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Numerous scholars have used traditional thermal anomaly extraction methods and time series prediction models to study seismic anomalies based on longwave infrared radiation data. This paper selected bidirectional long short-term memory (BILSTM) as the research algorithm after analyzing and comparing the prediction performance of five time series prediction models. Based on the outgoing longwave radiation (OLR) data, the time series prediction model was used to predict the infrared longwave radiation values in the spatial area of 5° × 5° at the epicenter for 30 days before the earthquake. The confidence interval was used as the evaluation criterion to extract anomalies. The examples of earthquakes selected for study were the Yangbi Ms6.4-magnitude earthquake in Yunnan on 21 May 2021 and the Luding Ms6.8-magnitude earthquake in Sichuan on 5 September 2022. The results showed that the observed values of the Yangbi earthquake 15 to 16 days before the earthquake (5 May to 6 May) exceeded the prediction confidence interval over a wide area and to a large extent. This indicates a strong and concentrated OLR anomaly before the Yangbi earthquake. The observations at 27 days (9 August), 18 days (18 August), and 8 days (28 August) before the Luding earthquake exceeded the prediction confidence interval in a local area and by a large extent, indicating a strong and scattered OLR anomaly before the Luding earthquake. Overall, the method used in this paper extracts anomalies in both spatial and temporal dimensions and is an effective method for extracting infrared longwave radiation anomalies. [ABSTRACT FROM AUTHOR]

• The vegetation drought was alleviating during 1982–2020 across China. • Spring drought and summer drought were more likely to occur in South China. • NPI-NAO-AO had the most significant impact on vegetation drought. • Soil moisture was the primary driving factor of vegetation drought. Vegetation drought can reduce the net ecosystem productivity, resulting in disturbances to the sustainable development of humans and nature. In this study, Vegetation Health Index (VHI) was used to identify vegetation drought. The spatial and temporal evolution of vegetation drought was characterized using the Pixel-based Trend Identification Method (PTIM) in China from 1982 to 2020. Moreover, multiple cross-wavelet techniques were used for revealing the correlations between atmospheric circulation factors and their combinations with vegetation drought. The results indicates that: (1) there is a decreasing trend of vegetation drought in China during the study period, while it will show an increasing trend in the future; (2) the South China is prone to spring drought and summer drought, and the Sichuan Basin is prone to autumn drought and winter drought; (3) in spring, the most obvious trend of vegetation drought mitigation appears; (4) a combination of atmospheric circulation factors, North Pacific Index (NPI)-North Atlantic Oscillation (NAO)-Arctic Oscillation (AO), has the most significant impact on vegetation drought across China. [ABSTRACT FROM AUTHOR]