1. Remote Sensing on land Surface Properties Menglin Jin Paolo Antonelli CIMSS, University of Wisconsin-Madison, 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 1 Evergreen Needleleaf Forest 2 Evergreen Broadleaf Forest 3 Deciduous Needleleaf Forest 4 Deciduous Broadleaf Forest 5 Mixed Forests 6 Closed Shrublands 7 Open Shrublands 8 Woody Savannas 9 Savannas 10 Grasslands 11 Permanent Wetlands 12 Croplands 13 Urban and Built-Up 14 Cropland/Natural Veg. Mosaic 15 Snow and Ice 16 Barren or Sparsely Vegetated 17 Tundra

4. Reflectance The physical quantity is the Reflectance i.e. the fraction of solar energy reflected by the observed targetThe 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 propertyTo 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 atmosphereIn 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

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

14. 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).

16. 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.

17. NDVI represents greenness

19. 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

20. 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.MODIS, 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

21. Electromagnetic spectrum 0.001  m1m1m1000  m 1m1000m 1,000,000  m = 1m GammaX rays Ultraviolet (UV) Infrared (IR)MicrowaveRadio waves Red (0.7  m) Orange (0.6  m) Yellow Green (0.5  m) Blue Violet (0.4  m) Visible Longer waves Shorter waves

23. 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.gov for data access and further descriptions Spectral Surface Albedo (E. G. Moody, M. D. King, S. Platnick, C. B. Schaaf, F. Gao – GSFC, BU)

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

25. Indian Subcontinent during Monsoon June 10-26, 2002

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

27.  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) Spectral Albedo of Snow

28. Albedo by IGBP Ecosystem Northern Hemisphere Multiyear Average (2000-2004) urban cropland ???

30. 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)

31. 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). T skin b = B -1 ( L  ) Include emissivity effect: T skin b = B -1 [(L  -(1-  )L  )/  ] Two unknowns!!

32. 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

33. 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

34. 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!)

35. MODIS SST

36. Accuracy of Retrieved Tskin Accuracy of Tskin retrievals with SWT ranges from ≤ 1 to ≥ 5 K ( Prata 1993, Schmugge et al. 1998). 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.

37. MODIS 2000-2007 averaged monthly Tskin

41. Modis land cover. 1.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

42. Land Tskin vs Albedo

43. Land Tskin vs. Water Vapor

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

45. Band 26 1.38 micron Strong H 2 0

46. 1.38 μm Only High Clouds Are Visible

47. Band 26 1.38 µm

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

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

50. Emissive Bands Used to observe terrestrial energy emitted by the Earth system in the IR between 4 and 15 µm About 99% of the energy observed in this range is emitted by the EarthAbout 99% of the energy observed in this range is emitted by the Earth Only 1% is observed below 4 µmOnly 1% is observed below 4 µm At 4 µm the solar reflected energy can significantly affect the observations of the Earth emitted energyAt 4 µm the solar reflected energy can significantly affect the observations of the Earth emitted energy

51. Spectral Characteristics of Energy Sources and Sensing Systems IR 4 µm 11 µm

52. NIR (0.86 µm) Green (0.55 µm) Red (0.67 µm) RGB NIR Ocean NIR and VIS over Vegetation and Ocean Vegetation

53. Spectral Characteristics of Energy Sources and Sensing Systems IR NIR

54. Radiation is governed by Planck’s Law In wavelength: B(,T) = c 1 /{ 5 [e c2 / T -1] } (mW/m 2 /ster/cm) B(,T) = c 1 /{ 5 [e c2 / T -1] } (mW/m 2 /ster/cm) where = wavelength (cm) where = wavelength (cm) T = temperature of emitting surface (K) c 1 = 1.191044 x 10-8 (W/m 2 /ster/cm -4 ) c 2 = 1.438769 (cm K) In wavenumber: B(,T) = c 1 3 / [e c2 /T -1] (mW/m 2 /ster/cm -1 ) where = # wavelengths in one centimeter (cm-1) T = temperature of emitting surface (K) c 1 = 1.191044 x 10-5 (mW/m 2 /ster/cm -4 ) c 2 = 1.438769 (cm K)

55. B( max,T)~T 5 B( max,T)~T 3 B(,T) B(,T) versus B(,T) 201054 3. 3 6. 6 100 wavelength [µm] max ≠(1/ max ) max ≠(1/ max )

56. wavelength : distance between peaks (µm) Slide 4 Slide 4 wavenumber : number of waves per unit distance (cm) =1/ =1/ d =-1/ 2 d d =-1/ 2 d

57. Using wavenumbers Wien's Law dB( max,T) / dT = 0 where (max) = 1.95T indicates peak of Planck function curve shifts to shorter wavelengths (greater wavenumbers) with temperature increase. Note B( max,T) ~ T**3.   Stefan-Boltzmann Law E =   B(,T) d =  T 4, where  = 5.67 x 10-8 W/m 2 /deg 4. 0 0 states that irradiance of a black body (area under Planck curve) is proportional to T 4. Brightness Temperature c 1 3 c 1 3 T = c 2 /[ln( ______ + 1)] is determined by inverting Planck function B T = c 2 /[ln( ______ + 1)] is determined by inverting Planck function B Brightness temperature is uniquely related to radiance for a given wavelength by the Planck function

58. Planck Function and MODIS Bands MODIS

59. MODIS BAND 20 Window Channel: little atmospheric absorptionlittle atmospheric absorption surface features clearly visiblesurface features clearly visible Clouds are cold

60. MODIS BAND 31 Window Channel: little atmospheric absorptionlittle atmospheric absorption surface features clearly visiblesurface features clearly visible Clouds are cold

61. Clouds at 11 µm look bigger than at 4 µm

62. Temperature sensitivity dB/B =  dT/T The Temperature Sensitivity  is the percentage change in radiance corresponding to a percentage change in temperature The Temperature Sensitivity  is the percentage change in radiance corresponding to a percentage change in temperature Substituting the Planck Expression, the equation can be solved in  :  = c 2 /T

63. Planck’s function (review lecture 1 ) B (T) = c 1 -5 exp (c 2 / T ) -1 Irridance: Blackbody radiative flux for a single wavelength at temperature T (W m -2 ) Second radiation constant Absolute temperature First radiation constantWavelength of radiation Total amount of radiation emitted by a blackbody is a function of its temperature c 1 = 3.74x10 -16 W m -2 c 2 = 1.44x10 -2 m °K

64. ∆B 11 ∆B 4 ∆B 11 > ∆B 4 T=300 K T ref =220 K

65. ∆B 11 /B 11 =  11 ∆T/T ∆B 4 /B 4 =  4 ∆T/T ∆B 4 /B 4 >∆B 11 /B 11   4 >  11 (values in plot are referred to wavelength)

66. ∆B/B=  ∆T/T Integrating the Temperature Sensitivity Equation Between T ref and T (B ref and B): B=B ref (T/T ref )  Where  =c 2 /T (in wavenumber space) (Approximation of) B as f unction of  and T

67. B=B ref (T/T ref )   B=(B ref / T ref  ) T   B  T  The temperature sensitivity indicates the power to which the Planck radiance depends on temperature, since B proportional to T  satisfies the equation. For infrared wavelengths,  = c 2 /T = c 2 / T. _____________________________________________________ ____ WavenumberTypical SceneTemperature TemperatureSensitivity 9003004.32 250030011.99

68. Non-Homogeneous FOV N 1-N T cold =220 K T hot =300 K B=NB(T hot )+(1-N)B(T cold ) BT=NBT hot +(1-N)BT cold

69. For NON-UNIFORM FOVs: B obs =NB cold +(1-N)B hot B obs =N B ref (T cold /T ref )  + (1-N) B ref (T hot /T ref )  B obs = B ref (1/T ref )  (N T cold  + (1-N)T hot  ) For N=.5 B obs =.5B ref (1/T ref )  ( T cold  + T hot  ) B obs =.5B ref (1/T ref T cold )  (1+ (T hot / T cold )  ) The greater  the more predominant the hot term At 4 µm (  =12) the hot term more dominating than at 11 µm (  =4) N 1-N T cold T hot

70. Consequences: Cloud & Fire Detection At 4 µm (  =12) clouds look smaller than at 11 µm (  =4)At 4 µm (  =12) clouds look smaller than at 11 µm (  =4) In presence of fires the difference BT 4 - BT 11 is larger than the solar contributionIn presence of fires the difference BT 4 - BT 11 is larger than the solar contribution The different response in these 2 windows allow for cloud detection and for fire detectionThe different response in these 2 windows allow for cloud detection and for fire detection

71. The algorithm uses these thresholds to determine ice cloud: Band 31 (11  m) Brightness Temperature < 238 K or Band 29 – Band 31 difference >.5 K The water cloud algorithm thresholds are Band 31 (11  m) Brightness Temperature > 238 K and Band 29 – Band 31 difference < -1.0 K OR:Or Band 31 (11  m) Brightness Temperature > 285 K and Band 29 – Band 31 difference < -0.5 K Band 29 (8.6  m) Band 31 (11  m MODIS clouds algorithm (As an example)

72. Conclusions: Vegetation Detection Vegetation: highly reflective in the Near Infrared and highly absorptive in the visible red. The contrast between these channels is a useful indicator of the status of the vegetation;Vegetation: highly reflective in the Near Infrared and highly absorptive in the visible red. The contrast between these channels is a useful indicator of the status of the vegetation; Planck Function: at any wavenumber/wavelength relates the temperature of the observed target to its radiance (for Blackbodies)Planck Function: at any wavenumber/wavelength relates the temperature of the observed target to its radiance (for Blackbodies) Thermal Sensitivity: different emissive channels respond differently to target temperature variations. Thermal Sensitivity helps in explaining why, and allows for cloud and fire detection.Thermal Sensitivity: different emissive channels respond differently to target temperature variations. Thermal Sensitivity helps in explaining why, and allows for cloud and fire detection.