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Global Potential of Rare Earth Resources and Rare Earth Demand from Clean Technologies
Rare earth elements (REE) are widely used in high technologies, medical devices, and military defense systems, and are especially indispensable in emerging clean energy. Along with the growing market of green energy in the next decades, global demand for REE will increase continuously, which will put great pressure on the current REE supply chain. The global REE production is currently mainly concentrated in China and Australia; they respectively contributed 85% and 10% in 2016. However, there are 178 deposits widely distributed in the world, and reported REE resources as of 2017 totaled 478 megaton (Mt) rare earth oxides (REO); 58% of these deposits contained exceed 0.1 Mt REO; 59 deposits have been technically assessed. These resources could sustain the global REE production at the current pace for more than a hundred years. It is noted that REE demand from clean technologies will reach 51.9 thousand metric tons (kt) REO in 2030, Nd and Dy, respectively, comprising 75% and 9%, while these two elements comprise 15% and 0.52% of the global REE resources, respectively. This indicates that Nd and Dy will strongly influence the development of exploring new REE projects and clean technologies in the next decades.
Journal Article
Smart IoT-driven precision agriculture: Land mapping, crop prediction, and irrigation system
As the world population is increasing day by day, so is the need for more advanced automated precision agriculture to meet the increasing demands for food while decreasing labor work and saving water for crops. Recently, there have been many studies done in this field, but very few discuss implementing smart technologies to present a combined sustainable farming system. In this article, we present a complete integrated design of a smart IoT-based suitable agricultural land and crop selection, along with an irrigation system using agricultural mapping, machine learning, and fuzzy logic for precision agriculture. Multi-spectral band images from Landsat-8 satellite images of a chosen land are employed from USGS Earth Resources Observation and Science (EROS) Center for extracting indices that are used for agricultural analysis, determining the vegetation index, water index, and salinity index of that land using K-means. Furthermore, crop yield is predicted using Linear Regression and Random Forest, achieving accuracies of 93.49% and 95.87%, respectively, while using RMSE (Root Mean Squared Error) as the loss function. The LSTM model is used for healthy vegetation area forecasting highlighting the changes of the vegetation area over time. Such analysis helps to decide whether that land is suitable for farming or not. Multiple soil-parameter measuring sensors are used to identify suitable crop and fertilizer requirements for that land using IoT and machine learning. The ML model-based crop prediction showed 97.35% accuracy utilizing random forest algorithm. Finally, a fuzzy logic-based solar-powered irrigation system is used to monitor the water requirements of those crops and irrigate them according to their needs. The experimental results demonstrated that fuzzy logic has faster calibration rate of 66.23% and helps to save around 61% water in comparison to average logic algorithm. The implementation of a fuzzy logic algorithm significantly optimized water usage compared to traditional manual irrigation methods. These findings highlight the effectiveness of advanced computational techniques in enhancing agricultural practices and resource management.
Journal Article
Geoinformatics : cyberinfrastructure for the solid Earth sciences
Advanced information technology infrastructure is increasingly being employed in the Earth sciences to provide researchers with efficient access to massive central databases and to integrate diversely formatted information from a variety of sources. These geoinformatics initiatives enable manipulation, modeling and visualization of data in a consistent way, and are helping to develop integrated Earth models at various scales, and from the near surface to the deep interior. This book uses a series of case studies to demonstrate computer and database use across the geosciences. Chapters are thematically grouped into sections that cover data collection and management; modeling and community computational codes; visualization and data representation; knowledge management and data integration; and web services and scientific workflows. Geoinformatics is a fascinating and accessible introduction to this emerging field for readers across the solid Earth sciences and an invaluable reference for researchers interested in initiating new cyberinfrastructure projects of their own.
Book
Cloud Top Phase Characterization of Extratropical Cyclones over the Northeast and Midwest United States: Results from IMPACTS
Cloud top phase (CTP) impacts cloud albedo and pathways for ice particle nucleation, growth, and fallout within extratropical cyclones. This study uses airborne lidar, radar, and Rapid Refresh analysis data to characterize CTP within extratropical cyclones as a function of cloud top temperature (CTT). During the 2020, 2022, and 2023 Investigation of Microphysics and Precipitation for Atlantic Coast-Threatening Snowstorms (IMPACTS) field campaign deployments, the Earth-Resources 2 (ER-2) aircraft flew 26 research flights over the Northeast and Midwest U.S. to sample the cloud tops of a variety of extratropical cyclones. A training dataset was developed to create probabilistic phase classifications based on Cloud Physics Lidar measurements of known ice and liquid clouds. These classifications were then used to quantify dominant CTP in the top 150 m of clouds sampled by the Cloud Physics Lidar in storms during IMPACTS. Case studies are presented illustrating examples of supercooled liquid water at cloud top at different CTT ranges (-3°C -20°C. Liquid-bearing cloud tops were found at CTTs as cold as -37ºC.
Journal Article
Co-engineering and participatory water management : organisational challenges for water governance
\"Effective participatory water management requires effective co-engineering - the collective process whereby organisational decisions are made on how to bring stakeholders together. This trans-disciplinary book highlights the challenges involved in the collective initiation, design, implementation and evaluation of water planning and management processes. It demonstrates how successful management requires the effective handling of two participatory processes: the stakeholder water management process and the co-engineering process required to organise this. The book provides practical methods for supporting improved participatory processes, including the application of theory and models to aid decision-making. International case studies of these applications from Australia, Europe and all over the world including Africa, are used to examine negotiations and leadership approaches, and their effects on the participatory stakeholder processes. This international review of participatory water governance forms an important resource for academic researchers in hydrology, environmental management and water policy, and also practitioners and policy-makers working in water management\"-- Provided by publisher.
Book
Validity of the Landsat Surface Reflectance Archive for Aquatic Science: Implications for Cloud-Based Analysis
Originally developed for terrestrial science and applications, the US Geological Survey Landsat surface reflectance (SR) archive spanning ~ 40 yr of observations has been increasingly utilized in large‐scale water‐quality studies. These products, however, have not been rigorously validated using in situ measured reflectance. This letter quantifies and demonstrates the quality of the SR products by harnessing a sizeable global dataset ( N = 1100). We found that the Landsat 8/9 SR in the green and red bands marginally meet the targeted accuracy requirements (30%), whereas the uncertainties in the blue and coastal‐aerosol bands ranged from 48% to 110%. We further observed > +25% biases in the visible bands of Landsat 5/7 SR, which can introduce an apparent downward trend when applied in time‐series analyses combined with Landsat 8/9. Users must exercise caution when using this archive for trend analyses, and progress in atmospheric correction is required to foster advanced applications of the Landsat archive for aquatic science.
Journal Article
Prelaunch Spectral Characterization of the Operational Land Imager-2
The Landsat-9 satellite, launched in September 2021, carries the Operational Land Imager-2 (OLI-2) as one of its payloads. This instrument is a clone of the Landsat-8 OLI and its mission is to continue the operational land imaging of the Landsat program. The OLI-2 instrument is not significantly different from OLI though the instrument-level pre-launch spectral characterization process was much improved. The focal plane modules used on OLI-2 were manufactured as spares for OLI and much of the spectral characterization of the components was performed for OLI. However, while the spectral response of the fully assembled OLI was characterized by a double monochromator system, the OLI-2 spectral characterization made use of the Goddard Laser for Absolute Measurement of Radiance (GLAMR). GLAMR is a system of tunable lasers that cover 350–2500 nm which are fiber-coupled to a 30 in integrating sphere permanently monitored by NIST-traceable radiometers. GLAMR allowed the spectral characterization of every detector of the OLI-2 focal plane in nominal imaging conditions. The spectral performance of the OLI-2 was, in general, much better than requirements. The final relative spectral responses (RSRs) represent the best characterization any Landsat instrument spectral response. This paper will cover the results of the spectral characterization from the component-level to the instrument-level of the Landsat-9 OLI-2.
Journal Article