Shabanov N.V., Bartalev S.A., Kobayashi H., Shin N., Кhovratovich T.S., Zharko V.O., Medvedev A.A., Telnova Kh.O. A Semiempirical Approach for Decomposition of Remotely Sensed Leaf Area Index into Overstory and Understory Components over Russian Forests // IEEE Transactions on Geoscience and Remote Sensing. 2023. DOI: 10.1109/TGRS.2023.3287075.
Abstract: Forest is a multilayered canopy, where overstory and understory implement different biogeochemical cycles, phenology, and functional role. Remote sensing products typically estimate forest total leaf area index (LAI), while few quantify its components. The theoretical understanding of foliage distribution between layers is still quite limited. In this study, we have developed a semiempirical model for decomposition of forest total LAI between layers. Decomposition was implemented over the full extent of Russian forests, exhibiting a wide dynamic range of the forest total LAI. This article addresses both the theoretical and practical aspects of the problem. In terms of theory, we formalized the principles of forest layers “biological/ radiometric coupling into a parametric model allowing to analyze the relationship between overstory/understory LAI and overstory crown fraction. The model captures various features of layers of foliage growth, including the “understory seasonal dip effect.” In terms of practical aspects, we generated time series of the moderate resolution imaging spectroradiometer (MODIS)-layered LAI product for 2001–2020 at the spatial resolution of 230 m over Russian forests. We calculated the mean layered LAI of species and contrasted with typical values from the literature surveys. According to our estimates, the relative contribution of understory LAI increases from South to North—28% of forest pixels have understory LAI, which exceeds that of overstory, and those pixels are located in the northern part of the eastern Siberia and occupied by larch forests. The layered LAI product was intercompared/validated with multiple ground and remote sensing data.
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