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A regional model-based algorithm to simulate root zone soil moisture with multi-source datasets
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AI
DOI：10.1016/j.agwat.2025.109803.png)
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摘要
En 中文
• 同化后的根区土壤湿度与原始数据相比，平均绝对误差降低了19.7%。
• 区域模型在半湿润和半干旱地区表现优于邻近站点模型。
• 适宜的气候和下垫面变量对土壤湿度模拟至关重要。
Keyword:
SM
soil moisture
RZSM
root zone soil moisture
DL
deep learning
LSTM
long short-term memory
RNN
recurrent neural network
PRE
precipitation
PREx
x-day cumulative precipitation
TEM
air temperature
RHU
relative humidity
WIN
wind speed
SSD
sunshine duration
PRS
atmospheric pressure
NDVI
normalized difference vegetation index
DEM
digital elevation model
EL
elevation
RS
remote sensing
CC
Pearson correlation coefficient
MAE
mean absolute error
RMSE
root mean square error
UbRMSE
unbiased root mean square error
IDW
inverse distance weighed method
RM
regional model
NM
nearby-station model
Obv
observed value
Sim
simulated value
ERA5
ECMWF reanalysis version 5
ECMWF
European Centre for Medium-Range Weather Forecasts
GLDAS
global land data assimilation system
GLEAM
global land evaporation assimilation model
SMAP
Soil Moisture Active Passive
DAAC
Distributed Active Archive Center
NSIDC
National Snow and Ice Data Center
NASA
National Aeronautics and Space Administration
GSFC
Goddard Space Flight Center
NOAA
National Oceanic and Atmospheric Administration
NCEP
National Centers for Environmental Prediction
CLM
community land model
VIC
variable infiltration capacity
CMA
China Meteorological Administration
GES DISC
Goddard Earth Sciences Data and Information Services Center
FAO
Food and Agriculture Organization
GPU
graphic processing unit
predictor variable
Root zone soil moisture
Long short-term memory
Regional model
Multi-source datasets
Data assimilation
AI总结
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期刊
IF:
6.5
论文数:
8.8K
被引数:
3.5W
机构
引用论文
SoilGrids 2.0: producing soil information for the globe with quantified spatial uncertaintySoilGrids 2.0: 以量化的空间不确定性为全球生产土壤信息.png)
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分享收藏Jia, Y.W., Sun, C.J., Wu, H.X., Luan, G. ze, Zhu, S. jin, Zhao, F., Yang, X., Deng, Z.T., 2023. Spatiotemporal distribution characteristics of land resources in the middle reaches of the Yellow River in China from 1980 to 2018: An asset perspective based on multi-source data. Nat. Resour. Res. 32, 1823–1838. https://doi.org/10.1007/s11053-023-10208-1.贾，Y.W.，孙，C.J.，吴，H.X.，奕，G. ze，朱，S. jin，赵，F.，杨，X.，邓，Z.T.，2023. 1980年至2018年中国黄河中游土地资源的时空分布特征：基于多源数据的资产视角。Nat. Resour. Res. 32，1823–1838。https://doi.org/10.1007/s11053-023-10208-1.
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