LEADER 04906nam 2201189z- 450 001 9910619465703321 005 20231214133658.0 010 $a3-0365-5326-6 035 $a(CKB)5670000000391617 035 $a(oapen)https://directory.doabooks.org/handle/20.500.12854/93215 035 $a(EXLCZ)995670000000391617 100 $a20202210d2022 |y 0 101 0 $aeng 135 $aurmn|---annan 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aRemote Sensing of Land Surface Phenology 210 $cMDPI - Multidisciplinary Digital Publishing Institute$d2022 215 $a1 electronic resource (276 p.) 311 $a3-0365-5325-8 330 $aLand surface phenology (LSP) uses remote sensing to monitor seasonal dynamics in vegetated land surfaces and retrieve phenological metrics (transition dates, rate of change, annual integrals, etc.). LSP has developed rapidly in the last few decades. Both regional and global LSP products have been routinely generated and play prominent roles in modeling crop yield, ecological surveillance, identifying invasive species, modeling the terrestrial biosphere, and assessing impacts on urban and natural ecosystems. Recent advances in field and spaceborne sensor technologies, as well as data fusion techniques, have enabled novel LSP retrieval algorithms that refine retrievals at even higher spatiotemporal resolutions, providing new insights into ecosystem dynamics. Meanwhile, rigorous assessment of the uncertainties in LSP retrievals is ongoing, and efforts to reduce these uncertainties represent an active research area. Open source software and hardware are in development, and have greatly facilitated the use of LSP metrics by scientists outside the remote sensing community. This reprint covers the latest developments in sensor technologies, LSP retrieval algorithms and validation strategies, and the use of LSP products in a variety of fields. It aims to summarize the ongoing diverse LSP developments and boost discussions on future research prospects. 606 $aTechnology: general issues$2bicssc 606 $aHistory of engineering & technology$2bicssc 606 $aEnvironmental science, engineering & technology$2bicssc 610 $aclimate change 610 $adigital camera 610 $aMODIS 610 $aMongolian oak 610 $aphenology 610 $asap flow 610 $aurbanization 610 $aplant phenology 610 $aspatiotemporal patterns 610 $astructural equation model 610 $aGoogle Earth Engine 610 $aThree-River Headwaters region 610 $aGPP 610 $acarbon cycle 610 $aarctic 610 $aphotosynthesis 610 $aremote sensing 610 $acrop sowing date 610 $adevelopment stage 610 $ayield gap 610 $ayield potential 610 $aprocess-based model 610 $aland surface temperature 610 $aurban heat island effect 610 $acontribution 610 $aHangzhou 610 $aland surface phenology 610 $aNDVI 610 $aspatiotemporal dynamics 610 $adifferent drivers 610 $arandom forest model 610 $adata suitability 610 $asatellite data 610 $aspatial scaling effects 610 $athe Loess Plateau 610 $aautumn phenology 610 $aturning point 610 $aclimate changes 610 $ahuman activities 610 $aQinghai-Tibetan Plateau 610 $asnow phenology 610 $adriving factors 610 $aspatiotemporal variations 610 $aNortheast China 610 $avegetation indexes 610 $aseasonally dry tropical forest 610 $avegetation phenology 610 $aclimatic limitation 610 $asolar-induced chlorophyll fluorescence 610 $aenhanced vegetation index 610 $agross primary production 610 $aevapotranspiration 610 $awater use efficiency 610 $aNDPI 610 $aQilian Mountains 610 $asnow cover 610 $ahigh elevation 610 $asoil moisture 610 $avegetation dynamics 610 $acarbon exchange 615 7$aTechnology: general issues 615 7$aHistory of engineering & technology 615 7$aEnvironmental science, engineering & technology 700 $aMa$b Xuanlong$4edt$01315239 702 $aJin$b Jiaxin$4edt 702 $aZhu$b Xiaolin$4edt 702 $aZhou$b Yuke$4edt 702 $aXie$b Qiaoyun$4edt 702 $aMa$b Xuanlong$4oth 702 $aJin$b Jiaxin$4oth 702 $aZhu$b Xiaolin$4oth 702 $aZhou$b Yuke$4oth 702 $aXie$b Qiaoyun$4oth 906 $aBOOK 912 $a9910619465703321 996 $aRemote Sensing of Land Surface Phenology$93032286 997 $aUNINA