A new method for pixel classification for rice variety identification using spectral and time series data from Sentinel-2 satellite imagery

Mapping Rice Paddy Based on Machine Learning with Sentinel-2 Multi-Temporal Data: Model Comparison and Transferability

(2020) Weichun Zhang et al.   Remote Sensing

Crop Type Identification and Mapping Using Machine Learning Algorithms and Sentinel-2 Time Series Data

(2019) Siwen Feng et al.   IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing

Finer Classification of Crops by Fusing UAV Images and Sentinel-2A Data

(2019) Licheng Zhao et al.   Remote Sensing

Mapping croplands, cropping patterns, and crop types using MODIS time-series data

(2018) Yaoliang Chen et al.   International Journal of Applied Earth Observation and Geoinformation

The effect of EVI time series density on crop classification accuracy

(2018) Yulin Zhan et al.   OPTIK

A high-performance and in-season classification system of field-level crop types using time-series Landsat data and a machine learning approach

(2018) Yaping Cai et al.   REMOTE SENSING OF ENVIRONMENT

Spatio-temporal fusion for daily Sentinel-2 images

(2018) Qunming Wang et al.   REMOTE SENSING OF ENVIRONMENT

Capability of Sentinel-2 data for estimating maximum evapotranspiration and irrigation requirements for tomato crop in Central Italy

(2018) Silvia Vanino et al.   REMOTE SENSING OF ENVIRONMENT

Monitoring cotton root rot by synthetic Sentinel-2 NDVI time series using improved spatial and temporal data fusion

(2018) Mingquan Wu et al.   Scientific Reports

3D Convolutional Neural Networks for Crop Classification with Multi-Temporal Remote Sensing Images

(2018) Shunping Ji et al.   Remote Sensing

Mapping Paddy Rice Using a Convolutional Neural Network (CNN) with Landsat 8 Datasets in the Dongting Lake Area, China

(2018) Meng Zhang et al.   Remote Sensing

Deep Learning Classification of Land Cover and Crop Types Using Remote Sensing Data

(2017) Nataliia Kussul et al.   IEEE Geoscience and Remote Sensing Letters

A new method for crop classification combining time series of radar images and crop phenology information

(2017) Damian Bargiel   REMOTE SENSING OF ENVIRONMENT

Understanding the temporal behavior of crops using Sentinel-1 and Sentinel-2-like data for agricultural applications

(2017) Amanda Veloso et al.   REMOTE SENSING OF ENVIRONMENT

An evaluation of time-series smoothing algorithms for land-cover classifications using MODIS-NDVI multi-temporal data

(2016) Yang Shao et al.   REMOTE SENSING OF ENVIRONMENT

An Object-Based Paddy Rice Classification Using Multi-Spectral Data and Crop Phenology in Assam, Northeast India

(2016) Mrinal Singha et al.   Remote Sensing

Comparison of support vector machine, artificial neural network, and spectral angle mapper algorithms for crop classification using LISS IV data

(2015) Pradeep Kumar et al.   INTERNATIONAL JOURNAL OF REMOTE SENSING

A scalable satellite-based crop yield mapper

(2015) David B. Lobell et al.   REMOTE SENSING OF ENVIRONMENT

How Could Agricultural Land Systems Contribute to Raise Food Production Under Global Change?

(2014) Wen-bin WU et al.   Journal of Integrative Agriculture

Global characterization and monitoring of forest cover using Landsat data: opportunities and challenges

(2012) John R. Townshend et al.   International Journal of Digital Earth

A review of large area monitoring of land cover change using Landsat data

(2012) Matthew C. Hansen et al.   REMOTE SENSING OF ENVIRONMENT