Temperature and Emissivity from AHS data in the framework of the AGRISAR and EAGLE campaigns

Transcription

1 AGRISAR and EAGLE Campaigns Final Workshop October 2007 (ESA/ESTEC, Noordwijk, The Netherlands) Temperature and Emissivity from AHS data in the framework of the AGRISAR and EAGLE campaigns J. A. Sobrino, J.C. Jiménez nez-muñoz, V. Hidalgo, A. Barella-Ortiz, G. Sòria, M. Romaguera and B. Franch Global Change Unit Dpt. of Earth Physics and Thermodynamics Faculty of Physics University of Valencia

2 PRESENTATION SCHEME 1. OBJECTIVES 2. METHODOLOGY 3. ON-GROUND CALIBRATION OF AHS TIR BANDS 4. LAND SURFACE TEMPERATURE/EMISSIVITY RETRIEVAL 5. SIMPLIFIED METHOD FOR ET RETRIEVAL 6. EFFECTS OF CLOUDS/SHADOWS: BRIEF ANALYSIS 7. CONCLUSIONS

3 1. OBJECTIVES MAIN OBJECTIVES To produce Land Surface Temperature (LST) validated products from AHS data. To solve energy budget equation to produce EvapoTranspiration (ET) products from AHS data using a simple and operative methodology. SECONDARY OBJECTIVES To produce Land Surface Emissivity (LSE) products from AHS data. Analysis of Emissivity spectra over high spectral contrast areas. Effects of Clouds and Shadows on our measurements.

4 1. OBJECTIVES 2. METHODOLOGY 3. ON-GROUND CALIBRATION OF AHS TIR BANDS 4. LAND SURFACE TEMPERATURE/EMISSIVITY RETRIEVAL 5. SIMPLIFIED METHOD FOR ET RETRIEVAL 6. EFFECTS OF CLOUDS/SHADOWS: BRIEF ANALYSIS 7. CONCLUSIONS

5 2. METHODOLOGY TEST AREA: AGRISAR Maize (P222) Barley (P200) Wheat (P250) Rape (P140) Grass (P823)

6 2. METHODOLOGY TEST AREA: EAGLE

7 2. METHODOLOGY TEST AREA: EAGLE Speulderbos forest Sand (Kootwijk)

8 2. METHODOLOGY EQUIPMENT Thermal radiometers: CIMEL CE 312-1& 1& 2 Raytek ST Raytek MID OPTRIS MiniSight Plus EVEREST 4000 Calibration sources: GALAI 204-P EVEREST 1000 Thermal camera: NEC TH9100 pro Masts and Tripods to place the radiometers

9 2. METHODOLOGY MAIN RADIOMETERS CIMEL (4 & 6 bands) Temperature Range: -80 to +50 ºC Operating Environment: -20 to +50 ºC Resolution: 8 mk (broadband) 50 mk (other bands) (at 20 ºC) Response time: 1 s Field of view: 10 º Readout data: Local display. Transferable on PC

10 2. METHODOLOGY... also thermal images Thermal camera NEC TH9100 pro Spectral Pass-Band: Single Band: 8-14 μm Temperature Range: -40 to +120 ºC Sensitivity: 0.08 K Field of view: 21º x 16º Other characteristics: Adjustable emissivity. Visible image can be acquired

11 2. METHODOLOGY MEASUREMENT OF LAND SURFACE TEMPERATURE Radiometric temperatures measured directly with the thermal radiometer need to be transformed into LST: emissivity (ε) + downwelling atmospheric radiance (L atm ) correction surface i L = ε B ( T ) + (1 ε ) L i i s i atm (i: thermal band of the radiometer) L surface : directly measured by the radiometer L surface =B(T rad ) L atm : measured with the radiometer pointing to the sky ε: TES algorithm applied to field measurements This correction is specially important over areas with low emissivity

12 2. METHODOLOGY Some examples of measured values Vegetated plot, with high emissivity Differences between LST and Trad = 0.5 ºC P140 (Rape) Emissivity=0.99 Radiometric Corrected (LST) AGRISAR Temperature (ºC) :06 12:07 13:07 14:08 15:08 16:09 17:09 18:10 Local Time

13 2. METHODOLOGY Some examples of measured values Bare soil plot, with low emissivity Differences between LST and Trad = 4 ºC P222 (Soil/Corn) Emissivity=0.916 AGRISAR Temperature (ºC) Radiometric Corrected (LST) 15 9:50 10:04 10:19 10:33 10:48 11:02 11:16 11:31 11:45 12:00 Local Time

14 2. METHODOLOGY Some examples of measured values: Emissivity Spectra Soil in P222 Emissivity CIMEL6b (8-JUNE) CIMEL6b (9-JUNE) Wavelength (microns) Asphalt Emissivity CIMEL6b (5-JULY) Wavelength (microns)

15 2. METHODOLOGY Some examples of measured values EAGLE 13-June-2006 Sand High spectral contrast

16 2. METHODOLOGY Some examples of measured values EAGLE Speulderbos forest Differences between nadir (R1) and 50º (R3). Influence of wind speed R1-R3 Windspeed(m/s) Difference around 2 K when wind speed is near to 0. T (K) Windspeed (m/s) :29 12:59 13:29 13:59 14:29 14:59 Time

17 2. METHODOLOGY Thermal Camera Some examples of measured values 12:32 LT, u=1.15m/s 14:45 LT, u=0.82m/s Differences in time 14:45 (local time) 12:32 (local time)

18 2. METHODOLOGY Thermal Video Composite From 10:30 to 16:30 (local time) 1 image/minute We can observe thermal fluctuations and thermal homogeneization due to the effect of wind

19

20 2. METHODOLOGY THERMAL AIRBORNE IMAGERY: AHS Operated by INTA Our Interest: Port 4, 10 TIR bands (71-80) Low flight (975 m) Satellite altitude (700 km) atmospheric transmissivity BAND 71 ( 8.18 μm) BAND 72 ( 8.66 μm) BAND 73 ( 9.15 μm) BAND 74 ( 9.60 μm) BAND 75 (10.07 μm) BAND 76 (10.59 μm) BAND 77 (11.18 μm) BAND 78 (11.78 μm) BAND 79 (12.35 μm) BAND 80 (12.93 μm) wavelength ( m) Bands 71, 74, 80: Absorption Regions A priori bands for LST retrieval: 72, to 79

21 2. METHODOLOGY AHS DATABASE AGRISAR 06-JUNE (12 flights) 10-JUNE (06 flights) 04-JULY (12 flights but only 4 images with IGM files) 05-JULY (12 flights) Total: 42 images EAGLE 13-JUNE (13 flights) Total AGRISAR+EAGLE: 55 images Field measurements are available for all the flights

22 1. OBJECTIVES 2. METHODOLOGY 3. ON-GROUND CALIBRATION OF AHS TIR BANDS 4. LAND SURFACE TEMPERATURE/EMISSIVITY RETRIEVAL 5. SIMPLIFIED METHOD FOR ET RETRIEVAL 6. EFFECTS OF CLOUDS/SHADOWS: BRIEF ANALYSIS 7. CONCLUSIONS

23 3. ON-GROUND CALIBRATION The predicted at-sensor radiance (or brightness temperature) for each AHS band (using field measurements and MODTRAN) has been compared with the AHS measured radiance. Theoretical Basis: Temperature-Based Method L F i = ε B( Ts) + (1 ε ) τ + π at sensor i i i i i L i ε: surface emissivity B: Planck function T s : surface kinetic temperature (LST) F : atmospheric downwelling irradiance τ: atmospheric transmissivity L : path radiance (Each spectral magnitud is convolved using the AHS filter functions) T s : measured in situ ε i : measured in situ or extracted from spectral libraries τ, F, L : calculated from atmospheric soundings and MODTRAN-4 No atmospheric sounding was available on 6-June-2006 MODIS products

24 3. ON-GROUND CALIBRATION Results obtained for the AGRISAR database P140 (Rape) Comparison in terms of brightness temperature (AHS values have been extracted for 3x3 pixels)

25 3. ON-GROUND CALIBRATION P140 (Rape) Problems for AHS band 78 were found in these particular flights (P01CD and P03CD) FINAL RESULT: RMSE = 1.5 K

26 3. ON-GROUND CALIBRATION P250 (Wheat Bowen Station)

27 3. ON-GROUND CALIBRATION P250 (Wheat Bowen Station)

28 3. ON-GROUND CALIBRATION P250 (Wheat Bowen Station)

29 3. ON-GROUND CALIBRATION P222 (Soil/Corn) FINAL RESULT: RMSE = 1.5 K

30 3. ON-GROUND CALIBRATION P823 (Grass) FINAL RESULT: RMSE = 1.3 K

31 3. ON-GROUND CALIBRATION P200 (Barley)

32 3. ON-GROUND CALIBRATION P200 (Barley) FINAL RESULT: RMSE = 0.9 K

33 3. ON-GROUND CALIBRATION ANALYSIS BY BAND

34 3. ON-GROUND CALIBRATION All bands show similar RMSE 1 K OVERALL RMSE 1.1 K

35 1. OBJECTIVES 2. METHODOLOGY 3. ON-GROUND CALIBRATION OF AHS TIR BANDS 4. LAND SURFACE TEMPERATURE/EMISSIVITY RETRIEVAL 5. SIMPLIFIED METHOD FOR ET RETRIEVAL 6. EFFECTS OF CLOUDS/SHADOWS: BRIEF ANALYSIS 7. CONCLUSIONS

36 4. LST/LSE RETRIEVAL Methods for LST retrieval (based on the Radiative Transfer Equation): SINGLE-CHANNEL (1 thermal band) SPLIT-WINDOW (2 thermal bands) A priori knowledge of emissivity is required, which is the major disadvantage of these methods. Over well-known areas emissivity can be estimated from NDVI. TEMPERATURE/EMISSIVITY SEPARATION (TES) It requires multispectral TIR data (at least 4 thermal bands). It provides simultaneously surface temperature and emissivity, which is the main advantage of this algorithm. TES has been used to obtain the LST products. Problems in emissivity retrieval over areas with low spectral contrast (water, vegetation), and it is very sensitive to atmospheric correction.

37 4. LST/LSE RETRIEVAL TES algorithm (Gillespie et al., 1998: IEEE Trans. Geosci. Rem. Sens.) Atm. Correc. L surface, L NEM (iterative procedure) Ts=max(T i ) ε initial AHS bands: 72,73,75,76 77,78,79 RATIO β-spectrum (normalized ε) (Note that it can be also applied to field measurements made with a multiband radiometer) Coefficients a, b, c obtained from ASTER spectral library MMD ε min =a+bmmd c (semi-empirical) Result: Ts, ε i

38 4. LST/LSE RETRIEVAL LST VALIDATION FROM FIELD MEASUREMENTS (AGRISAR) 53 test points RMSE < 1.9 K

39 4. LST/LSE RETRIEVAL LST VALIDATION FROM FIELD MEASUREMENTS (EAGLE) LOW FLIGHT (975 m AGL) Bias (K) st dev (K) RMSE (K) CIMEL4b CAMERA HIGH FLIGHT (2745 m AGL) Bias (K) st dev (K) RMSE (K) CIMEL4b CAMERA

40 4. LST/LSE RETRIEVAL Examples of LST maps EAGLE AGRISAR

41 4. LST/LSE RETRIEVAL LST PRODUCT PROVIDED BY INTA (level 2b) Obtained from ATCOR-4 software Single-channel method using AHS band 74 Problems found: LST is lower than at-sensor brightness temperature. Validation from field measurements shows RMSE = 4.7 K

42 4. LST/LSE RETRIEVAL RESULTS FOR LAND SURFACE EMISSIVITY RETRIEVAL Problems related to the emissivity spectra retrieval from multispectral TIR data: TES is very sensitive to atmospheric correction. Not all the surface acomplish the relationship between ε min and MMD. Problems over low-mmd surfaces, but it can be partly solved assuming ε min = AGRISAR: vegetated areas, high vegetation cover (P140 and P250)

43 4. LST/LSE RETRIEVAL AREAS WITH HIGH SPECTRAL CONTRAST: acceptable results Direct comparison between CIMEL-2 bands 5, 4, 3, 2 and AHS bands 72, 73, 76, 77 RMSE = Emissivity P222 (Soil/Corn) MMD= AHS In-Situ Wavelenght (microns)

44 4. LST/LSE RETRIEVAL EAGLE: Sand site (high spectral contrast) High bias probably due to angular effects or problems in thedynamicrangeofahs TIR bands Emissivity In-Situ AHS-TES Wavelength (μm)

45 1. OBJECTIVES 2. METHODOLOGY 3. ON-GROUND CALIBRATION OF AHS TIR BANDS 4. LAND SURFACE TEMPERATURE/EMISSIVITY RETRIEVAL 5. SIMPLIFIED METHOD FOR ET RETRIEVAL 6. EFFECTS OF CLOUDS/SHADOWS: BRIEF ANALYSIS 7. CONCLUSIONS

46 5. EVAPOTRANSPIRATION EVAPOTRANSPIRATION - Knowledge of ET allows the irrigation water use optimization (hydrological cycle). - Direct estimation of ET by solving the energy balance equation requires TIR data. ET Retrieval from high-resolution data: Sobrino et al. (2005), A simple algorithm to estimate evapotranspiration from DAIS data: Application to the DAISEX campaigns, Journal of Hydrology, 315: Gómez et al. (2005), Retrieval of evapotranspiration over the Alpilles/ReSeDA experimental site using airborne POLDER sensor and a thermal camera, Remote Sensing of Environment, 96: The methodology is based in the S-SEBI model [Roerink et al. 2000, Phys. Chem. Earth (B), 25(2): ] Instantaneous ET: LET i = Λ i ( Rni Gi ) R ni : instanteneous net radiation G i : instantaneous soil heat flux Λ i : evaporative fraction Daily ET ET d = Λ C R L i di ni (Λ i Λ d, G d 0) C di R = R nd ni C di depends on the day of the year and time (Bastiaanssen 2000, Journal of Hydrology, 229:87-100)

47 5. EVAPOTRANSPIRATION Net Radiation flux: R = (1 α ) R + εr εσt 4 ni s sw lw S (σ: Stefan-Boltzman constant) R sw, R lw : incoming shortwave and longwave radiation Measured in-situ. ε, T s : land surface emissivity and temperature TES algorithm Surface Albedo (α s ): roughly estimated from at-surface reflectances provided by INTA (level 2b, using ATCOR-4) Soil Heat Flux: G = 0.5Rn exp( 2.13MSAVI) Evaporative Fraction: Λ= T T H H T T s LET (T H, T LET : temperatures graphically obtained from Ts versus albedo)

48 5. EVAPOTRANSPIRATION Some values in AGRISAR (P250, bowen station) DOY Local Time ETd (mm) σ (ETd) (mm) ETi (mmd -1 ) σ(eti) (mmd -1 ) : ETinst In situ (mmd -1 ) 13 Relative error (ETinst) 38% : % : % : % Some values in EAGLE (Speulderbos, Pine forest) Local time Flight Altitude ETd (mm) ETi (mmd -1 ) 12: m : m : m : m : m : m 6 12

49 5. EVAPOTRANSPIRATION Examples of ET maps AGRISAR 9:10 GMT EAGLE 12:32 GMT Daily Evapotranspiration (mm)

50 1. OBJECTIVES 2. METHODOLOGY 3. ON-GROUND CALIBRATION OF AHS TIR BANDS 4. LAND SURFACE TEMPERATURE/EMISSIVITY RETRIEVAL 5. SIMPLIFIED METHOD FOR ET RETRIEVAL 6. EFFECTS OF CLOUDS/SHADOWS: BRIEF ANALYSIS 7. CONCLUSIONS

51 6. CLOUDS/SHADOWS In previous campaigns (DAISEX, SPARC, SEN2FLEX) airborne imagery was acquired under clear-sky conditions. During AGRISAR, some measurements were affected by clouds/shadows:

52 6. CLOUDS/SHADOWS Values extracted from cloud pixels clearly provides wrong results. Pixels near clouds are somehow affected. Difficult interpretation.

53 6. CLOUDS/SHADOWS Simple test for Cloud masking: LST < 290 K and NDVI < 0.5 Percentage of shadows (obtained with ATCOR-4, M. Jiménez from INTA) It would be useful to have also cloud/shadows masking products!

54 1. OBJECTIVES 2. METHODOLOGY 3. ON-GROUND CALIBRATION OF AHS TIR BANDS 4. LAND SURFACE TEMPERATURE/EMISSIVITY RETRIEVAL 5. SIMPLIFIED METHOD FOR ET RETRIEVAL 6. EFFECTS OF CLOUDS/SHADOWS: BRIEF ANALYSIS 7. CONCLUSIONS

55 7. CONCLUSIONS A complete database of field measurements and airborne imagery was collected in the AGRISAR/EAGLE field campaigns. On-ground calibration of AHS TIR bands showed good results over land: Plots with high emissivity and vegetation cover: RMSE < 0.9 K Other plots (soil): RMSE < 1.5 K Overall RMSE: 1.1 K Land Surface Temperature products are available (validation over land shows a RMSE < 1.9 K) Land Surface Emissivity products are also available, but some problems can be found over areas with low MMD (however, in these cases one can assume near-blackbody behaviour ε 0.99). Evapotranspiration maps can be obtained from a simple methodology. These products require a deeper validation (and also comparison with other models).