ABSTRACT

The advent of lab diffuse reflectance spectroscopy (LDRS) that exploits the fundamental vibration, overtones and combination of functional groups of soil components makes the soil study easier. The present research intends to predict sand content utilizing the proximal soil sensing (PSS) tech. Thus, in accord with the supplementary data layers and stratified randomized sampling (SRS) method, eventually, 128 samples were gathered from 20 cm of soil surface of Mazandaran Province, Iran. First of all, the sample set was subdivided into two subsets: calibration (96) and validation (32). Using the multivariate regression analysis-partial least squares regression (PLSR) algorithm with leave-one-out cross-validation (LOOCV) technique and some pre-processing algorithms, such as spectral averaging, smoothing and 1st derivative (1st-D), the definitive calibration model with two & four latent vectors (LVs/LFs) and correlation coefficient (R_P), determination coefficient (R²_P), root mean square error (RMSE_P), ratio of performance to deviation (RPD_P) and ratio of performance to interquartile distance (RPIQ_P) respectively: 0.83&0.82, 0.68&0.67,8.68&8.83%,1.78&1.75,2.45&2.41, were validated and spotted as the most appropriate predictive model for the sand content prediction in the study region. Last, the potentiality of the visible-near infrared diffuse reflectance spectroscopy (VNIR-DRS) for sand content estimation in Mazandaran soils was proved. Also, it is feasible to upscale the sand prediction process utilizing the principal resulted model and the key spectral domains via airborne/satellite hyperspectral data, which emphatically shows the LDRS importance as a commencement point for characterizing the informative optical wavelengths. Likewise, that will be the infrastructure for spaceborne data modeling and upscaling process.

摘要

利用土壤成分的基本振动、泛音和官能团组合的实验室漫反射光谱 (LDRS) 的出现使土壤研究变得更加容易。本研究旨在利用近端土壤传感 (PSS) 技术预测沙子含量。因此，根据补充数据层和分层随机抽样（SRS）方法，最终从伊朗马赞达兰省 20 厘米的土壤表面收集了 128 个样本。首先，样本集被细分为两个子集：校准（96）和验证（32）。使用带有留一法交叉验证 (LOOCV) 技术的多元回归分析-偏最小二乘回归 (PLSR) 算法和一些预处理算法，例如光谱平均、平滑和一阶导数 (1st-D)，_P )、决定系数 (R ² _P )、均方根误差 (RMSE _P )、性能与偏差的比率 (RPD _P ) 和性能与四分位间距的比率 (RPIQ _P) 分别为：0.83&0.82、0.68&0.67、8.68&8.83%、1.78&1.75、2.45&2.41，被验证并发现是研究区域含沙量预测最合适的预测模型。最后，证明了可见-近红外漫反射光谱 (VNIR-DRS) 在马赞达兰土壤中估算含沙量的潜力。此外，通过机载/卫星高光谱数据利用主要结果模型和关键光谱域来升级沙子预测过程是可行的，这强调了 LDRS 作为表征信息光波长的起点的重要性。同样，这将是星载数据建模和升级过程的基础设施。