LEADER 03688nam 22008293a 450 001 9910367566003321 005 20250203235427.0 010 $a9783039210657 010 $a3039210653 024 8 $a10.3390/books978-3-03921-065-7 035 $a(CKB)4100000010106086 035 $a(oapen)https://directory.doabooks.org/handle/20.500.12854/40380 035 $a(ScCtBLL)cd5f259a-58fd-4c76-83f9-c313ca3ce5e1 035 $a(OCoLC)1163823212 035 $a(EXLCZ)994100000010106086 100 $a20250203i20192019 uu 101 0 $aeng 135 $aurmn|---annan 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 00$aAdvancing Earth Surface Representation via Enhanced Use of Earth Observations in Monitoring and Forecasting Applications$fFatima Karbou, Vanessa M. Escobar, Gianpaolo Balsamo, Benjamin Ruston 210 1$aBasel, Switzerland :$cMDPI,$d2019. 215 $a1 electronic resource (262 pages) 311 08$a9783039210640 311 08$a3039210645 330 $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. 610 $adirect and inverse methods 610 $aabsorption coefficient 610 $aemissivity 610 $aland-surface model 610 $avariational retrieval 610 $atemporal autocorrelation 610 $aBayesian bias correction 610 $ahyperspectral 610 $ainfrared 610 $aBRDF 610 $asatellite rainfall 610 $aMCD43C1 610 $apenetration depth 610 $aRTTOV 610 $aearth-observations 610 $aearth system modelling 610 $arepresentative depth 610 $aland 610 $aChangjiang (Yangtze) estuary 610 $aCDOM 610 $asoil moisture 610 $asurface 610 $aMaqu network 610 $asurface soil moisture 610 $aMODIS 610 $asoil effective temperature 610 $aGOCI 610 $amicrowave remote sensing 610 $arain gauge 610 $aQAA inversion 610 $abroadband emissivity 610 $aradiation 610 $asurface parameters 610 $asatellite data 610 $aEast Africa 700 $aKarbou$b Fatima$01787269 702 $aEscobar$b Vanessa M 702 $aBalsamo$b Gianpaolo 702 $aRuston$b Benjamin 801 0$bScCtBLL 801 1$bScCtBLL 906 $aBOOK 912 $a9910367566003321 996 $aAdvancing Earth Surface Representation via Enhanced Use of Earth Observations in Monitoring and Forecasting Applications$94320135 997 $aUNINA