Multispectral high resolution sensor fusion for smoothing and gap-filling in the cloud

Álvaro Moreno-Martínez, Emma Izquierdo-Verdiguier, Marco P. Maneta, Gustau Camps-Valls, Nathaniel Robinson, Jordi Muñoz-Marí, Fernando Sedano, Nicholas Clinton, Steven W. Running

Research output: Contribution to journalArticlepeer-review

72 Scopus citations


Remote sensing optical sensors onboard operational satellites cannot have high spectral, spatial and temporal resolutions simultaneously. In addition, clouds and aerosols can adversely affect the signal contaminating the land surface observations. We present a HIghly Scalable Temporal Adaptive Reflectance Fusion Model (HISTARFM) algorithm to combine multispectral images of different sensors to reduce noise and produce monthly gap free high resolution (30 m) observations over land. Our approach uses images from the Landsat (30 m spatial resolution and 16 day revisit cycle) and the MODIS missions, both from Terra and Aqua platforms (500 m spatial resolution and daily revisit cycle). We implement a bias-aware Kalman filter method in the Google Earth Engine (GEE) platform to obtain fused images at the Landsat spatial-resolution. The added bias correction in the Kalman filter estimates accounts for the fact that both model and observation errors are temporally auto-correlated and may have a non-zero mean. This approach also enables reliable estimation of the uncertainty associated with the final reflectance estimates, allowing for error propagation analyses in higher level remote sensing products. Quantitative and qualitative evaluations of the generated products through comparison with other state-of-the-art methods confirm the validity of the approach, and open the door to operational applications at enhanced spatio-temporal resolutions at broad continental scales.

Original languageEnglish
Article number111901
JournalRemote Sensing of Environment
StatePublished - Sep 15 2020


  • Data fusion
  • Gap filling
  • Kalman filter
  • Landsat
  • Smoothing


Dive into the research topics of 'Multispectral high resolution sensor fusion for smoothing and gap-filling in the cloud'. Together they form a unique fingerprint.

Cite this