Estimating Gross and Net Primary Productivities Using Earth Observation Products: a Review
Considerable research has been conducted on the estimation of Gross Primary Productivity (GPP) and Net Primary Productivity (NPP) from different viewpoints. Each vision has proposed special applied materials and methodologies and has provided specific insight into the results and overall conclusions...
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Veröffentlicht in: | Environmental modeling & assessment 2024-02, Vol.29 (1), p.179-200 |
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Sprache: | eng |
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Zusammenfassung: | Considerable research has been conducted on the estimation of Gross Primary Productivity (GPP) and Net Primary Productivity (NPP) from different viewpoints. Each vision has proposed special applied materials and methodologies and has provided specific insight into the results and overall conclusions. As NPP generally declines once crops start to begin competing intensively for water resources, the identification of precise NPP estimation approaches can be of importance in water and agricultural studies. Due to the lack of a comprehensive overview of the subject that investigates GPP and NPP studies from both environmental and agricultural perspectives, we reviewed the existing approaches in estimating these two parameters with a special focus on estimating NPP for agricultural studies. Categorization of the diverse proposed models and approaches, with special attention to the role of newly introduced materials, such as chlorophyll proxies, is provided on GPP and NPP estimations. After introducing the different categories, from the environmental perspective, we addressed the most recent improvements in GPP estimations in the biosphere by applying a chlorophyll-based fraction of Absorbed Photosynthetically Active Radiation (fPAR) into the light use efficiency (LUE) model. Then, by considering the agricultural perspectives, the application of both chlorophyll-based fPAR and a new chlorophyll-based predictor was discussed to estimate NPP and yield. It is expected that the outlined vision on estimating NPP in agricultural studies helps to identify the current boundary of knowledge in these types of studies. |
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ISSN: | 1420-2026 1573-2967 |
DOI: | 10.1007/s10666-023-09927-9 |