1 Digital Imaging and Remote Sensing Laboratory Frank Padula

2 Digital Imaging and Remote Sensing Laboratory frankPadula update 1/2 Buoy DataBuoy Data »Yearly info on: » data availability » deployed instruments & sensor specs -temperature ± 1.0 C -wind Speed ± 1.0 m/s - 8min avg. // not an hourly avg. » sensor heights -Conversation with Rex Harvey from NDBC regarding sensor depth T water sensor depth T water -advised to use 0.6 m as sensor depth -Temp specs  situation dependent -Buoy itself, radiation reaching the sensor -who’s the manufacturer -buoy changes as a fcn of time

3 Digital Imaging and Remote Sensing Laboratory frankPadula update 2/2 ModtranModtran »Inputs  Dew points » prior to 1994 Td were only sampled to ~ 9km » Where T were sampled to ~ km » either case one has to use a modeled profile to fill in the void »What model? » Modtran - Mid Latitude Summer/Winter, 1976 Std. Atm., etc. » but what do these profiles look like, esp. compared to the real data data » Std. Modtran atm’s we currently have uses the moisture parameter RH. parameter RH. » Convert RH to Td RH = w/w s ≈ e/e s RH = w/w s ≈ e/e s

4 Digital Imaging and Remote Sensing Laboratory frankPadula 3/3

5 Digital Imaging and Remote Sensing Laboratory Jake Ward

6 Digital Imaging and Remote Sensing Laboratory Shape from Shading Introduction Azimuth/Elevation have tremendous impact on brightness temperature (i.e. largest sources of error) Want to lock down these two variables before optimizing other thermodynamic parameters, if possible (DEMs not good enough, but multi-temporal imaging helps!) Shape from Shading—Understanding how the shape of a 3D object may be recovered from shading in 2D image Most methods assume stereo or some sort of spatial difference imaging to create 3D model Need multi-temporal thermal IR imaging method –Trona WASP TIR resolution not good enough to rely on shadow geometry  new approach is needed!

7 Digital Imaging and Remote Sensing Laboratory 180° 0° 90° 270° TC=0.5 HC=0.5 SGP=0 T=4.0 EA=1.0 SA=0.88 TE= °= 11.2°, 12.3°, 21.2° 0°= 11.0°, 24.2°, 24.8° 45°= 11.2°, 26.3°, 21.2° 0°= 11.0°, 24.2°, 24.8° 45°= 11.2°, 29.5°, 28.1° 0°= 11.0°, 24.2°, 24.8° 45°= 11.2°, 15.2°, 33.9° 0°= 11.0°, 24.2°, 24.8° Therm Gives Us… We care more about pixels with larger surface normal deviation (elevation) angles Getting azimuth incorrect on flat pixels is not so bad

8 Digital Imaging and Remote Sensing Laboratory 180° 0° 90° 270° 45°= 11.2°, 12.3°, 21.2° 0°= 11.0°, 24.2°, 24.8° 45°= 11.2°, 26.3°, 21.2° 0°= 11.0°, 24.2°, 24.8° 45°= 11.2°, 29.5°, 28.1° 0°= 11.0°, 24.2°, 24.8° 45°= 11.2°, 15.2°, 33.9° 0°= 11.0°, 24.2°, 24.8° Azimuth/Elevation 11 AM Temp < 20.2° 11 AM Temp > 20.2°

9 Digital Imaging and Remote Sensing Laboratory 180° 0° 90° 270° 45°= 11.2°, 12.3°, 21.2° 0°= 11.0°, 24.2°, 24.8° 45°= 11.2°, 26.3°, 21.2° 0°= 11.0°, 24.2°, 24.8° 45°= 11.2°, 29.5°, 28.1° 0°= 11.0°, 24.2°, 24.8° 45°= 11.2°, 15.2°, 33.9° 0°= 11.0°, 24.2°, 24.8° Azimuth/Elevation 5 PM Temp ↓ 5 PM Temp ↑ 5 PM Temp ↑ 5 PM Temp ↓

10 Digital Imaging and Remote Sensing Laboratory 180° 0° 90° 270° 45°= 11.2°, 29.5°, 28.1° 35°= XX°, 28.4°, XX° 25°= XX°, 27.4°, XX° 15°= XX°, 26.3°, XX° 5°= XX°, 24.9°, XX° 0°= 11.0°, 24.2°, 24.8° Azimuth/Elevation Choose most ‘meaningful’ time per quadrant to base threshold 90°/270° – 5 PM 0°/180°– 11 AM

Broadband Phase Diversity Brian Daniel August 8, 2007 Update

12 Digital Imaging and Remote Sensing Laboratory What I’ve been up to Now that I understand the theory of chromatic incoherent imaging, I’m writing it down. Proposal currently at 30 (good) pages –Was at 20 (crappy) pages Studying Broadband Phase Diversity

13 Digital Imaging and Remote Sensing Laboratory Broadband definitions and assumptions

14 Digital Imaging and Remote Sensing Laboratory Broadband phase diversity

15 Digital Imaging and Remote Sensing Laboratory Broadband phase diversity Where:

16 Digital Imaging and Remote Sensing Laboratory Reflections Whole thing is based on Gray-world assumption Broadband PD infeasible without Gray-world assumption Broadband PSF is not too hard –Wavelength sampling still a possible issue Finding appropriate gradient constant might be a little tricky Optimization seems as complex as monochromatic case –Good, because I haven’t figured that out yet

Marvin Boonmee Land surface temperature and emissivity retrieval from thermal infrared hyperspectral imagery

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20 Digital Imaging and Remote Sensing Laboratory Black Tarp

21 Digital Imaging and Remote Sensing Laboratory Gray Tarp

22 Digital Imaging and Remote Sensing Laboratory Therm target

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LDCM Aaron Gerace

36 Digital Imaging and Remote Sensing Laboratory First attempt at atmosphere Idea is to find dark pixel in the scene and compare to Modtran-derived upwelled radiances to determine atmosphere

37 Digital Imaging and Remote Sensing Laboratory First attempt at atmosphere Use just NIR to determine atmosphere

Landsat Calibration Cliff Anderson

39 Digital Imaging and Remote Sensing Laboratory MODIS Imagery Good bandBad Band?

40 Digital Imaging and Remote Sensing Laboratory MODIS Imagery Other issues –Overlap btwn two scenes Will use continuous ones, not separate passes –How to process? Take averages of the calibration region for each band –Match MODIS bands to L5 and L7 spectral response curves Repeated pixels

41 Digital Imaging and Remote Sensing Laboratory MODIS Solar ZenithSolar Azimuth Sensor Zenith Sensor Azimuth

42 Digital Imaging and Remote Sensing Laboratory Ward BRDF

43 Digital Imaging and Remote Sensing Laboratory Jason Hamel

44 Digital Imaging and Remote Sensing Laboratory Video clip of spectral space