Early Detection of Crop Injury from Glyphosate on Soybean and Cotton Using Plant Leaf Hyperspectral Data
Early Detection of Crop Injury from Glyphosate on Soybean and Cotton Using Plant Leaf Hyperspectral Data
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利用植物叶片高光谱数据早期检测草甘膦对大豆和棉花造成的作物伤害
DOI：
10.3390/rs6021538
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发表时间：
2014-02
期刊：
影响因子：
5
通讯作者：
Thomson Steven J.
中科院分区：
文献类型：
--
作者：
Zhao Feng;Huang Yanbo;Guo Yiqing;Reddy Krishna N.;Lee Matthew A.;Fletcher Reginald S.;Thomson Steven J.
在这篇论文中，我们的目标是通过传统使用的光谱指数和新提取的特征来检测除草剂草甘膦对作物的伤害，并结合非抗草甘膦大豆和非转基因棉花的叶片高光谱反射数据。通过典型分析技术提取新的特征，能够提供最大的分离度来区分受害叶片和健康叶片。根据基于物理的叶片辐射传输模型(叶片光学特性光谱模型，PROSPECT)的灵敏度分析结果，选择了用于构建这些新特征的光谱波段，该模型可以帮助将这些特征的有效性扩展到广泛的叶片结构和生长条件。这种方法已经用草甘膦处理实验中获得的温室测量数据进行了验证。结果表明，归一化差值植被指数(NDVI)、比值植被指数(RVI)、土壤调节植被指数(SAVI)和差值植被指数(DVI)在处理后48h(HAT)和72HAT(棉花)均能检测到草甘膦的伤害，而其他光谱指数对大豆和棉花的区分作用不大，或者对健康和受害的大豆和棉花的区分不一致。与传统的光谱指数相比，新的特征更适合于草甘膦伤害的早期检测，喷洒草甘膦溶液的叶片具有更大的特征值。随着时间的推移，这一趋势变得越来越明显。喷施不同草甘膦用量的叶片在新特征下表现出一定的24帽分离度，在48帽以上大豆和棉花都能完全区分开来。这些发现表明，利用这些新提出的特征，应用叶片高光谱反射率测量来早期检测草甘膦伤害是可行的。
In this paper, we aim to detect crop injury from glyphosate, a herbicide, by both traditionally used spectral indices and newly extracted features with leaf hyperspectral reflectance data for non-Glyphosate-Resistant (non-GR) soybean and non-GR cotton. The new features were extracted by canonical analysis technique, which could provide the largest separability to distinguish the injured leaves from the healthy ones. Spectral bands used for constructing these new features were selected based on the sensitivity analysis results of a physically-based leaf radiation transfer model (leaf optical PROperty SPECTra model, PROSPECT), which could help extend the effectiveness of these features to a wide range of leaf structures and growing conditions. This approach has been validated with greenhouse measured data acquired in glyphosate treatment experiments. Results indicated that glyphosate injury could be detected by NDVI (Normalized Difference Vegetation Index), RVI (Ratio Vegetation Index), SAVI (Soil Adjusted Vegetation Index), and DVI (Difference Vegetation Index) in 48 h After the Treatment (HAT) for soybean and in 72 HAT for cotton, but the other spectral indices either showed little use for separation, or did not show consistent separation for healthy and injured soybean and cotton. Compared with the traditional spectral indices, the new features were more feasible for the early detection of glyphosate injury, with leaves sprayed with a higher rate of glyphosate solution having larger feature values. This trend became more and more pronounced with time. Leaves sprayed with different glyphosate rates showed some separability 24 HAT using the new features and could be totally distinguished at and beyond 48 HAT for both soybean and cotton. These findings demonstrated the feasibility of applying leaf hyperspectral reflectance measurements for the early detection of glyphosate injury using these newly proposed features.
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影响因子：
2.5
作者：
Andrea Saltelli;S. Tarantola;K. Chan
通讯作者：
Andrea Saltelli;S. Tarantola;K. Chan
影响因子：
8.2
作者：
Pingheng Li;Quan Wang
通讯作者：
Pingheng Li;Quan Wang
影响因子：
4.1
作者：
K. N. Reddy;W. Ding;W. Ding;R. M. Zablotowicz;S. Thomson;Yanbo Huang;L. Krutz
通讯作者：
K. N. Reddy;W. Ding;W. Ding;R. M. Zablotowicz;S. Thomson;Yanbo Huang;L. Krutz
影响因子：
1.3
作者：
通讯作者：
--
影响因子：
1.3
作者：
W. Ding;K. N. Reddy;L. Krutz;S. Thomson;Yanbo Huang;R. M. Zablotowicz
通讯作者：
W. Ding;K. N. Reddy;L. Krutz;S. Thomson;Yanbo Huang;R. M. Zablotowicz