Prediction of soil calcium carbonate with soil visible-near- infrared reflection (Vis-NIR) spectral in Shaanxi province, China: soil groups vs. spectral groups

Qi Yanbing; Qie Xin; Qin Qianru; Shukla Manoj K.

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Prediction of soil calcium carbonate with soil visible-near- infrared reflection (Vis-NIR) spectral in Shaanxi province, China: soil groups vs. spectral groups

机译：中国陕西省土壤可见近红外反射（Vis-NIR）碳酸钙预测，中国：土壤群与光谱群体

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Soil visible-near-infrared reflection (Vis-NIR; 350 to 2500 nm) spectra is a comprehensive reflection of various soil physical and chemical properties, resulting in significant differences of hyperspectral characteristics and diversity of soil property prediction models. Calcium carbonate regulates several soil properties that were widely used to describe soil types and to quantify vulnerability to erosion. Therefore, spectral-based determination of soil calcium carbonate content is essential for agricultural management and environmental evaluation. Based on their reflectance similarity, the soil Vis-NIR spectra data itself can be classified into different spectral groups to predict soil calcium carbonate. To obtain the best hyperspectral quantitative prediction model for soil calcium carbonate based on soil group spectral data and spectral group data, a total of 246 soil samples were collected from seven soil types in Shaanxi province, China, and the soil Vis-NIR spectra were measured. All the soil spectral data were smoothed using Savitzky-Golay and continuum removal method. In this paper, we combined spectral angle cosine (SAC) algorithm with spectral correlation coefficient (SCC) algorithm to classify soil spectra. The results indicated that the coefficient of correlations between measured and predicted soil properties for seven prediction models based on random regression forest (RFR) were low (Ratio of performance to deviation, RPD 2.00). When soil spectra were classified into three types based on the SAC-SCC spectral similarity measurement method, accuracy of all the prediction models increased significantly (R (2) = 0.96; RPD 2.00) compared with the individual soil type models. These results provide a novel method for soil spectral classification and soil attribute estimation.

机译：土壤可见近红外反射（VIS-NIR; 350至2500nm）光谱是各种土壤物理和化学性质的全面反映，导致近光谱特性和土壤性质预测模型的多样性差异显着差异。碳酸钙调节几种土壤性质，这些土壤属性被广泛用于描述土壤类型并量化侵蚀脆弱性。因此，基于光谱的土壤碳酸钙含量的测定对于农业管理和环境评估至关重要。基于它们的反射相似性，土壤vis-nir光谱数据本身可以分为不同的光谱基团以预测土壤碳酸钙。为了基于土壤群光谱数据和光谱群数据获得土壤碳酸钙的最佳高光谱定量预测模型，从陕西省七种土壤类型收集了总共246种土壤样品，并测量了土壤vis-nir光谱。使用Savitzky-Golay和连续拆卸方法平滑所有土壤光谱数据。本文用光谱相关系数（SCC）算法组合光谱角余弦（SAC）算法对土壤谱进行分类。结果表明，基于随机回归森林（RFR）的七种预测模型的测量和预测土壤性质之间的相关系数低（偏差比率，RPD <2.00）。当基于SAC-SCC光谱相似性测量方法将土壤光谱分为三种类型时，与各种土壤型模型相比，所有预测模型的准确性显着增加（R（2）> = 0.96; RPD> 2.00）。这些结果为土壤光谱分类和土壤属性估算提供了一种新的方法。

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来源
《International journal of remote sensing》 |2021年第8期|2502-2516|共15页
作者
Qi Yanbing; Qie Xin; Qin Qianru; Shukla Manoj K.;
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作者单位

Northwest A&F Univ Coll Nat Resources & Environm Yangling 712100 Shaanxi Peoples R China;

Northwest A&F Univ Coll Nat Resources & Environm Yangling 712100 Shaanxi Peoples R China;

Northwest A&F Univ Coll Nat Resources & Environm Yangling 712100 Shaanxi Peoples R China;

New Mexico State Univ Coll Agr Consumer & Environm Sci Dept Plant & Environm Sci Las Cruces NM 88003 USA;

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收录信息美国《科学引文索引》(SCI);美国《工程索引》(EI);
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正文语种 eng
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Prediction of soil calcium carbonate with soil visible-near- infrared reflection (Vis-NIR) spectral in Shaanxi province, China: soil groups vs. spectral groups

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