EG2234Earth Observation

AGRICULTURE

TOPICS

Introduction Physical Theory (spectroscopy and biology) NOAA-AVHRR system NDVI Other Vegetation indices

Crop yield measurement Discriminating between different crop species

Introduction

Agriculture is big business A nation’s food supply affects both its

economic status and political stability In Europe and the US the greatest concern is

profit and getting crops to market and obtaining a good price – whilst staying cheap

In the developing world the issue is food security and the wellbeing of the population

Introduction

Monitoring of crops from space or aircraft aids decision making

When to irrigate and what fertilizer is needed Management of crop rotation and set-aside

Most large-scale commercial farms are so large that monitoring cannot be made on foot so remote sensing is the clear choice

Physical theory #1

Physical theory #1

Physical theory #1

Physical theory #1

•Reflectance curve shows LOW values in the RED (0.6 - 0.7µm) and BLUE (0.4 - 0.5µm) regions of the visible spectrum

•There is a reflectance peak in the GREEN region (0.5 - 0.6µm)

•Sharp rise in reflectivity at about 0.75 µm is the ‘red-edge’. Reflectivity remains high in the NIR

Physical theory #2

Plants use pigments called CHLOROPHYLL (a and b) to absorb mostly RED and BLUE visible light. The pigments are contained within the CHLOROPLASTS of the plant cell

Physical theory #2

Chlorophyll is concentrated in the GRANA which are just visible with a light microscope as dark green spots inside a chloroplast

Physical theory #2

Physical theory #2

Solar energy is converted into the potential energy of organic molecules in a pathway of reactions called PHOTOSYNTHESIS.

A product of photosynthesis is a hexose sugar called glucose. 2880 kJ of solar energy is used in synthesising a single mole of glucose:

6CO2 + 6H2O C6H12O6 + 6O2

ΔG + 2880 kJ

Physical theory #2

•Chlorophyll does NOT absorb solar radiation equally - rather it absorbs chiefly RED and BLUE light (as much as 70-90%) in these spectral regions

•Little of the green light is absorbed, and so is reflected making vegetation appear green

•In the near infrared spectrum (unseen by human eyes), reflection is controlled by plant tissue

Physical theory #2•The CUTICLE and EPIDERMIS are almost transparent to infrared (IR) radiation - thus very little IR radiation is reflected from the outside of a leaf

•Mesophyll scatters radiation entering from the upper epidermis - with most (around 60%) being scattered upwards - as reflected energy

•The internal structure of a living leaf is largely responsible for the bright IR reflectance measured by radiometers above vegetation

Physical theory #2

Lambertian Reflectance:

Non-specular (i.e. diffuse) reflectance is known as a Lambertian reflectance

Because of the scattering of radiation entered and leaving the mesophyll tissue of a living leaf, we can say that the degree of IR reflectance from the leaf is uniform from many angles

NOAA-AVHRR System

Advanced Very High Resolution Radiometer

NOAA-AVHRR System

•4 or 5 channels (depending on model)•Senses visible, infrared and near infrared•Carried on NOAA’s polar orbiting satellites

•High resolution data (HRPT and LAC) – 1.1km•Reduced (sub-sampled) GAC data – 8km

•Data provides global coverage (pole to pole)•Each pass provides a 2399km swath•Satellite orbits 14 times each day from 833km

NDVINormalised Difference Vegetation Index (NDVI):

NDVI provides a good assessment of photosynthesising vegetation – but caution must be exercised with this type of index as other factors can affect the NDVI other than leaf reflectance: Viewing angle, Soil background, Atmospheric degradation and Leaf orientation

2 12 1

IR R CH CHNDVIIR R CH CH

CHANNEL SPECTRAL LIMITS REGION1 0.58 - 0.68µm visible2 0.72 - 1.10µm near infrared3 3.55 - 3.93µm thermal infrared4 10.3 - 11.3µm thermal infrared5 11.5 - 12.5µm thermal infrared

NDVIAtmospheric interference:

•Energy travelling from the earth to the satellite sensor must pass through the atmosphere•Energy is subject to interference by gases, aerosols, dust, smoke, water vapour etc•No simple way exists for removing these effects from NDVI which conspires to lower the NDVI value•One “trick” is to use maximum value composite data measured over some period of time (usually 10 days)

Other vegetation indices

PVI (perpendicular vegetation index):One of the problems associated with remote sensing vegetation is mixed pixels – a combination of both soil and vegetation. Hutchinson (1982) found that it was difficult to separate soil and vegetation reflectance when vegetation cover is less than 30%.

A solution to this problem is the exploitation of the SOIL BRIGHTNESS LINE (Richardson and Wiegand, 1977). Essentially the soil brightness tends to fall on a straight line – as soil becomes brighter in the near infrared (high NIR reflectance), so it is brighter in the red.

Other vegetation indices

PVI (perpendicular vegetation index):

R E D R E FLE C TA N C E

NE

AR

INFR

AR

ED

RE

FLE

CTA

NC

E

AB

CW

X Y

soil b righ tness lineB = dark wet soils

C = dry soils

X = pure vegetation pixel

Y = mixed pixel

Other vegetation indices

PVI (perpendicular vegetation index):

Richardson and Wiegand quantified the difference between soil/mixed pixels and vegetation pixels by defining the PVI:

2 2( ) ( )R R IR IRPVI S V S V

S = soil reflectance R = red radiation

V = vegetation reflectance IR = near infrared radiation

Other vegetation indices

A “family” of other vegetation indices exist that try and correct for soil influences:

SAVI soil adjusted vegetation indexTSAVI transformed soil adjusted vegetation indexGEMI global environment monitoring indexAnd, more recently:OSAVI optimised soil adjusted vegetation index:

0.16NIR ROSAVI

NIR R

Flowchart for crop-yield estimation. Source: Kastens et al, 2005

Spectral signatures of sugar cane species. Source: Galvao et al, 2005