Temperature-Vegetation-soil Moisture-Precipitation Drought Index (TVMPDI); 21-year drought monitoring in Iran using satellite imagery within Google Earth Engine

• DOI: 10.5281/zenodo.6137154
• Publication Date: 2021-12-01
• Authors: Soroosh Mehravar; Meisam Amani; Armin Moghimi; Farzaneh Dadrass ...; Farhad Samadzadegan; Arsalan Ghorbanian; Alfred Stein; Ali Mohammadzadeh; S. Mohammad Mirma...
• Keywords: TVPMDI; Monitoring; Droughts--Iran; Remote Sensing (RS); UT-Hybrid-D; Radiometers; Moderate res-olution imaging spectrometers; Remote-sensing; Mapes per teledetecció; 01 natural sciences; Drought in iran; Google earths; Remote-sensing maps; SDG 13 - Climate Action; Soil temperature; Engines; Àrees temàtiques de la UPC::Enginyeria de la telecomunicació; Remote sensing (RS); 0105 earth and related environmental sciences; Drought in Iran; Vegetation; Spectrometers; Drought; 22/3 OA procedure; Drought monitoring; Precipitation temperature; Drought index; Temperature; Google Earth; Satellite imagery; Soil surveys; Sequeres--Iran; Remote sensing; :Enginyeria de la telecomunicació [Àrees temàtiques de la UPC]; 15. Life on land; 6. Clean water; MODIS; 13. Climate action; ITC-ISI-JOURNAL-ARTICLE; Satèl·lits artificials en teledetecció; Soil moisture; Artificial satellites in remote sensing

We appreciate the I.R. of Iran Meteorological Organiza-tion (IRIMO) that provided us with meteorological data.<br/>Remote Sensing (RS) offers efficient tools for drought monitoring, especially in countries with a lack of reliable and consistent in-situ multi-temporal datasets. In this study, a novel RS-based Drought Index (RSDI) named Temperature-Vegetation-soil Moisture-Precipitation Drought Index (TVMPDI) was proposed. To the best of our knowledge, TVMPDI is the first RSDI using four different drought indicators in its formulation. TVMPDI was then validated and compared with six conventional RSDIs including VCI, TCI, VHI, TVDI, MPDI and TVMDI. To this end, precipitation and soil temperature in-situ data have been used. Different time scales of meteorological Standardized Precipitation Index (SPI) index have also been used for the validation of the RSDIs. TVMPDI was highly correlated with the monthly precipitation and soil temperature in-situ data at 0.76 and 0.81 values respectively. The correlation coefficients between the RSDIs and 3-month SPI ranged from 0.07 to 0.28, identifying the TVMPDI as the most suitable index for subsequent analyses. Since the proposed TVMPDI could considerably outperform the other selected RSDIs, all spatiotemporal drought monitoring analyses in Iran were conducted by TVMPDI over the past 21 years. In this study, different products of the Moderate Resolution Imaging Spectrometer (MODIS), Tropical Rainfall Measuring Mission (TRMM), and Global Precipitation Measurement (GPM) datasets containing 15,206 images were used on the Google Earth Engine (GEE) cloud computing platform. According to the results, Iran experienced the most severe drought in 2000 with a 0.715 TVMPDI value lasting for almost two years. Conversely, the TVMPDI showed a minimum value equal to 0.6781 in 2019 as the lowest annual drought level. The drought severity and trend in the 31 provinces of Iran have also been mapped. Consequently, various levels of decrease over the 21 years were found for different provinces, while Isfahan and Gilan were the only provinces showing an ascending drought trend (with a 0.004% and 0.002% trendline slope respectively). Khuzestan also faced a worrying drought prevalence that occurred in several years. In summary, this study provides updated information about drought trends in Iran using an advanced and efficient RSDI implemented in the cloud computing GEE platform. These results are beneficial for decision-makers and officials responsible for environmental sustainability, agriculture and the effects of climate change.<br/>