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Uncertainty in the measurementand mapping of soil attributesEstimation and reporting of uncertaintyPresentation by Dr Davi...
Uncertainty Quantification• What is it and how does uncertainty arise• Why we need to care about it• Importance / relevanc...
Sources of uncertaintyPositional errorsField vs lab measurementRaw data – gold standard or derived quantityConversion to s...
Reality CheckDespite our best efforts we don’t knoweverything• We know a lot about some soil processes• We know a lot abou...
The need for quantifying uncertaintyAvoiding the illusion of certaintyImprove the quality of inference based on TERNproduc...
Reporting of uncertaintyPreviously reported via a scale – an implicitmap-wide approachMap our estimates of uncertaintyDiff...
Improving quality of map productsData from Geosciences Australia
Improving quality of map productsData from Geosciences Australia
Improving quality of map productsData from Geosciences Australia
Merging TERN Products
Merging TERN Products
Merging TERN ProductsApproaches for merging:• Picking the most accurate approach• Bayesian Model Averaging• Equal weightin...
Merging TERN Products
Merging TERN Products
ConclusionsUncertainty is important to quantifyProject raising new challenges in modelling and   understanding itDrawing o...
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David Clifford_Uncertainty of soil attribute estimates based on disaggregation

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David Clifford_Uncertainty of soil attribute estimates based on disaggregation

  1. 1. Uncertainty in the measurementand mapping of soil attributesEstimation and reporting of uncertaintyPresentation by Dr David Clifford (CSIRO)
  2. 2. Uncertainty Quantification• What is it and how does uncertainty arise• Why we need to care about it• Importance / relevance for TERN• Incorporation as part of TERN infrastructureGoal: How can we understand, calculate and communicate uncertainty of our products
  3. 3. Sources of uncertaintyPositional errorsField vs lab measurementRaw data – gold standard or derived quantityConversion to standard depthsModels: – covariate layers – transformation of data – structure – estimation and prediction – multiple outputs & joint uncertainty
  4. 4. Reality CheckDespite our best efforts we don’t knoweverything• We know a lot about some soil processes• We know a lot about some regions of Australia• Our knowledge is not uniform• Our uncertainty estimates should reflect this
  5. 5. The need for quantifying uncertaintyAvoiding the illusion of certaintyImprove the quality of inference based on TERNproductsHelp highlight gaps in our knowledge‘Error Budget’ - relative contributions of sourcesDecide where / how to spend future funding
  6. 6. Reporting of uncertaintyPreviously reported via a scale – an implicitmap-wide approachMap our estimates of uncertaintyDifferent products have different uncertaintiesGlobal Soil Map workshop on uncertainty –USDA, Nebraska (Aug 2012)
  7. 7. Improving quality of map productsData from Geosciences Australia
  8. 8. Improving quality of map productsData from Geosciences Australia
  9. 9. Improving quality of map productsData from Geosciences Australia
  10. 10. Merging TERN Products
  11. 11. Merging TERN Products
  12. 12. Merging TERN ProductsApproaches for merging:• Picking the most accurate approach• Bayesian Model Averaging• Equal weighting of the two predictions• Variance weighting
  13. 13. Merging TERN Products
  14. 14. Merging TERN Products
  15. 15. ConclusionsUncertainty is important to quantifyProject raising new challenges in modelling and understanding itDrawing on diverse data resourcesBringing different components of project together to form best soil predictionsStrategically improve future products

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