College of Geography and Environment, Shandong Normal University, Jinan 250014, China.
Zhongke Shandong Dongying Institute of Geographic Sciences, Dongying 257000, China.
Int J Environ Res Public Health. 2023 Feb 6;20(4):2853. doi: 10.3390/ijerph20042853.
Hyperspectral technology has proven to be an effective method for monitoring soil salt content (SSC). However, hyperspectral estimation capabilities are limited when the soil surface is partially vegetated. This work aimed to (1) quantify the influences of different fraction vegetation coverage (FVC) on SSC estimation by hyperspectra and (2) explore the potential for a non-negative matrix factorization algorithm (NMF) to reduce the influence of various FVCs. Nine levels of mixed hyperspectra were measured from simulated mixed scenes, which were performed by strictly controlling SSC and FVC in the laboratory. NMF was implemented to extract soil spectral signals from mixed hyperspectra. The NMF-extracted soil spectra were used to estimate SSC using partial least squares regression. Results indicate that SSC could be estimated based on the original mixed spectra within a 25.76% FVC (R = 0.68, RMSE = 5.18 g·kg, RPD = 1.43). Compared with the mixed spectra, NMF extraction of soil spectrum improved the estimation accuracy. The NMF-extracted soil spectra from FVC below 63.55% of the mixed spectra provided acceptable estimation accuracies for SSC with the lowest results of determination of the estimation R = 0.69, RMSE = 4.15 g·kg, and RPD = 1.8. Additionally, we proposed a strategy for the model performance investigation that combines spearman correlation analysis and model variable importance projection analysis. The NMF-extracted soil spectra retained the sensitive wavelengths that were significantly correlated with SSC and participated in the operation as important variables of the model.
高光谱技术已被证明是监测土壤盐分含量 (SSC) 的有效方法。然而，当土壤表面部分植被时，高光谱估计能力会受到限制。本研究旨在：(1) 量化不同植被覆盖度 (FVC) 对高光谱估算 SSC 的影响；(2) 探索非负矩阵分解算法 (NMF) 降低各种 FVC 影响的潜力。从实验室严格控制 SSC 和 FVC 下的模拟混合场景中测量了九级混合高光谱。实施 NMF 从混合高光谱中提取土壤光谱信号。使用偏最小二乘回归 (PLSR) ，从 NMF 提取的土壤光谱中估算 SSC。结果表明，在 25.76%的 FVC 范围内，可以基于原始混合光谱估算 SSC (R = 0.68, RMSE = 5.18 g·kg, RPD = 1.43)。与混合光谱相比，土壤光谱的 NMF 提取提高了估算精度。当混合光谱的 FVC 低于 63.55%时，NMF 提取的土壤光谱对 SSC 提供了可接受的估算精度，最低的估计 R 值为 0.69，RMSE 为 4.15 g·kg，RPD 为 1.8。此外，我们提出了一种结合 Spearman 相关分析和模型变量重要性投影分析的模型性能研究策略。NMF 提取的土壤光谱保留了与 SSC 显著相关的敏感波长，并作为模型的重要变量参与运算。
