Export Image Collection and retain band/channel names and image properties

[qtyree]

Hello!

I used the geemap.ee_export_image_collection function to export each image in an image collection. However, I can't find a way to access the image (sentinel 2) properties or band names. Does anyone know how I can find the band names and properties in the downloaded tif, or how I can modify the function to retain that information? Thank you!

[giswqs]

Depending on the temporal frequency you use, each image in the collection may be computed based on a number of images. It would be challenging for the function to figure out which metadata to use from which image to pass to the computed image. You can look into the source code to modify it to suit you needs.

[qtyree]

Thank you. My code is below. I was able to modify the function to include more information in the file name, but I am not sure how to include other image properties.

# Define geometries


geometry1 = ee.Geometry.Point([-73.12672302960141, 42.14593436884982])

geometry2 = ee.Geometry.Point([-73.16242859600766, 42.511428462343346])

geometry3 = ee.Geometry.Point([-72.48951599835141, 42.24360708804471])

geometry4 = ee.Geometry.Point([-72.40162537335141, 42.47092289574001])


# Define FeatureCollection
BoxCollection = ee.FeatureCollection([
    ee.Feature(geometry1, {'id': 'A1', 'Dam': 'yes', 'Region': 'River', 'Date': '2021-01-01'}),
    ee.Feature(geometry2, {'id': 'A2', 'Dam': 'no', 'Region': 'Grassland', 'Date': '2018-01-01'}),
    ee.Feature(geometry3, {'id': 'A3', 'Dam': 'yes', 'Region': 'River', 'Date': '2020-01-01'}),
    ee.Feature(geometry4, {'id': 'A4', 'Dam': 'no', 'Region': 'Grassland', 'Date': '2019-01-01'})
])

# Define dam collection
Dam_data = BoxCollection

# Standardize date of image taken
def StandardizeDate(box):
    date = box.get("Date")
    return box.set("Survey_Date", ee.Date(date))

# Add new date column
Dam_data = Dam_data.map(StandardizeDate)
Dam_data = Dam_data.select(['id', 'Dam', 'Survey_Date'])

# Define a function of buffering and bounding
def buffer_func(feature):
    buffer_size = feature.buffer(1000)  # buffer radius, half your box width in m
    buffer_box = buffer_size.bounds()  # Draw a bounding box around the circle
    return buffer_box  # Return the bounding box

Dam_data = Dam_data.map(buffer_func)

# Define collections and metrics
CHIRPS_collection = ee.ImageCollection("UCSB-CHG/CHIRPS/DAILY")
ECMWF_collection = ee.ImageCollection("ECMWF/ERA5_LAND/MONTHLY_BY_HOUR")

soilType = ee.Image("OpenLandMap/SOL/SOL_TEXTURE-CLASS_USDA-TT_M/v02")
top_soils = soilType.expression(
    'soil_0 + soil_10 + soil_30',
    {
        'soil_0': soilType.select('b0'),
        'soil_10': soilType.select('b10'),
        'soil_30': soilType.select('b30')
    }
).rename('Top_soil')
AddBand = soilType.addBands(top_soils)
Soil_metric = AddBand.select('Top_soil')

dataset = ee.Image('USGS/3DEP/10m')
elevation_select = dataset.select('elevation')
elevation = ee.Image(elevation_select)
slope_select = ee.Terrain.slope(elevation)
slope = ee.Image(slope_select)

USGS_Vegetation = ee.Image('USGS/GAP/CONUS/2011')

# Define a function to add the masked image as a band to the S2 images
def addAcquisitionDate(image):
    date = ee.Date(image.get('system:time_start'))
    return image.set('acquisition_date', date)

# Load Sentinel imagery
S2 = ee.ImageCollection("COPERNICUS/S2")
oldBandNames = ['B1', 'B2', 'B3', 'B4', 'B5', 'B6', 'B7', 'B8', 'B8A', 'B9', 'B10', 'B11', 'B12']
newBandNames = ['S2_Aerosols', 'S2_Blue', 'S2_Green', 'S2_Red', 'S2_Red_Edge1', 'S2_Red_Edge2', 'S2_Red_Edge3', 'S2_NIR', 'S2_Red_Edge4', 'S2_Water_Vapor', 'S2_Cirrus', 'S2_SWIR1', 'S2_SWIR2']

S2_named_bands = S2.map(lambda image: image.select(oldBandNames).rename(newBandNames))
S2_cloud_filter = S2_named_bands.filterMetadata('CLOUDY_PIXEL_PERCENTAGE', 'less_than', 0.5).map(addAcquisitionDate)

def S2_PixelExtraction(Dam_Collection):
    def extract_pixels(box):
        imageDate = ee.Date(box.get("Survey_Date"))
        StartDate = imageDate.advance(-6, 'month').format("YYYY-MM-dd")
        EndDate = imageDate.advance(6, 'month').format("YYYY-MM-dd")
        boxArea = box.geometry()
        damId = box.get("id")

        filteredCollection = S2_cloud_filter.filterDate(StartDate, EndDate).filterBounds(boxArea)

        def add_climate_metrics(image):
            index = image.get("system:index")
            ECMWF = ECMWF_collection.filterDate(StartDate, EndDate).filterBounds(boxArea).mean()
            CHIRPS = CHIRPS_collection.filterDate(StartDate, EndDate).filterBounds(boxArea).mean()
            CHIRPS_precipitation = CHIRPS.select('precipitation').rename('CHIRPS_precipitation_2yr_avg')
            temperature = ECMWF.select('temperature_2m').rename('ECMWF_temperature_2m_2yr_avg')
            surface_runoff = ECMWF.select("surface_runoff").rename("ECMWF_surface_runoff_2yr_avg")
            surface_net_solar_radiation = ECMWF.select("surface_net_solar_radiation").rename("ECMWF_surface_net_solar_radiation_2yr_avg")
            ECMWF_precipitation = ECMWF.select("total_precipitation").rename("ECMWF_precipitation_2yr_avg")

            return image.addBands([
                CHIRPS_precipitation,
                temperature,
                surface_runoff,
                surface_net_solar_radiation,
                ECMWF_precipitation,
                USGS_Vegetation.rename("Vegetation"),
                elevation.rename("elevation"),
                slope.rename("slope"),
                Soil_metric
            ]).set("id", ee.String(index).cat("_").cat(damId)).clip(boxArea)


        filteredCollectionBands = filteredCollection.map(add_climate_metrics)
        return filteredCollectionBands

    ImageryCollections = Dam_Collection.map(extract_pixels).flatten()
    return ee.ImageCollection(ImageryCollections)

S2_Export_values = S2_PixelExtraction(Dam_data)

ImageCollect = ee.ImageCollection(S2_Export_values)
filename_id = ImageCollect.aggregate_array("id").getInfo()

geemap.ee_export_image_collection(ImageCollect, filenames = filename_id ,out_dir=Data_directory)

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