Remote Sensing of Land Surface Phenology

• Autres auteurs: Ma, Xuanlong (Éditeur intellectuel), Jin, Jiaxin (Éditeur intellectuel), Zhu, Xiaolin (Éditeur intellectuel), Zhou, Yuke (Éditeur intellectuel), Xie, Qiaoyun (Éditeur intellectuel)
• Format: Électronique Chapitre de livre
• Langue: anglais
• Publié: MDPI - Multidisciplinary Digital Publishing Institute 2022
• Sujets: Technology: general issues; History of engineering & technology; Environmental science, engineering & technology; climate change; digital camera; MODIS; Mongolian oak; phenology; sap flow; urbanization; plant phenology; spatiotemporal patterns; structural equation model; Google Earth Engine; Three-River Headwaters region; GPP; carbon cycle; arctic; photosynthesis; remote sensing; crop sowing date; development stage; yield gap; yield potential; process-based model; land surface temperature; urban heat island effect; contribution; Hangzhou; land surface phenology; NDVI; spatiotemporal dynamics; different drivers; random forest model; data suitability; satellite data; spatial scaling effects; the Loess Plateau; autumn phenology; turning point; climate changes; human activities; Qinghai-Tibetan Plateau; snow phenology; driving factors; spatiotemporal variations; Northeast China; vegetation indexes; seasonally dry tropical forest; vegetation phenology; climatic limitation; solar-induced chlorophyll fluorescence; enhanced vegetation index; gross primary production; evapotranspiration; water use efficiency; NDPI; Qilian Mountains; snow cover; high elevation; soil moisture; vegetation dynamics; carbon exchange; n/a
• Accès en ligne: DOAB: download the publication; DOAB: description of the publication

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 ro...