College of Geographical Sciences and Planning, Ningxia University, Yinchuan 750021, China.
Xi'an Meihang Remote Sensing Information Co. Ltd., Xi'an 710199, China.
Ying Yong Sheng Tai Xue Bao. 2023 Mar;34(3):717-725. doi: 10.13287/j.1001-9332.202303.020.
Soil organic matter (SOM) is a crucial indicator of soil fertility. Field hyperspectral reflectance and laboratory SOM data of soil samples from the Yinchuan Plain were used to explore the performance of models based on fractional derivative combined with different spectral indices. Following reciprocal and logarithmic transformation, the reflectance data were processed using fractional derivative from 0 to 2 orders (interval 0.20). Then, the difference index (DI), ratio index (RI), brightness index (BI), normalized difference index (NDI), renormalized difference index (RDI), and generalized difference index (GDI) were constructed. The two-dimensional correlation between the six indices and SOM content were analyzed. The optimal spectral indices were selected to establish SOM estimation models with principal component regression (PCR), partial least square regression (PLSR), back propagation neural network (BPNN), support vector machine (SVM), and geographically weighted regression (GWR). Results showed that the maximum absolute correlation coefficient (MACC) values between DI, RI, NDI, BI, GDI, RDI, and SOM contents increased firstly and then decreased, with the highest values observed at 1.0, 0.6, 1.4, and 1.6 orders. The 0.2-2.0 order RDI under fractional derivative variation could be used for subsequent model construction, in which the optimal combinations of bands for MACC values were mainly concentrated at 400-600 nm and 1300-1700 nm. Among the different models based on the single spectral index RDI, the model based on SVM achieved the highest estimation accuracy, whose modeling determination coefficient, verification determination coefficient and relative percentage difference reached 0.86, 0.87 and 2.32. Our results would provide a scientific reference for quick and accurate SOM assessment and mapping in areas with relatively low SOM content.
土壤有机质(SOM)是土壤肥力的一个关键指标。本研究利用银川平原野外高光谱反射率和实验室 SOM 数据,探讨了基于分数导数与不同光谱指数相结合的模型的性能。对反射率数据进行倒数和对数变换后,用分数导数从 0 阶到 2 阶(间隔为 0.20)进行处理。然后构建了差分值(DI)、比值指数(RI)、亮度指数(BI)、归一化差值指数(NDI)、再归一化差值指数(RDI)和广义差值指数(GDI)。分析了 6 个指数与 SOM 含量的二维相关性。选择最优光谱指数,利用主成分回归(PCR)、偏最小二乘回归(PLSR)、反向传播神经网络(BPNN)、支持向量机(SVM)和地理加权回归(GWR)建立 SOM 估算模型。结果表明,DI、RI、NDI、BI、GDI、RDI 与 SOM 含量的最大绝对相关系数(MACC)值先增加后减小,在 1.0、0.6、1.4、1.6 阶时达到最大值。分数导数变化下 0.2-2.0 阶的 RDI 可用于后续模型构建,其中 MACC 值的最佳波段组合主要集中在 400-600nm 和 1300-1700nm。在基于单一光谱指数 RDI 的不同模型中,基于 SVM 的模型达到了最高的估计精度,其建模决定系数、验证决定系数和相对百分比误差分别达到 0.86、0.87 和 2.32。本研究结果为相对低 SOM 含量地区的快速、准确 SOM 评估和制图提供了科学参考。