College of Resources and Environment, Shandong Agricultural University, Taian 271018, China; National Engineering Laboratory for Efficient Utilization of Soil and Fertilizer Resources, Shandong Agricultural University, Taian 271018, China.
College of Resources and Environment, Shandong Agricultural University, Taian 271018, China; National Engineering Laboratory for Efficient Utilization of Soil and Fertilizer Resources, Shandong Agricultural University, Taian 271018, China.
Spectrochim Acta A Mol Biomol Spectrosc. 2021 Nov 5;260:119963. doi: 10.1016/j.saa.2021.119963. Epub 2021 May 18.
Soil organic matter (SOM) is an important part of soil fertility and the main nutrient source for crop growth. The establishment of an effective SOM content estimation model can provide technical support for the improvement of saline soil and the implementation of precision agriculture. In this paper, a laboratory spectrometer was used to measure the spectral reflectance of saline soils with particle sizes of 1 mm, 0.50 mm, 0.25 mm and 0.15 mm collected from Kenli County. After spectral preprocessing and spectral transformation, the characteristic bands of the SOM spectrum were extracted by the successive projections algorithm (SPA). Finally, stepwise multiple linear regression (SMLR), principal component regression (PCR) and partial least squares regression (PLSR) were used to establish SOM content estimation models based on soil particle size. The results showed the following. (i) Soil particle size had a significant impact on soil spectral reflectance. The smaller the soil particle size was, the greater the soil spectral reflectance. (ii) The sensitive bands for SOM were mainly concentrated in the visible light region (400-760 nm). First derivative (FD) transformation can effectively improve the characteristic spectral information obtained from SOM. (iii) Among the three models established with the characteristic bands, the estimation ability of the PLSR model was better than that of the PCR and SMLR models. (iv) The FD of the original spectral reflectance of the 0.25 mm particles combined with the PLSR model gave the best estimation of the SOM content. When the soil particle size was less than 0.25 mm, the estimation results of the model were not improved. These results provide a basis for effective estimation of the SOM content and improvement of saline-alkali soil in Kenli County in the Yellow River Delta.
土壤有机质（SOM）是土壤肥力的重要组成部分，也是作物生长的主要养分来源。建立有效的 SOM 含量估算模型可为改良盐碱地和实施精准农业提供技术支持。本研究采用实验室光谱仪对黄河三角洲垦利县采集的粒径为 1mm、0.50mm、0.25mm 和 0.15mm 的盐渍土进行光谱反射率测量。经过光谱预处理和光谱变换后，采用连续投影算法（SPA）提取 SOM 光谱的特征波段。最后，基于土壤粒径，采用逐步多元线性回归（SMLR）、主成分回归（PCR）和偏最小二乘回归（PLSR）建立 SOM 含量估算模型。结果表明：（i）土壤粒径对土壤光谱反射率有显著影响，土壤粒径越小，土壤光谱反射率越大；（ii）SOM 的敏感波段主要集中在可见光区（400-760nm），一阶导数（FD）变换可以有效提高从 SOM 中获取的特征光谱信息；（iii）在利用特征波段建立的 3 个模型中，PLSR 模型的估算能力优于 PCR 和 SMLR 模型；（iv）0.25mm 粒径原始光谱反射率 FD 与 PLSR 模型相结合，对 SOM 含量的估算效果最佳，当土壤粒径小于 0.25mm 时，模型的估算结果不再提高。本研究结果为有效估算黄河三角洲垦利县 SOM 含量、改良盐碱地提供了依据。
