Multi-Source Remote Sensing Based Modeling of Vegetation Productivity in the Boreal: Issues & Opportunities
Understanding the processes driving terrestrial vegetation productivity dynamics in boreal ecosystems is critical for accurate assessments of carbon dynamics. Monitoring these dynamics typically requires a fusion of broad-scale remote sensing observations, climate information and other geospatial da...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Taylor & Francis Group
2023-01-01
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| Series: | Canadian Journal of Remote Sensing |
| Online Access: | http://dx.doi.org/10.1080/07038992.2023.2256895 |
| _version_ | 1797366169141772288 |
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| author | Ramon Melser Nicholas C. Coops Michael A. Wulder Chris Derksen |
| author_facet | Ramon Melser Nicholas C. Coops Michael A. Wulder Chris Derksen |
| author_sort | Ramon Melser |
| collection | DOAJ |
| description | Understanding the processes driving terrestrial vegetation productivity dynamics in boreal ecosystems is critical for accurate assessments of carbon dynamics. Monitoring these dynamics typically requires a fusion of broad-scale remote sensing observations, climate information and other geospatial data inputs, which often have unknown errors, are difficult to obtain, or limit spatial and temporal resolutions of productivity estimates. The past decade has seen notable advances in technologies and the diversity of observed wavelengths from remote sensing instruments, offering new insights on vegetation carbon dynamics. In this communication, we review key current approaches for modeling terrestrial vegetation productivity, followed by a discussion on new remote sensing instruments and derived products including Sentinel-3 Land Surface Temperature, freeze & thaw state from the Soil Moisture & Ocean Salinity (SMOS) mission, and soil moisture from the Soil Moisture Active Passive (SMAP) mission. We outline how these products can improve the spatial detail and temporal representation of boreal productivity estimates driven entirely by a fusion of remote sensing observations. We conclude with a demonstration of how these different elements can be integrated across key land cover types in the Hudson plains, an extensive wetland-dominated region of the Canadian boreal, and provide recommendations for future model development. |
| first_indexed | 2024-03-08T17:00:28Z |
| format | Article |
| id | doaj.art-f035073c8d7c42cab9b48f456a9e3444 |
| institution | Directory Open Access Journal |
| issn | 1712-7971 |
| language | English |
| last_indexed | 2024-03-08T17:00:28Z |
| publishDate | 2023-01-01 |
| publisher | Taylor & Francis Group |
| record_format | Article |
| series | Canadian Journal of Remote Sensing |
| spelling | doaj.art-f035073c8d7c42cab9b48f456a9e34442024-01-04T15:59:06ZengTaylor & Francis GroupCanadian Journal of Remote Sensing1712-79712023-01-0149110.1080/07038992.2023.22568952256895Multi-Source Remote Sensing Based Modeling of Vegetation Productivity in the Boreal: Issues & OpportunitiesRamon Melser0Nicholas C. Coops1Michael A. Wulder2Chris Derksen3Department of Forest Resource Management, University of British ColumbiaDepartment of Forest Resource Management, University of British ColumbiaCanadian Forest Service, Natural Resources CanadaEnvironment and Climate Change Canada, Climate Research DivisionUnderstanding the processes driving terrestrial vegetation productivity dynamics in boreal ecosystems is critical for accurate assessments of carbon dynamics. Monitoring these dynamics typically requires a fusion of broad-scale remote sensing observations, climate information and other geospatial data inputs, which often have unknown errors, are difficult to obtain, or limit spatial and temporal resolutions of productivity estimates. The past decade has seen notable advances in technologies and the diversity of observed wavelengths from remote sensing instruments, offering new insights on vegetation carbon dynamics. In this communication, we review key current approaches for modeling terrestrial vegetation productivity, followed by a discussion on new remote sensing instruments and derived products including Sentinel-3 Land Surface Temperature, freeze & thaw state from the Soil Moisture & Ocean Salinity (SMOS) mission, and soil moisture from the Soil Moisture Active Passive (SMAP) mission. We outline how these products can improve the spatial detail and temporal representation of boreal productivity estimates driven entirely by a fusion of remote sensing observations. We conclude with a demonstration of how these different elements can be integrated across key land cover types in the Hudson plains, an extensive wetland-dominated region of the Canadian boreal, and provide recommendations for future model development.http://dx.doi.org/10.1080/07038992.2023.2256895 |
| spellingShingle | Ramon Melser Nicholas C. Coops Michael A. Wulder Chris Derksen Multi-Source Remote Sensing Based Modeling of Vegetation Productivity in the Boreal: Issues & Opportunities Canadian Journal of Remote Sensing |
| title | Multi-Source Remote Sensing Based Modeling of Vegetation Productivity in the Boreal: Issues & Opportunities |
| title_full | Multi-Source Remote Sensing Based Modeling of Vegetation Productivity in the Boreal: Issues & Opportunities |
| title_fullStr | Multi-Source Remote Sensing Based Modeling of Vegetation Productivity in the Boreal: Issues & Opportunities |
| title_full_unstemmed | Multi-Source Remote Sensing Based Modeling of Vegetation Productivity in the Boreal: Issues & Opportunities |
| title_short | Multi-Source Remote Sensing Based Modeling of Vegetation Productivity in the Boreal: Issues & Opportunities |
| title_sort | multi source remote sensing based modeling of vegetation productivity in the boreal issues opportunities |
| url | http://dx.doi.org/10.1080/07038992.2023.2256895 |
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