Add chapter on fluxtower footprint calculations

[khufkens]

Check in with Yujie Liu

[YujieLiu666]

To assess leaf or canopy traits within the fetch of flux footprint would be a better title for this analysis.
An example dataset for Bartlett Experiment Forest (https://ameriflux.lbl.gov/sites/siteinfo/US-Bar) in Aug 2017 can be found here (https://drive.google.com/drive/folders/1CuvTYB1kRfbrZTf2ZqXV0iLqiZLsl9SW?usp=sharing)
The example dataset contains data from AmeriFlux (siteID: US-Bar) and NEON (siteID: US-xBR)

All the raw data can downloaded from:

1. Footprint weighted maps
   This is already a dataset about flux footprint for all AmeriFlux sites published by Chu et al., 2021: https://zenodo.org/record/4015350. Monthly_footprint_climatology_weight_map.zip are monthly aggregation of flux footprint for different site-month combinations. I would suggest using data for two sites located in Bartlett (AmeriFlux: US-Bar and NEON:US-xBR) as an example.
   Please note,
   NEON EC tower is 35.68 m, while the AmeriFlux tower is 24.5 m, which explains the footprint size difference.
   Footprints change over time. The footprints are computed based on half-hourly meteorological data and can be aggregated to footprint climatology. More descriptions about flux footprint can be found here.
2. EVImax maps
   The map of EVImax (maximum of EVI during growing season) can be downloaded here. The dataset is published by Moon et al., 2022.

Aim and objectives
The aim of the this analysis is to evaluate footprint-to-target-area representativeness following the method proposed by Chu et al. 2021.
More specifically, the objectives of the analysis are:

1. read data from .nc file
2. align two layers with same projection, extent, and resolution
3. basic raster calculation, such as mean of values, differences of two layers
4. conversion of raster and vector data
5. better visualization (overlaying with google map?)

[khufkens]

Thanks @YujieLiu666 will get to this later next month!

[khufkens]

One thing I would like to add is the actual footprint calculation. So not relying on the pre-calculated values but using a small FLUXNET datasets to calculate the values from scratch. Then applying the analysis listed in the objectives. This to underscore that one can recalculate these values (depending on need - say during "weird" years/months when weather patterns shifted and the fetch of the tower is different from the long term mean).