...
  • 主题
高级搜索  >
历史搜索 清空历史
首页> 外文期刊>Remote Sensing >Estimation of Maize Residue Cover Using Landsat-8 OLI Image Spectral Information and Textural Features
【24h】

Estimation of Maize Residue Cover Using Landsat-8 OLI Image Spectral Information and Textural Features

机译：利用Landsat-8 OLI图像光谱信息和纹理特征估算玉米残留量

获取原文
           
页面导航
  • 摘要
  • 著录项
  • 相似文献
  • 相关主题

摘要

The application of crop residue has become increasingly important for providing a barrier against water and wind erosion and improving soil organic matter content, infiltration, evaporation, temperature, and soil structure. The objectives of this work were to: (i) estimate maize residue cover (MRC) from Landsat-8 OLI images using seven vegetation indices (VIs) and eight textural features; and (ii) compare the VI method, textural feature method, and combination method (integration of textural features and spectral information) for estimating MRC with partial least squares regression (PLSR). The results showed that the normalized difference tillage index (NDTI), simple tillage index (STI), normalized difference index 7 (NDI7), and shortwave red normalized difference index (SRNDI) were significantly correlated with MRC. The MRC model based on NDTI outperformed (R2 = 0.84 and RMSE = 12.33%) the models based on the other VIs. Band3mean, Band4mean, and Band5mean were highly correlated with MRC. The regression between Band3mean and MRC was stronger (R2 = 0.71 and RMSE = 15.21%) than those between MRC and the other textural features. The MRC estimation accuracy using the combination method (R2 = 0.96 and RMSE = 8.11%) was better than that based on only the VI (R2 = 0.88 and RMSE = 11.34%) or textural feature (R2 = 0.90 and RMSE = 9.82%) methods. The results suggest that the combination method can be used to estimate MRC on a regional scale.
机译：为了提供抗水和风蚀的屏障并改善土壤有机质含量，渗透，蒸发，温度和土壤结构，作物残渣的应用变得越来越重要。这项工作的目的是：（i）利用7个植被指数（VI）和8个纹理特征，从Landsat-8 OLI图像估算玉米残留量（MRC）; （ii）比较用偏最小二乘回归（PLSR）估计MRC的VI方法，纹理特征方法和组合方法（纹理特征和光谱信息的集成）。结果表明，归一化耕作指数（NDTI），简单耕作指数（STI），归一化差异指数7（NDI7）和短波红色归一化差异指数（SRNDI）与MRC显着相关。基于NDTI的MRC模型优于基于其他VI的模型（R 2 = 0.84和RMSE = 12.33％）。 Band3 mean ，Band4 mean 和Band5 mean 与MRC高度相关。 Band3mean和MRC之间的回归比RRC和其他纹理特征之间的回归更强（R 2 = 0.71和RMSE = 15.21％）。使用组合方法（R 2 = 0.96和RMSE = 8.11％）的MRC估计精度优于仅基于VI的MRC估计精度（R 2 = 0.88和RMSE =方法）（11.34％）或纹理特征（R 2 = 0.90和RMSE = 9.82％）方法。结果表明，该组合方法可用于估计区域范围内的MRC。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱：kefu@zhangqiaokeyan.com

京公网安备：11010802029741号 ICP备案号：京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有

  • 客服微信

  • 服务号