clouds-everywhere 0.1.0

Check satellite imagery availability over any AOI by cloud cover — and find the data gaps

Clouds-Everywhere

Find out when cloud-free satellite imagery is available over your study area — and where the data gaps are.

You give it an area, a date range, and a cloud limit. It answers in plain language: which days, weeks, or months have usable imagery, and which tiles are missing.

Install

pip install clouds-everywhere

Or from source:

git clone https://github.com/mohammadanwarx/Clouds-Everywhere.git
cd Clouds-Everywhere
pip install -e .

Quick start

from clouds_everywhere import query

report = query(
    aoi        = "my_area.geojson",   # bbox, polygon, GeoJSON, or shapefile
    start_date = "2024-01-01",
    end_date   = "2024-02-29",
    max_cloud  = 20,                  # max cloud cover in %
    group_by   = "week",              # "day" | "week" | "month"
    satellites = ["sentinel2", "landsat"],
)

print(report)

  Sentinel-2  -  needs 9 tiles to fully cover your area
    [OK]  8-14 Jan 2024      All tiles have usable imagery  (15 images across 9 tiles)
    [GAP] 15-21 Jan 2024     Data gap - some tiles missing  (3/9 tiles; missing: 30SVJ, ...)
    [--]  5-11 Feb 2024      No usable imagery

    Summary: 3 fully-covered weeks, 5 with gaps, 1 empty  (of 9 weeks)

Each period gets one of three statuses:

Status	Meaning
available	every tile has at least one usable image
gap	some tiles have imagery, but at least one is missing
missing	no usable imagery at all

Usable means cloud cover is at or below your threshold.

Your area, any format

The AOI can be any of these — the CRS is handled for you (everything is reprojected to WGS84 automatically):

• a bbox: [minX, minY, maxX, maxY]
• polygon coordinates: [[lon, lat], ...]
• a GeoJSON dict (Feature, FeatureCollection, or geometry)
• a file: .geojson, .json, .shp, or .zip (zipped shapefile)

Tables and plots

report.to_dataframe()        # one row per period per satellite
report.tile_dataframe()      # one row per tile per period
report.available_periods()   # fully covered periods
report.gap_periods()         # periods with holes

from clouds_everywhere.viz import plot_availability_calendar
plot_availability_calendar(report)   # green / amber / red calendar

Lower-level functions

• search_images(...) — flat list of matching scenes
• check_coverage(...) — tile coverage per date
• viz.plot_coverage_heatmap, plot_cloud_timeline, plot_satellite_comparison

Demos

• demo.ipynb — search and coverage basics
• demo2.ipynb — the query() workflow and all plots

Tests

pytest

Fast and offline — all API calls are mocked.

Data sources

Sentinel-2 and Landsat from the Element84 Earth Search STAC API. MODIS from NASA CMR STAC.