Agricultural drought dynamics in China during 1982–2020: a depiction with satellite remotely sensed soil moisture

ABSTRACT

Agricultural drought (AD) is a serious threat to food security for many regions worldwide. Understanding the dynamics of AD contributes to preventing or mitigating its adverse impacts. Soil moisture (SM) anomaly is a relatively straightforward indicator of AD. However, most of the previous studies on AD dynamics of China were conducted with non-remotely sensed SM indicators due to the lack of long-term and spatial-continuous SM datasets. Here, such an SM dataset was created by enhancing a satellite remote sensing SM dataset with a machine learning method XGBoost, various remote sensing datasets, and some surface or meteorological parameters from reanalysis data. The new SM dataset has a period of 1982–2020, a spatial resolution of 0.25°, and a temporal resolution of 1 month. Furthermore, Standardized SM Index at one-month scale (SSMI1) was calculated, and AD events were identified using the SSMI1 and a 3-dimensional clustering method. Results demonstrated that 1) the new SM presented comparable or even better performances with the original SM as evaluated with spatial distributions, in-situ SM observations, and manufactured data gaps. 2) The AD was most frequent in North China, followed by the western parts of East China, Northeast, and Southwest China. The centroids of identified AD events were found chiefly in the Northeast, North, Southwest, and western parts of East China. 3) The severity of AD events presented a decreasing trend from 1982 to 2020, while significant drying trends were found mostly in the southern parts of North China, western parts of East China, and Southwest China. 4) The AD dynamics revealed in this study are basically consistent with other studies but also have unique features such as more space details and less drought frequency and count than that of meteorological drought. Further studies are expected to create a long-term satellite SM with faster timeliness, higher resolution, and greater depth.

KEYWORDS:

• Remote sensing of drought
• CCI soil moisture
• gap-filling method
• agricultural drought events

Acknowledgments

The author would like to thank any agency or organization that provided the remote sensing data resources, reanalysis data resources, and in-situ meteorological data resources used in this study.

Disclosure statement

No potential conflict of interest was reported by the authors.

Data availability statement

The data supporting the findings of this research are publicly available at https://www.esa-soilmoisture-cci.org/data, https://doi.org/10.5067/MODIS/MOD13C2.006, https://developers.google.com/earth-engine/datasets/catalog/NASA_GIMMS_3GV0, http://data.cma.cn/data/cdcdetail/dataCode/NAFP_CRA40_FTM_MON_NC.html, https://www.fao.org/soils-portal/data-hub/soil-maps-and-databases/harmonized-world-soil-database-v12/en/, https://earthexplorer.usgs.gov/, https://ismn.earth/en/, https://www.scidb.cn/en/detail?dataSetId=434a6fad0daa4c32a7396dc7db575fc8.

Additional information

Funding

This work was supported by the Beijing Natural Science Foundation under Grant 6222045.