DOI: 10.3390/rs12030455
论文题名: A soil moisture spatial and temporal resolution improving algorithm based on multi-source remote sensing data and GRNN model
作者: Cui Y. ; Chen X. ; Xiong W. ; He L. ; Lv F. ; Fan W. ; Luo Z. ; Hong Y.
刊名: Remote Sensing
ISSN: 20724292
出版年: 2020
卷: 12, 期: 3 语种: 英语
英文关键词: Downscaling
; FY-3B
; Machine learning
; Remote sensing
; Soil moisture
; Tibetan plateau
Scopus关键词: Climate change
; Land surface temperature
; Learning systems
; Machine learning
; Neural networks
; Soil moisture
; Surface measurement
; Surveying
; Digital elevation model
; Down-scaling
; General regression neural network
; Machine learning models
; Normalized difference vegetation index
; Spatial and temporal resolutions
; Spatio-temporal resolution
; Tibetan Plateau
; Remote sensing
英文摘要: Surface soil moisture (SM) plays an essential role in the water and energy balance between the land surface and the atmosphere. Low spatio-temporal resolution, about 25-40 km and 2-3 days, of the commonly used global microwave SM products limits their application at regional scales. In this study, we developed an algorithm to improve the SM spatio-temporal resolution using multi-source remote sensing data and a machine-learning model named the General Regression Neural Network (GRNN). First, six high spatial resolution input variables, including Land Surface Temperature (LST), Normalized Difference Vegetation Index (NDVI), albedo, Digital Elevation Model (DEM), Longitude (Lon) and Latitude (Lat), were selected and gap-filled to obtain high spatio-temporal resolution inputs. Then, the GRNN was trained at a low spatio-temporal resolution to obtain the relationship between SM and input variables. Finally, the trained GRNN was driven by the high spatio-temporal resolution input variables to obtain high spatio-temporal resolution SM. We used the Fengyun-3B (FY-3B) SM over the Tibetan Plateau (TP) to test the algorithm. The results show that the algorithm could successfully improve the spatio-temporal resolution of FY-3B SM from 0.25° and 2-3 days to 0.05° and 1-day over the TP. The improved SM is consistent with the original product in terms of both spatial distribution and temporal variation. The high spatio-temporal resolution SM allows a better understanding of the diurnal and seasonal variations of SM at the regional scale, consequently enhancing ecological and hydrological applications, especially under climate change. © 2020 by the authors. Citation statistics:
资源类型: 期刊论文
标识符: http://119.78.100.158/handle/2HF3EXSE/159789
Appears in Collections: 气候变化与战略
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作者单位: Institute of RS and GIS, School of Earth and Space Sciences, Peking University, Beijing, 100871, China; State Key Laboratory of Hydroscience and Engineering, Department of Hydraulic Engineering, Tsinghua University, Beijing, 100084, China; School of Civil Engineering and Environmental Science, University of Oklahoma, Norman, OK 73019, United States
Recommended Citation:
Cui Y.,Chen X.,Xiong W.,et al. A soil moisture spatial and temporal resolution improving algorithm based on multi-source remote sensing data and GRNN model[J]. Remote Sensing,2020-01-01,12(3)