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Summary

This study introduces the Highly Scalable Temporal Adaptive Reflectance Fusion Model (HISTARFM) to create gap-free, high-resolution land surface observations. The HISTARFM algorithm fuses satellite data, improving spatial and temporal resolution for enhanced remote sensing applications.

Keywords:
Data fusionGap fillingKalman filterLandsatMODISSmoothing

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Area of Science:

  • Remote Sensing
  • Earth Observation
  • Geospatial Analysis

Background:

  • Operational satellite optical sensors face trade-offs between spectral, spatial, and temporal resolutions.
  • Clouds and aerosols contaminate land surface observations, impacting data quality.

Purpose of the Study:

  • To develop a Highly Scalable Temporal Adaptive Reflectance Fusion Model (HISTARFM) algorithm.
  • To produce monthly, gap-free, high-resolution (30m) land surface observations by fusing multisensor satellite imagery.
  • To reduce noise and improve land surface reflectance estimates.

Main Methods:

  • Fusion of Landsat (30m) and MODIS (500m) multispectral images.
  • Implementation of a bias-aware Kalman filter method within the Google Earth Engine (GEE) platform.
  • Estimation of uncertainty in reflectance, enabling error propagation analysis.

Main Results:

  • Generated fused images at Landsat spatial resolution with enhanced spatio-temporal coverage.
  • Bias correction in Kalman filter estimates accounted for temporally auto-correlated errors.
  • Reliable uncertainty estimation for final reflectance products.

Conclusions:

  • The HISTARFM algorithm effectively fuses multisensor data for improved land surface monitoring.
  • The approach enables operational applications requiring enhanced spatio-temporal resolutions at continental scales.
  • Validated through quantitative and qualitative evaluations against state-of-the-art methods.