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Estimation of Soil Salinity Under Various Soil Moisture Conditions Using Laboratory Based Thermal Infrared Spectra
Journal of the Indian Society of Remote Sensing ( IF 2.5 ) Pub Date : 2020-11-24 , DOI: 10.1007/s12524-020-01271-9
Lu Xu , Zhichun Wang , Jinshan Hu , Shuguo Wang , John Maina Nyongesah

Soil salinization is a world-wide phenomenon that threatens ecological environment and agricultural production. Modeling soil salt content (SSC) is a big challenge because of its huge spatiotemporal variation and the interference of soil water content (SWC) and soil salt types. Prior studies showed more interest in the use of hyperspectral reflectance, while few studies focused on thermal infrared band domain. In this study, we arranged samples with three salt types and several levels of SWC and measured the soil emissivity for each sample at each level of SWC. We employed both original and derivate emissivity to figure out the relationship between SSC and soil thermal infrared spectra, then used partial least squares regression to estimate SSC. Finally, the optimal model was determined with the evaluation criteria, RPD (ratio of performance to deviation) for predictive ability and AICc (corrected Akaike Information Criterion) for simplicity. The models were applied to estimate SSC and coefficient of determination (R2) of 0.67, 0.71, 0.69 and 0.7, and root mean relative error of 4.03, 3.78, 3.92, 3.86 (g/100 g) was obtained, respectively, for NaCl, Na2SO4, Na2CO3 and all salt types. The study provided a comparison result of three salt types for soil salinity estimation and a criterion for modeling effectively and succinctly and should have potential applications in the future.

中文翻译:

使用基于实验室的热红外光谱估算各种土壤湿度条件下的土壤盐度

土壤盐渍化是威胁生态环境和农业生产的世界性现象。由于其巨大的时空变化以及土壤含水量 (SWC) 和土壤盐分类型的干扰,模拟土壤盐分 (SSC) 是一个巨大的挑战。先前的研究表明对高光谱反射率的使用更感兴趣,而很少有研究关注热红外波段域。在本研究中,我们安排了具有三种盐类型和几个 SWC 级别的样本,并测量了每个样本在每个 SWC 级别的土壤发射率。我们使用原始和衍生发射率来计算 SSC 和土壤热红外光谱之间的关系,然后使用偏最小二乘回归来估计 SSC。最后根据评价标准确定最优模型,为简单起见,用于预测能力的 RPD(性能与偏差的比率)和 AICc(更正的 Akaike 信息准则)。应用这些模型估计 SSC 和决定系数 (R2) 为 0.67、0.71、0.69 和 0.7,分别获得了 4.03、3.78、3.92、3.86 (g/100 g) 的根平均相对误差 (g/100 g)。 Na2SO4、Na2CO3 和所有盐类。该研究为土壤盐分估计提供了三种盐类的比较结果,并为有效和简洁的建模提供了一个标准,并在未来具有潜在的应用价值。分别用于 NaCl、Na2SO4、Na2CO3 和所有盐类。该研究为土壤盐分估计提供了三种盐类的比较结果,并为有效和简洁的建模提供了一个标准,并在未来具有潜在的应用价值。分别用于 NaCl、Na2SO4、Na2CO3 和所有盐类。该研究为土壤盐分估计提供了三种盐类的比较结果,并为有效和简洁的建模提供了一个标准,并在未来具有潜在的应用价值。
更新日期:2020-11-24
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