Stacking-Based Solar-Induced Chlorophyll Fluorescence Downscaling for Soil EC Estimation

The Xinjiang Province of China, characterized as a typical arid to semi-arid region, is increasingly facing severe issues related to soil salinization. Timely and accurate estimation of soil salinization in this region is crucial for the sustainable development of agriculture and food security. However, current methods for detecting soil salinization primarily rely on various environmental covariates, which assess the extent of soil salinization by analyzing the relationship between environmental factors and the accumulation of soil salts. Nonetheless, these conventional environmental covariates often suffer from response delays, making it challenging to promptly reflect the dynamic changes in soil salinity. Solar-induced chlorophyll fluorescence (SIF) has been widely used to assess vegetation photosynthetic efficiency and is considered a direct indicator of plant photosynthetic activity. In contrast, SIF provides a timely means of monitoring the status of plant photosynthesis, indirectly reflecting the impact of soil salinization on plant growth. However, the spatial resolution of SIF products derived from satellites is typically low, which significantly limits the accurate estimation of soil salinization in Xinjiang. This study proposes a novel method for monitoring soil salinization, based on SIF data. The approach employs a Stacking ensemble learning model to downscale SIF data, thereby improving the spatial resolution of soil salinity monitoring. Using the GOSIF dataset, combined with environmental covariates, such as MODIS, the Stacking framework facilitates the fine-scale downscaling of SIF data, generating high-resolution SIF products, ranging from 0.05° to 0.005°, with a spatial resolution of 30 m. This refined SIF data is then used to predict soil electrical conductivity (EC). The experimental results demonstrate that: (1) the proposed Stacking-based SIF downscaling method is highly effective, with a high degree of fit to reference SIF data (R2 > 0.85); (2) the high-resolution SIF data, after downscaling, more accurately reflects the spatial heterogeneity of soil salinization, especially in shallow soils (r < −0.6); and (3) models combining SIF and environmental covariates exhibit superior accuracy compared to models that rely solely on SIF or traditional environmental covariates (R2 > 0.65). This research provides new data support and methodological advancements for precision agriculture and ecological environmental monitoring.