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Analysis of Temporal Variability of MODIS Leaf Area Index (LAI) Product over Temperate Forest in Korea

机译:温带韩国温带林型叶片区域指数(LAI)产品的时间变异性分析

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MODIS LAI product is very important because it has potential for the monitoring of global scale ecosystem, global change of carbon and water. This study aims to analyze the temporal variability of annual MODIS LAI product using field measured LAI dataset for four vegetation types. The MODIS annual LAI pattern shows the high variety or inaccuracy of LAI during rainy season over South Korea. This phenomenon is caused by high cloud coverage and fails of processing by a radiative transfer model in this rainy season. Annual LAI pattern estimated by a radiative transfer model has least variance of LAI values during this time. In coniferous forest, MODIS annual LAI pattern shows the abnormal decreasing of LAI during non-growing season comparing with field-measured LAI. This decrease of LAI being an inaccurate phenomenon is caused by rather higher cloud coverage and decreasing of understory plants. MODIS LAI product in deciduous and grass coverage is underestimated by comparing with field-measured LAI. In rice paddy, MODIS LAI shows longer growing season than field measured LAI. For the accurate analysis of temporal MODIS LAI product, it needs considering of vegetation coverage types, cloud coverage, and algorithm types of MODIS LAI.
机译:Modis Lai产品非常重要,因为它具有监测全球规模生态系统,全球碳和水的变化。本研究旨在使用现场测量的Lai DataSet分析每年Modis Lai产品的时间变异性,用于四种植被类型。 Modis年度Lai模式显示韩国雨季赖的高品种或不准确性。这种现象是由高云覆盖率引起的,并且在这个雨季通过辐射转移模型进行了处理。通过辐射转移模型估算的年度LAI模式在此期间赖值的变化最小。在针叶林中,MODIS年度LAI图案显示与现场测量的LAI相比,在非生长季节期间莱的异常下降。 Lai的这种减少是不准确的现象是由相当更高的云覆盖率和抑制植物的降低引起的。通过与现场测量的Lai进行比较,低估了落叶和草地覆盖的Modis Lai产品。在稻米稻谷,Modis Lai表现出比田野的季节更长。为了准确分析时间MODIS LAI产品,需要考虑植被覆盖类型,云覆盖率和MODIS LAI的算法类型。

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