---
title: AMERI-FAR25 Dataset for Carbon Flux Mapping
url: https://www.emergentmind.com/topics/ameri-far25-dataset
type: topic
---

# AMERI-FAR25 Dataset for Carbon Flux Mapping

The AMERI-FAR25 dataset is a publicly available resource constructed for high-resolution carbon flux prediction across diverse North American ecosystems. It pairs eddy-covariance tower flux measurements with co-located Landsat 8 and 9 imagery, forming the basis of the Footprint-Aware Regression (FAR) deep-learning framework for pixel-level ecosystem carbon flux estimation at 30 m spatial resolution [2512.01917].

## 1. Dataset Composition and Scope

AMERI-FAR25 comprises data from 209 distinct AmeriFlux sites contributing a total of 439 site-years between 2013 and 2023. The geographical range includes the United States, Canada, Mexico, and Peru. The dataset contains 7,697,145 half-hourly records of net ecosystem carbon flux (FC), spanning a variety of ecosystem types as defined by IGBP codes:
- DBF (deciduous broadleaf forest)
- ENF (evergreen needleleaf forest)
- EBF (evergreen broadleaf forest)
- WET (wetlands)
- GRA (grasslands)
- CSH/OSH (shrublands)
- CRO (croplands)
- SAV (savannas)
- MF (mixed forests)
- CVM/BSV and additional minor classes

Each sample is spatially represented as a 128×128 pixel Landsat scene (≈3.84 km × 3.84 km), centered on the tower location.

| Dimension    | Value         | Ecosystem Coverage                 |
|--------------|--------------|------------------------------------|
| Sites        | 209          | DBF, ENF, WET, GRA, CRO, etc.      |
| Site-years   | 439          | 2013–2023 (varies by site)         |
| Flux records | 7,697,145    | Half-hour, spatially co-located    |

## 2. Data Types and Variables

The dataset integrates flux, meteorological, and remote-sensing inputs:

**Tower-Measured Variables**:  
- Net ecosystem exchange (FC) at half-hour intervals (Mg C ha⁻¹ half-hour⁻¹)

**Meteorological/Environmental Drivers**:  
- For footprint modeling (`X_footprint`): wind direction (WD), wind speed (WS), friction velocity (USTAR), air temperature (TA), sensible heat flux (H), tower height.
- For flux prediction (`X_drivers`): shortwave incoming radiation (SW_IN), air temperature (TA), relative humidity (RH)
- PRISM normals (800 m grid): daily TA, RH; monthly solar transmission; SW_IN estimated via pysolar

**Satellite Inputs**:
- Landsat 8 & 9 bands resampled to 30 m: coastal aerosol, blue, green, red, NIR, SWIR1, SWIR2, cirrus, TIRS1, TIRS2—excluding the panchromatic band.
- Ancillary: sun/sensor azimuth and zenith
- Cloud-filtered scenes: 45,124 valid patches

## 3. Spatial and Temporal Resolution

AMERI-FAR25 delivers high spatial precision:
- Pixel size: 30 m × 30 m (thermal bands resampled from native 100 m)
- Patch size: 128 × 128 pixels (~4 km side length)
- Tower sampling: every 30 minutes
- Landsat revisit: nominally every 16 days; "most recent available" scene per flux record
- Aggregation for model evaluation: monthly and annual sums of flux

Footprint modeling leverages soft attention-based masks $F_P \in \mathbb{R}^{128 \times 128}$, normalized such that $\sum_{l=1}^{128} \sum_{w=1}^{128} F_{P,lw}(t)=1$. Pixel-level predictions for $FC_{pixel}(l,w,t)$ are aggregated using this mask:

$$
\hat y_t = \sum_{l=1}^{128} \sum_{w=1}^{128} \bigl[FC_{pixel}(l,w,t)\times F_{P,lw}(t)\bigr]
$$

## 4. Preprocessing and Quality Control

**Tower Data Cleaning**:
- Inclusion: AmeriFlux BASE, CC-BY-4.0 license, required meteorological and flux variables
- SW_IN derived from PPFD_IN via linear scaling where necessary
- Metadata harmonized (highest sensor, tower height required)
- Outlier removal: FC outside 0.5–99.5% percentiles; negative SW_IN; night drawdown exclusion (FC<0 with SW_IN=0)

**Satellite Imagery Processing**:
- Download via landsatxplore with custom reliability fixes
- QA/QC using PIXEL_QA flags (clear land/water: codes 21824, 21888, 21952)
- Missing/cloudy pixels filled via temporal back-fill from latest valid observation

**Co-Registration**:
- Patches centered on tower coordinates; resampled bands; orientation angles concatenated as four supplementary channels

## 5. Data Splitting and Modeling Protocols

**Spatial Splitting**:
- Sites grouped by IGBP ecosystem class
- For classes with ≥10 sites: 40% withheld (20% validation, 20% test)
- Classes with <10 sites: used exclusively for training

**Temporal Splitting**:
- val_future/test_future: final year from multi-year sites, targeted at temporal-drift studies
- val: 20% random holdout of remaining half-hour records

| Split                  | Tower Records    | Landsat Patches    |
|------------------------|------------------|--------------------|
| Training (train)       | ~5.6 million     | ~35,000            |
| Validation/Test (site) | ~2.1 million     | ~10,000            |
| val/test (future year) | ~0.5 million     | ~5,000             |

A plausible implication is that these splits facilitate ecosystem- and temporally robust generalization studies for upscaling models.

## 6. Metadata and Annotations

AMERI-FAR25 includes extensive metadata relevant for both scientific reproducibility and ecological interpretation:
- Land cover and ecosystem codes (IGBP type per site)
- Tower metadata: height, coordinates, operation years, documented disturbance events (e.g., clear-cut, fire scar)
- Sun/sensor azimuth and zenith per scene (enabling bidirectional reflectance modeling)

Data are made available on Zenodo (DOI pending) with FAR code, model weights, and full site list published at github.com/jsearcy1/FAR and a corresponding archive.

## 7. Context, Intended Use, and Implications

AMERI-FAR25 underpins the Footprint-Aware Regression (FAR) framework, which achieves an $R^2 = 0.78$ for monthly net ecosystem exchange prediction on holdout test sites. The dataset allows pixel-level upscaling of ground-validated fluxes, addressing the mismatch between tower and satellite spatial scales. Researchers may apply AMERI-FAR25 directly for landscape-scale, high-resolution carbon flux mapping in heterogeneous environments, facilitating cross-ecosystem analyses and method development for natural climate solutions [2512.01917].

Source: https://www.emergentmind.com/topics/ameri-far25-dataset