gis-remote-sensing-guide

GIS and Remote Sensing Guide

A comprehensive skill for conducting geospatial analysis and remote sensing research. Covers data acquisition from satellite platforms, spatial analysis with open-source tools, and publication-quality map production.

Satellite Data Sources

Key Earth Observation Platforms

Data Download with Python

import ee

# Initialize Google Earth Engine
ee.Initialize()

def get_sentinel2_composite(aoi: ee.Geometry, start: str, end: str,
                             cloud_max: int = 20) -> ee.Image:
    """
    Create a cloud-free Sentinel-2 composite.

    Args:
        aoi: Area of interest as ee.Geometry
        start: Start date (YYYY-MM-DD)
        end: End date (YYYY-MM-DD)
        cloud_max: Maximum cloud cover percentage
    """
    collection = (ee.ImageCollection('COPERNICUS/S2_SR_HARMONIZED')
                  .filterBounds(aoi)
                  .filterDate(start, end)
                  .filter(ee.Filter.lt('CLOUDY_PIXEL_PERCENTAGE', cloud_max)))

    # Cloud masking using SCL band
    def mask_clouds(image):
        scl = image.select('SCL')
        mask = scl.neq(3).And(scl.neq(8)).And(scl.neq(9)).And(scl.neq(10))
        return image.updateMask(mask)

    return collection.map(mask_clouds).median().clip(aoi)

# Define study area
study_area = ee.Geometry.Rectangle([116.0, 39.5, 117.0, 40.5])  # Beijing region
composite = get_sentinel2_composite(study_area, '2024-06-01', '2024-09-30')