Seamless high-resolution soil moisture from the synergistic merging of the FengYun-3 satellite observations series
Authors/Creators
- 1. Hydro-Climate Extremes Lab (H-CEL), Ghent University, Ghent, Belgium
- 2. Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters, Nanjing University of Information Science and Technology, Nanjing, China.
- 3. Department of Civil Engineering, Kyung Hee University, 1732 Deogyeong-daero, Giheung-gu, Yongin-si 17104, Republic of Korea
- 4. Planet Labs, Haarlem, the Netherlands
- 5. School of Civil and Environmental Engineering, University of New South Wales, Australia
- 6. GeoHydrodynamics and Environment Research (GHER), University of Liège, Liège, Belgium
- 7. Faculty of Defense and Security, Rabdan Academy, Abu Dhabi, United Arab Emirates
- 8. School of Atmospheric Science & Remote Sensing, Wuxi University, Wuxi 214105, People's Republic of China
- 9. Department of Geology & Geophysics, Bacha Khan University Charsadda, Pakistan
Description
These datasets are results from merging three FengYun passive microwave soil moisture observations at a 15kmx15km spatial resolution from 2011 to 2020 with continuous extension as data becomes available. Here, we rely on a merging technique that minimizes mean square error (MSE) using the signal-to-noise ratio (SNRopt) of the input parent products to first merge subdaily soil moisture products into dail averages. From these, these are gap-filled using a Data INterpolating Convolutional Auto-Encoder, DINCAE (FY3_Reoconstructed_*). The advantage of this method is that it comes with error variances(FY3_ErVar_*) for each pixel and time step which are useful for sevral applications.
Notes
Manuscript for this data is under peer-review. Data could change based on feedback from reviewers.
Files
FY3_ErVar_2011.zip
Files
(14.2 GB)
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