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Open AccessArticle

Integrated Drought Monitoring and Evaluation through Multi-Sensor Satellite-Based Statistical Simulation

1
State Key Laboratory of Water Resources and Hydropower Engineering Science, Wuhan University, Wuhan 430072, China
2
Department of Civil Engineering, Joongbu University, Gyeonggi-do 10279, Korea
3
School of Resources and Environment, Hubei University, Wuhan 430062, China
4
Department of Civil and Environmental Engineering, Hanyang University (ERICA), Gyeonggi-do 15588, Korea
*
Author to whom correspondence should be addressed.
Remote Sens. 2021, 13(2), 272; https://doi.org/10.3390/rs13020272
Received: 20 November 2020 / Revised: 25 December 2020 / Accepted: 11 January 2021 / Published: 14 January 2021
To proactively respond to changes in droughts, technologies are needed to properly diagnose and predict the magnitude of droughts. Drought monitoring using satellite data is essential when local hydrogeological information is not available. The characteristics of meteorological, agricultural, and hydrological droughts can be monitored with an accurate spatial resolution. In this study, a remote sensing-based integrated drought index was extracted from 849 sub-basins in Korea’s five major river basins using multi-sensor collaborative approaches and multivariate dimensional reduction models that were calculated using monthly satellite data from 2001 to 2019. Droughts that occurred in 2001 and 2014, which are representative years of severe drought since the 2000s, were evaluated using the integrated drought index. The Bayesian principal component analysis (BPCA)-based integrated drought index proposed in this study was analyzed to reflect the timing, severity, and evolutionary pattern of meteorological, agricultural, and hydrological droughts, thereby enabling a comprehensive delivery of drought information. View Full-Text
Keywords: remote sensing; integrated drought monitoring; meteorological drought; hydrological drought; agricultural drought; Bayesian principal component analysis (BPCA); statistical simulation remote sensing; integrated drought monitoring; meteorological drought; hydrological drought; agricultural drought; Bayesian principal component analysis (BPCA); statistical simulation
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MDPI and ACS Style

Kim, J.-S.; Park, S.-Y.; Lee, J.-H.; Chen, J.; Chen, S.; Kim, T.-W. Integrated Drought Monitoring and Evaluation through Multi-Sensor Satellite-Based Statistical Simulation. Remote Sens. 2021, 13, 272. https://doi.org/10.3390/rs13020272

AMA Style

Kim J-S, Park S-Y, Lee J-H, Chen J, Chen S, Kim T-W. Integrated Drought Monitoring and Evaluation through Multi-Sensor Satellite-Based Statistical Simulation. Remote Sensing. 2021; 13(2):272. https://doi.org/10.3390/rs13020272

Chicago/Turabian Style

Kim, Jong-Suk; Park, Seo-Yeon; Lee, Joo-Heon; Chen, Jie; Chen, Si; Kim, Tae-Woong. 2021. "Integrated Drought Monitoring and Evaluation through Multi-Sensor Satellite-Based Statistical Simulation" Remote Sens. 13, no. 2: 272. https://doi.org/10.3390/rs13020272

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