基于MODIS数据的长江三角洲地区近地表气温遥感反演

    Estimation of near surface air temperature from MODIS data in the Yangtze River Delta

    • 摘要: 近地表气温是一个重要的气候参数,为了给农业研究提供空间上连续的气温信息,以长江三角洲为研究区,根据MODIS地表温度和NDVI数据运用温度-植被指数方法反演了2005年全年的气温,并通过进一步去除温度-植被指数空间窗口的残余云和水体信息扩大了该方法的适用范围。最后利用气象站点观测气温数据对遥感反演值进行了精度验证,分析了误差的分布特征和变化规律。常规温度-植被指数方法的气温反演误差为2.39℃,但是只有72.23%的样本能适用该方法。在去除温度-植被指数窗口内残余云和水体信息之后,温度-植被指数方法适用样本比例提高到了80.15%,误差为2.44℃。温度-植被指数方法的反演精度在很大程度上受到空间窗口内植被覆盖及地表异质性的影响,在植被覆盖度较高的区域误差明显偏低。论文提出的改进温度-植被指数方法在农田区域及农作物生长期内具有很好的适用性和精度,为有效获取大范围农田气温提供了一种新的思路。

       

      Abstract: Near surface air temperature is an important meteorological parameter and is closely related to agriculture production. Comparing with the traditional meteorological observation, satellite remote sensing provides a straightforward and consistent way to obtain air temperature over regional and global scales with more spatially detailed information. In this paper, the temperature-vegetation index method was used to retrieve the air temperature throughout 2005 in the Yangtze River Delta by MODIS land surface temperature and NDVI data. The retrieved air temperatures were validated by the meteorological observed air temperatures. The estimating error was about 2.39℃ with the normal temperature-vegetation index method, but only 72.23% of the samples could be used by this method. After some additional rules were made to broaden the applied range of temperature-vegetation index method, the percentage of valid estimates increased to 80.15% and the estimating error slightly rose to 2.44℃. Further more, the characters and variations of retrieval error were also analyzed. Results show that the retrieval accuracy of temperature-vegetation index method is significantly influenced by the vegetation coverage and land surface heterogeneity in temperature-vegetation index context windows. Estimation errors in high vegetation covered areas are obviously lower than in other areas. The improved temperature-vegetation index method shows good applicability and accuracy in cropland areas during crop growing seasons, which can provide a new approach for acquisition of air temperature of cropland in large-scale.

       

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