Conference Agenda

Solar-induced fluorescence (SIF) has become a promising remote sensing proxy for photosynthetic activity and thus plant health, but its broad application across vegetation types and climate regimes remains underexplored. Here, we present the first continental-scale assessment of seasonal SIF signatures for 33 vegetation types across 24 climate zones in the contiguous United States, enabled by a new open-access visualization tool. The analysis uses TROPOMI satellite SIF data (2019-2021), along with MODIS-derived gross primary productivity (GPP), normalized difference vegetation index (NDVI), and vapor pressure deficit (VPD). Our results show that SIF has consistently stronger and more reliable correlations with GPP than NDVI across vegetation types and environmental conditions. This relationship remains robust even under high VPD conditions (except for several perennial crops), confirming the ability of SIF to track productivity even in dry environments. While NDVI retains structural sensitivity, it often decouples from GPP under stress, particularly in arid climates and perennial crops. We also identify clear differences in SIF-NDVI and GPP-NDVI relationships by vegetation type and climate, with NDVI showing limited responsiveness to dynamic changes in canopy physiology. Despite the coarse spatial resolution of TROPOMI, these results demonstrate the feasibility of constructing climate-specific SIF signatures for agricultural and ecological monitoring. By identifying these climate-specific signatures at the continental scale, this work highlights the value of SIF for climate-smart crop management, productivity assessment, and satellite-based ecosystem modeling.

Accurate estimation of gross primary production (GPP) is fundamental for quantifying the terrestrial carbon cycle. However, coarse-resolution products often fail to capture fine-scale spatial variations in carbon uptake across heterogeneous landscapes. While recent studies have begun to employ 10 m Sentinel-1 and Sentinel-2 imagery, they typically reduce these data to pixel-wise spectral indices, discarding the two-dimensional spatial structure (canopy architecture, land-cover transitions, within-stand heterogeneity) that the imagery encodes. This study investigates whether explicitly exploiting this spatial context via convolutional neural networks yields robust, transferable gains over tabular machine-learning baselines. We curate a quality-controlled dataset of 23,528 eight-day multi-sensor composites from 222 AmeriFlux sites (2015–2025), evaluated under site-wise cross-validation, temporal generalisation, and geographic transfer to an 18-site upper Midwest forest holdout. Under temporal transfer to unseen years (2023–2025), the best convolutional model achieves R² = 0.77 and RMSE = 1.95 gC m⁻² d⁻¹, an 18.6% RMSE reduction over ridge regression (R² = 0.65, RMSE = 2.40 gC m⁻² d⁻¹). Although this advantage narrows under geographic transfer to structurally novel regions (R² = 0.59 vs. 0.54), the convolutional models still outperform all tabular baselines. Spatial structure at 10 m therefore supports more robust temporal generalisation than spectral aggregates alone.

Accurate gross primary productivity (GPP) estimation is crucial for understanding ecosystem function and the global carbon cycle. Remote sensing offers promising GPP proxies, including solar-induced chlorophyll fluorescence (SIF) and the structural proxy NIRvP. However, their performance and underlying drivers of effectiveness vary significantly across biomes. This study comprehensively evaluated the accuracy and limitations of SIF and NIRvP against flux GPP across diverse biomes (CRO, GRA, DBF, ENF), also investigating physiological and structural controls on LUE.

We found that proxy performance was highly biome-specific. Notably, the removal of canopy escape probability (fesc) from observed SIF (SIFobs) to derive total emitted SIF (SIFall) did not consistently improve, and sometimes even diminished, its correlation with GPP, particularly in CRO and GRA. Furthermore, we elucidated distinct dominant controls on seasonal LUE variations: apparent SIF emission yield (ΦF×fesc) was paramount in ENF, while canopy structure (fesc) predominated in CRO, GRA, and DBF. Seasonal analysis in ENF further revealed a temporal decoupling, with fesc decline lagging LUE in winter, and ΦF failing to track autumnal LUE reductions. These findings underscore the biome-specific necessity for optimal GPP proxy selection, establishing a robust scientific foundation for improved remote sensing monitoring.

Accurate quantification of gross primary productivity (GPP) is essential for understanding carbon dynamics under climate change. However, satellite-based GPP estimates face spatial–temporal trade-offs, limiting accuracy in heterogeneous landscapes. To overcome this challenge, we proposed a novel framework named UNified, high-resolution Intelligent carbon QUantification and Explanation (UNIQUE), which produces daily 30 m GPP maps by integrating spatial relationships between 500 m MODIS and 30 m Landsat imagery. UNIQUE consists of two components. First, two AI models were trained using MODIS- and Landsat-based vegetation indices combined with meteorological reanalysis data and validated with 309 eddy-covariance flux tower observations across the Northern Hemisphere. The Light Gradient Boosting Machine (LGBM) showed the best performance, achieving r = 0.80 and RMSE = 2.47 gC/m²/day for MODIS-based GPP, and r = 0.83 with RMSE = 2.43 gC/m²/day for Landsat-based GPP. Second, a diffusion-based deep learning model was used to downscale MODIS-based GPP to 30 m resolution. The diffusion model from MODIS to Landsat GPP exhibited good performance, demonstrating an RMSE of 2.12 gC/m²/day for the testing sites. The proposed approach enabled the analysis of spatiotemporal characteristics of GPP across different plant functional types, facilitating enhanced high-resolution carbon flux monitoring in diverse ecosystems.

Insights into crop productivity have long been of great interest to almond growers, as they enable effective planning to optimise economic returns. Advances in sensor technology have made it possible to collect hyperspectral imagery, which captures detailed information across a continuous range of wavelengths and has become a powerful tool for assessing crop physiological status. Solar-induced chlorophyll fluorescence (SIF), along with other plant pigments and structural traits retrieved through radiative transfer modelling, can effectively track crop photosynthetic activity. However, the ability to quantify SIF is strongly influenced by the spectral resolution of the sensor.

This study examines how the spectral resolution of airborne hyperspectral sensors affects the ability to explain yield variability in a commercial almond orchard, by comparing SIF derived from the 760 nm and 687 nm oxygen absorption bands at different spectral resolutions.

Green roofs (GRs) are constructed systems that replicate natural ecosystems and provide runoff reduction, cooling effect, habitat support and improved air quality services. Over 1,000 GRs have been constructed in Toronto since the GR Bylaw was enacted. As they are dispersed and primarily small-scale stormwater assets on private properties, it is crucial yet difficult to assess these roofs' condition to ensure they continue to deliver the desired advantages and adhere to maintenance regulations. To maintain green stormwater infrastructures in ideal conditions, it is advised that the vegetation be maintained with 80% coverage. GR vegetation experiences plant loss, water stress, and other maintenance concerns requiring regular inspections. This study presents a framework to enable remote assessment of green roof conditions utilizing Sentinel-2A satellite imagery, which captures images every five days. This method overcomes logistical challenges associated with drone imagery inspections, which are limited in frequency and require permits. The study was conducted using Google Earth Engine, focusing on the intra- and inter-annual variation of four GR modules. The study assessed the vegetation health from 2018 to 2025 using NDVI, EVI and NDMI, highlighting the long-term dynamics and distribution of GR vegetation. The results present the effectiveness of NDVI, EVI, and NDMI in assessing plant coverage and moisture content, with low NDMI being an important factor resulting in low NDVI and EVI. The study contributes to the potential of satellite images for scalable and continuous monitoring of GRs and supports efficient and complementary inspection.