Remote Sensing
and
Environmental Plant
Physiology
What is Remote Sensing?
KRReddy@pss.msstate.edu
Department of Plant and Soil Sciences
Mississippi State University
Mississippi State, MS
Remote Sensing
• Remote sensing is defined as the art and
science of obtaining information about an
object without in direct physical contact with
the object.
• It is a scientific technology that can be used
to measure and monitor important
biophysical characteristics and human
activities on Earth.
Remote Sensing Includes ….
• Remote sensing often involves collection of diverse
pieces of information or data for a particular site,
field or a target.
• The data is combined and interpreted to provide
useful information concerning the site or the target.
• Geographic Information Systems (GIS),Geographic
Position Systems (GPS) and geo(spatial)-statistics
are sometimes used to organize spatial data.
Scientific Principles
• Sunlight strikes the Earth’s surface and certain
wavelengths are either absorbed, reflected or
transmitted.
• Various materials absorb sunlight over specific
wavelength intervals resulting in absorption
features in reflectance spectra.
• The location and shape of these unique absorption
features provide information on the chemical
composition of materials.
A-Energy source or Illumination
B-Radiation and atmosphere
C-Interaction with target
D-Recording energy of target by sensor
E-Transmission, reception & processing
F-Interpretation & Analysis
G-Application
1
Spectral Characteristics of the Vegetation
• A healthy leaf intercepts incident radiant flux directly from the
Sun or from diffuse skylight scatted on to the leaf.
Spectral Characteristics of the Vegetation
Energy Balance
• Using the energy balance equation, we can keep track of what
happens to all the incident energy.
• The general equation for interaction of spectral radiant flux on
and within the leaf is
Incident radiant flux = reflectance + transmittance + absorbance
Reflectance = Incident radiant flux - (Absorbance +
Transmittance)
Percent reflectance = (target / reference) * 100
Typical Reflectance, Transmittance and
Absorption Characteristics of Big bluestem Leaf
Peep through Spectrally
Cotton Leaves and Spectral Images
Typical reflectance spectrum of cotton leaf
0.7
Internal tissue strongly
reflective, biomass content
0.6
Reflectance
COTTON
Moisture content
of vegetation,
contrast between
vegetation types
0.5
Typical Leaf Spectrum and Atmospheric Water Absorption Bands
589
0.4
700
0.3
0.2
Chlorophyll
0.1
Phenolics
0
400
800
1200
1600
Wavelength (nm)
2000
2400
760
800
2
What causes the reflectance to change?
Reflectance and leaf structure
Absorption Spectra of Chlorophyll a, b
Beta Carotene, and other pigments
Incident radiation
Reflected radiation
WAX LAYER
UPPER EPIDERMIS
PALISADE LAYER
CHLOROPLASTS
MESOPHYLL CELLS
SUB-STOMATAL
CAVITY
GUARD CELLS
LOWER EPIDERMIS
What can we measure?
•
Cellulose, Pigments (chlorophyll, carotenoids),
lignin, nonstructural carbohydrates, protein, oil
etc.
•
•
Greenness per surface area
Canopy coverage
What can Remote Sensing Contribute?
•
•
•
•
•
Site-specific history for field
Stand establishment
Early detection of disease
Presence of buried structures, tracks
Phenology or Developmental stages (e.g.,
bloom)
What can Remote Sensing Contribute?
•
•
•
•
•
•
Topography for site-specific management
Soil properties for site-specific management
Plant properties to estimate insect populations for
site-specific management
Site-specific identification of water stress?
Sites with late-season nutrient deficiency
Site-specific identification and management of
weeds.
Remote Sensing
Multi-spectral
or
Hyperspectral
3
What is Multispectral Imaging?
What is Hyperspectral Imaging?
• Collection of reflected, emitted, or
backscattered energy from an object or area
of interest in multiple bands (less than 10)
or regions of the electromagnetic spectrum.
• Collection of reflected, emitted, or backscattered
energy from an object or area of interest in multiple
bands (from 10 to hundreds) or regions of the
electromagnetic spectrum with each channel
covering a narrow and contiguous portion of the
light spectrum.
• Multispectral imaging allows the analyst to
perform reflectance spectroscopy on few
spatial elements of the image scene.
• Hyperspectral imaging allows the analyst to
perform reflectance spectroscopy on each spatial
element of the image scene.
Multispectral / Hyperspectral
Comparison
Hyperspectral Imaging - Rationale
Principal objective is to identify and map specific
vegetation, rock, and soil types.
• Many disciplines require
Alunite as seen by Multispectral
and Hyperspectral systems
quantitative results.
systems under sample the
spectrum.
Landsat TM
reflectance
• Multispectral imaging
AVIRIS
• Identification of vegetation
types, rock and soil types
requires high-resolution
spectra.
4 bands @ 80 m
Landsat 4, 5
• Space-based Sensors
(raw data)
7 bands @ 30 m
SPOT
GER
4 bands @ 20 m, Pan @ 10 m
63 bands @ 10 m
AVIRIS
224 bands @ 20 m
1990
HYDICE
1998
PROBE-1
210 bands @ 1-3 m
128 bands @ 3 m
3
• Aerial-based Sensors
(raw data)
• Value-added reseller
(processed data)
Millions of Dollars
1,000
900
800
700
600
500
400
300
200
100
0
Space
Aerial
VAR
VAR
Landsat 1, 2, 3
AERIAL
1972
2
Global Remote Sensing Market
SPACE
Hyperspectral Remote Sensing is one part of
an Inevitable Progression in Technology
1
wavelength (µm)
Greater Spectral and Spatial Resolution
4
Sensors
Remote Sensing Includes ….
Ground-based
•
•
•
•
•
Satellite imagery
Aerial photography
Radar and Lidar
Tractor-mounted sensors
Hand-held instruments or sensors
GER Radiometer -Typical Spectra
GER
ASD FieldSpec
Space/
Aerial
PHOTOGRAPHY Satellite
RADAR, LIDAR
CASI
MEIS II
ITD RDACS
ASD FieldSpec Radiometer -Typical Spectra
Well-watered and well-fertilized
0.7
0.6
CORN
SORGHUM
Reflectance
0.5
SOYBEAN
COTTON
0.4
0.3
0.2
0.1
0
400
900
1400
1900
2400
2900
Wavelength (mm)
Remote Sensing
Examples:
•
High-resolution black-and-white or color photography.
•
Radar (RAdio Detection And Ranging, active
microwave) and LIDAR (laser radar).
Lidar is an acronym which stands for LIght Detection
And Ranging.
•
Sonar (Transmission of sound waves through water
column and then recording the energy backscattered
from the bottom or from objects within the water
column.
High-Resolution Black-and-White
Photography
• Identification of landscape features
5
Remote Sensing – Radar and LIDAR
High-Resolution Color Photography
• Identification of landscape features
•
•
•
•
•
•
Topography
Tree height
Canopy water content
Vegetation type
Biomass by component
Canopy structure
Remote Sensing – Images to Information
Remote Sensing - LIDAR
http://www.csc.noaa.gov/products/sccoasts/html/tutlid.htm
Light Detection and Ranging (LIDAR) is a remote
sensing system used to collect topographic data.
This technology is being used by the National
Oceanic and Atmospheric Administration (NOAA)
and NASA scientists to document topographic
changes along shorelines. These data are collected
with aircraft-mounted lasers capable of recording
elevation measurements at a rate of 2,000 to 5,000
pulses per second and have a vertical precision of
15 centimeters (6 inches). After a baseline data set
has been created, follow-up flights can be used to
detect shoreline changes.
Spatial res olution
Satellites
• ADEOS
• CORONA
• COSMOS
Sensors
TOMS
Optical
X
LOW
M EDIUM
HIGH
VERY HIGH
>2KM
2KM -100M
100M -10M
<10M
Radar
X
X
TK-350
X
X
X
KVR-1000
• EARTH PROBE
• ENVISAT
• ERS
TOMS
• JERS
• LANDSAT
•
•
•
•
•
•
•
•
•
METEOR 3
METEOSAT
NIMBUS 7
NOAA
ORBVIEW2
RADARSAT
RESURS-F
RESURS 01
SPOT
• SRTM
X
X
X
X
AMI-SAR
X
ATSR
X
GOME
• IKONOS
• IRS-1C
X
X
X
X
X
X
X
X
PAN
X
X
LISS-III
X
WIFS
X
SAR
X
X
TM
X
X
RBV
X
X
ETM+
X
MSS
X
TOMS
X
X
MSR
X
X
X
X
SEAWIFS
X
SAR
Large area
Inventories
X
Regional Crop
Mapping
X
X
OPS
AVHRR
X
X
X
X
TOMS
Remote Sensing – Agriculture
In the beginning …
X
X
X
X
X
X
X
KFA-1000
X
MSU-SK
X
HRV
X
X
HRVIR
X
X
X
VGT
X
X
X
X
X
6
Remote Sensing – Agriculture
Today, We do Several Things
Estimating Grain Yield of Maize
Landsat-TM Data
Mean grain yield measured for 22 fields in
October 1995 = 9.36 t/ha
Mean of corresponding satellite derived
yield = 9.18 t/ha
• Pest Management (Insects and
weeds)
• Irrigation Scheduling
• Crop Growth Monitoring
• Nutrient Management
• Stress Detection
• Invasive Species Detection
• Natural Resource Management
and Applications
Estimating Grain Yield of Maize
5-7
7-8
8-9
9-9.5
9.5-10
t/ha
t/ha
t/ha
t/ha
t/ha
Analyses of deviations: RMS-error=1.38t/ha
R²=0.50
Estimating Crop Yield in Hungary
Crop maps for the 6 counties in Hungary derived from multitemporal high-resolution satellite data
(Landsat TM and IRS-1C LISS III.) from the early May-August period of 1997.
Estimating Crop Yield in Hungary
Winter wheat yields
per county and Hungary
Winter barley yields
per county and Hungary
COSH
Central Statistical Office,
Hungary
Maize yields
per county and Hungary
Sunflower yields
per county and Hungary
FOMI RSC
FOMI
Remote sensing Center
Winter wheat yield forecast for the 6 counties in Hungary using our developed
Landsat/IRS + NOAA AVHRR model.
7
Remote Sensing
and
Biodiversity
Typical Spectra - Species Differences
Well-watered and well-fertilized
Remote Sensing and Weed Species
25
Cocklebur
0.5
Sicklepod
Ipomea
Reflectance
0.4
Reflectance
0.75
Prickly Sida
Sesbania
0.3
0.2
0.1
0.50
0.0
400
Corn
Sorghum
Cotton
0.25
Total Chlorophyll (µg cm-2)
0.6
1.00
20
15
10
5
0
900
1400
Wavelength (nm)
1900
2400
Ipomea
Cocklebur
Prickly
Sida
Sesbania Sicklepod
Weed species
Soybean
0.00
500
1000
1500
2000
2500
Wavelength, nm
Interpretation and Analysis – Data Types
Spectral Space
Feature Space
Band 2
Image Space
Interpretation and Analysis – Interpretation
Analog
Digital
Band 1
1. Image space: Display of data in relation to one another in a geometric or geographic
sense, and provides a way to associate each pixel with a location on the ground.
2. Spectral space: High degree of spectral detail or response to the type of material on
the ground (grass, trees, soil, water etc.).
3. Feature space: Provides pattern recognition.
1.
2.
3.
4.
5.
6.
7.
Tone
Shape
Size
Pattern
Texture
Shadow
Association
1. Preprocessing
2. Image Enhancement
3. Image Transformation
4. Image Classification and Analysis
8
Interpretation and Analysis – Analysis
Single Bands (Any given band)
Simple Ratios (Band1/Band2)
Indices (Band1-Band2)/(Band1+Band2)
Fourier transformation
Wavelet decomposition
Principle Component Analysis
Supervised Classification
Unsupervised Classification
Hyperspectral Technology Applications
Statistical Packages
SAS
GENSTAT
MATLAB
Software Tools
Arc View
Arc Info
IDRISI
ENVI
IMAGINE
Agriculture Applications
Agriculture and Forestry
•
Geology
•
Environmental
•
Marine and inland waters
•
Civil
– vegetation type identification, assessment of vegetative
stress, crop yield, resource monitoring
– mapping of minerals and rock types for mineral and
hydrocarbon exploration
– detection of spills, baseline studies, land use planning
– mapping of shoreline materials, bathymetry, water
quality
– Transportation corridors, city planning
Forestry Applications
1.
2.
3.
4.
5.
6.
Crop type classification
Crop condition assessment
Crop yield estimation
Mapping of soil characteristics
Mapping of soil management practices
Compliance monitoring (farming practices)
Geological Applications
1.
2.
3.
4.
5.
6.
7.
8.
9.
10.
11.
12.
13.
•
Surface deposit / bedrock mapping
lithological mapping
structural mapping
sand and gravel (aggregate) exploration/ exploitation
mineral exploration
hydrocarbon exploration
environmental geology
geobotany
baseline infrastructure
sedimentation mapping and monitoring
event mapping and monitoring
geo-hazard mapping
planetary mapping
1) Reconnaissance mapping:
• forest cover type discrimination
• agroforestry mapping
2) Commercial forestry:
• clear cut mapping / regeneration assessment
• burn delineation
• infrastructure mapping / operations support
• forest inventory
• biomass estimation
• species inventory
3) Environmental monitoring
• deforestation (rainforest, mangrove colonies)
• species inventory
• watershed protection (riparian strips)
• coastal protection (mangrove forests)
• forest health and vigor
Hydrology Applications
1.
2.
3.
4.
5.
6.
7.
8.
9.
10.
11.
12.
wetlands mapping and monitoring,
soil moisture estimation,
snow pack monitoring / delineation of extent,
measuring snow thickness,
determining snow-water equivalent,
river and lake ice monitoring,
flood mapping and monitoring,
glacier dynamics monitoring (surges, ablation)
river /delta change detection
drainage basin mapping and watershed modeling
irrigation canal leakage detection
irrigation scheduling
9
Sea Ice Applications
1.
2.
3.
4.
5.
6.
7.
8.
9.
10.
Land Use Applications
1.
2.
3.
4.
5.
ice concentration
ice type / age /motion
iceberg detection and tracking
surface topography
tactical identification of leads: navigation: safe shipping routes/rescue
ice condition (state of decay)
historical ice and iceberg conditions and dynamics for planning purposes
wildlife habitat
pollution monitoring
meteorological / global change research
Mapping Applications
1.planimetry
2.digital elevation models (DEM's)
3.baseline thematic mapping / topographic mapping
Need More
Visit Remote sensing tutorial sites
http://rst.gsfc.nasa.gov/
http://www.vtt.fi/aut/rs/virtual/
http://www.remotesensing.org/
http://www.research.umbc.edu/~tbenja1/
http://dynamo.ecn.purdue.edu/~biehl/MultiSpec
http://www.cla.sc.edu/GEOG/rslab/images.html
http://www.cla.sc.edu/geog/rslab/rsccnew/fmod1.html
http://www.ccrs.nrcan.gc.ca/ccrs/eduref/tutorial/tutore.html
http://www.agso.gov.au/education/remote_sensing/TOC.html
6.
7.
8.
natural resource management
wildlife habitat protection
baseline mapping for GIS input
urban expansion / encroachment
routing and logistics planning for seismic /
exploration / resource extraction activities
damage delineation (tornadoes, flooding, volcanic,
seismic, fire)
legal boundaries for tax and property evaluation
target detection - identification of landing strips,
roads, clearings, bridges, land/water interface
Ocean and Coastal Monitoring Applications
•Oil spill
• Ocean pattern identification:
• mapping and predicting oil-spill extent
• currents, regional circulation
and drift
patterns, shears
• strategic support for oil spill
• frontal zones, internal waves,
emergency response decisions
gravity waves, eddies,
• identification of natural oil seepage
upwelling zones, shallow water
areas for exploration
bathymetry
•Shipping
• Storm forecasting
• navigation routing
• wind and wave retrieval
• traffic density studies
• Fish stock and marine mammal
• operational fisheries surveillance
assessment
• near-shore bathymetry mapping
• water temperature monitoring
•Intertidal
zone
• water quality
• tidal and storm effects
• ocean productivity,
• delineation of the land /water interface
phytoplankton concentration
• mapping shoreline features / beach
and drift
dynamics
• aquaculture inventory and
• coastal vegetation mapping
monitoring
• human activity / impact
Early Sensitive Indication of Particular Stresses
Mild
Intensity of stress
Severe
Development of Stress
10
Remote Sensing – Atmospheric Carbon Dioxide
Remote Sensing environmental limitation of
crop growth, development and yield
0.6
360 ppm CO2
720 ppm CO2
¾ Atmospheric Carbon Dioxide
¾ Solar Radiation
¾ Temperature
¾ Water (indirect)
¾ Wind
¾ Nutrients (N, P, K)
¾ Ozone, UV-B etc.,
¾ Growth Regulators
Remote Sensing environmental limitation of
crop growth, development and yield
Reflectance
0.5
0.4
0.3
0.2
0.1
0
0
500
1000
1500
2000
2500
3000
Wavelength (nm)
Crop Growth and Development - Environment
Response to Temperature
4-week old cotton seedlings
¾ Atmospheric Carbon Dioxide
¾ Solar Radiation
¾ Temperature
¾ Water (indirect)
¾ Wind
¾ Nutrients (N, P, K)
¾ Ozone, UV-B etc.,
¾ Growth Regulators
Environment Factors
Temperature:
20
25
30
35
40
Remote Sensing and Environment
Thermal Imagery
http://wwwghcc.msfc.nasa.gov/irgrp/lst_goes_UHEI.html
¾ Strongly Affects:
-- Phenology
-- Vegetative growth, LAI, LAD
-- Fruit Growth and Retention
-- Respiration
-- Water-loss and Water-Use
¾ Moderately Affects:
-- Photosynthesis on a canopy basis
11
Remote Sensing and Environment
Thermal Imagery
Relationships between NDVI and plant height, the number of nodes,
LAI or biomass
1.0
0.9
0N
50 N
100 N
150 N
NDVI
0.8
0.7
0.6
0.5
Y = 0.944(1-e
2
R = 0.99
0.4
-0.030x
-0.057x
)
Y = 1.48(1-e
2
R = 0.97
)
0.3
0.2
0
20
40
60
80
100
0
5
0.8
0.7
NDVI
15
20
Col 10 vs Col 7
Col 10 vs Col 7
x column 2 vs y column 2
Col 14 vs Col 8
Col 18 vs Col 8
Col 22 vs Col 8
0.9
Thermal image of a cotton canopy that was part of a water and nitrogen
study in Arizona. Blues and greens represent lower temperatures than
yellow and orange. The image was acquired with a thermal scanner on
board a helicopter. Most of the blue rectangles (plots) in the image
correspond to high water treatments.
10
MSN (No. plant-1)
Plant Height (cm)
1.0
0.6
0.5
0.296
0.4
Y = 0.231X0.215
2
R = 0.94
Y = 0.669X
R2 = 0.97
0.3
0.2
0
1
2
3
0
Remote Sensing environmental limitation of
crop growth, development and yield
200
400
600
800
Above Ground Biomass (g m-2)
LAI
Water
Water plays essential roles in plants as a:
¾ Constituent
¾ Atmospheric Carbon Dioxide
¾ Solar Radiation
¾ Temperature
¾ Water (indirect)
¾ Wind
¾ Nutrients (N, P, K)
¾ Ozone, UV-B etc.,
¾ Growth Regulators
¾ Solvent
¾ Reactant in various chemical processes
¾ Maintenance of turgidity
The physiological importance of water is reflected in its
ecological importance.
The distribution plants over the earth’s surface is controlled by
the availability of the water (amount and seasonal distribution of
precipitation) where ever temperature permits growth.
Environment Factors
Water Deficits:
¾ Strongly affects:
-- Vegetative growth, LAI, LAD
-- Fruit Growth and Retention
-- Water-loss and Water-Use
-- Photosynthesis
¾ Moderately affects certain phenological events:
-- Phenology (leaf development)
12
Environment - Water
Role of water in cotton
Sensitivity to water deficit
Leaf GR
LAI
Stem GR
Cell expansion
Vegetative
yield
height
Root GR
Water
deficit
Mesophyll
Resistance
Canopy
Rate of
photosynthesis
Photosynthesis
Stomatal
Resistance
Rate of
Transpiration
Cell division &
differentiation
Rate of squaring
Node production
Seasonal
transpiration
bolls
Economic
shedding yield
Boll GR
Sequence of events
Remote Sensing – Water Stress
Plant water status
Remote Sensing – Water Stress
Indices to Define Water Status
Plant water status is usually defined by measuring leaf
water content
Relative Leaf Water Content = (FW-DW)/(TW-DW)
Relative Water Content
= (FW-DW)/FW
As it is the seat for the major plant events like
photosynthesis, transpiration, respiration
Relative Drought Index
= WSDact / WSDcrit
Leaf Water Content
= FW-DW
Irrigation is provided to crop plants to maintain the
plant water status so as not to limit growth and
development processes
Equivalent Water Thickness = (FW-DW)/A (g cm-2) or cm
Fuel Moisture Content
= (FW-DW)/FW or DW
Leaf water potential (ψW)
= ψs + ψp + ψg
13
Leaf water potential – indicator of plant health
Remote Sensing – Water Stress
LWP is measured as negative pressure in bars or MPa.
The LWP depends on
- soil moisture (in the root zone)
- resistance to move from root surface
to water conducting vessels
- resistance to flow through xylem
- resistance from xylem to leaf cells
- resistance to water loss from stomates
depending on degree of opening
Leaf water potential is measured using:
- Psychrometer
- Cryoscopic osmometer
- Pressure probe
- Pressure chamber
Identifying the hyperspectral reflectance index
Co-ordination of measurements
¾
Simple correlation analysis between leaf physiological
parameters and spectral reflectance
¾
Linear regression analysis to determine the relation between
leaf water potential and reflectance ratios
LWPR = Xij / Xij
where i and j indicate the reflectance
values in a given spectrum and at a
given leaf water potential
10:00:00
10:02:00
10:02:15
10:02:30
Photosynthesis
Hyperspectral
reflectance
Fresh weight
Leaf water
potential
LWPI = (Xij - Xij) / (Xij+ Xij)
Diurnal trends of leaf water potential and
transpiration rate in cotton
Leaf water potential
-0.5
-0.2
10
Leaf water potentail (MPa)
8
-0.6
-0.8
6
-1.0
4
-1.2
2
-1.4
-1.6
0:00
4:00
8:00
12:00
16:00
20:00
0
0:00
Transpiration rate (mm)
-0.4
Leaf water potential (MPa)
12
0.0
y = -0.0017x - 0.0614
R2 = 0.9976
-1.0
y = -0.0036x + 0.7141
R2 = 0.9877
-1.5
-2.0
y = -0.003x + 0.1888
R2 = 0.9818
-2.5
-3.0
480
8
540
9
600
10
660
11
720
12
780
13
840
14
Time of day (h)
Time of Day
14
Leaf water potential vs. Photosynthesis
Hyperspectral reflectance spectra of 90 leaves
0.7
50
0.6
40
0.5
35
Reflectance
Photosynthesis (µmol m-2 s-1)
45
30
25
20
P n = 39.29-0.097*0.7732 LW P
R 2 = 0.78
P < 0.001
15
10
0.4
0.3
0.2
5
0.1
0
-2.5
-2.0
-1.5
-1.0
-0.5
0.0
Leaf water potential (MPa)
0
500
1000
1500
2000
2500
3000
Wavelength (nm)
Hyperspectral reflectance ratio vs. Leaf water potential
Prediction of weekly measured data leaf water potential
0.0
-0.5
Predicted LWP (MPa)
Reflectance Ratio (1689/1657)
1.00
0.99
0.99
y = 0.005x + 0.9954
R2 = 0.6636
0.98
-1.0
-1.5
-2.0
y = 0.9091x - 0.1827
R2 = 0.6263
-2.5
-3.0
0.98
-2.5
-2.0
-1.5
-1.0
-0.5
-3.5
-3.5
Leaf water potential (MPa)
34
Reflectance Predicted Pn (µmol m-2 s-1)
-2.5
-2.0
-1.5
-1.0
-0.5
0.0
Observed LWP (MPa)
Photosynthesis prediction using LWP and hyperspectral ratio
y = 0.9272x + 2.0227
R2 = 0.6444
r = 0.80
32
-3.0
30
28
26
Indices reported in literature – Vegetative indices
Reflectance Ratio
WS Treatments
R750/R650
-0.718
R935/R661
-0.736
R695/R760
0.609
R605/R760
0.54
(R750-R650)/(R750+R650)
-0.713
(R935-R661)/(R935+R661)
-0.728
(R850-R680)/(R850+R680)
-0.727
(R900-R680)/(R900+R680)
-0.728
(R790-R670)/(R790+R670)
-0.739
(R531-R570)/(R531+R570)
0.835
(R550-R530)/(R550+r530)
0.449
R900/R970
-0.75
Time of day
24
24
26
28
30
32
34
LWP Predicted Pn (µmol m-2 s-1)
15
Remote Sensing environmental limitation of
crop growth, development and yield
Indices reported in literature – Water indices
Reflectance Ratio
WS Treatments
Time of day
R750/R550
0.901
R695/R420
-0.904
R710/R760
-0.906
R970/R900
-0.74
(R415-R435)/(R415+R435)
0.857
(R790-R720)/(R790-R720)
0.848
¾ Atmospheric Carbon Dioxide
¾ Solar Radiation
¾ Temperature
¾ Water (indirect)
¾ Wind
¾ Nutrients (N, P, K)
¾ Ozone, UV-B etc.,
¾ Growth Regulators
R1450/R900
R970/R902
R790/R760
0.942
0.7
1689/1657
0.901
-0.928
Cotton and Nitrogen
Physiology and Spectral Properties
Photosynthesis - Variability Among Species
Response to Leaf Nitrogen
Photosynthesis, mg CO2 m-2 s
-1
3
Maize
Sorghum
Cotton
Sunflower
2
Rice
Soybean
1
0
0.0
0.5
1.0
1.5
2.0
2.5
3.0
3.5
Leaf Nitrogen, g m-2
Environment - Nitrogen
Cotton Nitrogen
•Cotton
- Physiology
Nitrogenand
Experiment
Spectral Properties
•Physiology•Leaf
andReflectance
Spectral Properties
0.6
•0.6
Control
•Control
Nitrogen
deficit
•Nitrogen
•-•Deficient
1.0
0.8
0.6
Reflectance
•Reflectance
Environmental Productivity
Idices for Nitrogen
1.2
Stem Growth
0.4
•0.4
Photosynthesis
0.2
•0.2
Leaf Development
0.4
0.2
Leaf Growth
0.0
1.00
1.25
1.50
1.75
2.00
Leaf Nitrogen, g m-2 leaf area
2.25
0.0
•0.0
2.50
•500
500
•1000
1000
•1500
1500
•2000
2000
•2500
2500
•nm nm
Wavelength,
16
Cotton Leaf N Concentration vs. R517/R413
Cotton Leaf Chlorophyll vs. Reflectance Ratios
(Field N and Pix Studies)
(Field N and Pix Studies)
2.5
2001
0.4
2001
2001
R551/R915
N study
MC study
2.0
1.5
R517/R413
1.0
N study
MC study
0.2
Y = -0.2784X + 2.0559
r2 = 0.69, n = 120
Y = -0.0176X + 1.9947
r2 = 0.78, n = 120
2002
0.3
Y = -0.0043X + 0.4549
Y = -0.00029X + 0.4821
2
2
r = 0.67
0.5
2002
2002
r = 0.83
0.1
2001
2002
0.6
R708/R915
2.0
1.5
1.0
0.5
0.4
Y = -0.3303X + 2.3375
r2 = 0.73, n = 156
Y = -0.0205X + 2.2225
r2 = 0.65, n = 156
0
10
20
30
40
50
60
1.0
3.0
0.2
20
4.0
2
r = 0.88
30
-2
Leaf N (g kg )
Y = -0.00045X + 0.7552
2
r = 0.76
2.0
-1
Y = -0.0077X + 0.7667
0.3
0.5
Leaf N (g m )
40
50
350
500
650
800
950
Chlorophyll (mg m-2)
SPAD reading
n = 120 (5 times × 4 N treatments × 3 reps × 2 studies )
Correlation Coefficient ( r ) between Leaf Pigments, Leaf N
Content, Phenolics, or Specific Leaf Weight (SLW) and Leaf
Hyperspectral Reflectance.
Corn Nitrogen Experiment
Physiology and Spectral Properties
1.0
1.0
0.8
Correlation Coefficient (r)
0.6
1.0
0.8
Chl a
Chl b
Carot
T.Chl
0.6
0.6
0.4
0.4
0.2
0.2
0.2
0.0
0.0
0.0
-0.2
-0.2
-0.2
-0.4
-0.4
-0.4
-0.6
-0.6
-0.6
-0.8
-0.8
0.4
-0.8
570 nm
-1.0
300
720 nm
Chl a/b
Phenolic
0.8
Leaf N (%)
Leaf N (mg/m2)
Chl/Carot
SLW
-1.0
-1.0
700 1100 1500 1900 2300 2700 300
700 1100 1500 1900 2300 2700 300
700 1100 1500 1900 2300 2700
Wavelength (nm)
Dotted lines represent significant levels at P = 0.05, n = 36)
Relationship between Leaf N Content and Leaf Net
Photosynthesis (Leaf N Was Expressed on Both a Dry Weight %
and a Leaf Area Basis).
Linear Regression of Leaf Pigment Concentrations
with a Specific Reflectance Ratio
•1.000
50
30
30
Y = 7.068X + 11.62
R2 = 0.551
Y = 21.601X + 8.448
R2 = 0.793
10
10
1.0
1.5
2.0
2.5
3.0
Leaf N (%)
3.5
4.0
0.4
0.6
0.8
1
1.2
Leaf N (g m-2)
1.4
1.6
• 0% N
• 0% NL
• 20% N
•100% N
•0.997
•0.996
•0.995
•0.9996
•0.9994
•0.9992
•y = -9E-05x + 0.9999
•2
•R • = 0.6128
•y = -0.0001x + 1.0001
•2
•R • = 0.5978
•0.994
•0.9990
•0
•10
•20
•30
•40
•50
•2
•1.000
•4
•6
•8
•0.9990
Carotenoids
Total Chl.
•0.999
•R756/R758
20
20
•0.998
•R764/R765
•R756/R758
40
0% NL
Reflectance Ratio
Leaf Pn (µmol m-2 s-1)
20% N
0% N
Chl. b
•0.9998
100% N
40
•1.0000
Chl. a
•0.999
•0.9980
•R756/R759
50
•0.998
•0.997
•0.996
•0.9960
•y = -0.0007x + 1.0003
•2
•R • = 0.5347
•0.9950
•y = -8E-05x + 1.0001
•2
•R • = 0.639
•0.995
•0.9970
•0.9940
•10
•20
•30
•40
•50
•2
•4
•6
•8
Leaf Pigment Concentration (µg cm-2)
17
Linear Regression of Leaf N Content with a Specific
Reflectance Ratio
•0.80
•1.15
•1.05
•1.00
•0.95
Y = 0.0369X +0.8949
R2 = 0.750
•0.90
•0.0 •1.0 •2.0 •3.0 •4.0 •5.0 •6.0
•0.70
•0.60
•0.50
•0.40
Y = -0.2168X + 0.7437
R2 = 0.510
•0.30
•0.4 •0.6 •0.8 •1 •1.2 •1.4
Leaf N (%)
•1.6
Leaf N (g m-2)
Sorghum Leaf Chlorophyll or N vs. Reflectance Ratio
(Pot Study)
0.5
1.4
R712/R1088
1.2
0.3
1.1
0.2
Y = 0.0003 X + 1.0124
r2 = 0.662
1.0
Y = 0.0047 X + 0.1591
r2 = 0.676
1.04
0.6
1.00
500
650
800
15
25
35
45
55
Leaf N (g kg-1)
Crop
N sensitive
bands (nm)
Algorithm†
N
r2
Reference
Corn
555, 715
Chl = [(R712/R1088) – 0.845] ÷ (-0.00086)
36
0.59
Zhao et al., 2003a
N = [(R575/R526) – 1.016] ÷ (-0.0017)
36
0.69
555, 710
555, 715
0.4
Chl = [(R708/R915) – 0.722] ÷ (-0.00040)
156
0.76
N = [(R517/R413) – 1.9947] ÷ (-0.0176)
120
0.78
Chl = [(R1075/R735) – 1.012] ÷ (0.00030)
21
0.66
N = [(R405/R715) – 0.159] ÷ (0.0047)
21
0.68
0.96
0.92
Y = -0.00086X + 0.845, r 2 = 0.59
Nitrogen, Physiology and Spectral
Properties – Various Crops
Sorghum
0.8
Y = -0.0017X + 1.016, r 2 = 0.687
0.0
Leaf chlorophyll (mg m-2)
Cotton
1.08
0.2
0.1
350
1.0
0.4
R405/R715
R1075/R735
(B)
(A)
100% N
20% N
0% N
1.3
0.9
200
Corn Leaf Chlorophyll or N vs. Reflectance Ratio
(SPAR Study)
R575/R526
R505/R645
R550/R1280
•100% N
• 20% N
• 0% N
• 0% NL
•1.10
† Chl
Sorghum Nitrogen Experiment
Physiology and Spectral Properties
0.88
0
100
200
300
400
Chlorophyll (mg m-2)
500
0
10
20
30
40
50
Leaf N (g kg-1)
Nutrient Deficiency – Potassium
Zhao et al., 2004b
Zhao et al., 2004c
= chlorophyll (mg m-2); N = leaf nitrogen (g kg-1); R = reflectance and subscript values are the wavelengths (nm).
18
Environment Factors
Environment - Nutrients
Potassium - Cotton Growth and Development
Environmental Productivity Indices
1.6
Environmental Productivity Indices
for Growth and Development
Fertilizers Deficits - Potassium:
¾ Strongly Affects:
-- Leaf growth, LAI, LAD
-- Fruit Retention
¾ Moderately Affects:
-- Photosynthesis
-- Stem growth
1.4
Stem Elongation
1.2
Photosynthesis
1.0
0.8
0.6
Leaf Initiation Rates
0.4
Leaf Growth
0.2
0.0
-0.2
0.0
0.5
1.0
1.5
2.0
2.5
3.0
3.5
Leaf Potassium, %
Cotton Potassium Experiment
Physiology and Spectral Properties
Remote Sensing environmental limitation of
crop growth, development and yield
70
60
Sufficient K
Medium K
Low K
Reflectance, %
50
40
30
20
10
0
400
500
600
700
800
900
1000
¾ Atmospheric Carbon Dioxide
¾ Solar Radiation
¾ Temperature
¾ Water (indirect)
¾ Wind
¾ Nutrients (N, P, K)
¾ Ozone, UV-B etc.,
¾ Growth Regulators
Wavelength, nm
Atmospheric CO2 and UV Radiation
Physiology and Spectral Properties
UV-B Radiation Effect on Cotton Leaf Anatomy
19
Environment Factors
UV-B Radiation – Phenology
EPI Factors for various Developmental Processes
1.2
Ultraviolet-B Radiation:
1.0
¾ Strongly Affects:
-- Photosynthesis
-- Stem growth
UV-B index
0.8
Node addition
2
2
y = -0.0003x + 0.0014x + 0.9997 R = 0.4619
0.6
Leaf are a e xpansion
2
¾ Moderately Affects:
-- Leaf growth
-- Leaf aging
¾ No Effects:
-- Phenology
2
0.4
y = -0.0015x + 0.0102x + 0.9914 R = 0.8136
0.2
Ste m e longation
2
2
y = -0.0023x + 0.0105x + 0.9926 R = 0.9331
0.0
-4
0
4
8
12
16
20
UV-B treatments (kJ m-2 d-1)
Hyperspectral Reflectances of Cotton Leaves Exposed
to Different UV-B and CO 2 Treatments
Breakdown of spectral reflectances of cotton leaves into specific waveband regions
with known properties exposed to different UV-B and CO2 treatments
0.20
0.6
0.15
0 kJ
8 kJ
16 kJ
360 ppm
0.5
0.4
0.60
Chlorophyll reflectance
0.55
0.10
0.50
0.05
0.45
Internal tissue reflectance
0.3
Reflectance
Reflectance
0.2
0.1
0
0
500
1000
1500
2000
2500
3000
0.6
0.5
0.00
500
525
550
575
600
625
650
675
700
0.40
750
0.35
0.20
0.30
0.15
850
950
1050
1150
1250
720 ppm
0.4
0.3
0.2
0.25
0.10
Leaf water reflectance
0.1
0
0
500
1000
1500
2000
2500
0.20
1550
3000
Wavelength(nm)
1600
1650
1700
1750
1800
1850
0.05
2000
2050 2100
2150 2200
2250 2300
2350 2400
Wavelength (nm)
Deviation of Spectral Reflectances of Cotton Leaves
Exposed to Different UV-B Treatments from Control
at Squaring and Flowering Stages
Reflectance deviation from
control (0 kJ UVB) treatment
0.08
Remote Sensing environmental limitation of
crop growth, development and yield
0.08
At squaring
0.06
At flowering
0.06
0.04
0.04
0.02
0.02
0.00
0 kJ UVB
8 kJ UVB
16 kJ UVB
0.00
-0.02
-0.02
0 kJ UVB
-0.04
-0.04
8 kJ UVB
-0.06
16 kJ UVB
-0.08
-0.06
-0.08
400
900
1400
1900
2400
400
Wavelength (nm)
900
1400
1900
2400
¾ Atmospheric Carbon Dioxide
¾ Solar Radiation
¾ Temperature
¾ Water (indirect)
¾ Wind
¾ Nutrients (N, P, K)
¾ Ozone, UV-B etc.,
¾ Growth Regulators
20
Remote Sensing and Plant
Growth Regulators
Environment Factors
Growth Regulators - Mepiquat Chloride
(PIX):
¾ Moderately Affects:
-- Leaf, stem and branch growth and LAI
¾ Slightly Affects:
-- Photosynthesis
Mepiquat Chloride (PIX) - Growth
EPI Factors
Mepiquat Chloride (PIX) and
Cotton Growth and Remote Sensing
1.2
80
MC Application Rate (g a.i. ha-1)
(A)
70
Leaf growth
0.8
Reflectance (%)
1.0
50
40
30
20
Photosynthesis
0.6
10
0
(B)
2
0.4
Stem growth
0.2
0.0
Reflectance Difference (%)
PIX and EPI Indices
0
25
50
100
60
0
-2
-4
-6
-8
-10
0.00
0.01
0.02
0.03
-12
300
400
Mepiquat Chloride, mg g-1 dry weight
Mepiquat Chloride (PIX) and
Cotton Growth and Remote Sensing
• As expected, application of MC decreased plant
growth, increased leaf chlorophyll concentration,
and decreased leaf reflectance.
• Reflectance values at 420, 545, 810, and 935 nm
separated MC-treated plants from untreated plants
under favorable growth conditions, but we were
unable to distinguish the different application rates
of MC.
• Forward stepwise regression and discriminant
analysis suggested that changes in leaf reflectance
from MC application were due to increased
chlorophyll and nitrogen concentrations.
500
600
700
800
900
1000
Wavelength (nm)
Remote Sensing environmental limitation of
crop growth, development and yield
¾ Atmospheric Carbon Dioxide
¾ Solar Radiation
¾ Temperature
¾ Water (indirect)
¾ Wind
¾ Nutrients (N, P, K)
¾ Ozone, UV-B etc.,
¾ Growth Regulators
21
Remote Sensing and Environment
Thermal Imagery
Remote Sensing – Atmospheric Carbon Dioxide
0.6
360 ppm CO2
720 ppm CO2
Reflectance
0.5
0.4
0.3
0.2
0.1
0
0
500
1000
1500
2000
2500
Wavelength (nm)
3000
Thermal image of a cotton canopy that was part of a water and nitrogen
study in Arizona. Blues and greens represent lower temperatures than
yellow and orange. The image was acquired with a thermal scanner on
board a helicopter. Most of the blue rectangles (plots) in the image
correspond to high water treatments.
Nitrogen, Physiology and Spectral
Properties – Various Crops
Hyperspectral reflectance ratio vs. Leaf water potential
Reflectance Ratio (1689/1657)
1.00
Crop
N sensitive
bands (nm)
Algorithm†
N
r2
Reference
Corn
555, 715
Chl = [(R712/R1088) – 0.845] ÷ (-0.00086)
36
0.59
Zhao et al., 2003a
N = [(R575/R526) – 1.016] ÷ (-0.0017)
36
0.69
Cotton
555, 710
Chl = [(R708/R915) – 0.722] ÷ (-0.00040)
156
0.76
N = [(R517/R413) – 1.9947] ÷ (-0.0176)
120
0.78
Chl = [(R1075/R735) – 1.012] ÷ (0.00030)
21
0.66
N = [(R405/R715) – 0.159] ÷ (0.0047)
21
0.68
0.99
0.99
Sorghum
555, 715
y = 0.005x + 0.9954
R2 = 0.6636
Zhao et al., 2004b
Zhao et al., 2004c
0.98
† Chl
= chlorophyll (mg m-2); N = leaf nitrogen (g kg-1); R = reflectance and subscript values are the wavelengths (nm).
0.98
-2.5
-2.0
-1.5
-1.0
-0.5
Leaf water potential (MPa)
Cotton Potassium Experiment
Physiology and Spectral Properties
Hyperspectral Reflectances of Cotton Leaves Exposed
to Different UV-B and CO 2 Treatments
0.6
70
0.3
0.2
40
Reflectance
Reflectance, %
0.4
Sufficient K
Medium K
Low K
50
0 kJ
8 kJ
16 kJ
360 ppm
0.5
60
30
20
10
0.1
0
0
500
1000
1500
2000
2500
3000
1000
1500
2000
2500
3000
0.6
0.5
720 ppm
0.4
0.3
0.2
0
0.1
0
400
500
600
700
800
Wavelength, nm
900
1000
0
500
Wavelength(nm)
22
Remote Sensing and Insect Infestation
Genotype Study – Nematode Damage – MSU North Farm
NDVI
Jul 08, 2002
Genotype Study North Farm, MSU
Aug 10, 2002
Aug 21, 2002
Can we identify insect pest damage?
Field Study
Environmental Factors, Crop Growth and Remote Sensing
• Treatments
•
•
•
•
N study treatments (0, 50, 100, and 150 lbs/A)
PIX study treatment (0, 8, 16, and 32 oz./A)
Irrigation study (Irrigated, Non-irrigated)
Genotype study (38 genotypes)
• Measurements
•
•
•
•
•
•
Location and Field Map for 2001 Field Study
(North Farm, MSU)
Leaf and canopy hyperspectral reflectance (weekly)
Growth analysis (Plant height, nodes, LAI, biomass) (5 times)
Pigments and phenolics (weekly)
Yield and yield components
Photosynthesis
Leaf mineral nutrient contents
Questions We Try to Answer
• Which index or ratio is the best to estimate
cotton growth and yield?
• Which growth stage is the best date to use
the index in estimating cotton yield?
Irrig.
Plots
Pix Plots
Nitrogen Plots
• Which function can be used to estimate crop
physiology, growth and yield?
The image was taken on July 17 (First Flower Stage)
23
Indices
6/25
7/3
7/24
7/31
8/15
8/22
NIR/R (R750/R650)
0.524
0.354 0.421
7/10
0.589
0.391
0.202
0.009
(R750/R550)
0.647
0.482 0.403
0.199
0.295
0.433
0.219
(R935/R661)
0.523
0.364 0.480
0.673
0.523
0.040
0.150
NDVI (R750 R650)
0.501
0.394 0.653
0.723
0.372
0.234
0.047
(R935 R661)
0.497
0.401 0.815
0.752
0.508
0.080
0.189
(R850 R680)
0.475
0.389 0.763
0.747
0.434
0.183
0.152
(R900 R680)
0.477
0.390 0.774
0.760
0.466
0.142
0.180
(R790 R670)
0.476
0.391 0.676
0.768
0.416
0.173
0.131
12
24
30
30
30
30
Sample size (n)
30
Relationship between NDVI at One Week Prior to
First Flower and Seedcotton Yield in 2001
N D V I [(R 9 3 5 -R 6 6 1 )/(R 9 3 5 + R 6 6 1 )]
Correlation Coefficient (R) of Seedcotton Yield with
Several Published Indices
0.92
Irrigation Study
PIX Study
N Study
0.91
0.90
0.89
0.88
0.87
Y = 3.27×10-5X+0.763
R2 = 0.665
0.86
0.85
0.84
2500
3000
Planting date: 5/14; Squaring date: 6/24; First flower: 7/17
3500
4000
4500
Seedcotton Yield (kg ha-1)
Relationship between NIR and Plant Height or the
Number of Mainstem Nodes (n =96)
Relationship between NIR or NDVI and Biomass
(n =96)
25
9.0
8.0
8
8.0
7
7.0
4
3
2
Y = 2.379Ln(X) - 4.006
1
R = 0.63
0
2
R = 0.67
15
30
45
60
75
6
90 105 120 135 150
Plant Height (cm)
8
10
12
14
16
18
20
22
4.0
3.0
100 200 300 400 500 600 700 800 900 1000
20
15
10
Y =21.6(1-0.99X)
R2 = 0.76
5
Mainstem Nodes (no. plant-1)
5.0
Y =6.62(1-0.98X)
R2 = 0.69
0.0
25
Y = 0.331X + 1.197
6.0
1.0
30
3.0
0.0
0
Y = 17.1(1-0.99 )
R2 = 0.0.78
0
4.0
7.0
2.0
X
5.0
1.0
0
10
5
2.0
2
15
0
0
0
NDVI [(R935-R661)/R935+R661)]
5
6.0
NIR (R935/R661)
6
NIR (R750/R550)
9.0
NIR (R750/R650)
9
N IR (R 7 5 0 /R 5 5 0 )
N IR (R 7 5 0 /R 5 5 0 )
20
100 200 300 400 500 600 700 800 900 1000
0.95
0.90
0.85
0.80
0.75
0.70
0.65
Y =0.9X/(12.34+X)
R2 = 0.63
0.60
0.55
0.50
0
100 200 300 400 500 600 700 800 900 1000
100 200 300 400 500 600 700 800 900 1000
Biomass (g m-2, from 5-time harvest data)
Relationship between NIR or NDVI and Leaf Area Index
(n =150)
25
Relationship between NIR or NDVI and Leaf Area Index
(n =96)
8
25
9.0
8.0
7
Y = -39.9+54.9(1-0.22X)
R2 = 0.68
5
4
Y = -5.65+12.27(1-0.21X)
R2 = 0.56
3
0
0
1
2
3
4
5
6
0
1
2
3
4
5
6
15
10
X
Y = -50.0+71.0(1-0.23 )
R2 = 0.70
5
0
0.75
0.70
0.65
X
Y = -0.359+1.25(1-0.156 )
R2 = 0.62
0.60
0.55
0
1
2
3
4
5
6
1
2
3
4
5
15
10
X
Y = -1.4+25.5(1-0.51 )
R2 = 0.72
0
0
1
2
3
Leaf Area Index (from LAI-2000 instrument)
4
5
6
4.0
3.0
6
20
5
0.50
0
5.0
Y =2.11+5.11(1-0.47X)
R2 = 0.74
0.0
25
0.80
6.0
1.0
30
0.85
7.0
2.0
Y = 0.17+18.79(1-0.51X)
R2 = 0.0.73
0.90
NIR (R935/R661)
NDVI [(R935-R661)/(R935+R661)]
20
10
0
0.95
25
15
5
2
30
NIR (R935/R661)
5
NIR (R750/R550)
10
6
0
NDVI [(R935-R661)/R935+R661)]
15
20
NIR (R750/R650)
NIR (R750/R550)
NIR (R750/R650)
20
1
2
3
4
5
6
0.95
0.90
0.85
0.80
0.75
0.70
0.65
Y = 0.59+0.32(1-0.0.44X)
R2 = 0.67
0.60
0.55
0.50
0
1
2
3
4
5
6
0
1
2
3
4
5
6
Leaf Area Index (from 5-time harvest data)
24
Changes in Plant height, the number of mainstem nodes, leaf area index,
and above ground biomass during cotton growth
under field conditions in 2001.
80
60
40
20
0.90
10
5
0.85
0
-2
Above ground Biomass (g m )
0
15
NDVI
Mainstem Nodes (no. plant-1)
0N
50 N
100 N
150 N
100
Plant Height (cm)
0.95
20
120
4
Lead Area Index
Changes in NDVI of different treatments during field cotton growth
3
2
1
0
20
40
60
80
800
0.80
600
0N
50 N
100 N
150 N
400
0.75
200
0
0.70
20
40
Days after Emergence
60
80
40
60
80
100
Days after Emergence
Days after Emergence
Relationships between NDVI and plant height, the number of nodes,
LAI or biomass
Relationships between NDVI and plant height, the number of nodes,
LAI or biomass
1.0
120
0.6
0.5
Y = 0.944(1-e
2
R = 0.99
0.4
-0.030x
-0.057x
)
Y = 1.48(1-e
2
R = 0.97
)
0.3
0.2
0
20
40
60
80
100
0
5
15
1.0
0.7
0.5
0.296
Y = 0.231X0.215
2
R = 0.94
Y = 0.669X
R2 = 0.97
0.3
60
0N
50 N
100 N
150 N
40
600
0.6
0.4
80
1
2
LAI
3
0
200
400
16
2
Y = 6.12-1.29X+2.94X
2
R = 0.99
14
12
10
8
6
4
2
5
Y=e
R2 = 0.95
4
400
200
0
600
800
Above Ground Biomass (g m-2)
0.2
2
Y = 2.35-1.22X+1.54X
R2 = 0.99
3
2
1
0
0.2
0
18
7.25X
Biomass (g m-2)
0.8
100
0
Col 10 vs Col 7
Col 10 vs Col 7
x column 2 vs y column 2
Col 14 vs Col 8
Col 18 vs Col 8
Col 22 vs Col 8
0.9
Y = 4.24-171X+266X2
2
R = 0.99
20
20
MSN (No. plant-1)
Plant Height (cm)
NDVI
10
20
Mainstem Nodes (No. plant-1)
0.7
Plant Height (cm)
NDVI
140
0N
50 N
100 N
150 N
0.8
Leaf Area index
0.9
0.4
0.6
NDVI
0.8
1.0
-1
0.2
0.4
0.6
0.8
1.0
NDVI
What’s Next?
Remote Sensing (RS):
Provide spatial variable data for crop health and
yield, and soil conditions, and some predictive
capabilities.
Crop Simulation Models and Decision Support
Systems(CSM-DSS):
Provide predictive capabilities and verification of
RS features.
RS and CSM-DSS:
Deliver site-specific input management or
optimization.
25