A Semi-empirical Approach for Decomposition of Remotely Sensed Leaf Area Index into Overstory and Understory Components over Russian Forests
Forest is a multi-layered 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’ve developed a semi-empirical 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 paper 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 foliage growth, including the "understory seasonal dip effect". In terms of practical aspects, we generated time series of the MODIS layered LAI product for 2001-2020 at the spatial resolution of 230-m over Russian forests. We calculated mean layered LAI of species and contrasted with typical values from the literature surveys. According to our estimates relative contribution of understory LAI increases from South to North- 28% of forests pixels have understory LAI which exceeds that of overstory, 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.