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Online measurement of soil organic carbon as correlated with wheat normalised difference vegetation index in a vertisol field

机译:垂直农田中土壤有机碳的在线测量与小麦归一化植被指数的相关性

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摘要

This study explores the potential of visible and near infrared (vis-NIR) spectroscopy for onlinemeasurement of soil organic carbon (SOC). It also attempts to explore correlations and similarities between the spatial distribution of SOC and normalized differential vegetation index (NDVI) of a wheat crop. The online measurement was carried out in a clay vertisol field covering 10 ha of area in Karacabey, Bursa, Turkey. Kappa statistics were carried out between different SOC and NDVI data to investigate potential similarities. Calibration model of SOC in full cross-validationresulted in a good accuracy ( 2 = 0.75, root mean squares error of prediction (RMSEP) = 0.17%, and ratio of prediction deviation (RPD) = 1.81). The validation of the calibration model using laboratory spectra provided comparatively better prediction accuracy ( 2 = 0.70, RMSEP = 0.15%, and RPD = 1.78), as compared to the online measured spectra ( 2 = 0.60, RMSEP = 0.20%, and RPD = 1.41). Although visual similarity was clear, low similarity indicated by a lowKappa value of 0.259 was observed between the online vis-NIR predicted full-point (based on all pointsmeasured in the field, e.g., 6486 points)map of SOC and NDVI map.
机译:这项研究探索了可见和近红外(vis-NIR)光谱技术在线测量土壤有机碳(SOC)的潜力。它还尝试探索SOC的空间分布与小麦作物的标准化差分植被指数(NDVI)之间的相关性和相似性。在线测量是在占地10公顷的土耳其布尔萨Karacabey的粘土粉砂岩田中进行的。在不同的SOC和NDVI数据之间进行了Kappa统计,以研究潜在的相似性。完全交叉验证中的SOC校准模型具有良好的准确性(2 = 0.75,预测均方根误差(RMSEP)= 0.17%,预测偏差比(RPD)= 1.81)。与在线测量光谱(2 = 0.60,RMSEP = 0.20%,RPD = 2)相比,使用实验室光谱对校准模型的验证提供了相对更好的预测精度(2 = 0.70,RMSEP = 0.15%,RPD = 1.78)。 1.41)。尽管视觉上的相似性很明显,但在SOC与NDVI的在线vis-NIR预测全点图(基于现场测量的所有点,例如6486点)之间,观察到的低相似度由低Kappa值0.259表示。

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