Integrated drought monitoring index: A tool to monitor agricultural drought by using time-series datasets of space-based earth observation satellites. Issue 1 (1st January 2021)

• Record Type: Journal Article
• Authors: Arun Kumar, K.C.; Reddy, G.P. Obi; Masilamani, P.; Turkar, Satish Y.; Sandeep, P.
• Is Part Of: Advances in space research. Volume 67:Issue 1(2021)
• Page Start: 298
• Page End: 315
• Publication Date: 2021-01-01
• Subjects: Agricultural drought -- Earth observation satellites -- IDMI -- MODIS -- NDVI; Space sciences -- Periodicals; Astronautics -- Periodicals; Geophysics -- Periodicals; 500.505
• DOI: 10.1016/j.asr.2020.10.003
• Languages: English
• ISSNs: 0273-1177

Abstract

Highlights: IDMI was proposed by integrating scaled CHIRPS, soil moisture, LST and NDVI datasets with PCA. The study shows the intensity and severity of agricultural drought during the period from 2000 to 2016. Correlation of IDMI and 3-month SPI shows a strong positive correlation during wet and dry years. Sensitivity analysis shows PCI and VCI found to be influencing parameters in computation of IDMI. Time-series earth observation datasets play a key role in assessment of agricultural drought. Abstract: In this study, integrated drought monitoring index (IDMI) was proposed as a tool to assess and monitor the spatio-temporal dynamics of agricultural drought during the northeast monsoon season for the period from 2000 to 2016 in Tamil Nadu state, south-eastern part of Indian peninsula. The IDMI is characterized as the principal component of precipitation condition index (PCI), soil moisture condition index (SMCI), temperature condition index (TCI), and vegetation condition index (VCI) derived from time-series satellite observations of climate hazards group infra-red precipitation with stations (CHIRPS), European space agency climate change initiative (ESA-CCI) and moderate resolution imaging spectroradiometer (MODIS). The study shows that in the year 2016, about 44.4 and 17.8% of Tamil Nadu state was under extreme and severe drought conditions, respectively. Sensitivity analysis of the study shows that PCI is the most influential parameter to IDMI, followed by VCI and TCI. The validation of IDMI with 3-month standardized precipitation index (SPI) by using Pearson correlation test shows a strong positive correlation between IDMI and 3-month SPI with correlation coefficient (r) value of 0.73 and 0.77 for the wet (2005) and dry year (2016), respectively. The study clearly demonstrates the potential of IDMI derived from time-series datasets of earth observation satellites as a tool in assessment and monitoring of spatio-temporal dynamics of agricultural drought. The proposed IDMI could be effectively used as a reliable tool to monitor agricultural drought and develop its mitigation strategies to minimise the adverse effects of drought on agriculture, water resources, and livelihoods of the people.