Field evolution and zonal stats

Using Agriculture cloud mask from EarthDaily, and data from L2A, zonal stats for evolution

Import librairies

import geopandas as gpd
from matplotlib import pyplot as plt
from earthdaily import datasets, EarthDataStore

Load plot

# load geojson
pivot = datasets.load_pivot()

Init earthdatastore with environment variables or default credentials

eds = EarthDataStore()

Search for collection items for June 2022. where at least 50% of the field is clear according to the native cloudmask.

pivot_cube = eds.datacube(
    "sentinel-2-l2a",
    intersects=pivot,
    datetime=["2022-06"],
    assets=["red", "green", "blue", "nir"],
    mask_with="native",
    clear_cover=50,
)
pivot_cube.clear_percent.plot.scatter(x="time")

<matplotlib.collections.PathCollection object at 0x7fc530fc18d0>

Add spectral indices using spyndex from earthdaily accessor

pivot_cube = pivot_cube.ed.add_indices(['NDVI'])

Plots cube with SCL with at least 50% of clear data

pivot_cube = pivot_cube.load()
pivot_cube.ed.plot_rgb(col_wrap=4, vmin=0, vmax=.3)
plt.title("Pivot evolution masked with native cloudmasks")
plt.show()

Compute zonal stats for the pivot

zonal_stats = pivot_cube.ed.zonal_stats(pivot, ['mean','max','min'])

zonal_stats.isel(feature=0).to_array(dim="band").plot.line(
    x="time", col="band", hue="zonal_statistics", col_wrap=3
)
plt.show()