Article
Biochemical Research Methods
Graham Alvare, Abiel Roche-Lima, Brian Fristensky
Summary: BioLegato is a programmable graphical user interface that can run any program or pipeline launched as a command. It reads tool specifications from PCD files, a simple language for specifying GUI components. Adding new tools to BioLegato is as simple as copying and modifying existing PCD files.
BMC BIOINFORMATICS
(2023)
Article
Chemistry, Analytical
Puneet Mishra
Summary: This study proposes a method based on chemometric calibration transfer and model update, which transfers point spectroscopy models to spectral cameras in high-throughput setups to enable shareable and widely applicable spectral calibrations.
ANALYTICA CHIMICA ACTA
(2021)
Article
Biochemical Research Methods
Dongliang Song, Yishen Chen, Jie Li, Haifeng Wang, Tian Ning, Shuang Wang
Summary: The integrated Raman spectral analysis software provides a user-friendly interface for preprocessing tasks and various multivariate analysis algorithms, ensuring accuracy and reliability in analyzing spectral data. The software's performance was evaluated using spectral data from two different samples, demonstrating its ability to quickly and accurately meet functional requirements.
JOURNAL OF BIOPHOTONICS
(2021)
Article
Optics
Jun Zhang, Yukun Luo, Zilong Tao, Jie You
Summary: A novel graphic-processable deep neural network (DNN) is proposed for automatically predicting and elucidating the optical chirality of two-dimensional chiral metamaterials, showing potential applications through the study of four classes of metamaterials with different material components. The DNN algorithm is found to handle metamaterial images beyond human intuition and capture the influence of parameters such as thickness and material composition on optical chirality response.
Article
Physics, Applied
Finn-Frederik Stiewe, Tristan Winkel, Yuta Sasaki, Tobias Tubandt, Tobias Kleinke, Christian Denker, Ulrike Martens, Nina Meyer, Tahereh Sadat Parvini, Shigemi Mizukami, Jakob Walowski, Markus Muenzenberg
Summary: We investigate the generation of local THz fields using spintronic THz emitters to improve the resolution for micrometer-sized imaging. By employing optical laser pulses as a pump, the THz field generation can be localized to the area of laser beam focusing. Through the use of scanning techniques and gold test patterns, we achieve sub-micrometer spatial resolution at the dimensions of the laser spot size.
APPLIED PHYSICS LETTERS
(2022)
Article
Computer Science, Interdisciplinary Applications
Tam Nguyen, Joerg Dietrich, Thanh Duc Dang, Dang An Tran, Binh Van Doan, Fanny J. Sarrazin, Karim Abbaspour, Raghavan Srinivasan
Summary: This study introduces R-SWAT, an interactive graphical user interface tool for parameter calibration, sensitivity analysis, uncertainty analysis, and visualization of the widely-used eco-hydrological model, SWAT. R-SWAT incorporates various R functions/packages for calibration, sensitivity analysis, and uncertainty analysis, and allows for easy integration of third-party packages. The tool's functionality is demonstrated through a test case study. Overall, R-SWAT has the potential to facilitate the development and testing of new sensitivity or optimization packages, and enhance the understanding of hydrological processes using open-source SWAT and R.
ENVIRONMENTAL MODELLING & SOFTWARE
(2022)
Article
Chemistry, Multidisciplinary
Cheng Guo, Jin Zhang, Wensheng Cai, Xueguang Shao
Summary: This study investigates the transferability of NIR models between different fruits using two case studies. A model for predicting strawberry soluble solids content (SSC) was established with acceptable accuracy, but directly applying this model to grape and apple spectra led to degraded performance. Spectral preprocessing improved predictions, but bias remained. Calibration transfer using SS-PFCE and PLS correction was found to effectively improve the prediction of grape and apple spectra. Therefore, calibration transfer may be a feasible way to improve the transferability of NIR models for multiple fruits.
APPLIED SCIENCES-BASEL
(2023)
Article
Instruments & Instrumentation
Kenji Ueda, Masaki Nishiura, Naoki Kenmochi, Zensho Yoshida, Kaori Nakamura
Summary: The research upgraded the CIS system by implementing an electron multiplier CCD and a CIS cell. It validated the linearity of the phase relation on the wavelength near the He II line and found that using two spectral lines of Ti and Zn lamps for calibration is reliable and sufficient.
REVIEW OF SCIENTIFIC INSTRUMENTS
(2021)
Article
Materials Science, Multidisciplinary
Tao Ma
Summary: VecMap is a python-based graphic user interface tool developed to analyze atomic displacements in perovskite ceramics. It automatically outputs displacement vector maps of A-site or B-site cations, and oxygen vector maps if O columns are visible, based on high-resolution STEM images. A specially designed Coupled HAADF-ABF function helps find atoms in ABF images with close atomic numbers. VecMap greatly simplifies the analysis of atomic displacement in perovskite structures.
MICROSCOPY AND MICROANALYSIS
(2023)
Article
Soil Science
Jonathan Sanderman, Asa Gholizadeh, Zampela Pittaki-Chrysodonta, Jingyi Huang, Jose Lucas Safanelli, Richard Ferguson
Summary: Large and publicly available soil spectral libraries are valuable resources for estimating soil properties. In this study, it was found that models developed using the USDA NSSC-KSSL MIR library could be successfully transferred to a secondary instrument with appropriate preprocessing and calibration transfer techniques.
SOIL SCIENCE SOCIETY OF AMERICA JOURNAL
(2023)
Article
Chemistry, Analytical
Bai Xue, Glenn Cloud, Sergey Vishnyakov, Zubin Mehta, Evan Ramer, Feng Jin, Meiping Song, Chein- Chang
Summary: A novel method for NIR spectroscopy spectra standardization is proposed in this paper. Most existing methods for standardization require spectral data sets from both primary and secondary instruments for validation, which limits their usage. This paper investigates the issue of spectrum data order and develops a different approach based on statistical signal processing. The developed method compensates for distortion and transfers the second order statistic from the primal spectra to the secondary spectra, allowing estimation regardless of the sample statistic order. Application-driven experiments and a comparison to PDS are conducted to demonstrate the extended usage of the method in NIR spectra standardization.
ANALYTICA CHIMICA ACTA
(2023)
Article
Chemistry, Multidisciplinary
Ashok Zachariah Samuel, Ryo Mukojima, Shumpei Horii, Masahiro Ando, Soshi Egashira, Takuji Nakashima, Masato Iwatsuki, Haruko Takeyama
Summary: Raman spectra are useful for chemical identification, but variations in signal to noise ratios and baseline fluctuations can affect the accuracy of spectral library searches. Careful consideration of small changes in baseline and noise levels is necessary in developing mathematical methods for general applications of Raman spectroscopy.
Article
Environmental Sciences
Matthew Davies, Mary B. Stuart, Matthew J. Hobbs, Andrew J. S. McGonigle, Jon R. Willmott
Summary: This article introduces a new method to improve the utility of portable hyperspectral imaging instruments by correcting spatial distortions, performing spectral calibration, and removing biases, allowing low-skilled operators to use low-cost hyperspectral imagers for applications in agriculture and environmental monitoring.
Article
Agronomy
Qinlin Xiao, Wentan Tang, Chu Zhang, Lei Zhou, Lei Feng, Jianxun Shen, Tianying Yan, Pan Gao, Yong He, Na Wu
Summary: Rapid determination of chlorophyll content is crucial for evaluating cotton's nutritional and physiological status. The use of spectral preprocessing combined with deep transfer learning can provide an effective approach to estimate chlorophyll content between different cotton varieties, offering a new possibility for evaluating the nutritional status of cotton in the field.
Article
Computer Science, Software Engineering
Jonas Lynge Vishart, Jaime Castillo-Leon, Winnie E. Svendsen
Summary: The article introduces a graphical user interface (GUI) created with Tkinter in Python for simplifying data analysis of electrochemical techniques such as cyclic voltammetry and electrochemical impedance.
Article
Environmental Sciences
Christine Y. Chang, Luis Guanter, Christian Frankenberg, Philipp Kohler, Lianhong Gu, Troy S. Magney, Katja Grossmann, Ying Sun
JOURNAL OF GEOPHYSICAL RESEARCH-BIOGEOSCIENCES
(2020)
Article
Computer Science, Interdisciplinary Applications
Luigi Ranghetti, Mirco Boschetti, Francesco Nutini, Lorenzo Busetto
COMPUTERS & GEOSCIENCES
(2020)
Article
Environmental Sciences
Niklas Bohn, Luis Guanter, Theres Kuester, Rene Preusker, Karl Segl
REMOTE SENSING OF ENVIRONMENT
(2020)
Article
Multidisciplinary Sciences
Gustau Camps-Valls, Manuel Campos-Taberner, Alvaro Moreno-Martinez, Sophia Walther, Gregory Duveiller, Alessandro Cescatti, Miguel D. Mahecha, Jordi Munoz-Mari, Francisco Javier Garcia-Haro, Luis Guanter, Martin Jung, John A. Gamon, Markus Reichstein, Steven W. Running
Summary: This study generalized commonly used vegetation indices by exploiting higher-order relations between spectral channels, resulting in increased sensitivity to vegetation physiological and biophysical parameters. The nonlinear NDVI consistently improved accuracy in monitoring key parameters, suggesting potential for more precise measurements of terrestrial carbon dynamics.
Article
Multidisciplinary Sciences
Itziar Irakulis-Loitxate, Luis Guanter, Yin-Nian Liu, Daniel J. Varon, Joannes D. Maasakkers, Yuzhong Zhang, Apisada Chulakadabba, Steven C. Wofsy, Andrew K. Thorpe, Riley M. Duren, Christian Frankenberg, David R. Lyon, Benjamin Hmiel, Daniel H. Cusworth, Yongguang Zhang, Karl Segl, Javier Gorrono, Elena Sanchez-Garcia, Melissa P. Sulprizio, Kaiqin Cao, Haijian Zhu, Jian Liang, Xun Li, Ilse Aben, Daniel J. Jacob
Summary: The study revealed a significant number of extreme point sources in the Permian basin, contributing to a large portion of methane emissions. New facilities and inefficient flaring operations were identified as major contributors. These findings question current practices and are crucial for guiding emission reduction efforts.
Article
Agronomy
Michele Meroni, Francois Waldner, Lorenzo Seguini, Herve Kerdiles, Felix Rembold
Summary: This study evaluates the performance of machine learning algorithms with small datasets to forecast crop yields on a monthly basis within the growing season. Machine learning models generally outperformed benchmark models, especially in low-yield years, but the differences in accuracy were not always practically significant. Proper model calibration and selection are crucial, especially when dealing with small datasets, in order to fully achieve superiority over simple benchmarks.
AGRICULTURAL AND FOREST METEOROLOGY
(2021)
Article
Engineering, Environmental
Itziar Irakulis-Loitxate, Javier Gorrono, Daniel Zavala-Araiza, Luis Guanter
Summary: Mitigation of methane emissions from fossil fuel extraction is effective for slowing global warming, and the use of satellite observations to detect offshore methane plumes represents a significant breakthrough in monitoring industrial methane emissions from space.
ENVIRONMENTAL SCIENCE & TECHNOLOGY LETTERS
(2022)
Article
Agronomy
Paul Naethe, Tommaso Julitta, Christine Yao-Yun Chang, Andreas Burkart, Mirco Migliavacca, Luis Guanter, Uwe Rascher
Summary: Remote sensing utilizes SIF as a proxy for photosynthesis, commonly extracting SIF from atmospheric oxygen bands with complex signal correction. However, the PLS method using solar Fraunhofer lines can provide more precise and faster retrieval of SIF values.
AGRICULTURAL AND FOREST METEOROLOGY
(2022)
Article
Engineering, Electrical & Electronic
Agnes Begue, Simon Madec, Louise Lemettais, Louise Leroux, Roberto Interdonato, Inbal Becker-Reshef, Brian Barker, Christina Justice, Herve Kerdiles, Michele Meroni
Summary: The GEOGLAM crop monitor for early warning is based on the integration of regional systems' crop condition assessments. Discrepancies between these assessments can be attributed to interpretation of vegetation and climate data, and this article argues that discrepancies related to the data must also be considered. An experiment comparing four operational crop monitoring systems in West Africa was conducted, revealing relatively low per-pixel similarity and indicating that preprocessing methods, especially the choice of reference period, are the main reasons for discrepancies. Negative alarm agreement maps can serve as a useful tool for early warning with synthesized information and confidence levels.
IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING
(2023)
Article
Multidisciplinary Sciences
Zhaoying Zhang, Alessandro Cescatti, Ying-Ping Wang, Pierre Gentine, Jingfeng Xiao, Luis Guanter, Alfredo R. Huete, Jin Wu, Jing M. Chen, Weimin Ju, Josep Penuelas, Yongguang Zhang
Summary: Photosynthesis and evapotranspiration in Amazonian forests have significant impacts on global carbon and water cycles. However, their diurnal patterns and responses to atmospheric warming and drying at regional scale are still not well understood. Using proxies from the International Space Station, we found a significant decrease in afternoon photosynthesis and evapotranspiration during the dry season, while morning photosynthesis showed a positive response to vapor pressure deficit (VPD) and afternoon photosynthesis showed a negative response. Furthermore, we projected that the decrease in afternoon photosynthesis will be compensated by an increase in the morning in future dry seasons. These findings provide new insights into the complex interplay between climate and carbon and water fluxes in Amazonian forests and improve the reliability of future projections.
Article
Meteorology & Atmospheric Sciences
Javier Gorrono, Daniel J. Varon, Itziar Irakulis-Loitxate, Luis Guanter
Summary: The use of satellite instruments to detect and quantify methane emissions from fossil fuel production activities is highly beneficial. The Sentinel-2 (S2) mission has the potential to detect large emissions globally and frequently, despite its limited spectral design. Benchmark datasets have been created to validate the S2 methane retrieval algorithms, and the results show that the detection limit ranges from 1000 to 2000 kg h-1 for homogeneous surfaces and can only detect plumes in excess of 500 kg h-1 for heterogeneous surfaces.
ATMOSPHERIC MEASUREMENT TECHNIQUES
(2023)
Review
Environmental Sciences
Daniel J. Jacob, Daniel J. Varon, Daniel H. Cusworth, Philip E. Dennison, Christian Frankenberg, Ritesh Gautam, Luis Guanter, John Kelley, Jason McKeever, Lesley E. Ott, Benjamin Poulter, Zhen Qu, Andrew K. Thorpe, John R. Worden, Riley M. Duren
Summary: This article reviews the current and planned satellite observations of atmospheric methane, discussing the methods and instruments used for quantifying emissions. The satellites are classified into area flux mappers for regional to global scale measurements and point source imagers for individual source detection. The current satellites provide valuable data for interpreting long-term methane trends and quantifying national emissions, while future satellites will enhance the resolution and capability for emissions quantification.
ATMOSPHERIC CHEMISTRY AND PHYSICS
(2022)
Article
Meteorology & Atmospheric Sciences
Elena Sanchez-Garcia, Javier Gorrono, Itziar Irakulis-Loitxate, Daniel J. Varon, Luis Guanter
Summary: This study explores the potential of using the WorldView-3 satellite for methane mapping. The results show that the satellite's high spatial resolution and spectral sampling of methane absorption feature in the shortwave infrared part of the spectrum contribute to its good performance in methane mapping. Analysis of simulated data and real data further demonstrates the potential of this satellite for detecting and locating industrial methane emissions, especially in bright and homogeneous areas.
ATMOSPHERIC MEASUREMENT TECHNIQUES
(2022)
Article
Geosciences, Multidisciplinary
Luis Guanter, Cedric Bacour, Andreas Schneider, Ilse Aben, Tim A. van Kempen, Fabienne Maignan, Christian Retscher, Philipp Kohler, Christian Frankenberg, Joanna Joiner, Yongguang Zhang
Summary: This work introduces a global SIF dataset produced from TROPOMI measurements within the TROPOSIF project funded by the European Space Agency, covering the period between May 2018 and April 2021. The high quality and dense spatial and temporal sampling of TROPOMI data promise to improve the application of global SIF datasets in small or fragmented ecosystems.
EARTH SYSTEM SCIENCE DATA
(2021)
Article
Geosciences, Multidisciplinary
Gregory Duveiller, Federico Filipponi, Sophia Walther, Philipp Kohler, Christian Frankenberg, Luis Guanter, Alessandro Cescatti
EARTH SYSTEM SCIENCE DATA
(2020)
Article
Computer Science, Interdisciplinary Applications
Yapo Abole Serge Innocent Oboue, Yunfeng Chen, Sergey Fomel, Wei Zhong, Yangkang Chen
Summary: Strong noise can disrupt the recorded seismic waves and negatively impact subsequent seismological processes. To improve the signal-to-noise ratio (S/N) of seismological data, we introduce MATamf, an open-source MATLAB code package based on an advanced median filter (AMF) that simultaneously attenuates various types of noise and improves S/N. Experimental results demonstrate the usefulness and advantages of the proposed AMF workflow in enhancing the S/N of a wide range of seismological applications.
COMPUTERS & GEOSCIENCES
(2024)
Article
Computer Science, Interdisciplinary Applications
Upkar Singh, P. N. Vinayachandran, Vijay Natarajan
Summary: The Bay of Bengal maintains its salinity distribution due to the cyclic flow of high salinity water and the mixing with freshwater. This paper introduces an advection-based feature definition and algorithms to track the movement of high salinity water, validated through comparison with observed data.
COMPUTERS & GEOSCIENCES
(2024)
Article
Computer Science, Interdisciplinary Applications
Bijal Chudasama, Nikolas Ovaskainen, Jonne Tamminen, Nicklas Nordback, Jon Engstro, Ismo Aaltonen
Summary: This contribution presents a novel U-Net convolutional neural network (CNN)-based workflow for automated mapping of bedrock fracture traces from aerial photographs acquired by unmanned aerial vehicles (UAV). The workflow includes training a U-Net CNN using a small subset of photographs with manually traced fractures, semantic segmentation of input images, pixel-wise identification of fracture traces, ridge detection algorithm and vectorization. The results show the effectiveness and accuracy of the workflow in automated mapping of bedrock fracture traces.
COMPUTERS & GEOSCIENCES
(2024)
Article
Computer Science, Interdisciplinary Applications
Ruizhen Wang, Siyang Wan, Weitao Chen, Xuwen Qin, Guo Zhang, Lizhe Wang
Summary: This paper proposes a novel framework to generate a finer soil strength map based on RCI, which uses ensemble learning models to obtain USCS soil classification and predict soil moisture, in order to improve the resolution and reliability of existing soil strength maps.
COMPUTERS & GEOSCIENCES
(2024)
Article
Computer Science, Interdisciplinary Applications
Zhanlong Chen, Xiaochuan Ma, Houpu Li, Xuwei Xu, Xiaoyi Han
Summary: Simulated terrains are important for landform and terrain research, disaster prediction, rescue and disaster relief, and national security. This study proposes a deep learning method, IGPN, that integrates global information and pattern features of the local terrain to generate accurate simulated terrains quickly.
COMPUTERS & GEOSCIENCES
(2024)
Article
Computer Science, Interdisciplinary Applications
Daniele Secci, Vanessa A. Godoy, J. Jaime Gomez-Hernandez
Summary: Neural networks excel in various machine learning applications, but lack physical interpretability and constraints, limiting their accuracy and reliability in predicting complex physical systems' behavior. Physics-Informed Neural Networks (PINNs) integrate neural networks with physical laws, providing an effective tool for solving physical problems. This article explores recent developments in PINNs, emphasizing their application in solving unconfined groundwater flow, and discusses challenges and opportunities in this field.
COMPUTERS & GEOSCIENCES
(2024)
Article
Computer Science, Interdisciplinary Applications
Renguang Zuo, Ying Xu
Summary: This study proposes a hybrid deep learning model consisting of a one-dimensional convolutional neural network (1DCNN) and a graph convolutional network (GCN) to extract joint spectrum-spatial features from geochemical survey data for mineral exploration. The physically constrained hybrid model performs better in geochemical anomaly recognition compared to other models.
COMPUTERS & GEOSCIENCES
(2024)
