1. EG2234 Earth Observation
AGRICULTURE

2. TOPICS Introduction Physical Theory (spectroscopy and biology)
NOAA-AVHRR system
NDVI
Other Vegetation indices
Crop yield measurement
Discriminating between different crop species

3. 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

4. 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

5. Physical theory #1

6. Physical theory #1

7. Physical theory #1

8. Physical theory #1
Reflectance curve shows LOW values in the RED ( µm) and BLUE ( µm) regions of the visible spectrum
There is a reflectance peak in the GREEN region ( µm)
Sharp rise in reflectivity at about 0.75 µm is the ‘red-edge’. Reflectivity remains high in the NIR

9. 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

10. Physical theory #2
Chlorophyll is concentrated in the GRANA which are just visible with a light microscope as dark green spots inside a chloroplast

11. Physical theory #2

12. Physical theory #2 6CO2 + 6H2O C6H12O6 + 6O2
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 kJ of solar energy is used in synthesising a single mole of glucose:
6CO2 + 6H2O C6H12O6 + 6O2
ΔG kJ

13. 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

14. 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

15. 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

16. Advanced Very High Resolution Radiometer
NOAA-AVHRR System
Advanced Very High Resolution Radiometer

17. 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

18. NDVI Normalised 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

19. NDVI Atmospheric 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)

20. 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.

21. Other vegetation indices
PVI (perpendicular vegetation index):
B = dark wet soils
C = dry soils
X = pure vegetation pixel
Y = mixed pixel

22. Other vegetation indices
PVI (perpendicular vegetation index):
Richardson and Wiegand quantified the difference between soil/mixed pixels and vegetation pixels by defining the PVI:
S = soil reflectance R = red radiation
V = vegetation reflectance IR = near infrared radiation

23. Other vegetation indices
A “family” of other vegetation indices exist that try and correct for soil influences:
SAVI soil adjusted vegetation index
TSAVI transformed soil adjusted vegetation index
GEMI global environment monitoring index
And, more recently:
OSAVI optimised soil adjusted vegetation index:

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

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