Remote Sensing of Land Surface Phenology
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...
שמור ב:
| פורמט: | Online |
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| שפה: | אנגלית |
| יצא לאור: |
MDPI - Multidisciplinary Digital Publishing Institute
2022
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| נושאים: | |
| גישה מקוונת: | ONIX_20221025_9783036553252_69 |
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| _version_ | 1869520462044725248 |
|---|---|
| collection | Directory of Open Access Books |
| description | 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. |
| format | Online |
| id | doab-20.500.12854ir-93215 |
| institution | Directory of Open Access Books |
| language | eng |
| publishDate | 2022 |
| publishDateRange | 2022 |
| publishDateSort | 2022 |
| publisher | MDPI - Multidisciplinary Digital Publishing Institute |
| publisherStr | MDPI - Multidisciplinary Digital Publishing Institute |
| record_format | ojs |
| spelling | doab-20.500.12854ir-932152024-04-11T15:11:04Z Remote Sensing of Land Surface Phenology Ma, Xuanlong Jin, Jiaxin Zhu, Xiaolin Zhou, Yuke Xie, Qiaoyun 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 thema EDItEUR::T Technology, Engineering, Agriculture, Industrial processes::TB Technology: general issues thema EDItEUR::T Technology, Engineering, Agriculture, Industrial processes::TB Technology: general issues::TBX History of engineering and technology thema EDItEUR::T Technology, Engineering, Agriculture, Industrial processes::TQ Environmental science, engineering and technology 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. 2022-10-25T09:02:31Z 2022-10-25T09:02:31Z 2022 book ONIX_20221025_9783036553252_69 9783036553252 9783036553269 https://directory.doabooks.org/handle/20.500.12854/93215 eng application/octet-stream Attribution 4.0 International https://mdpi.com/books/pdfview/book/6113 https://mdpi.com/books/pdfview/book/6113 MDPI - Multidisciplinary Digital Publishing Institute 10.3390/books978-3-0365-5326-9 10.3390/books978-3-0365-5326-9 46cabcaa-dd94-4bfe-87b4-55023c1b36d0 9783036553252 9783036553269 276 open access |
| spellingShingle | 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 thema EDItEUR::T Technology, Engineering, Agriculture, Industrial processes::TB Technology: general issues thema EDItEUR::T Technology, Engineering, Agriculture, Industrial processes::TB Technology: general issues::TBX History of engineering and technology thema EDItEUR::T Technology, Engineering, Agriculture, Industrial processes::TQ Environmental science, engineering and technology Remote Sensing of Land Surface Phenology |
| title | Remote Sensing of Land Surface Phenology |
| title_full | Remote Sensing of Land Surface Phenology |
| title_fullStr | Remote Sensing of Land Surface Phenology |
| title_full_unstemmed | Remote Sensing of Land Surface Phenology |
| title_short | Remote Sensing of Land Surface Phenology |
| title_sort | remote sensing of land surface phenology |
| topic | 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 thema EDItEUR::T Technology, Engineering, Agriculture, Industrial processes::TB Technology: general issues thema EDItEUR::T Technology, Engineering, Agriculture, Industrial processes::TB Technology: general issues::TBX History of engineering and technology thema EDItEUR::T Technology, Engineering, Agriculture, Industrial processes::TQ Environmental science, engineering and technology |
| topic_facet | 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 thema EDItEUR::T Technology, Engineering, Agriculture, Industrial processes::TB Technology: general issues thema EDItEUR::T Technology, Engineering, Agriculture, Industrial processes::TB Technology: general issues::TBX History of engineering and technology thema EDItEUR::T Technology, Engineering, Agriculture, Industrial processes::TQ Environmental science, engineering and technology |
| url | ONIX_20221025_9783036553252_69 |