作者：Miao, S., Lyu, H., Liu, H., Li, Y., Ruan, X. & Liu, W.

Terrestrial dissolved organic matter (tDOM), a key regulator of lake ecosystems and the global carbon cycle, significantly influences underwater light regimes, thermal structure, and biogeochemical cycles. However, quantifying the fluorescence intensity and spatiotemporal patterns of tDOM is a persistent challenge for traditional monitoring techniques, which creates a critical gap in regional carbon budget estimations and the understanding of large-scale ecosystem effects. To overcome this limitation, a novel remote sensing algorithm was developed to retrieve tDOM, which is characterized by the fluorescence intensity of fulvic acid-like and humic acid-like components, based on the absorption characteristics of colored dissolved organic matter (CDOM). This approach was subsequently applied to assess the spatiotemporal dynamics and long-term trends of tDOM across Yangtze River Delta (YRD) lakes from 1986 to 2024. The robustness of the algorithm was confirmed using both independent in-situ and satellite-ground match-up datasets (median absolute percentage error < 30%). The satellite-derived tDOM revealed significant spatiotemporal heterogeneity in the YRD. Trend analysis indicated that tDOM in the most lakes (63.46%) significantly increased, whereas it decreased in 17.31% and remained stable in 19.23% of the lakes. Furthermore, the random forest model results revealed that temperature and wind speed were the key drivers influencing the tDOM dynamics, acting synergistically with anthropogenic pressure which was associated with elevated tDOM levels. This study provides a feasible algorithm for large-scale tDOM monitoring and highlights the critical influences of climate change and anthropogenic activities on the estimation of lake carbon storage in rapidly developing regions.

陆源溶解有机质（tDOM）是湖泊生态系统和全球碳循环的关键调节因子，显著影响水下光场、热力结构和生物地球化学循环。然而，量化tDOM的荧光强度和时空格局对传统监测技术而言始终是一个挑战，这导致区域碳收支估算和对大规模生态系统效应理解方面存在关键空白。为克服这一局限，本研究基于有色溶解有机质（CDOM）的吸收特性，开发了一种新型遥感算法来反演tDOM，并以类富里酸和类腐殖酸组分的荧光强度对其进行表征。随后将该方法应用于评估1986至2024年间长三角（YRD）湖泊中tDOM的时空动态和长期趋势。该算法的稳健性通过独立的现场实测数据集和星地匹配数据集得到验证（中位绝对百分比误差 < 30%）。卫星反演的tDOM揭示了长三角地区显著的时空异质性。趋势分析表明，大多数湖泊（63.46%）的tDOM显著上升，17.31%的湖泊下降，19.23%的湖泊保持稳定。此外，随机森林模型结果显示，温度和风速是影响tDOM动态的关键驱动因子，且与导致tDOM水平升高的人为压力协同作用。本研究为大规模tDOM监测提供了一种可行算法，并凸显了气候变化和人为活动对快速发展地区湖泊碳储量估算的重要影响。

（来源：Journal of Hydrology 2026  DOI: 10.1016/j.jhydrol.2026.135037）