Spatio-Temporal Drought Monitoring in the Chi River Basin from 2001–2020 Using MODIS Time Series and Google Earth Engine
Drought is a recurring issue in South Asia (SA) caused by extreme climate events, posing ongoing challenges for food management, sustainable agricultural practices, and livelihoods, especially in frequently affected areas. Earth Observation (EO) data provides valuable information for long-term drought monitoring across wide regions. However, there remains a need to map and monitor spatial drought events over large regions and extended periods, particularly in the Chi River Basin. In this region, droughts have increasingly occurred with high frequency, leading to low water-holding capacity and adversely affecting agricultural production and productivity.
In this context, this study aimed: i) to identify spatial drought from 2001 to 2020 over the Chi River Basin, Thailand using MODIS image time series via Google Earth Engine (GEE); ii) analyse the correlation between Temperature Vegetation Dryness Index (TVDI), Standardized Precipitation Index (SPI), and Streamflow Drought Index (SDI); and compare severe drought areas to land use maps provided by the Land Development Department (LDD).
In this study, we collected MODIS data using the MYD09Q1 (250m spatial resolution) and MOD11A1 products (1000m pixel size) across the h27v07 and h28v07 tiles. Image pre-processing was implemented, consisting of image resampling, data compositing, and image mosaicking through the GEE platform. Subsequently, the TVDI index was generated for both dry and wet seasons to map and monitor the spatial distribution of drought events over 21 years. The spatial drought based on TVDI and meteorological datasets (SPI and SDI) was determined to identify their relationship. Additionally, this study compared the spatiotemporal distribution of drought to land use groups.
Historical droughts were most frequent during the dry seasons of 2005 (82%), 2013 (80%), and 2004 (78%), and appeared in the wet seasons of 2019 (41%), 2017 (41%), and 2009 (38%). The TVDI drought map had a slightly low coefficient of determination (R²) for SPI and SDI, ranging from 0.12 to 0.22. However, these findings showed similar trends of drought across all study years, with drought events predominantly occurring in the central and northeast parts of the region. In comparison, the spatial drought map in 2021 showed severe droughts, mostly impacting cassava and rice fields during the dry season and urban areas during the wet season. Our proposed workflow is reliable and robust, providing spatial drought areas with confidence in the accuracy and validity of our results.
This study produced spatial drought maps using MODIS image time series datasets. The mapped results were well smoothed and effectively distributed drought areas across large regions. The highly severe spatial droughts in 2005 align with Thailand's extreme drought due to the El Niño event, demonstrating high severity compared to other years. This confirms that the TVDI index provides excellent and efficient map results for mapping the spatial distribution of droughts in cloudy regions and complex landscapes of the Chi River Basin. The proposed workflow can generate drought maps in cloudy regions and complex landscapes over large or national regions, particularly in zones like Thailand. Our findings can be used to manage future droughts and serve as a significant tool for drought mitigation planning and management, as well as for warning systems, providing an integrated model under climate change conditions.
Keywords: Drought; earth observation; Temperature Vegetation Dryness Index (TVDI); Land use; Google Earth Engine