Ecological Processes (Jun 2023)

Comparing leaf area index estimates in a Mediterranean forest using field measurements, Landsat 8, and Sentinel-2 data

  • Alessandro Sebastiani,
  • Riccardo Salvati,
  • Fausto Manes

DOI
https://doi.org/10.1186/s13717-023-00441-0
Journal volume & issue
Vol. 12, no. 1
pp. 1 – 13

Abstract

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Abstract 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 8. Results 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 R 2 of 0.55 and NRMSE of 14.74%, compared to R 2 of 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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