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Training sample selection for robust multi-year within-season crop classification using machine learning

https://doi.org/10.1016/j.compag.2023.107927
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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(91)90048-bopenalex-snapshot
A review of assessing the accuracy of classifications of remotely sensed data
inferredcites →https://doi.org/10.1016/j.agsy.2022.103462openalex-snapshot
Towards automation of in-season crop type mapping using spatiotemporal crop information and remote sensing data
inferredcites →https://doi.org/10.1016/j.compag.2015.05.001openalex-snapshot
Crop classification of upland fields using Random forest of time-series Landsat 7 ETM+ data
inferredcites →https://doi.org/10.1016/j.compag.2019.105164openalex-snapshot
Pre-harvest classification of crop types using a Sentinel-2 time-series and machine learning
inferredcites →https://doi.org/10.1016/j.isprsjprs.2016.03.008openalex-snapshot
Optical remotely sensed time series data for land cover classification: A review
inferredcites →https://doi.org/10.1016/j.jag.2013.12.015openalex-snapshot
Early season monitoring of corn and soybeans with TerraSAR-X and RADARSAT-2
inferredcites →https://doi.org/10.1016/j.knosys.2014.06.004openalex-snapshot
ForesTexter: An efficient random forest algorithm for imbalanced text categorization
inferredcites →https://doi.org/10.1016/j.rse.2004.06.017openalex-snapshot
Toward intelligent training of supervised image classifications: directing training data acquisition for SVM classification
inferredcites →https://doi.org/10.1016/j.rse.2006.03.004openalex-snapshot
Training set size requirements for the classification of a specific class
inferredcites →https://doi.org/10.1016/j.rse.2017.04.026openalex-snapshot
Early season large-area winter crop mapping using MODIS NDVI data, growing degree days information and a Gaussian mixture model
inferredcites →https://doi.org/10.1016/j.rse.2017.06.031openalex-snapshot
Google Earth Engine: Planetary-scale geospatial analysis for everyone
inferredcites →https://doi.org/10.1016/j.rse.2018.02.045openalex-snapshot
A high-performance and in-season classification system of field-level crop types using time-series Landsat data and a machine learning approach
inferredcites →https://doi.org/10.1016/j.rse.2018.12.026openalex-snapshot
Crop type mapping without field-level labels: Random forest transfer and unsupervised clustering techniques
inferredcites →https://doi.org/10.1016/j.rse.2020.112048openalex-snapshot
Mapping crops within the growing season across the United States
inferredcites →https://doi.org/10.1016/j.scitotenv.2020.138869openalex-snapshot
Transfer Learning for Crop classification with Cropland Data Layer data (CDL) as training samples
inferredcites →https://doi.org/10.1016/s2095-3119(19)62812-1openalex-snapshot
Early-season crop type mapping using 30-m reference time series
inferredcites →https://doi.org/10.1038/s41597-020-00646-4openalex-snapshot
Mapping twenty years of corn and soybean across the US Midwest using the Landsat archive
inferredcites →https://doi.org/10.1038/srep36240openalex-snapshot
Rapid corn and soybean mapping in US Corn Belt and neighboring areas
inferredcites →https://doi.org/10.1080/01431161.2014.885152openalex-snapshot
Assessing the impact of training sample selection on accuracy of an urban classification: a case study in Denver, Colorado
inferredcites →https://doi.org/10.1080/01431161.2016.1194545openalex-snapshot
Towards a set of agrosystem-specific cropland mapping methods to address the global cropland diversity
inferredcites →https://doi.org/10.1109/tkde.2008.239openalex-snapshot
Learning from Imbalanced Data
inferredcites →https://doi.org/10.18637/jss.v087.c03openalex-snapshot
ggtern : Ternary Diagrams Using ggplot2
inferredcites →https://doi.org/10.3390/agriculture9010017openalex-snapshot
In-Season Major Crop-Type Identification for US Cropland from Landsat Images Using Crop-Rotation Pattern and Progressive Data Classification
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/rs12111735openalex-snapshot
Assessing the Accuracy of Multiple Classification Algorithms for Crop Classification Using Landsat-8 and Sentinel-2 Data
inferredcites →https://doi.org/10.3390/rs14040997openalex-snapshot
Enhancing the Accuracy and Temporal Transferability of Irrigated Cropping Field Classification Using Optical Remote Sensing Imagery
inferredcites →https://doi.org/10.3390/rs2092274openalex-snapshot
Remote Sensing of Irrigated Agriculture: Opportunities and Challenges
inferredcites →https://doi.org/10.3390/rs70505347openalex-snapshot
Feature Selection of Time Series MODIS Data for Early Crop Classification Using Random Forest: A Case Study in Kansas, USA
inferredcites →https://doi.org/10.3390/rs70708489openalex-snapshot
On the Importance of Training Data Sample Selection in Random Forest Image Classification: A Case Study in Peatland Ecosystem Mapping
inferredcites →https://doi.org/10.3390/rs71012859openalex-snapshot
In-Season Mapping of Crop Type with Optical and X-Band SAR Data: A Classification Tree Approach Using Synoptic Seasonal Features
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/rs9111184openalex-snapshot
In-Season Crop Mapping with GF-1/WFV Data by Combining Object-Based Image Analysis and Random Forest
inferredcites →https://doi.org/10.5721/eujrs20164920openalex-snapshot
Assessing in-season crop classification performance using satellite data: a test case in Northern Italy

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.4762397openalex-extracted
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/s40003-024-00801-yopenalex-snapshot
Machine Learning Based Hybrid Approach for Estimation of Jute Area Using Optical and Microwave Satellite Imagery
inferred← citeshttps://doi.org/10.1016/j.agwat.2024.108715openalex-snapshot
Exploring key factors driving farm-level seasonal irrigation water usage with Bayesian hierarchical modelling
inferred← citeshttps://doi.org/10.1016/j.atech.2026.102272openalex-snapshot
A novel and transferable Chlorophyll and Water Content Composite Index for cotton mapping using Sentinel-2 multispectral images on Google Earth Engine
inferred← citeshttps://doi.org/10.1016/j.atech.2026.102462openalex-snapshot
Towards reliable data augmentation in machine learning: Practices to prevent data leakage
inferred← citeshttps://doi.org/10.1016/j.compag.2024.108967openalex-snapshot
An iterative and subsequent proximation method to map historical crop information with satellite images
inferred← citeshttps://doi.org/10.1016/j.compag.2025.109949openalex-snapshot
Customized crop feature construction using genetic programming for early- and in-season crop mapping
inferred← citeshttps://doi.org/10.1016/j.compag.2025.110670openalex-snapshot
Generating crop type labels from historical annual crop inventory data with an ensemble learning method
inferred← citeshttps://doi.org/10.1016/j.compag.2025.110676openalex-snapshot
Impact of sampling techniques on crop type mapping using multi-temporal composites from Harmonized Landsat-Sentinel images
inferred← citeshttps://doi.org/10.1016/j.compag.2025.110689openalex-snapshot
Crop sample prediction and early mapping based on historical data: Exploration of an explainable FKAN framework
inferred← citeshttps://doi.org/10.1016/j.compag.2026.111699openalex-snapshot
Short-term time series predictions of greenhouse gas emission from dairy houses based on diverse machine learning models
inferred← citeshttps://doi.org/10.1016/j.isprsjprs.2024.08.006openalex-snapshot
A novel soybean mapping index within the global optimal time window
inferred← citeshttps://doi.org/10.1016/j.jia.2025.07.021openalex-snapshot
Uncovering the spatiotemporal evolution and driving mechanisms of soybean planting area in China from 2000 to 2022
inferred← citeshttps://doi.org/10.1016/j.rsase.2025.101629openalex-snapshot
The role of image aggregation techniques in enhancing in-season crop classification accuracy: A multi-sensor approach
inferred← citeshttps://doi.org/10.1038/s41598-025-03351-7openalex-snapshot
A precise spatiotemporal fusion crop classification framework based on parcels
inferred← citeshttps://doi.org/10.1080/10095020.2023.2275622openalex-snapshot
Mapping 30-m cotton areas based on an automatic sample selection and machine learning method using Landsat and MODIS images
inferred← citeshttps://doi.org/10.1088/2976-601x/aea22copenalex-snapshot
Improving smallholder representation in crop maps
inferred← citeshttps://doi.org/10.1109/access.2025.3567774openalex-snapshot
A New Lightweight Hybrid Model for Pistachio Classification Using Transformers and EfficientNet
inferred← citeshttps://doi.org/10.1109/agro-geoinformatics66479.2025.11136407openalex-snapshot
WPS-Compliant End to End Interactive Platform for Road-side Field View Crop Identification
inferred← citeshttps://doi.org/10.1109/earthsense66084.2025.11297329openalex-snapshot
Sentinel-2-Based Crop and Land Pattern Classification Using Machine Learning
inferred← citeshttps://doi.org/10.1109/igarss53475.2024.10642187openalex-snapshot
Early-Season Crop Classification with Planet Fusion
inferred← citeshttps://doi.org/10.33245/2310-9262-2025-201-2-48-60openalex-snapshot
Methodological foundations of applying three-axis graphs in the analysis of agricultural production efficiency
inferred← citeshttps://doi.org/10.33245/2310-9262-2025-201-2-61-78openalex-snapshot
Organizational and economic principles of the functioning of the beef market in modern realities
inferred← citeshttps://doi.org/10.3390/agriculture15131427openalex-snapshot
UniHSFormer X for Hyperspectral Crop Classification with Prototype-Routed Semantic Structuring
inferred← citeshttps://doi.org/10.3390/app14093545openalex-snapshot
Incorporating Multi-Temporal Remote Sensing and a Pixel-Based Deep Learning Classification Algorithm to Map Multiple-Crop Cultivated Areas
inferred← citeshttps://doi.org/10.3390/geographies5040059openalex-snapshot
Strategic Ground Data Planning for Efficient Crop Classification Using Remote Sensing and Mobile-Based Survey Tools
inferred← citeshttps://doi.org/10.3390/rs16020235openalex-snapshot
Time-Series-Based Spatiotemporal Fusion Network for Improving Crop Type Mapping
inferred← citeshttps://doi.org/10.3390/rs17071274openalex-snapshot
Assessing the Impact of Mixed Pixel Proportion Training Data on SVM-Based Remote Sensing Classification: A Simulated Study
inferred← citeshttps://doi.org/10.3390/su152115348openalex-snapshot
Soil Potassium Balance in the Hilly Region of Central Sichuan, China, Based on Crop Distribution
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