The interrelationship among vegetation, soil water volume and soil temperature across various altitude classes and land uses in Iran using land degradation approach

This study investigates vegetation degradation across Iran by utilizing Soil Temperature (ST) and Soil Water Volumetric (SWV) data, with a focus on understanding the spatiotemporal dynamics and relationships among these variables.

The Normalized Difference Vegetation Index (NDVI), derived from MODIS satellite products, served as the primary indicator for vegetation health and coverage. Concurrently, ST and SWV data were sourced from the ERA5 reanalysis dataset, spanning a temporal range from 2001 to 2022 and offering a spatial resolution of 10 km. To ensure robust analysis, the Mann-Kendall nonparametric test and Sen’s slope estimator were employed, enabling the detection of statistically significant trends in the time series data. Additionally, Pearson correlation tests were conducted to assess the relationships between NDVI, ST, and SWV.

The results revealed intriguing patterns in the temporal and spatial behavior of NDVI, SWV, and ST across Iran. Specifically, NDVI demonstrated a positive trend in 81.75% of the study area, predominantly in the northwest and southeast regions. Similarly, SWV and ST exhibited positive trends in 47.09% and 75.84% of the country’s area, respectively. These trends highlight notable regional variations in vegetation response to soil moisture and temperature conditions over the two-decade study period. The correlation analysis provided deeper insights into the interplay among these variables. NDVI and SWV were positively correlated in 90.49% of the study area, with strong positive correlations observed in 42.25% of the regions, indicating that vegetation health improves with increasing soil water content. Conversely, negative correlations were noted in only 9.5% of the area, with strong negative correlations accounting for a mere 0.82%, emphasizing the overall beneficial role of soil water availability. In contrast, the relationship between NDVI and ST exhibited predominantly negative correlations, covering 71.16% of the study area. Strong negative correlations were found in 16.26% of the regions, suggesting that rising soil temperatures could adversely affect vegetation health, particularly in temperature-sensitive ecosystems. Positive correlations were limited to 25.84% of the area, with strong positive correlations observed in just 1.87%. Land-use-specific analysis further refined these results. NDVI displayed a positive correlation with SWV in grasslands, barelands, croplands, and pasturelands across all altitude classes. However, in forested regions situated at altitudes between 1500–2000 meters and above 2000 meters, the correlation turned negative, reflecting unique hydrological and ecological dynamics. Similarly, NDVI correlated positively with ST in barelands and grasslands but showed a negative correlation in forests, croplands, and pasturelands, underscoring the complex interaction of vegetation types with soil temperature and water dynamics in varying environmental contexts.

Combining remote sensing and reanalysis data provides a reliable database for monitoring and modeling vegetation coverage, soil moisture, and temperature, and consequently, desertification and land degradation. It is important to note that this research was conducted over an annual timescale. Further investigations are warranted to explore the monthly relationships between the analyzed indices and their implications for land degradation.