aquamatch 0.1.3

Python package and script to validate, model water quality parameters with remote sensing data

Aquamatch

Bridging the gap between satellite observations and field data for water quality monitoring.

Aquamatch is a Python package for discovering and downloading Sentinel-2 imagery based on field sampling events, applies atmospheric correction and extracts water quality parameters via ACOLITE, and exports to multiple formats, including data cube and Cloud Optimized GeoTIFF. Designed for reproducible, automated environmental monitoring workflows — from a single Python call, CLI command, or YAML config.

Overview

Color coding: teal for the five pipeline steps, gray/neutral for data artifacts (CSVs, SAFE folders, outputs), amber for the YAML orchestration layer, and purple for the SCL/datacube components.
Dashed arrows: used for two relationships that are optional or indirect: the SCL polygon clip path (only when use_scl=True), and the Step 5 orchestration edges back to Steps 1–4 (since the YAML config drives the others rather than receiving data from them).

---

Installation

Requirements: Python ≥ 3.12

Clone the repository and install dependencies with Poetry:

git clone https://github.com/FelipeSBarros/aquamatch.git
cd aquamatch
poetry install

Or with pip (using the lock file for reproducibility):

pip install .

pyyaml is required for the pipeline config system. It is included in the project dependencies.

---

Environment Setup

Create a .env file in the project root with your API credentials before running any step:

• SH for SentinelHub (see documentation)
• DATASPACE for Copernicus Dataspace (see documentation)

SH_CLIENT_ID=your_sentinelhub_client_id    # SentinelHub
SH_CLIENT_SECRET=your_sentinelhub_client_secret    # SentinelHub
DATASPACE_ACCESS_KEY=your_copernicus_dataspace_access_key
DATASPACE_SECRET_KEY=your_copernicus_dataspace_secret_key

---

Step-by-step Workflow

Step 1 — Prepare in situ data

This package was developed to work with in-situ data from the OAN. You are therefore expected to save your campaign and station datasets in the './data/original_data' folder. This step involves reading field campaign data, cleaning measurement values and, if specified, assigning each station its Sentinel-2 tile. Two outputs are produced:

• campaigns_organized.csv — full cleaned dataset for analysis
• campaigns_unique_data.csv — one row per unique (date, tile) pair, used to drive the satellite search

from aquamatch import run_insitu_pipeline

run_insitu_pipeline(
    stations="data/original_data/my_stations.xlsx",
    campaigns="data/original_data/my_export.xlsx",
)

Pass skip_clean=True if the OAN export was already cleaned before download. See OAN's documention

CLI equivalent:

python aquamatch/insitu_data.py --mode campaigns \
  --stations your_path/my_stations.xlsx \
  --campaigns your_path/my_export.xlsx

---

Step 2 — Build the satellite catalog

Searches for Sentinel-2 L1C scenes that match each field date and location from campaigns_unique_data.csv. Only scenes whose MGRS tile matches the station's assigned tile are kept. For each L1C scene, the corresponding L2A scene is looked up to retrieve the SCL (Scene Classification) asset URL.

from aquamatch import run_sentinel_pipeline

run_sentinel_pipeline(
    csv="data/monitoring_data/campaigns_unique_data.csv",
    time_delta=2,
    cloud_cover=20,
    mode="catalog"
)

The result is a sentinel_catalog.json file listing matched scenes per field date. Scenes are grouped into three temporal buckets relative to the field date — same_day, previous (acquired before), and posterior (acquired after) — each sorted by delta_days then cloud_cover ascending so the best candidate is always bucket[0]:

[
  {
    "field_date": "2025-11-05",
    "images_found": {
      "same_day": [],
      "previous": [
        {
          "id": "S2B_MSIL1C_20251103T134659_N0511_R024_T21HVD_20251103T170338.SAFE",
          "datetime": "2025-11-03T14:01:38.729Z",
          "cloud_cover": 0.0,
          "href": "s3://eodata/Sentinel-2/MSI/L1C/2025/11/03/S2B_MSIL1C_20251103T134659_N0511_R024_T21HVD_20251103T170338.SAFE/",
          "delta_days": 2,
          "l2a_scl": "https://sentinel-cogs.s3.us-west-2.amazonaws.com/sentinel-s2-l2a-cogs/21/H/VD/2025/11/S2B_21HVD_20251103_0_L2A/SCL.tif"
        }
      ],
      "posterior": []
    }
  }
]

CLI equivalent:

python aquamatch/sentinel_data.py --mode catalog \
  --csv data/monitoring_data/campaigns_unique_data.csv \
  --time-delta 2 \
  --cloud-cover 20

---

Step 3 — Download imagery

Download the SAFE products and SCL assets listed in the catalogue. Any scenes that have already been downloaded are skipped automatically.

The strategy parameter controls which scenes are downloaded per field date:

Strategy	Behaviour
best (default)	Prefer same_day, fall back to previous, then posterior. Picks up to max_per_date scenes.
same_day	Only download scenes acquired on the exact field date.
previous	Prefer same_day, fall back to previous only. Never uses posterior.
posterior	Prefer same_day, fall back to posterior only. Never uses previous.
all	Download every scene found across all buckets.

from aquamatch import run_sentinel_pipeline

# Download best matching scene per date (default)
run_sentinel_pipeline(
    strategy="best",
    max_per_date=1,
    download_scl=True,
    mode="download",
)

# Download only same-day scenes, up to 2 per date, cloud cover ≤ 15 %
run_sentinel_pipeline(
    strategy="same_day",
    max_per_date=2,
    max_cloud_cover=15,
    download_scl=True,
    mode="download",
)

# Download everything found
run_sentinel_pipeline(strategy="all", mode="download")

Steps 2 and 3 can also be run together by passing mode="all".

CLI equivalent:

python aquamatch/sentinel_data.py --mode download \
  --strategy best \
  --max-per-date 1 \
  --download-scl

---

Step 4 — Atmospheric correction

Runs ACOLITE on the downloaded SAFE folders to produce surface reflectance and water quality products (turbidity, SPM, chlorophyll-a, and others) as NetCDF files.

from aquamatch import run_acolite_pipeline

run_acolite_pipeline(
    acolite_executable="/path/to/acolite",
    safe_dir="data/sentinel_downloads/S2A_MSIL1C_20170713T135111_N0500_R024_T21HUD.SAFE",
    output="data/acolite_output",
    limit = [-33.25, -58.45, -33.17, -58.33])

A GeoJson's path can also be passed:

run_acolite_pipeline(
    acolite_executable="/path/to/acolite",
    polygon = "/path/polygon.json")

For SCL-based water masking, use with_scl_polygon() to restrict processing to water pixels only:

run_acolite_pipeline(
    acolite_executable="/path/to/acolite",
    use_scl=True,
    scl_dir="data/sentinel_downloads/scl",
    scl_kwargs={"min_area_m2": 5000},
)

For batch processing across multiple scenes with per-tile spatial restrictions:

from aquamatch import run_acolite_pipeline
from aquamatch.pipeline_config import TilesSection

# Per-tile restrictions (different polygon or limit per MGRS tile)
tiles = TilesSection.from_dict({
    "21HUD": {"polygon": "data/polygons/21HUD.geojson"},
    "21HVD": {"limit": [-34.2, -56.8, -33.0, -55.1]},
})

result = run_acolite_pipeline(
    acolite_executable="/path/to/acolite",
    safe_dir="data/sentinel_downloads",
    output="data/acolite_output",
    tile_config=tiles,
)

If tile_config is omitted, a 0.1° bounding box is derived automatically from row["latitud"]/row["longitud"].

CLI equivalent:

python -m aquamatch.acolite_spec \
    --executable /path/to/acolite \
    --safe-dir data/sentinel_downloads/S2A_MSIL1C_20170713T135111_N0500_R024_T21HUD.SAFE \
    --output data/acolite_output

To run acolite with limit or polygon parameters, used YAML config. see "Run the full pipeline from a YAML config"

---

Run the full pipeline from a YAML config

For automated or version-controlled campaigns, the entire workflow can be driven from a single YAML file rather than calling each step individually. This is the recommended approach for production runs and shared projects.

Generate a template with all parameters at their defaults:

python -m aquamatch.pipeline_config --generate campaign_2025.yaml

Running from YAML

The generated file includes every parameter at its default value, with inline comments documenting units and valid options. Edit it for your campaign, then run:

python -m aquamatch.pipeline_config --run campaign_2025.yaml

Individual steps can be disabled by setting enabled: false:

insitu:
  enabled: false   # skip — already prepared

sentinel:
  enabled: true
  time_delta: 2
  cloud_cover: 20

download:
  enabled: true
  # Download selection strategy. One of: best | same_day | previous | posterior | all
  # best: prefer same_day, fall back to previous then posterior (default)
  strategy: best
  # Maximum scenes to download per field date (ignored for strategy: all)
  max_per_date: 1
  # Optional secondary cloud cover ceiling applied at download time
  max_cloud_cover: null
  download_scl: true

acolite:
  enabled: true
  acolite_executable: /path/to/acolite/acolite.py
  scl:
    use_scl: true
    min_area_m2: 5000

tiles:
  21HUD:
    polygon: data/polygons/21HUD.geojson
  21HVD:
    limit: [-34.2, -56.8, -33.0, -55.1]

Dry-run

Validate config and log steps without executing:

python -m aquamatch.pipeline_config --run campaign_2025.yaml --dry-run

Force process

Force reprocess — ignore existing outputs and reprocess all scenes:

python -m aquamatch.pipeline_config --run campaign_2025.yaml --force

Per-tile spatial restrictions

The tiles: section assigns a spatial restriction to each Sentinel-2 MGRS tile. The same boundary is then applied consistently across every scene processed for that tile. Restrictions are resolved in this precedence order:

1. Static polygon from tiles: — highest priority. If a tile has a polygon configured, it is applied directly to ACOLITE and SCL-based clipping (use_scl) is suppressed for that tile, since the static polygon already defines the water boundary precisely.
2. SCL-derived polygon (use_scl: true) — used when the tile has no static polygon. A water mask is extracted from the SCL asset and applied as the processing boundary.
3. Static limit from tiles: — applied when no polygon is available from either source above.
4. No restriction — full scene is processed when the tile is not listed in tiles: and SCL clipping is disabled or unavailable.

tiles:
  21HUD:
    polygon: data/polygons/21HUD.geojson   # hand-drawn or pre-processed boundary
  21HVD:
    limit: [-34.2, -56.8, -33.0, -55.1]   # bounding box [S, W, N, E]
  21HWD:
    # no entry — full scene processed

The tile ID is extracted automatically from the SAFE folder filename (e.g. T21HUD in S2A_MSIL1C_20250801T101031_N0500_R024_T21HUD_...SAFE), so no manual mapping between files and tiles is needed.

The same precedence logic applies when using run_batch() programmatically — see Step 4 above.

---

Utilities

Temporal opportunity cost analysis

Before committing to a temporal tolerance for your campaign, it is useful to understand how many field dates will have a valid Sentinel-2 acquisition available and what cloud cover to expect at each tolerance level. analyze_temporal_opportunity quantifies this directly from the catalog produced by Step 2.

For every tolerance d from 0 to max_delta_days the function:

1. Determines whether at least one image exists with delta_days <= d for each field date.
2. Computes availability (% of field dates with a valid image) and opportunity cost (1 - availability).
3. Selects the best image per date (minimum delta_days, then minimum cloud_cover) and reports mean and median cloud cover of the selected set.

A publication-quality three-panel PNG figure is saved to output_figure:

Panel	Content
Left	Availability (%) vs temporal tolerance (days)
Centre	Opportunity cost (%) vs temporal tolerance (days)
Right	Mean and median cloud cover (%) vs temporal tolerance (days)

from aquamatch.utils import analyze_temporal_opportunity

df = analyze_temporal_opportunity(
    catalog_json="data/sentinel_downloads/sentinel_catalog.json",
    output_figure="reports/temporal_opportunity.png",
    max_delta_days=7,
)

print(df[["delta_days", "availability", "opportunity_cost", "mean_cloud_cover"]])

   delta_days  availability  opportunity_cost  mean_cloud_cover
0           0          42.9              57.1               3.2
1           1          71.4              28.6               4.8
2           2          85.7              14.3               5.1
3           3          92.9               7.1               6.3
4           4          92.9               7.1               6.3
5           5         100.0               0.0               7.0
6           6         100.0               0.0               7.0
7           7         100.0               0.0               7.0

The returned DataFrame contains one row per tolerance value with columns delta_days, n_dates, n_available, availability, opportunity_cost, mean_cloud_cover, and median_cloud_cover. Pass return_dataframe=False if only the figure is needed.

What to consider when choosing a tolerance The table frames a trade-off between temporal fidelity and spatial coverage:

A strict same-day requirement (d=0) gives the most scientifically defensible matchups but leaves 57% of your field dates unmatched — a significant loss of usable data. d=1 or d=2 is usually the pragmatic sweet spot for water quality remote sensing, where surface conditions can change meaningfully over days but same-day coverage is rare. If your target variable changes slowly (e.g. long-term turbidity trends in a large reservoir), d=3 or d=5 may be acceptable and recovers nearly all dates. If your variable is highly dynamic (e.g. algal bloom events, flood pulses), even d=1 may introduce unacceptable temporal mismatch and you should be cautious about any row beyond d=0.

The cloud cover column is a sanity check — if mean cloud cover were climbing sharply with tolerance (say, above 20–30%), it would signal that the additional scenes being recovered are systematically cloudy and may not produce usable retrievals even after atmospheric correction.