I’m quite new to Earth Engine/QGIS (don’t have an ArcGIS license), and I want to use a simple ML model to estimate ground-level O3 using satellite VCD, NDVI, and meteorological data.

I am very lost in this world of GIS/geospatial data processing, so I have tried my best to explain my reasoning frantic Googling and reading some articles.

THE DATA I WANT TO USE IS THE FOLLOWING:

EE datasets:

• Daymet V4 Daily climate variables (https://developers.google.com/earth-engine/datasets/catalog/NASA_ORNL_DAYMET_V4#bands)
• MOD13A2 NDVI product (https://developers.google.com/earth-engine/datasets/catalog/MODIS_061_MOD13A2)
• Sentinel-5P (TROPOMI) O3 VCD data (https://developers.google.com/earth-engine/datasets/catalog/COPERNICUS_S5P_NRTI_L3_O3#bands)

LABELS: – ground-level O3 data from EPA (https://epa.maps.arcgis.com/apps/webappviewer/index.html?id=5f239fd3e72f424f98ef3d5def547eb5&extent=-146.2334,13.1913,-46.3896,56.5319), GO TO THE ‘SELECT LAYERS’ ICON IN THE TOP RIGHT AND SELECT O3 ACTIVE/INACTIVE, THEN HOVER OVER ANY OF THE POINTS

• It seems like this EPA data can be directly exported as a CSV and includes longitude, latitude, and ozone measurement.

Assumption — I need the data from the different sources I am pulling from to be at the same spatial/temporal resolution in order to use some simple machine learning algorithms (RF/linear regression) on it.

In the dataset I’m envisioning, each “row” of data will be the meteorological variable value, NDVI, and VCD value for a given pixel on a given day, on which I can perform basic RF/regression (either in EE or Python). In my mind, for this to work all of the datasets need to agree on what “a pixel” is, and to be/become daily temporal resolution (Daymet and TROPOMI are already daily, and I’m assuming I can just take the nearest 16-day NDVI value).

BASED ON THIS ASSUMPTION, I want to get all of the data to be at the same spatial resolution, so I am trying to figure out how to “reproject” the TROPOMI data (which is currently at a resolution of 1111.3km) to a 1km resolution (which is the resolution of all of the Daymet data AND the MODIS data, according to what I know). *I do not know what the process is to make the “nearest pixel” from the EPA data, which comes from point and not raster data, match up so that it can be used as labels for the data, but this seems like a more common problem, so I’ll look around for how to fix that after I get the rest of this sorted.

So the main operation I want to complete here is standardizing spatial resolution: either converting TROPOMI data to 1km pixels or converting Daymet/MODIS data to 1.113km pixels.