109 citations as recorded by crossref.

1. Improvement of evapotranspiration simulation in a physically based ecohydrological model for the groundwater–soil–plant–atmosphere continuum K. Zhang et al. 10.1016/j.jhydrol.2022.128440
2. Dynamics of global dryland vegetation were more sensitive to soil moisture: Evidence from multiple vegetation indices H. Liu et al. 10.1016/j.agrformet.2023.109327
3. Opposite eco-hydrological processes in flood and drought years caused comparable anomaly in dry-season canopy growth over southern Amazon H. Zhang & Y. Liu 10.1088/1748-9326/ac9852
4. Untangling the importance of dynamic and thermodynamic drivers for wet and dry spells across the Tropical Andes C. Klein et al. 10.1088/1748-9326/acb72b
5. Influence of Vegetation Height, Plant Area Index, and Forest Intactness on SMOS L-VOD, for Different Seasons and Latitude Ranges C. Vittucci et al. 10.1109/TGRS.2023.3299529
6. Water Ages Explain Tradeoffs Between Long‐Term Evapotranspiration and Ecosystem Drought Resilience J. Knighton & W. Berghuijs 10.1029/2023GL103649
7. Global-scale assessment and inter-comparison of recently developed/reprocessed microwave satellite vegetation optical depth products X. Li et al. 10.1016/j.rse.2020.112208
8. Sensitivity of remote sensing-based vegetation proxies to climate and sea surface temperature variabilities in Australia and parts of Southeast Asia S. Arjasakusuma et al. 10.1080/01431161.2020.1782509
9. Reliability of using vegetation optical depth for estimating decadal and interannual carbon dynamics Y. Dou et al. 10.1016/j.rse.2022.113390
10. The importance of antecedent vegetation and drought conditions as global drivers of burnt area A. Kuhn-Régnier et al. 10.5194/bg-18-3861-2021
11. Little evidence that Amazonian rainforests are approaching a tipping point S. Tao et al. 10.1038/s41558-023-01853-8
12. Are Remote Sensing Evapotranspiration Models Reliable Across South American Ecoregions? D. Melo et al. 10.1029/2020WR028752
13. Reliability of resilience estimation based on multi-instrument time series T. Smith et al. 10.5194/esd-14-173-2023
14. Towards constraining soil and vegetation dynamics in land surface models: Modeling ASCAT backscatter incidence-angle dependence with a Deep Neural Network X. Shan et al. 10.1016/j.rse.2022.113116
15. Reliability of vegetation resilience estimates depends on biomass density T. Smith & N. Boers 10.1038/s41559-023-02194-7
16. Toward the Removal of Model Dependency in Soil Moisture Climate Data Records by Using an $L$-Band Scaling Reference R. Madelon et al. 10.1109/JSTARS.2021.3137008
17. Global Change in Terrestrial Ecosystem Detected by Fusion of Microwave and Optical Satellite Observations H. Nara & Y. Sawada 10.3390/rs13183756
18. Assessing Model Predictions of Carbon Dynamics in Global Drylands D. Fawcett et al. 10.3389/fenvs.2022.790200
19. A global long-term, high-resolution satellite radar backscatter data record (1992–2022+): merging C-band ERS/ASCAT and Ku-band QSCAT S. Tao et al. 10.5194/essd-15-1577-2023
20. Impacts of climate extremes on vegetation dynamics in a transect along the Hu Line of China H. Wang et al. 10.1016/j.ecolind.2023.111043
21. Quantifying and Reducing Uncertainty in Global Carbon Cycle Predictions: Lessons and Perspectives From 15 Years of Data Assimilation Studies With the ORCHIDEE Terrestrial Biosphere Model N. MacBean et al. 10.1029/2021GB007177
22. Satellite Retrieval of Microwave Land Surface Emissivity under Clear and Cloudy Skies in China Using Observations from AMSR-E and MODIS J. Hu et al. 10.3390/rs13193980
23. Seasonal variations in vegetation water content retrieved from microwave remote sensing over Amazon intact forests H. Wang et al. 10.1016/j.rse.2022.113409
24. Sentinel-1 Cross Ratio and Vegetation Optical Depth: A Comparison over Europe M. Vreugdenhil et al. 10.3390/rs12203404
25. Carbon loss from forest degradation exceeds that from deforestation in the Brazilian Amazon Y. Qin et al. 10.1038/s41558-021-01026-5
26. Increasing and widespread vulnerability of intact tropical rainforests to repeated droughts S. Tao et al. 10.1073/pnas.2116626119
27. Decline in stability of forest productivity in the tropics as determined by canopy water content F. Liu et al. 10.1016/j.isci.2023.107211
28. L-band vegetation optical depth as an indicator of plant water potential in a temperate deciduous forest stand N. Holtzman et al. 10.5194/bg-18-739-2021
29. First assessment of optical and microwave remotely sensed vegetation proxies in monitoring aboveground carbon in tropical Asia T. Cui et al. 10.1016/j.rse.2023.113619
30. Combining Microwave and Optical Remote Sensing to Characterize Global Vegetation Water Status X. Wang et al. 10.1109/TGRS.2023.3294948
31. China's Terrestrial Carbon Sink Over 2010–2015 Constrained by Satellite Observations of Atmospheric CO2 and Land Surface Variables W. He et al. 10.1029/2021JG006644
32. Microwave remote sensing for agricultural drought monitoring: Recent developments and challenges M. Vreugdenhil et al. 10.3389/frwa.2022.1045451
33. A twenty-year dataset of soil moisture and vegetation optical depth from AMSR-E/2 measurements using the multi-channel collaborative algorithm L. Hu et al. 10.1016/j.rse.2023.113595
34. Estimating leaf moisture content at global scale from passive microwave satellite observations of vegetation optical depth M. Forkel et al. 10.5194/hess-27-39-2023
35. The Impacts of Climate and Wildfire on Ecosystem Gross Primary Productivity in Alaska N. Madani et al. 10.1029/2020JG006078
36. Assimilation of passive microwave vegetation optical depth in LDAS-Monde: a case study over the continental USA A. Mucia et al. 10.5194/bg-19-2557-2022
37. A new global C-band vegetation optical depth product from ASCAT: Description, evaluation, and inter-comparison X. Liu et al. 10.1016/j.rse.2023.113850
38. Reappraisal of SMAP inversion algorithms for soil moisture and vegetation optical depth L. Gao et al. 10.1016/j.rse.2021.112627
39. Characterizing natural variability in complex hydrological systems using passive microwave-based climate data records: a case study for the Okavango Delta R. van der Schalie et al. 10.5194/hess-26-3611-2022
40. Estimating Aboveground Carbon Dynamic of China Using Optical and Microwave Remote-Sensing Datasets from 2013 to 2019 Z. Chang et al. 10.34133/remotesensing.0005
41. Soil Moisture Drydown Detection Is Hindered by Model-Based Rescaling N. Raoult et al. 10.1109/LGRS.2022.3178685
42. Changes in land use and management led to a decline in Eastern Europe’s terrestrial carbon sink K. Winkler et al. 10.1038/s43247-023-00893-4
43. Human and climate drivers of global biomass burning variability E. Chuvieco et al. 10.1016/j.scitotenv.2021.146361
44. Satellite‐Observed Vegetation Responses to Intraseasonal Precipitation Variability B. Harris et al. 10.1029/2022GL099635
45. Linking remotely sensed ecosystem resilience with forest mortality across the continental United States X. Tai et al. 10.1111/gcb.16529
46. Increased drought effects on the phenology of autumn leaf senescence C. Wu et al. 10.1038/s41558-022-01464-9
47. Simulation of SMAP and AMSR2 observations and estimation of multi-frequency vegetation optical depth using a discrete scattering model in the Tibetan grassland X. Bai et al. 10.1016/j.rse.2023.113592
48. Bidirectional drought‐related canopy dynamics across pantropical forests: a satellite‐based statistical analysis L. Liu et al. 10.1002/rse2.229
49. Optimal ranges of social-environmental drivers and their impacts on vegetation dynamics in Kazakhstan K. Venkatesh et al. 10.1016/j.scitotenv.2022.157562
50. The evolution of ecological security and its drivers in the Yellow River Basin G. Zhang et al. 10.1007/s11356-023-25667-5
51. Effect of Socioeconomic Variables in Predicting Global Fire Ignition Occurrence T. Mukunga et al. 10.3390/fire6050197
52. A deep learning-based hybrid model of global terrestrial evaporation A. Koppa et al. 10.1038/s41467-022-29543-7
53. Uncovering Dryland Woody Dynamics Using Optical, Microwave, and Field Data—Prolonged Above-Average Rainfall Paradoxically Contributes to Woody Plant Die-Off in the Western Sahel P. Bernardino et al. 10.3390/rs12142332
54. Integrating plant physiology into simulation of fire behavior and effects L. Dickman et al. 10.1111/nph.18770
55. The influence of vegetation water dynamics on the ASCAT backscatter–incidence angle relationship in the Amazon A. Petchiappan et al. 10.5194/hess-26-2997-2022
56. An alternative AMSR2 vegetation optical depth for monitoring vegetation at large scales M. Wang et al. 10.1016/j.rse.2021.112556
57. Parameterizing Vegetation Traits With a Process‐Based Ecohydrological Model and Xylem Water Isotopic Observations K. Li et al. 10.1029/2022MS003263
58. Continuous ground monitoring of vegetation optical depth and water content with GPS signals V. Humphrey & C. Frankenberg 10.5194/bg-20-1789-2023
59. Past and future adverse response of terrestrial water storages to increased vegetation growth in drylands K. Liu et al. 10.1038/s41612-023-00437-9
60. Rethinking Satellite Data Merging: From Averaging to SNR Optimization S. Kim et al. 10.1109/TGRS.2021.3107028
61. VODCA2GPP – a new, global, long-term (1988–2020) gross primary production dataset from microwave remote sensing B. Wild et al. 10.5194/essd-14-1063-2022
62. Exploring the use of satellite observations of soil moisture, solar-induced chlorophyll fluorescence and vegetation optical depth to monitor droughts across India M. Likith et al. 10.1007/s12040-022-01848-7
63. Observed increasing water constraint on vegetation growth over the last three decades W. Jiao et al. 10.1038/s41467-021-24016-9
64. Global Monitoring of the Vegetation Dynamics from the Vegetation Optical Depth (VOD): A Review F. Frappart et al. 10.3390/rs12182915
65. Assessing the sensitivity of multi-frequency passive microwave vegetation optical depth to vegetation properties L. Schmidt et al. 10.5194/bg-20-1027-2023
66. SMOS-IC data record of soil moisture and L-VOD: Historical development, applications and perspectives J. Wigneron et al. 10.1016/j.rse.2020.112238
67. Greening drylands despite warming consistent with carbon dioxide fertilization effect A. Gonsamo et al. 10.1111/gcb.15658
68. Drought effects on leaf fall, leaf flushing and stem growth in the Amazon forest: reconciling remote sensing data and field observations T. Janssen et al. 10.5194/bg-18-4445-2021
69. The first global soil moisture and vegetation optical depth product retrieved from fused SMOS and SMAP L-band observations X. Li et al. 10.1016/j.rse.2022.113272
70. Detecting forest response to droughts with global observations of vegetation water content A. Konings et al. 10.1111/gcb.15872
71. Monitoring vegetation condition using microwave remote sensing: the standardized vegetation optical depth index (SVODI) L. Moesinger et al. 10.5194/bg-19-5107-2022
72. Observational evidence of wildfire-promoting soil moisture anomalies S. O et al. 10.1038/s41598-020-67530-4
73. Exploring the potential role of environmental and multi-source satellite data in crop yield prediction across Northeast China Z. Li et al. 10.1016/j.scitotenv.2021.152880
74. Impact of Drought on Isoprene Fluxes Assessed Using Field Data, Satellite-Based GLEAM Soil Moisture and HCHO Observations from OMI B. Opacka et al. 10.3390/rs14092021
75. Global vegetation resilience linked to water availability and variability T. Smith & N. Boers 10.1038/s41467-023-36207-7
76. Increased sensitivity of greening to afforestation in China over the recent 20 years Z. Jin et al. 10.1016/j.agrformet.2023.109561
77. Response to Comments on “Recent global decline of CO 2 fertilization effects on vegetation photosynthesis” S. Wang et al. 10.1126/science.abg7484
78. Rising ecosystem water demand exacerbates the lengthening of tropical dry seasons H. Xu et al. 10.1038/s41467-022-31826-y
79. Tracking Global Patterns of Drought‐Induced Productivity Loss Along Severity Gradient Y. Wang et al. 10.1029/2021JG006753
80. Pronounced loss of Amazon rainforest resilience since the early 2000s C. Boulton et al. 10.1038/s41558-022-01287-8
81. Reply to: Little evidence that Amazonian rainforests are approaching a tipping point C. Boulton et al. 10.1038/s41558-023-01854-7
82. The sensitivity of the West African monsoon circulation to intraseasonal soil moisture feedbacks J. Talib et al. 10.1002/qj.4274
83. Artificial Neural Network-Based Microwave Satellite Soil Moisture Reconstruction over the Qinghai–Tibet Plateau, China J. Wang & D. Xu 10.3390/rs13245156
84. Satellite observed aboveground carbon dynamics in Africa during 2003–2021 M. Wang et al. 10.1016/j.rse.2023.113927
85. Crop yield anomaly forecasting in the Pannonian basin using gradient boosting and its performance in years of severe drought E. Bueechi et al. 10.1016/j.agrformet.2023.109596
86. Integrating satellite-based passive microwave and optically sensed observations to evaluating the spatio-temporal dynamics of vegetation health in the red soil regions of southern China Q. Yang et al. 10.1080/15481603.2021.2023841
87. Incorporating Plant Access to Groundwater in Existing Global, Satellite‐Based Evaporation Estimates P. Hulsman et al. 10.1029/2022WR033731
88. Assimilation of vegetation optical depth retrievals from passive microwave radiometry S. Kumar et al. 10.5194/hess-24-3431-2020
89. Asymmetric response of primary productivity to precipitation anomalies in Southwest China G. Dong et al. 10.1016/j.agrformet.2023.109350
90. Empirical evidence for recent global shifts in vegetation resilience T. Smith et al. 10.1038/s41558-022-01352-2
91. SMAP underestimates soil moisture in vegetation-disturbed areas primarily as a result of biased surface temperature data X. Fan et al. 10.1016/j.rse.2020.111914
92. Increased atmospheric moisture demand induced a reduction in the water content of boreal forest during the past three decades T. Wang et al. 10.1016/j.agrformet.2023.109759
93. Time-variations of zeroth-order vegetation absorption and scattering at L-band M. Baur et al. 10.1016/j.rse.2021.112726
94. ASCAT IB: A radar-based vegetation optical depth retrieved from the ASCAT scatterometer satellite X. Liu et al. 10.1016/j.rse.2021.112587
95. A consistent record of vegetation optical depth retrieved from the AMSR-E and AMSR2 X-band observations M. Wang et al. 10.1016/j.jag.2021.102609
96. Impact of temperature and water availability on microwave-derived gross primary production I. Teubner et al. 10.5194/bg-18-3285-2021
97. Untangling the impacts of socioeconomic and climatic changes on vegetation greenness and productivity in Kazakhstan K. Venkatesh et al. 10.1088/1748-9326/ac8c59
98. Surface‐driven amplification of Madden–Julian oscillation circulation anomalies across East Africa and its influence on the Turkana jet J. Talib et al. 10.1002/qj.4487
99. Increasingly negative tropical water–interannual CO2 growth rate coupling L. Liu et al. 10.1038/s41586-023-06056-x
100. 2019–2020 Australia Fire and Its Relationship to Hydroclimatological and Vegetation Variabilities M. Ehsani et al. 10.3390/w12113067
101. Forest Canopy Changes in the Southern Amazon during the 2019 Fire Season Based on Passive Microwave and Optical Satellite Observations H. Zhang et al. 10.3390/rs13122238
102. Quantifying the contributions of climate change and human activities to vegetation dynamic in China based on multiple indices Y. Liu et al. 10.1016/j.scitotenv.2022.156553
103. Anatomy of the 2018 agricultural drought in the Netherlands using in situ soil moisture and satellite vegetation indices J. Buitink et al. 10.5194/hess-24-6021-2020
104. SMOS L-VOD Retrieved by Level 2 Algorithm and its Correlation With GEDI LIDAR Products C. Vittucci et al. 10.1109/JSTARS.2021.3128022
105. Assessing the sensitivity of multi-frequency passive microwave vegetation optical depth to vegetation properties L. Schmidt et al. 10.5194/bg-20-1027-2023
106. Earth Observation for agricultural drought monitoring in the Pannonian Basin (southeastern Europe): current state and future directions L. Crocetti et al. 10.1007/s10113-020-01710-w
107. Assimilation of passive microwave vegetation optical depth in LDAS-Monde: a case study over the continental USA A. Mucia et al. 10.5194/bg-19-2557-2022
108. “Late-stage” deforestation enhances storm trends in coastal West Africa C. Taylor et al. 10.1073/pnas.2109285119
109. Reliability of resilience estimation based on multi-instrument time series T. Smith et al. 10.5194/esd-14-173-2023