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Solar-induced chlorophyll fluorescence (SIF) serves as a valuable proxy for photosynthesis. The TROPOspheric Monitoring Instrument (TROPOMI) aboard the Copernicus Sentinel-5P mission offers nearly global coverage with a fine spectral resolution for reliable SIF retrieval. However, the present satellite-derived SIF datasets are accessible only at coarse spatial resolutions, constraining its applications at fine scales. Here, we utilized a weighted stacking algorithm to generate a high spatial resolution SIF dataset (500 m, 8-day) in China (HCSIF) from 2000 to 2022 from the TROPOMI with a spatial resolution at a nadir of 3.5 km by 5.6-7 km. Our algorithm demonstrated high accuracy on validation datasets (R = 0.87, RMSE = 0.057 mW/m/nm/sr). The HCSIF dataset was evaluated against OCO-2 SIF, GOME-2 SIF tower-based measurements of SIF, and gross primary productivity (GPP) from flux towers. We expect this dataset can potentially advance the understanding of fine-scale terrestrial ecological processes, allowing for monitoring of ecosystem biodiversity and precise assessment of crop health, productivity, and stress levels in the long term.
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http://dx.doi.org/10.1038/s41597-024-04101-6 | DOI Listing |
Photosynth Res
September 2025
Optics of Photosynthesis Laboratory, Institute for Atmospheric and Earth System Research (INAR)/Forest Sciences, Viikki Plant Science Centre (ViPS), University of Helsinki, Helsinki, 00014, Finland.
Pulse-amplitude modulated (PAM) chlorophyll fluorescence (ChlF) measurements provide a non-invasive method to study the regulation of the light reactions of photosynthesis in situ. PAM ChlF contributes also to the advancement of the interpretation of long-term observations of remotely sensed solar induced fluorescence by revealing the mechanistic connection between ChlF and photosynthetic function. However, long-term field PAM ChlF measurements remain uncommon due to challenges associated with the outdoor environment, instrument installation and maintenance, or data processing and interpretation.
View Article and Find Full Text PDFPhotosynthetica
August 2025
ICAR-National Rice Research Institute, Cuttack, Odisha, India.
Ensuring global food security requires noninvasive techniques for optimizing resource use and monitoring crop health. Hyperspectral imaging (HSI) enables the precise analysis of plant physiology by capturing spectral data across narrow bands. This review explores HSI's role in agriculture, particularly its integration with unmanned aerial vehicles, AI-driven analytics, and machine learning.
View Article and Find Full Text PDFFront Plant Sci
July 2025
College of Geography and Remote Sensing Sciences, Xinjiang University, Urumqi, China.
Introduction: Soil salinization in Central Asia and Xinjiang, China, poses serious threats to agriculture and ecosystems. Solar-induced chlorophyll fluorescence (SIF), which reflects plant photosynthetic status and stress, shows promise for monitoring salinity but remains underutilized in this region.
Methods: This study integrated SIF-derived indices (SIFI) with soil salinity data to build a region-specific prediction model.
Front Plant Sci
June 2025
Data and Geospatial Intelligence, Scion, Christchurch, New Zealand.
Introduction: Phenotyping is critical in tree breeding, but traditional methods are often labour-intensive and not easily scalable. Resistance to biotic and abiotic stress is a key focus in tree breeding programmes. While heritable traits derived from spectral remote sensing have been identified in trees, their application to tree phenotyping remains unexplored.
View Article and Find Full Text PDFFront Plant Sci
May 2025
Electronic Information School, Wuhan University, Wuhan, China.
The coupling between Gross Primary Productivity (GPP) and Solar-Induced Chlorophyll Fluorescence (SIF) is crucial for understanding terrestrial carbon cycles, with the GPP/SIF ratio regulated by canopy structure, environmental change, and other factors. While studies on canopy structure focus on how internal structure regulates light use efficiency, the impact of remotely sensed canopy structural parameters, particularly Fractional Vegetation Cover (FVC) and Leaf Area Index (LAI), on GPP-SIF coupling remains understudied. Investigating the response of canopy structure to GPP-SIF in large-scale forests supports high-accuracy GPP estimation.
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