首页> 外文会议>Conference on remote sensing for agriculture, ecosystems, and hydrology >Detecting leaf phosphorus content in arbuscular mycorrhizal fungi-inoculated soybean using hyperspectral remote sensing data
【24h】

Detecting leaf phosphorus content in arbuscular mycorrhizal fungi-inoculated soybean using hyperspectral remote sensing data

机译:使用超光谱遥感数据检测丛枝菌根真菌菌接种大豆叶磷含量

获取原文

摘要

Phosphorus (P) is an important parameter participated in the process of metabolism, photosynthesis, and energy exchange of crops. A growing number of studies have focused on effects of arbuscular mycorrhizal fungi (AMF) inoculation on crop P uptake. In this context, efficient and nondestructive monitoring of the changes of leaf P content (LPC) in inoculated crops is of vital. In this study, hyperspectral remote sensing was explored in an attempt to diagnose P deficiency in the inoculated and non-inoculated soybean plants. Greenhouse pot experiment was conducted under drought stresses, and measurements of leaf spectral reflectance and LPC were carried out at the 30*, 45th and 64* days after inoculation. We transformed the raw spectral reflectance (R) into the first derivation (FD), second derivation (SD), reciprocal (1/R), reciprocal logarithm (log(1/R)) and first derivation of log(1/R) (log"(1/R))- Results indicated that the AMF-inoculated plant had significantly higher LPC than the counterparts under different drought stresses. Analysis of the correlation between LPC and the raw and five transformed reflectance in the 350-2500 nm spectral range indicated that the green bands center around 545 nm and 567 nm, as well as near infrared (NIR) band center around 832 nm were the most sensitive (r>0.73). The kernel ridge regression (KRR) of LPC with the sensitive bands selected from the raw/ transformed reflectance was performed, showing that FD, 1/R and log(l/R) produced excellent results in LPC assessment, with determination of coefficient (R~2) all larger than 0.70. Validation with independent samples revealed that the log(l/R)-K.RR model achieved the strongest and superior prediction accuracy, with R~2 of 0.93, RMSE of 0.23 g/kg and RRMSE of 7.8%, respectively. Our results indicate that the log(1/R)-KRR derived from hyperspectral remote sensing data can provide the most suitable estimation model for describing the dynamic changes of LPC in the AMF-inoculated soybean.
机译:磷(P)是参与代谢,光合作用和作物能量交换过程的重要参数。越来越多的研究侧重于丛枝菌根真菌(AMF)接种对作物P吸收的影响。在这种情况下,对接种作物中叶P含量(LPC)变化的有效和非破坏性监测是至关重要的。在这项研究中,探讨了高光谱遥感,试图诊断接种和非接种的大豆植物中的P缺乏。温室罐实验在干旱胁迫下进行,并且在接种后的30×,第45次和64天内进行叶谱反射率和LPC的测量。我们将原始光谱反射(R)转换为第一个推导(FD),第二阶级(SD),互换(1 / R),互换对数(LOG(1 / R))和日志的第一次推导(1 / R) (log“(1 / R)) - 结果表明,AMF接种植物的LPC显着高于不同干旱胁迫下的对应物。在350-2500nm光谱中,LPC与原料和五种转化反射率之间的相关性分析范围表示,在832 nm左右约545nm和567nm的绿色带中心,以及近832 nm的近红外线(nir)是最敏感的(r> 0.73)。LPC与敏感乐队的内核脊回归(KRR)进行从原始/转化的反射率选择,显示FD,1 / R和Log(L / R)在LPC评估中产生出色的结果,测定系数(R〜2)大于0.70。与独立样本的验证显示出来Log(L / R)-k.rr模型实现了最强和卓越的预测交流固化,R〜2的0.93,RMSE分别为0.23克/千克,分别为7.8%。我们的结果表明,从高光谱遥感数据导出的日志(1 / R)-KRR可以提供最合适的估计模型,用于描述AMF接种大豆中LPC的动态变化。

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