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Constructing 10-m NDVI Time Series From Landsat 8 and Sentinel 2 Images Using Convolutional Neural Networks

https://doi.org/10.1109/lgrs.2020.3003322
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What this work says

Outgoing claims: the papers this work cites, builds on, or argues with.

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An enhanced spatial and temporal adaptive reflectance fusion model for complex heterogeneous regions
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Toward near real-time monitoring of forest disturbance by fusion of MODIS and Landsat data
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Dryland vegetation phenology across an elevation gradient in Arizona, USA, investigated with fused MODIS and Landsat data
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A comparison of STARFM and an unmixing-based algorithm for Landsat and MODIS data fusion
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A flexible spatiotemporal method for fusing satellite images with different resolutions
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Characterization of Sentinel-2A and Landsat-8 top of atmosphere, surface, and nadir BRDF adjusted reflectance and NDVI differences
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An Improved Flexible Spatiotemporal DAta Fusion (IFSDAF) method for producing high spatiotemporal resolution normalized difference vegetation index time series
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Fmask 4.0: Improved cloud and cloud shadow detection in Landsats 4–8 and Sentinel-2 imagery
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Deep learning-based fusion of Landsat-8 and Sentinel-2 images for a harmonized surface reflectance product
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Harmonizing Landsat 8 and Sentinel-2: A time-series-based reflectance adjustment approach
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Mapping cropping intensity in China using time series Landsat and Sentinel-2 images and Google Earth Engine
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Generation of high spatial and temporal resolution NDVI and its application in crop biomass estimation
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Spatiotemporal Satellite Image Fusion Using Deep Convolutional Neural Networks
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On the blending of the Landsat and MODIS surface reflectance: predicting daily Landsat surface reflectance
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Spatiotemporal Reflectance Fusion via Sparse Representation
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Fusion of Landsat 8 OLI and Sentinel-2 MSI Data
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StfNet : A Two-Stream Convolutional Neural Network for Spatiotemporal Image Fusion
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Image Super-Resolution Using Deep Convolutional Networks
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High-Resolution Global Maps of 21st-Century Forest Cover Change
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Deriving High Spatiotemporal Remote Sensing Images Using Deep Convolutional Network
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“Regression-then-Fusion” or “Fusion-then-Regression”? A Theoretical Analysis for Generating High Spatiotemporal Resolution Land Surface Temperatures
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An Enhanced Deep Convolutional Model for Spatiotemporal Image Fusion
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An Object-Based Strategy for Improving the Accuracy of Spatiotemporal Satellite Imagery Fusion for Vegetation-Mapping Applications
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An Improved Image Fusion Approach Based on Enhanced Spatial and Temporal the Adaptive Reflectance Fusion Model
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Blending Landsat and MODIS Data to Generate Multispectral Indices: A Comparison of “Index-then-Blend” and “Blend-then-Index” Approaches
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An Improved Method for Producing High Spatial-Resolution NDVI Time Series Datasets with Multi-Temporal MODIS NDVI Data and Landsat TM/ETM+ Images
inferredcites →https://doi.org/10.3390/rs9111125openalex-snapshot
A Spatio-Temporal Data Fusion Model for Generating NDVI Time Series in Heterogeneous Regions

What points at this work

Incoming claims: where this work appears in other papers’ reference lists and arguments.

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Deep Learning-Based Spatiotemporal Data Fusion Using a Patch-to-Pixel Mapping Strategy and Model Comparisons
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Random forest and artificial neural network-based tsunami forests classification using data fusion of Sentinel-2 and Airbus Vision-1 satellites: A case study of Garhi Chandan, Pakistan
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Billion Tree Tsunami Forests Classification Using Image Fusion Technique and Random Forest Classifier Applied to Sentinel-2 and Landsat-8 Images: A Case Study of Garhi Chandan Pakistan
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A Study on Deep Learning-Based Predictive Modeling of Vegetation Dynamics in Kazakhstan through the Integration of CNNs, RNNs, and Satellite Imagery for Ecological Monitoring
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A Deep Learning-Based Spatio-Temporal NDVI Data Fusion Model
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