ee_ndvi_example.jl

using EarthEngine

Initialize()

# base ndvi function to do the calculation
function ndvi(n::EE.AbstractEEObject, r::EE.AbstractEEObject)
    return (n - r) / (n + r)
end

# ndvi function for image, will extract out the correct bands
# and then pass to base ndvi function
function ndvi(img::EE.Image)
    n = select(img, ["B5"])
    r = select(img, ["B4"])
    ndvi(n,r)
end

# ndvi function for image, will extract out the correct values
# and then pass to base ndvi function
function ndvi(feature::EE.Feature)
    n = get(feature, "B5")
    r = get(feature, "B4")
    ndvi(n,r)
end

# get a Landsat 8 image collection
ic = sort(
    limit(
        filterDate(
            EE.ImageCollection("LANDSAT/LC08/C01/T1_SR"),
            "2018-01-01", "2019-01-01"
        ),
        100, "CLOUD_COVER"),
    "system:time_start", false
)

# get fist image from collection
img = first(ic)

# sample the image so we have a feature collection
fc = sample(img; scale=30, numPixels=500)

# apply ndvi function on the feature collection
# we have to use @eefunc and provide the input type to force 
# the Python side to do the correct thing with a mapped function
ndvi_fc = map(fc, @eefunc ndvi EE.Feature)

# apply ndvi function on the image collection
# we have to use @eefunc and provide the input type to force 
# the Python side to do the correct thing with a mapped function
ndvi_ic = map(fc, @eefunc ndvi EE.Image)

# apply ndvi on image
ndvi_img = ndvi(img)


n_value = EE.Number(0.1)
r_value = EE.Number(0.2)

# apply ndvi on ee.Number values
ndvi_value = ndvi(n_value, r_value)