Solar-induced chlorophyll fluorescence (SIF) and hyperspectral sensing offer complementary perspectives on vegetation physiology, biochemistry and structure. Connecting these observations with ecosystem processes presents opportunities to improve assessments of photosynthesis, vegetation stress and productivity in terrestrial and aquatic ecosystems for climate change adaptation and mitigation. We welcome contributions on SIF and hyperspectral sensing across leaf, canopy, landscape and global scales. Topics of interest include, but are not limited to. (1) Remote and proximal sensing observations. Advances in satellite SIF and imaging spectroscopy, retrieval algorithms, atmospheric correction, cal&val, cross-sensor harmonization, and integration with ground and airborne data. (2) Radiative transfer modelling. Simulation and interpretation of SIF and reflectance; effects of canopy structure, spectral reabsorption and scattering, and illumination and viewing geometry; and coupling with photosynthesis and land-surface models. (3) Machine learning and model-data fusion. Retrieval of vegetation properties, fusion of SIF and hyperspectral data, integration with ecosystem models, hybrid modelling, uncertainty, and transferability across ecosystems. (4) Vegetation traits and ecosystem processes. Estimation of plant pigments, water content and other functional traits; relationships among SIF, photosynthetic efficiency and gross primary productivity. (5) Natural or managed ecosystem applications. Monitoring crop phenotyping, yield and precision agriculture. Assessment of forest functional diversity, canopy condition, photosynthesis and carbon uptake. Detection of disturbance, recovery, and disease impacts. Monitoring phytoplankton photosynthetic activity and aquatic productivity. (6) Integration across sensors and scales. Combining SIF and hyperspectral observations with thermal, microwave, LiDAR and ecosystem flux measurements, as well as atmospheric CO₂ and carbonyl sulfide (OCS) observations, to connect vegetation structure and function with carbon and water cycling. (7) Climate resilience, mitigation and adaptation. Linking stress indicators with hazard exposure and ecosystem vulnerability to assess risks to productivity and ecosystem function; quantifying resistance, recovery time and legacy effects; evaluating adaptation outcomes under climate extremes; and assessing implications for carbon uptake and climate change mitigation.