Comparing leaf area index estimates in a Mediterranean forest using field measurements, Landsat 8, and Sentinel-2 data
作者机构：Research Centre for Forestry and Wood(FL) Council for Agricultural Research and Economics(CREA) Research Institute On Terrestrial Ecosystems(IRET) National Research Council of Italy(CNR) Presidential Estate of Castelporziano Department of Environmental Biology Sapienza University of Rome
出 版 物：《Ecological Processes》 (生态过程(英文))
年 卷 期：2023年第12卷第2期
页 面：187-199页
核心收录：
学科分类：0830[工学-环境科学与工程（可授工学、理学、农学学位）] 09[农学] 0903[农学-农业资源与环境] 0713[理学-生态学]
基 金：Servizi Ecosistemici e Infrastrutture Verdi urbane e peri-urbane nell’area Metropolitana Romana:stima del contributo delle foreste naturali di Castelporziano nel miglioramento della qualità dell’aria della città di Roma Accademia Nazionale delle Scienze detta dei XL, in collaborazione con Segretariato Generale della Presidenza della Repubblica PRO-ICOS_MED Potenziamento della Rete di Osservazione ICOS-Italia nel Mediterraneo—Rafforzamento del capitale umano” funded by the Ministry of Research PNRR, Missione 4, Componente 2, Avviso 3264/2021, IR0000032—ITINERIS—Italian Integrated Environmental Research Infrastructures System CUP B53C22002150006
主 题：Mediterranean forest Leaf area index Field measurement Multispectral satellite imagery Sentinel-2 Landsat 8 Spectral vegetation index Global change
摘 要：Background Leaf area index（LAI） is a key indicator for the assessment of the canopy s processes such as net primary production and evapotranspiration. For this reason, the LAI is often used as a key input parameter in ecosystem services modeling, which is emerging as a critical tool for steering upcoming urban reforestation strategies. However, LAI field measures are extremely time-consuming and require remarkable economic and human resources. In this context, spectral indices computed using high-resolution multispectral satellite imagery like Sentinel-2 and Landsat 8, may represent a feasible and economic solution for estimating the LAI at the city scale. Nonetheless, as far as we know, only a few studies have assessed the potential of Sentinel-2 and Landsat 8 data doing so in Mediterranean forest ecosystems. To fill such a gap, we assessed the performance of 10 spectral indices derived from Sentinel-2 and Landsat 8 data in estimating the LAI, using field measurements collected with the LI-COR LAI 2200c as a reference. We hypothesized that Sentinel-2 data, owing to their finer spatial and spectral resolution, perform better in estimating vegetation s structural parameters compared to Landsat *** We found that Landsat 8-derived models have, on average, a slightly better performance, with the best model（the one based on NDVI） showing an R2of 0.55 and NRMSE of 14.74%, compared to R2of 0.52 and NRMSE of 15.15% showed by the best Sentinel-2 model, which is based on the NBR. All models were affected by spectrum saturation for high LAI values（e.g., above 5）.Conclusion In Mediterranean ecosystems, Sentinel-2 and Landsat 8 data produce moderately accurate LAI estimates during the peak of the growing season. Therefore, the uncertainty introduced using satellite-derived LAI in ecosystem services assessments should be systematically accounted for.
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