# Remote Sensing and Machine Learning Tools to Support
## Abbreviations
[CrossRef]
* (118) [PERSON]; [PERSON]; [PERSON]; [PERSON] Spectral Analysis of Wetlands Using Multi-Source Optical Satellite Imagery. _ISPRS J. Photogramm. Remote Sens._**2018,** _144_, 119-136. [CrossRef]
* (119) [PERSON]; [PERSON] Polarimetric Synthetic Aperture Radar Application for Tropical Peatlands Classification: A Case Study in Siak River Transect, Riau Province, Indonesia. _J. Appl. Remote Sens._**2017,** _11_, 016040. [CrossRef]
* (120) [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON] Joint Classification of Hyperspectral and Multispectral Images for Mapping Coastal Wetlands. _IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens._**2021,** _14_, 982-996. [CrossRef]
* (121) [PERSON]; [PERSON]; [PERSON] [PERSON]; [PERSON]; [PERSON] [PERSON]; [PERSON]; [PERSON] [PERSON]; [PERSON] [PERSON] Synergy of High-Resolution Radar and Optical Images Satellite for Identification and Mapping of Wetland Macrophytes on the Danube Delta. _Remote Sens._**2020,** _12_, 2188. [CrossRef]
* (122) [PERSON]; [PERSON]; [PERSON]; [PERSON] [PERSON]; [PERSON]; [PERSON]; [PERSON] [PERSON]; [PERSON] Testing the Contribution of Multi-Source Remote Sensing Features for Random Forest Classification of the Greater Amanzule Tropical Peatland. _Sensors_**2021,** _21_, 3399. [CrossRef]* (123) [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON] The Integration of Optical, Topographic, and Radar Data for Wetland Mapping in Northern Minnesota. _Can. J. Remote Sens._**2011**, _37_, 564-582. [CrossRef]
* (124) [PERSON]; [PERSON]; [PERSON] [PERSON]; [PERSON]; [PERSON] Multi-Temporal, Multi-Frequency, and Multi-Polarization Coherence and SAR Backscatter Analysis of Wetlands. _ISPRS J. Photogramm. Remote Sens._**2018**, _142_, 78-93. [CrossRef]
* (125) [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON] Scattering Feature Set Optimization and Polarimetric SAR Classification Using Object-Oriented RF-SFS Algorithm in Coastal Wetlands. _Remote Sens._**2020**, _12_, 407. [CrossRef]
* (126) [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON] [PERSON]; [PERSON] [PERSON]; [PERSON]; [PERSON] [PERSON] Mapping Boreal Fealand Ecosystem Types from Multitemporal Radar and Optical Satellite Imagery. _Can. J. For. Res._**2017**, _47_, 545-559. [CrossRef]
* (127) [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON] [PERSON] The Third Generation of Pan-Canadian Wetland Map at 10 m Resolution Using Multisource Earth Observation Data on Cloud Computing Platform. _IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens._**2021**, _14_, 8789-8803. [CrossRef]
* (128) [PERSON] [PERSON]; [PERSON]; [PERSON]; [PERSON] [PERSON]; [PERSON]; [PERSON] The Contribution of ALOS/PALSAR-1 Multi-Temporal Data to Map Permanently and Temporarily Flooded Coastal Wetlands. _Int. J. Remote Sens._**2020**, _41_, 1582-1602. [CrossRef]
* (129) [PERSON]; [PERSON]; [PERSON]; [PERSON] Multi-Temporal SAR Image Classification of Coastal Plain Wetlands Using a New Feature Selection Method and Random Forests. _Remote Sens. Lett._**2019**, _10_, 312-321. [CrossRef]
* (130) [PERSON]; [PERSON]; [PERSON] [PERSON]; [PERSON] [PERSON]; [PERSON]; [PERSON] Multitemporal Classification of TerraSAR-X Data for Wetland Vegetation Mapping. _J. Appl. Remote Sens._**2014**, \(8\), 083648. [CrossRef]
* (131) [PERSON]; [PERSON]; [PERSON]; [PERSON] Wetland Mapping and Flood Extent Monitoring Using Optical and Radar Remotely Sensed Data and Ancillary Topographical Data in the Zhalong National Natural Reserve, China. In Proceedings of the Earth Resources and Environmental Remote Sensing/GIS Applications IV, Dresden, Germany, 23-25 September 2013; SPIE: Breda, The Netherlands, 2013; Volume 8893, pp. 390-396. * (132) [PERSON]; [PERSON]; [PERSON] Comparison and Assessment of NDVI Time Series for Seasonal Wetland Classification. _Int. J. Digit. Earth_**2018**, _11_, 1103-1131. [CrossRef]
* (133) [PERSON]; [PERSON]; [PERSON]; [PERSON] Mapping Wetland Using the Object-Based Stacked Generalization Method Based on Multi-Temporal Optical and SAR Data. _Int. J. Appl. Earth Obs. Geoinf._**2020**, _92_, 102164.