Solar-inspired chlorophyll fluorescence (SIF) is an important indicator of vegetation photosynthesis, and in recent years, tower-based SIF measure It is done. However, current SIF recover algorithms are plagued by uncertainties, especially introduced by atmospheric conditions and measurements geometry. These uncertainty can distort the full pattern of SIF, making it difficult to monitor photosynthesis throughout the day. To resolve these challenges, there is a growing need for progress in the tower-based SIF recovery algorithms.
On January 17, 2025, a research team of Aerospace Information Research Institute of Chinese Academy of Sciences published Study (doi: 10.34133/remotesensing.0429) In JOur remote sensing This SIF deal with these uncertainties in the recovery algorithm. The purpose of his research is to increase the accuracy of monitoring vegetation photosynthesis by improving the reliability of SIF data, addressing significant gaps in ecological and agricultural research.
The main innovation of the study lies in the comprehensive evaluation of the three algorithms to recover away from the tower-based comments. The band shape fitting (BSF) algorithm emerged as the most reliable, especially during the afternoon, performing better performance in capturing the full pattern of SIF. BSF algorithm excels by dismaling atmospheric absorption from SIF signals without the need for atmospheric reforms – a profit on traditional methods. In contrast, the monotonal vector decomposition (SVD) algorithm showed significant deviations, especially in the afternoon, while the three-band Fronhofer line discrimination (3fLD) algorithm required accurate atmospheric improvement to achieve comparable results. The BSF algorithm obtained a correlation coefficient (R of) of 0.85 with vegetation photosynthesis, which is greatly improved the other algorithm and reflects the ability to correctly capture the complete variation of vegetation photosynthesis. .
The research was conducted on two flux sites in China, with a height of a measure of 25 meters and 4 meters. The study recovered the SIF’s diadent pattern using various algorithms and assessed their correlation with vegetation photosynthesis and near-reflection reflection (NIRVR). The results showed that the BSF algorithm provided the most stable and accurate SIF recovery, especially during the period of high solar radiation. The study also emphasized the importance of refining atmospheric improvement techniques to further enhance SIF recovery accuracy. These findings outline the capacity of the BSF algorithm to improve the accuracy of vegetation monitoring, providing more reliable data for ecological and agricultural research.
“This study provides valuable insights for the development of the tower-based SIF recovery algorithms,” said the lead researcher. “By optimizing these algorithms, we are important to understand the dynamics of the botanical ecosystems, with more accuracy in flora photosynthesis.
The study collected three SIF recovery algorithms-BSF, 3fLD, and SVD- and collecting data from tower-based spectral and flux measurement on two sites. The performance of each algorithm was evaluated by comparing the full pattern of the recovered SIF with botanical photosynthesis and NIRVR data.
Progress in SIF recovering algorithms makes significant promises to applications in ecology, agricultural and climate change research. By providing accurate monitoring of complete variation in vegetation photosynthesis, these algorithms can provide deep insight into the mobility of the vegetation, which helps in indicating climate change mitigation strategies. In addition, better algorithms can eventually be applied to satellite remote sensing, which increases the accuracy and efficiency of global botanical monitoring efforts.
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Reference
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Original source url
https://doi.org/10.34133/remotesensing.0429
Money information
The research was funded by China’s National Key Research and Development Program (2022YFF1301900) and China’s National Natural Science Foundation (42071310, 42425001).
About this Remote sensing journal
Remote sensing journal, An online-open access journal, published in collaboration with Air-Case promotes the principle of remote sensing, science and technology as well as interdisciplinary research within Earth and Information Sciences.
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