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首页> 外文期刊>Remote Sensing >SPA-Based Methods for the Quantitative Estimation of the Soil Salt Content in Saline-Alkali Land from Field Spectroscopy Data: A Case Study from the Yellow River Irrigation Regions
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SPA-Based Methods for the Quantitative Estimation of the Soil Salt Content in Saline-Alkali Land from Field Spectroscopy Data: A Case Study from the Yellow River Irrigation Regions

机译：基于SPA的盐碱地土壤盐分定量估测方法的研究-以黄河灌区为例。

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The problem of soil salinization has always been a global problem involving resource, environmental, and ecological issues, and is closely related to the sustainable development of the social economy. Remote sensing provides an effective technical means for soil salinity identification and quantification research. This study focused on the estimation of the soil salt content in saline-alkali soils and applied the Successive Projections Algorithm (SPA) method to the estimation model; twelve spectral forms were applied in the estimation model of the spectra and soil salt content. Regression modeling was performed using the Partial Least Squares Regression (PLSR) method. Proximal-field spectral measurements data and soil samples were collected in the Yellow River Irrigation regions of Shizuishan City. A total of 60 samples were collected. The results showed that application of the SPA method improved the modeled determination coefficient ( R 2 ) and the ratio of performance to deviation (RPD), and reduced the modeled root mean square error (RMSE) and the percentage root mean square error (RMSE%); the maximum value of R 2 increased by 0.22, the maximum value of RPD increased by 0.97, the maximum value of the RMSE decreased by 0.098 and the maximum value of the RMSE% decreased by 8.52%. The SPA–PLSR model, based on the first derivative of reflectivity (FD), the FD–SPA–PLSR model, showed the best results, with an R 2 value of 0.89, an RPD value of 2.72, an RMSE value of 0.177, and RMSE% value of 11.81%. The results of this study demonstrated the applicability of the SPA method in the estimation of soil salinity, by using field spectroscopy data. The study provided a reference for a subsequent study of the hyperspectral estimation of soil salinity, and the proximal sensing data from a low distance, in this study, could provide detailed data for use in future remote sensing studies.
机译：土壤盐碱化问题一直是一个涉及资源，环境和生态问题的全球性问题，与社会经济的可持续发展息息相关。遥感为土壤盐分的识别和定量研究提供了有效的技术手段。本研究着重于盐碱地土壤盐分含量的估算，并将逐次投影算法（SPA）应用于估算模型。在光谱和土壤盐分含量的估算模型中采用了十二种光谱形式。使用偏最小二乘回归（PLSR）方法进行回归建模。在石嘴山市黄河灌区采集近场光谱测量数据和土壤样品。总共收集了60个样品。结果表明，SPA方法的应用提高了建模的确定系数（R 2）和性能偏差比（RPD），并降低了建模的均方根误差（RMSE）和均方根误差百分比（RMSE％ ）; R 2的最大值增加0.22，RPD的最大值增加0.97，RMSE的最大值减少0.098，RMSE％的最大值减少8.52％。 SPA-PLSR模型基于反射率（FD）的一阶导数，即FD-SPA-PLSR模型，显示了最佳结果，R 2值为0.89，RPD值为2.72，RMSE值为0.177，和RMSE％值，为11.81％。这项研究的结果表明，通过使用现场光谱数据，SPA方法可用于估算土壤盐度。该研究为随后的土壤盐分高光谱估算研究提供了参考，本研究中的近距离遥感数据可为将来的遥感研究提供详细的数据。

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