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<p>Soil salinity and sodicity are significant environmental concerns contributing to land degradation, hampering plant growth, disrupting microbial functions, reducing crop yields, and limiting arable land for agriculture. Understanding these soil properties from spatiotemporal perspectives is essential for decision-makers to devise effective crop management and agricultural planning strategies. This study aims to develop an approach integrating multi-source remote sensing data with an emsenble random forest (ERF) for soil salinity and sodicity assessment in Taiwan. The data spanning from 2012 to 2020 were processed, and predicted electrical conducitivty (EC) and sodium adsorption ratio (SAR) results were validated against field observations, demonstrating a robust agreement with correlation coefficient (<i>r</i>) values of 0.78 and 0.79, respectively. Although these models effectively captured the spatial patterns of soil salinity and sodicity, the root mean square error (RMSE) values of 1.04 and 1.4 for EC and SAR models indicated a room for further improving predictive accuracy and consistency. Salinity and sodicity classes derived from predicted results showed that slight salinity was widely distributed across the study region, whereas moderate to strong salinity and slight to moderate sodicity were more concentrated in low-lying coastal areas, highlighting the significant impact of saltwater intrusion and poor drainage. Finally, these salinity and sodicity classes were overlaid with township-level cultivated areas for targeted management interventions and improved agricultural practices.</p>

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Multi-source remote sensing for spatial mapping of salt-affected agricultural soils in Taiwan

  • 期刊论文

  • 发表日期：

    2026年3月6日

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  • Nguyen-Thanh Son,
  • Chein-Hui Syu,
  • Chi-Farn Chen,
  • Cheng-Ru Chen,
  • Huan-Sheng Lin,
  • Yi-Ting Zhang,
  • Tsang-Sen Liu,
  • Chun-Chien Yen

摘要

Soil salinity and sodicity are significant environmental concerns contributing to land degradation, hampering plant growth, disrupting microbial functions, reducing crop yields, and limiting arable land for agriculture. Understanding these soil properties from spatiotemporal perspectives is essential for decision-makers to devise effective crop management and agricultural planning strategies. This study aims to develop an approach integrating multi-source remote sensing data with an emsenble random forest (ERF) for soil salinity and sodicity assessment in Taiwan. The data spanning from 2012 to 2020 were processed, and predicted electrical conducitivty (EC) and sodium adsorption ratio (SAR) results were validated against field observations, demonstrating a robust agreement with correlation coefficient (r) values of 0.78 and 0.79, respectively. Although these models effectively captured the spatial patterns of soil salinity and sodicity, the root mean square error (RMSE) values of 1.04 and 1.4 for EC and SAR models indicated a room for further improving predictive accuracy and consistency. Salinity and sodicity classes derived from predicted results showed that slight salinity was widely distributed across the study region, whereas moderate to strong salinity and slight to moderate sodicity were more concentrated in low-lying coastal areas, highlighting the significant impact of saltwater intrusion and poor drainage. Finally, these salinity and sodicity classes were overlaid with township-level cultivated areas for targeted management interventions and improved agricultural practices.