Evaluate the Winter Wheat GVMI and NDMI correlations with the Rainfall

With the recent application of digital twin systems in smart agriculture, the intricate relationships between different data types have become increasingly significant, particularly the correlation between satellite data and weather data. This correlation is a critical aspect of advanced agricultural monitoring. This study examined the relationship between two satellitederived indices, GVMI and NDMI, which reflect leaf moisture content, and rainfall. Our analysis reveals a strong correlation between GVMI and NDMI, with a coefficient as high as 0.97, while their correlation with rainfall is minimal. Consequently, future research will further explore the impact of rainfall on these indices. Specifically, we investigated whether substantial increases in these indices are linked to rainfall and examined the correlation between their growth rates and rainfall. Our findings indicate that approximately 70% of significant increases are attributed to rainfall. Additionally, due to the inherent lag in satellite data’s response to rainfall-—here substantial changes may not be immediate but require some time to manifest—we explored the time lag needed for significant changes in NDMI and GVMI to occur after rainfall. The results demonstrate that NDMI is more sensitive and reacts more quickly to rainfall, with $\mathbf{9 2 \%}$ of the data showing significant increases or decreases within two days following rainfall, compared to $\mathbf{7 0 \%}$ for GVMI.