A two-dimensional bare soil separation framework using multi-temporal Sentinel-2 images across China

文献类型： 外文期刊

第一作者： Xue, Jie

作者： Xue, Jie;Zhang, Xianglin;Huang, Yuyang;Chen, Songchao;Dai, Lingju;Chen, Xueyao;Ye, Su;Shi, Zhou;Chen, Songchao;Yu, Qiangyi;Shi, Zhou

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关键词： Bare soil composite; Time-series Sentinel-2; Cropland; Two-dimensional index

期刊名称：INTERNATIONAL JOURNAL OF APPLIED EARTH OBSERVATION AND GEOINFORMATION （ 影响因子：8.6； 五年影响因子：8.6 ）

ISSN： 1569-8432

年卷期： 2024 年 134 卷

页码：

收录情况： SCI

摘要： Accurate and detailed spatial-temporal soil information is crucial for soil quality assessment worldwide, particularly in the countries with large populations and extensive agricultural areas. Using remote sensing technology to generate bare soil reflectance composites has been shown as a prerequisite for effectively modeling soil properties. However, most bare soil extraction methods rely on the single-period satellite imagery, making it difficult to produce a complete bare soil map. Although some developed methods have explored the advantages of multitemporal images, single indicators (e.g., Normalized Difference Vegetation Index and Normalized Burn Ratio 2) are prone to misidentifying bare soil as other land cover types such as impervious surface. Additionally, these methodologies were designed for specific areas and coarse spatial resolution images, leaving their generalizability to other areas or larger scales underexplored. Therefore, we proposed a Two-Dimensional Bare Soil Separation (TDBSS) framework to generate the bare soil composites of Chinese cropland at 10-m spatial resolution using multi-temporal Sentinel-2 images. This method employs the Normalized Difference Red/Green Redness Index and Soil Adjusted Vegetation Index as bidimensional indicators. We identified optimal thresholds for these indicators by analyzing ecoregion-specific samples and then implemented them across nine major agricultural zones in China. Additionally, we evaluated the framework against three prevalent bare soil extraction methods (i.e., Barest Pixel Composite, Soil Composite Mapping Processor, and Geospatial Soil Sensing System) based on spatial accuracy. The results showed that TDBSS outperformed the others with the highest overall accuracy of 78.28% and the lowest omission error of 0.198. The findings indicated that the TDBSS algorithm is competent in mapping bare soil at a national scale. The produced composite map of bare soil reflectance is particularly valuable for retrieving soil attributes in Chinese cropland. The TDBSS method can be easily implemented across broad areas with computational efficiency, contributing to land management, food security, and the development of policies for precision agriculture.

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