A satellite‐based method for monitoring seasonality in the overstory leaf area index of Siberian larch forest

Abstract

[1] Reliable monitoring of the leaf area index (LAI) is required to further understand the carbon, water, and energy cycles of forests. In this study, we proposed a new satellite-based method to estimate the overstory LAI (LAI o ) separately from the understory LAI (LAI u ) for larch forests covering eastern Siberia. We modeled forest scenes representative of larch forest structure, with particular consideration of the typical clumped shoot structure of larch. Three-dimensional radiative transfer simulations were then conducted under various forest conditions to establish the relationships between LAI o and seasonal increases in the normalized difference water index after leaf appearance. Model-based sensitivity analyses indicated a maximum error of up to 26% under known noise levels. Averaged at the continental scale, total LAI from our estimates, the CYCLOPES version 3.1, and the Moderate Resolution Imaging Spectroradiometer MOD15 Collection 5 (main algorithm) showed similar ranges in summer. However, spatial pattern of LAI was slightly different, with smoother variability for CYCLOPES LAI. Our LAI and CYLOPES effective LAI reproduced a realistic seasonal variation with exact timing of spring increase in LAI o . The main drawbacks of MOD15 Collection 5 were unrealistically strong temporal variability, and the fact that LAI began to increase earlier than the overstory leaf appearance date. Overall, the results show that our new method is a good alternative to MOD15 Collection 5 and CYCLOPES, as it provides separate estimates of LAI o and LAI u and true LAI instead of effective LAI.Citation: Kobayashi, H., N. Delbart, R. Suzuki, and K. Kushida (2010), A satellite-based method for monitoring seasonality in the overstory leaf area index of Siberian larch forest,

Mapping global seasonal forest background reflectivity with Multi‐angle Imaging Spectroradiometer data

“…The area with the highest density (dark red) strays slightly from the 1:1 line, indicating that the referenced LAIu is little smaller than our estimation. This may be attributed to the appearance of evergreen trees in larch stands which deduces the NDWI measurements and finally results in the underestimation of understory LAI [Kobayashi et al, 2010].…”

“…Given the challenges of direct validation of forest background reflectivities, the understory LAI (LAIu) estimated from forest background reflectivities was compared with a reference data set [Kobayashi et al, 2010] over the larch forest areas in North Asia (Figure 5). Based on the assumption that forest background has similar composition as grassland, the LAIu here was retrieved from SR (simple ratio) of background by using the global LAI mapping algorithm [Deng et al, 2006] combined with a technique of look-up table [Liu et al, 2007].…”

“…In addition, the lack of information about forest background would introduce large uncertainties in modeling forest canopy reflectance and in interpreting the measured reflectance spectra [Ahl et al, 2006;Eriksson et al, 2006;Wang et al, 2004]. For example, the overestimation of LAI in many global products would mainly be attributed to the limited optical information about understory vegetation [Garrigues et al, 2008;Kobayashi et al, 2010].…”

Exploiting Differential Vegetation Phenology for Satellite-Based Mapping of Semiarid Grass Vegetation in the Southwestern United States and Northern Mexico

“…Interpretation of such metrics is straightforward for land areas with a dominant vegetation type or a common phenology, but can be complex for areas with horizontal (surface cover) or vertical (understory and overstory) variation in vegetation type and phenology [41][42][43][44]. In the mixed-composition landscapes of the Southwest, the observed time signal may be composed of one or more discrete and potentially unique signals attributable to the individual grass, shrub, tree and succulent components of the landscape, with their respective contributions modulated by their fractional surface cover within the pixel.…”

Evaluation of Sub-Pixel Cloud Noises on MODIS Daily Spectral Indices Based on in situ Measurements

“…Remotely sensed spectral indices calculated from a combination of band reflectances are widely used to monitor biophysical quantities related to the terrestrial ecosystem, such as leaf area index [1][2][3][4] and leaf onset/offset phenology [5,6]. However, occasionally, clouds contaminate these data, and therefore, obscure the monitoring by optical remote sensing satellites.…”