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Use of Vis-NIR reflectance spectroscopy for estimating soil phosphorus sorption parameters at the watershed scale
Soil and Tillage Research ( IF 6.1 ) Pub Date : 2025-01-18 , DOI: 10.1016/j.still.2025.106460
Sanaz Saidi, Shamsollah Ayoubi, Mehran Shirvani, Seyed Ahmad Mireei, Yufeng Ge, Kaiguang Zhao, Artemi Cerdà

Measurement of soil phosphorus sorption parameters (PSPs) provides crucial information on P fertilization and P leaching. Traditional approaches for determining these indices are expensive and time-consuming. To develop rapid indirect methods, this study aims to assess the effectiveness of Vis-NIR spectroscopy ranging from 350 to 2500 nm for estimating various PSPs, including maximum buffering capacity (MBC), the standard buffering capacity (SBC), P sorption maximum (Qmax), soil P buffering capacity (PBC), and standard P requirement (SPR). We collected 100 soil samples in western Iran and related Vis-NIR data to the PSP parameters via Partial least squares regression (PLSR) and artificial neural network (ANN). The observed PSP values showed large variabilities across sites (CV> 48 %), attributed primarily to the wide variation in soil properties controlling PSPs. The PLSR model highlighted that efficient spectral peaks in the band-wise regression coefficients were strongly associated with signature wavelengths of clay minerals, soil organic carbon, and cation exchange capacity, all are key factors influencing the PSP indices. However, the PLSR models had limited predictive power for the PSPs, due to the complex relationships between spectral data and various soil properties indirectly influencing PSPs. Compared to PLSR, the nonlinear ANN model enhanced the prediction accuracy of MBC, PBC, Qmax, SBC, and SPR by 39.25 %, 50 %, 19.28 %, 39.41 %, and 59.32 %, respectively. The best coefficient of determination achieved in validation dataset by the ANN model ranged from 0.65 to 0.85, which is deemed acceptable for practical use on large scale by local farmers and decision-makers for P fertilization strategies.

中文翻译:


使用 Vis-NIR 反射光谱估计流域尺度的土壤磷吸附参数



土壤磷吸附参数 (PSP) 的测量提供了有关 P 施肥和 P 浸出的重要信息。确定这些指数的传统方法既昂贵又耗时。为了开发快速间接方法,本研究旨在评估 350 至 2500 nm 的 Vis-NIR 光谱法估计各种 PSP 的有效性,包括最大缓冲能力 (MBC)、标准缓冲能力 (SBC)、磷吸附最大值 (Qmax)、土壤磷缓冲能力 (PBC) 和标准磷需求 (SPR)。我们在伊朗西部收集了 100 个土壤样本,并通过偏最小二乘回归 (PLSR) 和人工神经网络 (ANN) 将 Vis-NIR 数据与 PSP 参数相关联。观察到的 PSP 值显示不同地点的差异很大 (CV> 48 %),这主要是由于控制 PSP 的土壤特性的广泛变化。PLSR 模型强调,带状回归系数中的有效光谱峰与粘土矿物的特征波长、土壤有机碳和阳离子交换能力密切相关,所有这些都是影响 PSP 指数的关键因素。然而,由于光谱数据与间接影响 PSP 的各种土壤特性之间的复杂关系,PLSR 模型对 PSP 的预测能力有限。与 PLSR 相比,非线性 ANN 模型将 MBC 、 PBC 、 Qmax 、 SBC 和 SPR 的预测精度分别提高了 39.25 % 、 50 % 、 19.28 % 、 39.41 % 和 59.32 %。ANN 模型在验证数据集中实现的最佳决定系数范围为 0.65 至 0.85,当地农民和决策者认为可以接受大规模用于磷肥策略的实际应用。
更新日期:2025-01-18
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