Satellite-derived aridity index reveals China's drying in recent two decades

Ling Yao; Jiaying Lu; Hou Jiang; Tang Liu; Jun Qin; Chenghu Zhou

doi:10.1016/j.isci.2023.106185

Satellite-derived aridity index reveals China's drying in recent two decades

iScience. 2023 Feb 13;26(3):106185. doi: 10.1016/j.isci.2023.106185. eCollection 2023 Mar 17.

Authors

Ling Yao^{1

2}, Jiaying Lu^{1

2

3}, Hou Jiang^{1

2}, Tang Liu^{1

2

4}, Jun Qin¹, Chenghu Zhou¹

Affiliations

¹ State Key Laboratory of Resources and Environmental Information System, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China.
² Jiangsu Center for Collaborative Innovation in Geographical Information Resource Development and Application, Nanjing Normal University, Nanjing 210023, China.
³ University of Chinese Academy of Sciences, Beijing 100101, China.
⁴ School of Information Engineering, China University of Geosciences (Beijing), Beijing 100083, China.

Abstract

The expansion of dryland has caused a huge impact on the natural environment and human society. Aridity index (AI) can effectively reflect the degree of dryness, but spatiotemporally continuous estimation of AI is still challenging. In this study, we develop an ensemble learning algorithm to retrieve AIs from MODIS satellite data in China from 2003 to 2020. The validation proves the high match between these satellite AIs and their corresponding station estimates with a root-mean-square error of 0.21, bias of -0.01, and correlation coefficient of 0.87. The analysis results indicate China has been drying in recent two decades. Moreover, the North China Plain is undergoing an intense drying process, whereas the Southeastern China is becoming significantly more humid. On the national scale, China's dryland area shows a slight expansion, while the hyper arid area has a decreasing trend. These understandings have contributed to China's drought assessment and mitigation.

Keywords: Artificial intelligence; Remote sensing; earth sciences; global change.