1. 1 Lecture 7 Land surface reflectance in the visible and RIR regions of the EM spectrum 25 September 2008

2. 2 Reading Assignment Campbell, Chapter 17, sections 1 to 3, pages 45-46 (BRDF)

3. 3 radiometer DN Flux Variations in the flux measured by a radiometer will result in variations in the digital number recorded by that radiometer Radiometer: an instrument to measure fluxes of electromagnetic radiation Question: what are the sources of variation in the fluxes detected by a radiometer

4. 4 DN  i Variations in incoming solar flux will cause variations in the DN recorded by a radiometer operating in any wavelength region Figure 1

5. 5 The amount of solar radiation reaching the earth’s surface varies as a function of latitude and the day of the year – these differences will cause variations in the digital number recorded by a radiometer Figure 2

6. 6 DN  i  - Atmospheric Extinction Coefficient Variations in  will cause variations in the digital number recorded by the radiometer – for example, as  increases, the digital number will decrease Figure 3

7. 7 Key components of VIS/NIR remote sensing 1. Sun is EM Energy Source 2. Energy emitted from sun based on Stephan/Boltzman Law, Planck’s formula, and Wein Displacement Law) 3. EM Energy interacts with the atmosphere 4. EM energy reflected from Earth’s Surface VIS/NIR Satellite EM energy 5. EM Energy interacts with the atmosphere Figure 4

8. 8 Lecture Topics 1.Topic of today’s lecture – factors that influence the reflection coefficient 2.Types of surface reflection 3.Reflectance curves 4.Sources of variation in reflectance –Surface material composition –Moisture –Vegetation 5.Vegetation Index – NDVI and temporal variations in reflectance 6.Bidirectional reflectance

9. 9 Importance of surface reflectance Let R be the amount of EM flux leaving the earth’s surface (that eventually is going to be detected by the satellite - R is exitance) R =  i a r Where  i a is the incident flux after passing through the atmosphere r is the surface reflection coefficient The subscript denotes that all these values are wavelength specific

10. 10  i a r R radiometer DN pixel Variations in net reflectance (r ) result in variations in the flux reflected from the surface (R ), which when detected by a radiometer, will result in variations in the digital number recorded by the remote sensing system Figure 5

11. 11 Lecture Topics 1.Topic of today’s lecture – factors that influence the reflection coefficient 2.Types of surface reflection 3.Reflectance curves 4.Sources of variation in reflectance –Surface material composition –Moisture –Vegetation 5.Vegetation Index – NDVI and temporal variations in reflectance 6.Bidirectional reflectance

12. 12 Three types of surfaces or reflection Specular surfaces or reflection Diffuse surfaces or reflection Lambertian surfaces or reflection

13. 13 Specular Reflection Occurs from very smooth surfaces, where the height of features on the surface << wavelength of the incoming EM radiation In specular reflection, all energy is reflected in one direction, e.g., angle of incidence = angle of exitance Figure 6

14. 14 Diffuse Reflection Most surfaces are not smooth, and reflect incoming EM radiation in a variety of directions These are called diffuse reflectors Figure 7

15. 15 Lambertian Surface A perfectly diffuse reflector is called a Lambertian surface A Lambertian surface reflects equally in all directions Figure 8

16. 16 Lecture Topics 1.Topic of today’s lecture – factors that influence the reflection coefficient 2.Types of surface reflection 3.Reflectance curves 4.Sources of variation in reflectance –Surface material composition –Moisture –Vegetation 5.Vegetation Index – NDVI and temporal variations in reflectance 6.Bidirectional reflectance

17. 17 Reflectance curve – variations in reflectance (r ) as a function of wavelength expressed in percent Figure 9

18. 18 Measurement of reflectance A radiometer is a device that measures the amount of flux originating from a surface or body By measuring incoming flux as well as outgoing flux, reflectance can be calculated A spectroradiometer measures flux in narrow wavelength bands These data are then used to produce a reflectance curve

19. 19 Reflectance curve for a leaf generated from data collected by a spectroradiometer

20. 20 Lecture Topics 1.Topic of today’s lecture – factors that influence the reflection coefficient 2.Types of surface reflection 3.Reflectance curves 4.Sources of variation in reflectance –Surface material composition –Moisture –Vegetation 5.Vegetation Index – NDVI and temporal variations in reflectance 6.Bidirectional reflectance

21. 21 From Lillesand and Kiefer 1994 Comparison of surface reflectances from 3 common features observed by spaceborne remote sensing systems – water, bare soil, and vegetation Figure 10

22. 22 Reflectance from water, soil, and vegetation Water is a very good absorber of EM radiation in the visible/RIR EM regions  low reflectance in all wavelength regions Reflectance from soils generally is low in the shorter visible EM region, but increases in the NIR and SWIR regions Vegetation – has low reflection in visible regions, very high reflection in the near IR, and variable reflection in the SWIR

23. 23 http://www.ghcc.msfc.nasa.gov/precisionag/atlasremote.html Water absorption bands No data collected in these regions Data collected by an airborne spectroradiometer Figure 11

24. 24 Reflectance from soils and rocks Differences in the mineral composition of different soils and rocks lead to variations in reflectance curves Differences in reflectance in specific bands provide the basis for discrimination of mineral types

25. 25 http://www.itek.norut.no/vegetasjon/fenologi/introduction/ndvi.html Figure 12

26. 26 Lecture Topics 1.Topic of today’s lecture – factors that influence the reflection coefficient 2.Types of surface reflection 3.Reflectance curves 4.Sources of variation in reflectance –Surface material composition –Moisture –Vegetation 5.Vegetation Index – NDVI and temporal variations in reflectance 6.Bidirectional reflectance

27. 27 Water has a low reflectance because it absorbs EM radiation in the VIS/RIR region Because water absorbs EM energy throughout the VIS/RIR region, as moisture content increases, reflectance decreases Figure 13

28. 28 http://research.umbc.edu/~tbenja1/leblon/module9.html Reflection of soil with different moisture levels Values represent % water by volume Figure 14

29. 29 Lecture Topics 1.Topic of today’s lecture – factors that influence the reflection coefficient 2.Types of surface reflection 3.Reflectance curves 4.Sources of variation in reflectance –Surface material composition –Moisture –Vegetation 5.Vegetation Index – NDVI and temporal variations in reflectance 6.Bidirectional reflectance

30. 30 Figure 15 visible Near IR Shortwave IR Vegetation has a very characteristic reflectance curve What causes variations in reflectance in the 3 wavelength regions?

31. 31 Different plant/tree species have different reflectance curves Figure 15a

32. 32 Importance of vegetation cover in remote sensing of land surfaces 1.A high percentages of land surfaces have some level of vegetation cover 2.Types and amount of vegetation cover vary dramatically between biomes and regions 3.In many places, vegetation undergoes seasonal growth cycles, where the amount of living, green vegetation increase then decreases 4.Vegetation cover responds to variations in climate at annual and inter-annual time scales 5.Because of all of the above, vegetation causes variations in surface reflectance and hence the DN recorded by VIS/RIR remote sensing systems, both spatially and temporally

33. 33 - Plants and Trees are complex structures, with multiple layers of leaves, twigs and branches - Light interacts with individual leaves at a cellular level Light passing through a single leaf then interacts with the next canopy component it encounters Figure 16

34. 34 Vegetation and Surface Reflectance – Key Points 1.Key factors controlling reflectance from leaf surfaces 2.Multi-layer model of leaf/canopy reflectance 3.Temporal aspects of reflectance from vegetated surfaces

35. 35 Figure 16a Three factors control variations in reflectance from leaf/needle surfaces 1.Chlorophyll content 2.Water content 3.Leaf/needle structure

36. 36 Figure from Jensen Figure 17

37. 37 Internal Leaf Structure Chloroplasts Intercellular air labyrinth CO 2 in & O 2 out Figure 19

38. 38 What happens to EM flux in the VIS/RIR region when it interacts with leaf surface? Reflected from surface Absorbed by chloroplast Absorbed by water Reflected by cell wall Transmitted through leaf Figure 18

39. 39 Plant Pigments So, what absorbs EM energy in functioning leaves? (Reflectance = 100 - Absorption  Figure 20 Chlorophyll effects the Visible region the most!!! Importance of chlorophyll

40. 40 Absorption by plant pigments carrying out photosynthesis leads to low plant reflectances in the 0.40 to 0.65  m range Figure 21

41. 41 Leaves are green because chlorophyll absorbs little radiation in the 0.5 to 0.6  m region of the EM spectrum Figure 21a

42. 42 Photosynthesis Piers Seller’s PAR Diagram Leaf Photosynthesis Piers Seller’s PAR Diagram PAR = Photosynthetically active radiation Figure 22

43. 43 Photosynthetically active radiation PAR is the EM radiation between 0.4 and 0.7  m that is used for photosynthesis by plants FPAR – Fraction of PAR intercepted by a vegetation canopy (also called intercepted PAR)

44. 44 Figure 23

45. 45 Liquid Water Absorption While water is a strong absorber at all VIS/RIR wavelengths, it has peaks, at wavelengths of 1.45  m, 1.95  m, and > 2.2  m Figure 27 Importance of leaf water content

46. 46 Dips in spectral reflectance due to the absorption by water

47. 47 Reflectance from a vegetation canopy decreases as water content increases Water absorbs EM energy in the VIS/RIR region of the EM spectrum  higher water content results in lower reflections Changes in reflectance are greatest in SWIR region of EM spectrum Figure 28

48. 48 Maple & Pine reflectance maple pine - Pine trees have higher cellulose content than maple trees - Cellulose absorbs NIR radiation, and lowers reflectance Figure 29

49. 49 Role of leaf/needle structure Plant leaves contain varying amounts of structural material – cellulose and lignin The distribution and amounts of these materials, along with variations in the structure different types of leaves/ needles results in variations in absorbed and reflected VIS/RIR radiation These structural differences are most pronounced in the Near IR region

50. 50 http://research.umbc.edu/~tbenja1/leblon/module9.html Figure 30

51. 51 Green leaves from a broadleaf tree beginning to change color as nutrients withdraw into the tree core Deciduous broadleaf tree with its colors changed because chlorophyll dies In addition to changes in chlorophyll, leaves become drier Broadleaf Trees Changing Color in the Fall – What happens to spectral reflectance? Figure 24

52. 52 Leaf during middle of growing season Note low reflectance from 0.4 to 0.7  m Figure 25

53. 53 1. Reflectance from 0.4 to 0.53 decreases because of the loss of chlorophyll – color of leaves depends on which EM region has highest reflectance after loss of chlorophyll 2. Increase in near IR reflectance as leaf moisture decreases

54. 54 Vegetation and surface reflectance Key aspects of reflectance from leaf surfaces –Chlorophyll –Water content –Leaf structures Multi-layer model of leaf/canopy reflectance Temporal aspects of reflectance from vegetated surfaces

55. 55 Figure 32

56. 56 Figure 33

57. 57 Lecture Topics 1.Topic of today’s lecture – factors that influence the reflection coefficient 2.Types of surface reflection 3.Reflectance curves 4.Sources of variation in reflectance –Surface material composition –Moisture –Vegetation 5.Vegetation Index – NDVI and temporal variations in reflectance 6.Bidirectional reflectance

58. 58 Reflectance curve for a leaf generated from data collected by a spectroradiometer NIR Most digital VIS/RIR spaceborne sensors have radiometers with red and near infrared channels Ratios of these two channels are used to create indices of vegetation cover, e.g., vegetation indices Figure 34

59. 59 From Lillesand and Kiefer 1994 Figure 35

60. 60 Normalized Difference Vegetation Index NDVI Let R = radiance in the red channel Let IR = radiance in the near IR channel IR - R NDVI = __________ IR + R The value of NDVI is typically proportional to the amount of green biomass present on the land surface detected by the remote sensing system

61. 61 Soil NDVI = (35-27)/(35+27) = 0.13 Vegetation NDVI = (50-20)/(50+20) = 0.43

62. 62 Figure 36

63. 63 Figure 37

64. 64 Figure 38

65. 65 Lecture Topics 1.Topic of today’s lecture – factors that influence the reflection coefficient 2.Types of surface reflection 3.Reflectance curves 4.Sources of variation in reflectance –Surface material composition –Moisture –Vegetation 5.Vegetation Index – NDVI and temporal variations in reflectance 6.Bidirectional reflectance

66. 66 Hemispherical reflection r = R /  I What we have been discussing to date is hemispherical reflectance, e.g., ratios of total outgoing flux from a surface to total incoming flux

67. 67 VIS/IR Sensor Satellite VIS/IR sensor detects radiant flux over a range of different viewing angles, not just a single viewing angle To cover wide swaths, a remote sensing system views the earth’s surface over a range of viewing angles Figure 39

68. 68 Two things to consider when relating surface reflectance curves to satellite observations of radiant flux 1.The viewing direction of the sensor may not be fixed 2. A given surface type’s reflection may not be constant over different viewing angles

69. 69 Further information on this slide can be viewed at http://snrs.unl.edu/agmet/908/brdf_definition.htm If a surface were Lambertian, viewing angle would not matter because reflection would be equal in any direction Figure 40

70. 70 Further information on this slide can be viewed at http://snrs.unl.edu/agmet/908/brdf_definition.htm Most surfaces are not Lambertian; therefore, reflection is dependent on viewing angle Figure 41

71. 71 Reflectance from a Grass Field – Effects of differing viewing angles Figure 42

72. 72 Figure 43

73. 73 Bidirectional Reflectance Bidirectional reflectance is the relative amount of EM energy being detected from a surface given a specific solar illumination geometry and a specific sensor viewing geometry

74. 74 Bidirectional Reflectance Distribution Function (BRDF) Defines the reflectance of a surface in multiple viewing directions based on a specified irradiance azimuth and zenith angles

75. 75 Goniometer – a mechanical device used with a radiometer to measure BRDF Figure 45

76. 76 Anistrophy Factor vs. Reflectance Factor Anistrophy factor – the ratio of the radiance at a specific viewing geometry divided by the radiance at a nadir viewing geometry Reflectance Factor - the ratio of the radiance of the actual surface to the radiance of an ideal Lambertian surface illuminated and viewed in the same manner as the surface of interest.

77. 77 Figure 45

78. 78 The Hotspot – backscatter of light from the surface in the direction of the illumination (no shadow present, so reflection is higher) sensor Figure 44

79. 79 Figure 46

80. 80 Bottom – Line on BRDFs Remote sensing scientists can use BRDF curves to make corrections to digital satellite data to account for variations in surface reflectance