Calibration of SMOS geolocation biases

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Daniella Lindsey
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1 Calibration of SMOS geolocation biases F. Cabot, Y. Kerr, Ph Waldteufel CBSA AO-3282

2 Introduction Why is geolocalisation accuracy critical? Where do inaccuracies come from? General method for localisation biases determination Method assessment Enhancements, perspectives

3 Soil moisture vs localisation Sensitivity analysis on near coastal areas: 2.3%/km Soil moisture retrieval test, over Japan. Only SM is retrieved 99% land only

4 Soil moisture vs localisation Localization random error constant bias 100m 400m 1500m

5 Overall pointing budget is mainly biases Biases and perturbations Ground measurement accuracy and launch time variations Attitude determination accuracy Only time varying contributions are accuracy (random) and thermal cycling (harmonic) Deformations due to thermal cycling Arm deployment repeatability

6 Geolocation accuracy budget Geoloc Reference Points Reference points for localization error computation (from SRD ESA Iss. 4.1)

7 Geolocation accuracy budget Satellite Pointing Knowledge & Geoloc Budget Post RQS Satellite Pointing Knowledge Budget Typical error Maximal error in deg Without biases Bia ses Total (RSS) XS YS ZS in deg Without biases Bia ses Tota l (LS) XS YS ZS Typical Maximal P3 - FOV Lower Along P4 - FOV Upper P5 - FOV Narrow Position FOV P1 - Center FOV P2 - Nadir Track Corner Upper Corner Error (m) P3 - FOV Lower Along P4 - FOV Upper P5 - FOV Narrow Position FOV P1 - Center FOV P2 - Nadir Track Corner Upper Corner Error (m)

8 Timeline On ground measurements and analysis demonstrate that main contribution to budget is constant bias, acquired at launch time between the reference frame and actual instrument frame. This bias is not expected to vary through mission lifetime Must be assessed very early, make L1c processing relevant. But no need for high radiometric quality. Must also be assessed later into the mission lifetime, to verify stability.

9 Accessibility On average 15 orbits (asc+desc) overpass the selected area over 2 months. More could be used, with less efficiency, to improve statisitics.

10 Simulated data set Selected area All orbits over the area of interest for 6 weeks: 23 overpasses. L1PP with different values for BFP attitude (216)

11 Method The simplified coast model is based on the following assumptions: The coast is a perfect straight line at the pixel scale of known position. Land and sea temperatures are homogeneous The optimization process yields 4 values: xc which gives the horizontal shift between theoretical and actual coast position, brightness temperatures TLand and TSea for land and ocean and the width of the weighting function rmax x T ) + T B ( x0 ) = ( TLand TSea ) H ( x xc ) W ( x x0 Sea

Linear coastlines The proposed method makes use of the high accuracy that can be achieved in the direction perpendicular to the selected coastline.

12 Linear coastlines The proposed method makes use of the high accuracy that can be achieved in the direction perpendicular to the selected coastline. Use of ascending and descending passes over the same coast allows to corner down the geolocalisation biases Ascending track Different angles ensure accuracy on both axis Highest accuracy direction Descending track Highest accuracy direction

13 Results For each set of selected data points. Retrieved Land and Sea temperature, rmax insensitive to shift σ Ascending Descending TBland TBsea rmax Rmax appears to be sensitive to orbit direction. Need to account for WEF oblateness

14 Sensitivity Descending almost exclusively sensitive to roll. Ascending more sensitive to pitch and yaw. Yaw sensitivity prove to be sufficient at 42. Possibility of processing only L1c browse products. Correlation of retrieved shift vs euler angles Ascending Descending Roll Pitch Yaw

15 Achievements Tools available to process either L1c or L1c browse Capable of deriving BFP attitude or quaternion correction. Accuracy of the methods assessed to be fulfilling requirements To do Accounting for oblateness of synthetic antenna pattern Sensitivity to artifacts yet to be assessed

GEOG 4110/5100 Advanced Remote Sensing Lecture 4

GEOG 4110/5100 Advanced Remote Sensing Lecture 4 Geometric Distortion Relevant Reading: Richards, Sections 2.11-2.17 Review What factors influence radiometric distortion? What is striping in an image?