1. Calibration and validation of satellite sensors 1 Sharlene-Asia Naicker Maanda Rambau Sedzani Elia Muravha Amanda Forbes Busisiwe Nkuzani Busisiwe Nkuzani

2. Overview  Introduction  Definition and importance  Technology trends  Challenges  Best practices  Career scope  Conclusion  Questions  Bibliography 2

3. Definition 3  Calibration: Validation of specific techniques and measurements of equipment. Validation of specific techniques and measurements of equipment. Comparison of measurements with a known standard or other devices. Comparison of measurements with a known standard or other devices. Scientifically assessing a systems response to known controlled signal inputs. Scientifically assessing a systems response to known controlled signal inputs. Responses that are traceable to Cal/Val standards. Responses that are traceable to Cal/Val standards.

4. Definition 4  Validation: Product and process that conforms to the necessary user requirements and specifications. Product and process that conforms to the necessary user requirements and specifications. Process of assessing the quality data that is derived a systems output. Process of assessing the quality data that is derived a systems output.

5. Importance 5  Building blocks of all satellites programs.  Data from different sensors are processed.  Reliability and possibility of uncertainties are established.  Needed for airborne, space borne, images and data retrieval.  Can be done in a shuttle or on a satellite.  Instruments performance and calibration accuracy.

6. Importance 6  Needed to be done: Pre-launch: data to be accurate and reliable. Pre-launch: data to be accurate and reliable. In-orbit: High temporal resolution and prior data acquisition. In-orbit: High temporal resolution and prior data acquisition. Post-launch: Using in-situ measurements and provide reference data for future calibration and validation measurements. Post-launch: Using in-situ measurements and provide reference data for future calibration and validation measurements.

7. Importance 7  Generate consistent and accurate data.  To determine progress of validation.  One kind of calibration is enough.  Techniques such as relative and absolute which use uniform spatial radiance are efficient.  Technically demanding but require international standards.  Data quality, competency and aid detection methods.

8. Importance 8  Both provide consistency, reliability, quality and availability.  Biological, geological, environmental sciences.  Habitat change, biodiversity, vegetation, mapping are significant.  Confidence in data that is well calibrated and validated and provide traceability  Inter-comparison and long term studies and product specifications are achieved.

9. The challenges facing calibration and validation 9 General Challenges General Challenges  Lack of funding  Technical challenges  Lack of resources  Lack of regular comparison of instrumentation and methodologies.  Lack of endorsement and support.  Lack of framework development, guideline standards, best practices and recommendations.

10. The challenges facing calibration and validation 10 Calibration challenges: Calibration challenges:  Pre-launch calibration: Reproducing the essential features of the space environment. Reproducing the essential features of the space environment. Vibration, extreme temperatures and contamination. Vibration, extreme temperatures and contamination. Changes in time. Changes in time. Radiometric produces uncertainty and cost and spatial invariance or broad land coverage. Radiometric produces uncertainty and cost and spatial invariance or broad land coverage.

11. The challenges facing calibration and validation 11 Calibration challenges: OOOOn board calibration The full field view of the sensor is not available. The accuracy is not as high as pre-launch calibration. VVVVicarious calibration The target might not be homogenous or easily accessible. The size and complexity of the mission is increased.

12. The challenges facing calibration and validation 12 Calibration challenges: Calibration challenges:  Post-launch calibration Platform may be damaged or degraded in time. Platform may be damaged or degraded in time. Uncertainty in reliability by neglecting a measurement. Uncertainty in reliability by neglecting a measurement. Atmospheric characteristics. Atmospheric characteristics. Human errors. Human errors.

13. The technology trends in calibration and validation 13  Three things that need to be done to improve the technology for the calibration and validation process: Instrument Calibration Instrument Calibration Instrument Validation Instrument Validation Instrument Re-qualifications Instrument Re-qualifications

14. The technology trends in calibration and validation 14  Instruments Integrating Sphere Integrating Sphere

15.  Inexpensive Near-IR Sunphotometer The technology trends in calibration and validation 15

16.  CEOS  WGCV  Quality Assurance Framework for Earth Observation (QA4EO) Best Practices 16

17. QA4EO  7 Guidelines on Data Quality 17

18. QA4EO  2 Guidelines on Data Policy Guidelines on how to document the data and how to exchange the data Guidelines on how to document the data and how to exchange the data  DPK001 : Procedures and Policies DPK002: Metadata RequirementsDPK002: Metadata Requirements 18

19. QA4EO  1 Guideline on Communication and Education CEK001 CEK001 Peer review Peer review Common TerminologyCommon Terminology Cal/Val PortalCal/Val Portal 19

20. IVOS  Chaired by Nigel Fox  Mission is to monitor the quality of data from Infrared and Visible Optical Earth Observation Satellites through the quality of calibration and validation and international collaboration 20

21. Where to study for CalVal  University of Stellenbosch  University of Cape Town  University of Johannesburg  University of KZN  University of Limpopo  University of Fort Hare  University of Venda 21

22.  University of South Africa  University of Free state  Nelson Mandela Metropolitan  Rhode University  Wits University  University of Pretoria  North –West University 22

23. Career Scope  Studying Cal/Val can bring different career opportunities since it is a broad field that includes the following: Geoinformation Specialist Geoinformation Specialist Image processing researcher Image processing researcher GIS researcher GIS researcher Space science facilitator Space science facilitator 23

24.  Electronic technologist or engineer  Software engineer  Electronic engineer  Satellite system engineer  Control system engineer  Mechanical engineer  Electrical engineer  Remote sensing researcher 24

25. Conclusion  Cal/Val is becoming the most important part of the remote sensing process.  More research and awareness campaigns are required. 25

26. Thank you  Ms M Lubbe  Mr L Vhengani  Dr M Lysko  Mr D Griffith  Patricia Govender  CSIR  DST  Everyone that has helped us. Thank you for your attention. Any Questions??? 26

27.  Full bibliography is available in the final report submitted. Bibliography 27