MONITORING TEMPORAL AND SPATIAL VARIATION OF SOIL MOISTURE AND DROUGHT USING MODIS AND IN-SITU MEASUREMENT: A CASE STUDY OF ERBIL IRAQ

Abstract

Drought poses the most pressing challenges to our planet, often giving rise to severe economic, environmental, and social crises. Despite the increasing availability of biophysical, climate, and satellite data for the characterization and modeling of drought, much of the research has remained limited to the utilization of meteorological point data, such as rainfall measurements, for drought monitoring. The primary objective of this study is to monitor and assess drought occurrences, examining the temporal and spatial variations of soil moisture. The research emphasizes the utilization of Remote Sensing (RS) and Geographic Information Systems (GIS) in the realm of agriculture drought risk assessment. In this scope, satellite imagery data from the MODIS sensor and climatic data from meteorological stations have been used. The meteorological stations have been strategically distributed across the study area and provide invaluable hourly data for air temperature, soil temperature, soil moisture, and humidity. To gauge drought conditions in the study area, a standardized precipitation index (SPI) was calculated. Then, a Generalized Linear Model (GLM) was employed to establish a relationship between remote sensing data and climatic data. This model enabled to monitor drought and estimate spatial and temporal variability of soil moisture. Vulnerable areas to drought have been identified by analyzing rainfall data of 2018, 2019, and 2020. The climate data was used to compute the Standardized Precipitation Index (SPI). Additionally, MODIS satellite imagery aided to derive several spectral indices, such as NDVI, LSWI, NDWI, EVI, SM, and Land Surface Temperature (LST). The indices were used to estimate and monitor vegetation percentages, water availability, and temperatures. Linear regression revealed the relationships between the spectral indices and SPI. Strong correlation was obtained between LST and NDWI and SPI-3. The findings showed how drought severity fluctuates across different regions. The study area experienced drought conditions in 2019, whereas severe and extreme drought affected the eastern and northeastern parts of the study area in 2018 and 2020. The study e offers critical insights that help policymakers, farmers, and communities better understand and mitigate the impacts of drought. By harnessing the power of satellite imagery and weather station data, informed decision-making in agriculture, water resource management, and disaster preparedness can be possible. The correlations between remote sensing indicators and drought severity provide a valuable tool for early warning systems and resource allocation, ultimately building resilience in the face of an increasingly variable climate. In a world where droughts are becoming more frequent and severe, the findings of this study serve as a vital resource for protecting food security, ecosystems, and enhancing the well-being of our communities.