School on Modelling Tools and Capacity Building in Climate and Public Health April Remote Sensing
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1 School on Modelling Tools and Capacity Building in Climate and Public Health April 2013 Remote Sensing CECCATO Pietro International Research Institute for Climate and Society, IRI The Earth Institute Columbia University 61 Route 9W, Monell Building Lamont Campus Palisades NY U.S.A.
2 Remote Sensing Dr. Pietro Ceccato The International Research Institute for Climate and Society, The Earth Institute, Columbia University
3 Sensing Ultraviolet Infrared Visible spectrum Wavelength (μm) X-Rays Ultraviolet Near-Short Thermal Microwave Infrared Infrared
4 Sensor
5 An Image An image is composed by pixels which contain values (radiance) of electromagnetic radiation
6
7
8
9 Remote
10 Satellites
11 Geostationary or Polar-Orbiting Satellites
12 Geostationary Satellites Located at about 35,800 km above the equator Orbit at the same speed as earth s rotation Repeat coverage about 15 to 30 minutes Cover full earth disk Observes events and their evolution
13 Polar-Orbiting Satellites 350 to 1000 km height Narrow spatial coverage Less frequent observations
14 Spatial Resolution (Size of the Pixel)
15 Spatial Resolution (5 Km)
16 Spatial Resolution (1 Km)
17 Spatial Resolution (30 m)
18 Spatial Resolution (45 cm)
19 ICTP
20 Environmental Parameters Precipitation Vegetation Temperature Water Bodies
21 Remotely-Sensed Products Precipitation Estimation
22 What do satellite sensors see? VV, IR & Thermal IR MW (low frequencyemission by rain) MW (high frequency- scattering by ice) ICE Mixed Radar Liquid
23 Geostationary Satellites Located at about 35,800 km above the equator Visible, NIR and Thermal Infrared Repeat coverage about 15 to 30 minutes Observes events and their evolution
24 Polar-Orbiting Satellites Passive Microwave Defense Meteorological Satellite Program (DMSP) Special Sensor Microwave/Imager (SSM/ I) - Swath width: 1400-km - Seven passive MW channels
25 Radar and Passive Microwave on-board Satellites Tropical Rainfall Measurement Mission (TRMM) - Precipitation radar (PR) km Swath m vertical resolution - TMI - 9-channel MW km swath - VV/IR - Lightening detector trmm.gsfc.nasa.gov
26 Radar and Passive Microwave TRMM product
27 Merging IR, Passive and Active Microwave Rainfall Estimates Combines the best features of both approaches: Good space/time resolution of geostationary estimates Better accuracy of microwave estimates
28 Satellite Rainfall Estimates Products Time Res Space Res Existence PM Gauge CMORPH Daily 0.25 deg 2002-Pres Y N NRL 3-hourly 0.25 deg Y N PERSIANN 3-hourly 0.25 deg Y N TRMM-3B42 3-hourly 0.25 deg 1998-Pres Y Y TRMM-3B42RT 3-hourly 0.25 deg 2002-Pres Y N CPC-RFE Daily 0.1 deg 2001-Pres Y Y CPC-ARC Daily 0.1 deg 1995-Pres N Y GPCP-1DD Daily 1.0 deg 1996-Pres TAMSAT 10-daily ~0.05 deg 1996-Pres N N GPCP Monthly 2.5 deg Y Y CMAP Monthly 2.5 deg Y Y TRMM-3B43 Monthly 2.5 deg 1998-Pres Y Y
29 Global Precipitation Climatology Project (GPCP) - Merged satellites with gauge spatial resolution - monthly rain rate - Also 1-degree daily(1dd) (monthly) (1DD)
30 Satellite Products CPC-Merged Analysis of Precipitation (CMAP) - Merged satellites, numerical model predictions and gauge observations CMAP December Climatology( ) spatial resolution - monthly total rain - Also 5-day total From IRI data library SOURCES/.NOAA/.NCEP/.CPC/.Merged_Analysis/.monthly/.latest/.ver 2/.prcp_est/
31 Satellite Products TRMM - Active and passive microwave instruments spatial resolution - monthly total rain - Also 3-hourly current 01_Data_Products/02_Gridded/ 07_Monthly_Other_Data_Source_3B_43/index.html
32 Satellite Products RFE - Merged satellites and gauge (11 km) spatial resolution - Daily total rainfall - RFE1: RFE2: 2002-current
33 Rainfall Products available on IRIS Web Site myresearch/rainfall_2.html
34 Validation of Rainfall Products Validation: Comparing Rainfall Estimates with Rain Gauge Data
35 Validation of Rainfall Products Ethiopia
![Validation region and data Validation of Rainfall Products - 120 Stations used - Gauge data gridded using Climate Aided Interpolation - Kriging for interpolating the means 22 35 39 Elevation [meters]](/docs-images/91/105898732/images/36-0.jpg "22 Topography and distribution of gauges. The four 2.5 degree boxes are used for at 2.5 degree resolution, and the number of gauges in each box is given.")
36 Validation region and data Validation of Rainfall Products Stations used - Gauge data gridded using Climate Aided Interpolation - Kriging for interpolating the means Elevation [meters] 22 Topography and distribution of gauges. The four 2.5 degree boxes are used for at 2.5 degree resolution, and the number of gauges in each box is given. Stations in the larger box is used for validation at 1- degree resolution.
37 Validation of Rainfall Products Monthly at 2.5-degree
38 Validation of Rainfall Products 10-day total at 1 o x 1 o
39 Validation of Rainfall Products The following statistics were used to evaluate the accuracy of the rainfall estimate products to retrieve rainfall: coefficient of determination (R 2 ), mean error (ME), standard deviation (Stdv), root mean square error (RMSE), mean absolute error (MAE), and bias. Where R = reference rain gauge observation, G = rainfall estimate product, and N = number of data pairs. ME and MAE are in mm while R 2, Stdv, RMSE and Bias are unit-less.
40 Validation of Rainfall Products Monthly at 2.5-degree Satellite[mm] GPCP_SG CMAP TRMM_3B43 N = 360 GPCP CMAP 3B43 CC Bias ME Gauge[mm] Data:
41 Sep-00 Nov-00 Jan-00 Mar-00 May-00 Jul-00 Nov-99 Sep-99 Validation of Rainfall Products Monthly at 2.5-degree Jan-98 Mar-98 May-98 Jul-98 Sep-98 Nov-98 Jan-99 Mar-99 May-99 Jul-99 Date Gauge GPCP_MS GPCP_SG CMAP Rain[mm]
42 Validation of Rainfall Products 10 Days at 1ºx1º 1 o x 1 o N=306 1DD 3B42 TAMS AT CMO RPH CC Bias ME
43 Validation of Rainfall Products 0.25-deg RFE PERS NRL 3B42 3B42RT CMORPH CC Bias RMS[%] deg RFE 1DD 3B42T 3B42 TAMSAT CMORPH CC Bias RMS[%] deg GPCP CMAP 3B43 CC Bias RMS[%]
44 Improving Rainfall Estimates Calibration: Integrating Rainfall Gauges within Rainfall Estimates Derived from Satellites
45 Improving Rainfall Estimates A B C D Comparison of rain gauge data (A), satellite estimates (B), gauge-only gridded products(c), and combined gauge-satellite product (D), over Ethiopia for 7 July All products have spatial resolution of 0.1 o lat/long
46 Ethiopian Meteorology Agency
47 Remotely-Sensed Products Temperature
48 Air Temperature Land surface temperature retrieval from satellite Thermal IR sensors Longwave radiation Emissivity Shortwave radiation at ground converted in heat (longwave radiation) Land surface (skin) temperature (LST) retrieval from thermal infrared sensors on board geostationary or polar orbiting satellites
49 Air Temperature Comparison between MODIS Night LST and min air T
50 Air Temperature Comparison between MODIS Day LST and max air T
51 Air Temperature Minimum Air Temperature maps from MODIS Land Surface Temperature at night Warmest period Coolest period 18
52
53 Remotely-Sensed Products Vegetation
54 Vegetation Visible spectrum Infrared 10% 50% 100% Chlorophyll absorption Red Channel Multiple-scattering at leaf level Near Infrared Channel
55 Vegetation Visible spectrum Infrared 10% 50% 100% 30% Chlorophyll absorption Red Channel Multiple-scattering at leaf level Near Infrared Channel
56 Vegetation Vegetation Index: NDVI In the Red channel: Low reflectance values = Strong chlorophyll absorption = green vegetation In the NIR channel: High reflectance values = High quantity of biomass Normalised Difference Vegetation Index NDVI= (NIR-Red)/(NIR+Red)
57 Vegetation
58
59 NDVI Study (NIR-Red)/(NIR+Red) NIR NIR channel channel Linear (NDVI 0) Linear (NDVI 0.10) Linear (NDVI 0) Linear (NDVI 0) -0.10) Linear Linear (NDVI (NDVI 0.10) 0) 0) Linear Linear (NDVI (NDVI 0.10) 0.20) 0) Linear (NDVI (NDVI -0.10) Linear (NDVI-0.10) 0.70) Linear (NDVI 0.20) Linear (NDVI 0.80) Linear (NDVI 0.90) Red Red channel channel
60 NDVI Study (NIR-Red)/(NIR+Red) NIR channel Red Red channel vegetation vegetation vegetation sparse vegetation sparse vegetation vegetation sparse bright soils vegetation bright Linear sparse soils (NDVI vegetation 0.60) Linear dark soils (NDVI 0.60) 0.00) Linear bright (NDVI soils 0.00) 0.70) Linear (NDVI 0.70) 0.10) 0.00) Linear dark soils (NDVI 0.10) 0.80) Linear (NDVI 0.80) 0.20) 0.10) Linear (NDVI 0.20) Linear (NDVI 0.20)
61 NDVI example NDVI shows the presence of vegetation when there is no vegetation in the field
62 NDVI example Problem resolved using RGB composite image NDVI shows the presence of vegetation when there is no vegetation in the field
63 Remotely-Sensed Products Water Bodies
64 Hue as a Qualitative Index The various land cover types have specific ranges of Hue Temporal variations of Hue can be interpreted as a change of land cover type
65 Water Bodies Khartoum Blue Nile White Nile
66 To Obtain Images. 15 Years Ago
67 Today, Images are available via Internet Google Earth, Google Map
68 Today, Images are available via Internet... etc
69 Today, Images are available via Internet
70
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