夏传福, 李 静, 柳钦火. 基于MODIS 叶面积指数的遥感物候产品反演方法[J]. 农业工程学报, 2012, 28(19): 103-109.
    引用本文: 夏传福, 李 静, 柳钦火. 基于MODIS 叶面积指数的遥感物候产品反演方法[J]. 农业工程学报, 2012, 28(19): 103-109.
    Xia Chuanfu, Li Jing, Liu Qinhuo. Vegetation phenology monitoring method using time-series MODIS LAI data[J]. Transactions of the Chinese Society of Agricultural Engineering (Transactions of the CSAE), 2012, 28(19): 103-109.
    Citation: Xia Chuanfu, Li Jing, Liu Qinhuo. Vegetation phenology monitoring method using time-series MODIS LAI data[J]. Transactions of the Chinese Society of Agricultural Engineering (Transactions of the CSAE), 2012, 28(19): 103-109.

    基于MODIS 叶面积指数的遥感物候产品反演方法

    Vegetation phenology monitoring method using time-series MODIS LAI data

    • 摘要: 物候信息是研究植被与气候、环境间关系的重要资料。近年来,遥感技术为物候研究提供了新的手段, 物候遥感监测数据已经被广泛应用于植物物候监测、时空变化及其变化响应的研究。然而,有限的遥感物候产品已经限制了遥感物候监测的应用和发展。在总结已有遥感物候监测方法和遥感物候产品的基础上,该文提出一种更具普适性,相对稳定、可靠的遥感物候产品反演生产方法。以2007年中国区域为例,利用地面物候观测数据对结果验证,平均绝对误差为?20.6~15.3 d,均方根误差达4.0~33.5,二者的相关系数达0.404~0.887,生长周期数反演准确率达到90.12%,该文算法反演成功率达99.93%,较MODIS MLCD产品83.06%的成功率提高了16.87%。

       

      Abstract: Vegetation phenology can provide important informations which reflect the response of vegetation to climate and environment. Recently, remote sensing has become a new way to monitor vegetation phenology. Phenology from remote sensing has been widely used to monitor the response to climate. However, there were very few regional-to-global phenology products that limited the study and application of phenology. Based on the summary of existing methods, the paper proposed a new method which is more universal, stable and practical. This method was employed in China using time-series MODIS LAI data in 2007. The results were validated with ground phenology observations and MLCD data. The root mean square errors (RMSE) for different vegetation type were 4.0~33.5, the mean absolute errors were ?20.6~15.3 and the correlations were 0.404~0.887. By comparison with MLCD data, the success rate and the accuracy of the method have been highly improved.

       

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