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Remote Sensing on land Surface Properties

Remote Sensing on land Surface Properties. Menglin Jin. Modified from Paolo Antonelli CIMSS, University of Wisconsin-Madison, M. D. King UMCP lecture, and P. Mentzel. outline. Reflectance and albedo Vegetation retrieval Surface temperature retrieval Theory of clouds and fire retrieval.

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Remote Sensing on land Surface Properties

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  1. Remote Sensing on land Surface Properties Menglin Jin Modified from Paolo Antonelli CIMSS, University of Wisconsin-Madison, M. D. King UMCP lecture, and P. Mentzel

  2. outline • Reflectance and albedo • Vegetation retrieval • Surface temperature retrieval • Theory of clouds and fire retrieval

  3. MODIS Land Cover Classification(M. A. Friedl, A. H. Strahler et al. – Boston University) 0 Water 6 Closed Shrublands 12Croplands 1 Evergreen Needleleaf Forest 7 Open Shrublands 13 Urbanand Built-Up 2 Evergreen BroadleafForest 8 Woody Savannas 14 Cropland/NaturalVeg. Mosaic 3 Deciduous Needleleaf Forest 9 Savannas 15 Snowand Ice 4 Deciduous Broadleaf Forest 10 Grasslands 16 Barren or Sparsely Vegetated 5 Mixed Forests 11 Permanent Wetlands 17 Tundra

  4. Reflectance • The physical quantity is the Reflectance i.e. the fraction of solar energy reflected by the observed target • To properly compare different reflective channels we need to convert observed radiance into a target physical property • In the visible and near infrared this is done through the ratio of the observed radiance divided by the incoming energy at the top of the atmosphere

  5. Soil Vegetation Snow Ocean

  6. MODIS multi-channels • Band 1 (0.65 m) – clouds and snow reflecting • Band 2 (0.86 m) – contrast between vegetation and clouds diminished • Band 26 (1.38 m) – only high clouds and moisture detected • Band 20 (3.7 m) – thermal emission plus solar reflection • Band 31 (11 m) – clouds colder than rest of scene -- Band 35 (13.9 m) – only upper atmospheric thermal emission detected

  7. MODIS BAND 1 (RED) Low reflectance in Vegetated areas Higher reflectance in Non-vegetated land areas

  8. MODIS BAND 2 (NIR) Higher reflectance in Vegetated areas Lower reflectance in Non-vegetated land areas

  9. RED NIR Dense Vegetation Barren Soil

  10. Vegetation: NDVI The NDVI is calculated from these individual measurements as follows: • Subsequent work has shown that the NDVI is directly related to the photosynthetic capacity and hence energy absorption of plant canopies. NIR-RED NDVI = NIR+RED NDVI –Normalized Difference Vegetation Index

  11. Satellite maps of vegetation show the density of plant growth over the entire globe. The most common measurement is called the Normalized Difference Vegetation Index (NDVI). Very low values of NDVI (0.1 and below) correspond to barren areas of rock, sand, or snow. Moderate values represent shrub and grassland (0.2 to 0.3), while high values indicate temperate and tropical rainforests (0.6 to 0.8).

  12. NDVI • Vegetation appears very different at visible and near-infrared wavelengths. In visible light (top), vegetated areas are very dark, almost black, while desert regions (like the Sahara) are light. At near-infrared wavelengths, the vegetation is brighter and deserts are about the same. By comparing visible and infrared light, scientists measure the relative amount of vegetation.

  13. NDVI represents greenness

  14. NDVI as an Indicator of Drought August 1993 In most climates, vegetation growth is limited by water so the relative density of vegetation is a good indicator of agricultural drought

  15. Enhanced Vegetation Index (EVI) • In December 1999, NASA launched the Terra spacecraft, the flagship in the agency’s Earth Observing System (EOS) program. Aboard Terra flies a sensor called the Moderate-resolution Imaging Spectroradiometer, or MODIS, that greatly improves scientists’ ability to measure plant growth on a global scale. • EVI is calculated similarly to NDVI, it corrects for some distortions in the reflected light caused by the particles in the air as well as the ground cover below the vegetation. • does not become saturated as easily as the NDVI when viewing rainforests and other areas of the Earth with large amounts of chlorophyll

  16. Electromagnetic spectrum Red (0.7m) Orange (0.6m) Yellow Green (0.5m) Blue Violet (0.4m) Visible Ultraviolet (UV) Radio waves Microwave Infrared (IR) X rays Gamma 1000m 1m 1000 m 1m 0.001m Longer waves Shorter waves 1,000,000m = 1m

  17. Spectral Surface Albedo(E. G. Moody, M. D. King, S. Platnick, C. B. Schaaf, F. Gao – GSFC, BU) • Spectral albedo needed for retrievals over land surfaces • Spatially complete surface albedo datasets have been generated • Uses high-quality operational MODIS surface albedo dataset (MOD43B3) • Imposes phenological curve and ecosystem-dependent variability • White- and black-sky albedos produced for 7 spectral bands and 3 broadbands • See modis-atmos.gsfc.nasa.govfor data access and further descriptions

  18. Conditioned Spectral Albedo Maps(C. B. Schaaf, F. Gao, A. H. Strahler - Boston University) MOD43B3

  19. Indian Subcontinent during MonsoonJune 10-26, 2002

  20. Spatially Complete Spectral Albedo Maps(E. G. Moody, M. D. King, S. Platnick, C. B. Schaaf, F. Gao – GSFC, BU)

  21. Spectral Albedo of Snow • Used near real-time ice and snow extent (NISE) dataset • Distinguishes land snow and sea ice (away from coastal regions) • Identifies wet vs dry snow • Projected onto an equal-area 1’ angle grid (~2 km) • Aggregate snow albedo from MOD43B3 product • Surface albedo flagged as snow • Aggregate only snow pixels whose composite NISE snow type is >90% is flagged as either wet or dry snow in any 16-day period • Hemispherical multiyear statistics • Separate spectral albedo by ecosystem (MOD12Q1)

  22. Albedo by IGBP EcosystemNorthern Hemisphere Multiyear Average (2000-2004) ??? urban cropland ???

  23. Surface Temperature: Skin Temperature • The term “skin temperature” has been used for “radiometric surface temperature” (Jin et al. 1997). • can be measured by either a hand-held or aircraft-mounted radiation thermometer, as derived from upward longwave radiation based on the Stefan-Boltzmann law (Holmes 1969; Oke 1987)

  24. Surface Temperature: Skin Temperature • The retrieval techniques for obtaining Tskin from satellite measurements for land applications have developed substantially in the last two decades (Price 1984). Tskinb = B-1( L) Include emissivity effect: Tskinb = B-1 [(L-(1-  )L )/  ] Two unknowns!!

  25. Surface Temperature: Skin Temperature • Split Window Algorith • Retrieving Tskin using the two channels (i.e., SWT) was first proposed in the 1970s (Anding and Kauth 1970). For example: The NOAA Advanced Very High Resolution Radiometer (AVHRR), which has spectral channels centered around 10.5 μm and 11.2 μm, has been widely used in this regard for both land and sea surface temperature estimation

  26. Surface Temperature: Skin Temperature Split-window algorithms are usually written in “classical" form, as suggested by Prabhakara (1974)(after Stephens 1994): Tskin ≈ Tb,1 + f(Tb,1 – Tb,2), • where Tb,1 , Tb,2 are brightness measurements in two thermal channels, and f is function of atmospheric optical depth of the two channels. • A more typical form of the split-window is Tskin = aT1 + b(T1 –T2) – c where a, b and c are functions of spectral emissivity of the the two channels and relate radiative transfer model simulations or field measurements of Tskin to the remotely sensed observations

  27. MODIS SST Algorithm • Bands 31 (11 m) and 32 (12 m) of MODIS are sensitive to changes in sea surface temperature, because the atmosphere is almost (but not completely) transparent at these wavelengths. An estimate of the sea surface temperature (SST) can be made from band 31, with a water vapor correction derived from the difference between the band 31 and band 32 brightness temperatures: • SST ≈ B31 + (B31 – B32) (just this simple!)

  28. MODIS SST

  29. Accuracy of Retrieved Tskin • Accuracy of Tskin retrievals with SWT ranges from ≤ 1 to ≥ 5 K ( Prata 1993, Schmugge et al. 1998). • SST is more accurate than LST (land skin temperature) • Error sources: split window equation; Specifically, split window techniques rely on assumptions of Lambertian surface properties, surface spectral emissivity, view angle, and approximations of surface temperature relative to temperatures in the lower atmosphere (which vary more slowly). An assumption of invariant emissivity, for example, can induce errors of 1-2 K per 1% variation in emissivity.

  30. Lambertian surface properties

  31. MODIS 2000-2007 averaged monthly Tskin

  32. Modis land cover. • Evergreen Needleleaf Forest; • 2,Evergreen Broadleaf Forest; • 3,Deciduous Needleleaf Forest; • 4,Deciduous Broadleaf Forest; • 5,Mixed Forest; • 6,Closed Shrubland; • 7,Open Shrubland; • 8,Woody Savannas; • 9,Savannas; 10,Grassland; • 11,Permanent Wetland; 12,Croplands; • 13,Urban and Built-Up; • 14,Cropland/Narural Vegetation Mosaic; • 15,Snow and Ice; 16,Barren or Sparsely Vegetated

  33. Land Tskin vs Albedo

  34. Land Tskin vs. Water Vapor

  35. snow clouds desert sea Band 4 (0.56 µm) Transects of Reflectance Band 1 Band 4 Band 3

  36. Band 26 1.38 micron Strong H20

  37. 1.38 μm Only High Clouds Are Visible

  38. Band 26 1.38 µm

  39. Visible (Reflective Bands) Infrared (Emissive Bands)

  40. Visible (Reflective Bands) Infrared (Emissive Bands)

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