Retrieval of purification ability of urban forest to SO2 stress based on the coupling of radiative transfer and AO-DELM models

• Published in: International journal of applied earth observation and geoinformation 2024-03, Vol.127, p.103644, Article 103644
• Main Authors: Han, Aru, Bao, Yongbin, Tong, Zhijun, Liu, Xingpeng, Qing, Song, Bao, Yuhai, Zhang, Jiquan
• Format: Article
• Language: English
• Subjects: AO+DELM; PROSAIL; Remote sensing inversion; SO2 purification capacity; Spectral indices; Urban forest
• ISSN: 1569-8432; 1872-826X
• DOI: 10.1016/j.jag.2023.103644

•Simulating canopy reflectance of SO2 at different purification levels using PROSAIL model.•Hyperspectral indices NDI(653,688), DI(775, 772), RVI(653, 688) are the most sensitive.•Realizing the inversion of SO2 purification rates at the multispectral level. Currently, hyperspectral remote sensing technology used for vegetation monitoring mainly uses empirical and semi-empirical statistical methods to calculate heavy metal content. Combining physical models and machine learning algorithms is an effective method for estimating biochemical vegetation parameters without much ground measurement data. However, a deep extreme learning machine (DELM) has a faster training speed and better generalization ability. By introducing the Aquila Optimizer (AO) algorithm, the training process of the DELM method can be accelerated. This study combined the PROSAIL model, chlorophyll concentration information, and vegetation SO2 purification ability, and comprehensively applied physical and empirical models to analyze the optical characteristics of the urban forest SO2 purification rate and other factors. A coupled model (PROSAIL + AO + DELM) was constructed to simulate urban forests canopy SO2 purification ability and was then applied to remote sensing inversion. These results indicated that the Syringa oblate Lindl. (S. oblate) and Ulmus pumila “Jinye” (U. pumila) had moderate SO2 purification capacities, whereas that of Prunus cerasifera var. atropurpurea Jack. (P. cerasifera) were low. The SO2 purification rate was highly sensitive in the green, red, and red-edge spectral ranges. In the SO2 purification rate estimation model constructed by the subset (Corresponding to Sentinel-2 band), NDI, DI, and RVI indices, the PROSAIL + AO + DELM model had the best performance, with R2, root mean square error (RMSE), and relative percent deviation (RPD) of 0.73, 0.056, 1.61, and 0.68, 0.096, and 1.06 for T2 (low concentration) and T3 (high concentration) treatments, respectively. The PROSAIL + AO + DELM model was extended to multispectral images (Sentinel-2), where the results of the NDI model inversion were the closest to those of field monitoring. These results indicate that the urban forest SO2 purification rate model constructed in this study has the potential to be applied on a large scale. This study addresses the research gap regarding rapid, non-destructive, and low-input evaluation of plant purification capacity and achieves the non-destructive detection of plant purificatio