soil temperature and soil moisture

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High-resolution soil temperature and moisture patterns are critical for understanding environmental processes and managing agricultural systems. An interpretable machine learning modeling approach enables researchers to analyze these patterns across spatial and temporal dimensions, facilitating insights into how soil conditions influence plant growth and ecosystem dynamics. By integrating various data sources, such as remote sensing, meteorological records, and soil properties, these models can uncover complex relationships and trends. This interpretability allows stakeholders, including farmers and land managers, to make informed decisions based on the predictions, ultimately leading to optimized resource management and enhanced resilience to climate variability.

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