156 citations as recorded by crossref.

1. Evaluation of drought propagations with multiple indices in the Yangtze River basin M. Um et al. https://doi.org/10.1016/j.jenvman.2022.115494
2. Spatiotemporal Analysis of Compound Agrometeorological Drought and Hot Events in India Using a Standardized Index D. Muthuvel & A. Mahesha https://doi.org/10.1061/(ASCE)HE.1943-5584.0002101
3. Drought analysis using copula approach: a case study for Turkey O. Evkaya et al. https://doi.org/10.1080/23737484.2019.1635923
4. Simultaneous Monitoring of Different Drought Types Using Linear and Nonlinear Combination Approaches Z. Hosseini et al. https://doi.org/10.1007/s11269-022-03418-4
5. Can multiscalar meteorological drought indices detect soil moisture droughts? A study of Indian regions P. Das et al. https://doi.org/10.1080/02626667.2021.1942475
6. Plant and soil characteristics as key predictors for future agricultural drought A. Samantaray et al. https://doi.org/10.1088/1748-9326/adccdb
7. Monitoring water storage decline over the Middle East M. Khaki & I. Hoteit https://doi.org/10.1016/j.jhydrol.2021.127166
8. Drought Monitoring and Evaluation by ESA CCI Soil Moisture Products Over the Yellow River Basin L. Zhang et al. https://doi.org/10.1109/JSTARS.2019.2934732
9. Assessment of global meteorological, hydrological and agricultural drought under future warming based on CMIP6 J. Zeng et al. https://doi.org/10.1016/j.aosl.2021.100143
10. Drought Occurrence Probability Analysis Using Multivariate Standardized Drought Index and Copula Function Under Climate Change K. Naderi et al. https://doi.org/10.1007/s11269-022-03186-1
11. Atmospheric drought indices in future projections S. Vicente-Serrano et al. https://doi.org/10.1038/s44221-025-00416-9
12. Comparing Agriculture‐Related Characteristics of Flash and Normal Drought Reveals Heterogeneous Crop Response S. Ho et al. https://doi.org/10.1029/2023WR034994
13. Vegetation Water Stress Response-Driven Approach for Verifying Vegetation Drought Monitoring Results: A Case Study of the Yangtze River Basin, China L. Wu et al. https://doi.org/10.1109/TGRS.2026.3670187
14. Probabilistic forecasting of seasonal drought behaviors in the Huai River basin, China M. Xiao et al. https://doi.org/10.1007/s00704-016-1733-x
15. Status and prospects for drought forecasting: opportunities in artificial intelligence and hybrid physical–statistical forecasting A. AghaKouchak et al. https://doi.org/10.1098/rsta.2021.0288
16. Spatial-temporal dynamics of agricultural drought in the Loess Plateau under a changing environment: Characteristics and potential influencing factors Z. Han et al. https://doi.org/10.1016/j.agwat.2020.106540
17. Vulnerability assessment of agricultural production systems to drought stresses using robustness measures M. Cruz et al. https://doi.org/10.1038/s41598-021-98829-5
18. Soil moisture as an essential component for delineating and forecasting agricultural rather than meteorological drought S. Chatterjee et al. https://doi.org/10.1016/j.rse.2021.112833
19. Comparative analysis of drought indicators for effective vegetation and soil moisture monitoring in the Oueme River Basin, Benin A. Bio Sidi D. Bouko et al. https://doi.org/10.2166/wcc.2025.639
20. A global drought dataset of standardized moisture anomaly index incorporating snow dynamics (SZIsnow) and its application in identifying large-scale drought events L. Tian et al. https://doi.org/10.5194/essd-14-2259-2022
21. The hydrologic nature of swales uncovers remarkable influence of non-topographic factors on catchment-scale soil moisture variation Y. Zhang et al. https://doi.org/10.1016/j.jhydrol.2024.131196
22. The Process-Cognizant Vegetation Drought Model (PCVDM): Analyzing vegetation drought dynamics in Hunan Province, China Z. Zhang et al. https://doi.org/10.1016/j.jenvman.2025.124533
23. A generalized framework for deriving nonparametric standardized drought indicators A. Farahmand & A. AghaKouchak https://doi.org/10.1016/j.advwatres.2014.11.012
24. Advancing drought monitoring with a three-dimensional feature space framework M. Cheng et al. https://doi.org/10.1016/j.jhydrol.2026.135781
25. Crop yield response to climate change varies with crop spatial distribution pattern G. Leng & M. Huang https://doi.org/10.1038/s41598-017-01599-2
26. The development of a novel nonstationary meteorological and hydrological drought index using the climatic and anthropogenic indices as covariates T. Zhang et al. https://doi.org/10.1016/j.scitotenv.2021.147385
27. A methodology for deriving ensemble response from multimodel simulations L. Cheng & A. AghaKouchak https://doi.org/10.1016/j.jhydrol.2014.12.025
28. Spatiotemporal Patterns of Soil Moisture Drought Across Different Soil Layers and Their Relationships with Ecosystem Water Use Efficiency and Resilience in the Three-North Shelterbelt Forest Program Region E. Hu et al. https://doi.org/10.3390/su18157771
29. Stratified drought analysis using a stochastic ensemble of simulated and in-situ soil moisture observations V. Sehgal et al. https://doi.org/10.1016/j.jhydrol.2016.12.033
30. A novel multi-timescale comprehensive drought index for drought monitoring in the Xijiang River Basin L. Wang et al. https://doi.org/10.1016/j.ejrh.2025.102674
31. The development of a Nonstationary Standardized Precipitation Index using climate covariates: A case study in the middle and lower reaches of Yangtze River Basin, China Z. Song et al. https://doi.org/10.1016/j.jhydrol.2020.125115
32. Improved flash drought forecasting and attribution: A spatial-temporal causality-aware deep learning approach S. Tang et al. https://doi.org/10.1016/j.jhydrol.2026.134945
33. Multi-type assessment of global droughts and teleconnections Z. Nikraftar et al. https://doi.org/10.1016/j.wace.2021.100402
34. Unraveling drought propagation dynamics using causal inference from time series M. Pang & S. Yang https://doi.org/10.1088/1748-9326/ae151e
35. High Resolution Forecasting of Summer Drought in the Western United States R. Abolafia‐Rosenzweig et al. https://doi.org/10.1029/2022WR033734
36. Impacts of water regulation through a reservoir on drought dynamics and propagation in the Pilica River watershed T. Senbeta et al. https://doi.org/10.1016/j.ejrh.2024.101812
37. 中国大陆地区水文气象要素非平稳变化对不同类型干旱评估的影响 潇. 张 et al. https://doi.org/10.1360/SSTe-2025-0194
38. Can limits of plant available water be inferred from soil moisture distributions? M. Kukal & S. Irmak https://doi.org/10.1002/ael2.20113
39. Sequential hybridization enhances the reliability of a statistical crop yield model – exemplified by wheat and sugar beet yields in the provinces of Poland M. Eini et al. https://doi.org/10.1007/s00704-026-06322-8
40. Remote sensing of drought: Progress, challenges and opportunities A. AghaKouchak et al. https://doi.org/10.1002/2014RG000456
41. Projecting meteorological, hydrological and agricultural droughts for the Yangtze River basin F. Sun et al. https://doi.org/10.1016/j.scitotenv.2019.134076
42. Evaluation of Spatial Rainfall Products in Sparsely Gauged Region Using Copula Uncertainty Modeling with Triple Collocation A. Tanim et al. https://doi.org/10.1061/(ASCE)HE.1943-5584.0002071
43. A Spatial Downscaling Method for Solar-Induced Chlorophyll Fluorescence Product Using Random Forest Regression and Drought Monitoring in Henan Province Z. Zhang et al. https://doi.org/10.3390/rs16060963
44. Mapping future droughts under global warming across China: A combined multi-timescale meteorological drought index and SOM-Kmeans approach P. Zeng et al. https://doi.org/10.1016/j.wace.2021.100304
45. Future hydro-meteorological drought of the Johor River Basin, Malaysia, based on CORDEX-SEA projections M. Tan et al. https://doi.org/10.1080/02626667.2019.1612901
46. Probabilistic modeling of crop-yield loss risk under drought: a spatial showcase for sub-Saharan Africa B. Kamali et al. https://doi.org/10.1088/1748-9326/ac4ec1
47. High resolution monitoring and probabilistic prediction of meteorological drought in a Mediterranean environment M. Torres-Vázquez et al. https://doi.org/10.1016/j.wace.2023.100558
48. Unraveling the influence of atmospheric evaporative demand on drought and its response to climate change S. Vicente‐Serrano et al. https://doi.org/10.1002/wcc.632
49. Drought impacts on hydrology and water quality under climate change J. Qiu et al. https://doi.org/10.1016/j.scitotenv.2022.159854
50. Copula-Based Joint Drought Index Using Precipitation, NDVI, and Runoff and Its Application in the Yangtze River Basin, China H. Wei et al. https://doi.org/10.3390/rs15184484
51. Projection of meteorological drought in Türkiye’s Mediterranean region based on multi-model ensemble from CMIP6 V. Gumus & M. Seker https://doi.org/10.1080/02626667.2024.2364712
52. How do different drought types influence compound drought in the Yellow River Basin? B. Ding et al. https://doi.org/10.1016/j.jhydrol.2025.134021
53. Predicting High-Resolution Root-Zone Soil Moisture Using an Analytical Relationship Between Satellite-Derived Soil Water Content and Soil Properties for Drought Monitoring A. Kasim et al. https://doi.org/10.1109/TGRS.2026.3698964
54. Accuracy of agricultural drought indices and analysis of agricultural drought characteristics in China between 2000 and 2019 Y. Pan et al. https://doi.org/10.1016/j.agwat.2023.108305
55. Improved Drought Prediction Using Near Real-Time Climate Forecasts and Simulated Hydrologic Conditions H. Kang & V. Sridhar https://doi.org/10.3390/su10061799
56. Seasonal precipitation variability controls shallow soil water drought events across the southwestern United States T. McKellar & M. Crimmins https://doi.org/10.1016/j.agrformet.2025.110403
57. An impact-based drought classification method using real-world agricultural drought records and explainable automated machine learning K. Zhou et al. https://doi.org/10.1016/j.jhydrol.2026.135078
58. Identifying drought propagation by simultaneously considering linear and nonlinear dependence in the Wei River basin of the Loess Plateau, China W. Fang et al. https://doi.org/10.1016/j.jhydrol.2020.125287
59. Improving Drought Modeling Using Hybrid Random Vector Functional Link Methods R. Adnan et al. https://doi.org/10.3390/w13233379
60. A Combined Satellite-Derived Drought Indicator to Support Humanitarian Aid Organizations M. Enenkel et al. https://doi.org/10.3390/rs8040340
61. Long-term daily hydrometeorological drought indices, soil moisture, and evapotranspiration for ICOS sites F. Pohl et al. https://doi.org/10.1038/s41597-023-02192-1
62. Probabilistic drought classification using gamma mixture models G. Mallya et al. https://doi.org/10.1016/j.jhydrol.2014.11.008
63. A Vantage from Space Can Detect Earlier Drought Onset: An Approach Using Relative Humidity A. Farahmand et al. https://doi.org/10.1038/srep08553
64. Drought propagation and recovery dynamics across hydroclimatic pathways: Predictors and timescale dependence in Chile I. Fuentes et al. https://doi.org/10.1016/j.ejrh.2026.103797
65. Simulating the effects of climatic fluctuations on rice irrigation water requirement using AquaCrop N. Pirmoradian & N. Davatgar https://doi.org/10.1016/j.agwat.2018.10.003
66. Quantification of agricultural drought over Indian region: a multivariate phenology-based approach P. Das et al. https://doi.org/10.1007/s11069-020-03872-6
67. SMART-Soil: Satellite-based machine learning approach for reliable tracking of soil moisture across CONUS S. Asadollah et al. https://doi.org/10.1016/j.apsoil.2026.107201
68. Evaluation of two satellite-based products against ground-based observation for drought analysis in the southern part of Iran S. Jafari et al. https://doi.org/10.1007/s11069-020-03965-2
69. Composite Agrometeorological Drought Index Accounting for Seasonality and Autocorrelation M. Bateni et al. https://doi.org/10.1061/(ASCE)HE.1943-5584.0001654
70. Global characterization of hydrological and meteorological droughts under future climate change: The importance of timescales, vegetation‐CO2feedbacks and changes to distribution functions S. Vicente‐Serrano et al. https://doi.org/10.1002/joc.6350
71. Bayesian vine copulas improve agricultural drought prediction for long lead times H. Wu et al. https://doi.org/10.1016/j.agrformet.2023.109326
72. Global evaluation of the “dry gets drier, and wet gets wetter” paradigm from a terrestrial water storage change perspective J. Xiong et al. https://doi.org/10.5194/hess-26-6457-2022
73. Flash Drought: Review of Concept, Prediction and the Potential for Machine Learning, Deep Learning Methods S. Tyagi et al. https://doi.org/10.1029/2022EF002723
74. A combined drought monitoring index based on multi-sensor remote sensing data and machine learning H. Han et al. https://doi.org/10.1080/10106049.2019.1633423
75. Multivariate drought indices to analyse spatiotemporal drought propagation in a semi-arid river basin S. Gorugantula et al. https://doi.org/10.1007/s00704-024-05236-7
76. Quantifying the reliability of four global datasets for drought monitoring over a semiarid region P. Katiraie-Boroujerdy et al. https://doi.org/10.1007/s00704-014-1360-3
77. A near‐term drought assessment using hydrological and climate forecasting in the Mekong River Basin H. Kang & V. Sridhar https://doi.org/10.1002/joc.6860
78. Drought cascades across multiple systems in Central Asia identified based on the dynamic space–time motion approach L. Tian et al. https://doi.org/10.5194/hess-27-4115-2023
79. Multi-sensor integrated framework and index for agricultural drought monitoring X. Zhang et al. https://doi.org/10.1016/j.rse.2016.10.045
80. Spatial and temporal patterns of drought under different scenarios for Türkiye in the 21st century M. Yıldız et al. https://doi.org/10.1016/j.pce.2025.104028
81. Propagation and future projection of dry-wet abrupt alternation through the hydrological cycle using a GCM-VIC modeling framework S. Liu et al. https://doi.org/10.1016/j.ejrh.2026.103631
82. Do Derived Drought Indices Better Characterize Future Drought Change? Z. Jiang et al. https://doi.org/10.1029/2022EF003350
83. Monitoring and Predicting Drought Based on Multiple Indicators in an Arid Area, China Y. Wang et al. https://doi.org/10.3390/rs12142298
84. Analysing agricultural drought resilience over Indian mainland at sub-basin level using long-term (2002-23) precipitation, soil moisture and ancillary datasets P. Das et al. https://doi.org/10.1007/s11069-025-07508-5
85. Copula‐Based Convection‐Permitting Projections of Future Changes in Multivariate Drought Characteristics B. Zhang et al. https://doi.org/10.1029/2019JD030686
86. Copula-based stochastic uncertainty analysis of satellite precipitation products E. Sharifi et al. https://doi.org/10.1016/j.jhydrol.2019.01.035
87. The asymmetric impact of global warming on US drought types and distributions in a large ensemble of 97 hydro-climatic simulations S. Huang et al. https://doi.org/10.1038/s41598-017-06302-z
88. A multivariate approach for persistence-based drought prediction: Application to the 2010–2011 East Africa drought A. AghaKouchak https://doi.org/10.1016/j.jhydrol.2014.09.063
89. Generating High-Resolution and Long-Term SPEI Dataset over Southwest China through Downscaling EEAD Product by Machine Learning R. Fu et al. https://doi.org/10.3390/rs14071662
90. Spatial and temporal variations of ecosystem water use efficiency and its response to soil moisture drought in a water-limited watershed of northern China T. Zhang et al. https://doi.org/10.1016/j.jenvman.2024.120251
91. An integrated water deficit index to evaluate multifaceted impacts of drought in a semi-arid river basin S. Gorugantula & B. Kambhammettu https://doi.org/10.1007/s11069-025-07862-4
92. Understanding the global hydrological droughts of 2003–2016 and their relationships with teleconnections E. Forootan et al. https://doi.org/10.1016/j.scitotenv.2018.09.231
93. AI-driven U.S. drought prediction using machine learning and deep learning I. Tanriverdi & İ. Batmaz https://doi.org/10.1007/s00382-025-07720-w
94. Evaluation analysis of NASA SMAP L3 and L4 and SPoRT-LIS soil moisture data in the United States A. Tavakol et al. https://doi.org/10.1016/j.rse.2019.05.006
95. Prediction of Severe Drought Area Based on Random Forest: Using Satellite Image and Topography Data H. Park et al. https://doi.org/10.3390/w11040705
96. A comparison of large-scale climate signals and the North American Multi-Model Ensemble (NMME) for drought prediction in China L. Xu et al. https://doi.org/10.1016/j.jhydrol.2017.12.044
97. Drought monitoring and evaluation using ESA CCI and GLDAS-Noah soil moisture datasets across China G. Zhang et al. https://doi.org/10.1007/s00704-021-03609-w
98. The soil moisture data bank: The ground-based, model-based, and satellite-based soil moisture data A. Tavakol et al. https://doi.org/10.1016/j.rsase.2021.100649
99. Quantifying the Effects of Climate and Vegetation on Soil Moisture in an Arid Area, China Y. Wang et al. https://doi.org/10.3390/w11040767
100. Probabilistic Forecasting of Drought Events Using Markov Chain- and Bayesian Network-Based Models: A Case Study of an Andean Regulated River Basin A. Avilés et al. https://doi.org/10.3390/w8020037
101. Understanding the interactions between meteorological and soil moisture drought over Indian region P. Das et al. https://doi.org/10.1007/s12040-020-01460-7
102. A parametric multivariate drought index for drought monitoring and assessment under climate change L. Xu et al. https://doi.org/10.1016/j.agrformet.2021.108657
103. Transitional properties of droughts and related impacts of climate indices in the Pearl River basin, China M. Xiao et al. https://doi.org/10.1016/j.jhydrol.2016.01.012
104. Analysis of Trends in Drought with the Non-Parametric Approach in Vietnam: A Case Study in Ninh Thuan Province N. Tuan & T. Canh https://doi.org/10.4236/ajcc.2021.101004
105. Sub-seasonal soil moisture anomaly forecasting using combinations of deep learning, based on the reanalysis soil moisture records X. Wang et al. https://doi.org/10.1016/j.agwat.2024.108772
106. Robust Meteorological Drought Prediction Using Antecedent SST Fluctuations and Machine Learning J. Li et al. https://doi.org/10.1029/2020WR029413
107. Entropy-weight-based spatiotemporal drought assessment using MODIS products and Sentinel-1A images in Urumqi, China X. Tang et al. https://doi.org/10.1007/s11069-023-06131-6
108. Managing climate risks on the ranch with limited drought information T. Shrum et al. https://doi.org/10.1016/j.crm.2018.01.002
109. Spatiotemporal propagation dynamics of multiple droughts in Central Asia: A three-dimensional perspective L. Chen et al. https://doi.org/10.1016/j.jhydrol.2025.134640
110. Identifying Key Driving Processes of Major Recent Heat Waves K. Wehrli et al. https://doi.org/10.1029/2019JD030635
111. Probabilistic long‐term hydrological drought forecast using Bayesian networks and drought propagation J. Shin et al. https://doi.org/10.1002/met.1827
112. Evaluating the Credibility of Downscaling: Integrating Scale, Trend, Extreme, and Climate Event into a Diagnostic Framework F. Sun et al. https://doi.org/10.1175/JAMC-D-20-0078.1
113. An evaluation of statistical, NMME and hybrid models for drought prediction in China L. Xu et al. https://doi.org/10.1016/j.jhydrol.2018.09.020
114. Appraising standardized moisture anomaly index (SZI) in drought projection across China under CMIP6 forcing scenarios G. Zhang et al. https://doi.org/10.1016/j.ejrh.2021.100898
115. Validation of drought indices using environmental indicators: streamflow and carbon flux data U. Bhuyan-Erhardt et al. https://doi.org/10.1016/j.agrformet.2018.11.016
116. Drought monitoring using high resolution soil moisture through multi-sensor satellite data fusion over the Korean peninsula S. Park et al. https://doi.org/10.1016/j.agrformet.2017.02.022
117. Simple Short-Term Probabilistic Drought Prediction Using Mediterranean Teleconnection Information M. Bateni et al. https://doi.org/10.1007/s11269-018-2056-8
118. A Wireless Underground Sensor Network Field Pilot for Agriculture and Ecology: Soil Moisture Mapping Using Signal Attenuation S. Balivada et al. https://doi.org/10.3390/s22103913
119. Potential predictability sources of the 2012 U.S. drought in observations and a regional model ensemble D. PaiMazumder & J. Done https://doi.org/10.1002/2016JD025322
120. The climate hazards infrared precipitation with stations—a new environmental record for monitoring extremes C. Funk et al. https://doi.org/10.1038/sdata.2015.66
121. Global snow drought hot spots and characteristics L. Huning & A. AghaKouchak https://doi.org/10.1073/pnas.1915921117
122. A Bayesian Network-Based Probabilistic Framework for Drought Forecasting and Outlook J. Shin et al. https://doi.org/10.1155/2016/9472605
123. A Framework for Global Multicategory and Multiscalar Drought Characterization Accounting for Snow Processes B. Zhang et al. https://doi.org/10.1029/2019WR025529
124. Forecasting vegetation condition with a Bayesian auto-regressive distributed lags (BARDL) model E. Salakpi et al. https://doi.org/10.5194/nhess-22-2703-2022
125. An improved adaptive neuro-fuzzy inference system for hydrological drought prediction in Algeria M. Achite et al. https://doi.org/10.1016/j.pce.2023.103451
126. The influence of nonstationarity in hydro-meteorological variables on different drought types across the Chinese mainland X. Zhang et al. https://doi.org/10.1007/s11430-025-1836-1
127. Interpretability of compound drought-hot extreme index prediction model: a regional study in Iran M. Moghaddasi et al. https://doi.org/10.1007/s11356-025-36257-y
128. Spatial and temporal patterns of agricultural drought in China during 1960–2020 characterized by use of the crop water deficit Abnormal Index N. Jin et al. https://doi.org/10.1016/j.jhydrol.2023.130454
129. A climate-informed nonstationary trivariate drought index for regional assessment in Jiangsu Province, China C. Yu et al. https://doi.org/10.1016/j.ejrh.2026.103495
130. Continental drought monitoring using satellite soil moisture, data assimilation and an integrated drought index L. Xu et al. https://doi.org/10.1016/j.rse.2020.112028
131. Replacing Yield Detrending with Direct Spatiotemporal Inputs Improves LSTM-Based Rice Yield Estimation N. Chen et al. https://doi.org/10.3390/rs18132200
132. Drought in the Twenty-First Century in a Water-Rich Region: Modeling Study of the Wabash River Watershed, USA J. Dierauer & C. Zhu https://doi.org/10.3390/w12010181
133. A new approach to quantify propagation time from meteorological to hydrological drought S. Ho et al. https://doi.org/10.1016/j.jhydrol.2021.127056
134. Forest Fire Mapping Using Multi-Source Remote Sensing Data: A Case Study in Chongqing Y. Zhao et al. https://doi.org/10.3390/rs15092323
135. Identification of Drought Occurrences Using Ensemble Predictions up to 20-Days in Advance S. Shrivastava et al. https://doi.org/10.1007/s11269-018-1921-9
136. Future river basin health assessment through reliability-resilience-vulnerability: Thresholds of multiple dryness conditions P. Zeng et al. https://doi.org/10.1016/j.scitotenv.2020.140395
137. Assessment of meteorological, agricultural and hydrological droughts in Central Himalaya region under climate change L. Wang et al. https://doi.org/10.1016/j.ejrh.2025.102586
138. Remote Sensing Standardized Soil Moisture Index for Drought Monitoring: A Case Study in the Ebro Basin G. Sánchez Alcalde & M. Escorihuela https://doi.org/10.3390/rs17233916
139. Comparing 1-km Sentinel-1 surface soil moisture with coarser-resolution satellite data for agricultural drought monitoring in Mediterranean regions E. Ayari et al. https://doi.org/10.1016/j.agwat.2026.110341
140. Evaluation of SPI and Rainfall Departure Based on Multi-Satellite Precipitation Products for Meteorological Drought Monitoring in Tamil Nadu S. Pazhanivelan et al. https://doi.org/10.3390/w15071435
141. Mapping drought evolution in Ethiopia: trends, clustering, and Bayesian estimation of abrupt changes F. Di Nunno et al. https://doi.org/10.1007/s11069-024-06935-0
142. A conceptual prediction model for seasonal drought processes using atmospheric and oceanic standardized anomalies: application to regional drought processes in China Z. Liu et al. https://doi.org/10.5194/hess-22-529-2018
143. Predicting Hydrological Drought With Bayesian Model Averaging Ensemble Vine Copula (BMAViC) Model H. Wu et al. https://doi.org/10.1029/2022WR033146
144. Multisource data-based integrated drought monitoring index: Model development and application Q. Zhang et al. https://doi.org/10.1016/j.jhydrol.2022.128644
145. Detecting drought events over a region in Central Europe using a regional and two satellite-based precipitation datasets M. Eini et al. https://doi.org/10.1016/j.agrformet.2023.109733
146. Investigating the Long-Term Spatial and Temporal Characteristics of Vegetative Drought in the Contiguous United States S. Zhong et al. https://doi.org/10.1109/JSTARS.2019.2896159
147. The evolution, propagation, and spread of flash drought in the Central United States during 2012 J. Basara et al. https://doi.org/10.1088/1748-9326/ab2cc0
148. What roles can water-stressed vegetation play in agricultural droughts? M. Liu et al. https://doi.org/10.1016/j.scitotenv.2021.149810
149. Comparative assessment of gridded soil moisture data products (2000–2019) for drought detection: A study over Indian region P. Das et al. https://doi.org/10.1007/s11600-024-01418-z
150. A global drought dataset for Multivariate Composite Drought Index (MCDI) and its constituent drought indices M. Yuan et al. https://doi.org/10.1038/s41597-025-06320-x
151. Spatiotemporal drought monitoring using bottom-up precipitation dataset (SM2RAIN-ASCAT) over different regions of Iran S. Koohi et al. https://doi.org/10.1016/j.scitotenv.2021.146535
152. Soil Moisture-Derived SWDI at 30 m Based on Multiple Satellite Datasets for Agricultural Drought Monitoring J. Ning et al. https://doi.org/10.3390/rs16183372
153. Subseasonal Meteorological Drought Development over the Central United States during Spring B. Jong et al. https://doi.org/10.1175/JCLI-D-21-0435.1
154. A framework for drought monitoring and assessment from a drought propagation perspective under non-stationary environments X. Wei et al. https://doi.org/10.1016/j.scitotenv.2024.175981
155. Concurrent drought threatens wheat and maize production and will widen crop yield gaps in the future M. Hou et al. https://doi.org/10.1016/j.agsy.2024.104056
156. Monitoring the drought in Southern Africa from space-borne GNSS-R and SMAP data K. Edokossi et al. https://doi.org/10.1007/s11069-024-06546-9