Integrating Proximal Soil Sensing and Remote Sensing to Track Soil Constraints and Crop Responses in Salinization-Prone Environments.

Understanding soil–crop interactions in salinization-prone agroecosystems is critical for sustainable management, yet these interactions are highly dynamic and influenced by multiple drivers. Soil apparent electrical conductivity (ECa) serves as an integrative indicator of soil properties affecting crop performance, while vegetation indices such as NDVI provide high-resolution information on crop development. Linking these datasets across space and time can reveal how soil constraints emerge and evolve during the growing season, informing adaptive management strategies. We propose a spatiotemporal correlation framework combining proximal soil sensing (ECa) with remote sensing imagery (NDVI) to identify periods and zones where soil conditions either promote or limit crop growth. Positive correlations indicate favorable conditions, whereas negative correlations signal stress factors such as water scarcity or salinity. The approach was tested in irrigated systems in southern Spain—including maize, cotton, tomato, and sugar beet—under both non-saline and salinization-prone scenarios. Results show that correlation patterns shift throughout the season, reflecting changes in soil water and salinity dynamics and their impact on crop development. This integrative workflow demonstrates the potential of combining proximal and remote sensing for diagnosing soil-driven variability and guiding precision agriculture. Integrating proximal and remote sensing technologies enables more effective monitoring and management of soil–crop interactions across fields.

Acknowledgements
This work was supported by grant PID2023-149609OR-I00, funded by MICIU/AEI/10.13039/501100011033 and by FEDER, EU. JLGF and MRR acknowledge their PhD grants PRE2020-095133 and PREP2023-001774 funded by MICIU/AEI/10.13039/501100011033 and by “ESF Investing in your future”.