Abstract Synthetic Aperture Radar (SAR) observations, valued for their consistent acquisition
schedule and not being affected by cloud cover and variations between day and night, have …

CONTEXT Map** crop types from satellite images is a promising application in
agricultural systems. However, it is a challenge to automate in-season crop type map** …

Accurate crop type map** is essential for crop growth monitoring and yield estimation.
Recently, various machine learning methods have been increasingly used for crop type …

Remote sensing appears as an essential approach for crop map**, yet the interpretation
of satellite imageries requires for a large amount of labeled data as ground truth information …

Land cover classification in remote sensing is often faced with the challenge of limited
ground truth labels. Incorporating historical ground information has the potential to …

Accurate crop map** is of great importance for agricultural applications, and deep
learning methods have been applied on multi-temporal remotely sensed images to classify …

Existing crop map** models, rely heavily on reference (calibration) data obtained from
remote sensing observations. However, the transferability of such models in space and time …

Identifying spatial distribution of crop planting in large-scale is one of the most significant
applications of remote sensing imagery. As an active remote sensing system, synthetic …

Abstract Space-based crop identification and acreage estimation have played a significant
role in agricultural studies in recent years, due to the development of Remote Sensing …

Field-scale crop monitoring is essential for agricultural management and policy making for
food security and sustainability. Automating crop classification process while elaborating a …