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Documenting improvement in leaf area index estimates from MODIS using hemispherical photos for Australian savannas

机译：使用澳大利亚大草原的半球照片记录来自MODIS的叶面积指数估计的改进

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This paper compares estimates of Leaf Area Index (LAI) obtained from the MODIS (Moderate Resolution Imaging Spectroradiometer) collections 4.8 (MC4)and 5.0 (MC5) with ground-based measurements taken along a 900 km north-south transect through savanna in the Northern Territory, Australia. There was excellent agreement for both the magnitude and timing in the annual variation in LAI from MC5 and biometric estimates at Howard Springs, near Darwin, whereas MC4 overestimated LAI by 1-2 m(2) m(-2) for the first 200 days of the year. Estimates of LAI from MC5 were also compared with those obtained from the analysis of digital hemispherical photographs taken during the dry season (September 2008) based on algorithms that included random and clumped distribution of leaves. Linear regression of LAI from MC5 versus that using the clumping algorithm yielded a slope close to 1 (m = 0.98). The regression based on a random distribution of leaves yielded a slope significantly different from 1 (m = 1.37), with higher Mean Absolute Error (MAE) and bias compared to the clumped analysis. The intercept for either analysis was not significantly different from zero but inclusion of five additional sites that were visually bare or without green vegetation produced a statistically significant offset of +0.16 m(2) m(-2) by MC5. Overall, our results show considerable improvement of MC5 over MC4 LAI and good agreement between MC5 and ground-based LAI estimates from hemispherical photos incorporating clumping of leaves
机译：本文比较了从MODIS（中等分辨率成像光谱仪）集合4.8（MC4）和5.0（MC5）获得的叶面积指数（LAI）的估计值，以及从北部穿过热带稀树草原沿南北900 km断面进行的地面测量结果澳大利亚领土。 MC5的LAI年度变化的幅度和时机以及达尔文附近的霍华德斯普林斯的生物特征估计均达成了极好的一致性，而MC4在前200天高估了1-2 m（2）m（-2）的LAI。的一年。 MC5的LAI估计值也与干旱季节（2008年9月）根据包括随机和成簇分布的叶子在内的算法对数字半球照片进行分析所获得的估计值进行了比较。来自MC5的LAI与使用成簇算法的LAI线性回归得出的斜率接近1（m = 0.98）。与成簇分析相比，基于叶片的随机分布进行的回归得出的斜率明显不同于1（m = 1.37），具有更高的平均绝对误差（MAE）和偏差。两种分析的截距均无明显差异（零），但包含5个在视觉上裸露或没有绿色植被的其他地点，MC5产生了统计上显着的偏移+0.16 m（2）m（-2）。总体而言，我们的结果表明，与结合了叶子团块的半球照片相比，MC5相对于MC4 LAI有了显着改善，并且MC5与地面LAI估计值之间具有良好的一致性

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