Despite the potentially key role of land-mitigation techniques (LMTs) in climate mitigation strategies, the efficiency of LMTs to enhance the terrestrial biosphere’s carbon uptake (negative emissions) remains poorly understood. Recent developments of solar-induced fluorescence (SIF) measurements from satellite-based atmospheric sensors appear promising to monitor negative emissions from vegetation. SIF is a direct proxy for photosynthetic activity and is found to correlate linearly with carbon uptake. This project aims to utilize SIF to quantify negative emissions following from LMT implementations.

Retrieving reliable SIF records from satellite observations has become an active field of study over the last decade. We aim to improve on KNMI’s SIFTER v2 algorithm (Van Schaik, et al. 2020), to retrieve SIF estimates from the GOME-2A sensor, by further limiting the interference of water vapor absorption and improving on the degradation correction. Using the SIFTER principle, retrieval versions to be applied on GOME-2B and TROPOMI will be generated so that long-term SIF data-records of GOME-2 (2007-2021) and TROPOMI (2018-) can be realized.

The SIF data-records, together with vegetation optical depth (VOD) data, facilitate tracking of vegetation dynamics over LMT locations and adjacent undisturbed areas. Additionally, trace gas signals will be analyzed to examine potential dual benefits of LMTs for air quality. This study uses actual specific land management sites (varying from reforestation efforts in Spain to fire management in Venezuela) as monitoring case studies. Finally, to verify the effectiveness and detectability of specific LMTs, satellitederived proxies will be evaluated against modelled (landatmosphere exchange models) and ground-assessed negative emissions.