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首页> 外文期刊>Remote Sensing >Comparison of the Spatial Characteristics of Four Remotely Sensed Leaf Area Index Products over China: Direct Validation and Relative Uncertainties
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Comparison of the Spatial Characteristics of Four Remotely Sensed Leaf Area Index Products over China: Direct Validation and Relative Uncertainties

机译：中国四种遥感叶面积指数产品的空间特​​征比较：直接验证和相对不确定性

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摘要

Leaf area index (LAI) is a key input for many land surface models, ecological models, and yield prediction models. In order to make the model simulation and/or prediction more reliable and applicable, it is crucial to know the characteristics and uncertainties of remotely sensed LAI products before they are input into models. In this study, we conducted a comparison of four global remotely sensed LAI products—Global Land Surface Satellite (GLASS), Global LAI Product of Beijing Normal University (GLOBALBNU), Global LAI Map of Chinese Academy of Sciences (GLOBMAP), and Moderate-resolution Imaging Spectrometer (MODIS) LAI, while the former three products are newly developed by three Chinese research groups on the basis of the MODIS land reflectance product over China between 2001 and 2011. Direct validation by comparing the four products to ground LAI observations both globally and over China demonstrates that GLASS LAI shows the best performance, with R 2 = 0.70 and RMSE = 0.96 globally and R 2 = 0.94 and RMSE = 0.61 over China; MODIS performs worst ( R 2 = 0.55, RMSE = 1.23 globally and R 2 = 0.03, RMSE = 2.12 over China), and GLOBALBNU and GLOBMAP performs moderately. Comparison of the four products shows that they are generally consistent with each other, giving the smallest spatial correlation coefficient of 0.7 and the relative standard deviation around the order of 0.3. Compared with MODIS LAI, GLOBALBNU LAI is the most similar, followed by GLASS LAI and GLOBMAP. Large differences mainly occur in southern regions of China. LAI difference analysis indicates that evergreen needleleaf forest (ENF), woody savannas (SAV) biome types and temperate dry hot summer, temperate warm summer dry winter and temperate hot summer no dry season climate types correspond to high standard deviation, while ENF and grassland (GRA) biome types and temperate warm summer dry winter and cold dry winter warm summer climate types are responsible for the large relative standard deviation of the four products. Our results indicate that although the three newly developed products have improved the accuracy of LAI estimates, much work remains to improve the LAI products especially in ENF, SAV, and GRA regions and temperate climate zones. Findings from our study can provide guidance to communities regarding the performance of different LAI products over mainland China.
机译：叶面积指数（LAI）是许多土地表面模型，生态模型和产量预测模型的关键输入。为了使模型仿真和/或预测更加可靠和适用，在将遥感LAI产品输入模型之前了解它们的特性和不确定性至关重要。在这项研究中，我们对四种全球遥感LAI产品进行了比较：全球地面卫星（GLASS），北京师范大学全球LAI产品（GLOBALBNU），中国科学院全球LAI地图（GLOBMAP）和中度分辨率成像光谱仪（MODIS）LAI，而前三个产品是由三个中国研究小组在2001年至2011年间基于中国的MODIS地面反射率产品新开发的。通过将这四个产品与全球地面LAI观测值进行比较来直接验证中国各地的情况表明，GLASS LAI表现最佳，在全球范围内，R 2 = 0.70和RMSE = 0.96，R 2 = 0.94和RMSE = 0.61; MODIS的表现最差（全球范围内R 2 = 0.55，RMSE = 1.23，而在中国范围内R 2 = 0.03，RMSE = 2.12），而GLOBALBNU和GLOBMAP表现中等。四种产品的比较表明，它们通常彼此一致，最小的空间相关系数为0.7，相对标准偏差为0.3量级。与MODIS LAI相比，GLOBALBNU LAI最相似，其次是GLASS LAI和GLOBMAP。较大的差异主要发生在中国南部地区。 LAI差异分析表明，常绿针叶林（ENF），木本稀树草原（SAV）生物群落类型和温带干燥夏季，温带温暖夏季干燥冬季和温带夏季无干旱季节气候类型对应于高标准偏差，而ENF和草地（ GRA）生物群落类型和温带夏季干燥冬季和寒冷干燥冬季温暖夏季的气候类型是造成这四种产品相对标准偏差较大的原因。我们的结果表明，尽管这三种新开发的产品提高了LAI估算的准确性，但仍需进行大量工作来改进LAI产品，尤其是在ENF，SAV和GRA地区以及温带气候区。我们的研究结果可以为社区提供有关中国大陆不同LAI产品性能的指导。

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