54 citations as recorded by crossref.

1. Harmonized NDVI time-series from Landsat and Sentinel-2 reveal phenological patterns of diverse, small-scale cropping systems in East Africa A. Liepa et al. https://doi.org/10.1016/j.rsase.2024.101230
2. A Study on the Evolution of Forest Landscape Patterns in the Fuxin Region of China Combining SC-UNet and Spatial Pattern Perspectives F. Wang et al. https://doi.org/10.3390/su16167067
3. Quantifying post-fire live tree presence and spatial variation using Sentinel-2 time series S. Saberi et al. https://doi.org/10.1016/j.foreco.2025.123461
4. A Framework for the Estimation of Uncertainties and Spectral Error Correlation in Sentinel-2 Level-2A Data Products J. Gorroño et al. https://doi.org/10.1109/TGRS.2024.3435021
5. High Spatial Resolution Fractional Vegetation Coverage Inversion Based on UAV and Sentinel-2 Data: A Case Study of Alpine Grassland G. Zhong et al. https://doi.org/10.3390/rs15174266
6. Spatiotemporal variability of lake surface water temperature and water quality parameters and its interrelationship with water hyacinth biomass in Lake Tana, Ethiopia G. Bayable et al. https://doi.org/10.1007/s11356-024-34212-x
7. Tracking Phytoplankton Biomass Amid Wildfire Smoke Interference Using Landsat 8 OLI S. Mohammady et al. https://doi.org/10.3390/rs16193605
8. Harmonized Landsat and Sentinel-2 Data with Google Earth Engine E. Berra et al. https://doi.org/10.3390/rs16152695
9. Advancing Global Landslide Segmentation: A Coupled Multispectral Attention and Data Augmentation Approach Using the Novel MRGSLD Dataset G. Emani et al. https://doi.org/10.1007/s41748-025-00577-3
10. Delineation of Management Zones Based on the Agricultural Potential Concept for Potato Production Using Optical Satellite Images D. Ramirez-Gonzalez et al. https://doi.org/10.3390/rs17223709
11. Assessment of spectral indices and water color combinations for detecting algal blooms in coastal subtropical shallow lakes M. Cabezudo et al. https://doi.org/10.1016/j.rsase.2025.101678
12. An effective atmospheric correction method for the wide swath of Chinese GF-1 and GF-6 WFV images on lands Y. Dong et al. https://doi.org/10.1016/j.ejrs.2023.07.011
13. Retrieval of water quality parameters based on IOA-ML models and their response to short-term hydrometeorological factors W. Hu et al. https://doi.org/10.1016/j.ejrh.2024.102118
14. Characterization of Irrigated Rice Cultivation Cycles and Classification in Brazil Using Time Series Similarity and Machine Learning Models with Sentinel Imagery A. Garcia et al. https://doi.org/10.3390/agriengineering7030065
15. Quantifying Forest Cover Loss during the COVID-19 Pandemic in the Lubumbashi Charcoal Production Basin (DR Congo) through Remote Sensing and Landscape Analysis Y. Useni Sikuzani et al. https://doi.org/10.3390/resources13070095
16. Mapping the Cerrado–Amazon Transition Using PlanetScope–Sentinel Data Fusion and a U-Net Deep Learning Framework C. Li et al. https://doi.org/10.3390/rs17132138
17. GROUNDED EO: Data-driven Sentinel-2 LAI and FAPAR retrieval using Gaussian processes trained with extensive fiducial reference measurements L. Brown et al. https://doi.org/10.1016/j.rse.2025.114797
18. Predicting turbidity dynamics in small reservoirs in central Kenya using remote sensing and machine learning S. Steinbach et al. https://doi.org/10.1016/j.jag.2025.104390
19. Rangeland degradation and bush encroachment assessment using machine learning approaches in a semi-arid nature reserve S. Jombo & M. Abd Elbasit https://doi.org/10.1080/19475683.2025.2552158
20. MANGLEE: A Tool for Mapping and Monitoring MANgrove Ecosystem on Google Earth Engine—A Case Study in Ecuador L. Caiza-Morales et al. https://doi.org/10.1007/s41651-024-00175-3
21. Cost-effective statewide wetland inventory update using weakly supervised deep learning: A case study in Minnesota, USA V. Igwe et al. https://doi.org/10.1016/j.rsase.2026.101871
22. A spatiotemporal fusion algorithm based on Fourier transform is developed to generate daily surface soil moisture with 20 m spatial resolution X. Bai et al. https://doi.org/10.1016/j.geoderma.2025.117548
23. Optimal time-window for assessing soil salinity via Sentinel-2 multitemporal synthetic data in the arid agricultural regions of China J. Xiong et al. https://doi.org/10.1016/j.ecolind.2025.113642
24. LinkNet-Spectral-Spatial-Temporal Transformer Based on Few-Shot Learning for Mangrove Loss Detection with Small Dataset I. Panuntun et al. https://doi.org/10.3390/rs16061078
25. Climate change impacts on streamflow, sediment load and carbon fluxes in the Lena River delta S. Chalov et al. https://doi.org/10.1016/j.ecolind.2023.111252
26. GPP of a Chinese Savanna Ecosystem during Different Phenological Phases Simulated from Harmonized Landsat and Sentinel-2 Data X. Zhang et al. https://doi.org/10.3390/rs16183475
27. Archetypal crop trait dynamics for enhanced retrieval of biophysical parameters from Sentinel-2 MSI F. Yin et al. https://doi.org/10.1016/j.rse.2024.114510
28. Assessing Mangrove Forest Recovery in the British Virgin Islands After Hurricanes Irma and Maria with Sentinel-2 Imagery and Google Earth Engine M. Routhier et al. https://doi.org/10.3390/rs17142485
29. An Ecological Quality Evaluation of Large-Scale Farms Based on an Improved Remote Sensing Ecological Index J. Wang et al. https://doi.org/10.3390/rs16040684
30. The possible contribution of marine heatwaves on the declining healthy coral reef cover in Nusa Dua, Bali M. Prasetyo et al. https://doi.org/10.1080/01431161.2025.2592906
31. Need and vision for global medium-resolution Landsat and Sentinel-2 data products V. Radeloff et al. https://doi.org/10.1016/j.rse.2023.113918
32. Evaluation of Atmospheric Preprocessing Methods and Chlorophyll Algorithms for Sentinel-2 Imagery in Coastal Waters T. Wolters et al. https://doi.org/10.3390/rs17203503
33. Evaluation of Correction Algorithms for Sentinel-2 Images Implemented in Google Earth Engine for Use in Land Cover Classification in Northern Spain I. Teijido-Murias et al. https://doi.org/10.3390/f15122192
34. Earth observations reveal impacts of climate variability on maize cropping systems in sub-Saharan Africa A. Liepa et al. https://doi.org/10.1080/15481603.2025.2476248
35. Meteorological and potential climatic influence on high cyanobacterial biomass within Patos Lagoon (southern Brazil): A case study of the summer of 2019–2020 B. Caneve et al. https://doi.org/10.1590/2675-2824071.21099bfc
36. Synergistic use of satellite, legacy, and in situ data to predict spatio-temporal patterns of the invasive Lantana camara in a savannah ecosystem L. Schell et al. https://doi.org/10.3389/fpls.2025.1593110
37. Evaluating Sentinel‑2 vegetation indices for aboveground biomass estimation and forest management in the fragmented Atlantic Rainforest F. de Moraes et al. https://doi.org/10.1007/s44415-026-00086-9
38. Rapid Large-Scale Wetland Inventory Update Using Multi-Source Remote Sensing V. Igwe et al. https://doi.org/10.3390/rs15204960
39. Retrieving land surface reflectance anisotropy with Sentinel-3 observations and prior BRDF model constraints J. León-Tavares et al. https://doi.org/10.1016/j.rse.2023.113967
40. Estimating rice leaf area index at multiple growth stages with Sentinel-2 data: An evaluation of different retrieval algorithms T. Wu et al. https://doi.org/10.1016/j.eja.2024.127362
41. Photosynthesis, heat, and structure: an evident hierarchy of environmental conditions driving wetland carbon assimilation E. Romero et al. https://doi.org/10.1016/j.rse.2026.115339
42. Detecting Coseismic Landslides in GEE Using Machine Learning Algorithms on Combined Optical and Radar Imagery S. Peters et al. https://doi.org/10.3390/rs16101722
43. Improved prediction of rice yield at field and county levels by synergistic use of SAR, optical and meteorological data W. Yu et al. https://doi.org/10.1016/j.agrformet.2023.109729
44. Atmospheric Correction Inter-Comparison eXercise, ACIX-III Land: An Assessment of Atmospheric Correction Processors for EnMAP and PRISMA over Land N. Cremer et al. https://doi.org/10.3390/rs17233790
45. Lifecycle-Aware Multitemporal InSAR: Synergistic Optical-Radar Monitoring of Evolving Infrastructure B. Du et al. https://doi.org/10.1109/TGRS.2026.3696624
46. Remote sensing-based rice mapping in Brazil: Identifying the best approach for segmenting different spectral compositions using deep learning M. Islam et al. https://doi.org/10.1016/j.rsase.2025.101770
47. Irrigated rice-field mapping in Brazil using phenological stage information and optical and microwave remote sensing A. Garcia et al. https://doi.org/10.1016/j.acags.2025.100223
48. A framework for improved predictions of the climate impacts on potential yields of UK winter wheat and its applicability to other UK crops G. Hayman et al. https://doi.org/10.1016/j.cliser.2024.100479
49. Utilizing Remote Sensing and Random Forests to Identify Optimal Land Use Scenarios and Address the Increase in Landslide Susceptibility A. Putra et al. https://doi.org/10.3390/su17094227
50. Spatiotemporal analysis of turbidity in tropical reservoirs using Sentinel-2 satellite imagery under catchment dynamics A. Latwal et al. https://doi.org/10.1016/j.rsase.2025.101631
51. Artifact-induced bias and cross-sensor inconsistencies in landsat surface reflectance for global inland waters: evaluation and solutions C. Yang et al. https://doi.org/10.1016/j.isprsjprs.2026.04.003
52. Early-Stage Mapping of Winter Canola by Combining Sentinel-1 and Sentinel-2 Data in Jianghan Plain China T. Liu et al. https://doi.org/10.3390/rs16173197
53. Disturbance and incomplete recovery in the Cerrado-Amazon transition: Implications for conservation of a critical tropical ecotone C. Li et al. https://doi.org/10.1016/j.biocon.2026.111900
54. SUGARFuseNet: Diffusion‑driven domain adaptation and bimodal bitemporal fusion for advancing global landslide segmentation on novel GBMT‑SLID dataset G. Emani et al. https://doi.org/10.1016/j.neucom.2026.134060