Products derived from a single multispectral sensor are hampered by a limited spatial, spectral or temporal resolutions. Image fusion in general and downscaling/blending in particular allow to combine different multiresolution datasets. We present here an optimal interpolation approach to generate smoothed and gap-free time series of Landsat reflectance data. We fuse MODIS (moderate-resolution imaging spectroradiometer) and Landsat data globally using the Google Earth Engine (GEE) platform. The optimal interpolator exploits GEE ability to ingest large amounts of data (Landsat climatologies) and uses simple linear operations that scale easily in the cloud. The approach shows very good results in practice, as tested over five sites with different vegetation types and climatic characteristics in the contiguous US.
|Title of host publication
|2018 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2018 - Proceedings
|Institute of Electrical and Electronics Engineers Inc.
|Number of pages
|Published - Oct 31 2018
|38th Annual IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2018 - Valencia, Spain
Duration: Jul 22 2018 → Jul 27 2018
|International Geoscience and Remote Sensing Symposium (IGARSS)
|38th Annual IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2018
|07/22/18 → 07/27/18
- Data fusion
- Kalman filter
- Optimal interpolator