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Automatic 10 m Forest Cover Mapping in 2020 at China’s Han River Basin by Fusing ESA Sentinel-1/Sentinel-2 Land Cover and Sentinel-2 near Real-Time Forest Cover Possibility
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What this work says
Outgoing claims: the papers this work cites, builds on, or argues with.
inferredcites →https://doi.org/10.1016/0034-4257(95)00153-0openalex-snapshot
Forest classification of Southeast Asia using NOAA AVHRR data
inferredcites →https://doi.org/10.1016/j.ecoleng.2018.11.001openalex-snapshot
Forest restoration efforts drive changes in land-use/land-cover and water-related ecosystem services in China’s Han River basin
inferredcites →https://doi.org/10.1016/j.jag.2014.04.015openalex-snapshot
Forest cover classification using Landsat ETM+ data and time series MODIS NDVI data
inferredcites →https://doi.org/10.1016/j.jag.2017.07.017openalex-snapshot
Updating Landsat-based forest cover maps with MODIS images using multiscale spectral-spatial-temporal superresolution mapping
inferredcites →https://doi.org/10.1016/j.jag.2018.08.011openalex-snapshot
Combining Sentinel-1 and Sentinel-2 data for improved land use and land cover mapping of monsoon regions
inferredcites →https://doi.org/10.1016/j.rse.2003.07.010openalex-snapshot
IKONOS imagery for resource management: Tree cover, impervious surfaces, and riparian buffer analyses in the mid-Atlantic region
inferredcites →https://doi.org/10.1016/j.rse.2004.03.014openalex-snapshot
A simple method for reconstructing a high-quality NDVI time-series data set based on the Savitzky–Golay filter
inferredcites →https://doi.org/10.1016/j.rse.2007.02.014openalex-snapshot
Application of high spatial resolution satellite imagery for riparian and forest ecosystem classification
inferredcites →https://doi.org/10.1016/j.rse.2010.05.032openalex-snapshot
An enhanced spatial and temporal adaptive reflectance fusion model for complex heterogeneous regions
inferredcites →https://doi.org/10.1016/j.rse.2012.06.011openalex-snapshot
A comparative analysis of high spatial resolution IKONOS and WorldView-2 imagery for mapping urban tree species
inferredcites →https://doi.org/10.1016/j.rse.2012.08.022openalex-snapshot
A comparison of forest cover maps in Mainland Southeast Asia from multiple sources: PALSAR, MERIS, MODIS and FRA
inferredcites →https://doi.org/10.1016/j.rse.2014.04.014openalex-snapshot
New global forest/non-forest maps from ALOS PALSAR data (2007–2010)
inferredcites →https://doi.org/10.1016/j.rse.2017.03.021openalex-snapshot
Comparison of Sentinel-2 and Landsat 8 in the estimation of boreal forest canopy cover and leaf area index
inferredcites →https://doi.org/10.1016/j.rse.2017.05.011openalex-snapshot
Generating a series of fine spatial and temporal resolution land cover maps by fusing coarse spatial resolution remotely sensed images and fine spatial resolution land cover maps
inferredcites →https://doi.org/10.1016/j.rse.2018.05.010openalex-snapshot
Spatial-temporal fraction map fusion with multi-scale remotely sensed images
inferredcites →https://doi.org/10.1016/j.rse.2019.01.038openalex-snapshot
Mapping annual forest cover by fusing PALSAR/PALSAR-2 and MODIS NDVI during 2007–2016
inferredcites →https://doi.org/10.1016/j.rse.2019.111465openalex-snapshot
From woody cover to woody canopies: How Sentinel-1 and Sentinel-2 data advance the mapping of woody plants in savannas
inferredcites →https://doi.org/10.1016/j.rse.2019.111537openalex-snapshot
SFSDAF: An enhanced FSDAF that incorporates sub-pixel class fraction change information for spatio-temporal image fusion
inferredcites →https://doi.org/10.1016/j.rse.2020.112165openalex-snapshot
Mapping global forest canopy height through integration of GEDI and Landsat data
inferredcites →https://doi.org/10.1016/j.rse.2021.112470openalex-snapshot
Tracking small-scale tropical forest disturbances: Fusing the Landsat and Sentinel-2 data record
inferredcites →https://doi.org/10.1016/j.rse.2022.113043openalex-snapshot
Timeliness in forest change monitoring: A new assessment framework demonstrated using Sentinel-1 and a continuous change detection algorithm
inferredcites →https://doi.org/10.1016/s0034-4257(02)00079-2openalex-snapshot
Towards an operational MODIS continuous field of percent tree cover algorithm: examples using AVHRR and MODIS data
inferredcites →https://doi.org/10.1038/s41558-020-00976-6openalex-snapshot
Global maps of twenty-first century forest carbon fluxes
inferredcites →https://doi.org/10.1038/s41586-018-0411-9openalex-snapshot
Global land change from 1982 to 2016
inferredcites →https://doi.org/10.1038/s41597-022-01307-4openalex-snapshot
Dynamic World, Near real-time global 10 m land use land cover mapping
inferredcites →https://doi.org/10.1038/s41893-019-0336-9openalex-snapshot
Improved estimates of forest cover and loss in the Brazilian Amazon in 2000–2017
inferredcites →https://doi.org/10.1073/pnas.0912668107openalex-snapshot
Quantification of global gross forest cover loss
inferredcites →https://doi.org/10.1080/01431160903131000openalex-snapshot
Sampling designs for accuracy assessment of land cover
inferredcites →https://doi.org/10.1080/01431161.2014.930207openalex-snapshot
Estimating area and map accuracy for stratified random sampling when the strata are different from the map classes
inferredcites →https://doi.org/10.1080/01431161.2018.1479789openalex-snapshot
Assessing forest cover change in Mexico from annual MODIS VCF data (2000–2010)
inferredcites →https://doi.org/10.1080/01431161.2021.2022804openalex-snapshot
Forest cover change in China from 2000 to 2016
inferredcites →https://doi.org/10.1080/17538947.2013.786146openalex-snapshot
Global, 30-m resolution continuous fields of tree cover: Landsat-based rescaling of MODIS vegetation continuous fields with lidar-based estimates of error
inferredcites →https://doi.org/10.1080/22797254.2020.1806734openalex-snapshot
Near-real time forest change detection using PlanetScope imagery
inferredcites →https://doi.org/10.1088/1748-9326/abd0a8openalex-snapshot
Forest disturbance alerts for the Congo Basin using Sentinel-1
inferredcites →https://doi.org/10.1109/jstars.2023.3267796openalex-snapshot
Mapping Annual Global Forest Gain From 1983 to 2021 With Landsat Imagery
inferredcites →https://doi.org/10.1109/tgrs.2006.872081openalex-snapshot
On the blending of the Landsat and MODIS surface reflectance: predicting daily Landsat surface reflectance
inferredcites →https://doi.org/10.1126/sciadv.aax8574openalex-snapshot
The fate of tropical forest fragments
inferredcites →https://doi.org/10.1126/science.1244693openalex-snapshot
High-Resolution Global Maps of 21st-Century Forest Cover Change
inferredcites →https://doi.org/10.3390/f10020139openalex-snapshot
The NDVI-CV Method for Mapping Evergreen Trees in Complex Urban Areas Using Reconstructed Landsat 8 Time-Series Data
inferredcites →https://doi.org/10.3390/f10110937openalex-snapshot
Spatio–Temporal Changes of Forests in Northeast China: Insights from Landsat Images and Geospatial Analysis
inferredcites →https://doi.org/10.3390/f11121322openalex-snapshot
Forest Cover Mapping Based on a Combination of Aerial Images and Sentinel-2 Satellite Data Compared to National Forest Inventory Data
inferredcites →https://doi.org/10.3390/f12030297openalex-snapshot
Monitoring Forest Phenology in a Changing World
inferredcites →https://doi.org/10.3390/ijgi11080423openalex-snapshot
The Influence of Data Density and Integration on Forest Canopy Cover Mapping Using Sentinel-1 and Sentinel-2 Time Series in Mediterranean Oak Forests
inferredcites →https://doi.org/10.3390/rs11080961openalex-snapshot
Comparing Sentinel-2 MSI and Landsat 8 OLI Imagery for Monitoring Selective Logging in the Brazilian Amazon
inferredcites →https://doi.org/10.3390/rs12213502openalex-snapshot
Large Uncertainty on Forest Area Change in the Early 21st Century among Widely Used Global Land Cover Datasets
inferredcites →https://doi.org/10.3390/rs13122237openalex-snapshot
Forest Land Cover Mapping at a Regional Scale Using Multi-Temporal Sentinel-2 Imagery and RF Models
inferredcites →https://doi.org/10.3390/rs6098878openalex-snapshot
Annual Detection of Forest Cover Loss Using Time Series Satellite Measurements of Percent Tree Cover
inferredcites →https://doi.org/10.3390/rs70302781openalex-snapshot
A Comparison of Novel Optical Remote Sensing-Based Technologies for Forest-Cover/Change Monitoring
What points at this work
Incoming claims: where this work appears in other papers’ reference lists and arguments.
inferred← citeshttps://doi.org/10.2139/ssrn.4655449openalex-extracted
Land Cover Pixel Class Probabilities Create Customizable Layers for Forested and Urban Landscapes
inferred← citeshttps://doi.org/10.1080/15481603.2026.2702254openalex-snapshot
Mapping annual 10-meter evergreen and non-evergreen forests in China from 2017 to 2023 with multi-source remote sensing data
inferred← citeshttps://doi.org/10.1109/icct-pacific69083.2026.11519021openalex-snapshot
Synthetic Aperture Radar Imaging for Forest Cover Change Detection in Tesso Nilo National Park Using Cumulative Sums Method
inferred← citeshttps://doi.org/10.1109/jstars.2024.3417302openalex-snapshot
Monitoring Annual Forest Cover Fraction Change During 2000-2020 in China's Han River Basin Using Time-Series MODIS NDVI, VCF and Spatio-Temporal Regression
inferred← citeshttps://doi.org/10.1109/jstars.2025.3650357openalex-snapshot
Estimating Spatiotemporally Continuous GEDI Aboveground Biomass Density During 2015–2020 From Multisource Data Using Machine Learning and Deep Learning
inferred← citeshttps://doi.org/10.3390/f15101749openalex-snapshot
Estimation of Water Demand for Riparian Forest Vegetation Based on Sentinel-2 Data: A Case Study of the Kokyar River Basin
inferred← citeshttps://doi.org/10.3390/rs16162964openalex-snapshot
Assessment of Semi-Automated Techniques for Crop Mapping in Chile Based on Global Land Cover Satellite Data
inferred← citeshttps://doi.org/10.3390/rs16203855openalex-snapshot
A Framework for Subregion Ensemble Learning Mapping of Land Use/Land Cover at the Watershed Scale
inferred← citeshttps://doi.org/10.3390/rs17040722openalex-snapshot
Automatic Mapping of 10 m Tropical Evergreen Forest Cover in Central African Republic with Sentinel-2 Dynamic World Dataset
inferred← citeshttps://doi.org/10.3390/rs17071146openalex-snapshot
Spatial Distribution Pattern of Forests in Yunnan Province in 2022: Analysis Based on Multi-Source Remote Sensing Data and Machine Learning
inferred← citeshttps://doi.org/10.3390/rs17152570openalex-snapshot
Increasing the Thematic Resolution for Trees and Built Area in a Global Land Cover Dataset Using Class Probabilities
inferred← citeshttps://doi.org/10.64960/easr.2026.263460openalex-snapshot
Feature-level fusion of Sentinel-1 SAR and Sentinel-2 optical imagery for crop-type mapping in humid tropical mountainous terrain
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