应用线性混合模型遥感监测冬小麦种植面积

    Linear mixture modeling applied to remote sensing monitoring of winter wheat areas

    • 摘要: 中分辨率成像光谱仪(MODIS)具有多光谱、多时相以及免费接收使用的优势。该文利用冬小麦返青期间的MODIS多光谱数据,采用传统的监督分类和阈值方法研究冬小麦种植区域的分布情况,同时针对遥感像元多为混合像元的特点,重点将线性混合像元分解技术应用于冬小麦种植面积的分解计算研究。比较不同分类方法对冬小麦种植面积估算的精度分析表明,采用线性混合分解模型,绝大部分(98.45%)的均方根误差都小于0.01,对比实际冬小麦种植面积数据,相对误差约3%,明显优于传统遥感分类方法的精度。

       

      Abstract: Moderate Resolution Imaging Spectroradiometer (MODIS) has advantages in the following aspects: multi-spectra, multi-temporal and freely obtained. Using traditional classification methods, super-classification and vegetation index thresholds, based on MODIS data during the winter wheat regreening stage, the distribution of winter wheat area was investigated in this study. Meanwhile, aiming at the characteristics that most remote sensing pixels are mixed pixels, application of linear mixture modeling to unmixing the planting area of winter wheat was mainly studied. Comparing the precision of different classification methods for the planting area of winter wheat, using linear mixture modeling, the major part (98.45%) of the root-mean-square error is smaller than 0.01, the relative error is approximately 3% compared with the actual winter wheat field data, obviously superior to the precision of traditional remote sensing classification method.

       

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