收稿：2018-12-10，
纸质出版：2020-09-07
移动端阅览
作物茬覆盖度的估算对于探究农业耕作方式对周围环境的影响具有十分重要的意义。目前，基于多光谱影像的作物茬指数是作物茬覆盖度估算的常用方法。然而，在作物茬高覆盖区域，指数法容易出现“饱和”现象。已有研究结果表明结合影像的光谱与纹理信息有助于改善指数法的“饱和”问题。Sentinel-2作为一颗多光谱卫星，空间分辨率可达10 m，与Landsat OLI相比，具有更丰富的纹理信息。因此，探究Sentinel-2光谱与纹理信息相结合在作物茬覆盖度估算上的潜力具有重要意义。本文以山东省禹城市为研究区，分析了Sentinel-2各波段反射率、归一化差值指数以及不同窗口大小下灰度共生矩阵统计量等遥感因子与野外实测作物茬覆盖度的相关性，并利用最优子集法对遥感因子进行筛选，构建作物茬覆盖度的最优估算模型。同时，使用留一法交叉验证对模型进行评价。结果表明在单因子分析中，归一化差异耕作指数NDTI（Normalized Difference Residue Index）与作物茬覆盖度的相关性最好，相关系数达0.735。使用NDTI、5×5窗口下Sentinel-2 8A波段的相关性统计量以及12波段的方差统计量构建的多元方程是作物茬覆盖度估算的最优模型，相关系数为0.869，均方根误差为11%。与仅使用光谱信息的最优模型相比，相关系数提高了0.094，均方根误差下降了3.5%。可见，结合Sentinel-2的纹理信息有助于提高作物茬覆盖度的估算精度。
As an important element of farmland ecosystems
Crop Residues Cover (CRC) provides a barrier against water erosion and improves soil structure and organic matter content. Timely and accurate estimation of CRC at regional scale is essential for understanding the ecosystem condition and interactions with the surrounding environment. Satellite remote sensing is an effective method of regional CRC estimation. Tillage indices based on multi-spectral satellite imagery data are commonly used in CRC estimation. However
this method is ineffective in high coverage areas due to “saturation”. Previous studies have shown that a combination of image spectral and textural information can solve saturation problems to a certain extent. Sentinel-2 is a new satellite mission that can provide observations at multi-spectral bands with spatial resolutions of 10
20
and 60 m. Sentinel-2 can provide more information about texture compared with the commonly used multi-spectral satellite Landsat-8 Operational Land Imager. Therefore
exploring the potential of combining spectral and textural information from Sentinel-2 data is an important task in CRC estimation.
The objectives of this study are to (1) analyze correlation between field measured CRC and satellite-derived variables such as Sentinel-2 band reflectance
tillage indices
and gray-level co-occurrence matrix statistics in different windows
and (2) determine the optimal CRC estimation method from optimal subset regression with various combinations of tillage indices and image textural features.
The results showed that the Normalized Difference Tillage Index (NDTI)
B12_CO (contrast of band12
B12 in window 5×5)
and B12_DI (dissimilarity of B12 in window 5×5) were significantly correlated with the measured CRC with correlation coefficient R values of 0.765
–0.641
–0.553. The estimation model based on NDTI outperformed the models based on other single variables. The model that combined the spectral and textural information in an optimal window (
R
=0.869， RMSE=11.0%) provided a more precise result than that based solely on spectral information (
R
=0.775 and RMSE=14.5%). The results demonstrated that a combination of spectral and textural information can improve the accuracy of CRC estimation.
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