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Early Crop Classification via Multi-Modal Satellite Data Fusion and Temporal Attention

https://doi.org/10.3390/rs15030799
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What this work says

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

inferredcites →https://doi.org/10.1007/s41064-018-0050-7openalex-snapshot
A Progressive Crop-Type Classification Using Multitemporal Remote Sensing Data and Phenological Information
inferredcites →https://doi.org/10.1016/j.isprsjprs.2019.01.011openalex-snapshot
DuPLO: A DUal view Point deep Learning architecture for time series classificatiOn
inferredcites →https://doi.org/10.1016/j.isprsjprs.2020.01.001openalex-snapshot
Examining earliest identifiable timing of crops using all available Sentinel 1/2 imagery and Google Earth Engine
inferredcites →https://doi.org/10.1016/j.isprsjprs.2020.06.006openalex-snapshot
Self-attention for raw optical Satellite Time Series Classification
inferredcites →https://doi.org/10.1016/j.isprsjprs.2022.03.012openalex-snapshot
Multi-modal temporal attention models for crop mapping from satellite time series
inferredcites →https://doi.org/10.1016/j.rse.2016.10.010openalex-snapshot
Assessing the robustness of Random Forests to map land cover with high resolution satellite image time series over large areas
inferredcites →https://doi.org/10.1016/j.srs.2021.100018openalex-snapshot
An evaluation of Landsat, Sentinel-2, Sentinel-1 and MODIS data for crop type mapping
inferredcites →https://doi.org/10.1080/01431161.2019.1569791openalex-snapshot
Crop type classification using a combination of optical and radar remote sensing data: a review
inferredcites →https://doi.org/10.1080/15481603.2017.1351149openalex-snapshot
Assessing the suitability of data from Sentinel-1A and 2A for crop classification
inferredcites →https://doi.org/10.1093/insilicoplants/diab017openalex-snapshot
Evolution and application of digital technologies to predict crop type and crop phenology in agriculture
inferredcites →https://doi.org/10.1109/agro-geoinformatics.2018.8476020openalex-snapshot
A Review of Crop Classification Using Satellite-Based Polarimetric SAR Imagery
inferredcites →https://doi.org/10.1109/cvpr42600.2020.01234openalex-snapshot
Satellite Image Time Series Classification With Pixel-Set Encoders and Temporal Self-Attention
inferredcites →https://doi.org/10.1109/igarss.2018.8518427openalex-snapshot
Crop-Rotation Structured Classification using Multi-Source Sentinel Images and LPIS for Crop Type Mapping
inferredcites →https://doi.org/10.1109/tgrs.1984.6499157openalex-snapshot
A summary of the history of the development of automated remote sensing for agricultural applications
inferredcites →https://doi.org/10.20944/preprints202004.0316.v2openalex-snapshot
Early Crop-Type Mapping Under Climate Anomalies
inferredcites →https://doi.org/10.21236/ada247651openalex-snapshot
Datums, Ellipsoids, Grids, and Grid Reference Systems
inferredcites →https://doi.org/10.25165/j.ijabe.20201301.5285openalex-snapshot
Integration of optical and SAR remote sensing images for crop-type mapping based on a novel object-oriented feature selection method
inferredcites →https://doi.org/10.3389/fpls.2019.01750openalex-snapshot
A CNN-RNN Framework for Crop Yield Prediction
inferredcites →https://doi.org/10.3390/agriculture11100999openalex-snapshot
Review on Multitemporal Classification Methods of Satellite Images for Crop and Arable Land Recognition
inferredcites →https://doi.org/10.3390/agronomy9090556openalex-snapshot
A Copernicus Sentinel-1 and Sentinel-2 Classification Framework for the 2020+ European Common Agricultural Policy: A Case Study in València (Spain)
inferredcites →https://doi.org/10.3390/app10103456openalex-snapshot
Systematic Mapping Study on Remote Sensing in Agriculture
inferredcites →https://doi.org/10.3390/data4030093openalex-snapshot
Towards Sentinel-1 SAR Analysis-Ready Data: A Best Practices Assessment on Preparing Backscatter Data for the Cube
inferredcites →https://doi.org/10.3390/ijgi7040129openalex-snapshot
Multi-Temporal Land Cover Classification with Sequential Recurrent Encoders
inferredcites →https://doi.org/10.3390/rs10101642openalex-snapshot
Synergistic Use of Radar Sentinel-1 and Optical Sentinel-2 Imagery for Crop Mapping: A Case Study for Belgium
inferredcites →https://doi.org/10.3390/rs11020118openalex-snapshot
In-Season Mapping of Irrigated Crops Using Landsat 8 and Sentinel-1 Time Series
inferredcites →https://doi.org/10.3390/rs11131569openalex-snapshot
Analyzing Temporal and Spatial Characteristics of Crop Parameters Using Sentinel-1 Backscatter Data
inferredcites →https://doi.org/10.3390/rs13142790openalex-snapshot
Evaluation of Five Deep Learning Models for Crop Type Mapping Using Sentinel-2 Time Series Images with Missing Information
inferredcites →https://doi.org/10.3390/rs13224668openalex-snapshot
Crop Type Mapping from Optical and Radar Time Series Using Attention-Based Deep Learning
inferredcites →https://doi.org/10.3390/rs13245036openalex-snapshot
Detecting Phenological Development of Winter Wheat and Winter Barley Using Time Series of Sentinel-1 and Sentinel-2
inferredcites →https://doi.org/10.3390/rs14102404openalex-snapshot
Cropping Patterns of Annual Crops: A Remote Sensing Review
inferredcites →https://doi.org/10.3390/rs14132981openalex-snapshot
Mapping Crop Types of Germany by Combining Temporal Statistical Metrics of Sentinel-1 and Sentinel-2 Time Series with LPIS Data
inferredcites →https://doi.org/10.3390/rs70912356openalex-snapshot
Assessment of an Operational System for Crop Type Map Production Using High Temporal and Spatial Resolution Satellite Optical Imagery
inferredcites →https://doi.org/10.3390/rs8050362openalex-snapshot
Improved Early Crop Type Identification By Joint Use of High Temporal Resolution SAR And Optical Image Time Series
inferredcites →https://doi.org/10.3390/rs8080666openalex-snapshot
Ready-to-Use Methods for the Detection of Clouds, Cirrus, Snow, Shadow, Water and Clear Sky Pixels in Sentinel-2 MSI Images
inferredcites →https://doi.org/10.3390/s19102401openalex-snapshot
Using of Multi-Source and Multi-Temporal Remote Sensing Data Improves Crop-Type Mapping in the Subtropical Agriculture Region
inferredcites →https://doi.org/10.3390/s22166106openalex-snapshot
Monitoring and Identification of Agricultural Crops through Multitemporal Analysis of Optical Images and Machine Learning Algorithms

What points at this work

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

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Manifold Structure of Multispectral-Spatial-Temporal Remote Sensing Data in Crop Type Mapping Based Temporal Feature Extractor
inferred← citeshttps://doi.org/10.2139/ssrn.4781001openalex-extracted
Manifold Structure of Multispectral-Spatial-Temporal Remote Sensing Data in Crop Type Mapping Based Temporal Feature Extractor
inferred← citeshttps://doi.org/10.1007/978-3-031-43548-5_12openalex-snapshot
Digital Yield Predictions
inferred← citeshttps://doi.org/10.1007/978-3-031-91398-3_29openalex-snapshot
An Analysis of Temporal Dropout in Earth Observation Time Series for Regression Tasks
inferred← citeshttps://doi.org/10.1007/978-981-96-8796-1_19openalex-snapshot
VegFormer: A Transformer-Based Multimodal Fusion Framework for Harvest Date Prediction and Control
inferred← citeshttps://doi.org/10.1007/978-981-97-7762-4_9openalex-snapshot
Deep Learning and Reinforcement Learning Methods for Advancing Sustainable Agricultural and Natural Resource Management
inferred← citeshttps://doi.org/10.1007/s10661-025-14420-9openalex-snapshot
Improved early-stage crop classification using a novel fusion-based machine learning approach with Sentinel-2A and Landsat 8–9 data
inferred← citeshttps://doi.org/10.1007/s11042-023-16653-7openalex-snapshot
Deep learning: crop selection based on weather conditions in Tarakeswar village of Hooghly district in West Bengal
inferred← citeshttps://doi.org/10.1016/b978-0-443-13985-7.00012-9openalex-snapshot
Net-zero emission of the agricultural power sector through land-based renewable energy
inferred← citeshttps://doi.org/10.1016/j.isprsjprs.2023.11.016openalex-snapshot
Spatio-temporal multi-level attention crop mapping method using time-series SAR imagery
inferred← citeshttps://doi.org/10.1016/j.isprsjprs.2024.08.018openalex-snapshot
Review of synthetic aperture radar with deep learning in agricultural applications
inferred← citeshttps://doi.org/10.1016/j.isprsjprs.2024.10.013openalex-snapshot
Improving crop type mapping by integrating LSTM with temporal random masking and pixel-set spatial information
inferred← citeshttps://doi.org/10.1016/j.jag.2025.104823openalex-snapshot
In the search for optimal multi-view learning models for crop classification with global remote sensing data
inferred← citeshttps://doi.org/10.1016/j.rsase.2025.101527openalex-snapshot
Advancements in crop mapping through remote sensing: A comprehensive review of concept, data sources, and procedures over four decades
inferred← citeshttps://doi.org/10.1016/j.rsase.2026.101965openalex-snapshot
Integrating Dual-pol SAR parameters and Multi-spectral vegetation indices for Wheat crop classification in fragmented land parcels
inferred← citeshttps://doi.org/10.1016/j.rse.2024.114110openalex-snapshot
Boosting crop classification by hierarchically fusing satellite, rotational, and contextual data
inferred← citeshttps://doi.org/10.1016/j.rse.2025.114950openalex-snapshot
Robust and timely within-season conterminous United States crop type mapping using Landsat Sentinel-2 time series and the transformer architecture
inferred← citeshttps://doi.org/10.1016/j.rse.2025.115208openalex-snapshot
Integrating classifier transfer and sample transfer strategies for in-season crop mapping based on sample weighting technique
inferred← citeshttps://doi.org/10.1016/j.rse.2026.115416openalex-snapshot
Advancing the earliest identifiable timing of cotton fields by the unique mechanized plastic-mulching practices in Xinjiang
inferred← citeshttps://doi.org/10.1080/01431161.2023.2205984openalex-snapshot
Crop mapping using supervised machine learning and deep learning: a systematic literature review
inferred← citeshttps://doi.org/10.1080/01431161.2025.2565837openalex-snapshot
Satellite remote sensing-based crop cover classification over Europe: accuracy of different methodological approaches
inferred← citeshttps://doi.org/10.1109/access.2024.3508271openalex-snapshot
A Comprehensive Review on Deep Learning-Based Data Fusion
inferred← citeshttps://doi.org/10.1109/access.2025.3610649openalex-snapshot
A Data-Driven Review of Remote Sensing-Based Data Fusion in Precision Agriculture From Foundational to Transformer-Based Techniques
inferred← citeshttps://doi.org/10.1109/igarss53475.2024.10642187openalex-snapshot
Early-Season Crop Classification with Planet Fusion
inferred← citeshttps://doi.org/10.1109/igarss53475.2024.10642827openalex-snapshot
Early-Season Crop Classification Utilizing Time Series Based Deep Learning with Multi-Sensor Remote Sensing Data
inferred← citeshttps://doi.org/10.1109/intercon67304.2025.11244653openalex-snapshot
Detection of Potato Crops in Advanced Vegetative Stage Using Sentinel 2 with Remote Sensing in the Peruvian Andes
inferred← citeshttps://doi.org/10.1109/jstars.2024.3361556openalex-snapshot
Common Practices and Taxonomy in Deep Multiview Fusion for Remote Sensing Applications
inferred← citeshttps://doi.org/10.1109/jstars.2024.3378348openalex-snapshot
Multimodal Colearning Meets Remote Sensing: Taxonomy, State of the Art, and Future Works
inferred← citeshttps://doi.org/10.1109/tgrs.2025.3603859openalex-snapshot
A Multimodal Federated Learning Framework for Remote Sensing Image Classification
inferred← citeshttps://doi.org/10.1515/geo-2025-0793openalex-snapshot
Assessing green area changes in Najran City, Saudi Arabia (2013–2022) using hybrid deep learning techniques
inferred← citeshttps://doi.org/10.2139/ssrn.5938033openalex-snapshot
U-PARCEL: Object-Oriented Parcel-Level Crop Classification for Fragmented Fields
inferred← citeshttps://doi.org/10.2139/ssrn.6026304openalex-snapshot
Advancing the Earliest Identifiable Timing of Cotton Fields by the Unique Mechanized Plastic-mulching Practices of Xinjiang Cotton
inferred← citeshttps://doi.org/10.23919/fusion59988.2024.10706276openalex-snapshot
Enhancing Peatland Classification using Sentinel-1 and Sentinel-2 Fusion with Encoder-Decoder Architecture
inferred← citeshttps://doi.org/10.3389/frai.2026.1803320openalex-snapshot
Mapping seasonal dynamics of forage and cereal crops in a hyper-arid environment using Sentinel-1 and Sentinel-2 time series
inferred← citeshttps://doi.org/10.3390/agriculture13050965openalex-snapshot
Deep Learning Models for the Classification of Crops in Aerial Imagery: A Review
inferred← citeshttps://doi.org/10.3390/agriculture13081633openalex-snapshot
Multi-Year Cereal Crop Classification Model in a Semi-Arid Region Using Sentinel-2 and Landsat 7–8 Data
inferred← citeshttps://doi.org/10.3390/agriculture16090920openalex-snapshot
Crop Type Mapping in an Irrigation District Using Multi-Source Remote Sensing and LSTM-Based Time Series Analysis
inferred← citeshttps://doi.org/10.3390/agronomy15092199openalex-snapshot
Maize Yield Prediction via Multi-Branch Feature Extraction and Cross-Attention Enhanced Multimodal Data Fusion
inferred← citeshttps://doi.org/10.3390/geographies3030029openalex-snapshot
Investigating the Use of Street-Level Imagery and Deep Learning to Produce In-Situ Crop Type Information
inferred← citeshttps://doi.org/10.3390/rs15030853openalex-snapshot
Investigating the Potential of Crop Discrimination in Early Growing Stage of Change Analysis in Remote Sensing Crop Profiles
inferred← citeshttps://doi.org/10.3390/rs16173115openalex-snapshot
Multi-Stage Feature Fusion of Multispectral and SAR Satellite Images for Seasonal Crop-Type Mapping at Regional Scale Using an Adapted 3D U-Net Model
inferred← citeshttps://doi.org/10.3390/rs16244620openalex-snapshot
Field-Level Classification of Winter Catch Crops Using Sentinel-2 Time Series: Model Comparison and Transferability
inferred← citeshttps://doi.org/10.3390/rs17101783openalex-snapshot
Cascade Learning Early Classification: A Novel Cascade Learning Classification Framework for Early-Season Crop Classification
inferred← citeshttps://doi.org/10.3390/rs17244011openalex-snapshot
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inferred← citeshttps://doi.org/10.3390/rs18111828openalex-snapshot
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inferred← citeshttps://doi.org/10.3390/rs18152622openalex-snapshot
MSF-Net: A Multimodal SAR–Optical Fusion Network for Agricultural Land Use Classification in Smallholder Landscapes of Northern Benin
inferred← citeshttps://doi.org/10.3390/rs18172860openalex-snapshot
CL-LGFM: Early-Season Winter Wheat Mapping by Integrating Sentinel-2 NDVI and GPM Precipitation Data—A Case Study in the Chaohu Basin, China
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