• 主题
高级搜索  >
历史搜索 清空历史
首页> 外文期刊>Remote Sensing >Spatial Variability Mapping of Crop Residue Using Hyperion (EO-1) Hyperspectral Data
【24h】

Spatial Variability Mapping of Crop Residue Using Hyperion (EO-1) Hyperspectral Data

机译：使用Hyperion（EO-1）高光谱数据对作物残渣进行空间变异性制图

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

摘要

Soil management practices that maintain crop residue cover and reduce tillage improve soil structure, increase organic matter content in the soil, positively influence water infiltration, evaporation and soil temperature, and play an important role in fixing CO2 in the soil. Consequently, good residue management practices on agricultural land have many positive impacts on soil quality, crop production quality and decrease the rate of soil erosion. Several studies have been undertaken to develop and test methods to derive information on crop residue cover and soil tillage using empirical and semi-empirical methods in combination with remote sensing data. However, these methods are generally not sufficiently rigorous and accurate for characterizing the spatial variability of crop residue cover in agricultural fields. The goal of this research is to investigate the potential of hyperspectral Hyperion (Earth Observing-1, EO-1) data and constrained linear spectral mixture analysis (CLSMA) for percent crop residue cover estimation and mapping. Hyperion data were acquired together with ground-reference measurements for validation purposes at the beginning of the agricultural season (prior to spring crop planting) in Saskatchewan (Canada). At this time, only bare soil and crop residue were present with no crop cover development. In order to extract the crop residue fraction, the images were preprocessed, and then unmixed considering the entire spectral range (427 nm–2355 nm) and the pure spectra (endmember). The results showed that the correlation between ground-reference measurements and extracted fractions from the Hyperion data using CLMSA showed that the model was overall a very good predictor for crop residue percent cover (index of agreement (D) of 0.94, coefficient of determination (R2) of 0.73 and root mean square error (RMSE) of 8.7%) and soil percent cover (D of 0.91, R2 of 0.68 and RMSE of 10.3%). This performance of Hyperion is mainly due to the spectral band characteristics, especially the availability of contiguous narrow bands in the short-wave infrared (SWIR) region, which is sensitive to the residue (lignin and cellulose absorption features).
机译：维持作物残茬覆盖率和减少耕作的土壤管理措施可以改善土壤结构，增加土壤有机质含量，对水的渗透，蒸发和土壤温度产生积极影响，并在固定CO 2 方面起重要作用。土壤。因此，良好的耕地残留管理措施对土壤质量，农作物生产质量和降低土壤侵蚀速度有许多积极影响。已经进行了几项研究，以开发和测试方法，以经验和半经验方法结合遥感数据，得出有关农作物残茬覆盖率和土壤耕作的信息。但是，这些方法通常不足以精确地表征农作物中农作物残茬覆盖率的空间变异性。这项研究的目的是研究高光谱Hyperion（Earth Observing-1，EO-1）数据和约束线性光谱混合分析（CLSMA）潜在的农作物残茬覆盖率估算和绘图的潜力。在农业季节开始时（春季作物种植之前）在加拿大萨斯喀彻温省（加拿大）获取了Hyperion数据以及地面参考测量数据，以进行验证。此时，仅存在裸露的土壤和农作物残渣，而没有农作物覆盖。为了提取农作物残留物部分，对图像进行预处理，然后考虑整个光谱范围（427 nm–2355 nm）和纯光谱（最终成员）进行不混合。结果表明，地面参考测量与使用CLMSA从Hyperion数据中提取的馏分之间的相关性表明，该模型总体上可以很好地预测作物残渣覆盖率（一致性指数（D）为0.94，测定系数（R 2 ）为0.73，均方根误差（RMSE）为8.7％）和土壤覆盖率（D为0.91，R 2 为0.68，RMSE为10.3％）。 Hyperion的这种性能主要归因于光谱带的特性，尤其是短波红外（SWIR）区域中连续窄带的可用性，该区域对残留物（木质素和纤维素吸收特性）敏感。

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