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Application of VIS-NIR spectroscopy for estimation of soil organic carbon using different spectral preprocessing techniques and multivariate methods in the middle Indo-Gangetic plains of India
Geoderma Regional ( IF 3.1 ) Pub Date : 2020-11-13 , DOI: 10.1016/j.geodrs.2020.e00349
Seema , A.K. Ghosh , B.S. Das , N. Reddy

Soil organic carbon (SOC) is the main source of soil nutrients, which are essential for the growth and development of agricultural crops. Precise and quick quantification of SOC is of utmost importance in crop husbandry and soil health/carbon sequestration quantification. In order to evaluate visible and near-infrared diffuse reflectance spectroscopy (VIS-NIR) as an alternative to precise and quick method of quantification of SOC in the Indo-Gangetic plains, 280 soil samples were collected covering Inceptisols, Entisols and Alfisols and their spectra recorded. Six preprocessing techniques ((reflectance, absorbance, multiplicative scatter correction (MSC), standard normal variate (SNV), Savitzky–Golay smoothing first derivative (SG-FD) and Savitzky–Golay smoothing second derivative (SG-SD)) and four multivariate methods (partial least-squares regression (PLSR), random forest (RF), support vector regression (SVR) and multivariate adaptive regression splines (MARS)) were evaluated to predict SOC from VIS-NIR spectra. The considerable prediction accuracy and robustness were achieved using the PLSR model (RV2 = 0.73, RMSEV = 0.07, and RPDV = 1.90), RF model (RV2 = 0.69, RMSEV = 0.07, and RPDV = 1.74), SVR model (RV2 = 0.57, RMSEV = 0.08), and RPDV = 1.50), and MARS model (RV2 = 0.63, RMSEV = 0.10, and RPDV = 1.05). Findings from this study identified the reliability of SOC determinations by examining how preprocessing techniques and multivariate methods affect spectral analyses.



中文翻译:

VIS-NIR光谱在印度中部-恒河平原中使用不同的光谱预处理技术和多元方法估算土壤有机碳的应用

土壤有机碳(SOC)是土壤养分的主要来源,对农业作物的生长和发育至关重要。SOC的精确和快速定量在农牧业和土壤健康/碳固存定量中至关重要。为了评估可见和近红外漫反射光谱法(VIS-NIR),作为精确,快速的印度-恒河平原SOC定量方法的替代方法,收集了280个土壤样品,包括Inceptisols,Entisol和Alfisols及其光谱记录下来。六种预处理技术((反射率,吸收率,乘法散射校正(MSC),标准正态变量(SNV),Savitzky-Golay平滑一阶导数(SG-FD)和Savitzky-Golay平滑二阶导数(SG-SD))和四种多元方法(偏最小二乘回归(PLSR),随机森林(RF),支持向量回归(SVR)和多元自适应回归样条(MARS)进行了评估,以根据VIS-NIR光谱预测SOC。使用PLSR模型(RV 2  = 0.73,RMSE V  = 0.07,RPD V  = 1.90),RF模型(R V 2  = 0.69,RMSE V  = 0.07,RPD V  = 1.74),SVR模型(R V 2  = 0.57,RMSE V  = 0.08),RPD V  = 1.50)和MARS模型(R V 2  = 0.63,RMSE V  = 0.10和RPD V  = 1.05)。这项研究的发现通过检查预处理技术和多元方法如何影响光谱分析,确定了SOC确定的可靠性。

更新日期:2020-11-25
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