36 citations as recorded by crossref.

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4. Coastal wetland degradation and ecosystem service value change in the Yellow River Delta, China J. Yan et al. 10.1016/j.gecco.2023.e02501
5. An improved global land cover mapping in 2015 with 30 m resolution (GLC-2015) based on a multisource product-fusion approach B. Li et al. 10.5194/essd-15-2347-2023
6. Exploring the impact of urbanization on flood characteristics with the SCS-TRITON method H. Yu et al. 10.1007/s11069-023-06324-z
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8. Policy instruments as a trigger for urban sprawl deceleration: monitoring the stability and transformations of green areas K. Filepné Kovács et al. 10.1038/s41598-024-52637-9
9. The distribution of global tidal marshes from Earth observation data T. Worthington et al. 10.1111/geb.13852
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12. Potential of Sample Migration and Explainable Machine Learning Model for Monitoring Spatiotemporal Changes of Wetland Plant Communities K. Feng et al. 10.1109/JSTARS.2024.3399791
13. National Scale Land Cover Classification Using the Semiautomatic High-Quality Reference Sample Generation (HRSG) Method and an Adaptive Supervised Classification Scheme A. Naboureh et al. 10.1109/JSTARS.2023.3241620
14. A review of regional and Global scale Land Use/Land Cover (LULC) mapping products generated from satellite remote sensing Y. Wang et al. 10.1016/j.isprsjprs.2023.11.014
15. Mapping Phenology of Complicated Wetland Landscapes through Harmonizing Landsat and Sentinel-2 Imagery C. Fan et al. 10.3390/rs15092413
16. Agreement Analysis and Accuracy Assessment of Multiple Mangrove Datasets in Guangxi Beibu Gulf and Guangdong-Hong Kong-Macau Greater Bay, China, for 2000–2020 Z. Xiao et al. 10.1109/JSTARS.2024.3353251
17. Land cover dataset of the China Central-Asia West-Asia Economic Corridor from 1993 to 2018 A. Naboureh et al. 10.1038/s41597-023-02623-z
18. Remote Data for Mapping and Monitoring Coastal Phenomena and Parameters: A Systematic Review R. Cavalli 10.3390/rs16030446
19. Vegetation classification in a subtropical region with Sentinel-2 time series data and deep learning M. Zhang et al. 10.1080/10095020.2024.2336604
20. Spatiotemporal Dynamics and Driving Factors of Ecosystem Services in the Yellow River Delta, China S. Xue et al. 10.3390/su16083466
21. Automated and refined wetland mapping of Dongting Lake using migrated training samples based on temporally dense Sentinel 1/2 imagery Y. Deng et al. 10.1080/17538947.2023.2241428
22. Assessing urban wetlands dynamics in Wuhan and Nanchang, China Y. Deng et al. 10.1016/j.scitotenv.2023.165777
23. Distribution and Storage Characteristics of Soil Organic Carbon in Tidal Wetland of Dandou Sea, Guangxi M. Wang et al. 10.3390/atmos15040431
24. Evaluation of Remote Sensing Products for Wetland Mapping in the Irtysh River Basin K. Luo et al. 10.3390/geosciences14010014
25. A New Method for Bare Permafrost Extraction on the Tibetan Plateau by Integrating Machine Learning and Multi-Source Information X. Li et al. 10.3390/rs15225328
26. Mapping global non-floodplain wetlands C. Lane et al. 10.5194/essd-15-2927-2023
27. Automated Mapping of Global 30-m Tidal Flats Using Time-Series Landsat Imagery: Algorithm and Products X. Zhang et al. 10.34133/remotesensing.0091
28. Mapping wetlands in Northeast China by using knowledge-based algorithms and microwave (PALSAR-2, Sentinel-1), optical (Sentinel-2, Landsat), and thermal (MODIS) images C. Zhang et al. 10.1016/j.jenvman.2023.119618
29. Characterizing the Accelerated Global Carbon Emissions from Forest Loss during 1985–2020 Using Fine-Resolution Remote Sensing Datasets W. Liu et al. 10.3390/rs16060978
30. Vegetation Classification and Evaluation of Yancheng Coastal Wetlands Based on Random Forest Algorithm from Sentinel-2 Images Y. Wang et al. 10.3390/rs16071124
31. The Wetland Intrinsic Potential tool: mapping wetland intrinsic potential through machine learning of multi-scale remote sensing proxies of wetland indicators M. Halabisky et al. 10.5194/hess-27-3687-2023
32. Fine extraction of plateau wetlands based on a combination of object-oriented machine learning and ecological rules: a case study of Dianchi Basin F. Cai et al. 10.1109/JSTARS.2024.3356656
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34. Fine-grained wetland classification for national wetland reserves using multi-source remote sensing data and Pixel Information Expert Engine (PIE-Engine) H. Liu et al. 10.1080/15481603.2023.2286746
35. GWL_FCS30: a global 30 m wetland map with a fine classification system using multi-sourced and time-series remote sensing imagery in 2020 X. Zhang et al. 10.5194/essd-15-265-2023
36. Precise mapping of coastal wetlands using time-series remote sensing images and deep learning model L. Ke et al. 10.3389/ffgc.2024.1409985