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

REMOTE SENSING OF ENVIRONMENT

卷:247

Article

Moreno-Martinez, Alvaro^1,2  Izquierdo-Verdiguier, Emma³  Maneta, Marco P.^4,5  Camps-Valls, Gustau¹  Robinson, Nathaniel⁶  Munoz-Mari, Jordi¹  Sedano, Fernando⁷  Clinton, Nicholas⁸  Running, Steven W.² 

[1] Univ Valencia, Image Proc Lab IPL, Valencia, Spain

[2] Univ Montana, Numer Terradynam Simulat Grp NTSG, WA Franke Coll Forestry & Conservat, Missoula, MT 59812 USA

[3] Univ Nat Resources & Life Sci, Inst Geomat, Vienna, Austria

[4] Univ Montana, Dept Geosci, Missoula, MT 59812 USA

[5] Univ Montana, WA Franke Coll Forestry & Conservat, Dept Ecosyst & Conservat Sci, Missoula, MT 59812 USA

[6] Panthera, New York, NY USA

[7] Univ Maryland, Dept Geophys Sci, College Pk, MD 20742 USA

[8] Google Inc, Mountain View, CA USA

关键词: Landsat;    MODIS;    Gap filling;    Smoothing;    Kalman filter;    Data fusion;   

DOI  :  10.1016/j.rse.2020.111901

来源: Elsevier

【 摘 要 】

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 (HIST-ARFM) 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 (30m spatial resolution and 16 day revisit cycle) and the MODIS missions, both from Terra and Aqua platforms (500m 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.

【 授权许可】

Free