Article
Multidisciplinary Sciences
Thomas Miraglio, Nicholas C. Coops, Christine I. B. Wallis, Anna L. Crofts, Margaret Kalacska, Mark Vellend, Shawn P. Serbin, Juan Pablo Arroyo-Mora, Etienne Laliberte
Summary: The advent of new spaceborne imaging spectrometers offers new opportunities for ecologists to map vegetation traits at global scales. In this paper, we present a new method that utilizes historical spaceborne imaging spectroscopy data to map vegetation traits at the landscape scale and upscale them to the continental level, demonstrating the potential of these spectrometers for plant biodiversity monitoring and conservation efforts.
SCIENTIFIC REPORTS
(2023)
Article
Optics
Jianwei Zhou, Hongxing Cai, Yu Ren, Shuang Li, Chunxu Jiang, Zhong Lv, Guannan Qu, Yong Tan, Jing Shi, Tingting Wang, Quansheng Liu
Summary: This paper proposes a broadband NIR detector imaging scheme based on nonlinear crystal frequency conversion, which utilizes silicon-based detectors for direct detection, overcoming the limitations of existing NIR detectors in terms of high cost and noise. The effectiveness of the scheme is validated through theoretical investigation and experimental measurement.
Article
Environmental Sciences
Arie Dwika Rahmandhana, Muhammad Kamal, Pramaditya Wicaksono
Summary: Mangrove mapping at the species level was conducted by classifying and grouping mangrove species based on their spectral reflectance characteristics. The mapping results were evaluated using different algorithms and found to have varying levels of accuracy. The study highlights the importance of accurately mapping mangrove species for coastal ecosystem management.
Article
Chemistry, Analytical
Lei Feng, Xiaoying He, Yacan Li, Lidong Wei, Yunfeng Nie, Juanjuan Jing, Jinsong Zhou
Summary: This article presents a compact prism imaging spectrometer method, utilizing a catadioptric curved prism to optimize the optical design process and reduce system volume. The laboratory testing results demonstrate excellent optical performance and a significant decrease in spectrometer length under the same specifications.
Article
Environmental Sciences
Jussi Juola, Aarne Hovi, Miina Rautiainen
Summary: In this study, a new close-range sensing technology - a portable, pushbroom hyperspectral camera - was tested for in situ collection of stem bark spectra in forests. The study found that changes in measurement conditions, particularly in illumination, had a significant effect on the quality of the collected data. Diffuse overcast days with clouds were found to be practical for acquiring hyperspectral images of stem bark.
REMOTE SENSING OF ENVIRONMENT
(2023)
Article
Biodiversity Conservation
Franziska Wolff, Tiina H. M. Kolari, Miguel Villoslada, Teemu Tahvanainen, Pasi Korpelainen, Pedro A. P. Zamboni, Timo Kumpula
Summary: Plant communities of mires are important for ecological processes such as carbon storage and gas fluxes. Mapping mire vegetation using UAVs can provide valuable information for ecosystem assessment. However, accurate mapping of plant communities remains challenging due to overlapping spectral signatures of plant species.
ECOLOGICAL INDICATORS
(2023)
Article
Multidisciplinary Sciences
Jingang Zhang, Runmu Su, Qiang Fu, Wenqi Ren, Felix Heide, Yunfeng Nie
Summary: Hyperspectral imaging captures abundant spatial and spectral information, but the expensive and complicated devices hinder its application in consumer electronics. Computational spectral imaging methods can reconstruct hyperspectral information from RGB images, eliminating the need for spectral camera hardware. This review investigates state-of-the-art spectral reconstruction methods and categorizes them into prior-based and data-driven methods. It identifies challenges and trends for future work, highlighting the potential of learnable methods with fine feature representation abilities.
SCIENTIFIC REPORTS
(2022)
Article
Optics
Yingli Liu, Yijie Dai, Fanqi Shen, Lin Yang, Zhanghao Ding, Zhenrong Zheng, Rengmao Wu, Liu Xu
Summary: In this paper, a novel computational imaging modality is presented, which achieves high-performance imaging using a simple non-image-forming optical system. The system utilizes an optimized non-imaging lens to transfer light radiation between an object and a detector, and recovers a high-quality image of the object using a full-path optical diffraction calculation method.
Article
Agronomy
Wenhui Zhang, Liangwei Cheng, Ruitao Xu, Xiaohua He, Weihan Mo, Jianbo Xu
Summary: Soil fertility is crucial for crop growth, and studying its spatial distribution and variation is important for agricultural management. Traditional methods for assessing soil fertility are time-consuming and costly, and don't capture the spatial variation across continuous geographic space. Digital soil mapping techniques, particularly spatial interpolation models, have been widely used in recent years. However, further research is needed on these models for regions with complex terrains and variable climates. This study compares the performances of four popular spatial interpolation models for digital soil mapping and analyzes the spatial variation and driving factors of available phosphorus in a hilly area in Gaozhou, Guangdong Province, China. The study also demonstrates the correlations between environmental variables and available phosphorus in different spatial positions, and provides insights into the influence of vegetation and topography on the spatial variations of available phosphorus.
Article
Materials Science, Multidisciplinary
Zhihan Hong, Yuanyuan Sun, Piaoran Ye, Douglas A. Loy, Rongguang Liang
Summary: A 3D printed glass lightguide array is developed to address the challenges of high spatial resolution in snapshot hyperspectral imaging. It samples the intermediate image in high spatial resolution and redistributes the pixels to achieve high spectral resolution. This technology simplifies the imaging system, reduces complexity and cost, and has demonstrated good performance with biological samples. It will catalyze the development of new hyperspectral imaging systems and enable new applications from UV to infrared.
ADVANCED OPTICAL MATERIALS
(2023)
Article
Remote Sensing
Wang Li, Wenyong Guo, Yuchu Qin, Li Wang, Zheng Niu, Jens-Christian Svenning
Summary: Tree cover, an essential indicator of forest productivity, is widely studied using remote sensing techniques. However, the spatial heterogeneity of tree cover has received less attention despite its significant impact on critical phenomena like biodiversity and forest landscape dynamics. This study mapped the spatio-temporal dynamics of global tree cover heterogeneity over a 35-year period and found that changes in tree cover do not always correspond to changes in tree cover heterogeneity, emphasizing the importance of considering local spatial heterogeneity in understanding forest dynamics.
INTERNATIONAL JOURNAL OF APPLIED EARTH OBSERVATION AND GEOINFORMATION
(2021)
Article
Chemistry, Multidisciplinary
Xinwen Liu, Lixue Shi, Lingyan Shi, Mian Wei, Zhilun Zhao, Wei Min
Summary: Understanding metabolism is crucial for deciphering physiological and pathogenic processes. In this study, the authors used mid-infrared imaging coupled with heavy water metabolic labeling to generate a metabolic atlas for mouse organs and tissues. They successfully identified inter-organ and intra-tissue metabolic signatures and revealed spatially-resolved metabolic profiles of brain tissues. This integrated platform allows for mapping the metabolic tissue atlas in complex mammalian systems.
Article
Environmental Sciences
Florian M. Hellwig, Martyna A. Stelmaszczuk-Gorska, Clemence Dubois, Marco Wolsza, Sina C. Truckenbrodt, Herbert Sagichewski, Sergej Chmara, Lutz Bannehr, Angela Lausch, Christiane Schmullius
Summary: This study investigates the early detection of spruce infestation by bark beetles using hyperspectral data, showing a new index with high accuracy and ability to detect more infested spruces. The index demonstrates great potential in the red-edge domain for distinguishing infested spruces at an early stage, with applications in forest preservation strategies.
Article
Nanoscience & Nanotechnology
Petr Bouchal, Petr Dvorak, Martin Hrton, Katarina Rovenska, Radim Chmelik, Tomas Sikola, Zdenek Bouchal
Summary: This study presents a wide-field optical imaging technique that can restore the aspect ratio and orientation of individual nanoparticles by measuring the polarization anisotropy of scattered light. This method enables the assessment and categorization of nanoparticles in various media and time-resolved imaging.
Article
Optics
Yanyu Zhao, Bowen Song, Ming Wang, Yang Zhao, Yubo Fan
Summary: This article introduces a new imaging modality called halftone spatial frequency domain imaging (halftone-SFDI), which enables high-speed, label-free, non-contact, wide-field quantification of optical properties in strongly turbid media. By mapping the spatial frequency response obtained from halftone binary patterned illumination to optical properties using model-based analysis, the method has been successfully validated in phantoms with a wide range of optical properties and in vivo human tissue.
LIGHT-SCIENCE & APPLICATIONS
(2021)
Article
Geochemistry & Geophysics
Jorge Vicent Servera, Juan Pablo Rivera-Caicedo, Jochem Verrelst, Jordi Munoz-Mari, Neus Sabater, Beatrice Berthelot, Gustau Camps-Valls, Jose Moreno
Summary: A systematic assessment of emulating MODTRAN was conducted to find that Gaussian processes regression (GPR) is the most accurate emulator, and principal component analysis remains a robust dimensionality reduction method, with around 20 components sufficient for precision.
IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING
(2022)
Article
Environmental Sciences
Matias Salinero-Delgado, Jose Estevez, Luca Pipia, Santiago Belda, Katja Berger, Vanessa Paredes Gomez, Jochem Verrelst
Summary: Monitoring crop phenology using optical satellite data is challenging due to cloud and atmospheric interference. This study proposes a processing chain using Sentinel-2 data and Google Earth Engine platform to overcome these challenges and generate spatiotemporal maps and time series of crop traits. The results show good performance in estimating canopy-level traits and the ability to reconstruct maps using GPR gap-filling time series.
Review
Environmental Sciences
Angela Lausch, Michael E. Schaepman, Andrew K. Skidmore, Eusebiu Catana, Lutz Bannehr, Olaf Bastian, Erik Borg, Jan Bumberger, Peter Dietrich, Cornelia Glaesser, Jorg M. Hacker, Rene Hoefer, Thomas Jagdhuber, Sven Jany, Andras Jung, Arnon Karnieli, Reinhard Klenke, Toralf Kirsten, Uta Koedel, Wolfgang Kresse, Ulf Mallast, Carsten Montzka, Markus Moeller, Hannes Mollenhauer, Marion Pause, Minhaz Rahman, Franziska Schrodt, Christiane Schmullius, Claudia Schuetze, Peter Selsam, Ralf-Uwe Syrbe, Sina Truckenbrodt, Michael Vohland, Martin Volk, Thilo Wellmann, Steffen Zacharias, Roland Baatz
Summary: This paper provides a comprehensive overview of using remote sensing techniques for monitoring geomorphology and introduces a new perspective for defining and recording the characteristics of geomorphodiversity using remote sensing data. The five characteristics discussed in this paper are geomorphic genesis diversity, geomorphic trait diversity, geomorphic structural diversity, geomorphic taxonomic diversity, and geomorphic functional diversity. The paper also discusses the challenges and limitations of monitoring geomorphodiversity using remote sensing and presents new approaches and methods for monitoring geomorphodiversity. The importance of the digitization process and data science in geomorphology research is emphasized.
Review
Environmental Sciences
Katja Berger, Miriam Machwitz, Marlena Kycko, Shawn C. Kefauver, Shari Van Wittenberghe, Max Gerhards, Jochem Verrelst, Clement Atzberger, Christiaan van der Tol, Alexander Damm, Uwe Rascher, Ittai Herrmann, Veronica Sobejano Paz, Sven Fahrner, Roland Pieruschka, Egor Prikaziuk, Ma. Luisa Buchaillot, Andrej Halabuk, Marco Celesti, Gerbrand Koren, Esra Tunc Gormus, Micol Rossini, Michael Foerster, Bastian Siegmann, Asmaa Abdelbaki, Giulia Tagliabue, Tobias Hank, Roshanak Darvishzadeh, Helge Aasen, Monica Garcia, Isabel Pocas, Subhajit Bandopadhyay, Mauro Sulis, Enrico Tomelleri, Offer Rozenstein, Lachezar Filchev, Gheorghe Stancile, Martin Schlerf
Summary: This study provides an overview of the main spectral technologies and retrieval approaches for detecting crop stress in agriculture. Remote sensing technology can capture different plant responses under stress through specific light interaction processes. The analysis of research papers reveals the increasing usage of satellite and unmanned aerial vehicle data and a shift towards more advanced models. However, most studies still focus on proxies calculated from single-source sensor domains, and future research should explore simultaneous analysis of multiple stress responses, integration of multi-domain models and machine learning methods, and assimilation of estimated plant traits into integrated crop growth models.
REMOTE SENSING OF ENVIRONMENT
(2022)
Article
Remote Sensing
Marina Ranghetti, Mirco Boschetti, Luigi Ranghetti, Giulia Tagliabue, Cinzia Panigada, Marco Gianinetto, Jochem Verrelst, Gabriele Candiani
Summary: The PRISMA mission, launched by the Italian Space Agency, provides new opportunities in various scientific domains, and this study combines the PROSAIL-PRO radiative transfer model and machine learning algorithms to retrieve canopy chlorophyll content and canopy nitrogen content from PRISMA data. The results are used to define relationships with plant nitrogen uptake, and the model is applied to actual PRISMA images.
EUROPEAN JOURNAL OF REMOTE SENSING
(2022)
Article
Environmental Sciences
Gabriel Caballero, Alejandro Pezzola, Cristina Winschel, Alejandra Casella, Paolo Sanchez Angonova, Juan Pablo Rivera-Caicedo, Katja Berger, Jochem Verrelst, Jesus Delegido
Summary: Earth observation provides a unique opportunity to monitor cultivated areas and assess fertilizer needs and crop water uptake. This study presents a hybrid retrieval workflow using Sentinel-2 imagery to accurately estimate vegetation traits of irrigated winter wheat. The established models were validated and applied to time series analysis, demonstrating the effectiveness of the approach in supporting agricultural management decisions.
Article
Engineering, Electrical & Electronic
Miguel Morata, Bastian Siegmann, Adrian Perez-Suay, Jose Luis Garcia-Soria, Juan Pablo Rivera-Caicedo, Jochem Verrelst
Summary: This study demonstrates the possibility of emulating a hyperspectral satellite image using statistical learning, specifically the use of neural network algorithms. The emulator was trained using a large dataset and showed superior performance compared to other algorithms. The emulated hyperspectral image was evaluated and showed potential in terms of accuracy, with an R-2 value between 0.75 and 0.9. The emulator was also able to generate a hyperspectral reflectance datacube with the texture of S2, showcasing the future possibilities of satellite imaging spectroscopy.
IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING
(2023)
Article
Environmental Sciences
Na Wang, Peiqi Yang, Jan G. P. W. Clevers, Sebastian Wieneke, Lammert Kooistra
Summary: In this study, the effects of water stress on sun-induced chlorophyll fluorescence (SIF) were investigated using an Unmanned Aerial Vehicle (UAV). The results showed that both physiological and non-physiological factors influenced SIF variations caused by water stress, but the contribution of non-physiological factors was relatively small.
REMOTE SENSING OF ENVIRONMENT
(2023)
Article
Environmental Sciences
Gabriel Caballero, Alejandro Pezzola, Cristina Winschel, Paolo Sanchez Angonova, Alejandra Casella, Luciano Orden, Matias Salinero-Delgado, Pablo Reyes-Munoz, Katja Berger, Jesus Delegido, Jochem Verrelst
Summary: Optical Earth Observation is often limited by weather conditions, but radar sensors have the potential to overcome these limitations. We propose a data fusion approach that focuses on the cross-correlation between radar and optical data streams to benefit from both domains. By analyzing multiple-output Gaussian processes models, we demonstrate the efficient fusion of Sentinel-1 Radar Vegetation Index (RVI) and Sentinel-2 vegetation water content (VWC) time series over a dry agri-environment in southern Argentina.
Article
Environmental Sciences
Soraya Bandak, Seyed Ali Reza Movahedi Naeini, Chooghi Bairam Komaki, Jochem Verrelst, Mohammad Kakooei, Mohammad Ali Mahmoodi
Summary: This study aimed to assess the potential of using optical satellite imagery for estimating soil moisture content (SMC) over cropland areas. The relationships between several spectral indices and surveyed SMC were analyzed using machine learning regression algorithms, revealing a high correlation and the sensitivity of MNDWI, NDWI, and NDSI in estimating SMC.
Article
Environmental Sciences
Sajad Khoshnood, Aynaz Lotfata, Maryam Mombeni, Alireza Daneshi, Jochem Verrelst, Khalil Ghorbani
Summary: In recent years, remote sensing technology has been used to study evapotranspiration in different land use/cover classes. The study in Lake Urmia Basin from 2016 to 2020 showed changes in evapotranspiration rates for different land types. The results indicated decreases in grassland, savanna, and wetland, while cropland, urban areas, shrubland, and water bodies experienced increases. The study also demonstrated the accuracy of using SEBS and satellite images to extract evapotranspiration and land use/cover information.
Article
Environmental Sciences
David D. Kovacs, Pablo Reyes-Munoz, Matias Salinero-Delgado, Viktor Ixion Meszaros, Katja Berger, Jochem Verrelst
Summary: Global mapping of essential vegetation traits (EVTs) using Earth-observing satellite data provides a spatially explicit way to analyze the current vegetation states and dynamics of our planet. This study presents a processing chain for the continuous production of four EVTs at a global scale, including FAPAR, LAI, FVC, and LCC. The proposed workflow utilizes hybrid retrieval models and temporal reconstruction techniques to produce cloud-free maps at 5 km spatial resolution and 10-day time intervals.
Article
Environmental Sciences
Elahe Akbari, Ali Darvishi Boloorani, Jochem Verrelst, Stefano Pignatti, Najmeh Neysani Samany, Saeid Soufizadeh, Saeid Hamzeh
Summary: This study proposes a kernel-based machine learning algorithm for spatio-temporal estimation of vegetation biophysical variables using Sentinel-2 images. The developed GPR-PSO algorithm outperformed other algorithms in terms of robustness and accuracy, and it is capable of generating pixel-based uncertainty maps for prediction purposes.
Article
Geochemistry & Geophysics
Jorge Vicent Servera, Luca Martino, Jochem Verrelst, Gustau Camps-Valls
Summary: Atmospheric radiative transfer models (RTMs) are crucial for satellite data processing. We propose a solution that uses multifidelity methods to improve the accuracy and runtime of Gaussian process (GP) emulators. The optimal multifidelity emulator achieves high accuracy and efficient atmospheric correction for hyperspectral PRISMA satellite data.
IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING
(2023)
Article
Remote Sensing
Veronika Doepper, Alby Duarte Rocha, Katja Berger, Tobias Graenzig, Jochem Verrelst, Birgit Kleinschmit, Michael Foerster
Summary: This study explores the use of unmanned aerial systems (UAS) for precise monitoring of soil moisture content (SMC) and validates the methods using large scale SMC products. The study compares data-driven and physically-based approaches and identifies challenges and limitations in different scenarios. The results show promising potential for hybrid methods in certain conditions, but also highlight the impact of complex canopy structures on the accuracy of SMC retrieval. The study concludes with the need for further research and development of robust models for high resolution SMC retrieval from UAS-borne remote sensing observations.
INTERNATIONAL JOURNAL OF APPLIED EARTH OBSERVATION AND GEOINFORMATION
(2022)
