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Part II Remote Sensing using Reflected Visible and Infrared Radiation

Lecture 7 Land surface reflectance in the visible and RIR regions of the EM spectrum March 4th 2009. Part II Remote Sensing using Reflected Visible and Infrared Radiation 6 02-Mar 7 Surface reflectance – Land Surfaces Ch 17.1-17.3 04-Mar Surface reflectance – Land Surfaces II

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Part II Remote Sensing using Reflected Visible and Infrared Radiation

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  1. Lecture 7Land surface reflectancein the visible and RIR regions of the EM spectrumMarch 4th 2009

  2. Part II Remote Sensing using Reflected Visible and Infrared Radiation 6 02-Mar 7 Surface reflectance – Land Surfaces Ch 17.1-17.3 04-Mar Surface reflectance – Land Surfaces II 05-Mar Lab 2 Contrast stretching and DN to reflectance conversion in ENVI 7 09-Mar 8 Surface reflectance – Water Bodies Ch 19.1-19.6 11-Mar 9 Detection of EM Radiation by a Vis/IR Radiometer 12-Mar Lab 3 Visual Analysis and High Resolution Visual Analysis 8 16-Mar Spring Break 18-Mar 9 23-Mar 9 Detection of EM Radiation by a Vis/IR Radiometer II 25-Mar 10 Multispectral Remote Sensing Systems I Ch 6,21 26-Mar Lab 4 Reflectance Spectra Compared to RS Images and Veg Index 10 30-Mar Multispectral Remote Sensing Systems II Ch 6,21 01-Apr 11 Multispectral Remote Sensing Data Analyses I Ch 12,17.9-17.10 02-Apr Lab 5 Image Classification 11 06-Apr 11 Multispectral Remote Sensing Data Analyses II 08-Apr Exam 2 – will cover material presented in Lectures 7-11 09-Apr Lab 6 Multi-temporal change detection

  3. Part II Remote Sensing using Reflected Visible and Infrared Radiation 6 02-Mar Campus Closed Ch 17.1-17.3 04-Mar 7 Surface reflectance – Land Surfaces I 05-Mar Lab 2 Contrast stretching and DN to reflectance conversion in ENVI 7 09-Mar 7 extended, 8 Surface reflectance – Water Bodies Ch 19.1-19.6 11-Mar 9 Detection of EM Radiation by a Vis/IR Radiometer 12-Mar Lab 3 Visual Analysis and High Resolution Visual Analysis 8 16-Mar Spring Break 18-Mar 9 23-Mar 9 Detection of EM Radiation by a Vis/IR Radiometer II 25-Mar 10 Multispectral Remote Sensing Systems I Ch 6,21 26-Mar Lab 4 Reflectance Spectra Compared to RS Images and Veg Index 10 30-Mar Multispectral Remote Sensing Systems II Ch 6,21 01-Apr 11 Multispectral Remote Sensing Data Analyses I Ch 12,17.9-17.10 02-Apr Lab 5 Image Classification 11 06-Apr 11 Multispectral Remote Sensing Data Analyses II 08-Apr Exam 2 – will cover material presented in Lectures 7-11 09-Apr Lab 6 Multi-temporal change detection

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

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

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

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

  8. Figure 2 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

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

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

  11. 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 = ia r Where ia 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

  12. Figure 5 radiometer pixel DN ia R r 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

  13. Lecture Topics • Topic of today’s lecture – factors that influence the reflection coefficient • Types of surface reflection • Reflectance curves • Sources of variation in reflectance • Surface material composition • Moisture • Vegetation • Vegetation Index – NDVI and temporal variations in reflectance • Bidirectional reflectance

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

  15. 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

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

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

  18. Lecture Topics • Topic of today’s lecture – factors that influence the reflection coefficient • Types of surface reflection • Reflectance curves • Sources of variation in reflectance • Surface material composition • Moisture • Vegetation • Vegetation Index – NDVI and temporal variations in reflectance • Bidirectional reflectance

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

  20. 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 spectroradiometermeasures flux in narrow wavelength bands These data are then used to produce a reflectance curve

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

  22. Lecture Topics • Topic of today’s lecture – factors that influence the reflection coefficient • Types of surface reflection • Reflectance curves • Sources of variation in reflectance • Surface material composition • Moisture • Vegetation • Vegetation Index – NDVI and temporal variations in reflectance • Bidirectional reflectance

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

  24. 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

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

  26. 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

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

  28. Lecture Topics • Topic of today’s lecture – factors that influence the reflection coefficient • Types of surface reflection • Reflectance curves • Sources of variation in reflectance • Surface material composition • Moisture • Vegetation • Vegetation Index – NDVI and temporal variations in reflectance • Bidirectional reflectance

  29. Figure 13 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

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

  31. Lecture Topics • Topic of today’s lecture – factors that influence the reflection coefficient • Types of surface reflection • Reflectance curves • Sources of variation in reflectance • Surface material composition • Moisture • Vegetation • Vegetation Index – NDVI and temporal variations in reflectance • Bidirectional reflectance

  32. 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

  33. Figure 15 Near IR Shortwave IR visible 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

  35. Importance of vegetation cover in remote sensing of land surfaces • A high percentages of land surfaces have some level of vegetation cover • Types and amount of vegetation cover vary dramatically between biomes and regions • In many places, vegetation undergoes seasonal growth cycles, where the amount of living, green vegetation increase then decreases • Vegetation cover responds to variations in climate at annual and inter-annual time scales • 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

  36. - 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

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

  38. Figure 16a • Three factors control variations in reflectance from leaf/needle surfaces • Chlorophyll content • Water content • Leaf/needle structure

  39. Figure 17 Figure from Jensen

  40. Internal Leaf Structure Figure 19 Intercellular air labyrinth CO2 in & O2 out Chloroplasts

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

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

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

  44. True & False Color Ikonos Satellite Data Beltsville Agricultural Research Center Visible region only Near infrared separates confiers from deciduous trees 400 500 600 700nm

  45. 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 (absorbed) by a vegetation canopy (also called FAPAR)

  46. LeafPhotosynthesis Piers Seller’s PAR Diagram Figure 22 PAR = Photosynthetically active radiation

  47. Figure 23

  48. 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

  49. 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

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