Shanshan Du

Canopy chlorophyll content (CCC) is a critical biochemical indicator of vegetation growth status and photosynthetic capacity, with broad applications in global climate change monitoring, agricultural productivity assessment, and ecosystem management. The PROSAIL model has been extensively applied for remote sensing retrieval of CCC; however, it shows notable limitations in accurately simulating canopy reflectance within the red-edge spectral region (680–780 nm), which in turn reduces the reliability of CCC estimation. To overcome this limitation, this study proposes a novel canopy spectral simulation approach, termed the measured-leaf-driven SAIL (mSAIL) model, which incorporates measured leaf spectra into the SAIL model to improve the spectral simulation accuracy of the red-edge region. Firstly, measured leaf spectra from the ANGERS dataset (a widely used laboratory-measured leaf spectral dataset) were incorporated into the SAIL model to simulate mSAIL canopy reflectance under a range of leaf and canopy parameter conditions. Subsequently, the mSAIL-simulated spectra were validated using field measurements and compared with the corresponding PROSAIL simulations. Finally, empirical statistical models were constructed using the mSAIL and PROSAIL simulated spectra to retrieve CCC. Validation using field measurements collected in 2002, 2004, and 2006 indicates that mSAIL performs substantially better than PROSAIL in simulating red-edge reflectance, with corresponding improvements in the accuracy of red-edge vegetation indices (VIs). Furthermore, in empirical CCC retrieval applications, mSAIL consistently outperformed PROSAIL across all validation years. The three-year combined validation results show that mSAIL achieved RMSE reductions of 43.53%, 29.17%, and 15.18% for MTCI, CIRE, and ND705, respectively. In summary, the mSAIL approach establishes a practical hybrid framework that effectively improves the accuracy of vegetation canopy spectral simulations, particularly in the red-edge region, thereby providing strong support for precise retrieval of CCC.

1 Solar-induced chlorophyll fluorescence (SIF) is a weak signal emitted by chlorophyll molecules after absorbing solar radiation. The primary emission wavelengths approximately range from 650 to 80...

Solar-Induced Chlorophyll Fluorescence (SIF), spanning 650–800 nm, is a powerful indicator of plant vitality and productivity. The full SIF spectrum offers comprehensive insights into plant photosynthetic dynamics, surpassing single-band SIF. While current full-spectrum SIF reconstruction algorithms often rely on mathematical functions or basis spectra (principal components or singular vectors) derived from simulated datasets to model reflectance shapes. These methods struggle to accurately capture reflectance, especially in the red and red-edge regions, leading to significant retrieval errors. To address this limitation, we developed an improved full-spectrum SIF reconstruction algorithm using Principal Component Analysis (PCA) based on an SIF-free reflectance dataset (FSIF-iPCA). The SIF-free leaf spectra, obtained using an ASD field spectrometer with a FluoWat leaf clip and long-pass filter and under clear-sky condition, were upscaled to the canopy level using the SCOPE model to create a new SIF-free canopy reflectance dataset. FSIF-iPCA algorithm derives the principal components (PCs) through PCA optimization on this SIF-free reflectance dataset, thereby minimizing reflectance reconstruction artifacts induced by conventional simulated training data. Finally, validation experiments using field leaf datasets and tower-based canopy measurements demonstrated that FSIF-iPCA significantly outperformed previous methods. At the leaf level, FSIF-iPCA achieved an average RRMSE of 11.20%, with RRMSEs of 5.67% and 5.93% and R² values of 0.989 and 0.987 for the O₂-A and O₂-B bands, respectively. Tower-based measurements confirmed its superior performance, with O₂-A band RRMSEs of 3.56% (DM site) and 2.49% (GC site), and O₂-B band values of 13.98% and 10.47%. Compared to other methods, FSIF-iPCA consistently delivered higher retrieval accuracy, particularly in the red and red-edge regions, demonstrating the efficacy of PCs derived from SIF-free reflectance dataset for improving SIF spectral reconstruction.

Solar-induced chlorophyll fluorescence (SIF) is a crucial proxy of photosynthetic processes in vegetation. In recent decades, advancements in remote sensing technology have facilitated long-term global SIF monitoring, significantly enhancing our understanding of vegetation dynamics on a global scale. Despite this progress, current SIF datasets face major challenges, including temporal inconsistencies among various satellite-derived products and a lack of long-term, high-resolution observations. In this study, we developed a “Long-term Harmonized SIF” (LHSIF) dataset spanning 1995 to 2024 with a fine spatial resolution of 0.05° by coordinating SIF satellite observations from GOME, SCIAMACHY, GOME-2, and OCO-2. Light use efficiency (LUE)-based spatial downscaling models were employed for each SIF product to generate fine-resolution global SIF maps. The long-term dataset was constructed using temporally corrected GOME-2A SIF (TCSIF) as a benchmark and was combined with a cumulative distribution function (CDF) normalization method for far-red SIF harmonization across satellite sensors from GOME, SCIAMACHY, and OCO-2. The resulting harmonized dataset shows a 49 % reduction in inter-sensor differences compared to the uncorrected data and exhibits a stable interannual increase of 0.31 ± 0.07 % yr−1. This result strongly aligns with the growth rate of gross primary production (GPP, 0.47 ± 0.03 % yr−1) and is consistent with ground-based SIF observations (R>0.60). Therefore, the long-term harmonized SIF dataset with a fine 0.05° resolution is valuable for estimating global photosynthesis over extended periods. The LHSIF dataset is available at https://doi.org/10.5281/zenodo.16394372 (Zou et al., 2025).

Remotely sensed the canopy-level solar-induced chlorophyll fluorescence (SIF) is only a small fraction of the total SIF emitted by all leaves due to the scattering and reabsorption effects within the canopy. Obtaining the total SIF for all leaf emissions by accurately estimating the canopy fluorescence escape ratio (f_esc(Ω_v)) can help to mitigate the influence of non-physiological structural factors on the correlation between SIF and gross primary production (GPP); however, mechanistically robust and highly practical f_esc(Ω_v) estimation models in the near-infrared (NIR) region that account for interactions between the soil and vegetation canopy are lacking. In this study, according to the radiative transfer process of SIF photons inside a canopy, the canopy NIR f_esc(Ω_v) was divided into three parts: i) the scattering contribution from vegetation with no interaction with the soil, ii) the single scattering contribution from the soil, and iii) the contribution of multiple scatterings between the soil and canopy. By quantifying each of these components of NIR f_esc(Ω_v), a mechanistic model considering the interactions between the soil and canopy (MSC model) was derived for estimating NIR f_esc(Ω_v). The MSC model was validated using the Soil Canopy Observation of Photosynthesis and Energy fluxes (SCOPE) model and the Discrete Anisotropic Radiative Transfer (DART) model simulations, with an R² > 0.94 and an RMSE < 0.040, and the model continued to perform exceptionally well even for sparse canopies (SCOPE: R² = 0.93, RMSE = 0.037; DART: R² = 0.97, RMSE = 0.031). Specifically, it is applicable to conditions of sparse vegetation with bright soil backgrounds. It should be noted that although we proposed a parameterization scheme to simplify model calculations, limitations still remain. Nevertheless, the MSC model has excellent performance in retrieving the total SIF emitted by all leaves within the canopy for different canopy structures and soil backgrounds, paving the way for better understanding large-scale plant photosynthetic activities.

Global observations of solar-induced chlorophyll fluorescence (SIF) serve as a robust proxy for monitoring vegetation photosynthetic activity and elucidating the terrestrial carbon cycle. To date, several atmospheric remote sensing satellites have been deployed to generate global SIF products. However, accurate mapping of dual-band (red and far-red) SIF with daily temporal resolution and kilometer-level spatial resolution remains a critical gap, despite its significance for various applications. The Chinese next-generation greenhouse gas monitoring satellite, TanSat-2, is set to succeed the original TanSat satellite, aiming to record the fraction of greenhouse gases, pollutants, and SIF measurements from space. According to current schedules, TanSat-2 is slated for launch in 2026. This satellite will feature a wide swath of 2900 km, a high spatial resolution of 2 km at an orbit altitude of 7000 km, and near-daily global coverage. TanSat-2 is equipped with two spectral channels, 747–777 and 672–702 nm, operating at a spectral resolution of 0.12 nm, thereby offering significant potential for mapping SIF in both the red and the far-red bands. In this study, we explore the prospective capabilities of TanSat-2 for SIF retrieval through simulation experiments. First, we simulated the satellite's radiative transfer processes using the Moderate Resolution Atmospheric Transmission 5 (MODTRAN 5) and Soil Canopy Observation of Photosynthesis and Energy (SCOPE) models. An end-to-end orbit simulation dataset for TanSat-2 was generated by aggregating global bottom-of-atmosphere (BOA) reflectance, meteorological datasets, and the global Orbiting Carbon Observatory-2 (OCO-2) SIF dataset (GOSIF). We then assessed the theoretical accuracy of TanSat-2 based on this spectral simulation dataset, yielding root mean square error (RMSE) values of 0.24 and 0.19 mW m ⁻² sr ⁻¹ nm ⁻¹ for SIF retrievals at 740 and 685 nm, respectively. Finally, we examined the global prospects of TanSat-2 SIF retrievals using the end-to-end orbit simulations. Comparisons between the anticipated TanSat-2 SIF retrievals and GOSIF inputs revealed excellent correlation at both bands, with R ² values of 0.88 and 0.61 and RMSE values of 0.082 and 0.061 mW m ⁻² sr ⁻¹ nm ⁻¹ , respectively. Thus, TanSat-2 is poised to provide a valuable data resource for reliable SIF retrievals in the red and far-red bands, characterized by high spatiotemporal resolution on a global scale.