• 主题
高级搜索  >
历史搜索 清空历史
首页> 外文会议>Remote Sensing for Agriculture, Ecosystems, and Hydrology >Use of imaging spectroscopy to assess different organic carbon fractions of agricultural soils
【24h】

Use of imaging spectroscopy to assess different organic carbon fractions of agricultural soils

机译：使用成像光谱法评估农业土壤的不同有机碳级分

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

摘要

The site for this study - located in Rhineland-Palatinate, Germany ("Bitburger Gutland") - covered different geological substrates and agro-pedological zones. In total, 42 plots were sampled in the field; soil samples from the top horizon were analysed in the laboratory for total organic carbon (OC), hot water-extractable C (HWE-C) and microbial C (Cmic). In parallel to the ground campaign, a data set of the HyMap~(TM) airborne imaging sensor was acquired on 27th of August 2009. After pre-processing, HyMap spectra were used to assess the contents of OC, HWE-C and Cmic. As calibration method we used partial least squares regression (PLSR), as it allows a handling of large input spaces and noisy patterns. Since calibration quality was poor for HWE-C and Cmic (cross-validated r~(2) values were less than 0.5), we additionally combined PLSR with a genetic algorithm (GA) to preselect an optimum set of spectral features instead of using the full spectrum. With this GA-PLSR approach, results improved considerably for all constituents in the cross-validation (r~(2) >= 0.72). Very similar GA selection patterns for all carbon fractions suggest that spurious (indirect) correlations may be relevant for assessing HWE-C and Cmic. For the GA approach, some overfitting due to a selection based on chance correlations between C fractions and spectral variables cannot be excluded.
机译：这项研究的网站 - 位于德国莱茵兰 - 普法尔茨（“Bitburger Gutland”） - 覆盖了不同的地质基质和农业小说。总共有42个地块在该领域取样;在实验室中分析来自顶部地平线的土壤样品，用于总有机碳（OC），热水可提取的C（HWE-C）和微生物C（CMIC）。与地面活动并行，在2009年8月27日获取了Hymap〜（TM）空中成像传感器的数据集。预处理后，使用Hymap光谱来评估OC，HWE-C和CMIC的含量。作为校准方法，我们使用部分最小二乘回归（PLSR），因为它允许处理大输入空间和嘈杂的图案。由于HWE-C和CMIC（交叉验证的R〜（2）值小于0.5的校准质量差，因此我们将PLSR与遗传算法（GA）组合，以预先选择最佳的光谱特征集，而不是使用全谱。通过该GA-PLSR方法，对于交叉验证中的所有成分（R〜（2）> = 0.72），结果可以显着提高。所有碳级分的非常相似的GA选择模式表明，虚假（间接）相关性可能与评估HWE-C和CMIC相关。对于GA方法，不能排除基于C分数和光谱变量之间的机会相关性的选择的一些过度装备。

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