123 citations as recorded by crossref.

1. Evaluation of real-time global flood modeling with satellite surface inundation observations from SMAP H. Wu et al. 10.1016/j.rse.2019.111360
2. Improving Surface Soil Moisture Estimates in Humid Regions by an Enhanced Remote Sensing Technique P. Song et al. 10.1029/2020GL091459
3. Remote Sensing of Environmental Changes in Cold Regions: Methods, Achievements and Challenges J. Du et al. 10.3390/rs11161952
4. Assessment with remotely sensed soil moisture products and ground-based observations over three dense network G. Yang et al. 10.1007/s12145-020-00454-9
5. Can vegetation optical depth reflect changes in leaf water potential during soil moisture dry-down events? Y. Zhang et al. 10.1016/j.rse.2019.111451
6. L-Band Microwave Satellite Data and Model Simulations Over the Dry Chaco to Estimate Soil Moisture, Soil Temperature, Vegetation, and Soil Salinity F. Vincent et al. 10.1109/JSTARS.2022.3193636
7. Leaf surface water, not plant water stress, drives diurnal variation in tropical forest canopy water content X. Xu et al. 10.1111/nph.17254
8. P-band radiometry for enhanced vegetation optical depth (VOD) and soil moisture retrieval in dense crop canopies X. Shen et al. 10.1016/j.rse.2024.114353
9. Dynamic evolution of recent droughts in Central Asia based on microwave remote sensing satellite products P. Yang et al. 10.1016/j.jhydrol.2023.129497
10. A fusion approach using GIS, green area detection, weather API and GPT for satellite image based fertile land discovery and crop suitability A. Balasundaram et al. 10.1038/s41598-024-67070-1
11. Regulation of the global carbon and water cycles through vegetation structural and physiological dynamics W. Li et al. 10.1088/1748-9326/ad5858
12. Joint assimilation of satellite-based surface soil moisture and vegetation conditions into the Noah-MP land surface model Z. Heyvaert et al. 10.1016/j.srs.2024.100129
13. Satellite-based vegetation optical depth as an indicator of drought-driven tree mortality K. Rao et al. 10.1016/j.rse.2019.03.026
14. A Multivariable Approach for Estimating Soil Moisture from Microwave Radiation Imager (MWRI) S. Zhang et al. 10.1007/s13351-020-9203-x
15. Assessing the dynamics of vegetation productivity in circumpolar regions with different satellite indicators of greenness and photosynthesis S. Walther et al. 10.5194/bg-15-6221-2018
16. Reliability of using vegetation optical depth for estimating decadal and interannual carbon dynamics Y. Dou et al. 10.1016/j.rse.2022.113390
17. Improving precipitable water vapor estimations of the Moderate Resolution Imaging Spectroradiometer (MODIS) using metaheuristic algorithms M. Ghaderi & M. Rahimzadegan 10.1016/j.asr.2022.03.035
18. Generating seamless global daily AMSR2 soil moisture (SGD-SM) long-term products for the years 2013–2019 Q. Zhang et al. 10.5194/essd-13-1385-2021
19. Analysis of the Radar Vegetation Index and Potential Improvements C. Szigarski et al. 10.3390/rs10111776
20. An improved surface soil moisture downscaling approach over cloudy areas based on geographically weighted regression P. Song et al. 10.1016/j.agrformet.2019.05.022
21. Rain-on-snow events in Alaska, their frequency and distribution from satellite observations C. Pan et al. 10.1088/1748-9326/aac9d3
22. Vegetation Water Content Retrieval from Spaceborne GNSS-R and Multi-Source Remote Sensing Data Using Ensemble Machine Learning Methods Y. Zhang et al. 10.3390/rs16152793
23. 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
24. Global Coordination in Plant Physiological and Rooting Strategies in Response to Water Stress Y. Liu et al. 10.1029/2020GB006758
25. ASCAT IB: A radar-based vegetation optical depth retrieved from the ASCAT scatterometer satellite X. Liu et al. 10.1016/j.rse.2021.112587
26. Partitioning of three phenology rhythms in American tropical and subtropical forests using remotely sensed solar-induced chlorophyll fluorescence and field litterfall observations F. Gong et al. 10.1016/j.jag.2022.102698
27. Estimating rainfall depth from satellite-based soil moisture data: A new algorithm by integrating SM2RAIN and the analytical net water flux models M. Saeedi et al. 10.1016/j.jhydrol.2022.127868
28. Estimation of global ecosystem isohydricity from solar-induced chlorophyll fluorescence and meteorological datasets J. Xue et al. 10.1016/j.rse.2024.114168
29. Microwave-based soil moisture improves estimates of vegetation response to drought in China J. Qiu et al. 10.1016/j.scitotenv.2022.157535
30. Influences of Using Different Satellite Soil Moisture Products on SM2RAIN for Rainfall Estimation Across the Tibetan Plateau L. Miao et al. 10.1109/JSTARS.2023.3296455
31. A Comparative Analysis of Remote Sensing Soil Moisture Datasets Fusion Methods: Novel LSTM Approach Versus Widely Used Triple Collocation Technique H. Zhao et al. 10.1109/JSTARS.2024.3455549
32. 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
33. Snow Phenology and Hydrologic Timing in the Yukon River Basin, AK, USA C. Pan et al. 10.3390/rs13122284
34. 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
35. Global Change in Terrestrial Ecosystem Detected by Fusion of Microwave and Optical Satellite Observations H. Nara & Y. Sawada 10.3390/rs13183756
36. 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
37. Coupling of ecosystem-scale plant water storage and leaf phenology observed by satellite F. Tian et al. 10.1038/s41559-018-0630-3
38. Increasing and widespread vulnerability of intact tropical rainforests to repeated droughts S. Tao et al. 10.1073/pnas.2116626119
39. Space‐Based Observations for Understanding Changes in the Arctic‐Boreal Zone B. Duncan et al. 10.1029/2019RG000652
40. Effects of Vegetation Restoration on Regional Soil Moisture Content in the Humid Karst Areas—A Case Study of Southwest China X. Wei et al. 10.3390/w13030321
41. Climatological Changes in Soil Moisture during the 21st Century over the Indian Region Using CMIP5 and Satellite Observations C. Sravani et al. 10.3390/rs14092108
42. A Long-Term Passive Microwave Snowoff Record for the Alaska Region 1988–2016 C. Pan et al. 10.3390/rs12010153
43. Quantifying the effects of freeze-thaw transitions and snowpack melt on land surface albedo and energy exchange over Alaska and Western Canada Y. Kim et al. 10.1088/1748-9326/aacf72
44. Influence of surface water variations on VOD and biomass estimates from passive microwave sensors E. Bousquet et al. 10.1016/j.rse.2021.112345
45. Widespread spring phenology effects on drought recovery of Northern Hemisphere ecosystems Y. Li et al. 10.1038/s41558-022-01584-2
46. Widespread and complex drought effects on vegetation physiology inferred from space W. Li et al. 10.1038/s41467-023-40226-9
47. Association and driving factors of meteorological drought and agricultural drought in Ningxia, Northwest China P. Yang et al. 10.1016/j.atmosres.2023.106753
48. Siberian carbon sink reduced by forest disturbances L. Fan et al. 10.1038/s41561-022-01087-x
49. Mapping global drought-induced forest mortality based on multiple satellite vegetation optical depth data X. Zhang et al. 10.1016/j.rse.2024.114406
50. The Earth‐Observing Aqua Satellite Mission: 20 Years and Counting C. Parkinson 10.1029/2022EA002481
51. Evaluation of optical and microwave-derived vegetation indices for monitoring aboveground biomass over China Z. Chang et al. 10.1080/10095020.2024.2311858
52. Detecting forest response to droughts with global observations of vegetation water content A. Konings et al. 10.1111/gcb.15872
53. Grouping Small Spacecraft for Global Meteorological Observations Using a Microwave Radiometer–Spectrometer A. Kuzmin et al. 10.1134/S000143382109053X
54. Global L-band equivalent AI-based vegetation optical depth dataset O. Skulovich et al. 10.1038/s41597-024-03810-2
55. Improving the AMSR-E/NASA Soil Moisture Data Product Using In-Situ Measurements from the Tibetan Plateau Q. Xie et al. 10.3390/rs11232748
56. Estimating Global Ecosystem Isohydry/Anisohydry Using Active and Passive Microwave Satellite Data Y. Li et al. 10.1002/2017JG003958
57. A comprehensive framework for seasonal controls of leaf abscission and productivity in evergreen broadleaved tropical and subtropical forests X. Yang et al. 10.1016/j.xinn.2021.100154
58. A new composite index for global soil plant atmosphere continuum drought monitoring combing remote-sensing based terrestrial water storage and vapor pressure deficit anomalies J. Han et al. 10.1016/j.jhydrol.2022.128622
59. An alternative AMSR2 vegetation optical depth for monitoring vegetation at large scales M. Wang et al. 10.1016/j.rse.2021.112556
60. Satellite Flood Inundation Assessment and Forecast Using SMAP and Landsat J. Du et al. 10.1109/JSTARS.2021.3092340
61. Use of a new Tibetan Plateau network for permafrost to characterize satellite-based products errors: An application to soil moisture and freeze/thaw J. Zheng et al. 10.1016/j.rse.2023.113899
62. 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
63. Spatiotemporal variability and controlling factors of ecosystem water use efficiency in India V. Bejagam et al. 10.1007/s00704-023-04418-z
64. A long term global daily soil moisture dataset derived from AMSR-E and AMSR2 (2002–2019) P. Yao et al. 10.1038/s41597-021-00925-8
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. Observational evidence of wildfire-promoting soil moisture anomalies S. O et al. 10.1038/s41598-020-67530-4
67. Vegetation Mapping by Using GPM/DPR over the Mongolian Land B. Nyamsuren et al. 10.3390/rs11202386
68. Soil moisture variation drives canopy water content dynamics across the western U.S. D. Lyons et al. 10.1016/j.rse.2020.112233
69. Remotely Sensed Winter Habitat Indices Improve the Explanation of Broad-Scale Patterns of Mammal and Bird Species Richness in China L. Zhu & Y. Guo 10.3390/rs14030794
70. Mapping paddy rice agriculture over China using AMSR-E time series data P. Song et al. 10.1016/j.isprsjprs.2018.08.015
71. Local Scale (3-m) Soil Moisture Mapping Using SMAP and Planet SuperDove J. Du et al. 10.3390/rs14153812
72. Land Surface Phenologies and Seasonalities in the US Prairie Pothole Region Coupling AMSR Passive Microwave Data with the USDA Cropland Data Layer W. Alemu et al. 10.3390/rs11212550
73. Remote sensing-based multi-scale characterization of ecohydrological indicators (EHIs) in India V. Bejagam & A. Sharma 10.1016/j.ecoleng.2022.106841
74. A carbon sink-driven approach to estimate gross primary production from microwave satellite observations I. Teubner et al. 10.1016/j.rse.2019.04.022
75. Spatio-Temporal Variations of Precipitable Water Vapor and Horizontal Tropospheric Gradients from GPS during Typhoon Lekima M. Tu et al. 10.3390/rs13204082
76. Satellite observed aboveground carbon dynamics in Africa during 2003–2021 M. Wang et al. 10.1016/j.rse.2023.113927
77. Inter-Sensor Calibration between HY-2B and AMSR2 Passive Microwave Data in Land Surface and First Result for Snow Water Equivalent Retrieval S. Gao et al. 10.3390/s19225023
78. Soil Moisture Feedbacks on Precipitation in the Yangtze River Catchment Based on Transfer Entropy 巍. 娄 10.12677/JWRR.2021.101003
79. Satellite‐Observed Major Greening and Biomass Increase in South China Karst During Recent Decade M. Brandt et al. 10.1029/2018EF000890
80. Multicomponent Satellite Assessment of Drought Severity in the Contiguous United States From 2002 to 2017 Using AMSR‐E and AMSR2 J. Du et al. 10.1029/2018WR024633
81. Reviews and syntheses: Recent advances in microwave remote sensing in support of terrestrial carbon cycle science in Arctic–boreal regions A. Mavrovic et al. 10.5194/bg-20-2941-2023
82. Estimation of soil moisture from remote sensing products using an ensemble machine learning model: a case study of Lake Urmia Basin, Iran S. Asadollah et al. 10.1007/s12145-023-01172-8
83. Assessing global surface water inundation dynamics using combined satellite information from SMAP, AMSR2 and Landsat J. Du et al. 10.1016/j.rse.2018.04.054
84. Maps with 1 km resolution reveal increases in above- and belowground forest biomass carbon pools in China over the past 20 years Y. Chen et al. 10.5194/essd-15-897-2023
85. 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
86. Towards species‐level forecasts of drought‐induced tree mortality risk M. De Kauwe et al. 10.1111/nph.18129
87. Interannual variability of ecosystem iso/anisohydry is regulated by environmental dryness G. Wu et al. 10.1111/nph.17040
88. Reappraisal of SMAP inversion algorithms for soil moisture and vegetation optical depth L. Gao et al. 10.1016/j.rse.2021.112627
89. 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
90. Estimation of flood-damaged cropland area using a convolutional neural network R. Lazin et al. 10.1088/1748-9326/abeba0
91. Linking remotely sensed ecosystem resilience with forest mortality across the continental United States X. Tai et al. 10.1111/gcb.16529
92. Conversion of satellite passive microwave signals to land surface “skin” temperature for extremely dry deserts P. Song et al. 10.1016/j.rse.2023.113857
93. Soil Moisture Variability in India: Relationship of Land Surface–Atmosphere Fields Using Maximum Covariance Analysis K. Pangaluru et al. 10.3390/rs11030335
94. Phenological Characteristics of Global Ecosystems Based on Optical, Fluorescence, and Microwave Remote Sensing M. Dannenberg et al. 10.3390/rs12040671
95. 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
96. Global Monitoring of the Vegetation Dynamics from the Vegetation Optical Depth (VOD): A Review F. Frappart et al. 10.3390/rs12182915
97. Introducing hydrometeor orientation into all-sky microwave and submillimeter assimilation V. Barlakas et al. 10.5194/amt-14-3427-2021
98. Advancing Scientific Understanding of the Global Methane Budget in Support of the Paris Agreement A. Ganesan et al. 10.1029/2018GB006065
99. Reconstruction of Global Long-Term Gap-Free Daily Surface Soil Moisture from 2002 to 2020 Based on a Pixel-Wise Machine Learning Method P. Mi et al. 10.3390/rs15082116
100. Assessing land surface drying and wetting trends with a normalized soil water index on the Loess Plateau in 2001–2016 Y. Wang et al. 10.1016/j.scitotenv.2019.04.195
101. Retrieving the global distribution of the threshold of wind erosion from satellite data and implementing it into the Geophysical Fluid Dynamics Laboratory land–atmosphere model (GFDL AM4.0/LM4.0) B. Pu et al. 10.5194/acp-20-55-2020
102. Compound hot–dry events greatly prolong the recovery time of dryland ecosystems Y. Yao et al. 10.1093/nsr/nwae274
103. Satellite Observations and Malaria: New Opportunities for Research and Applications M. Wimberly et al. 10.1016/j.pt.2021.03.003
104. Modeling Microwave Emission of Corn Crop Considering Leaf Shape and Orientation Under the Physical Optics Approximation J. Liu et al. 10.1109/TGRS.2020.2986256
105. Hydraulic diversity of forests regulates ecosystem resilience during drought W. Anderegg et al. 10.1038/s41586-018-0539-7
106. Divergent forest sensitivity to repeated extreme droughts W. Anderegg et al. 10.1038/s41558-020-00919-1
107. Estimating Land Surface Temperature from Satellite Passive Microwave Observations with the Traditional Neural Network, Deep Belief Network, and Convolutional Neural Network S. Wang et al. 10.3390/rs12172691
108. Dynamic response law and instability mechanism of steep inclined soft-hard interphase bedding slope under strong earthquakes G. Yan & G. Lou 10.1080/00150193.2021.1905733
109. Globally Consistent Patterns of Asynchrony in Vegetation Phenology Derived From Optical, Microwave, and Fluorescence Satellite Data X. Wang et al. 10.1029/2020JG005732
110. Applying Remotely Sensed Environmental Information to Model Mosquito Populations M. Kofidou et al. 10.3390/su13147655
111. An Approach to Improve the Spatial Resolution and Accuracy of AMSR2 Passive Microwave Snow Depth Product Using Machine Learning in Northeast China Y. Wei et al. 10.3390/rs14061480
112. Retrieval of High-Resolution Vegetation Optical Depth from Sentinel-1 Data over a Grassland Region in the Heihe River Basin Z. Zhou et al. 10.3390/rs14215468
113. Widespread decline in winds delayed autumn foliar senescence over high latitudes C. Wu et al. 10.1073/pnas.2015821118
114. A UGV-Based Forest Vegetation Optical Depth Mapping Using GNSS Signals A. Ghosh et al. 10.1109/JSTARS.2024.3365798
115. Occurrence of drought events at the land–atmosphere interface in Central Asia assessed via advanced microwave scanning radiometer data P. Yang et al. 10.1002/joc.7655
116. Interacting Effects of Leaf Water Potential and Biomass on Vegetation Optical Depth M. Momen et al. 10.1002/2017JG004145
117. Synergistic Satellite Assessment of Global Vegetation Health in Relation to ENSO‐Induced Droughts and Pluvials J. Du et al. 10.1029/2020JG006006
118. Assessing the responsiveness of multiple microwave remote sensing vegetation optical depth indices to drought on crops in Midwest US J. Cao et al. 10.1016/j.jag.2024.104072
119. Identifying areas at risk of drought‐induced tree mortality across South‐Eastern Australia M. De Kauwe et al. 10.1111/gcb.15215
120. Applications of Deep Learning-Based Super-Resolution Networks for AMSR2 Arctic Sea Ice Images T. Feng et al. 10.3390/rs15225401
121. Global Satellite Retrievals of the Near-Surface Atmospheric Vapor Pressure Deficit from AMSR-E and AMSR2 J. Du et al. 10.3390/rs10081175
122. Interannual Variations of Vegetation Optical Depth are Due to Both Water Stress and Biomass Changes A. Konings et al. 10.1029/2021GL095267
123. Divergent forest sensitivity to repeated extreme droughts W. Anderegg et al. 10.1038/s41558-020-00919-1