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2 Part II Remote Sensing using Reflected Visible and Infrared Radiation 602-Mar7 Surface reflectance – Land SurfacesCh Mar Surface reflectance – Land Surfaces II 05-MarLab 2 Contrast stretching and DN to reflectance conversion in ENVI 709-Mar8 Surface reflectance – Water BodiesCh Mar9 Detection of EM Radiation by a Vis/IR Radiometer 12-MarLab 3 Visual Analysis and High Resolution Visual Analysis 816-MarSpring Break 18-Mar 923-Mar9 Detection of EM Radiation by a Vis/IR Radiometer II 25-Mar10 Multispectral Remote Sensing Systems ICh 6,21 26-MarLab 4 Reflectance Spectra Compared to RS Images and Veg Index 1030-Mar Multispectral Remote Sensing Systems IICh 6,21 01-Apr11 Multispectral Remote Sensing Data Analyses ICh 12, AprLab 5 Image Classification 1106-Apr11 Multispectral Remote Sensing Data Analyses II 08-AprExam 2 – will cover material presented in Lectures AprLab 6 Multi-temporal change detection

3 Part II Remote Sensing using Reflected Visible and Infrared Radiation 602-MarCampus ClosedCh Mar7 Surface reflectance – Land Surfaces I 05-MarLab 2 Contrast stretching and DN to reflectance conversion in ENVI 709-Mar7 extended, 8 Surface reflectance – Water BodiesCh Mar9 Detection of EM Radiation by a Vis/IR Radiometer 12-MarLab 3 Visual Analysis and High Resolution Visual Analysis 816-MarSpring Break 18-Mar 923-Mar9 Detection of EM Radiation by a Vis/IR Radiometer II 25-Mar10 Multispectral Remote Sensing Systems ICh 6,21 26-MarLab 4 Reflectance Spectra Compared to RS Images and Veg Index 1030-Mar Multispectral Remote Sensing Systems IICh 6,21 01-Apr11 Multispectral Remote Sensing Data Analyses ICh 12, AprLab 5 Image Classification 1106-Apr11 Multispectral Remote Sensing Data Analyses II 08-AprExam 2 – will cover material presented in Lectures AprLab 6 Multi-temporal change detection

 Campbell, Chapter 17, sections 1 to 3, pages (BRDF) 4

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 5

6 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

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

8 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

9 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

10 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

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 11

12  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

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 13

 Specular surfaces or reflection  Diffuse surfaces or reflection  Lambertian surfaces or reflection 14

 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 15 Figure 6

 Most surfaces are not smooth, and reflect incoming EM radiation in a variety of directions  These are called diffuse reflectors 16 Figure 7

 A perfectly diffuse reflector is called a Lambertian surface  A Lambertian surface reflects equally in all directions 17 Figure 8

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 18

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

 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 20

21 Reflectance curve for a leaf generated from data collected by a spectroradiometer

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 22

23 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

 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 24

25 Water absorption bands No data collected in these regions Data collected by an airborne spectroradiometer Figure 11

 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 26

27 Figure 12

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 28

29 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

30 Reflection of soil with different moisture levels Values represent % water by volume Figure 14

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 31

 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 32

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

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

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 35

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37 - 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

1. Key factors controlling reflectance from leaf surfaces 2. Multi-layer model of leaf/canopy reflectance 3. Temporal aspects of reflectance from vegetated surfaces 38

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

40 Figure from Jensen Figure 17

41 Chloroplasts Intercellular air labyrinth CO 2 in & O 2 out Figure 19

42 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

43 So, what absorbs EM energy in functioning leaves? (Reflectance = Absorption  Figure 20 Chlorophyll effects the Visible region the most!!! Importance of chlorophyll

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

True & False Color Ikonos Satellite Data Beltsville Agricultural Research Center Visible region only Near infrared separates confiers from deciduous trees nm

 PAR is the EM radiation between 0.4 and 0.7  m that is used for photosynthesis by plants  FPAR – Fraction of PAR intercepted (absorbed) by a vegetation canopy (also called FAPAR) 46

47 PAR = Photosynthetically active radiation Figure 22

48 Figure 23

 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 49

50 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

51 Dips in spectral reflectance due to the absorption by water

52 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

 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 53

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

 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 55

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59 Part II Remote Sensing using Reflected Visible and Infrared Radiation 602-MarCampus ClosedCh Mar7 Surface reflectance – Land Surfaces I 05-MarLab 2 Contrast stretching and DN to reflectance conversion in ENVI 709-Mar7 extended, 8 Surface reflectance – Water BodiesCh Mar9 Detection of EM Radiation by a Vis/IR Radiometer 12-MarLab 3 Visual Analysis and High Resolution Visual Analysis 816-MarSpring Break 18-Mar 923-Mar9 Detection of EM Radiation by a Vis/IR Radiometer II 25-Mar10 Multispectral Remote Sensing Systems ICh 6,21 26-MarLab 4 Reflectance Spectra Compared to RS Images and Veg Index 1030-Mar Multispectral Remote Sensing Systems IICh 6,21 01-Apr11 Multispectral Remote Sensing Data Analyses ICh 12, AprLab 5 Image Classification 1106-Apr11 Multispectral Remote Sensing Data Analyses II 08-AprExam 2 – will cover material presented in Lectures AprLab 6 Multi-temporal change detection

60 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

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

62 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

 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 63

 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 64

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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 67

68 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

69 From Lillesand and Kiefer 1994 Figure 35

 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 70

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

Spectral intervals for studying vegetation--which ones & why Information all locales, all conditions, etc. vegetation is goal ! vive photosynthesis ! reduce atmospheric effects to minimize errors Avoid redundancy --keep it simple stupid Information all locales, all conditions, etc. vegetation is goal ! vive photosynthesis ! reduce atmospheric effects to minimize errors Avoid redundancy --keep it simple stupid

Normalized Difference Vegetation Index: NDVI = (NIR-Red)/(NIR+Red) Normalized Difference Vegetation Index: NDVI = (NIR-Red)/(NIR+Red)

Characteristics of NDVI The NDVI is based on the difference between the maximum absorption of radiation in the red (due to the chlorophyll pigments) vs. the maximum reflection of radiation in the NIR (due to the leaf cellular structure), and the fact that soil spectra, lacking these mechanisms, typically do not show such a dramatic spectral difference. The values of the NDVI range from –1 to +1 NDVI uses radiance, surface reflectance (  ), or apparent reflectance (measured at the top of the atmosphere) Characteristics of NDVI The NDVI is based on the difference between the maximum absorption of radiation in the red (due to the chlorophyll pigments) vs. the maximum reflection of radiation in the NIR (due to the leaf cellular structure), and the fact that soil spectra, lacking these mechanisms, typically do not show such a dramatic spectral difference. The values of the NDVI range from –1 to +1 NDVI uses radiance, surface reflectance (  ), or apparent reflectance (measured at the top of the atmosphere)

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In situ measurements at Pawnee National Grasslands, Colorado

Nondestructive Biological Mass (or biomass) Objective Grassland system -- simple, >>herbaceous Plant biomass is source of energy for system Anything is better than clipping!!! Clipping is limited for time & space extrapolations

Spectrometer mini-computer combo

HP-2100 mini-computer data collection system (circa 1971) 8196 bytes of RAM no disk drive Paper tape input/output 2-step/pass Fortran compiler Control panel assembly language operation Teletype used to punch paper tapes Believe it or not--this was state of the art!

Thorough documentation of every plot, before & after clipping

Summary of r 2 values vs. Summary of r 2 values vs. for visible, a close association of regression significance between reflectance l and leaf water content with chlorophyll a & b (leaf water vs. live veg.) for visible, a close association of regression significance between reflectance l and leaf water content with chlorophyll a & b (leaf water vs. live veg.)

All dead leaf biomass--no pigments remain

[(ir-red)/(ir+red)] aka ndvi

Winter wheat total dry matter  ndvi over growing season ndvi-days

Winter wheat grain yield  ndvi over growing season ndvi-days

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 95

97  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

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 98

99 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

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 100

101 Further information on this slide can be viewed at If a surface were Lambertian, viewing angle would not matter because reflection would be equal in any direction Figure 40

102 Further information on this slide can be viewed at Most surfaces are not Lambertian; therefore, reflection is dependent on viewing angle Figure 41

103 Figure 42

104 Figure 43

 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 105

 Defines the reflectance of a surface in multiple viewing directions based on a specified irradiance azimuth and zenith angles 106

107 Figure 45

108 Figure 46

 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.

110 Figure 45

111 sensor

 This means that light is NOT reflected to the sensor irrespective of the direction of solar radiation or the sensor viewing geometry.  Bidirectional Reflectance Distribution Function or BRDF is the name for the variable reflectance from a target, depending on the direction of illumination and the sensor.  This means that light is NOT reflected to the sensor irrespective of the direction of solar radiation or the sensor viewing geometry.  Bidirectional Reflectance Distribution Function or BRDF is the name for the variable reflectance from a target, depending on the direction of illumination and the sensor.

Black Spruce Forest Backward Scatter View Forward Scatter View

Soybean Field Backward Scatter ViewForward Scatter View

Backward Scatter ViewForward Scatter View Soil

 Remote sensing scientists can use BRDF curves to make corrections to digital satellite data to account for variations in surface reflectance 116

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 117

118 Part II Remote Sensing using Reflected Visible and Infrared Radiation 602-MarCampus ClosedCh Mar7 Surface reflectance – Land Surfaces I 05-MarLab 2 Contrast stretching and DN to reflectance conversion in ENVI 709-Mar7 extended, 8 Surface reflectance – Water BodiesCh Mar9 Detection of EM Radiation by a Vis/IR Radiometer 12-MarLab 3 Visual Analysis and High Resolution Visual Analysis 816-MarSpring Break 18-Mar 923-Mar9 Detection of EM Radiation by a Vis/IR Radiometer II 25-Mar10 Multispectral Remote Sensing Systems ICh 6,21 26-MarLab 4 Reflectance Spectra Compared to RS Images and Veg Index 1030-Mar Multispectral Remote Sensing Systems IICh 6,21 01-Apr11 Multispectral Remote Sensing Data Analyses ICh 12, AprLab 5 Image Classification 1106-Apr11 Multispectral Remote Sensing Data Analyses II 08-AprExam 2 – will cover material presented in Lectures AprLab 6 Multi-temporal change detection