A Survey of Active Learning for Quantifying Vegetation Traits from Terrestrial Earth Observation Data

This study explores the use of Active Learning (AL) techniques to improve the analysis of Earth Observation (EO) data for monitoring vegetation. As satellite data streams increase, efficient methods are needed to translate this data into useful information about vegetation properties. The research focuses on hybrid retrieval approaches, which combine physical models (Radiative Transfer Models) with machine learning algorithms. AL can help by intelligently selecting the most informative data samples for training these algorithms. The paper summarizes AL theory, reviews its application in EO, and presents a case study demonstrating how AL can enhance the accuracy and speed of estimating vegetation traits like leaf pigment and water content. The findings suggest that AL-based sampling is a valuable method for optimizing regression accuracy, reducing processing time, and facilitating data analysis for vegetation monitoring.

Careers

While this paper does not directly describe a career path, the research contributes to the field of Earth Observation and vegetation monitoring. Expertise in this area is valuable for roles in environmental science, remote sensing, data analysis, and agricultural management.

• Remote Sensing Scientist
• Data Analyst
• Environmental Scientist
• Geospatial Analyst
• Agricultural Scientist