nm

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Optimum Bandwidth

• Optimum bandwidth required differed for different wavelength regions

• 700-800 nm region: ~5 nm, 600-700 & 800-900 nm: upto 15 nm; 400-500nm and 900-

1000nm: upto 25

Reflectance

difference

at peaks

RMSE at

varying

Bandwidth

RMSE at varying Bandwidth

0

0.05

0.1

0.15

0.2

5 10 15 20 25 30

Bandwidth

RM

SE

(40

0-5

00

,70

0-8

00

,90

0-1

00

0)

0

0.01

0.02

0.03

0.04

0.05

0.06

RM

SE

(50

0-6

00

, 6

00-7

00

, 8

00-9

00

)

400-500 700-800 900-1000

500-600 600-700 800-900

Bandwidth (nm) Reflectance (%); Difference from 3 nm 3 5 10 15 20 25 30

Reflectance 13.0 13. 12.8 12.5 12 11.8 11.3 Green Maximum Difference -- 0 -0.2 -0.5 -1 -1.2 -1.7

Reflectance 2.5 2.5 2.6 2.7 2.5 2.75 2.9 Red Minimum Difference -- 0 0.1 0.2 0 0.25 0.4

Reflectance 58.0 57.0 56.0 55.0 52.0 52.0 53.0 NIR maximum Difference -- -1.0 -2.0 -3.0 -6.0 -6.0 -5.0

Conclusion

• All these studies have been carried out under ‘Hyperspectral Remote Sensing Applications’ project of Space Applications Centre, in collaboration with large number of organizations including IARI.

• Narrowband data have shown higher potential in assessing crop stresses, vegetation type discrimination, and so on.

• It was also found suitable for more accurate bio-chemical and bio-

physical parameter retrieval

• Various software and database have been developed for better interpretation of hyperspectral data.

• Methodologies have been developed for selection of optimum bands and bandwidth for vegetation studies.