Examine outliers from canopy cover time series

[max-zilla]

You can select Season 6 Canopy Cover on traitvis and hover over the spikes to see which days have the spikes (you've probably done this already): https://traitvis.workbench.terraref.org/

My approach for these was to use files on Globus, e.g. for May 6:
/ua-mac/Level_2/rgb_fullfield/2018-05-06/rgb_fullfield_L2_ua-mac_2018-05-06_stereovis_ir_sensors_fullfield_sorghum6_shade_may2018_mask_canopycover_bety.csv
This contains a row for each plot with the CC value, easier to find which plots are above 5%.

/ua-mac/Level_2/rgb_fullfield/2018-05-06/rgb_fullfield_L2_ua-mac_2018-05-06_stereovis_ir_sensors_fullfield_sorghum6_shade_may2018_mask.tif
The _mask.tif files were actually used to calculate the CC, but they can be large.

/ua-mac/Level_2/rgb_fullfield/2018-05-06/rgb_fullfield_L2_ua-mac_2018-05-06_stereovis_ir_sensors_fullfield_sorghum6_shade_may2018_rgb_10pct.tif
The RGB 10% resolution files don't have the soil mask, but they are smaller in size and you can still see targets or other things that might affect the CC estimation.

I've attached a JSON plotmap file you can drag and drop into QGIS to overlay the plots onto the GeoTIFF images. The plot names are included as fields. This makes it fast to see where in the image the higher values come from: https://app.zenhub.com/files/41696258/9b78b746-deec-4df6-a0b8-1ba3f1d89334/download

[ZongyangLi]

To coordinate with May 7 error data, I ran a full day canopy cover ration process. 52 image will return a value over 5% canopy cover, in totally 9096 images.

Cases show below:

Some caused by calibration target in the field, some caused by some unexpected object, and also there are some area the algorithm can not work well. But a 0.5% error data seems not a huge mistake.