000			03716naaaa2200757uu 4500
001			https://directory.doabooks.org/handle/20.500.12854/40380
005			20220714160845.0
020			_abooks978-3-03921-065-7
020			_a9783039210640
020			_a9783039210657
024	7		_a10.3390/books978-3-03921-065-7 _cdoi
041	0		_aEnglish
042			_adc
100	1		_aRuston, Benjamin _4auth _91571132
700	1		_aKarbou, Fatima _4auth _91571133
700	1		_aTrigo, Isabel F. _4auth _91571134
700	1		_aBalsamo, Gianpaolo _4auth _91571135
700	1		_aEscobar, Vanessa M. _4auth _91571136
700	1		_aDrusch, Matthias _4auth _91571137
700	1		_aMecklenburg, Susanne _4auth _91571138
245	1	0	_aAdvancing Earth Surface Representation via Enhanced Use of Earth Observations in Monitoring and Forecasting Applications
260			_bMDPI - Multidisciplinary Digital Publishing Institute _c2019
300			_a1 electronic resource (262 p.)
506	0		_aOpen Access _2star _fUnrestricted online access
520			_aThe representation of the Earth's surface in global monitoring and forecasting applications is moving towards capturing more of the relevant processes, while maintaining elevated computational efficiency and therefore a moderate complexity. These schemes are developed and continuously improved thanks to well instrumented field-sites that can observe coupled processes occurring at the surface-atmosphere interface (e.g., forest, grassland, cropland areas and diverse climate zones). Approaching global kilometer-scale resolutions, in situ observations alone cannot fulfil the modelling needs, and the use of satellite observation becomes essential to guide modelling innovation and to calibrate and validate new parameterization schemes that can support data assimilation applications. In this book, we review some of the recent contributions, highlighting how satellite data are used to inform Earth surface model development (vegetation state and seasonality, soil moisture conditions, surface temperature and turbulent fluxes, land-use change detection, agricultural indicators and irrigation) when moving towards global km-scale resolutions.
540			_aCreative Commons _fhttps://creativecommons.org/licenses/by-nc-nd/4.0/ _2cc _4https://creativecommons.org/licenses/by-nc-nd/4.0/
546			_aEnglish
653			_adirect and inverse methods
653			_aabsorption coefficient
653			_aemissivity
653			_aland-surface model
653			_an/a
653			_avariational retrieval
653			_atemporal autocorrelation
653			_aBayesian bias correction
653			_ahyperspectral
653			_ainfrared
653			_aBRDF
653			_asatellite rainfall
653			_aMCD43C1
653			_apenetration depth
653			_aRTTOV
653			_aearth-observations
653			_aearth system modelling
653			_arepresentative depth
653			_aland
653			_aChangjiang (Yangtze) estuary
653			_aCDOM
653			_asoil moisture
653			_asurface
653			_aMaqu network
653			_asurface soil moisture
653			_aMODIS
653			_asoil effective temperature
653			_aGOCI
653			_amicrowave remote sensing
653			_arain gauge
653			_aQAA inversion
653			_abroadband emissivity
653			_aradiation
653			_asurface parameters
653			_asatellite data
653			_aEast Africa
856	4	0	_awww.oapen.org _uhttps://mdpi.com/books/pdfview/book/1510 _70 _zDOAB: download the publication
856	4	0	_awww.oapen.org _uhttps://directory.doabooks.org/handle/20.500.12854/40380 _70 _zDOAB: description of the publication
999			_c2978544 _d2978544