Ground-based long-term remote sensing of solar-induced chlorophyll fluorescence: Methods, challenges and opportunities

Solar-induced chlorophyll fluorescence (SIF) is a direct indicator of vegetation photosynthesis. Time-series in-situ SIF data enable better understanding of SIF-photosynthesis relationship. However, continuous seasonal SIF data is extremely lack due to several technical problems for ground-based long-term SIF observation systems. One of the major problems is it is difficult to measure radiance and irradiance simultaneously, both of which are inputs of traditional SIF retrieval methods. Statistical approaches for SIF retrieval is independent of synchronous irradiance data and therefore provides opportunities for improving long-term SIF observation systems. This report reviewed the state of the art of the automated SIF observation systems and evaluated the potential of using statistical approaches to simplify the design and data processing of these systems. Results showed that reliable SIF could be retrieved using statistical method without irradiance data once good train set was provided. Opportunities and challenges for improving ground-based long-term SIF measurement systems were discussed.