Sar Temporal Series Interpretation And Backscattering Modelling For Maize Growth Monitoring
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SAR Temporal Series Interpretation and Backscattering Modelling for Maize Growth Monitoring
The research objective is to develop a methodology for the extraction of bio- and geophysical variables from Synthetic Aperture Radars (SAR) and for their use in the perspective of maize monitoring in an operational context. SARs interest agronomists because they present some advantages for vegetation monitoring. However, the actual revisit cycle of SARs is not sufficient for crop monitoring. The image processing chain we developed overcomes this issue and meets the 4 requirements for operational crop monitoring: a high temporal resolution, a high geometric accuracy, a short processing time and the preservation of the signal content. From the literature, we know that the interactions between the signal backscattered by the vegetation and by the underlying soil are very complex. To understand these interactions, we carried out very intensive ground campaigns. The resulting data set is very rich. It covers 3 growing seasons during which 30 ERS SAR images and 13 RADARSAT SAR images were acquired and processed. In total, 612 fields, i.e. 581 maize fields and 31 sugar beet fields were located and visited. These field campaigns represent 2500 field visits and more or less 7500 measurements of 8 variables. One of the major outputs of this research comes from the analysis of the temporal behaviour of the SAR signal distribution at both field and regional levels. The SAR signal is analysed by the mean of the per-field backscattering coefficient. Previous results concerning the respective contribution of soil and crop are confirmed. The research also addresses the use of several regional indicators. We point out a drop of per-field variation coefficient averaged at regional level and we link it to the decrease of the infra-parcel variability of the soil roughness and to the progressive masking effect of the crop canopy on different sources of variability. The spatial variability of the ERS per-field backscattering coefficients is related to the variability of the sowing dates. Finally, existing and new versions of the cloud model are calibrated and validated. The cloud model is adapted to account for the data available from the field campaigns. The results show that SAR do not allow the prediction of the maize biomass at the field level but they can be used to give an indication on the crop status at a regional level.
Global Urban Monitoring and Assessment through Earth Observation
Cities and towns are the original producers of many of the global environmental problems related to waste disposal, and air and water pollution. There is a rapidly growing need for technologies that will enable monitoring of the world’s natural resources and urban assets, and managing exposure to natural and man-made risks. The Group on Earth Observation (GEO) calls for strengthening the cooperation and coordination among global observing systems and research programs. Global Urban Monitoring and Assessment through Earth Observation introduces this important international collaborative effort, reviews the current state of global urban remote sensing, and expands on future directions in the field. The book reviews the current state of global urban monitoring, assessment, modeling, and prediction through Earth observation and related technologies. It then introduces GEO’s important international collaborative effort—Global Urban Observation and Information Task—and the current state of global urban remote sensing and future directions. It explores groundbreaking work in urban remote sensing and examines how it could contribute to the development of innovative concepts and techniques for sustainable urban development. Despite significant progress in recent years, there remain substantial gaps in ongoing national, regional, and global efforts to address environmental challenges. Edited by a well-known expert in the field of remote sensing, GIS, and other geospatial technologies, this book addresses the gaps in an effective and long-term manner, highlighting the importance of increased coordination and networking among major stakeholders and of working together with other key international mechanisms. Drawing on the expertise of pioneers in the field from across the globe, the book details emerging research in the theory, methods, and techniques of urban remote sensing that provide insight into how to solve the major issues of sustainable development—one of the most important issues facing society in the future.
Computational Science and Its Applications – ICCSA 2024 Workshops
This eleven-volume set LNCS 14815 – 14825 constitutes the refereed workshop proceedings of the 24th International Conference on Computational Science and Its Applications, ICCSA 2024, held at Hanoi, Vietnam, during July 1–4, 2024. The 281 full papers, 17 short papers and 2 PHD showcase papers included in this volume were carefully reviewed and selected from a total of 450 submissions. In addition, the conference consisted of 55 workshops, focusing on very topical issues of importance to science, technology and society: from new mathematical approaches for solving complex computational systems, to information and knowledge in the Internet of Things, new statistical and optimization methods, several Artificial Intelligence approaches, sustainability issues, smart cities and related technologies.