Article
Engineering, Electrical & Electronic
Zhijun Zhen, Shengbo Chen, Tiangang Yin, Jean-Philippe Gastellu-Etchegorry
Summary: This study conducted a quantitative analysis of the distribution of mixed pixels and their impact on temperature and emissivity separation method using a radiative transfer model. The results showed that the spatial resolution variation has a significant effect on the accuracy of the method when applied to urban areas.
IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING
(2022)
Article
Environmental Sciences
Qiaoli Wu, Shaoyuan Chen, Yulong Zhang, Conghe Song, Weimin Ju, Li Wang, Jie Jiang
Summary: The variability of leaf photosynthetic capacity (V-cmax) significantly affects carbon cycle estimations. This study improved the estimation of gross primary production (GPP) by introducing a spatially and temporally explicit V-cmax derived from satellite data. The results showed the need for accurate V-cmax products to reduce uncertainties in global carbon modeling.
Article
Multidisciplinary Sciences
Bernhard Lehner, Mathis L. Messager, Maartje C. Korver, Simon Linke
Summary: The LakeAT LAS dataset provides a wide range of hydro-environmental characteristics for over 1.4 million lakes and reservoirs globally, and its standardized format allows for versatile applicability in hydro-ecological assessments from regional to global scales.
Article
Geochemistry & Geophysics
Xin Ye, Huazhong Ren, Pengxin Wang, Jinshun Zhu, Jian Zhu
Summary: The land surface temperature (LST) of urban areas plays a crucial role in urban environmental monitoring, and thermal infrared remote sensing has proven to be an effective method for obtaining LST. However, the traditional thermal radiance transfer model assumes a flat land surface, which does not hold true for the complex urban landscape. This study proposes a new ultrahigh spatial resolution urban thermal radiance transfer model (UHURT) that successfully quantifies multiple scattering and adjacent effects, leading to improved LST retrieval results and reduced errors in the urban landscape.
IEEE GEOSCIENCE AND REMOTE SENSING LETTERS
(2023)
Article
Geochemistry & Geophysics
Xiaopo Zheng, Zhihao Huang, Tianxing Wang, Youying Guo, Hui Zeng, Xin Ye
Summary: This study proposed a new method for simultaneously retrieving 250-m LST and LSE from the TIR measurements of MERSI-II onboard a Chinese FengYun-3D (FY-3D) satellite. Validation results using ground measurements and cross-validation showed that the proposed method can accurately retrieve LST and LSE, which can facilitate their applications in relevant fields.
IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING
(2023)
Article
Environmental Sciences
Lina Huang, Shupeng Zhang, Guo-Yue Niu, Nan Wei, Hua Yuan, Zhongwang Wei, Xingjie Lu, Jingman Peng, Wenyao Li, Yongjiu Dai
Summary: In order to accurately represent the physical processes in hyper-resolution land surface modeling, a catchment-based spatial tessellation method is proposed. This method divides the land surface into a hierarchical structure of catchments, height bands along hillslopes within a catchment, and land cover patches within a height band. The catchment-based structure can better represent land surface heterogeneity and has higher aggregation skill through the height band representation. Testing the method at different resolutions and terrain slopes, it was found to outperform the conventional grid-based structure.
WATER RESOURCES RESEARCH
(2022)
Article
Engineering, Civil
Solmaz Fathololoumi, Mohammad Karimi Firozjaei, Asim Biswas
Summary: This study presents a machine learning based approach to improve the spatial resolution of the Soil Water Index (SWI) obtained from satellite imagery. It was found that land surface temperature has the greatest effect on the spatial distribution of SWI, and the impact of surface biophysical properties is greater than topographical and geographical properties.
JOURNAL OF HYDROLOGY
(2022)
Article
Geosciences, Multidisciplinary
Yufeng Li, Cheng Wang, Alan Wright, Hongyu Liu, Huabing Zhang, Ying Zong
Summary: The prediction and mapping of soil salinity in coastal wetlands is crucial for biodiversity protection and ecological integrity. This study successfully established soil salinity maps from 2015 to 2019 using multiple linear regression and random forest models, with the random forest model being more suitable. The distribution of soil salinity is influenced by factors such as distance from sea and vegetation type.
Article
Environmental Sciences
Xiaojie Gao, Josh M. Gray, Brian J. Reich
Summary: Utilizing a Bayesian hierarchical model to retrieve land surface phenology (LSP) from Landsat imagery has proven to be a reliable method for accurately estimating vegetation phenological changes, especially when analyzing long-term time series data.
REMOTE SENSING OF ENVIRONMENT
(2021)
Article
Geochemistry & Geophysics
Yitao Li, Hua Wu, Hong Chen, Xinming Zhu
Summary: LST products with high spatial resolution and short revisiting cycles are important for environmental studies. However, such data is not directly available due to the tradeoff between spatial and temporal resolutions of satellite observations. We propose a Robust Framework for Combining Downscaling and spatiotemporal Fusion methods (RFCDF) to generate high-resolution LST with high accuracy in different landscapes. RFCDF introduces a novel weighting strategy and can generate more accurate estimations and preserve more spatial details compared to individual or combination methods.
IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING
(2023)
Article
Engineering, Electrical & Electronic
Paulina Bartkowiak, Mariapina Castelli, Alice Crespi, Georg Niedrist, Damiano Zanotelli, Roberto Colombo, Claudia Notarnicola
Summary: In this article, a new method is presented to predict satellite-derived land surface temperature under cloudy skies over vegetated areas in the Alps. The method utilizes ground-measured temperature data and other geo-biophysical variables in conjunction with the ESRA radiation model to establish predictive models. The results show the feasibility and reliability of the method in heterogeneous ecosystems.
IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING
(2022)
Article
Environmental Studies
Hao Wang, Huimin Yan, Yunfeng Hu, Yue Xi, Yichen Yang
Summary: This study evaluated multiple land use/land cover datasets in the Indochina Peninsula and found differences in their consistency and accuracy. The findings provide a reference for data selection in land use studies in the region and reliability assessment of multi-source land use/land cover datasets in other areas.
Article
Environmental Sciences
Mady Mohamed, Abdullah Othman, Abotalib Z. Abotalib, Abdulrahman Majrashi
Summary: Contemporary cities are facing significant geoenvironmental challenges due to rapid urbanization, with governments worldwide considering green cities. The study in Makkah city reveals differences in urban heat island effects between districts with different urban fabrics, showing that organic/compact urban fabric is more effective in mitigating UHI.
Article
Multidisciplinary Sciences
Rui Wang, Wenjia Cai, Le Yu, Wei Li, Lei Zhu, Bowen Cao, Jin Li, Jianxiang Shen, Shihui Zhang, Yaoyu Nie, Can Wang
Summary: Assessing biomass resource potential is crucial for China's goals of carbon neutrality, rural revitalization, and poverty eradication. This study estimates the biomass resource potential for various types of biomass feedstock in China at a high spatial resolution of 1 km. The assessment framework developed in this study combines statistical accounting and GIS-based methods, ensuring transparency and compliance with principles of food security, land and biodiversity protection. The reliability of the dataset is verified through comparisons with existing literature.
Article
Meteorology & Atmospheric Sciences
Xin Ma, Aihui Wang
Summary: This study simulated land surface processes in China using the CLM5 model and validated the results using multiple high-quality datasets. The simulations accurately captured most of the hydrological variables and energy fluxes, with some biases in certain aspects.
JOURNAL OF HYDROMETEOROLOGY
(2022)
Letter
Multidisciplinary Sciences
Jeff C. Ho, Anna M. Michalak, Nima Pahlevan
Review
Environmental Sciences
Ghada Y. H. El Serafy, Blake A. Schaeffer, Merrie-Beth Neely, Anna Spinosa, Daniel Odermatt, Kathleen C. Weathers, Theo Baracchini, Damien Bouffard, Laurence Carvalho, Robyn N. Conmy, Liesbeth De Keukelaere, Peter D. Hunter, Cedric Jamet, Klaus D. Joehnk, John M. Johnston, Anders Knudby, Camille Minaudo, Nima Pahlevan, Ils Reusen, Kevin C. Rose, John Schalles, Maria Tzortziou
Summary: Water quality measures can be obtained from professional and volunteer monitoring programs as well as automated sensors, with the integration of these data resulting in a more holistic understanding of dynamic ecosystems and improved water resource management. Combining data from various sources to answer scientific questions is common, but methods for scaling and integrating data globally have only recently emerged.
Article
Geochemistry & Geophysics
Kiana Zolfaghari, Nima Pahlevan, Caren Binding, Daniela Gurlin, Stefan G. H. Simis, Antonio Ruiz Verdu, Lin Li, Christopher J. Crawford, Andrea VanderWoude, Reagan Errera, Arthur Zastepa, Claude R. Duguay
Summary: This study aims to evaluate the impact of spectral resolution on the determination of phycocyanin (PC), and results show that using a multilayer perceptron (MLP) neural network to estimate PC in hyperspectral data delivers significant improvements compared to retrievals from multispectral data, showing promise for developing a globally applicable cyanobacteria measurement approach.
IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING
(2022)
Article
Environmental Sciences
Ryan E. O'Shea, Nima Pahlevan, Brandon Smith, Mariano Bresciani, Todd Egerton, Claudia Giardino, Lin Li, Tim Moore, Antonio Ruiz-Verdu, Steve Ruberg, Stefan G. H. Simis, Richard Stumpf, Diana Vaiciute
Summary: The study developed a machine-learning model, MDNs, trained on a large dataset, to estimate phycocyanin concentration from hyperspectral satellite remote sensing measurements. The model demonstrated superior performance on HICO and PRISMA datasets compared to multispectral algorithms, particularly in accurately estimating low PC values.
REMOTE SENSING OF ENVIRONMENT
(2021)
Review
Environmental Sciences
Rabia Munsaf Khan, Bahram Salehi, Masoud Mahdianpari, Fariba Mohammadimanesh, Giorgos Mountrakis, Lindi J. Quackenbush
Summary: This study analyzed a large number of journal articles to explore spatiotemporal trends in harmful algal bloom (HAB) monitoring, identified research gaps and future directions, and suggested the need for standardized reporting methods and potential technological advancements and data fusion for HAB detection and monitoring.
Article
Environmental Sciences
Nima Pahlevan, Brandon Smith, Krista Alikas, Janet Anstee, Claudio Barbosa, Caren Binding, Mariano Bresciani, Bruno Cremella, Claudia Giardino, Daniela Gurlin, Virginia Fernandez, Cedric Jamet, Kersti Kangro, Moritz K. Lehmann, Hubert Loisel, Bunkei Matsushita, Nguyen Ha, Leif Olmanson, Genevieve Potvin, Stefan G. H. Simis, Andrea VanderWoude, Vincent Vantrepotte, Antonio Ruiz-Verdu
Summary: This research applies machine learning models to multi-source satellite data for the detection and monitoring of water quality indicators. By conducting comprehensive analyses and optimizations of different satellite data and water quality indicators, the accuracy and consistency of detection are improved, providing new methods and technologies for water quality monitoring.
REMOTE SENSING OF ENVIRONMENT
(2022)
Article
Environmental Sciences
Michael A. Wulder, David P. Roy, Volker C. Radeloff, Thomas R. Loveland, Martha C. Anderson, David M. Johnson, Sean Healey, Zhe Zhu, Theodore A. Scambos, Nima Pahlevan, Matthew Hansen, Noel Gorelick, Christopher J. Crawford, Jeffrey G. Masek, Txomin Hermosilla, Joanne C. White, Alan S. Belward, Crystal Schaaf, Curtis E. Woodcock, Justin L. Huntington, Leo Lymburner, Patrick Hostert, Feng Gao, Alexei Lyapustin, Jean-Francois Pekel, Peter Strobl, Bruce D. Cook
Summary: Since 1972, the Landsat program has provided 50 years of digital, multispectral, medium spatial resolution observations, playing a crucial role in scientific and technical advancements. The program's early years brought technological breakthroughs and established a template for global Earth observation missions. The knowledge gained from Landsat has been recognized for its economic and scientific value, leading to continuous improvement and increased usage through the introduction of free and open access to data.
REMOTE SENSING OF ENVIRONMENT
(2022)
Article
Environmental Sciences
Guoqing Lin, Robert E. Wolfe, Ping Zhang, John J. Dellomo, Bin Tan
Summary: This paper summarizes the on-orbit geolocation calibration and validation (Cal/Val) activities for the VIIRS sensors aboard the SNPP and NOAA-20 satellites in the past 10 years. It includes nominal geolocation Cal/Val activities, risk reduction activities, and improvements for the on-orbit VIIRS sensor operations. The activities have achieved sub-pixel geolocation accuracy and improved the overall performance of the sensors.
Article
Geochemistry & Geophysics
Arun M. Saranathan, Brandon Smith, Nima Pahlevan
Summary: This study validates the use of an uncertainty metric calculated directly from the chlorophyll-a (Chla) estimates of mixture density networks (MDNs) for water-quality indicators. The suggested uncertainty metric accurately captures the reduced confidence associated with test data drawn from a different distribution than the training data, which could be caused by random noise, uncertainties in atmospheric correction, and novel data.
IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING
(2023)
Article
Environmental Sciences
Kiana Zolfaghari, Nima Pahlevan, Stefan G. H. Simis, Ryan E. O'Shea, Claude R. Duguay
Summary: In this study, the accuracy of remote sensing reflectance (Rrs) derived from three different atmospheric correction (AC) algorithms was evaluated, and the effects of Rrs uncertainties on pigment concentration estimations were investigated. The results showed that iCOR and ACOLITE performed closely, while POLYMER had poorer performance. It was concluded that iCOR combined with MDNs produced adequate pigment products for studying and monitoring Lake Erie's chlorophyll-a concentration.
JOURNAL OF GREAT LAKES RESEARCH
(2023)
Article
Environmental Sciences
J. M. Barreneche, B. Guigou, F. Gallego, A. Barbieri, B. Smith, M. Fernandez, V. Fernandez, N. Pahlevan
Summary: Uruguay's freshwater network is threatened by Harmful Algal Blooms (HABs) triggered by human activities. The existing field-based monitoring practices are limited in scope and coverage. This study explores remote sensing techniques to estimate water quality parameters and identify the best combination of algorithms and sensors for accurate assessment. The results show that the Mixture Density Networks (MDN) algorithm is among the top performers, along with other algorithms such as Gons, Moses, and Normalized Difference Chla Index. The study also highlights the importance of atmospheric correction methods and the MDN model for reporting national Sustainable Development Goal (SDG) 6.3.2 and other monitoring applications.
REMOTE SENSING APPLICATIONS-SOCIETY AND ENVIRONMENT
(2023)
Article
Environmental Sciences
Andrea Pellegrino, Alice Fabbretto, Mariano Bresciani, Thainara Munhoz Alexandre de Lima, Federica Braga, Nima Pahlevan, Vittorio Ernesto Brando, Susanne Kratzer, Marco Gianinetto, Claudia Giardino
Summary: This study aims to evaluate the accuracy of PRISMA's standard Level 2d (L2d) products for water bodies in the visible and near-infrared spectral regions. An analytical comparison with in situ water reflectance data from AERONET-OC was performed, and the results showed significant levels of uncertainty in the L2d reflectance products, especially in oligotrophic waters. It suggests the need to develop and test water-specific atmospheric correction algorithms to fully exploit PRISMA data.
Letter
Limnology
Daniel Andrade Maciel, Nima Pahlevan, Claudio Clemente Faria Barbosa, Evlyn Marcia Leao de Moraes de Novo, Rejane Souza Paulino, Vitor Souza Martins, Eric Vermote, Christopher J. Crawford
Summary: The US Geological Survey Landsat surface reflectance (SR) archive, originally developed for terrestrial science, is increasingly being used in large-scale water-quality studies. However, these products have not been rigorously validated using in situ measured reflectance. This study quantifies and demonstrates the quality of the SR products using a global dataset (N = 1100). It found that the Landsat 8/9 SR in the green and red bands meet the accuracy requirements, but there are uncertainties and biases in the blue, coastal-aerosol, and visible bands that need to be addressed for advanced applications.
LIMNOLOGY AND OCEANOGRAPHY LETTERS
(2023)
Article
Remote Sensing
Amir M. Chegoonian, Nima Pahlevan, Kiana Zolfaghari, Peter R. Leavitt, John-Mark Davies, Helen M. Baulch, Claude R. Duguay
Summary: We developed a support vector regression (SVR) model using satellite-derived remote-sensing reflectance spectra (R-rs(d)) to retrieve near-surface chlorophyll-a (Chla) concentration in Buffalo Pound Lake (BPL), Canada. The SVR model outperformed other models and showed comparable performance to a locally trained model, producing accurate Chla distribution maps and time series.
CANADIAN JOURNAL OF REMOTE SENSING
(2023)
Article
Geochemistry & Geophysics
Zhigang Cao, Ronghua Ma, Nima Pahlevan, Miao Liu, John M. Melack, Hongtao Duan, Kun Xue, Ming Shen
Summary: This study examines the quality of water quality products generated from VIIRS observations and develops a deep neural network for improving the retrieval accuracy of chlorophyll-a and suspended particulate matter. The results provide high-quality VIIRS-derived water quality products in eastern China over the past decade.
IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING
(2022)