Pre- and within-season crop type classification trained with archival land cover information
Published 2021 View Full Article
- Home
- Publications
- Publication Search
- Publication Details
Title
Pre- and within-season crop type classification trained with archival land cover information
Authors
Keywords
Crop, Land cover, Classification, Predictive, Real-time, Landsat, Sentinel-2, Random forest, Without training data, Cloud-based
Journal
REMOTE SENSING OF ENVIRONMENT
Volume 264, Issue -, Pages 112576
Publisher
Elsevier BV
Online
2021-07-04
DOI
10.1016/j.rse.2021.112576
References
Ask authors/readers for more resources
Related references
Note: Only part of the references are listed.- Pre-harvest classification of crop types using a Sentinel-2 time-series and machine learning
- (2020) Mmamokoma Grace Maponya et al. COMPUTERS AND ELECTRONICS IN AGRICULTURE
- Crop-Type Classification for Long-Term Modeling: An Integrated Remote Sensing and Machine Learning Approach
- (2020) Henrique G. Momm et al. Remote Sensing
- Mapping twenty years of corn and soybean across the US Midwest using the Landsat archive
- (2020) Sherrie Wang et al. Scientific Data
- Scalable pixel-based crop classification combining Sentinel-2 and Landsat-8 data time series: Case study of the Duero river basin
- (2019) Laura Piedelobo et al. AGRICULTURAL SYSTEMS
- A random forest-based framework for crop mapping using temporal, spectral, textural and polarimetric observations
- (2019) Iman Khosravi et al. INTERNATIONAL JOURNAL OF REMOTE SENSING
- Current status of Landsat program, science, and applications
- (2019) Michael A. Wulder et al. REMOTE SENSING OF ENVIRONMENT
- Early Season Mapping of Sugarcane by Applying Machine Learning Algorithms to Sentinel-1A/2 Time Series Data: A Case Study in Zhanjiang City, China
- (2019) Hao Jiang et al. Remote Sensing
- Smallholder maize area and yield mapping at national scales with Google Earth Engine
- (2019) Zhenong Jin et al. REMOTE SENSING OF ENVIRONMENT
- Large-Scale Crop Mapping Based on Machine Learning and Parallel Computation with Grids
- (2019) Ning Yang et al. Remote Sensing
- Machine Learning-Based Spectral Library for Crop Classification and Status Monitoring
- (2019) Zhang et al. Agronomy-Basel
- Using the Landsat archive to map crop cover history across the United States
- (2019) David M. Johnson REMOTE SENSING OF ENVIRONMENT
- Overall Methodology Design for the United States National Land Cover Database 2016 Products
- (2019) Suming Jin et al. Remote Sensing
- Fusion of Moderate Resolution Earth Observations for Operational Crop Type Mapping
- (2018) Nathan Torbick et al. Remote Sensing
- How much does multi-temporal Sentinel-2 data improve crop type classification?
- (2018) Francesco Vuolo et al. International Journal of Applied Earth Observation and Geoinformation
- The Harmonized Landsat and Sentinel-2 surface reflectance data set
- (2018) Martin Claverie et al. REMOTE SENSING OF ENVIRONMENT
- Prior Season Crop Type Masks for Winter Wheat Yield Forecasting: A US Case Study
- (2018) Inbal Becker-Reshef et al. Remote Sensing
- Improving the mapping of crop types in the Midwestern U.S. by fusing Landsat and MODIS satellite data
- (2017) Likai Zhu et al. International Journal of Applied Earth Observation and Geoinformation
- Measuring land-use and land-cover change using the U.S. department of agriculture’s cropland data layer: Cautions and recommendations
- (2017) Tyler J. Lark et al. International Journal of Applied Earth Observation and Geoinformation
- Early season large-area winter crop mapping using MODIS NDVI data, growing degree days information and a Gaussian mixture model
- (2017) Sergii Skakun et al. REMOTE SENSING OF ENVIRONMENT
- A multi-resolution approach to national-scale cultivated area estimation of soybean
- (2017) LeeAnn King et al. REMOTE SENSING OF ENVIRONMENT
- National-scale soybean mapping and area estimation in the United States using medium resolution satellite imagery and field survey
- (2017) Xiao-Peng Song et al. REMOTE SENSING OF ENVIRONMENT
- Google Earth Engine: Planetary-scale geospatial analysis for everyone
- (2017) Noel Gorelick et al. REMOTE SENSING OF ENVIRONMENT
- Toward mapping crop progress at field scales through fusion of Landsat and MODIS imagery
- (2017) Feng Gao et al. REMOTE SENSING OF ENVIRONMENT
- A Global Analysis of Sentinel-2A, Sentinel-2B and Landsat-8 Data Revisit Intervals and Implications for Terrestrial Monitoring
- (2017) et al. Remote Sensing
- Nominal 30-m Cropland Extent Map of Continental Africa by Integrating Pixel-Based and Object-Based Algorithms Using Sentinel-2 and Landsat-8 Data on Google Earth Engine
- (2017) Jun Xiong et al. Remote Sensing
- A comprehensive assessment of the correlations between field crop yields and commonly used MODIS products
- (2016) David M. Johnson International Journal of Applied Earth Observation and Geoinformation
- High-resolution mapping of global surface water and its long-term changes
- (2016) Jean-François Pekel et al. NATURE
- Conterminous United States crop field size quantification from multi-temporal Landsat data
- (2016) L. Yan et al. REMOTE SENSING OF ENVIRONMENT
- Preliminary analysis of the performance of the Landsat 8/OLI land surface reflectance product
- (2016) Eric Vermote et al. REMOTE SENSING OF ENVIRONMENT
- Mapping paddy rice planting area in northeastern Asia with Landsat 8 images, phenology-based algorithm and Google Earth Engine
- (2016) Jinwei Dong et al. REMOTE SENSING OF ENVIRONMENT
- Crop classification of upland fields using Random forest of time-series Landsat 7 ETM+ data
- (2015) Kenichi Tatsumi et al. COMPUTERS AND ELECTRONICS IN AGRICULTURE
- Early-season mapping of crops and cultural operations using very high spatial resolution Pléiades images
- (2015) E. Vaudour et al. International Journal of Applied Earth Observation and Geoinformation
- An analysis of cropland mask choice and ancillary data for annual corn yield forecasting using MODIS data
- (2015) Yang Shao et al. International Journal of Applied Earth Observation and Geoinformation
- Evaluation of the Landsat-5 TM and Landsat-7 ETM+ surface reflectance products
- (2015) Martin Claverie et al. REMOTE SENSING OF ENVIRONMENT
- Feature Selection of Time Series MODIS Data for Early Crop Classification Using Random Forest: A Case Study in Kansas, USA
- (2015) Pengyu Hao et al. Remote Sensing
- An Automated Method for Annual Cropland Mapping along the Season for Various Globally-Distributed Agrosystems Using High Spatial and Temporal Resolution Time Series
- (2015) Nicolas Matton et al. Remote Sensing
- Identifying representative crop rotation patterns and grassland loss in the US Western Corn Belt
- (2014) Ritvik Sahajpal et al. COMPUTERS AND ELECTRONICS IN AGRICULTURE
- Efficiency assessment of using satellite data for crop area estimation in Ukraine
- (2014) Francisco Javier Gallego et al. International Journal of Applied Earth Observation and Geoinformation
- Evidence for increased monoculture cropping in the Central United States
- (2013) James D. Plourde et al. AGRICULTURE ECOSYSTEMS & ENVIRONMENT
- Efficient corn and soybean mapping with temporal extendability: A multi-year experiment using Landsat imagery
- (2013) Liheng Zhong et al. REMOTE SENSING OF ENVIRONMENT
- High-Resolution Global Maps of 21st-Century Forest Cover Change
- (2013) M. C. Hansen et al. SCIENCE
- Changes of crop rotation in Iowa determined from the United States Department of Agriculture, National Agricultural Statistics Service cropland data layer product
- (2012) Alan J. Stern et al. Journal of Applied Remote Sensing
- Opening the archive: How free data has enabled the science and monitoring promise of Landsat
- (2012) Michael A. Wulder et al. REMOTE SENSING OF ENVIRONMENT
- Evaluation of random forest method for agricultural crop classification
- (2012) Asli Ozdarici Ok et al. European Journal of Remote Sensing
- Monitoring US agriculture: the US Department of Agriculture, National Agricultural Statistics Service, Cropland Data Layer Program
- (2011) Claire Boryan et al. Geocarto International
Publish scientific posters with Peeref
Peeref publishes scientific posters from all research disciplines. Our Diamond Open Access policy means free access to content and no publication fees for authors.
Learn MoreBecome a Peeref-certified reviewer
The Peeref Institute provides free reviewer training that teaches the core competencies of the academic peer review process.
Get Started