open-geodata-api 0.2.7

Unified Python client for open geospatial data APIs: Planetary Computer, EarthSearch, and more

Open Geodata API - Complete User Guide

Table of Contents

1. Introduction
2. Open Geodata API - Complete Tool Summary
3. Installation
4. Quick Start
5. Core Concepts
6. API Reference
7. Usage Examples
8. Best Practices
9. Troubleshooting
10. Advanced Usage
11. Utility Functions
12. CLI Usage
13. FAQ

Introduction

What is Open Geodata API?

Open Geodata API is a unified Python client library that provides seamless access to multiple open geospatial data APIs. It focuses on API access, search, and URL management while maintaining maximum flexibility for data reading and processing.

Open Geodata API - Complete Tool Summary

🛰️ Unified Python Client for Satellite Data Access

What It Does

✅ One Interface - Access Microsoft Planetary Computer & AWS EarthSearch APIs
✅ Smart URLs - Automatic signing, validation, and expiration handling
✅ Your Choice - Use any raster package (rioxarray, rasterio, GDAL)
✅ Complete Workflow - Search → Filter → Download → Analyze

Key Capabilities

✅ Python API - Programmatic access with full flexibility
✅ Command Line - ogapi CLI for all operations with help
✅ Smart Downloads - Progress tracking, resume, batch processing
✅ Data Filtering - Cloud cover, temporal, quality-based filtering
✅ URL Management - Export, validate, and refresh URLs automatically

Supported APIs

API	Provider	Authentication	URL Handling
Planetary Computer	Microsoft	API Key + Signing	Automatic signing
EarthSearch	Element84/AWS	None required	URL validation

Philosophy

🎯 Core Focus: We provide URLs - you choose how to read them!
📦 Use Any Package: rioxarray, rasterio, GDAL, or any package you prefer
🚀 Maximum Flexibility: Zero restrictions on your workflow

Installation

Basic Installation

# Install core package
pip install open-geodata-api

Optional Dependencies

# For spatial analysis (shapely, geopandas)
pip install open-geodata-api[spatial]

# For raster reading suggestions (rioxarray,rasterio, xarray)
pip install open-geodata-api[io]  # rioxarray + xarray

# For complete examples (shapely, geopandas, rioxarray, rasterio, xarray)
pip install open-geodata-api[complete]

# Development dependencies
pip install open-geodata-api[dev]

Verify Installation

import open_geodata_api as ogapi
ogapi.info()

Quick Start

30-Second Example

import open_geodata_api as ogapi

# Get clients for both APIs
clients = ogapi.get_clients(pc_auto_sign=True)
pc = clients['planetary_computer']
es = clients['earth_search']

# Search for Sentinel-2 data
results = pc.search(
    collections=["sentinel-2-l2a"],
    bbox=[-122.5, 47.5, -122.0, 48.0],
    datetime="2024-01-01/2024-03-31"
)

# Get items and URLs
items = results.get_all_items()
item = items[0]

# Get ready-to-use URLs
blue_url = item.get_asset_url('B02')  # Automatically signed!
all_urls = item.get_all_asset_urls()  # All assets

# Use with ANY raster package
import rioxarray
data = rioxarray.open_rasterio(blue_url)

# Or use with rasterio
import rasterio
with rasterio.open(blue_url) as src:
    data = src.read(1)

5-Minute Tutorial

# 1. Import and setup
import open_geodata_api as ogapi

# 2. Create clients
pc = ogapi.planetary_computer(auto_sign=True)
es = ogapi.earth_search()

# 3. Search for data
search_params = {
    'collections': ['sentinel-2-l2a'],
    'bbox': [-122.5, 47.5, -122.0, 48.0],
    'datetime': '2024-01-01/2024-03-31',
    'query': {'eo:cloud_cover': {'lt': 30}}
}

pc_results = pc.search(**search_params, limit=10)
es_results = es.search(**search_params, limit=10)

# 4. Work with results
pc_items = pc_results.get_all_items()
es_items = es_results.get_all_items()

print(f"Found: PC={len(pc_items)}, ES={len(es_items)} items")

# 5. Get URLs and use with your preferred package
item = pc_items[0]
item.print_assets_info()

# Get specific bands
rgb_urls = item.get_band_urls(['B04', 'B03', 'B02'])  # Red, Green, Blue
print(f"RGB URLs: {rgb_urls}")

# Use URLs with any package you want!

Core Concepts

STAC (SpatioTemporal Asset Catalog)

Open Geodata API works with STAC-compliant APIs. Key STAC concepts:

• Collections: Groups of related datasets (e.g., "sentinel-2-l2a")
• Items: Individual products/scenes with metadata
• Assets: Individual files (bands, thumbnails, metadata)

Package Architecture

open-geodata-api/
├── Core Classes (Universal)
│   ├── STACItem           # Individual products
│   ├── STACItemCollection # Groups of products  
│   ├── STACAsset          # Individual files
│   └── STACSearch         # Search results
├── API Clients
│   ├── PlanetaryComputerCollections
│   └── EarthSearchCollections
└── Utilities
    ├── URL signing (PC)
    ├── URL validation (ES)
    └── Filtering functions

Provider-Specific Handling

Feature	Planetary Computer	EarthSearch
Authentication	Automatic via planetary-computer package	None required
URL Signing	Automatic (auto_sign=True)	Not applicable
Asset Naming	B01, B02, B03...	coastal, blue, green...
Cloud Cover	eo:cloud_cover	eo:cloud_cover

API Reference

Factory Functions

planetary_computer(auto_sign=False)

Creates a Planetary Computer client.

Parameters:

• auto_sign (bool): Automatically sign URLs for immediate use

Returns: PlanetaryComputerCollections instance

earth_search(auto_validate=False)

Creates an EarthSearch client.

Parameters:

• auto_validate (bool): Validate URLs (currently placeholder)

Returns: EarthSearchCollections instance

get_clients(pc_auto_sign=False, es_auto_validate=False)

Creates both clients simultaneously.

Returns: Dictionary with 'planetary_computer' and 'earth_search' keys

Client Methods

search(collections, bbox=None, datetime=None, query=None, limit=100)

Search for STAC items.

Parameters:

• collections (list): Collection IDs to search
• bbox (list): Bounding box [west, south, east, north]
• datetime (str): Date range "YYYY-MM-DD/YYYY-MM-DD"
• query (dict): Additional filters like {"eo:cloud_cover": {"lt": 30}}
• limit (int): Maximum results to return

Returns: STACSearch instance

list_collections()

Get list of available collection names.

Returns: List of collection ID strings

get_collection_info(collection_name)

Get detailed information about a specific collection.

Returns: Collection metadata dictionary

STACItem Methods

get_asset_url(asset_key, signed=None)

Get ready-to-use URL for a specific asset.

Parameters:

• asset_key (str): Asset name (e.g., 'B02', 'blue', 'red')
• signed (bool): Override automatic signing behavior

Returns: URL string ready for any raster package

get_all_asset_urls(signed=None)

Get URLs for all available assets.

Returns: Dictionary {asset_key: url}

get_band_urls(bands, signed=None)

Get URLs for specific bands/assets.

Parameters:

• bands (list): List of asset names

Returns: Dictionary {asset_key: url}

list_assets()

Get list of available asset names.

Returns: List of asset key strings

print_assets_info()

Print detailed information about all assets.

STACItemCollection Methods

get_all_urls(asset_keys=None, signed=None)

Get URLs from all items in the collection.

Parameters:

• asset_keys (list, optional): Specific assets to get URLs for
• signed (bool, optional): Override signing behavior

Returns: Dictionary {item_id: {asset_key: url}}

to_dataframe(include_geometry=True)

Convert collection to pandas/geopandas DataFrame.

Parameters:

• include_geometry (bool): Include spatial geometry (requires geopandas)

Returns: DataFrame with item metadata

export_urls_json(filename, asset_keys=None)

Export all URLs to JSON file for external processing.

Usage Examples

Example 1: Simple Data Discovery

import open_geodata_api as ogapi

# Setup
pc = ogapi.planetary_computer(auto_sign=True)

# Find available collections
collections = pc.list_collections()
sentinel_collections = [c for c in collections if 'sentinel' in c.lower()]
print(f"Sentinel collections: {sentinel_collections}")

# Get collection details
s2_info = pc.get_collection_info('sentinel-2-l2a')
print(f"Sentinel-2 L2A: {s2_info['title']}")
print(f"Description: {s2_info['description'][:100]}...")

Example 2: Geographic Search

# Search around San Francisco Bay Area
bbox = [-122.5, 37.5, -122.0, 38.0]

results = pc.search(
    collections=['sentinel-2-l2a'],
    bbox=bbox,
    datetime='2024-06-01/2024-08-31',
    query={'eo:cloud_cover': {'lt': 20}},  # Less than 20% clouds
    limit=20
)

items = results.get_all_items()
print(f"Found {len(items)} items with <20% cloud cover")

# Convert to DataFrame for analysis
df = items.to_dataframe()
print(f"Date range: {df['datetime'].min()} to {df['datetime'].max()}")
print(f"Cloud cover range: {df['eo:cloud_cover'].min():.1f}% to {df['eo:cloud_cover'].max():.1f}%")

Example 3: Multi-Provider Comparison

# Compare results from both providers
bbox = [-122.2, 47.6, -122.1, 47.7]  # Seattle area

pc_results = pc.search(
    collections=['sentinel-2-l2a'],
    bbox=bbox,
    datetime='2024-01-01/2024-03-31'
)

es_results = es.search(
    collections=['sentinel-2-l2a'], 
    bbox=bbox,
    datetime='2024-01-01T00:00:00Z/2024-03-31T23:59:59Z'
)

pc_items = pc_results.get_all_items()
es_items = es_results.get_all_items()

print(f"Planetary Computer: {len(pc_items)} items")
print(f"EarthSearch: {len(es_items)} items")

# Compare asset availability
if pc_items and es_items:
    pc_assets = pc_items[0].list_assets()
    es_assets = es_items[0].list_assets()
    
    print(f"PC assets: {pc_assets[:5]}")
    print(f"ES assets: {es_assets[:5]}")

Example 4: URL Export for External Processing

# Get URLs for specific bands across multiple items
items = pc_results.get_all_items()

# Export RGB band URLs
rgb_urls = items.get_all_urls(['B04', 'B03', 'B02'])  # Red, Green, Blue

# Save to JSON for external processing
items.export_urls_json('sentinel2_rgb_urls.json', ['B04', 'B03', 'B02'])

# Use the URLs with any package
first_item_urls = rgb_urls[list(rgb_urls.keys())[0]]
print(f"Red band URL: {first_item_urls['B04']}")

# Example with different raster packages
import rioxarray
import rasterio
from osgeo import gdal

red_url = first_item_urls['B04']

# Option 1: rioxarray
red_data_xr = rioxarray.open_rasterio(red_url)

# Option 2: rasterio
with rasterio.open(red_url) as src:
    red_data_rio = src.read(1)

# Option 3: GDAL
red_ds = gdal.Open(red_url)
red_data_gdal = red_ds.ReadAsArray()

print(f"Data shapes - XR: {red_data_xr.shape}, RIO: {red_data_rio.shape}, GDAL: {red_data_gdal.shape}")

Example 5: Batch Processing Setup

# Setup for batch processing
import json

# Search for monthly data
results = pc.search(
    collections=['sentinel-2-l2a'],
    bbox=[-120.0, 35.0, -119.0, 36.0],
    datetime='2024-01-01/2024-12-31',
    query={'eo:cloud_cover': {'lt': 15}},
    limit=100
)

items = results.get_all_items()
print(f"Found {len(items)} low-cloud scenes")

# Group by month
df = items.to_dataframe()
df['month'] = df['datetime'].str[:7]  # YYYY-MM
monthly_counts = df.groupby('month').size()
print("Monthly data availability:")
print(monthly_counts)

# Export all URLs for batch processing
all_urls = items.get_all_urls(['B04', 'B03', 'B02', 'B08'])  # RGB + NIR

# Save configuration for external processing
config = {
    'search_params': {
        'bbox': [-120.0, 35.0, -119.0, 36.0],
        'datetime': '2024-01-01/2024-12-31',
        'collections': ['sentinel-2-l2a']
    },
    'items_found': len(items),
    'urls': all_urls
}

with open('batch_processing_config.json', 'w') as f:
    json.dump(config, f, indent=2)

print("Batch processing configuration saved!")

Example 6: EarthSearch Specific Features

# EarthSearch uses different asset names
es = ogapi.earth_search()

es_results = es.search(
    collections=['sentinel-2-l2a'],
    bbox=[-122.5, 47.5, -122.0, 48.0],
    datetime='2024-06-01T00:00:00Z/2024-08-31T23:59:59Z',
    limit=5
)

es_items = es_results.get_all_items()
item = es_items[0]

# EarthSearch asset names
item.print_assets_info()

# Get URLs using EarthSearch naming
rgb_urls = item.get_band_urls(['red', 'green', 'blue'])
nir_url = item.get_asset_url('nir')

print(f"RGB URLs: {list(rgb_urls.keys())}")
print(f"NIR URL ready: {nir_url[:50]}...")

# All URLs (no signing needed for EarthSearch)
all_urls = item.get_all_asset_urls()
print(f"Total assets available: {len(all_urls)}")

Best Practices

1. Client Configuration

# Recommended setup
import open_geodata_api as ogapi

# Auto-sign PC URLs for immediate use
pc = ogapi.planetary_computer(auto_sign=True)
es = ogapi.earth_search()

# Or get both at once
clients = ogapi.get_clients(pc_auto_sign=True)

2. Search Strategy

# Start with broad search, then refine
results = pc.search(
    collections=['sentinel-2-l2a'],
    bbox=your_bbox,
    datetime='2024-01-01/2024-12-31',
    query={'eo:cloud_cover': {'lt': 50}},  # Start broad
    limit=100
)

# Filter further based on your needs
df = results.get_all_items().to_dataframe()
filtered_df = df[df['eo:cloud_cover'] < 20]  # Refine cloud cover

3. URL Management

# Let the package handle URL signing automatically
item = items[0]

# This automatically handles signing based on provider
blue_url = item.get_asset_url('B02')  # PC: signed, ES: validated

# Override if needed
unsigned_url = item.get_asset_url('B02', signed=False)

4. Asset Name Handling

# Handle different naming conventions gracefully
def get_rgb_urls(item):
    """Get RGB URLs regardless of provider naming."""
    assets = item.list_assets()
    
    # Try Planetary Computer naming
    if all(band in assets for band in ['B04', 'B03', 'B02']):
        return item.get_band_urls(['B04', 'B03', 'B02'])
    
    # Try EarthSearch naming  
    elif all(band in assets for band in ['red', 'green', 'blue']):
        return item.get_band_urls(['red', 'green', 'blue'])
    
    else:
        print(f"Available assets: {assets}")
        return {}

# Use the function
rgb_urls = get_rgb_urls(item)

5. Error Handling

# Robust search with error handling
def safe_search(client, **kwargs):
    """Search with comprehensive error handling."""
    try:
        results = client.search(**kwargs)
        items = results.get_all_items()
        
        if len(items) == 0:
            print("No items found. Try adjusting search parameters.")
            return None
            
        print(f"Found {len(items)} items")
        return items
        
    except Exception as e:
        print(f"Search failed: {e}")
        return None

# Use robust search
items = safe_search(
    pc,
    collections=['sentinel-2-l2a'],
    bbox=your_bbox,
    datetime='2024-01-01/2024-03-31'
)

6. Memory Management

# For large datasets, process in batches
def process_in_batches(items, batch_size=10):
    """Process items in batches to manage memory."""
    for i in range(0, len(items), batch_size):
        batch = items[i:i+batch_size]
        
        # Get URLs for this batch
        batch_urls = {}
        for item in batch:
            try:
                batch_urls[item.id] = item.get_band_urls(['B04', 'B03', 'B02'])
            except Exception as e:
                print(f"Failed to get URLs for {item.id}: {e}")
        
        # Process batch_urls as needed
        yield batch_urls

# Use batch processing
for batch_urls in process_in_batches(items):
    print(f"Processing batch with {len(batch_urls)} items")
    # Your processing logic here

Troubleshooting

Common Issues and Solutions

Issue: "planetary-computer package not found"

Problem: PC URL signing fails

# Error: planetary-computer package not found, returning unsigned URL

Solution:

pip install planetary-computer

Issue: No items found

Problem: Search returns empty results

Solutions:

# 1. Check collection names
available_collections = pc.list_collections()
print("Available collections:", available_collections)

# 2. Expand search area
bbox = [-123.0, 47.0, -121.0, 48.0]  # Larger area

# 3. Expand date range
datetime = '2023-01-01/2024-12-31'  # Larger time window

# 4. Relax cloud cover
query = {'eo:cloud_cover': {'lt': 80}}  # More permissive

Issue: Asset not found

Problem: KeyError: Asset 'B02' not found

Solutions:

# 1. Check available assets
item.print_assets_info()

# 2. Use correct naming for provider
# PC: B01, B02, B03...
# ES: coastal, blue, green...

# 3. Handle gracefully
try:
    url = item.get_asset_url('B02')
except KeyError:
    # Try alternative naming
    url = item.get_asset_url('blue')

Issue: EarthSearch datetime format

Problem: EarthSearch requires RFC3339 format

Solution:

# Use proper format for EarthSearch
datetime_es = '2024-01-01T00:00:00Z/2024-03-31T23:59:59Z'

# Package handles this automatically in most cases

Issue: Large data downloads

Problem: Memory issues with large datasets

Solutions:

# 1. Use overview levels (if your raster package supports it)
import rioxarray
data = rioxarray.open_rasterio(url, overview_level=2)

# 2. Use chunking
data = rioxarray.open_rasterio(url, chunks={'x': 512, 'y': 512})

# 3. Read windows
import rasterio
with rasterio.open(url) as src:
    window = rasterio.windows.Window(0, 0, 1024, 1024)
    data = src.read(1, window=window)

Debug Mode

# Enable debug information
import logging
logging.basicConfig(level=logging.DEBUG)

# Check what URLs are being generated
item = items[0]
print(f"Item ID: {item.id}")
print(f"Provider: {item.provider}")

all_urls = item.get_all_asset_urls()
for asset, url in all_urls.items():
    print(f"{asset}: {url[:50]}...")

Validation Steps

# Validate your setup
def validate_setup():
    """Validate package installation and API access."""
    try:
        import open_geodata_api as ogapi
        print("✅ Package imported successfully")
        
        # Test client creation
        pc = ogapi.planetary_computer()
        es = ogapi.earth_search()
        print("✅ Clients created successfully")
        
        # Test collection listing
        pc_collections = pc.list_collections()
        print(f"✅ PC collections: {len(pc_collections)} available")
        
        # Test simple search
        test_results = pc.search(
            collections=['sentinel-2-l2a'],
            bbox=[-122.0, 47.0, -121.0, 48.0],
            limit=1
        )
        test_items = test_results.get_all_items()
        print(f"✅ Test search: {len(test_items)} items found")
        
        return True
        
    except Exception as e:
        print(f"❌ Validation failed: {e}")
        return False

# Run validation
validate_setup()

Advanced Usage

Custom Processing Workflows

# Example: Multi-temporal analysis setup
def setup_temporal_analysis(bbox, date_ranges, max_cloud_cover=20):
    """Setup data for temporal analysis."""
    
    all_data = {}
    
    for period_name, date_range in date_ranges.items():
        print(f"Searching for {period_name}...")
        
        results = pc.search(
            collections=['sentinel-2-l2a'],
            bbox=bbox,
            datetime=date_range,
            query={'eo:cloud_cover': {'lt': max_cloud_cover}},
            limit=50
        )
        
        items = results.get_all_items()
        urls = items.get_all_urls(['B04', 'B03', 'B02', 'B08'])  # RGB + NIR
        
        all_data[period_name] = {
            'count': len(items),
            'date_range': date_range,
            'urls': urls
        }
        
        print(f"  Found {len(items)} items")
    
    return all_data

# Use for seasonal analysis
seasonal_data = setup_temporal_analysis(
    bbox=[-120.0, 35.0, -119.0, 36.0],
    date_ranges={
        'spring_2024': '2024-03-01/2024-05-31',
        'summer_2024': '2024-06-01/2024-08-31',
        'fall_2024': '2024-09-01/2024-11-30'
    }
)

Integration with Other Libraries

Install Required Packages

pip install stackstac pystac

The Custom Functions

# Example: Integration with STAC-tools
def integrate_with_stac_tools(items):
    """Convert to format compatible with other STAC tools."""
    
    # Export as standard STAC format
    stac_collection = items.to_dict()  # GeoJSON FeatureCollection
    
    # Use with pystac
    try:
        import pystac
        
        # Convert items for pystac
        pystac_items = []
        for item_data in items.to_list():
            pystac_item = pystac.Item.from_dict(item_data)
            pystac_items.append(pystac_item)
        
        print(f"Converted {len(pystac_items)} items to pystac format")
        return pystac_items
        
    except ImportError:
        print("pystac not available")
        return stac_collection

# Example: Integration with stackstac
def prepare_for_stackstac(items, bands=['B04', 'B03', 'B02']):
    """Prepare data for stackstac processing."""
    
    try:
        import stackstac
        
        # Get STAC items in proper format
        stac_items = [item.to_dict() for item in items]
        
        # Note: URLs need to be properly signed
        # The package handles this automatically
        
        print(f"Prepared {len(stac_items)} items for stackstac")
        print(f"Bands: {bands}")
        
        return stac_items
        
    except ImportError:
        print("stackstac not available")
        return None

if __name__ == "__main__":
    # Use the functions
    stac_items = integrate_with_stac_tools(items)
    stackstac_items = prepare_for_stackstac(items)
    print(f"STAC items: {stac_items} \nStackSTAC items: {stackstac_items}")
    print(f"STAC items: {len(stac_items)} \nStackSTAC items: {len(stackstac_items)}")
    print("Integration and preparation complete!")

Custom URL Processing

# Example: Custom URL validation and processing
def process_urls_custom(items, custom_processor=None):
    """Process URLs with custom logic."""
    
    def default_processor(url):
        """Default URL processor."""
        # Add custom headers, caching, etc.
        return url
    
    processor = custom_processor or default_processor
    
    processed_urls = {}
    
    for item in items:
        item_urls = item.get_all_asset_urls()
        processed_item_urls = {}
        
        for asset, url in item_urls.items():
            processed_url = processor(url)
            processed_item_urls[asset] = processed_url
        
        processed_urls[item.id] = processed_item_urls
    
    return processed_urls

# Example custom processor
def add_caching_headers(url):
    """Add caching parameters to URL."""
    if '?' in url:
        return f"{url}&cache=3600"
    else:
        return f"{url}?cache=3600"

# Use custom processing
cached_urls = process_urls_custom(items, add_caching_headers)
print(f"Cached URLs: {cached_urls}")

Utils Functions

Utils Functions - Usage Examples

import open_geodata_api as ogapi
from open_geodata_api.utils import (
    filter_by_cloud_cover,
    download_datasets,
    download_url,
    download_from_json,
    download_seasonal,
    download_single_file,
    download_url_dict,
    download_items,
    download_seasonal_data,
    create_download_summary,
    is_url_expired,
    is_signed_url,
    re_sign_url_if_needed
)

# Setup clients
pc = ogapi.planetary_computer(auto_sign=True)
es = ogapi.earth_search()

Example 1: Complete Workflow - Search and Filter by Cloud Cover

print("🔍 Searching for Sentinel-2 data...")
results = pc.search(
    collections=["sentinel-2-l2a"],
    bbox=[-122.5, 47.5, -122.0, 48.0],  # Seattle area
    datetime="2024-06-01/2024-08-31",
    limit=20
)

items = results.get_all_items()
print(f"Found {len(items)} items")

# Filter by cloud cover using utils
clear_items = filter_by_cloud_cover(items, max_cloud_cover=15)
print(f"After filtering: {len(clear_items)} clear items (<15% clouds)")

Example 2: Download Single Asset from Search Results

print("\n📥 Downloading single asset...")
first_item = clear_items[^0]
first_item.print_assets_info()

# Get a single band URL and download
red_url = first_item.get_asset_url('B04')  # Red band, auto-signed
downloaded_file = download_single_file(
    red_url, 
    destination="./data/red_band.tif",
    provider="planetary_computer"
)
print(f"Downloaded: {downloaded_file}")

Example 3: Download RGB Bands from Multiple Items

print("\n🎨 Downloading RGB bands from multiple items...")
rgb_downloads = download_items(
    clear_items[:3],  # First 3 clear items
    base_destination="./rgb_data/",
    asset_keys=['B04', 'B03', 'B02'],  # Red, Green, Blue
    create_product_folders=True
)

print(f"Downloaded RGB data for {len(rgb_downloads)} items")

Example 4: Multi-Provider Data Collection and Download

print("\n🌍 Comparing data from multiple providers...")

# Search both providers
search_params = {
    'collections': ['sentinel-2-l2a'],
    'bbox': [-120.0, 35.0, -119.0, 36.0],  # California
    'datetime': '2024-07-01/2024-07-31',
    'limit': 5
}

pc_results = pc.search(**search_params)
es_results = es.search(**search_params)

pc_items = pc_results.get_all_items()
es_items = es_results.get_all_items()

print(f"PC found: {len(pc_items)} items")
print(f"ES found: {len(es_items)} items")

# Filter both collections
pc_clear = filter_by_cloud_cover(pc_items, max_cloud_cover=20)
es_clear = filter_by_cloud_cover(es_items, max_cloud_cover=20)

# Download from both providers
print("📦 Downloading from Planetary Computer...")
pc_downloads = download_items(
    pc_clear[:2], 
    base_destination="./pc_data/",
    asset_keys=['B08', 'B04'],  # NIR, Red for NDVI
)

print("📦 Downloading from EarthSearch...")
es_downloads = download_items(
    es_clear[:2], 
    base_destination="./es_data/",
    asset_keys=['nir', 'red'],  # ES naming convention
)

Example 5: Seasonal Analysis Workflow

print("\n🌱 Setting up seasonal analysis...")

def collect_seasonal_data(bbox, year):
    """Collect data for seasonal analysis."""
    seasons = {
        'spring': f'{year}-03-01/{year}-05-31',
        'summer': f'{year}-06-01/{year}-08-31', 
        'fall': f'{year}-09-01/{year}-11-30',
        'winter': f'{year}-12-01/{year+1}-02-28'
    }
    
    seasonal_data = {}
    
    for season, date_range in seasons.items():
        print(f"🔍 Searching {season} {year} data...")
        
        results = pc.search(
            collections=['sentinel-2-l2a'],
            bbox=bbox,
            datetime=date_range,
            query={'eo:cloud_cover': {'lt': 25}},
            limit=10
        )
        
        items = results.get_all_items()
        filtered_items = filter_by_cloud_cover(items, max_cloud_cover=20)
        
        # Get URLs for NDVI calculation
        urls = filtered_items.get_all_urls(['B08', 'B04'])  # NIR, Red
        
        seasonal_data[season] = {
            'count': len(filtered_items),
            'date_range': date_range,
            'urls': urls
        }
        
        print(f"  Found {len(filtered_items)} clear scenes")
    
    return seasonal_data

# Collect seasonal data
bbox = [-121.0, 38.0, -120.5, 38.5]  # Northern California
seasonal_data = collect_seasonal_data(bbox, 2024)

# Download seasonal data using utils
seasonal_downloads = download_seasonal_data(
    seasonal_data,
    base_destination="./seasonal_analysis/",
    seasons=['spring', 'summer'],  # Only spring and summer
    asset_keys=['B08', 'B04']  # NIR and Red bands
)

Example 6: URL Management and Re-signing

print("\n🔐 URL management example...")

# Get some URLs from items
item = pc_items[^0] if pc_items else clear_items[^0]
all_urls = item.get_all_asset_urls()

# Check URL status
for asset, url in list(all_urls.items())[:3]:
    print(f"\n🔗 Asset: {asset}")
    print(f"   Signed: {is_signed_url(url)}")
    print(f"   Expired: {is_url_expired(url)}")
    
    # Re-sign if needed
    fresh_url = re_sign_url_if_needed(url, provider="planetary_computer")
    if fresh_url != url:
        print(f"   ✅ URL was re-signed")

Example 7: Batch Processing with URL Dictionary

print("\n📊 Batch processing workflow...")

# Create a custom URL dictionary from search results
custom_urls = {}
for i, item in enumerate(clear_items[:3]):
    item_id = f"sentinel2_{item.id[-8:]}"  # Shortened ID
    # Get specific bands for analysis
    item_urls = item.get_band_urls(['B02', 'B03', 'B04', 'B08'])
    custom_urls[item_id] = item_urls

print(f"Created custom URL dictionary with {len(custom_urls)} items")

# Download using URL dictionary
batch_downloads = download_url_dict(
    {k: v for k, v in list(custom_urls.items())[^0].items()},  # First item only
    base_destination="./batch_data/",
    provider="planetary_computer",
    create_subfolders=True
)

Example 8: Export and Import Workflow

print("\n💾 Export/Import workflow...")

# Export URLs to JSON for later processing
import json
with open('./data_urls.json', 'w') as f:
    json.dump(custom_urls, f, indent=2)

print("📤 URLs exported to data_urls.json")

# Download from JSON file using utils
json_downloads = download_from_json(
    './data_urls.json',
    destination="./from_json/",
    asset_keys=['B04', 'B08'],  # Only specific bands
    create_folders=True
)

Example 9: Download Summary and Reporting

print("\n📋 Creating download summary...")

# Combine all download results
all_downloads = {
    'rgb_downloads': rgb_downloads,
    'pc_downloads': pc_downloads,
    'es_downloads': es_downloads,
    'seasonal_downloads': seasonal_downloads,
    'batch_downloads': batch_downloads,
    'json_downloads': json_downloads
}

# Create comprehensive summary
summary = create_download_summary(
    all_downloads, 
    output_file="./download_report.json"
)

print(f"📊 Download Summary:")
print(f"   Total files: {summary['total_files']}")
print(f"   Successful: {summary['successful_downloads']}")
print(f"   Failed: {summary['failed_downloads']}")
print(f"   Success rate: {summary['success_rate']}")

Example 10: Advanced Filtering and Processing

print("\n🔬 Advanced processing workflow...")

# Multi-step filtering
def advanced_processing_workflow(bbox, max_cloud=10):
    """Advanced workflow with multiple filtering steps."""
    
    # Step 1: Search with broader criteria
    results = pc.search(
        collections=['sentinel-2-l2a'],
        bbox=bbox,
        datetime='2024-06-01/2024-09-30',
        limit=50
    )
    
    items = results.get_all_items()
    print(f"Step 1: Found {len(items)} total items")
    
    # Step 2: Filter by cloud cover
    clear_items = filter_by_cloud_cover(items, max_cloud_cover=max_cloud)
    print(f"Step 2: {len(clear_items)} items with <{max_cloud}% clouds")
    
    # Step 3: Convert to DataFrame for advanced filtering
    df = clear_items.to_dataframe(include_geometry=False)
    
    # Step 4: Filter by date (summer months only)
    summer_mask = df['datetime'].str.contains('2024-0[^678]')  # June, July, August
    summer_items_ids = df[summer_mask]['id'].tolist()
    
    # Step 5: Get items for summer period
    summer_items = [item for item in clear_items if item.id in summer_items_ids]
    print(f"Step 3: {len(summer_items)} summer items")
    
    # Step 6: Download analysis-ready data
    analysis_downloads = download_items(
        summer_items[:5],  # Top 5 summer items
        base_destination="./analysis_ready/",
        asset_keys=['B02', 'B03', 'B04', 'B08', 'B11', 'B12'],  # Multi-spectral
        create_product_folders=True
    )
    
    return analysis_downloads, summer_items

# Run advanced workflow
analysis_results, summer_items = advanced_processing_workflow(
    bbox=[-122.0, 37.0, -121.5, 37.5],  # San Francisco Bay
    max_cloud=5
)

print(f"✅ Analysis-ready data downloaded for {len(analysis_results)} items")

Example 11: Error Handling and Resilient Downloads

print("\n🛡️ Resilient download example...")

def resilient_download(items, max_retries=3):
    """Download with retry logic and error handling."""
    
    successful_downloads = {}
    failed_downloads = {}
    
    for item in items[:2]:  # Process first 2 items
        item_id = item.id
        retries = 0
        
        while retries < max_retries:
            try:
                # Try to download key bands
                downloads = download_items(
                    [item],
                    base_destination=f"./resilient_data/attempt_{retries+1}/",
                    asset_keys=['B04', 'B08'],
                    create_product_folders=True
                )
                
                successful_downloads[item_id] = downloads
                print(f"✅ Successfully downloaded {item_id}")
                break
                
            except Exception as e:
                retries += 1
                print(f"❌ Attempt {retries} failed for {item_id}: {e}")
                
                if retries >= max_retries:
                    failed_downloads[item_id] = str(e)
                    print(f"💀 Gave up on {item_id} after {max_retries} attempts")
    
    return successful_downloads, failed_downloads

# Run resilient download
successful, failed = resilient_download(clear_items)
print(f"Resilient download completed: {len(successful)} successful, {len(failed)} failed")

print("\n🎉 All utils function examples completed!")
print(f"Check your './data/' directory for downloaded files")

CLI Usage

Command Line Interface (CLI) Usage

Open Geodata API provides a comprehensive CLI for satellite data discovery, filtering, and downloading. After installation, use the ogapi command to access all functionality.

Show package information

ogapi info

Get help for any command

ogapi --help

ogapi collections --help

ogapi search items --help

Collections Management

List all collections from both providers

ogapi collections list

List from specific provider

ogapi collections list --provider pc

ogapi collections list --provider es

Filter collections by keyword

ogapi collections list --filter sentinel

Save results to file

ogapi collections list --output collections.json

Search Collections

Find collections by keyword

ogapi collections search sentinel

ogapi collections search landsat --provider pc

ogapi collections search modis --provider both

Get Collection Information

Get detailed collection info

ogapi collections info sentinel-2-l2a

ogapi collections info sentinel-2-l2a --provider es

ogapi collections info landsat-c2-l2 --output collection_info.json

Data Search

Search for Sentinel-2 data in Seattle area

ogapi search items --collections sentinel-2-l2a --bbox "-122.5,47.5,-122.0,48.0" --datetime "2024-06-01/2024-08-31" --limit 10

Search with cloud cover filter

ogapi search items --collections sentinel-2-l2a --bbox "-122.5,47.5,-122.0,48.0" --cloud-cover 20 --output search_results.json

Search multiple collections

ogapi search items --collections "sentinel-2-l2a,landsat-c2-l2" --bbox "-120.0,35.0,-119.0,36.0" --datetime "2024-01-01/2024-12-31"

Advanced Search with JSON Query

ogapi search items --collections sentinel-2-l2a --bbox "-122.5,47.5,-122.0,48.0" --query '{"eo:cloud_cover":{"lt":15},"platform":{"eq":"sentinel-2a"}}' --output advanced_search.json

Find recent clear data (last 30 days)

ogapi search quick sentinel-2-l2a "-122.5,47.5,-122.0,48.0"

Customize time range and cloud threshold

ogapi search quick sentinel-2-l2a "-122.5,47.5,-122.0,48.0" --days 7 --cloud-cover 10 --limit 5

Save results for later processing

ogapi search quick landsat-c2-l2 "-120.0,35.0,-119.0,36.0" --output recent_landsat.json

Compare Data Availability Between Providers

Compare Sentinel-2 availability

ogapi search compare --collections sentinel-2-l2a --bbox "-122.5,47.5,-122.0,48.0" --datetime "2024-06-01/2024-08-31"

Compare with cloud filtering

ogapi search compare --collections sentinel-2-l2a --bbox "-122.5,47.5,-122.0,48.0" --cloud-cover 25 --output comparison_results.json

Working with Items

Show info for first item in search results

ogapi items info search_results.json

Show info for specific item by index

ogapi items info search_results.json --item-index 2

Show detailed metadata

ogapi items info search_results.json --show-all --output item_details.json

List all assets for an item

ogapi items assets search_results.json

Filter assets by pattern

ogapi items assets search_results.json --type "image/tiff"

Show URLs for assets

ogapi items assets search_results.json --show-urls

Get URLs for RGB bands

ogapi items urls search_results.json --assets "B04,B03,B02"

Get all asset URLs

ogapi items urls search_results.json --output all_urls.json

Get URLs by pattern

ogapi items urls search_results.json --pattern "B0" --output optical_bands.json

Get unsigned URLs

ogapi items urls search_results.json --unsigned

Compare Items by Quality

Compare items by cloud cover (find clearest)

ogapi items compare search_results.json

Compare by date (find most recent)

ogapi items compare search_results.json --metric date

Compare asset availability

ogapi items compare search_results.json --metric assets --max-items 10

Data Download

Download all assets from search results

ogapi download search-results search_results.json

Download specific bands only

ogapi download search-results search_results.json --assets "B04,B03,B02" --destination "./rgb_data/"

Download with additional filtering

ogapi download search-results search_results.json --cloud-cover 15 --max-items 5 --assets "B08,B04"

Create flat file structure

ogapi download search-results search_results.json --flat-structure --destination "./satellite_data/

Download single file

ogapi download url "https://example.com/sentinel2_B04.tif"

Download to specific location

ogapi download url "https://example.com/B04.tif" --destination "./data/red_band.tif"

Download with provider specification (it will handdle url validation)

ogapi download url "https://pc.example.com/B04.tif" --provider pc

Download from exported URLs

ogapi download urls-json exported_urls.json

Custom destination

ogapi download urls-json urls.json --destination "./downloads/" --flat-structure

Download all seasons from seasonal JSON

ogapi download seasonal seasonal_data.json

Download specific seasons and assets

ogapi download seasonal seasonal_data.json --seasons "spring,summer" --assets "B08,B04" --destination "./time_series/"

Filter by Cloud Cover

Filter search results by cloud cover

ogapi utils filter-clouds search_results.json --max-cloud-cover 20

Filter and save results

ogapi utils filter-clouds search_results.json --max-cloud-cover 15 --output clear_results.json --show-stats

Export URLs

Export all URLs from search results

ogapi utils export-urls search_results.json --output all_urls.json

Export specific assets

ogapi utils export-urls search_results.json --output rgb_urls.json --assets "B04,B03,B02"

Export in simple format

ogapi utils export-urls search_results.json --output simple_urls.json --format simple

Export unsigned URLs

ogapi utils export-urls search_results.json --output unsigned_urls.json --unsigned

Validate URLs

Basic URL validation

ogapi utils validate-urls urls.json

Check accessibility (HTTP requests)

ogapi utils validate-urls urls.json --check-access

Fix expired URLs

ogapi utils validate-urls urls.json --fix-expired --output fixed_urls.json

Skip expiry check for speed

ogapi utils validate-urls urls.json --no-check-expiry

Analyze Search Results

Analyze cloud cover distribution

ogapi utils analyze search_results.json

Temporal analysis

ogapi utils analyze search_results.json --metric temporal

Asset availability analysis

ogapi utils analyze search_results.json --metric assets --output analysis_report.json

Create Download Summaries

Create detailed download summary

ogapi utils download-summary download_results.json

Brief summary

ogapi utils download-summary results.json --format brief

Save summary report

ogapi utils download-summary results.json --output report.json

Complete Workflow Examples

1.1: Search for data (Basic RGB Download Workflow)

ogapi search items --collections sentinel-2-l2a --bbox "-122.5,47.5,-122.0,48.0" --datetime "2024-06-01/2024-08-31" --cloud-cover 20 --output search_results.json

1.2: Filter for very clear imagery

ogapi utils filter-clouds search_results.json --max-cloud-cover 10 --output clear_results.json

1.3: Download RGB bands

ogapi download search-results clear_results.json --assets "B04,B03,B02" --destination "./rgb_analysis/"

1.4: Create summary

ogapi utils download-summary download_results.json

2.1: Search for data (NDVI Analysis Workflow)

ogapi search items --collections sentinel-2-l2a --bbox "-120.0,35.0,-119.0,36.0" --datetime "2024-01-01/2024-12-31" --cloud-cover 25 --output yearly_search.json

2.2: Filter by seasons and export URLs

ogapi utils filter-clouds yearly_search.json --max-cloud-cover 15 --output clear_yearly.json

ogapi utils export-urls clear_yearly.json --assets "B08,B04" --output ndvi_urls.json

2.3: Download NDVI bands

ogapi download urls-json ndvi_urls.json --destination "./ndvi_analysis/"

3.1 Compare data availability (Multi-Provider Comparison)

ogapi search compare --collections sentinel-2-l2a --bbox "-122.5,47.5,-122.0,48.0" --datetime "2024-06-01/2024-08-31" --output comparison.json

3.2 Search both providers separately

ogapi search items --provider pc --collections sentinel-2-l2a --bbox "-122.5,47.5,-122.0,48.0" --cloud-cover 20 --output pc_results.json

ogapi search items --provider es --collections sentinel-2-l2a --bbox "-122.5,47.5,-122.0,48.0" --cloud-cover 20 --output es_results.json

3.3 Download from best provider

ogapi download search-results pc_results.json --max-items 3 --destination "./pc_data/"

4.1 Search and analyze (Quality Assessment Workflow)

ogapi search items --collections sentinel-2-l2a --bbox "-122.5,47.5,-122.0,48.0" --limit 50 --output large_search.json

4.2 Analyze data quality

ogapi utils analyze large_search.json --metric cloud_cover

ogapi utils analyze large_search.json --metric temporal

ogapi utils analyze large_search.json --metric assets

4.3 Compare individual items

ogapi items compare large_search.json --metric cloud_cover

4.4 Download best items only

ogapi utils filter-clouds large_search.json --max-cloud-cover 10 --output best_quality.json

ogapi download search-results best_quality.json --max-items 5

Global Options

All commands support these global options:

Enable verbose output for debugging

ogapi --verbose [command]

Show version

ogapi --version

Get help for any command

ogapi [command] --help

ogapi [command] [subcommand] --help

Tips and Best Practices

1. Start Small: Use --limit and --max-items to test workflows before large downloads
2. Save Results: Always use --output to save search results for reprocessing
3. Filter Early: Use cloud cover filters to reduce data volume
4. Check Status: Use validation commands before large downloads
5. Resume Downloads: Most download commands support resuming interrupted transfers
6. Use Verbose Mode: Add --verbose for detailed debugging information

Environment Variables

Optional: Set default provider

export OGAPI_DEFAULT_PROVIDER=pc

Optional: Set default destination

export OGAPI_DEFAULT_DEST=./satellite_data/

FAQ

General Questions

Q: What makes this package different from using APIs directly?

A: Key advantages:

• Unified interface across multiple APIs
• Automatic URL signing/validation
• Consistent error handling
• No lock-in to specific data reading packages
• Built-in best practices

Q: Can I use this with my existing geospatial workflow?

A: Absolutely! The package provides URLs that work with any raster reading library:

url = item.get_asset_url('red')

# Use with your existing tools
import rioxarray; data = rioxarray.open_rasterio(url)
import rasterio; data = rasterio.open(url)
from osgeo import gdal; data = gdal.Open(url)

Q: Do I need API keys?

A: Only for Planetary Computer. EarthSearch is completely open.

Technical Questions

Q: How does automatic URL signing work?

A: When auto_sign=True, the package:

1. Detects the provider (PC vs ES)
2. For PC: Uses the planetary-computer package to sign URLs
3. For ES: Returns URLs as-is (no signing needed)
4. You can override with signed=False/True

Q: What about rate limiting?

A: Both APIs have rate limits:

• Planetary Computer: Generous limits for signed URLs
• EarthSearch: Standard HTTP rate limits

The package doesn't implement rate limiting - use your own if needed.

Q: Can I cache results?

A: Yes, several approaches:

# 1. Export URLs to JSON
items.export_urls_json('cache.json')

# 2. Save DataFrames
df = items.to_dataframe()
df.to_parquet('metadata_cache.parquet')

# 3. Use your own caching layer

Q: How do I handle different projections?

A: The package provides URLs - projection handling is up to your raster library:

import rioxarray
data = rioxarray.open_rasterio(url)
data_reprojected = data.rio.reproject('EPSG:4326')

Troubleshooting Questions

Q: Why am I getting "Asset not found" errors?

A: Different providers use different asset names:

• PC: B01, B02, B03, B04...
• EarthSearch: coastal, blue, green, red...

Use item.print_assets_info() to see available assets.

Q: Search returns no results but data should exist

A: Common issues:

1. Bbox order: Use [west, south, east, north]
2. Date format: PC accepts "YYYY-MM-DD", ES prefers RFC3339
3. Collection names: Use client.list_collections() to verify
4. Cloud cover: Try relaxing the threshold

Q: URLs work but data loading is slow

A: Optimization strategies:

1. Use overview levels: rioxarray.open_rasterio(url, overview_level=2)
2. Enable chunking: rioxarray.open_rasterio(url, chunks=True)
3. Read smaller windows with rasterio
4. Consider geographic proximity to data

Integration Questions

Q: Can I use this with Jupyter notebooks?

A: Yes! The package works great in Jupyter:

# Display asset info
item.print_assets_info()

# Show DataFrames
df = items.to_dataframe()
display(df)

# Plot with matplotlib/cartopy
import matplotlib.pyplot as plt
data = rioxarray.open_rasterio(url)
data.plot()

Q: How do I integrate with QGIS/ArcGIS?

A: Export URLs and use them directly:

# Get URLs
urls = item.get_all_asset_urls()

# In QGIS: Add Raster Layer -> use the URL directly
# In ArcGIS: Add Data -> Raster Dataset -> paste URL

Q: Can I use this in production systems?

A: Yes! The package is designed for production use:

• Robust error handling
• No forced dependencies
• Clean separation of concerns
• Comprehensive logging support

Q: How do I contribute or report issues?

A: Visit the GitHub repository:

• Report issues: GitHub Issues
• Contribute: Pull Requests welcome
• Documentation: Help improve this guide

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This completes the comprehensive user guide for Open Geodata API. The package provides a clean, flexible foundation for accessing open geospatial data while letting you maintain full control over data processing and analysis workflows.

--- End ---