基于特征空间的黄河三角洲垦利县土壤盐分遥感提取

土壤盐渍化是实现土地资源可持续利用所面临的重要挑战，在我国滨海的黄河三角洲区域遥感定量反演适宜方法可为区域盐渍化监测与防治提供技术方法参考。研究以Landsat 8 OLI数据和野外实测数据为基础，提取关键地表特征参量，定量化探讨土壤盐分与地表生物物理参数之间的规律及关系，建立黄河三角洲土壤盐分最优反演模型。结果表明:Albedo-MSAVI、SI-Albedo、SI-NDVI反演精度分别为83.4%、88.8%和80.6%。分析认为SI-Albedo模型最适用于滨海地区盐渍化程度反演，对滨海地区土壤盐分的预测能力较强；Albedo-MSAVI、SI-NDVI模型对内陆干旱、半干旱地区的盐渍化信息提取具有一定的参考意义。基于精度最高的SI-Albedo所反演的结果来看，垦利县盐渍化程度自东向西总体呈高低高走向，与该区域盐分积聚的成因机理相符。

Remote Sensing Extraction of Soil Salinity in Yellow River Delta Kenli County based on Feature Space

Soil salinization is an important challenge to achieve sustainable use of land resources. The appropriate method for remote sensing quantitative inversion in the coastal Yellow River Delta region of China can provide technical reference for regional salinization monitoring and prevention. Utilizing Landsat 8 OLI image and field measured data, we extracted key surface characteristic parameters, quantitatively discussed the law and relationship between soil salinity and surface biophysical parameters and established a soil salinity inversion model. The results show that the inversion precisions of Albedo-MSAVI, SI-Albedo and SI-NDVI feature space are 83.4%, 88.8% and 80.6% respectively. The analysis shows the SI-Albedo model is suitable for the inversion of salinization level in Binhai areas. For Albedo-MSAVI and SI-NDVI models, they have certain reference significance for salinization information extraction in inland arid and semi-arid areas. Based on the inversion of the SI-Albedo feature space with the highest accuracy, the level of salinization in Kenli County is generally high-low-high trends from the east to the west, which is consistent with the formation mechanism of salt accumulation in this area.