Estimation of Wavelet Based Spatially Enhanced Evapotranspiration Using Energy Balance Approach
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1 Estimation of Wavelet Based Spatially Enhanced Evapotranspiration Using Energy Balance Approach Dr.Gowri 1 Dr.Thirumalaivasan 2 1 Associate Professor, Jerusalem College of Engineering, Department of Civil Engineering, Chennai, gowrisenthilkumar@gmail.com 2 Professor, Institute of Remote Sensing, Department of Civil Engineering, Chennai 7/19/ SWAT International Conference 1
2 INTRODUCTION What is Evapotranspiration? Evapotranspiration = Evaporation + Transpiration Knowledge of ET Is critical for irrigation management Effective utilisation of water Manage the scarce resource 7/19/ SWAT International Conference 2
3 NEED FOR THE STUDY Conventional methods estimate ET based on point based measurements, Remote sensing based ET estimation- spatial distribution INPUT - Thermal Infrared (TIR) data and ground based meteorological data Spatial resolution of the thermal data is coarser Current ET estimating methods use coarse spatial thermal bands for analysis Spatial enhancement of the thermal data with the spatial resolution of visible bands 7/19/ SWAT International Conference 3
4 OBJECTIVES To spatially enhance the low spatial resolution TIR image by Multi Resolution Techniques To analyze the spatial variation of actual Evapotranspiration estimated from the enhanced TIR image. 7/19/ SWAT International Conference 4
5 R n ENERGY BALANCE FOR ET ET is calculated as a residual of the energy balance H LE Basic Truth: Evaporation consumes Energy LE = R - G - H n G R n is the net radiation LE is the latent heat flux G is the soil heat flux H is the sensible heat flux (all in W/m 2 units) 7/19/ SWAT International Conference 5
6 PRIMARY DATA FOR ENERGY BALANCE MODELS Surface temperature is derived information Thermal infrared images in the wavelength range 8 to 14 µm Sensor Satellite Visible/NIR bands Thermal band Revisit time ETM+ Landsat 7 30 m 60 m 16 days ASTER Terra 15 m 90 m On demand TM Landsat 5 30 m 120 m 16 days MODIS Terra, Aqua 250/500 m 1000 m 1 day AVHRR NOAA 1000 m 1000 m 1-2 days 7/19/ SWAT International Conference 6
7 REMOTE SENSING BASED ET According to Courault et al (2005), classified as follows: Direct simplified methods Semi empirical models (ET directly related to surface temperature) Residual methods of energy budget Empirical and physical modules Two Source Model Surface Energy Balance Algorithm for Land (SEBAL) Simplified Surface Energy Balance Index (S-SEBI) Evapotranspiration Mapping Algorithm (ETMA) Mapping Evapotranspiration with Internalized Calibration (METRIC) Deterministic methods Vegetation index based method 7/19/ SWAT International Conference 7
8 CHOICE OF SEBAL METHOD Developed by Bastiannssen et al 1998 Minimum amount of ground measurements including meteorological measurements Based on physical concept - applicable for various climates No need for landuse classification Validated at various countries Procedures are satellite independent - can be used with other satellite images having thermal bands SEBAL calculates actual ET and no satellite based crop classification is needed SEBAL eliminates the need for atmospheric correction of surface temperature 7/19/ SWAT International Conference 8
9 LIMITATION Estimating ET at high spatial resolution - for field level analysis Spatial resolution of the estimated ET actual is limited to the spatial resolution of the TIR image Spatial resolution of thermal band is often coarser than visible, near infrared and shortwave-infrared. Research need to utilize simultaneously acquired high resolution visible, VNIR and SWIR images to improve on the spatial resolution of the ET maps 7/19/ SWAT International Conference 9
10 DATA FUSION IMAGE FUSION IMAGES Same Sensor/Different Sensor To keep maximum spectral information while increasing its spatial resolution Research has developed that aims at proposing algorithms for fusing high spatial and high spectral resolution images, in order to synthesise images with highest spectral and spatial resolutions available in the sets of images (Ranchin et al 2003). 7/19/ SWAT International Conference 10
11 Location of the Study Area Acquisition data Over pass time Sun elevation (degrees) Sun azimuth (degrees) 4 Dec :28: Oct :25: /19/ SWAT International Conference 11
12 METHODOLOGY IMAGE SIMILARITY ANALYSIS SPATIAL ENHANCEMENT IMAGE QUALITY ASSESSMENT SEBAL ANALYSIS 7/19/ SWAT International Conference 12
13 SPATIAL ENHANCEMENT Add High spatial details = TIR image at 30 m Spectral properties preserved High spatial resolution image at 30m TIR image at 60 m IMAGE SIMILARITY ANALYSIS HIGH SPATIAL RESOLUTION IMAGE 30M REDUCE BY 2 HIGH SPATIAL RESOLUTION IMAGE 60M Spectral Comparison LOW SPATIAL RESOLUTION TIR IMAGE 60M 7/19/ SWAT International Conference 13
14 Wavelet Based Enhancement Scheme Bi-orthogonal Wavelets Low Spatial Resolution Resampled Image DWT A TIR V TIR H TIR D TIR Combined Wavelet Coefficients Input Images Wavelet Coefficients A TIR V VIS H VIS D VIS High Spatial Resolution DWT A VIS V VIS H VIS D VIS Inverse DWT Spatially Enhanced Image 7/19/ SWAT International Conference 14
15 IMAGE QUALITY ASSESSMENT VISUAL EVALUATION QUANTITATIVE EVALUATION SPECTRAL QUALITY Spatially Enhanced TIR Image (30M) REDUCE BY 2 Spatially Enhanced TIR Image (60M) Spectral Comparison Original TIR Image (60M) SPATIAL QUALITY Spatially Enhanced TIR Image (30M) Spatial Comparison High Spatial Resolution Image (30M) 7/19/ SWAT International Conference 15
16 Auxillary Parameters DOY Overpass Time Earth-Sun Distance Solar Declination Solar Angle Net Shortwave Radiation Net Longwave Radiation Net Radiation Albedo Soil Heat Flux Visible and NIR Reflectance Surface Emissivity Landsat 7 ETM+ Meteorological Data Red and NIR TIR Water vapour pressure Air Temperature Surface Temperature Atmospheric emissivity Friction Velocity NDVI Surface Roughness Sensible Heat Flux Iteration method Evaporative Fraction Daily Actual Evapotranspiration Daily Net Radiation 7/19/ SWAT International Conference 16 Wind speed Atmospheric Temperature Aerodynamic resistance
17 LANDSAT 7 ETM+ IMAGES Processing of Images and Atmospheric corrections Visible, NIR, MIR (30 m) Thermal Infrared (60 m) Field Data and Secondary data DN to Spectral Radiance Calc of Reflectance Calc of Broadband Albedo NDVI Flux Algorithms Net Radiation Soil Heat Flux Sensible Heat Flux Latent Heat Flux Resampled TIR (30 m) Resampling Pyramid based spatially enhanced TIR (30 m) Surface Temperature (For resampled and Spatially Enhanced TIR (30m)) Spatial Enhancement Comparison of Surface Temperature Wavelet based spatially enhanced TIR (30 m) Spectral Quality Assessment Meteorological Data Daily Actual Evapotranspiration (Using Resampled and Spatially Enhanced TIR) Validation Penman-Monteith Method based Reference ET 7/19/ SWAT International Conference 17 Comparison of Actual Evapotranspiration
18 Images Spectral Quality Assessment TIR Image RES RMSE CC ERGAS Q4 04-Dec Oct TIR Image WAV RMSE CC ERGAS Q4 04-Dec Oct Standard Deviation (SD) RESULTS Spatial Quality Assessment for Enhanced Images (at 30m Spatial Resolution) Mean Gradient (MG) Correlation Coefficient (CC) HighPass Correlation Coefficient (HPCC) TIR WAV TIR RES TIR WAV TIR WAV TIR WAV 04-Dec Oct /19/ SWAT International Conference 18
19 Spatial Variation of NDVI Image 7/19/ SWAT International Conference 19
20 Surface Temperature Images from TIR res and TIR wav Image 7/19/ SWAT International Conference 20
21 Statistics of Estimated Net Radiation Images Rn with TIR RES (W/m2) Rn with TIR WAV (W/m2) Min Max Mean Min Max Mean 04-Dec Oct Statistics of Estimated Soil Heat Flux Images G with TIR RES (W/m2) G with TIR WAV (W/m2) Min Max Mean Min Max Mean 04-Dec Oct Statistics of Estimated Sensible Heat Flux Images H with TIR RES (W/m2) H with TIR WAV (W/m2) Min Max Mean Min Max Mean 04-Dec Oct Statistics of Estimated Evaporative Fraction Images Evaporative fraction with TIRRES (-) Evaporative fraction with TIRWAV (-) Min Max Mean Min Max Mean 04-Dec Oct /19/ SWAT International Conference 21
22 Spatial Distribution of ETact Using TIR res Image and TIR wav Image 7/19/ SWAT International Conference 22
23 Images Summary of SEBAL Estimates at Different Scale and the Point Estimate of Reference ET ET o Penman- Monteith (mm/day) ET o (Pan evaporation) (mm/day) ET act (whole scene) mm/day ET act Station Pixel (mm/day) ETact- ETact- ET act Vegetation (mm/day) ETact- ETact- ETact- ETact- RES WAV RES WAV RES WAV 04-Dec Oct Images Percentage Difference between ET Estimates for Agricultural Land Type ETo ET act Penman- Vegetation (mm/day) Monteith (mm/day) Percentage Difference (%) ET act-res ET act-wav ET act-res ET act-wav 04-Dec Oct /19/ SWAT International Conference 23
24 CONCLUSION Wavelet enhanced images have the advantage of high spatial resolution while retaining spectral content from the original TIR images NDVI and surface temperature images have a negative correlation. ET and NDVI have a high agreement in terms of spatial distribution based on the positive relation of vegetation index and evapotranspiration. Overall accuracy of ET from SEBAL estimated using wavelet enhanced TIR image is within a margin of 10% compared to the Penman-Monteith method. 7/19/ SWAT International Conference 24
25 SCOPE FOR FURTHER STUDIES High spatial microwave data can be used to estimate ET To enhance the spatial resolution of freely available high temporal satellite images Stages of the crop growth can be incorporated in the estimation of actual ET need to formulate approaches that utilize lowresolution (greater than 1 km) satellite platforms, to inform upon the field scale 7/19/ SWAT International Conference 25
26 7/19/ SWAT International Conference 26
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