A 3 km spatially and temporally consistent European daily soil moisture reanalysis from 2000 to 2015

被引：40
作者：
Naz, Bibi S. [1 ,2 ]
Kollet, Stefan [1 ,2 ]
Franssen, Harrie-Jan Hendricks [1 ,2 ]
Montzka, Carsten [1 ,2 ]
Kurtz, Wolfgang [3 ]
机构：
[1] Julich Res Ctr GmbH, Inst Bio & Geosci Agrosphere IBG 3, D-52425 Julich, Germany
[2] Ctr High Performance Sci Comp Terr Syst, Geoverbund ABC J, D-52425 Julich, Germany
[3] Leibniz Supercomp Ctr, Boltzmannstr 1, D-85748 Garching, Germany
来源：
SCIENTIFIC DATA | 2020年 / 7卷 / 01期
基金：
欧盟地平线“2020”;
关键词：
DATA ASSIMILATION; DISCHARGE PREDICTIONS; MODEL SIMULATIONS; SURFACE; SMOS; IMPROVEMENT; RETRIEVALS; PRODUCTS; NETWORK; CLIMATE;
D O I：
10.1038/s41597-020-0450-6
中图分类号：
O [数理科学和化学]; P [天文学、地球科学]; Q [生物科学]; N [自然科学总论];
学科分类号：
07 ; 0710 ; 09 ;
摘要：
High-resolution soil moisture (SM) information is essential to many regional applications in hydrological and climate sciences. Many global estimates of surface SM are provided by satellite sensors, but at coarse spatial resolutions (lower than 25 km), which are not suitable for regional hydrologic and agriculture applications. Here we present a 16 years (2000-2015) high-resolution spatially and temporally consistent surface soil moisture reanalysis (ESSMRA) dataset (3 km, daily) over Europe from a land surface data assimilation system. Coarse-resolution satellite derived soil moisture data were assimilated into the community land model (CLM3.5) using an ensemble Kalman filter scheme, producing a 3 km daily soil moisture reanalysis dataset. Validation against 112 in-situ soil moisture observations over Europe shows that ESSMRA captures the daily, inter-annual, intra-seasonal patterns well with RMSE varying from 0.04 to 0.06 m(3)m(-3) and correlation values above 0.5 over 70% of stations. The dataset presented here provides long-term daily surface soil moisture at a high spatiotemporal resolution and will be beneficial for many hydrological applications over regional and continental scales.
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页数：14
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