Remote Sensing and Hydrology Using Thermal Infrared Sensors

Transcription

1 Remote Sensing and Hydrology Using Thermal Infrared Sensors J. van der Kwast & S.M. de Jong Faculty of Geosciences Utrecht University, the Netherlands Contents Introduction Theory of Thermal Remote Sensing Sensors Hydrological application Case study Barrax, Spain & Al Sehoul, Morocco Methods Assignment Aha

2 Radiant temperature Kinetic temperature Objects have different thermal properties Radiant temperature, Kinetic temperature Kinetic temperature is defined as the average energy of microscopic motions of particles. On the macroscopic scale, temperature is the unique physical property that determines the direction of heat flow between two objects placed in thermal contact. Radiant temperature: emitted energy by radiation

3 Thermal Energy Transfer: thermal convection thermal conduction radiation & Transpiration / condensation Etna, July 29th 2001 ASTER VNIR: 3,2,1 Introduction Introduction Study area Methods Results Discussion Conclusion

4 Etna, July 29th 2001 ASTER Thermal composite Introduction Introduction Study area Methods Results Discussion Conclusion RGB =??? Thermal Remote Sensing Satellites with thermal/optical sensors offer means to derive spatial and temporal values of surface temperature Useful for soil moisture estimates, irrigation, heat loss survey of buildings, ocean currents derived from sea surface temperature, surface energy balance models. Prerequisite for deriving reliable surface temperature: 1. accurate atmospheric corrections 2. account for surface emissivity

5 Important practical appliction of thermal scanners Visible wavelengths smoke Thermal wavelengths burning fire lines in yellow Attenuation to radiation of different wavelengths by atmospheric constituents and other particles 10

6 Draining (thermal) in Rotterdam Harbour Night time image Day time image Heat loss from buildings

7 Heat loss from buildings Airborne heat loss mapping in Nijmegen

8 Airborne heat loss mapping in Nijmegen Roof ridge Hollow walls Warm/cold cars Thermal imagery: Bank full conditions in the winter river bed: relative warm seepage water under the dike

9 TIR: Wavelengths from 3.5 to 20 μm Two useful TIR atmospheric windows and μm First window limited day time applications due to solar reflective component

10 TIR: Low solar irradiation low reflection Remotely sensed TIR emission Blackbody (hypothetical object): All objects at temperature above 0 K (-273 ºC) emit electromagnetic radiation. Jozef Stefan & Ludwig Boltzman law: 0 M ( λ) dλ = σt 4 M: total energy emitted in W m -2 σ: Stefan-Boltzman constant (5.6697*10-8 W m -2 K -4 ) T: Absolute temperature (K) λ: Wavelength Wien s law: A λ max = T λ max : wavelength of maximum spectral radiant exitance (μm) A: Wien s constant 2898 (μm K) T: Absolute temperature (K)

11 Theory BB TIR 10 5 Sun 6000 K Relative electromagnetic intensity Molten Lava 5400 K Forest Fire 1000 K Hot Spring 380 K Ambient 290 K Arctic Ice 220 K Wavelength (micrometers) Essentially all of the radiation from the human body and its ordinary surroundings is in the thermal infrared portion of the electromagnetic spectrum Engine Human body, λ max ~ 9.7µ

12 Real materials do not behave as blackbodies So, Trad Tkin Emissivity: the emitting ability of a real material, compared to that of a blackbody ε(λ) = radiant exitance of an object at a given temperature radiant exitance of a blackbody at the same temperature Values between 0 and 1 Varies with wavelength, viewing angle, temperature Besides blackbodies there are: Graybodies: ε<1 and constant at all wavelengths Selective radiator: ε varies with wavelength In practice when broadband thermal sensor are used, emissivity is considered constant in the 8 to 14 μm range

13 Rules of thumb for emissivity of objects: Emissivity (physical object property) function of: Colour: good absorbers (dark) are good emitters, good reflectors are poor emitters (Kirchoff); Surface roughness: greater the surface roughness, more surface for absorption and re-emittance Moisture content: more moisture, better emitter (like water bodies); Compaction more dense soils, higher emissivity Emissivity, some examples (8-13 μm) Vegetation: Grass: Sunflower: Maize: Pine: Wheat: Soils and rocks: Basalt: Dolomite: 0.97 Andosol: 0.96 Others: - Water: Aluminum: 0.03

14 Can of 5º C <- low radiant thermal flux high radiant thermal flux -> Thermal image of a beer can from fridge 5ºC Why is the can not homogeneously chilled? Small piece of tape on beer can changes the emissivity of the alluminium can Thermal sensors measure Radiance Temperature of an object (T rad ) For real bodies we need to convert radiance temperature to Kinetic Temperature (T kin ) 4 T rad = ε 1/ T kin

15 Effect of emissivity differences on radiant temperature 10.0 C Kinetic temperature 4 T rad = ε 1/ T kin Suppose kinetic temperature of beer can is 5ºC, what is the radiant flux?? what is radiant temperature?? Suppose ε of paint on beer can is 0.98: F rad = (278K) = W / cm.25 T rad = 0.98 Tkin.25 T rad = 0.02 Tkin ο = C = ο Suppose ε of aluminium of beer can is 0.02: F rad = (278K) = W / cm = C C = ο ο C

16 Sensors Infrared Thermometers: points Infrared Thermometer Blackbody calibration Device has fixed emissivity e.g Methods Temperature & Emissivity, non-imaging Field Emissivity Measurements ε = L L BB 2 La L a

17 Sensors - Satellite Sensor Channel Wavelength (μm) Resolution (m) Landsat TM 4 & Landsat TM ASTER Sensors - Satellite

18 Understanding & solving the Surface Energy Balance Example of the Surface Energy Balance

19 Thermal properties of the earth surface Thermal properties of the earth surface Heat Capacity (C) in J/(kg K) : measure of increase in thermal energy content (Q) per degree of temperature rise. The capacity of a material to store heat. Thermal Conductivity (K) in J/(sec m K): Rate at which heat passes through a specific thickness of a substance Thermal Inertia (P) in J/(m 2 sec 0.5 K): Resistance of material to temperature change, indicated by the time dependent variations in temperature during a full heating/cooling cycle (a 24-hour day for Earth); Thermal Diffusivity (D) in m²/s: rate at which heat passes through a specific thickness of a substance

20 Thermal properties of some objects: K: thermal Conductivity c: heat capacity d: therm. diffusivty P: therm. inertia Water Soil Basalt Steel TVX plot: integrating Surface temperature & NDVI Envi demo DAIS Scatterplots Full canopy Ts used to estimate patterns of air temperature Bare areas: used to estimate patterns of top soil moisture

21 TVX Plot La Peyne of DAIS7915 images Ts -> NDVI -> Original optical/thermal DAIS image Top soil moisture map for fallow land ƒ(top soil moisture,ts) ƒ(top soil moisture,ndvi)

22 48 hours simulation of surface temperature Temperature C 55 C N 15 C T e m p e r a t u r e Time Surface Energy Balance, model results Temperature [Celcius] Calibrated modelled and measured surface temperatures, for each landcover type Maquis modelled Maquis measured Garrigue modelled Garrigue measured Pine forest modelled Pine forest measured Vineyard modelled Vineyard measured Grassland modelled Grassland measured Bare soil modelled Bare soil measured 0 11:00 16:00 21:00 2:00 7:00 12:00 17:00 22:00 3:00 8:00 Time [hour] Land cover type Modelled Measured Difference Maquis Garrigue Pine forest Vineyard Grassland Bare soil

23 Hydrological application Pivot irrigation in the Barrax study site (Spain) ASTER VNIR, 321 low ASTER Temperature high Hydrological application using SEBS Pivot irrigation in the Barrax study site (Spain)

24 Instrumentation for Barrax Experiment Eddy Correlation (CSAT 3, Li-COR 7500), 2 sets Scintillometer, 2 Sets Radiation components Soil Heat flux plates Temperature profile (air & soil) Goniometer Thermal camera, Everest thermal radiometer ASD spectrometer Digital camera Li-COR LAI2000 (Thermal couples) Component temperatures ITC MSG-1 facility Hydrological Application Surface Energy Balance Energy Balance equation R n = G 0 + H + λe G 0 = soil heat flux density (W m -2 ) H = sensible heat flux (W m -2 ) λe = latent heat flux (W m -2 ) Partitioning of net radiation depends on exhalation of water through: Stomata Bare soil Open water bodies R n H λe G 0

25 Methods Simulations aims at understanding the entire energy balance What is the albedo? Empirical albedo relations for bare soil areas: - based on the Landsat Thematic Mapper - after radiometric calibration - developed for the central US (Duguay and LeDrew, 1991): α = 0.256R (2) R(4) R(7) Liang 2001: α sw = 0.356R(1) R(3) R(4) R(5) R(7) Empirical albedo relations for snow / ice surfaces: - based on the Landsat Thematic Mapper - after radiometric calibration - developed for Switzerland by (Knapp, 1997): α = 0.251R (2) R(4) R(4) 2

26 Barax Spain measurements of the components of the surface energy balance Net radiance, soil flux, sensible heat, latent heat Hydrological Application using SEBS Remote Sensing and hydrological modelling Photogrammetry Thermal IR Optical DEM Radiation Vegetation maps Meteorological parameters Evapotranspiration Soil parameters Soil Moisture Input from remote sensing Output from remote sensing Models Data assimilation algorithm

27 Case Study Study Area Community of Sehoul (Morocco) Rabat Casablanca Morocco MODIS image - January 26, 2003 Introduction Theory Sensors --- Hydrological Application Case Study Methods Assignment Case Study Study Area QuickBird image (June 5 th, 2002)

28 Case Study Sehoul basin Quercus suber (cork oak) forest Reservoir Lake Case Study Study Area Rainfed Agriculture Irrigated Cucurbita pepo (courgette) Submersion irrigation of Mentha piperita (mint) Arachis hypogaea (peanuts)

29 Case Study Surface Energy Balance System (SEBS) (Su, 2002): Estimates atmospheric turbulent fluxes and surface evaporation using satellite earth observation data in the visible, near infrared, and thermal infrared frequency range. Preprocessing to derive radiation balance Submodel to derive roughness length for heat transfer Submodel to derive stability parameters Energy balance at limiting cases Energy balance terms (R n, G 0, H, λe) Surface Energy Balance System (SEBS) 1. Derivation of energy balance terms Energy Balance equation R n = G 0 + H + λe Net Radiation R n = ( 1 α) R + ε R ε σt swd a lwd s 4 0 Soil Heat Flux [ Γ + (1 ) ( Γ Γ )] G = R n f 0 c c s c R n H λe Γc, Γs: ratio of soil heat flux for full canopy/bare soil to net radiation G 0

30 Surface Energy Balance System (SEBS) 2. Submodel to derive roughness length of heat transfer Roughness length expresses the roughness of the surface (Z 0 ). Affects intensity of Mechanical turbulence Fluxes Low roughness length less exchange between surface and atmosphere stronger wind near ground H Surface Energy Balance System (SEBS) 3. Submodel to derive stability parameters Atmospheric Boundary Layer Atmospheric Surface Layer (ASL): Monin-Obukhov Similarity (MOS) Hypothesis: Wind, temperature and humidity profiles are similar above extensive horizontal homogeneous terrain Relates surface fluxes to surface variables Mixed Layer ABL Bulk Atmospheric Boundary Layer (ABL) Similarity (BAS) Wind, temperature and humidity profiles are constant with height Relates surface fluxes to surface variables and mixed layer variables Mixed Layer ASL H

31 Energy Balance at limiting cases R n λe R n H H G 0 Wet low ASTER Temperature high G 0 Dry Case Study Surface Energy Balance System (SEBS) Λ R n H λe Λ: evaporative fraction between extremes wet/dry G 0

32 Meteo tower yield necessary field data (one location) Installation of meteo tower on representative site in order to obtain data for surface energy balance model Sensors: Anemometer (3.87 m) Pyranometer (3.87 m) Hygrometer (3.25 m) Thermometer (3.25 m) Barometer (2.55 m) Not logging: Evaporation pan Rain gauge Case Study Outputs from SEBS Net Radiation Soil Heat Flux Sensible Heat Flux Latent Heat Flux Relative Evaporation Evaporative Fraction Evapotranspiration rate

33 Al Sehoul study area ASTER image Lake Bare soil Open forest Golf course Agric fields Case Study Outputs from SEBS: R n

34 Case Study Outputs from SEBS: H Case Study Outputs from SEBS: H and the Oasis Effect irrigation golf court water bodies

35 Case Study Outputs from SEBS: H and the Oasis Effect Wind direction Desert boundary layer Moisture profile Oasis boundary layer H LE H LE H Increased moisture content Case Study Outputs from SEBS: LE

36 Case Study Outputs from SEBS: Evapotranspiration Rate Class Room Exercise

37 Pivot irrigation of agricultural crops Lecture Source: Thermal Hans Remote van Sensing der Kwast Utrecht

38 Lecture Source: Thermal Hans Remote van Sensing der Kwast Utrecht SEBS Energy Balance at limiting cases R n H λe R n H G 0 Wet low ASTER Temperature high G 0 Dry

39 Thank you for your attention