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http://dx.doi.org/10.7780/kjrs.2021.37.4.11

Satellite-based Drought Forecasting: Research Trends, Challenges, and Future Directions  

Son, Bokyung (School of Urban and Environmental Engineering, Ulsan National Institute of Science and Technology)
Im, Jungho (School of Urban and Environmental Engineering, Ulsan National Institute of Science and Technology)
Park, Sumin (School of Urban and Environmental Engineering, Ulsan National Institute of Science and Technology)
Lee, Jaese (School of Urban and Environmental Engineering, Ulsan National Institute of Science and Technology)
Publication Information
Korean Journal of Remote Sensing / v.37, no.4, 2021 , pp. 815-831 More about this Journal
Abstract
Drought forecasting is crucial to minimize the damage to food security and water resources caused by drought. Satellite-based drought research has been conducted since 1980s, which includes drought monitoring, assessment, and prediction. Unlike numerous studies on drought monitoring and assessment for the past few decades, satellite-based drought forecasting has gained popularity in recent years. For successful drought forecasting, it is necessary to carefully identify the relationships between drought factors and drought conditions by drought type and lead time. This paper aims to provide an overview of recent research trends and challenges for satellite-based drought forecasts focusing on lead times. Based on the recent literature survey during the past decade, the satellite-based drought forecasting studies were divided into three groups by lead time (i.e., short-term, sub-seasonal, and seasonal) and reviewed with the characteristics of the predictors (i.e., drought factors) and predictands (i.e., drought indices). Then, three major challenges-difficulty in model generalization, model resolution and feature selection, and saturation of forecasting skill improvement-were discussed, which led to provide several future research directions of satellite-based drought forecasting.
Keywords
Drought forecasting; Drought prediction; remote sensing; Forecast lead time;
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