12 citations as recorded by crossref.

1. Regional-scale cotton yield forecast via data-driven spatio-temporal prediction (STP) of solar-induced chlorophyll fluorescence (SIF) X. Kang et al. 10.1016/j.rse.2023.113861
2. Increasing Risks of Future Compound Climate Extremes With Warming Over Global Land Masses H. Wu et al. 10.1029/2022EF003466
3. Land-atmosphere and ocean–atmosphere couplings dominate the dynamics of agricultural drought predictability in the Loess Plateau, China J. Luo et al. 10.1016/j.jhydrol.2024.132225
4. A novel feature extraction-selection technique for long lead time agricultural drought forecasting M. Mohammadi Ghaleni et al. 10.1016/j.jhydrol.2024.132332
5. Compound climate extremes over the globe during 1951–2021: Changes in risk and driving factors H. Wu et al. 10.1016/j.jhydrol.2023.130387
6. Projections of the characteristics and probability of spatially concurrent hydrological drought in a cascade reservoirs area under CMIP6 T. Zhang et al. 10.1016/j.jhydrol.2022.128472
7. Status and prospects for drought forecasting: opportunities in artificial intelligence and hybrid physical–statistical forecasting A. AghaKouchak et al. 10.1098/rsta.2021.0288
8. Bayesian vine copulas improve agricultural drought prediction for long lead times H. Wu et al. 10.1016/j.agrformet.2023.109326
9. Evaluating vegetation vulnerability under compound dry and hot conditions using vine copula across global lands G. Zhang et al. 10.1016/j.jhydrol.2024.130775
10. Rising risks of hydroclimatic swings: A large ensemble study of dry and wet spell transitions in North America W. Na & M. Najafi 10.1016/j.gloplacha.2024.104476
11. Dynamic assessment of the impact of compound dry-hot conditions on global terrestrial water storage Z. Han et al. 10.1016/j.rse.2024.114428
12. Predicting Hydrological Drought With Bayesian Model Averaging Ensemble Vine Copula (BMAViC) Model H. Wu et al. 10.1029/2022WR033146