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基于地统计方法的土壤属性制图通常需要大量的采样与实验室测定。本研究提出利用可见光近红外(visible-nearinfrared spectroscopy,VNIR)光谱技术测定替代实验室测定,并与地统计方法相结合预测土壤质地的空间变异。通过建立砂粒(>0.02 mm),粉粒(0.002~0.02 mm),黏粒(<0.002 mm)含量的VNIR光谱预测模型,将模型预测得到的质地数据和建模点实测质地数据一同用于地统计分析和Kriging插值制图。以江苏北部黄淮平原地区为案例的研究结果表明,砂粒、粉粒、黏粒含量的预测值和实测值的均方根误差(RMSE)分别为8.67%、6.90%3、.51%,平均绝对误差(MAE)分别为6.46%、5.60%、3.05%,显示了较高的预测精度。研究为快速获取平原区土壤质地空间分布提供了新的可能的途径。
Soil attribute mapping based on geostatistical methods often requires extensive sampling and laboratory measurements. This study proposed the use of VNIR spectroscopy to measure alternative laboratory measurements and to predict the spatial variability of soil texture in combination with geostatistical methods. Through the establishment of the VNIR spectral prediction model of sand (> 0.02 mm), silt (0.002-0.02 mm) and clay (<0.002 mm), the predicted texture data and the measured data of the modeling point are used together Statistical Analysis and Kriging Interpolation Cartography. The results of the case study from Huanghe-Huaihe Plain in northern Jiangsu showed that the root mean square error (RMSE) of the predicted and measured values of grit, silt and clay content were 8.67%, 6.90%, 3.51%, respectively The absolute errors (MAE) were 6.46%, 5.60% and 3.05%, respectively, indicating a high prediction accuracy. The research provides a new possible approach for rapidly obtaining the spatial distribution of soil texture in the plain area.