Crop Identification with Monte Carlo Simulations and Rotation Models from Sentinel-2 Data

Multitemporal Machine Learning Approach for 10-m Pixel-Based Crop Classification in the Fragmented Agricultural Fields of the Nile Delta, Egypt

Annual agricultural censuses in Egypt are often delayed, leaving crop data outdated and less reliable. This study addresses the challenge of fine-scale crop mapping in the fragmented Nile Delta, where accurate classification is vital for water resource management and food security. It uses 10-m multitemporal Sentinel-1...

Ensemble Deep Learning for the Classification of Strategic Crops Using Sentinel-2 Time Series

Reliable and up-to-date information on crop distribution is essential for ensuring food security in the face of rapid population growth and climate change, and with recent advancements in remote sensing and artificial intelligence, large-scale analyses of agricultural landscapes can now be performed more quickly and ac...

A hybrid PCA and 1D-CNN approach with regional priors for accurate agricultural monitoring: a case study with EuroCropsML

Synergistic Use of Sentinel-1, Sentinel-2, & EnMAP Data towards Advancing Agricultural Monitoring in Greece

Accurate and frequently updated land cover and crop type maps are essential for environmental monitoring, agricultural management, and informed decision-making. While multi-temporal SAR and multispectral data from various satellite sensors have been widely assessed towards this end, the potential of spaceborne hyperspe...

On-farm soybean yield estimation using earth observation data and machine learning models

Soybean ( Glycine max (L.) Merr.) is a multi-purpose crop, with yields that have increased significantly since 2000s in the United States. Given the complex mechanisms involved in vegetation growth, soybean yield estimation is determined by many factors, including weather, topographical attributes (TA), managem...

Cross-Year Crop and Land-Cover Classification with Limited Labels

This study evaluated a cross-year crop and land-cover classification framework using Sentinel-2 imagery in Jiyuan City, China to demonstrate the practical applicability of a fixed source-year semantic-segmentation model for multi-year crop mapping within the study area.