11 citations as recorded by crossref.

1. Reconstructing long-term global satellite-based soil moisture data using deep learning method Y. Hu et al. 10.3389/feart.2023.1130853
2. Application of Synthetic DINCAE–BME Spatiotemporal Interpolation Framework to Reconstruct Chlorophyll–a from Satellite Observations in the Arabian Sea X. Yan et al. 10.3390/jmse11040743
3. Synthesizing Sea Surface Temperature and Satellite Altimetry Observations Using Deep Learning Improves the Accuracy and Resolution of Gridded Sea Surface Height Anomalies S. Martin et al. 10.1029/2022MS003589
4. A General Convolutional Neural Network to Reconstruct Remotely Sensed Chlorophyll-a Concentration X. Zhang & M. Zhou 10.3390/jmse11040810
5. Key factors for quantitative precipitation nowcasting using ground weather radar data based on deep learning D. Han et al. 10.5194/gmd-16-5895-2023
6. End-to-End Neural Interpolation of Satellite-Derived Sea Surface Suspended Sediment Concentrations J. Vient et al. 10.3390/rs14164024
7. Reconstruction Methods in Oceanographic Satellite Data Observation—A Survey L. Ćatipović et al. 10.3390/jmse11020340
8. Evaluation of Chlorophyll-a estimation using Sentinel 3 based on various algorithms in southern coastal Vietnam N. Binh et al. 10.1016/j.jag.2022.102951
9. A gap-filling method for satellite-derived chlorophyll-a time series based on neighborhood spatiotemporal information G. Zhou et al. 10.1016/j.jag.2024.103724
10. Empirical Function Method: A Precise Approach for Filling Data Gaps in Satellite Sea Surface Temperature Imagery G. Zheng & X. Li 10.1109/TGRS.2023.3335940
11. Remote sensing of sea surface salinity: challenges and research directions Y. Kim et al. 10.1080/15481603.2023.2166377