04906nam 2201189z- 450 991061946570332120231214133658.03-0365-5326-6(CKB)5670000000391617(oapen)https://directory.doabooks.org/handle/20.500.12854/93215(EXLCZ)99567000000039161720202210d2022 |y 0engurmn|---annantxtrdacontentcrdamediacrrdacarrierRemote Sensing of Land Surface PhenologyMDPI - Multidisciplinary Digital Publishing Institute20221 electronic resource (276 p.)3-0365-5325-8 Land 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.Technology: general issuesbicsscHistory of engineering & technologybicsscEnvironmental science, engineering & technologybicsscclimate changedigital cameraMODISMongolian oakphenologysap flowurbanizationplant phenologyspatiotemporal patternsstructural equation modelGoogle Earth EngineThree-River Headwaters regionGPPcarbon cyclearcticphotosynthesisremote sensingcrop sowing datedevelopment stageyield gapyield potentialprocess-based modelland surface temperatureurban heat island effectcontributionHangzhouland surface phenologyNDVIspatiotemporal dynamicsdifferent driversrandom forest modeldata suitabilitysatellite dataspatial scaling effectsthe Loess Plateauautumn phenologyturning pointclimate changeshuman activitiesQinghai-Tibetan Plateausnow phenologydriving factorsspatiotemporal variationsNortheast Chinavegetation indexesseasonally dry tropical forestvegetation phenologyclimatic limitationsolar-induced chlorophyll fluorescenceenhanced vegetation indexgross primary productionevapotranspirationwater use efficiencyNDPIQilian Mountainssnow coverhigh elevationsoil moisturevegetation dynamicscarbon exchangeTechnology: general issuesHistory of engineering & technologyEnvironmental science, engineering & technologyMa Xuanlongedt1315239Jin JiaxinedtZhu XiaolinedtZhou YukeedtXie QiaoyunedtMa XuanlongothJin JiaxinothZhu XiaolinothZhou YukeothXie QiaoyunothBOOK9910619465703321Remote Sensing of Land Surface Phenology3032286UNINA