Various forms of machine learning (ML) methods have historically played a valuable role in
environmental remote sensing research. With an increasing amount of “big data” from earth …

Reliable and timely crop-yield prediction and crop map** are crucial for food security and
decision making in the food industry and in agro-environmental management. The global …

Leaf area index (LAI) is a critical vegetation structural variable and is essential in the
feedback of vegetation to the climate system. The advancement of the global Earth …

Pre-harvest prediction of a crop yield may prevent a disastrous situation and help decision-
makers to apply more reliable and accurate strategies regarding food security. Remote …

Satellite remote sensing is becoming a widely used monitoring technique in coastal
sciences. Yet, no benchmarking studies exist that compare the performance of popular …

Phenoty** plays an important role in crop science research; the accurate and rapid
acquisition of phenotypic information of plants or cells in different environments is helpful for …

Timely and non-destructive assessment of crop yield is an essential part of agricultural
remote sensing (RS). The development of unmanned aerial vehicles (UAVs) has provided a …

This paper reviews the most important information fusion data-driven algorithms based on
Machine Learning (ML) techniques for problems in Earth observation. Nowadays we …

This paper introduces the use of single-layer and deep convolutional networks for remote
sensing data analysis. Direct application to multi-and hyperspectral imagery of supervised …

Hyperspectral remote sensing technology has advanced significantly in the past two
decades. Current sensors onboard airborne and spaceborne platforms cover large areas of …