Integrated drought monitoring index: A tool to monitor agricultural drought by using time-series datasets of space-based earth observation satellites
| Autor: | Satish Y. Turkar, P. Masilamani, G. P. Obi Reddy, K. C. Arun Kumar, P. Sandeep |
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| Rok vydání: | 2021 |
| Předmět: |
Atmospheric Science
010504 meteorology & atmospheric sciences Correlation coefficient Aerospace Engineering Climate change Astronomy and Astrophysics Vegetation 01 natural sciences Normalized Difference Vegetation Index Water resources Condition index Geophysics Space and Planetary Science Climatology 0103 physical sciences General Earth and Planetary Sciences Environmental science Moderate-resolution imaging spectroradiometer Precipitation 010303 astronomy & astrophysics 0105 earth and related environmental sciences |
| Zdroj: | Advances in Space Research. 67:298-315 |
| ISSN: | 0273-1177 |
| DOI: | 10.1016/j.asr.2020.10.003 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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