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首页> 外文期刊>Journal of Geophysical Research. Biogeosciences >A satellite-based method for monitoring seasonality in the overstory leaf area index of Siberian larch forest
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A satellite-based method for monitoring seasonality in the overstory leaf area index of Siberian larch forest

机译：基于卫星的西伯利亚落叶松林地上叶面积指数季节性监测方法

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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 LAl_u and true LAI instead of effective LAI.
机译：需要对叶面积指数（LAI）进行可靠的监视，以进一步了解森林的碳，水和能量循环。在这项研究中，我们针对覆盖西伯利亚东部的落叶松林提出了一种新的基于卫星的方法来分别估算上层LAI（LAI_o）和下层LAI（LAI_u）。我们对代表落叶松林结构的森林场景进行了建模，特别考虑了落叶松的典型丛生枝结构，然后在各种森林条件下进行了三维辐射传递模拟，以建立LAI_o与归一化差异水指数的季节性增加之间的关系。叶出现后。基于模型的灵敏度分析表明，在已知噪声水平下，最大误差高达26％。在大陆范围平均，我们估计的总LAI，CYCLOPES 3.1版和中分辨率成像光谱仪MOD15 Collection 5（主要算法）在夏季显示了相似的范围。但是，LAI的空间格局略有不同，而CYCLOPES LAI的变异性更平滑。我们的LAI和CYLOPES有效LAI再现了一个现实的季节变化，其中LAI_o的春季增加的确切时机。 MOD15 Collection 5的主要缺点是不切实际的强烈的时间变异性，以及LAI开始早于叶片过早出现的事实开始增加的事实。总体而言，结果表明我们的新方法是MOD15 Collection 5和CYCLOPES的很好替代方法，因为它提供了LAI_o和LAl_u以及真实LAI的单独估计，而不是有效LAI。

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