Abstract Land cover mixture at moderate- to coarse-resolution is an important cause for the uncertainty of global leaf area index (LAI) products. The accuracy of LAI retrievals over land-water mixed pixels is adversely impacted because water absorbs considerable solar radiation and thus can greatly lower pixel-level reflectance especially in the near-infrared wavelength. Here we proposed an approach named Reduced Water Effect (RWE) to improve the accuracy of LAI retrievals by accounting for water-induced negative bias in reflectances. The RWE consists of three parts: water area fraction (WAF) calculation, subpixel water reflectance computation in land-water mixed pixels and LAI retrieval using the operational MODIS LAI algorithm. The performance of RWE was carefully evaluated using the aggregated Landsat ETM+ reflectance of water pixels over different regions and observation dates and the aggregated 30-m LAI reference maps over three sites in the moderate-resolution pixel grid (500-m). Our results suggest that the mean absolute errors of water endmember reflectance in red and NIR bands were both

中文翻译：

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改进与水混合的异质表面的叶面积指数检索

摘要 中到粗分辨率的土地覆盖混合是导致全球叶面积指数（LAI）产品不确定性的重要原因。LAI 检索陆水混合像素的准确性受到不利影响，因为水吸收了大量的太阳辐射，因此可以大大降低像素级反射率，尤其是在近红外波长。在这里，我们提出了一种名为减少水效应 (RWE) 的方法，通过考虑水引起的反射率负偏差来提高 LAI 检索的准确性。RWE 由三部分组成：水域面积分数 (WAF) 计算、陆水混合像素中的亚像素水反射率计算和使用操作 MODIS LAI 算法的 LAI 检索。使用不同区域和观测日期上水像素的聚合 Landsat ETM+ 反射率以及中等分辨率像素网格 (500-m) 中三个站点的聚合 30-m LAI 参考图仔细评估了 RWE 的性能。我们的结果表明，红色和 NIR 波段的水端元反射率的平均绝对误差均为