Exercise 1: Uncertainty Propagation Basics (Click here to open exercise)

• Simple measurement functions

• Manual specification of input uncertainties

• MC and LPU uncertainty propagation methods

Exercise 2: Error Correlation in EO Datasets (Click here to open exercise)

• Simple measurement functions

• Propagating error correlation information

• Random and systematic uncertainties

Exercise 3: From Spectrometer Measurements to NDVI (Click here to open exercise)

• Spectrometer example from demo and uncertainty tree diagram session

• Propagate uncertainties from raw data, to reflectance and NDVI

• Random and systematic uncertainties

Exercise 4: Multi-Dimension Datasets (Click here to open exercise)

• Use obsarray to store error-correlation information for multi-dimensional measurement datasets - such as from Earth Observation.

• Propagate uncertainties from these datasets through measurement functions using punpy.

(If time allows) Exercise 5: Uncertainty propagation for inidividual products in a timeseries

• Often, products are processed one at a time, and thus uncertainties need to align with this

• This exercise explores handling error correlation in such situations.

(Friday) Exercise 6: Work on your own use case or HYPERNETS (Click here to open exercise)

• Get familiar with sample EO data and their uncertainties (HYPERNETS & S2)

• Perform validation comparisons between these sensors

• Generate and interpret uncertainty-aware outputs

✅ Solutions

Exercise solutions will be added after the workshop.