Solar-induced chlorophyll fluorescence (SIF) is a crucial indicator of vegetation photosynthesis, and in recent years, tower-based SIF measurements have become increasingly valuable in conjunction with gross primary productivity (GPP) for studying photosynthesis dynamics. However, current SIF retrieval algorithms are plagued by uncertainties, especially those introduced by atmospheric conditions and measurement geometries. These uncertainties can distort the diurnal patterns of SIF, making it harder to accurately monitor photosynthesis throughout the day. To address these challenges, there is a growing need for advancements in tower-based SIF retrieval algorithms. Solar-induced chlorophyll fluorescence (SIF) is a crucial indicator of vegetation photosynthesis, and in recent years, tower-based SIF measurements have become increasingly valuable in conjunction with gross primary productivity (GPP) for studying photosynthesis dynamics. However, current SIF retrieval algorithms are plagued by uncertainties, especially those introduced by atmospheric conditions and measurement geometries. These uncertainties can distort the diurnal patterns of SIF, making it harder to accurately monitor photosynthesis throughout the day. To address these challenges, there is a growing need for advancements in tower-based SIF retrieval algorithms. Molecular & Computational biology Agriculture Phys.org – latest science and technology news stories
Enhancing algorithms to boost vegetation photosynthesis monitoring
