Exploration and advancement of NDDI leveraging NDVI and NDWI in Indian semi-arid regions: A remote sensing-based study

Droughts are one of the most catastrophic natural disasters on the planet, impacting millions of people in various ways (e.g., food security, economic losses, and migration). The growing severity of droughts and their catastrophic effects on society in India's semi-arid regions necessitate better drought monitoring and assessment systems. Traditional mathematical methods like Standardized Precipitation Index (SPI), Palmer Drought Severity Index (PDSI), and Climate Moisture Index (CMI), etc., are used to analyze drought intensity. In the present study, analysis of historical rainfall data from the Parbhani district (2000–2020) was conducted to identify patterns in normal, actual, and % deviation of actual rainfall from normal for two contrasting years (2020: +4.1 % deviation, 2015: −53.73 % deficit), with a focus on drought analysis using dry spell data from the Government senses. The satellite images for the years 2015 and 2020 were gathered at 16-day intervals, and remote sensing techniques were employed to calculate NDDI (normalized difference drought index) using NDVI (normalized difference vegetation index) and NDWI (normalized difference water index). The comparative analysis of NDVI and NDWI interpreted greater value during normal rainy conditions in 2020, whereas it was low in 2015 due to less rainfall and higher dry spells. NDDI reflects positively; it was observed that the mean area under the Mild drought class in 2015 (90.9 %) was higher than in 2020 (84.9 %). Variable rainfall distribution and dry spell patterns caused severe drought in 2015, according to vegetation indices. The study utilised remote sensing to investigate drought and corroborated its findings with India Meteorological Department (IMD) rainfall and dry spell data. VIs can therefore be utilised as an independent indicator that complements conventional techniques.