1. “Assessing the Behavioural Responses of Small Cetaceans to Unmanned Aerial Vehicles”
by Joana, Castro et al.
Remote Sens. 2021, 13(1), 156; https://doi.org/10.3390/rs13010156
Available online: https://www.mdpi.com/2072-4292/13/1/156

2. “Deep Learning Based Thin Cloud Removal Fusing Vegetation Red Edge and Short Wave Infrared Spectral Information for Sentinel-2A Imagery”
by Jun, Li et al.
Remote Sens. 2021, 13(1), 157; https://doi.org/10.3390/rs13010157
Available online: https://www.mdpi.com/2072-4292/13/1/157

3. “Spatial Temporal Analysis of Traffic Patterns during the COVID-19 Epidemic by Vehicle Detection Using Planet Remote-Sensing Satellite Images”
by Yulu, Chen et al.
Remote Sens. 2021, 13(2), 208; https://doi.org/10.3390/rs13020208
Available online: https://www.mdpi.com/2072-4292/13/2/208

4. “A Remote Sensing-Based Assessment of Water Resources in the Arabian Peninsula”
by Youssef, Wehbe et al.
Remote Sens. 2021, 13(2), 247; https://doi.org/10.3390/rs13020247
Available online: https://www.mdpi.com/2072-4292/13/2/247

5. “A Comparison of Machine Learning Approaches to Improve Free Topography Data for Flood Modelling”
by Michael, Meadows et al.
Remote Sens. 2021, 13(2), 275; https://doi.org/10.3390/rs13020275
Available online: https://www.mdpi.com/2072-4292/13/2/275

6. “Imaging Spectroscopy for Conservation Applications”
by Megan, Seeley et al.
Remote Sens. 2021, 13(2), 292; https://doi.org/10.3390/rs13020292
Available online: https://www.mdpi.com/2072-4292/13/2/292

7. “Complex Principal Component Analysis of Antarctic Ice Sheet Mass Balance”
by Jingang, Zhan et al.
Remote Sens. 2021, 13(3), 480; https://doi.org/10.3390/rs13030480
Available online: https://www.mdpi.com/2072-4292/13/3/480

8. “Remote Sensing and Machine Learning in Crop Phenotyping and Management, with an Emphasis on Applications in Strawberry Farming”
by Caiwang, Zheng et al.
Remote Sens. 2021, 13(3), 531; https://doi.org/10.3390/rs13030531
Available online: https://www.mdpi.com/2072-4292/13/3/531

9. “Spatiotemporal Characteristics and Trend Analysis of Two Evapotranspiration-Based Drought Products and Their Mechanisms in Sub-Saharan Africa”
by Isaac Kwesi, Nooni et al.
Remote Sens. 2021, 13(3), 533; https://doi.org/10.3390/rs13030533
Available online: https://www.mdpi.com/2072-4292/13/3/533

10. “Spatial–Temporal Vegetation Dynamics and Their Relationships with Climatic, Anthropogenic, and Hydrological Factors in the Amur River Basin”
by Shilun, Zhou et al.
Remote Sens. 2021, 13(4), 684; https://doi.org/10.3390/rs13040684
Available online: https://www.mdpi.com/2072-4292/13/4/684

11. “Crop Biomass Mapping Based on Ecosystem Modeling at Regional Scale Using High Resolution Sentinel-2 Data”
by Liming, He et al.
Remote Sens. 2021, 13(4), 806; https://doi.org/10.3390/rs13040806
Available online: https://www.mdpi.com/2072-4292/13/4/806

12. “Landsat and Sentinel-2 Based Burned Area Mapping Tools in Google Earth Engine”
by Ekhi, Roteta et al.
Remote Sens. 2021, 13(4), 816; https://doi.org/10.3390/rs13040816
Available online: https://www.mdpi.com/2072-4292/13/4/816

13. “Diurnal Cycle of Passive Microwave Brightness Temperatures over Land at a Global Scale”
by Zahra, Sharifnezhad et al.
Remote Sens. 2021, 13(4), 817; https://doi.org/10.3390/rs13040817
Available online: https://www.mdpi.com/2072-4292/13/4/817

14. “Assessing within-Field Corn and Soybean Yield Variability from WorldView-3, Planet, Sentinel-2, and Landsat 8 Satellite Imagery”
by Zahra, Sharifnezhad et al.
Remote Sens. 2021, 13(5), 872; https://doi.org/10.3390/rs13050872
Available online: https://www.mdpi.com/2072-4292/13/5/872

15. “Mapping the Groundwater Level and Soil Moisture of a Montane Peat Bog Using UAV Monitoring and Machine Learning”
by Theodora, Lendzioch et al.
Remote Sens. 2021, 13(5), 907; https://doi.org/10.3390/rs13050907
Available online: https://www.mdpi.com/2072-4292/13/5/907

16. “Hyperspectral Image Classification Based on Superpixel Pooling Convolutional Neural Network with Transfer Learning”
by Fuding, Xie et al.
Remote Sens. 2021, 13(5), 930; https://doi.org/10.3390/rs13050930
Available online: https://www.mdpi.com/2072-4292/13/5/930

17. “Traditional vs. Machine-Learning Methods for Forecasting Sandy Shoreline Evolution Using Historic Satellite-Derived Shorelines”
by Floris, Calkoen et al.
Remote Sens. 2021, 13(5), 934; https://doi.org/10.3390/rs13050934
Available online: https://www.mdpi.com/2072-4292/13/5/934

18. “Hydrocarbon Pollution Detection and Mapping Based on the Combination of Various Hyperspectral Imaging Processing Tools”
by Véronique, Achard et al.
Remote Sens. 2021, 13(5), 1020; https://doi.org/10.3390/rs1305102
Available online: https://www.mdpi.com/2072-4292/13/5/1020

19. “The openEO API–Harmonising the Use of Earth Observation Cloud Services Using Virtual Data Cube Functionalities”
by Matthias, Schramm et al.
Remote Sens. 2021, 13(6), 1125; https://doi.org/10.3390/rs13061125
Available online: https://www.mdpi.com/2072-4292/13/6/1125

20. “A Technical Study on UAV Characteristics for Precision Agriculture Applications and Associated Practical Challenges”
by Nadia, Delavarpour et al.
Remote Sens. 2021, 13(6), 1204; https://doi.org/10.3390/rs13061204
Available online: https://www.mdpi.com/2072-4292/13/6/1204

21. “Trends in Satellite Earth Observation for Permafrost Related Analyses—A Review”
by Marius, Philipp et al.
Remote Sens. 2021, 13(6), 1217; https://doi.org/10.3390/rs13061217
Available online: https://www.mdpi.com/2072-4292/13/6/1217

22. “Photogrammetry Using UAV-Mounted GNSS RTK: Georeferencing Strategies without GCPs”
by Martin, Štroner et al.
Remote Sens. 2021, 13(7), 1336; https://doi.org/10.3390/rs13071336
Available online: https://www.mdpi.com/2072-4292/13/7/1336

23. “Applications of Unmanned Aerial Systems (UASs) in Hydrology: A Review”
by Mercedes, Vélez-Nicolás et al.
Remote Sens. 2021, 13(7), 1359; https://doi.org/10.3390/rs13071359
Available online: https://www.mdpi.com/2072-4292/13/7/1359

24. “Sea Ice Thickness Estimation Based on Regression Neural Networks Using L-Band Microwave Radiometry Data from the FSSCat Mission”
by Christoph, Herbert et al.
Remote Sens. 2021, 13(7), 1366; https://doi.org/10.3390/rs13071366
Available online: https://www.mdpi.com/2072-4292/13/7/1366

25. “The Road to Operationalization of Effective Tropical Forest Monitoring Systems”
by Carlos, Portillo-Quintero et al.
Remote Sens. 2021, 13(7), 1370; https://doi.org/10.3390/rs13071370
Available online: https://www.mdpi.com/2072-4292/13/7/1370

26. “Flood Monitoring in Rural Areas of the Pearl River Basin (China) Using Sentinel-1 SAR”
by Junliang, Qiu et al.
Remote Sens. 2021, 13(7), 1384; https://doi.org/10.3390/rs13071384
Available online: https://www.mdpi.com/2072-4292/13/7/1384

27. “Responses of Summer Upwelling to Recent Climate Changes in the Taiwan Strait”
by Caiyun, Zhang
Remote Sens. 2021, 13(7), 1386; https://doi.org/10.3390/rs13071386
Available online: https://www.mdpi.com/2072-4292/13/7/1386

28. “Rice-Yield Prediction with Multi-Temporal Sentinel-2 Data and 3D CNN: A Case Study in Nepal”
by Ruben, Fernandez-Beltran,et al.
Remote Sens. 2021, 13(7), 1391; https://doi.org/10.3390/rs13071391
Available online: https://www.mdpi.com/2072-4292/13/7/1391

29. “The Potential Role of News Media to Construct a Machine Learning Based Damage Mapping Framework”
by Genki, Okada et al.
Remote Sens. 2021, 13(7), 1401; https://doi.org/10.3390/rs13071401
Available online: https://www.mdpi.com/2072-4292/13/7/1401

30. “Automated Global Shallow Water Bathymetry Mapping Using Google Earth Engine”
by Jiwei, Li et al.
Remote Sens. 2021, 13(8), 1469; https://doi.org/10.3390/rs13081469
Available online: https://www.mdpi.com/2072-4292/13/8/1469

31. “High-Resolution Mangrove Forests Classification with Machine Learning Using Worldview and UAV Hyperspectral Data”
by Yufeng, Jiang et al.
Remote Sens. 2021, 13(8), 1529; https://doi.org/10.3390/rs13081529
Available online: https://www.mdpi.com/2072-4292/13/8/1529

32. “Joint Task Offloading, Resource Allocation, and Security Assurance for Mobile Edge Computing-Enabled UAV-Assisted VANETs”
by Yixin, He et al.
Remote Sens. 2021, 13(8), 1547; https://doi.org/10.3390/rs13081547
Available online: https://www.mdpi.com/2072-4292/13/8/1547

33. “High-Resolution Aerial Detection of Marine Plastic Litter by Hyperspectral Sensing”
by Marco, Balsi et al.
Remote Sens. 2021, 13(8), 1557; https://doi.org/10.3390/rs13081557
Available online: https://www.mdpi.com/2072-4292/13/8/1557

34. “On the Geopolitics of Fire, Conflict and Land in the Kurdistan Region of Iraq”
by Lina, Eklund et al.
Remote Sens. 2021, 13(8), 1575; https://doi.org/10.3390/rs13081575
Available online: https://www.mdpi.com/2072-4292/13/8/1575

35. “Leveraging River Network Topology and Regionalization to Expand SWOT-Derived River Discharge Time Series in the Mississippi River Basin”
by Cassandra, Nickles et al.
Remote Sens. 2021, 13(8), 1590; https://doi.org/10.3390/rs13081590
Available online: https://www.mdpi.com/2072-4292/13/8/1590

36. “Assessing Forest Phenology: A Multi-Scale Comparison of Near-Surface (UAV, Spectral Reflectance Sensor, PhenoCam) and Satellite (MODIS, Sentinel-2) Remote Sensing.”
by Shangharsha, Thapa et al.
Remote Sens. 2021, 13(8), 1597; https://doi.org/10.3390/rs13081597
Available online: https://www.mdpi.com/2072-4292/13/8/1597

37. “Development of Novel Classification Algorithms for Detection of Floating Plastic Debris in Coastal Waterbodies Using Multispectral Sentinel-2 Remote Sens. Imagery”
by Bidroha, Basu et al.
Remote Sens. 2021, 13(8), 1598; https://doi.org/10.3390/rs13081598
Available online: https://www.mdpi.com/2072-4292/13/8/1598

38. “In-Season Interactions between Vine Vigor, Water Status and Wine Quality in Terrain-Based Management-Zones in a ‘Cabernet Sauvignon’ Vineyard”
by Idan, Bahat et al.
Remote Sens. 2021, 13(9), 1636; https://doi.org/10.3390/rs13091636
Available online: https://www.mdpi.com/2072-4292/13/9/1636

39. “Hyperspectral Data Simulation (Sentinel-2 to AVIRIS-NG) for Improved Wildfire Fuel Mapping, Boreal Alaska”
by Anushree, Badola et al.
Remote Sens. 2021, 13(9), 1693; https://doi.org/10.3390/rs13091693
Available online: https://www.mdpi.com/2072-4292/13/9/1693

40. “Assessing the Accuracy of ALOS/PALSAR-2 and Sentinel-1 Radar Images in Estimating the Land Subsidence of Coastal Areas: A Case Study in Alexandria City, Egypt”
by Noura, Darwish et al.
Remote Sens. 2021, 13(9), 1838; https://doi.org/10.3390/rs13091838
Available online: https://www.mdpi.com/2072-4292/13/9/1838

41. “GIS-Based Urban Flood Resilience Assessment Using Urban Flood Resilience Model: A Case Study of Peshawar City, Khyber Pakhtunkhwa, Pakistan”
by Muhammad, Tayyab et al.
Remote Sens. 2021, 13(10), 1864; https://doi.org/10.3390/rs13101864
Available online: https://www.mdpi.com/2072-4292/13/10/1864

42. “Drone-Based Hyperspectral and Thermal Imagery for Quantifying Upland Rice Productivity and Water Use Efficiency after Biochar Application”
by Hongxiao, Jin et al.
Remote Sens. 2021, 13(10), 1866; https://doi.org/10.3390/rs13101866
Available online: https://www.mdpi.com/2072-4292/13/10/1866

43. “Using Uncrewed Aerial Vehicles for Identifying the Extent of Invasive Phragmites australis in Treatment Areas Enrolled in an Adaptive Management Program”
by Colin, Brooks et al.
Remote Sens. 2021, 13(10), 1895; https://doi.org/10.3390/rs13101895
Available online: https://www.mdpi.com/2072-4292/13/10/1895

44. “Combining Satellite InSAR, Slope Units and Finite Element Modeling for Stability Analysis in Mining Waste Disposal Areas”
by Juan, López-Vinielles et al.
Remote Sens. 2021, 13(10), 2008; https://doi.org/10.3390/rs13102008
Available online: https://www.mdpi.com/2072-4292/13/10/2008

45. “A Machine Learning-Based Approach for Surface Soil Moisture Estimations with Google Earth Engine”
by Felix, Greifeneder et al.
Remote Sens. 2021, 13(11), 2099; https://doi.org/10.3390/rs13112099
Available online: https://www.mdpi.com/2072-4292/13/11/2099

46. “Digital Ecosystems for Developing Digital Twins of the Earth: The Destination Earth Case”
by Stefano, Nativi et al.
Remote Sens. 2021, 13(11), 2119; https://doi.org/10.3390/rs13112119
Available online: https://www.mdpi.com/2072-4292/13/11/2119

47. “UAVs for Vegetation Monitoring: Overview and Recent Scientific Contributions”
by Ana I. , de Castro et al.
Remote Sens. 2021, 13(11), 2139; https://doi.org/10.3390/rs13112139
Available online: https://www.mdpi.com/2072-4292/13/11/2139

48. “Evaluation of the Performances of Radar and Lidar Altimetry Missions for Water Level Retrievals in Mountainous Environment: The Case of the Swiss Lakes”
by Frédéric, Frappart et al.
Remote Sens. 2021, 13(11), 2196; https://doi.org/10.3390/rs13112196
Available online: https://www.mdpi.com/2072-4292/13/11/2196

49. “SAMIRA-SAtellite Based Monitoring Initiative for Regional Air Quality”
by Kerstin, Stebel et al.
Remote Sens. 2021, 13(11), 2219; https://doi.org/10.3390/rs13112219
Available online: https://www.mdpi.com/2072-4292/13/11/2219

50. “Tropical Forest Monitoring: Challenges and Recent Progress in Research”
by Jennifer, Murrins Misiukas et al.
Remote Sens. 2021, 13(12), 2252; https://doi.org/10.3390/rs13122252
Available online: https://www.mdpi.com/2072-4292/13/12/2252

51. “Near-Real-Time Flood Mapping Using Off-the-Shelf Models with SAR Imagery and Deep Learning”
by Vaibhav, Katiyar et al.
Remote Sens. 2021, 13(12), 2334; https://doi.org/10.3390/rs13122334
Available online: https://www.mdpi.com/2072-4292/13/12/2334

52. “Advancing Floating Macroplastic Detection from Space Using Experimental Hyperspectral Imagery”
by Paolo, Tasseron et al.
Remote Sens. 2021, 13(12), 2335; https://doi.org/10.3390/rs13122335
Available online: https://www.mdpi.com/2072-4292/13/12/2335

53. “Remote Sensing Based Yield Estimation of Rice (Oryza Sativa L.) Using Gradient Boosted Regression in India”
by Ponraj, Arumugam et al.
Remote Sens. 2021, 13(12), 2379; https://doi.org/10.3390/rs13122379
Available online: https://www.mdpi.com/2072-4292/13/12/2379

54. “Self-Attention in Reconstruction Bias U-Net for Semantic Segmentation of Building Rooftops in Optical Remote Sensing Images”
by Ziyi, Chen et al.
Remote Sens. 2021, 13(13), 2524; https://doi.org/10.3390/rs13132524
Available online: https://www.mdpi.com/2072-4292/13/13/2524

55. “Assessing Repeatability and Reproducibility of Structure-from-Motion Photogrammetry for 3D Terrain Mapping of Riverbeds”
by Jessica, De Marco et al.
Remote Sens. 2021, 13(13), 2572; https://doi.org/10.3390/rs13132572
Available online: https://www.mdpi.com/2072-4292/13/13/2572

56. “Comparison of Random Forest, Support Vector Machines, and Neural Networks for Post-Disaster Forest Species Mapping of the Krkonoše/Karkonosze Transboundary Biosphere Reserve”
by Bogdan, Zagajewski et al.
Remote Sens. 2021, 13(13), 2581; https://doi.org/10.3390/rs13132581
Available online: https://www.mdpi.com/2072-4292/13/13/2581

57. “Linking Remotely Sensed Carbon and Water Use Efficiencies with In Situ Soil Properties”
by Bassil, El Masri et al.
Remote Sens. 2021, 13(13), 2593; https://doi.org/10.3390/rs13132593
Available online: https://www.mdpi.com/2072-4292/13/13/2593

58. “A Comparison of Multi-Temporal RGB and Multispectral UAS Imagery for Tree Species Classification in Heterogeneous New Hampshire Forests”
by Heather, Grybas et al.
Remote Sens. 2021, 13(13), 2631; https://doi.org/10.3390/rs13132631
Available online: https://www.mdpi.com/2072-4292/13/13/2631

59. “Systematic Water Fraction Estimation for a Global and Daily Surface Water Time-Series”
by Stefan, Mayr et al.
Remote Sens. 2021, 13(14), 2675; https://doi.org/10.3390/rs13142675
Available online: https://www.mdpi.com/2072-4292/13/14/2675

60. “The Surface Velocity Response of a Tropical Glacier to Intra and Inter Annual Forcing, Cordillera Blanca, Peru”
by Andrew, Kos et al.
Remote Sens. 2021, 13(14), 2694; https://doi.org/10.3390/rs13142694
Available online: https://www.mdpi.com/2072-4292/13/14/2694

61. “Utilizing the Available Open-Source Remotely Sensed Data in Assessing the Wildfire Ignition and Spread Capacities of Vegetated Surfaces in Romania”
by Artan, Hysa et al.
Remote Sens. 2021, 13(14), 2737; https://doi.org/10.3390/rs13142737
Available online: https://www.mdpi.com/2072-4292/13/14/2737

62. “Estimation of Northern Hardwood Forest Inventory Attributes Using UAV Laser Scanning (ULS): Transferability of Laser Scanning Methods and Comparison of Automated Approaches at the Tree- and Stand-Level”
by Bastien, Vandendaele et al.
Remote Sens. 2021, 13(14), 2796; https://doi.org/10.3390/rs13142796
Available online: https://www.mdpi.com/2072-4292/13/14/2796

63. “Improvement of a Dasymetric Method for Implementing Sustainable Development Goal 11 Indicators at an Intra-Urban Scale”
by Mariella, Aquilino et al.
Remote Sens. 2021, 13(14), 2835; https://doi.org/10.3390/rs13142835
Available online: https://www.mdpi.com/2072-4292/13/14/2835

64. “The Key Reason of False Positive Misclassification for Accurate Large-Area Mangrove Classifications”
by Chuanpeng, Zhao et al.
Remote Sens. 2021, 13(15), 2909; https://doi.org/10.3390/rs13152909
Available online: https://www.mdpi.com/2072-4292/13/15/2909

65. “Warm Arctic Proglacial Lakes in the ASTER Surface Temperature Product”
by Adrian, Dye et al.
Remote Sens. 2021, 13(15), 2987; https://doi.org/10.3390/rs13152987
Available online: https://www.mdpi.com/2072-4292/13/15/2987

66. “Mangrove Forest Cover and Phenology with Landsat Dense Time Series in Central Queensland, Australia”
by Debbie A., Chamberlain et al.
Remote Sens. 2021, 13(15), 3032; https://doi.org/10.3390/rs13153032
Available online: https://www.mdpi.com/2072-4292/13/15/3032

67. “Mangrove Forest Cover and Phenology with Landsat Dense Time Series in Central Queensland, Australia”
by Debbie A., Chamberlain et al.
Remote Sens. 2021, 13(15), 3032; https://doi.org/10.3390/rs13153032
Available online: https://www.mdpi.com/2072-4292/13/15/3032

68. “Mangrove Forest Cover and Phenology with Landsat Dense Time Series in Central Queensland, Australia”
by Debbie A., Chamberlain et al.
Remote Sens. 2021, 13(15), 3032; https://doi.org/10.3390/rs13153032
Available online: https://www.mdpi.com/2072-4292/13/15/3032

69. “Impervious Surfaces Mapping at City Scale by Fusion of Radar and Optical Data through a Random Forest Classifier”
by Binita, Shrestha et al.
Remote Sens. 2021, 13(15), 3040; https://doi.org/10.3390/rs13153040
Available online: https://www.mdpi.com/2072-4292/13/15/3040

70. “Regional-Scale Systematic Mapping of Archaeological Mounds and Detection of Looting Using COSMO-SkyMed High Resolution DEM and Satellite Imagery”
by Deodato, Tapete et al.
Remote Sens. 2021, 13(16), 3106; https://doi.org/10.3390/rs13163106
Available online: https://www.mdpi.com/2072-4292/13/16/3106

71. “Automatic Detection of Impervious Surfaces from Remotely Sensed Data Using Deep Learning”
by Jash R., Parekh et al.
Remote Sens. 2021, 13(16), 3166; https://doi.org/10.3390/rs13163166
Available online: https://www.mdpi.com/2072-4292/13/16/3166

72. “A Novel Framework for Rapid Detection of Damaged Buildings Using Pre-Event LiDAR Data and Shadow Change Information”
by Ying, Zhang et al.
Remote Sens. 2021, 13(16), 3297; https://doi.org/10.3390/rs13163297
Available online: https://www.mdpi.com/2072-4292/13/16/3297

73. “First Estimation of Global Trends in Nocturnal Power Emissions Reveals Acceleration of Light Pollution”
by Alejandro, Sánchez de Miguel et al.
Remote Sens. 2021, 13(16), 3311; https://doi.org/10.3390/rs13163311
Available online: https://www.mdpi.com/2072-4292/13/16/3311

74. “Sentinel-2 and Landsat-8 Multi-Temporal Series to Estimate Topsoil Properties on Croplands”
by Fabio, Castaldi et al.
Remote Sens. 2021, 13(17), 3345; https://doi.org/10.3390/rs13173345
Available online: https://www.mdpi.com/2072-4292/13/17/3345

75. “Hyperspectral and Lidar Data Applied to the Urban Land Cover Machine Learning and Neural-Network-Based Classification: A Review”
by Agnieszka, Kuras et al.
Remote Sens. 2021, 13(17), 3393; https://doi.org/10.3390/rs13173393
Available online: https://www.mdpi.com/2072-4292/13/17/3393

76. “Mapping Crop Types and Cropping Systems in Nigeria with Sentinel-2 Imagery”
by Esther Shupel, Ibrahim et al.
Remote Sens. 2021, 13(17), 3523; https://doi.org/10.3390/rs13173523
Available online: https://www.mdpi.com/2072-4292/13/17/3523

77. “Continuous Monitoring of the Flooding Dynamics in the Albufera Wetland (Spain) by Landsat-8 and Sentinel-2 Datasets”
by Carmela, Cavallo et al.
Remote Sens. 2021, 13(17), 3525; https://doi.org/10.3390/rs13173525
Available online: https://www.mdpi.com/2072-4292/13/17/3525

78. “Evaluation of a Statistical Approach for Extracting Shallow Water Bathymetry Signals from ICESat-2 ATL03 Photon Data”
by Heidi, Ranndal et al.
Remote Sens. 2021, 13(17), 3548; https://doi.org/10.3390/rs13173548
Available online: https://www.mdpi.com/2072-4292/13/17/3548

79. “Hyperspectral Imaging Combined with Machine Learning for the Detection of Fusiform Rust Disease Incidence in Loblolly Pine Seedlings”
by Piyush, Pandey et al.
Remote Sens. 2021, 13(18), 3595; https://doi.org/10.3390/rs13183595
Available online: https://www.mdpi.com/2072-4292/13/18/3595

80. “Assessing the Reliability of Satellite and Reanalysis Estimates of Rainfall in Equatorial Africa”
by Sharon E., Nicholson et al.
Remote Sens. 2021, 13(18), 3609; https://doi.org/10.3390/rs13183609
Available online: https://www.mdpi.com/2072-4292/13/18/3609

81. “High-Resolution Ocean Currents from Sea Surface Temperature Observations: The Catalan Sea (Western Mediterranean)”
by Jordi, Isern-Fontanet et al.
Remote Sens. 2021, 13(18), 3635; https://doi.org/10.3390/rs13183635
Available online: https://www.mdpi.com/2072-4292/13/18/3635

82. “The Role of Satellite InSAR for Landslide Forecasting: Limitations and Openings”
by Serena, Moretto et al.
Remote Sens. 2021, 13(18), 3735; https://doi.org/10.3390/rs13183735
Available online: https://www.mdpi.com/2072-4292/13/18/3735

83. “The Potential of Multispectral Imagery and 3D Point Clouds from Unoccupied Aerial Systems (UAS) for Monitoring Forest Structure and the Impacts of Wildfire in Mediterranean-Climate Forests”
by Sean, Reilly et al.
Remote Sens. 2021, 13(19), 3810; https://doi.org/10.3390/rs13193810
Available online: https://www.mdpi.com/2072-4292/13/19/3810

84. “Wood–Leaf Classification of Tree Point Cloud Based on Intensity and Geometric Information”
by Jingqian, Sun et al.
Remote Sens. 2021, 13(20), 4050; https://doi.org/10.3390/rs13204050
Available online: https://www.mdpi.com/2072-4292/13/20/4050

85. “Estimation of Plot-Level Burn Severity Using Terrestrial Laser Scanning”
by Michael R., Gallagher et al.
Remote Sens. 2021, 13(20), 4168; https://doi.org/10.3390/rs13204168
Available online: https://www.mdpi.com/2072-4292/13/20/4168

86. “Important Airborne Lidar Metrics of Canopy Structure for Estimating Snow Interception”
by Micah, Russell et al.
Remote Sens. 2021, 13(20), 4188; https://doi.org/10.3390/rs13204188
Available online: https://www.mdpi.com/2072-4292/13/20/4188

87. “Spatiotemporal Variations in Liquid Water Content in a Seasonal Snowpack: Implications for Radar Remote Sensing”
by Randall, Bonnell et al.
Remote Sens. 2021, 13(21), 4223; https://doi.org/10.3390/rs13214223
Available online: https://www.mdpi.com/2072-4292/13/21/4223

88. “Recognition of Sedimentary Rock Occurrences in Satellite and Aerial Images of Other Worlds—Insights from Mars”
by Kenneth S., Edgett et al.
Remote Sens. 2021, 13(21), 4296; https://doi.org/10.3390/rs13214296
Available online: https://www.mdpi.com/2072-4292/13/21/4296

89. “Opposite Spatiotemporal Patterns for Surface Urban Heat Island of Two “Stove Cities” in China: Wuhan and Nanchang”
by Yao, Shen et al.
Remote Sens. 2021, 13(21), 4447; https://doi.org/10.3390/rs13214447
Available online: https://www.mdpi.com/2072-4292/13/21/4447

90. “A Dual Network for Super-Resolution and Semantic Segmentation of Sentinel-2 Imagery”
by Saüc, Abadal et al.
Remote Sens. 2021, 13(22), 4547; https://doi.org/10.3390/rs13224547
Available online: https://www.mdpi.com/2072-4292/13/22/4547

91. “Application of a Convolutional Neural Network for the Detection of Sea Ice Leads”
by Jay P., Hoffman et al.
Remote Sens. 2021, 13(22), 4571; https://doi.org/10.3390/rs13224571
Available online: https://www.mdpi.com/2072-4292/13/22/4571

92. “Compact Thermal Imager (CTI) for Atmospheric Remote Sensing”
by Dong L., Wu et al.
Remote Sens. 2021, 13(22), 4578; https://doi.org/10.3390/rs13224578
Available online: https://www.mdpi.com/2072-4292/13/22/4578

93. “Comparative Study of Groundwater-Induced Subsidence for London and Delhi Using PSInSAR”
by Vivek, Agarwal et al.
Remote Sens. 2021, 13(23), 4741; https://doi.org/10.3390/rs13234741
Available online: https://www.mdpi.com/2072-4292/13/23/4741

94. “A Self-Adaptive Method for Mapping Coastal Bathymetry On-The-Fly from Wave Field Video”
by Matthijs, Gawehn et al.
Remote Sens. 2021, 13(23), 4742; https://doi.org/10.3390/rs13234742
Available online: https://www.mdpi.com/2072-4292/13/23/4742

95. “Accuracy of Sentinel-1 PSI and SBAS InSAR Displacement Velocities against GNSS and Geodetic Leveling Monitoring Data”
by Francesca, Cigna et al.
Remote Sens. 2021, 13(23), 4800; https://doi.org/10.3390/rs13234800
Available online: https://www.mdpi.com/2072-4292/13/23/4800

96. “Improvement of the Soil Moisture Retrieval Procedure Based on the Integration of UAV Photogrammetry and Satellite Remote Sensing Information”
by Amal, Chakhar et al.
Remote Sens. 2021, 13(24), 4968; https://doi.org/10.3390/rs13244968
Available online: https://www.mdpi.com/2072-4292/13/24/4968

97. “QDC-2D: A Semi-Automatic Tool for 2D Analysis of Discontinuities for Rock Mass Characterization”
by Lidia, Loiotine et al.
Remote Sens. 2021, 13(24), 5086; https://doi.org/10.3390/rs13245086
Available online: https://www.mdpi.com/2072-4292/13/24/5086

98. “Assessment of CYGNSS Wind Speed Retrievals in Tropical Cyclones”
by Lucrezia, Ricciardulli et al.
Remote Sens. 2021, 13(24), 5110; https://doi.org/10.3390/rs13245110
Available online: https://www.mdpi.com/2072-4292/13/24/5110