Physical concepts. Remote sensing of fires and vegetation. Applications of SEVIRI channels.

Transcription

1 Physical concepts Remote sensing of fires and vegetation Applications of SEVIRI channels

2 Contents Applications of SEVIRI channels Characteristics of the 3.9µm channel Differences between 3.9µm and 10.8µm channels Solar channels for monitoring vegetation Physical concepts Semitransparency and sub-pixels effects Remote sensing of fires and vegetation Smoke monitoring

3 30% of channel 3.9µm on top of HRV 2006-July-7 16:00

4 Satellite detection of fires Study in Croatia, N Strelec, for Average: 750 fires/year, Ha, <x> = 1.5 km average horizontal dimension Poor performance of satellites due to: small active surface, cloud or smoke cover, location uncertainty (for fire brigades)

5 SEVIRI CHANNELS Properties Channel Cloud Gases Application HRV 0.7 Absorption < > Scattering < Emissivity -----> Broad band VIS Surface, aerosol, cloud detail (1 km) 12 VIS 0.6 Narrow band Ice or snow VIS 0.8 Narrow band Vegetation NIR 1.6 Window Aerosols, snow<>cloud IR 3.8 Triple window SST, fog<>surface, ice cloud WV 6.2 Water vapour Upper troposphere 300 Hpa humidity WV 7.3 Water vapour Mid-troposphere 600 Hpa humidity IR 8.7 Almost window Water vapour in boundary layer, ice<>liquid IR 9.7 Ozone Stratospheric winds IR 10.8 Split window CTH, cloud analysis, PW IR 12.0 Split window Land and SST IR 13.4 Carbon dioxide +10.8: Semitransparent-cloud top, air mass analysis

6 3.9 µm and 10.8µm channels: IR window channels 10.8µm [215K K] 3.9µm µm [-12K.. +54K] Differences between those two channels due to sun, gas absorption, ground type and... Planck

7 3.9 µm and 10.8µm: window channels 3.9 µm Negligible absorption by atmospheric humidity Close to a CO2 absorption band, 4-7 Kelvin signal reduction High temperature sensitivity (big sub-pixel effects) ~T^14 Blinding effect by hot pixels, affecting measurements west of the saturated pixel Enhancement of signal by thin cloud is not a fire Sun enhancement during day, but only emission during night 10.8 µm 1-2 Kelvin absorption by atmospheric humidity No signal reduction by CO2 Lower temperature sensitivity (small subpixel effects) ~T^4 No sensor blinding by fires Low values compared with 3.9µm due to semitransparent cloud or smoke.

8 Channel IR3.9r: Cloud Particle Size Maputo Water Clouds (15%) Large Ice Particles (1%) Small Ice Particles (10%) MSG-1, 6 November 2004, 12:00 UTC, Channel 04r (IR3.9r) Range: 0 % (black) to +60 % (white), Gamma = 2.5

9 3.9 µm and 10.8µm channels: sensor blinding and filters 2006_08_ UTC rgb_ HRV can be combined with lower horizontal resolution for more spectral information measured real data For pixels west of the fire the sensors can be blinded, according to geometrical patterns -5

10 3.9 µm and 10.8µm: thin cloud, emissivity, thermal inertia rock sand BTD ch4-ch9 for pixels in an ocean area partly with thin cloud (colder in ch9) Warm water surfaces, rocky grounds and fires show in declouded images (maximum value in several days) Sand is less emissive and cools off faster than rocky ground, which has texture

11 Planck dependencies: wavelength and temperature 1% temperature change results in a S% increase in the energy count: S ~ / Wavelength(µm) / Temperature(K)) S ~ 14% at 3.9µm and scene temperature of 260 K S ~ 4% for a warm scene at the split window (11µm) Radiation = Temperature S (S-th power) Inside a pixel, S determines the bias towards the warm part of the signal Bigger bias for lower temperatures Fire onset is better detected than its progress. Cloud dissipation is better detected than cloud growth (airport warnings?)

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13 ch4 - ch9 difference: fog dissipation before midday Sun rising Fog starts dissipating Dry air over land Feb UTC

14 Sub-pixel effects = temperature sensitivity = warm bias Σ B C + (1-Σ) B G = B EC Cloud Equivalent Cloud Ground The equivalent temperature is not the average temperature, but shows a WARM BIAS! BRIGHTNESS TEMPERATURE Ground % cloud gives: Cloud 3.9 µm 10.8 µm CLOUD FRACTION Σ 282 K (at 3.9 µm) 264 K (at 10.8 µm) and NOT the average 250 K

15 Standard behaviour of green and dry soils 15 dry soil vegetation 6

16 Added value by 1.6 µm A E C B A Meteosat solar channels 16 B D Now, 2-1 in vertical. Your turn! C B A E D C E Channel 1.6µm reflects better than 0.8µm on dry ground (B), but worse in vegetated areas (C) D

17 landsaf.meteo.pt Meteosat solar channels 17

18 Land SAF LST product 18

19 Astronomical factor (sun apparent size) Meteosat solar channels 19 Sun 1 m K... 1/R m 2 Earth 288 K scattering µm Kelvin This factor explains the brightness difference between sun and moon (together with moon albedo), or between sun and bright cloud. As radiation expands in space, it gets the energy density of black bodies at much lower temperatures than 6000 K The solar radiation reflected by the Earth is equivalent of an emission at lower temperature. Earth radiation competes with the sun at 3.9 µm

20 6.2µm - 7.3µm difference: not just thermal signal 6.2µm - 7.3µm differences are typically negative, 6.2µm being better absorbed by water vapour than 7.3µm radiation Exceptions (positive differences): - warm pixels in convection overshooting the tropopause - sun glint (reflected sun energy enhancing 6.2µm channel values) Cyan big dot is the sun glint centre Other colours are warm pixels in other areas, only for very cold pixels around 200K at the cloud top

21 Fires on 1.6µm images

22 Solar reflection and emission together (3.9µm) 1-E E B(BT) = (1-E) * B(350K) + E * B(300K) Warm bias in brightness temperature towards 350K During night, brightness temperature (BT) is lower than 300K The apparent solar temperature depends on illumination, and is under 350K for oblique sun zenith angles

23 How hot is fire? 300K, neighbour pixels Abundant CO2..and soot... Which one is the fire temperature? 500K, air nearby 800K, orange gas 1100K, yellow 1400K, white tones 3.9µm Hotspots are easily detected Total absorption of ground radiation by CO2 BT is temperature of the CO2 layer above the fire 100m minimum fire size for Meteosat pixel Sun interference noticeable (~20 K), but truncated by 3.9µm channel dynamic range limit (333K) Difficult statistics due to man-made fire generation (e.g. after harvest)

24 How hot is fire? Karthala, Met-8, 29 May 2006, 12:15 UTC Natural colours RGB 1.6µm-0.8µm-0.6µm Abundant CO2..and soot... How hot is lava? 500K, air nearby 800K, orange gas 1100K, yellow 300K, neighbour pixels Which one is the fire temperature? 1400K, white tones

25 Detection domains NEAR INFRARED (e.g. 1.6µm) More adequate for smoke detection than 3.9µm Small fires not visible (below threshold) No CO2 absorption (higher fire temperature ) High sub-pixel sensitivity Karthala, Met-8, 29 May 2006, 12:15 UTC Natural colours RGB 1.6µm-0.8µm-0.6µm 3.9µm Hotspots are easily detected Total absorption of ground radiation by CO2 BT is temperature of the CO2 layer above the fire 100m minimum fire size for Meteosat pixel Sun interference noticeable (~20 K), but truncated by 3.9µm channel dynamic range limit (333K) Difficult statistics due to man-made fire generation (e.g. after harvest) How hot is lava?

26 Hot spots contributions in a pixel (3.9µm) 350 K 350 K *cos(sun) Reflectivity (15% - 50%) Emitted at 300 K Emitted at 500 K 500 K Burning Reflectivity DAY BT 3.9µm 15% 50% Forest Savannah Fraction burning NIGHT BT Fraction burning Reflectivity 3.9µm 15% 50% Sunrise increases 3.9µm BT by about 20K. Opposite for sunset. Not noted by SEVIRI: out of range values.

27 Sun influence and lower limits for detection Temperature (kelvin) Temperature (kelvin) Channel wavelength in µm Equivalent temperatures of the sun radiation at different wavelengths after reflection Fire size (meters) Minimum fire or flare temperature for easy detection on channel 1.6µm at night (blue line) or at day time (brown line) and on channel 3.9µm (green line) for different fire sizes

28 Astronomical factor (sun apparent size) Sun 1 m K... 1/R m 2 Earth 288 K scattering µm Kelvin This factor explains the brightness difference between sun and moon (together with moon albedo), or between sun and bright cloud. As radiation expands in space, it gets the energy density of black bodies at much lower temperatures than 6000 K The solar radiation reflected by the Earth is equivalent of an emission at lower temperature. Earth radiation competes with the sun at 3.9 µm

29 How hot is fire? Burning pixel fraction 11µm 3.9µm 2.2µm 1.6µm 0.8µm Min. size of fire (305 K) 400m 150m 40m 10m 3cm sun temperature 140K 350K 580K 750K 1300K Night threshold for the temperature of a 1% of pixel (300m) fire: 420K 600K 770K 1390K Fires not visible (below threshold) No CO2 absorption (higher fire temperature ) Total absorption of ground radiation by CO2 High sub-pixel sensitivity BT is temperature of the CO2 layer above the fire 150m minimum fire size for Meteosat pixel Sun interference noticeable (~20 K), but truncated by 3.9µm channel dynamic range limit (314K) Difficult statistics due to man-made fire generation (e.g. after harvest)

30 Meteosat third generation NIGHT detection thresholds Min* size in meters for NIGHT fire µm channel µm channel Only fires on the right and upper part of these curves are detected. The two curves are in fact closer together around 4µm due to CO2 absorption, which masks the fire temperature. 3.8µm channel sensitivity is not as good as in the red curve. Geostationary satellites reduce false detections by means of its frequent refresh time During the day (green line) the sun forces an increase in fire size for detection, equivalent of 500 kelvin at the fire core *Brightness temperature above neighbours and above detection threshold by 3%

31 Meteorites on 3.9 µm images

32 Subpixel detection at 3.9µm Colour from Meteosat-9 channel 3.9µm. Blue=270K Red=280K

33 1.6 µm and 3.9 µm SEVIRI detection thresholds fire Ø (km) 1.6µm 3.9µm CO2 kelvin The actual fire temperature is masked at 3.9 µm by CO2, which makes it colder: The same fire presents a higher temperature at 1.6 µm than at 3.9 µm. The two curves get closer together due to CO2 absorption Geostationary satellites reduce false detections by the frequent refresh time

34 Expected detection capabilities

35 Signal enhancement by sun The sun (with equivalent radiative power as a black body at 350K) enhances the signal at 3.9 µm from cold or reflective scenes

36 Fires in Galicia (Spain) Channel at 3.9 µm, colour enhanced 330

37 Hot spots, brightness temperature daily evolution ch4 ch4-ch9 ch9 ch9-ch10 Stronger response in 3.9µm than in 10.8µm or 12µm Optimal index is 3.9µm 10.8µm Alternative index 10.8µm 12µm, due to humidity increase?

38 Ozone, CO 2 and H 2 O vapour Ch8 (9.7 µm) Ch5 (6.2µm) Ch11 (13.4 µm) Ch6 (7.3 µm) No significant effect in O3 signal CO2 injection is not reducing the signal at 13.4µm Water vapour injection seems efficient to decrease the signal

39 Solar reflection ch1 ch2 ch3 ch12 ch2-ch1 0.6µm reflection increases after the forest fire! More moderately for for 0.8µm and 1.6µm

40 Smoke 5-6 September 2007, Meteosat-9 Around sunrise and sunset times for central south America Assuming no major smoke sink or source in 24 hours, the intensity difference is due to the sun angle

41 Image contrast for smoke or dust in solar images Early morning at E North Pole W E The solar radiation reflects mainly forwards on smoke particles, comparable in size to the wavelengths (Mie) Asymmetry forward / backward for a.m and p.m. Late afternoon at E At East in the early morning (and at West in the late afternoon) there is strong image contrast for smoke or dust Meteosat at 0 longitude

42 Meteosat9, UTC Smaller wavelengths favoured by forward scattering Blue-cyan colour due to 1.6 µm rather Rayleigh Scattering intensity higher in the western late afternoon What if smoke particles were smaller? More contrast (intense)? Redder or bluer in hue? +contrast -contrast +blue +red R1.6 Smaller Bigger B0.6 G0.8

43 Smoke natural colour Solar forward scattering 0.6(B) 0.8(G) wavelength 1.6(R) µm Curve on the left hand side (small smoke particles) shows less intense hue and just blue in the natural RGB Curve on the right hand side (bigger smoke particles) shows intense hue (large 0.8µm contribution) and blue-green (cyan) Typical diameter D ~ wavelength / pi. For example: 0.7 / pi = 0.25µm average diameter for cyan pixels. Rapid decay of response with wavelength due to Rayleigh, less forwarding

44 Ash or smoke is smaller than dust (solar) UTC Dust over Red Sea µm 0.8 µm 0.6 µm smoke dust UTC Haifa fire ash plume (% albedos for channels at 0.6, 0.8 and 1.6 µm). Plume maxima: 20%,15%, 3%

45 Images of the month: fires

46 Conclusions Solar channels at 0.6µm and 0.8µm are designed to measure vegetation growth. Channel at 1.6µm supplies an additional index Channel 3.9µm in Meteosat is an excellent detection tool for active fires above 200m across (4 Ha), and for measuring the burnt area as reflectivity change (for large burnt areas) Statistics on fires (natural or man-made) are missing or affected by sensor saturation. However, an approximate retrieval can be attempted based on frequency curves below saturation The Land SAF offers a large choice of vegetation products, even to assess vegetation stress and fire risk THANK YOU FOR YOUR ATTENTION!

47 Fire Planck spectrum