In iScience
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.
Yao Ling, Lu Jiaying, Jiang Hou, Liu Tang, Qin Jun, Zhou Chenghu
2023-Mar-17
Artificial intelligence, Remote sensing, earth sciences, global change
