74 citations as recorded by crossref.

1. Development of Continuous AMSR-E/2 Soil Moisture Time Series by Hybrid Deep Learning Model (ConvLSTM2D and Conv2D) and Transfer Learning for Reanalyses V. Sivaprasad et al. https://doi.org/10.1109/JSTARS.2025.3557956
2. AI in soil moisture remote sensing C. Montzka et al. https://doi.org/10.1016/j.jag.2025.105011
3. Generating a long-term (2003−2020) hourly 0.25° global PM2.5 dataset via spatiotemporal downscaling of CAMS with deep learning (DeepCAMS) Y. Xiao et al. https://doi.org/10.1016/j.scitotenv.2022.157747
4. Improved seamless mapping of surface O3 concentrations using an integrated deep learning framework T. Li et al. https://doi.org/10.1038/s41612-025-01007-x
5. A Physics-Constrained Network for Daily Gap-Free Gridded Wind Speed Product Considering High Wind Speeds From CYGNSS W. Sun et al. https://doi.org/10.1109/TGRS.2026.3651797
6. A consistent record of vegetation optical depth retrieved from the AMSR-E and AMSR2 X-band observations M. Wang et al. https://doi.org/10.1016/j.jag.2021.102609
7. A TCN-Transformer Parallel model for reconstruction of a global, daily, spatially seamless FY-3B soil moisture dataset Q. Wang et al. https://doi.org/10.1016/j.rse.2025.114841
8. Cross-calibration between MWRI and AMSR2 to improve consistency of snow depth products S. Wei et al. https://doi.org/10.1016/j.rcar.2024.08.002
9. A 1 km daily soil moisture dataset over China using in situ measurement and machine learning Q. Li et al. https://doi.org/10.5194/essd-14-5267-2022
10. Estimation of Evapotranspiration and Its Components across China Based on a Modified Priestley–Taylor Algorithm Using Monthly Multi-Layer Soil Moisture Data W. Xing et al. https://doi.org/10.3390/rs13163118
11. Generation of Spatial-Seamless AMSR2 Land Surface Temperature in China During 2012–2020 Using a Deep Neural Network Y. Lian et al. https://doi.org/10.1109/TGRS.2023.3247806
12. A global daily seamless 9 km vegetation optical depth (VOD) product from 2010 to 2021 D. Hu et al. https://doi.org/10.5194/essd-17-2849-2025
13. A decade-long seamless-continuity daily L-band soil moisture product derived from SMOS observations since 2010 Y. Bai et al. https://doi.org/10.1038/s41597-026-06756-9
14. Generation of global 1 km daily soil moisture product from 2000 to 2020 using ensemble learning Y. Zhang et al. https://doi.org/10.5194/essd-15-2055-2023
15. SGD-SST 2.0: Seamless global daily sea surface temperature products cross-sensors generating from 2003 to 2025 Q. Wang et al. https://doi.org/10.1016/j.eswa.2026.132259
16. An Integrated Learning Framework for Seamless High-Resolution Soil Moisture Estimation Y. Jing et al. https://doi.org/10.1109/TGRS.2024.3461717
17. InSAR-derived seasonal subsidence reflects spatial soil moisture patterns in Arctic lowland permafrost regions B. Widhalm et al. https://doi.org/10.5194/tc-19-1103-2025
18. Spatio-Temporal reconstruction to fill spatial gaps in global satellite vegetation optical depth products H. Wang et al. https://doi.org/10.1016/j.rse.2025.114787
19. Quantitative Assessment of Cropland Exposure to Agricultural Drought in the Greater Mekong Subregion W. Ming et al. https://doi.org/10.3390/rs15112737
20. A Hybrid Triple Collocation-Deep Learning Approach for Improving Soil Moisture Estimation from Satellite and Model-Based Data W. Ming et al. https://doi.org/10.3390/rs14071744
21. Spatial Gap-Filling of SMAP Soil Moisture Pixels Over Tibetan Plateau via Machine Learning Versus Geostatistics C. Tong et al. https://doi.org/10.1109/JSTARS.2021.3112623
22. A Knowledge Optimization-Driven Network With Normalizer-Free Group ResNet Prior for Remote Sensing Image Pan-Sharpening J. He et al. https://doi.org/10.1109/TGRS.2022.3186916
23. Refinement of NOAA AMSR-2 Soil Moisture Data Product: 1. Intercomparisons of the Commonly Used Machine-Learning Models J. Yin et al. https://doi.org/10.1109/TGRS.2023.3280173
24. Validation of Multiple Soil Moisture Products over an Intensive Agricultural Region: Overall Accuracy and Diverse Responses to Precipitation and Irrigation Events X. Fan et al. https://doi.org/10.3390/rs14143339
25. Hyperspectral Image Denoising: From Model-Driven, Data-Driven, to Model-Data-Driven Q. Zhang et al. https://doi.org/10.1109/TNNLS.2023.3278866
26. Reconstruction of Historical SMAP Soil Moisture Dataset From 1979 to 2015 Using CCI Time-Series H. Yang et al. https://doi.org/10.1109/TGRS.2024.3360092
27. Bridging spatio-temporal discontinuities in global soil moisture mapping by coupling physics in deep learning Z. Wei et al. https://doi.org/10.1016/j.rse.2024.114371
28. Space–Air–Ground Synergistic Approaches for Field Water Status Precision Monitoring: A Review T. Li et al. https://doi.org/10.3390/rs18101542
29. Combined deep prior with low-rank tensor SVD for thick cloud removal in multitemporal images Q. Zhang et al. https://doi.org/10.1016/j.isprsjprs.2021.04.021
30. The International Soil Moisture Network: serving Earth system science for over a decade W. Dorigo et al. https://doi.org/10.5194/hess-25-5749-2021
31. Remote sensing image gap filling based on spatial-spectral random forests Q. Wang et al. https://doi.org/10.1016/j.srs.2022.100048
32. Retrieval Methods for Microwave Remote Sensing of Soil Moisture, Above-Ground Biomass, and Freeze-Thaw Dynamics: A review A. Veneri et al. https://doi.org/10.1109/MGRS.2025.3623398
33. Sub-daily global vegetation optical depth reconstruction from SMAP using 3D partial convolutional networks J. Kou et al. https://doi.org/10.1080/01431161.2026.2619150
34. Satellite Soil Moisture Data Reconstruction in the Temporal and Spatial Domains: Latent Error Assessments and Performances for Tracing Rainstorms and Droughts Y. Liu et al. https://doi.org/10.3390/rs14194841
35. Physically-aware deep learning for reconstructing gap-free sea surface temperature in the South China Sea C. Su et al. https://doi.org/10.1016/j.isprsjprs.2026.04.016
36. Artificial Neural Network-Based Microwave Satellite Soil Moisture Reconstruction over the Qinghai–Tibet Plateau, China J. Wang & D. Xu https://doi.org/10.3390/rs13245156
37. Spatiotemporal Reconstruction of MODIS Normalized Difference Snow Index Products Using U-Net with Partial Convolutions D. Xing et al. https://doi.org/10.3390/rs14081795
38. Exploring diurnal variation in soil moisture via sub-daily estimates reconstruction Z. Wei et al. https://doi.org/10.1016/j.jhydrol.2025.134005
39. Influences of Using Different Satellite Soil Moisture Products on SM2RAIN for Rainfall Estimation Across the Tibetan Plateau L. Miao et al. https://doi.org/10.1109/JSTARS.2023.3296455
40. An innovative method for measuring the hysteresis effects of soil moisture on meteorological variables at various time scales and climate conditions Q. Tian et al. https://doi.org/10.1080/10095020.2023.2280574
41. Reconstructing long-term global satellite-based soil moisture data using deep learning method Y. Hu et al. https://doi.org/10.3389/feart.2023.1130853
42. Addressing spatial gaps in ESA CCI soil moisture product: A hierarchical reconstruction approach using deep learning model T. Ding et al. https://doi.org/10.1016/j.jag.2024.104003
43. Temporal Gap‐Filling of 12‐Hourly SMAP Soil Moisture Over the CONUS Using Water Balance Budgeting R. Zhang et al. https://doi.org/10.1029/2023WR034457
44. Estimating Surface Albedo of Arctic Sea Ice Using an Ensemble Back-Propagation Neural Network: Toward a Better Consideration of Reflectance Anisotropy and Melt Ponds Y. Ding et al. https://doi.org/10.1109/TGRS.2022.3202046
45. SGD-SM 2.0: an improved seamless global daily soil moisture long-term dataset from 2002 to 2022 Q. Zhang et al. https://doi.org/10.5194/essd-14-4473-2022
46. Spatiotemporal Reconstruction of FY-3B Soil Moisture Using a Hybrid Attention and Partial Convolution Neural Network R. Xu et al. https://doi.org/10.1109/JSTARS.2025.3632561
47. Generating a Long-Term Spatiotemporally Continuous Melt Pond Fraction Dataset for Arctic Sea Ice Using an Artificial Neural Network and a Statistical-Based Temporal Filter Z. Peng et al. https://doi.org/10.3390/rs14184538
48. Robust Thick Cloud Removal for Multitemporal Remote Sensing Images Using Coupled Tensor Factorization J. Lin et al. https://doi.org/10.1109/TGRS.2022.3140800
49. Space-time super-resolution for satellite video: A joint framework based on multi-scale spatial-temporal transformer Y. Xiao et al. https://doi.org/10.1016/j.jag.2022.102731
50. Estimation of Snow Depth from AMSR2 and MODIS Data based on Deep Residual Learning Network D. Xing et al. https://doi.org/10.3390/rs14205089
51. Reconstruction of a spatially seamless, daily SMAP (SSD_SMAP) surface soil moisture dataset from 2015 to 2021 H. Yang & Q. Wang https://doi.org/10.1016/j.jhydrol.2023.129579
52. A seamless global daily soil moisture dataset (2010–2015) harmonized from SMOS observations and SMAP-era assimilation modeling X. Wang et al. https://doi.org/10.1080/17538947.2025.2609469
53. Soil moisture downscaling using machine learning with scale-aware, physically informed feature constraints D. Zhang et al. https://doi.org/10.1016/j.soilad.2025.100096
54. Fusing ERA5-Land and SMAP L4 for an improved global soil moisture product (1950–2025) W. Wang et al. https://doi.org/10.5194/essd-18-1061-2026
55. Reconstruction of ESA CCI soil moisture based on DCT-PLS and in situ soil moisture X. Guo et al. https://doi.org/10.2166/nh.2022.058
56. A robust gap-filling approach for European Space Agency Climate Change Initiative (ESA CCI) soil moisture integrating satellite observations, model-driven knowledge, and spatiotemporal machine learning K. Liu et al. https://doi.org/10.5194/hess-27-577-2023
57. Hyperspectral image mixed noised removal via jointly spatial and spectral difference constraint with low-rank tensor factorization Q. Zhang et al. https://doi.org/10.1016/j.engappai.2025.110508
58. Evaluation of Several Satellite-Based Soil Moisture Products in the Continental US S. Feng et al. https://doi.org/10.3390/s22249977
59. Global intraday land surface temperature estimation of enhanced coverage by fusion of passive microwave data between different polar orbits P. Song et al. https://doi.org/10.1016/j.jag.2025.104873
60. Seamless global daily soil moisture mapping using deep learning based spatiotemporal fusion M. Jiang et al. https://doi.org/10.1016/j.jag.2025.104517
61. Coastline target detection based on UAV hyperspectral remote sensing images S. Zhao et al. https://doi.org/10.3389/fmars.2024.1452737
62. SGD-SST: Seamless global daily sea surface temperature products reconstruction and validation via deep spatio-temporal fusion model Q. Wang et al. https://doi.org/10.1016/j.eswa.2025.128703
63. Global daily 1 km gapless XCO₂ (2003−2023) derived from multi-satellite observations and a spatiotemporal deep learning framework J. Wang https://doi.org/10.1016/j.eiar.2025.108146
64. A Novel Merging Framework for Generating High-Accuracy Global Soil Moisture by Error Decomposition Through Multiple Collocation Analysis X. Min et al. https://doi.org/10.1109/TGRS.2024.3470800
65. Spatiotemporal Seamless Estimation of Global Surface Soil Moisture Using Triple Collocation, Machine Learning, and Data Assimilation L. Xu et al. https://doi.org/10.1109/TGRS.2025.3568034
66. SM2RAIN–dual: a global rainfall fusion product derived from multi-source satellite soil moisture observations L. Miao et al. https://doi.org/10.1016/j.jhydrol.2026.135261
67. Reconstructing a Gap-Free MODIS Normalized Difference Snow Index Product Using a Long Short-Term Memory Network J. Hou et al. https://doi.org/10.1109/TGRS.2022.3178421
68. Beyond being wise after the event: Combining spatial, temporal and spectral information for Himawari-8 early-stage wildfire detection Q. Zhang et al. https://doi.org/10.1016/j.jag.2023.103506
69. A spatial downscaling method for SMAP soil moisture considering vegetation memory and spatiotemporal fusion C. Cui et al. https://doi.org/10.1080/17538947.2024.2367729
70. A comprehensive review of spatial-temporal-spectral information reconstruction techniques Q. Wang et al. https://doi.org/10.1016/j.srs.2023.100102
71. DsTer: A dense spectral transformer for remote sensing spectral super-resolution J. He et al. https://doi.org/10.1016/j.jag.2022.102773
72. Retrieving long-term topsoil moisture in Qingtongxia irrigation district using a modified OPTRAM model S. Du et al. https://doi.org/10.1016/j.ejrh.2025.103074
73. 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. https://doi.org/10.3390/rs15082116
74. Enhancing Global Surface Soil Moisture Estimation From ESA CCI and SMAP Product With a Conditional Variational Autoencoder C. Shi et al. https://doi.org/10.1109/JSTARS.2024.3393828