Effective approaches to achieve food safety and security can prevent catastrophic situations.
Therefore, it is required to monitor agricultural crops on a regular basis. This can be easily …

Artificial intelligence and machine learning are ubiquitous in the domain of Earth
Observation (EO) and Remote Sensing. Congruent to their success in the domain of …

Monitoring agricultural systems becomes increasingly important in the context of global
challenges like climate change, biodiversity loss, population growth, and the rising demand …

Detailed parcel-level crop type map** for the whole European Union (EU) is necessary for
the evaluation of agricultural policies. The Copernicus program, and Sentinel-1 (S1) in …

Artificial intelligence and machine learning have been increasingly applied for prediction in
agricultural science. However, many models are typically black boxes, meaning we cannot …

Optical and radar satellite time series are synergetic: optical images contain rich spectral
information, while C-band radar captures useful geometrical information and is immune to …

Crop type information is essential for many practical applications, yet its map** is often
constrained by inherent characteristics of most farming areas, such as fragmentation and …

Crop classification plays a crucial role in ensuring food security, agricultural policy
development, and effective land management. Remote sensing data, particularly Sentinel-1 …

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

In recent years, the use of remote sensing data obtained from satellite or unmanned aerial
vehicle (UAV) imagery has grown in popularity for crop classification tasks such as yield …