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 Snow albedo Surface temperature retrieval

3. Soil Vegetation Snow Ocean

5. Videos 1.Northern Hemisphere albedo 2.NDVI 3.Snow Coverage 4.Landsat images

6. 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 Moody, E. G., M. D. King, S., Platnick, C. B. Schaaf, and F. Gao, 2005: Spatially complete global spectral surface albedos: Value-added datasets derived from Terra MODIS land products. IEEE Trans. Geosci. Remote Sens., 43, 144–158.

7. 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

8. 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

9. Planck Function and MODIS Bands MODIS

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

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

12. RED NIR Dense Vegetation Barren Soil

15. 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

16. 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).

18. 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.

19. NDVI represents greenness

20. 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

21. 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

22. EVI vs. NDVI http://gdata1.sci.gsfc.nasa.gov/daac- bin/G3/gui.cgi?instance_id=neespi EVI NDVI

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

24.  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 Spectral Albedo of Snow Moody, E. G., M. D. King, C. B. Schaaf, D. K. Hall, and S. Platnick, 2007: Northern Hemisphere five-year average (2000-2004) spectral albedos of surfaces in the presence of snow: Statistics computed from Terra MODIS land products. Remote Sens. Environ.

25. Remote Sensing of snow reflectance

26. Video Snow coverage

27. 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)

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

30. 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

31. 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

32. 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!)

33. 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.

34. METR180. Land Skin Temperature Remote Sensing Class discussion

35. The important effects of____________, ___________and ___________________ are considered and the current practice for removing these effects is specified.

36. The important effects of the atmosphere, surface emissivity and instrument noise are considered and the current practice for removing these effects is specified.

37. What are the differences between Tskin (LST) and 2m surface air temperature? Satellite sensor LW ↑ T air at 2m LW ↑ =εσΤ skin 4

38. Why do we need to use Tskin in land surface model development 1. Problems of conventional surface temperature observations: measured at 2m above surface from WMO weather stations, Insufficient spatial coverage Sites are irregularly distributed, Political boundary ….. 2. Advances of satellite observations: Global coverage High resolution High quality ….. Jin et al. 1997 J. of Climate

39. Since the AVHRR has been used operationally with some success to derive sea surface temperature (SST) it is natural to attempt to use the data over the land to derive land surface temperature…

40. good science is about identifying a good question and designing doable approach to solve the question, not about getting data and plotting them. This makes the difference between a scintist and a technician. -Robert E. Dickinson How to Use Satellite Data?

41. http://gdata1.sci.gsfc.nasa.gov/daac-bin/G3/gui.cgi?instance_id=neespi 1.Global Tskin from Terra for January 2009, July 2009 – where are the hottest regions? 1.Time series of daytime Tskin from Terra MODIS from July 2000 to Dec. 2009 over 10-20N, 20-40E (Sahara Desert) When the maximum occurred? How much was the maximum? When was the minimum? How much was the minimum? 3. Using daily data, examine Daytime and nighttime Tskin for July 2009 SF Sahara Greenland 10S-10N, 50-75W (tropical forest) 4. Select both NDVI and Tskin, on monthly Terra MODIS, calculate the correlation coefficient over Tibet Homework 6 (slightly corrected as indicated in red color in questions)

42. 10-minute in-class quiz What is split window algorithm (SWA)? What channels are used in SWA? What are the uncertainties in SWA?