How to remove Black Backgrounnd

[Muhammad-Waqas5012]

I am using this script to calculate Mean NDVI and export it into GeoTiff but there is a problem in each image black color is present in the output. Check Screenshot.

import geopandas as gpd
import pandas as pd
import ee
import geemap
import os
import glob
import matplotlib.pyplot as plt
import numpy as np
import datetime
from shapely.geometry import Polygon, MultiPolygon
import json

# Authenticate Earth Engine (Note: if already not) or Authentication is expired, Remove "#" to run the next line.
# ee.Authenticate()

# Initialize Earth Engine
ee.Initialize()

# Specify the folder name containing GeoJSON files (relative to the current working directory)
folder_name = 'R1'

# Specify the output folder name for NDVI GeoTIFF files (relative to the current working directory)
output_folder = 'Results/Tiff'

# Specify the output folder name for PNG files (relative to the current working directory)
png_folder = 'Results/PNG'

# Get the current working directory
current_dir = os.getcwd()

# Construct the absolute paths for input and output folders
folder_path = os.path.join(current_dir, folder_name)
output_folder_path = os.path.join(current_dir, output_folder)
png_folder_path = os.path.join(current_dir, png_folder)

# Get a list of all GeoJSON files in the folder
geojson_files = glob.glob(os.path.join(folder_path, '*.geojson'))

# Iterate over each GeoJSON file
for geojson_file in geojson_files:
    with open(geojson_file, 'r') as f:
        data = json.load(f)

    # Access the features in the GeoJSON file
    features = [feature for feature_list in data['features'] for feature in feature_list]

    # Create a GeoDataFrame to store the features
    gdf_features = []

    for feature in features:
        # Extract properties and geometry
        properties = feature['properties']
        coordinates = feature['geometry']['coordinates']  # Extracting the nested list of coordinates
        geometry = MultiPolygon([Polygon([(float(coord[0]), float(coord[1])) for coord in coordinates])])

        # Extract feature ID
        feature_id = properties.get('field_id')  # Assuming the ID property is named 'Id'
        farmer_id = properties['farmer_id']
        field_crop_id = properties['field_crop_id']
        if feature_id is None:
            raise ValueError("Field ID is missing in the GeoJSON file.")

        # Load the GeoJSON string into Earth Engine
        feature_gdf = gpd.GeoDataFrame.from_features([{'geometry': geometry, 'properties': properties}])
        feature_gdf = feature_gdf.set_crs('EPSG:4326')  
        # Load the GeoJSON string into Earth Engine
        feature_geojson = geemap.gdf_to_ee(feature_gdf, geodesic=False)
        # Set the CRS to WGS84 (EPSG:4326)# Calculate Mean NDVI for the feature
        start_date = ee.Date(datetime.datetime.now()).advance(-5, 'day')
        end_date = ee.Date(datetime.datetime.now())
        # Load both Sentinel-2 image collections
        s2_collection = ee.ImageCollection('COPERNICUS/S2_SR_HARMONIZED').filterDate(start_date, end_date).filterBounds(feature_geojson)
        
        # Check if the collection is empty
        image_count = s2_collection.size().getInfo()
        if image_count == 0:
            # No images available for this feature
            print(f"No images available for Feature ID {feature_id}. Skipping...")
            continue

        # Calculate Mean NDVI for the feature
        ndvi = s2_collection.map(lambda img: img.normalizedDifference(['B8', 'B4']).rename('NDVI'))
        ndvi_mean = ndvi.mean().clip(feature_geojson)
        ndvi_reclassified = ndvi_mean.visualize(**{
            'min': -1.0,
            'max': 1.0,
            'palette': ['FFFFFF', 'CE7E45', 'DF923D', 'F1B555', 'FCD163', '99B718',
                        '74A901', '66A000', '529400', '3E8601', '207401', '056201',
                        '004C00', '023B01', '012E01', '011D01', '011301']
        }) 
        # Iterate over each image in the collection
        for image in s2_collection.getInfo()['features']:
            # Get the date of image acquisition
            acquisition_date = ee.Date(image['properties']['system:time_start']).format('YYYY-MM-dd').getInfo()

            # Export NDVI images as GeoTIFF with date included in the filename
            timestamp = datetime.datetime.now().strftime("%Y_%m_%d_%I_%M_%p")
            ndvi_output_filename = f"{output_folder_path}/NDVI_of_{acquisition_date}_for_Farmer_{farmer_id}_Feature_{feature_id}.tif"
            geemap.ee_export_image(ndvi_reclassified, filename=ndvi_output_filename, region=feature_geojson.geometry(), scale=10, crs='EPSG:4326')
            print(f"NDVI for Date {acquisition_date} for Farmer ID '{farmer_id}' and Feature ID '{feature_id}' have been saved in '{output_folder_path}'")

[giswqs]

There are several issues with your question:

• The screenshot does not show up
• Code has absolute paths, making it difficult to debug
• Looping with .getInfo, which should be avoided.

Make sure you provide a minimal script that makes it easier for others to debug the issue without having to modify your code.