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- Kooistra, Lammert, et al.
(author)
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Reviews and syntheses : Remotely sensed optical time series for monitoring vegetation productivity
- 2024
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In: Biogeosciences. - 1726-4170. ; 21:2, s. 473-511
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Research review (peer-reviewed)abstract
- Vegetation productivity is a critical indicator of global ecosystem health and is impacted by human activities and climate change. A wide range of optical sensing platforms, from ground-based to airborne and satellite, provide spatially continuous information on terrestrial vegetation status and functioning. As optical Earth observation (EO) data are usually routinely acquired, vegetation can be monitored repeatedly over time, reflecting seasonal vegetation patterns and trends in vegetation productivity metrics. Such metrics include gross primary productivity, net primary productivity, biomass, or yield. To summarize current knowledge, in this paper we systematically reviewed time series (TS) literature for assessing state-of-the-art vegetation productivity monitoring approaches for different ecosystems based on optical remote sensing (RS) data. As the integration of solar-induced fluorescence (SIF) data in vegetation productivity processing chains has emerged as a promising source, we also include this relatively recent sensor modality. We define three methodological categories to derive productivity metrics from remotely sensed TS of vegetation indices or quantitative traits: (i) trend analysis and anomaly detection, (ii) land surface phenology, and (iii) integration and assimilation of TS-derived metrics into statistical and process-based dynamic vegetation models (DVMs). Although the majority of used TS data streams originate from data acquired from satellite platforms, TS data from aircraft and unoccupied aerial vehicles have found their way into productivity monitoring studies. To facilitate processing, we provide a list of common toolboxes for inferring productivity metrics and information from TS data. We further discuss validation strategies of the RS data derived productivity metrics: (1) using in situ measured data, such as yield; (2) sensor networks of distinct sensors, including spectroradiometers, flux towers, or phenological cameras; and (3) inter-comparison of different productivity metrics. Finally, we address current challenges and propose a conceptual framework for productivity metrics derivation, including fully integrated DVMs and radiative transfer models here labelled as "Digital Twin". This novel framework meets the requirements of multiple ecosystems and enables both an improved understanding of vegetation temporal dynamics in response to climate and environmental drivers and enhances the accuracy of vegetation productivity monitoring.
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| 2. |
- Tebaldini, S., et al.
(author)
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TomoSense: A unique 3D dataset over temperate forest combining multi-frequency mono- and bi-static tomographic SAR with terrestrial, UAV and airborne lidar, and in-situ forest census
- 2023
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In: Remote Sensing of Environment. - : Elsevier BV. - 0034-4257. ; 290
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Journal article (peer-reviewed)abstract
- The TomoSense experiment was funded by the European Space Agency (ESA) to support research on remote sensing of forested areas by means of Synthetic Aperture Radar (SAR) data, with a special focus on the use of tomographic SAR (TomoSAR) to retrieve information about the vertical structure of the vegetation at different frequency bands. The illuminated scene is the temperate forest at the Eifel National Park, North-West Germany. Dominant species are beech and spruce trees. Forest height ranges roughly from 10 to 30 m, with peaks up to over 40 m. Forest Above Ground Biomass (AGB) ranges from 20 to 300 Mg/ha, with peaks up to over 400 Mg/ha. SAR data include P-, L-, and C-band surveys acquired by flying up to 30 trajectories in two headings to provide tomographic imaging capabilities. L- and C-band data were acquired by simultaneously flying two aircraft to gather bistatic data along different trajectories. The SAR dataset is complemented by 3D structural canopy measurements made via terrestrial laser scanning (TLS), Unoccupied Aerial Vehicle lidar (UAV-L) and airborne laser scanning (ALS), and in-situ forest census. This unique combination of SAR tomographic and multi-scale lidar data allows for direct comparison of canopy structural metrics across wavelength and scale, including vertical profiles of canopy wood and foliage density, and per-tree and plot-level above ground biomass (AGB). The resulting TomoSense data-set is free and openly available at ESA for any research purpose. The data-set includes ALS-derived maps of forest height and AGB, forest parameters at the level of single trees, TLS raw data, and plot-average TLS vertical profiles. The provided SAR data are coregistered, phase calibrated, and ground steered, to enable a direct implementation of any kind of interferometric or tomographic processing without having to deal with the subtleties of airborne SAR processing. Moreover, the data-base comprises SAR tomographic cubes representing forest scattering in 3D both in Radar and geographical coordinates, intended for use by non-Radar experts. For its unique features and completeness, the TomoSense data-set is intended to serve as an important basis for future research on microwave scattering from forested areas in the context of future Earth Observation missions.
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