Autorentext
Dr. Peng Fu is an Assistant Professor of Geospatial Science and lead research scientist at the Center for Advanced Agriculture and Sustainability at with Louisiana State University. Previously a research fellow at the University of Illinois at Urbana-Champaign, he received funding from the Bill & Melinda Gates Foundation for his work on phenotyping of photosynthesis to enhance crop production. Dr. Fu's research interests encompass environmental science, plant physiology, natural resources, and urbanization. He has served as chair of the early-career professional council and is currently the deputy chair for the Awards Committee at the American Society for Photogrammetry and Remote Sensing. He also contributes as an editorial board member and special issue editor for journals, including Remote Sensing of Environment. Dr. Fu received the ASPRS Presidential Citation Award in 2021 for his exceptional management of scholarship programs, reflecting his expertise and dedication to the field of geospatial science.

Klappentext

Agricultural Applications of Earth Observation details the revolutionary impact of satellite and remote sensing technologies in enhancing agricultural practices. It bridges the knowledge gap by making advanced satellite and sensor data understandable and applicable, enabling readers to select the best tools for specific agricultural needs. Real-world case studies and practical examples demonstrate the successful implementation of Earth Observation technologies in agriculture, inspiring readers with tangible results and encouraging innovative thinking. Agricultural Applications of Earth Observation enables readers to apply Earth Observation technologies to enhance agronomical sustainability and contribute to more environmentally friendly practices. With detailed methodologies and a range of additional supplementary elements, the book equips readers with the knowledge and tools to drive the future of agriculture into a more sustainable era.

Inhalt

1. Introduction to Earth Observation in Agriculture
   
   2. Retrieval of key agricultural variables using radiative transfer modeling
   3. Retrievals of parameters characterising land surface interactions from the synergy of EO data with simulation process models
   4. Monitoring evapotranspiration in agricultural fields
   5. Monitoring crop phenology with remote sensing
   6. Mapping cropland extent with Landsat image using machine learning
   7. Satellite imagery-based mapping of crop yield
   8. Learning feature representations with recurrent neural networks for the Earth Sciences
   9. Drought Monitoring based on Remote Sensing and Multiple Data Fusion
   10. Carbon sequestration potential in abandoned cropland
   11. Remote sensing of soil moisture in agroecosystems