Request an EMIT point spectrum with AppEEARS¶

This notebook requests selected EMIT surface reflectance wavelengths for one point with the HyperCoast AppEEARS helpers.

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# %pip install "hypercoast[extra]"

# %pip install "hypercoast[extra]"

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from pathlib import Path

import pandas as pd
import hypercoast

from pathlib import Path import pandas as pd import hypercoast

Log in and select EMIT reflectance layers by wavelength.

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client = hypercoast.appeears_login()

# wavelengths = [490, 560, 650, 705, 865, 1640, 2210]
wavelengths = None
metadata = hypercoast.appeears_emit_layers(
    wavelengths,
    client=client,
    return_metadata=True,
)
layers = [{"product": item["product"], "layer": item["layer"]} for item in metadata]
layers

client = hypercoast.appeears_login() # wavelengths = [490, 560, 650, 705, 865, 1640, 2210] wavelengths = None metadata = hypercoast.appeears_emit_layers( wavelengths, client=client, return_metadata=True, ) layers = [{"product": item["product"], "layer": item["layer"]} for item in metadata] layers

Build and submit a point task. Point coordinates use (longitude, latitude).

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task = hypercoast.appeears_point_task(
    task_name="emit_hyperspectral_point",
    coordinates=(-118.33, 33.96),
    layers=layers,
    start_date="2024-04-01",
    end_date="2024-04-30",
)

response = hypercoast.appeears_submit_task(task, client=client)
response

task = hypercoast.appeears_point_task( task_name="emit_hyperspectral_point", coordinates=(-118.33, 33.96), layers=layers, start_date="2024-04-01", end_date="2024-04-30", ) response = hypercoast.appeears_submit_task(task, client=client) response

Wait for the task and download the CSV result.

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task_id = response["task_id"]
hypercoast.appeears_wait(task_id, client=client, interval=60)

files = hypercoast.appeears_download(
    task_id,
    out_dir="data/appeears_emit_point",
    client=client,
    file_types=["csv"],
    overwrite=True,
)
files

task_id = response["task_id"] hypercoast.appeears_wait(task_id, client=client, interval=60) files = hypercoast.appeears_download( task_id, out_dir="data/appeears_emit_point", client=client, file_types=["csv"], overwrite=True, ) files

Read the point table and plot the returned spectrum.

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csv_file = (
    Path(files[0]) if files else next(Path("data/appeears_emit_point").glob("*.csv"))
)
df = pd.read_csv(csv_file)
df.head()

csv_file = ( Path(files[0]) if files else next(Path("data/appeears_emit_point").glob("*.csv")) ) df = pd.read_csv(csv_file) df.head()

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if {"wavelength", "reflectance"}.issubset(df.columns):
    spectrum = (
        df.dropna(subset=["wavelength", "reflectance"])
        .sort_values("wavelength")
        .groupby("wavelength")["reflectance"]
        .mean()
    )
else:
    wavelength_by_layer = {item["layer"]: item["wavelength"] for item in metadata}
    band_columns = [column for column in df.columns if "EMIT_L2A_RFL_001_B" in column]
    wavelength_by_column = {
        column: wavelength_by_layer[column.split("_")[-1]] for column in band_columns
    }
    spectrum = df[band_columns].mean().rename(index=wavelength_by_column).sort_index()

spectrum.index.name = "wavelength"
spectrum.plot(marker="o", xlabel="Wavelength (nm)", ylabel="Reflectance")

if {"wavelength", "reflectance"}.issubset(df.columns): spectrum = ( df.dropna(subset=["wavelength", "reflectance"]) .sort_values("wavelength") .groupby("wavelength")["reflectance"] .mean() ) else: wavelength_by_layer = {item["layer"]: item["wavelength"] for item in metadata} band_columns = [column for column in df.columns if "EMIT_L2A_RFL_001_B" in column] wavelength_by_column = { column: wavelength_by_layer[column.split("_")[-1]] for column in band_columns } spectrum = df[band_columns].mean().rename(index=wavelength_by_column).sort_index() spectrum.index.name = "wavelength" spectrum.plot(marker="o", xlabel="Wavelength (nm)", ylabel="Reflectance")