期刊
REMOTE SENSING OF ENVIRONMENT
卷 247, 期 -, 页码 -出版社
ELSEVIER SCIENCE INC
DOI: 10.1016/j.rse.2020.111901
关键词
Landsat; MODIS; Gap filling; Smoothing; Kalman filter; Data fusion
资金
- NASA Earth Observing System MODIS project [NNX08AG87A]
- USDA AFRI [2016-67026-25067]
- NASA EPSCoR, United States [80NSSC18M0025M]
- European Research Council (ERC) under the ERC Consolidator Grant 2014 SEDAL (Statistical Learning for Earth Observation Data Analysis, European Union) project [647423]
- Google Earth Engine developers
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.
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