Statistics and Probability; Information Systems; Education; Computer Science Applications; Statistics, Probability and Uncertainty; Library and Information Sciences
Abstract :
[en] In the last years, more satellites with microwave imagers have been launched, making more observations available to obtain soil moisture estimates globally. China's endeavour has resulted in the launch of the FengYun (FY) passive microwave observations (FY-3B, C, D) capable of filling in global soil moisture data gaps. In this study, we develop a merged soil moisture dataset at a spatial resolution of 0.15° from the FY series which spans 2011 to present time (2020 in this study) by a merging technique that minimizes mean square error (MSE) using the signal-to-noise ratio of the input parent products. Here, we combine the ascending and descending observations from the three satellite observations to obtain sub-daily estimates. Finally, we averaged the merged sub-daily FY soil moisture into daily estimates and gap-fill it using a deep learning interpolation approach to reconstruct the missing days while preserving the characteristics of the Merged FY data. The results of this study aim to provide datasets that meet challenges in using global satellite soil moisture observations.
Disciplines :
Earth sciences & physical geography
Author, co-author :
Hagan, Daniel F T ; Hydro-Climate Extremes Lab (H-CEL), Ghent University, Department of Environment, Ghent, 9000Gent, Belgium ; Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters, Nanjing, 210044, China
Kim, Seokhyeon ; Kyung Hee University, 1732 Deogyeong-daero, Department of Civil Engineering, Giheung-gu, Yongin-si, 17104, Republic of Korea
Wang, Guojie; Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters, Nanjing, 210044, China. gwang@nuist.edu.cn
Ma, Xiaowen; Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters, Nanjing, 210044, China
Hu, Yifan; Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters, Nanjing, 210044, China
Liu, Yi Y; School of Civil and Environmental Engineering, University of New South Wales, Sydney, NSW 2052, Australia
Barth, Alexander ; Université de Liège - ULiège > Département d'astrophysique, géophysique et océanographie (AGO) > GeoHydrodynamics and Environment Research (GHER)
Liu, Haonan; Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters, Nanjing, 210044, China
Ullah, Waheed; Faculty of Defense and Security, Rabdan Academy, Abu Dhabi, United Arab Emirates
Nooni, Isaac K; School of Atmospheric Science & Remote Sensing, Wuxi University, Wuxi, 214105, China
Bhatti, Samuel A; Bacha Khan University Charsadda, Department of Geology & Geophysics, Peshawar, Pakistan
Language :
English
Title :
Seamless finer-resolution soil moisture from the synergistic merging of the FengYun-3 satellite series.
This study is supported by the National Natural Science Foundation of China (U23A2006, 42275028), the Postgraduate Research & Practice Innovation Program of Jiangsu Province (KYCX21_0935) and the European Research Council Consolidator Grant, HEAT(101088405). The authors also appreciate Dr. Robert Parinussa and Dr. Robin van der Schalie for all their work and contribution to the development of this dataset.
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