000886003 001__ 886003
000886003 005__ 20210628225835.0
000886003 0247_ $$2doi$$a10.3389/frwa.2020.578367
000886003 0247_ $$2Handle$$a2128/26041
000886003 0247_ $$2altmetric$$aaltmetric:93285150
000886003 0247_ $$2WOS$$aWOS:000659431100001
000886003 037__ $$aFZJ-2020-04225
000886003 082__ $$a333.7
000886003 1001_ $$0P:(DE-HGF)0$$aHuang, Jingyi$$b0$$eCorresponding author
000886003 245__ $$aRetrieving Heterogeneous Surface Soil Moisture at 100 m Across the Globe via Fusion of Remote Sensing and Land Surface Parameters
000886003 260__ $$aLausanne$$bFrontiers Media$$c2020
000886003 3367_ $$2DRIVER$$aarticle
000886003 3367_ $$2DataCite$$aOutput Types/Journal article
000886003 3367_ $$0PUB:(DE-HGF)16$$2PUB:(DE-HGF)$$aJournal Article$$bjournal$$mjournal$$s1604483799_18853
000886003 3367_ $$2BibTeX$$aARTICLE
000886003 3367_ $$2ORCID$$aJOURNAL_ARTICLE
000886003 3367_ $$00$$2EndNote$$aJournal Article
000886003 520__ $$aSuccessful monitoring of soil moisture dynamics at high spatio-temporal resolutions globally is hampered by the heterogeneity of soil hydraulic properties in space and complex interactions between water and the environmental variables that control it. Current soil moisture monitoring schemes via in situ station networks are sparsely distributed while remote sensing satellite soil moisture maps have a very coarse spatial resolution. In this study, an empirical surface soil moisture (SSM) model was established via fusion of in situ continental and regional scale soil moisture networks, remote sensing data (SMAP and Sentinel-1) and high-resolution land surface parameters (e.g., soil texture, terrain) using a quantile random forest (QRF) algorithm. The model had a spatial resolution of 100 m and performed well under cultivated, herbaceous, forest, and shrub soils (overall R2 = 0.524, RMSE = 0.07 m3 m−3). It has a relatively good transferability at the regional scale among different soil moisture networks (mean RMSE = 0.08–0.10 m3 m−3). The global model was applied to map SSM dynamics at 30–100 m across a field-scale soil moisture network (TERENO-Wüstebach) and an 80-ha cultivated cropland in Wisconsin, USA. Without the use of local training data, the model was able to delineate the variations in SSM at the field scale but contained large bias. With the addition of 10% local training datasets (“spiking”), the bias of the model was significantly reduced. The QRF model was relatively insensitive to the resolution of Sentinel-1 data but was affected by the resolution and accuracy of soil maps. It was concluded that the empirical model has the potential to be applied elsewhere across the globe to map SSM at the regional to field scales for research and applications. Future research is required to improve the performance of the model by incorporating more field-scale soil moisture sensor networks and assimilation with process-based models.
000886003 536__ $$0G:(DE-HGF)POF3-255$$a255 - Terrestrial Systems: From Observation to Prediction (POF3-255)$$cPOF3-255$$fPOF III$$x0
000886003 588__ $$aDataset connected to CrossRef
000886003 7001_ $$0P:(DE-HGF)0$$aDesai, Ankur R.$$b1
000886003 7001_ $$0P:(DE-Juel1)156366$$aZhu, Jun$$b2
000886003 7001_ $$0P:(DE-HGF)0$$aHartemink, Alfred E.$$b3
000886003 7001_ $$0P:(DE-HGF)0$$aStoy, Paul C.$$b4
000886003 7001_ $$0P:(DE-HGF)0$$aLoheide, Steven P.$$b5
000886003 7001_ $$0P:(DE-Juel1)129440$$aBogena, Heye$$b6
000886003 7001_ $$0P:(DE-HGF)0$$aZhang, Yakun$$b7
000886003 7001_ $$0P:(DE-HGF)0$$aZhang, Zhou$$b8
000886003 7001_ $$0P:(DE-HGF)0$$aArriaga, Francisco$$b9
000886003 773__ $$0PERI:(DE-600)2986721-6$$a10.3389/frwa.2020.578367$$gVol. 2, p. 578367$$p578367$$tFrontiers in water$$v2$$x2624-9375$$y2020
000886003 8564_ $$uhttps://juser.fz-juelich.de/record/886003/files/frwa-02-578367.pdf$$yOpenAccess
000886003 8564_ $$uhttps://juser.fz-juelich.de/record/886003/files/frwa-02-578367.pdf?subformat=pdfa$$xpdfa$$yOpenAccess
000886003 909CO $$ooai:juser.fz-juelich.de:886003$$pVDB$$pVDB:Earth_Environment$$pdriver$$popen_access$$popenaire$$pdnbdelivery
000886003 9101_ $$0I:(DE-588b)5008462-8$$6P:(DE-Juel1)129440$$aForschungszentrum Jülich$$b6$$kFZJ
000886003 9131_ $$0G:(DE-HGF)POF3-255$$1G:(DE-HGF)POF3-250$$2G:(DE-HGF)POF3-200$$3G:(DE-HGF)POF3$$4G:(DE-HGF)POF$$aDE-HGF$$bErde und Umwelt$$lTerrestrische Umwelt$$vTerrestrial Systems: From Observation to Prediction$$x0
000886003 9141_ $$y2020
000886003 915__ $$0StatID:(DE-HGF)0510$$2StatID$$aOpenAccess
000886003 915__ $$0LIC:(DE-HGF)CCBY4$$2HGFVOC$$aCreative Commons Attribution CC BY 4.0
000886003 920__ $$lyes
000886003 9201_ $$0I:(DE-Juel1)IBG-3-20101118$$kIBG-3$$lAgrosphäre$$x0
000886003 980__ $$ajournal
000886003 980__ $$aVDB
000886003 980__ $$aUNRESTRICTED
000886003 980__ $$aI:(DE-Juel1)IBG-3-20101118
000886003 9801_ $$aFullTexts