H. Babajafari, Sh. Paimozd, M. Moghaddasi, M. Hosseini Vardanjani,
Volume 26, Issue 3 (12-2022)
Abstract
Drought is one of the most complex natural disasters due to its slow onset and long-term impact. Today, the use of remote sensing techniques and satellite imagery has been considered a useful tool for monitoring agricultural drought. The objective of the present study was to evaluate spatial and temporal monitoring of agricultural drought in the lake Urmia catchment area with the ETDI drought index which is calculated from Nova satellite images based on actual evapotranspiration from the SEBS algorithm and compared with the ground index SPI. For this purpose, 248 AVHRR sensor images and NOAA satellites during the statistical period of 1998-2000 and 17 meteorological stations with a statistical period of 30 years were used to calculate the indicators. To determine agricultural lands, six thousand points were marked for different uses and their actual evapotranspiration was calculated using the SEBS algorithm. The results showed that with the onset of the drought period in 1998, the ETDI index indicated 9.4% in weak drought conditions in May and 90.6% in normal conditions. Over time, in June of 1998, the situation was different with 95% in a weak drought situation and 5% in a normal situation for the city of Tabriz. In July, the entire catchment area experiences a slight drought. Then, in August, 84% of the basin is in normal condition and 16% in Tabriz and Urmia are declared weak drought. It was also founded that the ETDI drought index due to the combination of visible and infrared bands and its combination with terrestrial data has a physical meaning and has high certainty and predicts drought faster and more accurately.
Homa Chegini, Chooghi Bairam Komaki, Majid Owneq, Hamidreza Asgari, Khalil Ghorbani,
Volume 30, Issue 1 (3-2026)
Abstract
This study aimed to analyze the spatial–temporal correlation between the Vegetation Health Index (VHI) and climatic variables, including precipitation, potential evapotranspiration (PET), and mean temperature, in Golestan Province during the period 2000–2024. MODIS satellite products were used for vegetation and land surface temperature data, while the TerraClimate dataset provided precipitation and PET variables. After spatial–temporal alignment, the Cross-Correlation Function (CCF) was applied to identify optimal time lags, and the Random Forest model was employed to assess the relative importance of the climatic drivers. Turning to the results, increasing trends in mean temperature and PET were observed, alongside a significant decrease in precipitation, which led to intensified climatic stress and reduced VHI across the province, especially during summer in croplands and rangelands. The relationship between VHI and precipitation was positive (maximum correlation of 0.299 in croplands), negative with PET (−0.287), and non-linear with temperature (0.275). Notably, VHI responded to precipitation with short-term lags (0–1 month), whereas PET and temperature effects emerged with longer lags (2–4 months). The Random Forest analysis highlighted precipitation as the most influential factor on VHI, followed by PET and temperature, achieving strong predictive performance (R² = 0.78, RMSE = 0.09). Overall, these findings emphasize precipitation as the immediate driver of vegetation health, while PET and temperature act as secondary, cumulative stressors. The results provide valuable insights for developing climate adaptation and sustainable resource management strategies in agriculture and natural ecosystems of Golestan Province.
