Homework: Solar Induced Fluorescence (SIF) Observations for Assessing Vegetation Changes Related to Floods, Drought, and Fire Impacts

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This homework includes questions from all 3 parts of the training series. You might want to record your answers on a sheet of paper or word document before submitting them here. You will not be able to save your answers and return to complete this form at a later time.

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Solar-Induced Fluorescence or SIF is a red to far-red signal emitted by excited chlorophyll molecules in vegetation.

5 points

True

False

What were some of the different science applications for using SIF?

5 points

Climate anomalies

Crop yield predictions

Drought impacts

All of the above

Which of the following statements describe advantages to using SIF for observing plant health compared to other measures?

A. SIF is a direct emission with mechanistic ties to photosynthesis

B. It can detect changes in vegetation stress before changes appear in reflectance indices (e.g., NDVI)

C. It has low sensitivity to deep clouds

D. It is correlated with carbon fixation rather than just greenness

5 points

Statement A only

Statements A, B and C

Statements A, B and D

Statements A, B, C and D

Space-based SIF measurements are available only from NASA.

5 points

True

False

In the lessons, we learned about two key challenges of using Level 2 SIF data from OCO-2 and OCO-3: single sounding retrievals may have negative values (which is not possible in a physical sense) and the samples cover narrow strips of area in each orbit. What are some of the ways we can mitigate these limitations?

5 points

a. Gap-filling with other data sets

b. Spatial gridding and averaging

c. Rejecting negative values

d. a & b

e. a & c

Which of the following is an advantage of using Snapshot Area Map (SAM) mode observations, like the ones used in the second Jupyter Notebook, , 2_oco3_sam.ipynb, over other types of SIF measurements from OCO-3?

5 points

SAM samples are more accurate than other OCO-3 data since they are all taken over a short time window.

SAM observations build up a record of a small region through repeated overflights, allowing for comparison with ground-based measurements.

SAM data are spatially contiguous so they can be used in place of gap-filled products.

All of the above

SIF and Gross Primary Production (GPP) are known to be directly correlated with one another, but in our Jupyter notebook exercise on SAMs, 2_oco3_sam.ipynb, we saw a good correlation at the Michigan site and poor correlation at the Oregon site. What are some reasons for the poor correlation at the Oregon site?

5 points

a. Changes in land cover type around the Oregon flux tower site over the course of the study period.

b. The data is noisy and more outlier points need to be discarded.

c. The Oregon tower site experienced a wildfire during the study period.

d. All of the above

e. a & c

True or false: Gap-filled SIF products like GOSIF are a form of generative AI (genAI) that synthesize values by being fine-tuned from remote sensing foundation models.

5 points

True

False

In the third code exercise on GOSIF, we looked at the 2019 Midwestern floods and how they impacted the corn belt in the midwestern US. One conclusion we made was that SIF was overall lower in 2019 (the flood year) compared to 2018, our control year. Review the time series plot we made at the end of the notebook. Why did we see a peak in SIF late in the 2019 season when SIF was already declining over the same period in 2018?

5 points

Planting was delayed in the 2019 season, resulting in a corresponding later harvest.

Late-season heat waves in 2018 caused crops to dry out too early.

More water in the soil due to the flood allowed the corn and soy to "catch up" later in the season.

None of the above

Take a moment to review the code in the Jupyter Notebook from exercise 1, 01_exploration.ipynb. What format does NASA use to store OCO-2 and OCO-3 data and what Python modules can we use to open it?

5 points

HDF4 format and the pyhdf module

NetCDF format and the netcdf4 module or xarray

GeoTIFF format and the gdal Python bindings

Zarr format and the zarr-python module

Which of the following is a reason to use a gap-filled product (like GOSIF) for studying vegetation change as opposed to a gridded raster of instrument data (like the ones we made in Part 1)?

5 points

Gap-filled products have reduced uncertainty compared to gridded rasters.

Gap-filled products account for biome type, and therefore ecosystem-level SIF behavior is easier to observe in the data compared to instrument data.

Gap-filled products have higher availability over time and space than instrument data, allowing for studies of regions and events that might not have been covered by the source instrument.

Gap-filled products take up less disk space than gridded rasters.

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