AUTHOR=Cui Kuangda , Ding Jianli , Wang Jinjie , Tan Jiao , Han Lijing , Li Jiangtao 

TITLE=Potential of solar-induced chlorophyll fluorescence for monitoring long-term dynamics of soil salinity in Central Asia the Xinjiang Region China

JOURNAL=Frontiers in Plant Science

VOLUME=Volume 16 - 2025

YEAR=2025

URL=https://www.frontiersin.org/journals/plant-science/articles/10.3389/fpls.2025.1603159

DOI=10.3389/fpls.2025.1603159

ISSN=1664-462X

ABSTRACT=IntroductionSoil salinization in Central Asia and Xinjiang, China, poses serious threats to agriculture and ecosystems. Solar-induced chlorophyll fluorescence (SIF), which reflects plant photosynthetic status and stress, shows promise for monitoring salinity but remains underutilized in this region.MethodsThis study integrated SIF-derived indices (SIFI) with soil salinity data to build a region-specific prediction model. Using a random forest algorithm, soil salinity was classified into five levels based on satellite data and ground references from 2000–2020. Model performance, seasonal sensitivity, and spatial variation were analyzed across Central Asian countries and Xinjiang.ResultsSIF effectively detected salinization dynamics, with highest sensitivity in Kazakhstan and Xinjiang. April was identified as the most responsive month, with SIFI1 being the key indicator. The model achieved over 80% accuracy in typical regions and around 70% in atypical regions. Kazakhstan had the largest salt-affected area, followed by Turkmenistan and Xinjiang. Tajikistan showed high variability, while Xinjiang remained relatively stable. Most areas exhibited increasing salinity and expansion of saline lands.DiscussionThese findings demonstrate the potential of SIF-based monitoring for large-scale salinity assessment. The integration of plant physiological signals with machine learning provides a valuable tool for early warning and sustainable land management in arid regions.