利用区域可见光和近红外光谱库及机器学习预测热带耕地中的土壤有机碳组分

IF 6.1 1区 农林科学 Q1 SOIL SCIENCE
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引用次数: 0

摘要

土壤有机碳(SOC)并不是一个单一和统一的实体,因此,了解土壤有机碳的组分,尤其是颗粒有机碳(POC)和矿物质相关有机碳(MAOC),能为了解土壤有机碳的动态提供宝贵的信息。然而,传统的 SOC 分馏实验室测量耗费大量人力和财力。因此,利用快速、低成本的土壤光谱技术有望解决这一难题。以往的研究主要集中在利用中红外(MIR)光谱预测 SOC 分量,而可见光和近红外(VNIR)光谱的潜力相对来说仍未被开发,尤其是在热带土壤中。为了填补这一空白,我们评估了六种机器学习方法,包括三种全局模型(Cubist、随机森林(RF)、偏最小二乘回归(PLSR))和三种局部模型(应用偏最小二乘回归(MBL-PLSR)和高斯过程局部回归(MBL-GPR)的基于记忆的学习)、非线性记忆学习(N-MBL)),以中国广东省热带地区红土红壤的区域土壤 VNIR 光谱库(224 个样本)为基础预测 POC 和 MAOC(g C kg-1 soil)。我们还评估了变量选择对提高模型性能的影响,通过迭代评估和剔除不重要的预测变量来确定最佳预测变量的数量。结果表明(1) MBL-PLSR 和 N-MBL 的预测性能值得称赞,在验证集上,POC 的判定系数(R2)分别为 0.73 和 0.72,MAOC 的判定系数(R2)分别为 0.53 和 0.55,优于 Cubist 和 PLSR;(2) 变量选择通过识别最佳谱带简化了预测模型,从而提高了 POC(R2 从 0.68 提高到 0.73)和 MAOC(R2 从 0.49 提高到 0.55);(3) 与 MAOC(R2 为 0.55)相比,可见近红外光谱对 POC 的总体预测性能更高(R2 为 0.73),而通过从 SOC 观测值中减去 POC 预测值(R2 为 0.73),可以更准确地预测 MAOC。良好的预测准确性凸显了 VNIR 光谱在预测 POC 方面的可行性。此外,从测量的 SOC 中减去预测的 POC,也能很好地预测 MAOC。这项研究的成果为利用近红外光谱预测 SOC 分数提供了宝贵的见解。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Prediction of soil organic carbon fractions in tropical cropland using a regional visible and near-infrared spectral library and machine learning

Soil organic carbon (SOC) is not a single and uniform entity, therefore understanding SOC fractions, particularly particulate organic carbon (POC) and mineral-associated organic carbon (MAOC), offers valuable insights into SOC dynamics. However, traditional laboratory measurements of SOC fractions are labor-intensive and costly. Therefore, leveraging rapid and cost-effective soil spectroscopy holds significant promise for addressing this challenge. While previous studies have concentrated on predicting SOC fractions using mid-infrared (MIR) spectroscopy, the potential of visible and near-infrared (VNIR) spectroscopy remains relatively unexplored, especially for tropical soils. To fill this gap, we evaluated six machine learning approaches, including three global models (Cubist, random forest (RF), partial least squares regression (PLSR)) and three local models (memory-based learning fitted by applying partial least squares regression (MBL-PLSR) and Gaussian process local regressions (MBL-GPR), non-linear memory-based learning (N-MBL)), for predicting POC and MAOC (g C kg−1 soil) based on a regional soil VNIR spectral library (224 samples) from lateritic red soil in the tropical region of Guangdong Province, China. We also assessed the impact of variable selection on improving model performance by iteratively evaluating and removing insignificant predictor variables to determine the optimal number of predictors. The results showed that: (1) MBL-PLSR and N-MBL demonstrated commendable predictive performance, attaining coefficients of determination (R2) of 0.73 and 0.72 for POC, and 0.53 and 0.55 for MAOC on the validation set, respectively, outperforming Cubist and PLSR; (2) variable selection simplified predictive models by identifying the best spectral bands, leading to improved predictive accuracy for both POC (R2 increased from 0.68 to 0.73) and MAOC (R2 increased from 0.49 to 0.55); (3) the overall predictive performance of VNIR spectroscopy was higher for POC (R2 of 0.73) compared to MAOC (R2 of 0.55), while MAOC could be predicted more accurately by subtracting POC predictions from SOC observations (R2 of 0.73). The favorable predictive accuracy underscores VNIR spectroscopy's viability for POC predictions. Additionally, MAOC can be well predicted by subtracting the predicted POC from the measured SOC. The outcomes of this study offers valuable insights for predicting SOC fractions using VNIR spectroscopy.

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来源期刊
Soil & Tillage Research
Soil & Tillage Research 农林科学-土壤科学
CiteScore
13.00
自引率
6.20%
发文量
266
审稿时长
5 months
期刊介绍: Soil & Tillage Research examines the physical, chemical and biological changes in the soil caused by tillage and field traffic. Manuscripts will be considered on aspects of soil science, physics, technology, mechanization and applied engineering for a sustainable balance among productivity, environmental quality and profitability. The following are examples of suitable topics within the scope of the journal of Soil and Tillage Research: The agricultural and biosystems engineering associated with tillage (including no-tillage, reduced-tillage and direct drilling), irrigation and drainage, crops and crop rotations, fertilization, rehabilitation of mine spoils and processes used to modify soils. Soil change effects on establishment and yield of crops, growth of plants and roots, structure and erosion of soil, cycling of carbon and nutrients, greenhouse gas emissions, leaching, runoff and other processes that affect environmental quality. Characterization or modeling of tillage and field traffic responses, soil, climate, or topographic effects, soil deformation processes, tillage tools, traction devices, energy requirements, economics, surface and subsurface water quality effects, tillage effects on weed, pest and disease control, and their interactions.
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