Spatio-temporal Monitoring of Drought using Machine Learning approach and Remote Sensing Techniques in Ningxia

Timely and accurate monitoring of the beginning and development of drought in China is significant in decreasing losses from drought. The present study contributes to a comprehensive spatio-temporal analysis of drought over the Ningxia Hui (northwestern China) from 2003–2023. We determined the moisture content and vegetation using MODIS satellite data. The Enhanced Vegetation Index (EVI), the Land Surface Temperature (LST), the Standardized Precipitation Index (SPI-1, SPI-3, SPI-6, SPI-9 and SPI-12), and the Standardized Precipitation-Evapotranspiration Index (SPEI-1, SPEI-3, SPEI-6, SPEI-9, and SPEI-12), were calculated. SPEI at 1–12 months timescales and the Keetch-Byram Drought Index (KBDI) were adopted to characterize drought events over the Ningxia region from 2003 to 2023. Future drought predictions were determined based on SPI at 1–12 months timescales using an artificial neural network (ANN) and cellular automata (CA) machine learning approaches. The CA-ANN model was used to validate drought prediction. The results showed: (1) the EVI declined from 0.38 to 0.33 from 2003–2023. This declining EVI indicates that the annual average of vegetation was decreased ; (2) The KBDI increased from 581.33 in 2003 to 681.091 in 2023, reflecting aggrading aridity with the soil moisture drying out; (3) SPI decreased from 0.7 in 2003 to -1.835 in 2023 and the SPEI varied from 0.5 to − 1.898 in the same period, (4) SPEI results in 2003 highlight western and southern parts highly affected by drought; (6) drought prediction from CA-ANN display that the SPI and SPEI expected in 2033 will further decrease and can cause more frequent drought. The study concluded that the ever-declining drought conditions in the Ningxia region over the past two decades have manifested drastic changes in the drought conditions.