Article
Environmental Sciences
Feinan Chen, Donggen Luo, Shuang Li, Benyong Yang, Liang Sun, Shule Ge, Jin Hong
Summary: The work presents a vicarious in-flight calibration method for the DPC directional polarimetric camera based on sea and land non-equipment sites. By comparing a massive amount of data, it is able to maintain the accuracy of radiation calibration coefficients and achieve calibration precision.
Article
Environmental Sciences
Zhipeng Wang, Kurtis Thome, Ronald Lockwood, Brian N. Wenny
Summary: This study focuses on achieving the accuracy requirement of a hyperspectral imager through the use of an independent calibration method, relying on a pre-determined absolute radiance measurement as the reference RadCal.
Article
Environmental Sciences
Honggeng Zhang, Hongzhao Tang, Xining Liu, Xianhui Dou, Yonggang Qian, Wei Chen, Kun Li
Summary: This study conducted on-orbit and vicarious calibration for the ZY1-02E satellite, comparing the results of these two methods. The findings showed that the vicarious calibration had a lower deviation in radiance compared to the on-orbit calibration, meeting the quantitative requirements of remote sensing data and providing a foundation for satellite calibration plans and algorithm enhancement.
Article
Environmental Sciences
Hongzhao Tang, Junfeng Xie, Xianhui Dou, Honggeng Zhang, Wei Chen
Summary: This paper presents a novel on-orbit absolute radiometric calibration technique based on ground observations for the ZY1-02E thermal infrared sensor. Various natural surface objects were selected as references and measurements were made using multiple instruments. Calibration coefficients were determined to improve the accuracy of on-orbit radiometric calibration.
Article
Geochemistry & Geophysics
Jun Lu, Tao He, Shunlin Liang, Yongjun Zhang
Summary: The study proposed a cross-calibration method for medium-resolution satellite data, successfully calibrating GF-4 and Landsat-8 by eliminating differences in illumination viewing geometry, resulting in a significant improvement in calibration results.
IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING
(2022)
Article
Geochemistry & Geophysics
Qu Zhou, Liqiao Tian, Jian Li, Hua Wu, Qun Zeng
Summary: This study demonstrates the effectiveness of the GS-based method for calibrating large-view-angle sensors, showing higher efficiency and accuracy compared to traditional methods. The GS method has the potential to correct BRDF during cross-calibration, freeing large-view-angle sensors from BRDF models and products.
IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING
(2021)
Article
Geochemistry & Geophysics
Jie Han, Zui Tao, Yong Xie, Huina Li, Xiaoguo Guan, Hang Yi, Tingting Shi, Gengke Wang
Summary: This study addresses the insufficiency of cross-calibration methods in the wide field of view imaging system of the GaoFen-6 satellite. By using MODIS as a reference sensor, cross-calibration methods based on the TOA and BOA BRDF models are developed. The results show that the BOA BRDF model provides higher consistency with the official calibration coefficients in the cross-calibration of eight spectral bands.
IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING
(2022)
Article
Chemistry, Analytical
Abdul Halim Bhuiyan, Jean-Emmanuel Clement, Zannatul Ferdous, Kentaro Mochizuki, Koji Tabata, James Nicholas Taylor, Yasuaki Kumamoto, Yoshinori Harada, Thomas Bocklitz, Katsumasa Fujita, Tamiki Komatsuzaki
Summary: A line illumination Raman microscope is used to extract spatial and spectral information from samples faster than raster scanning microscopes. Non-uniform laser line illumination can introduce artifacts and lower the accuracy of machine learning models. To address this issue, a detrending scheme based on random forest regression and position-dependent wavenumber calibration is proposed, which significantly reduces artifacts and improves the differentiability of sample states.
Article
Geochemistry & Geophysics
Jie Han, Zui Tao, Yong Xie, Qiyue Liu, Youju Huang
Summary: A new radiometric cross-calibration method of the PMS based on radiometric block adjustment (RBA) is proposed, which can reduce the radiometric difference between PMS images over different integration times. The method is validated at the Dunhuang radiometric calibration site and maintains high absolute radiometric calibration consistency with official calibration coefficients.
IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING
(2021)
Article
Geochemistry & Geophysics
Lin Yan, Jun Li, Chenchao Xiao
Summary: This article conducts several vicarious radiometric calibration experiments for the second hyperspectral imager of China, the AHSI onboard the ZY1E satellite, and validates its good on-orbit radiometric status.
IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING
(2022)
Article
Geochemistry & Geophysics
Aaron Pearlman, Matthew Montanaro, Boryana Efremova, Joel McCorkel, Brian Wenny, Allen Lunsford, Dennis Reuter
Summary: This paper reviews the key elements of instrument-level radiometric testing and on-orbit calibration for TIRS-2, using a Monte Carlo approach to address uncertainties and demonstrating the instrument's potential for enhancing understanding of the Earth's environment.
IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING
(2021)
Article
Chemistry, Analytical
Sudip Paul, Rohit Sharma, Prashant Tathireddy, Ricardo Gutierrez-Osuna
Summary: In this paper, a multi-calibration ensemble approach is proposed to compensate for sensor drift in long-term application of chemical sensor arrays. The method utilizes past sensor measurements and known ground-truth data to build a regression model for predicting the concentration of target analytes. Experimental and simulation results demonstrate the superiority of the proposed approach compared to existing methods under various conditions.
SENSORS AND ACTUATORS B-CHEMICAL
(2022)
Article
Engineering, Electrical & Electronic
Yongsheng Zhou, Li Zhuang, Jitong Duan, Fan Zhang, Wen Hong
Summary: This article proposes a method for selecting stable distributed targets using time-series stability analysis, and improves the accuracy of cross calibration for synthetic aperture radar (SAR) by adopting different stable pixel extraction methods and using the Oh model to correct the scattering difference caused by the incidence angle difference.
IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING
(2022)
Article
Geochemistry & Geophysics
Taeyoung Choi, Changyong Cao
Summary: The study evaluated the stability of radiometers on the satellite using a new method and found that the SD spectral response has a significant impact on SD degradation. The recalibration of coefficients based on the new findings suggested that detectors in the reflective solar bands showed very stable responses over two years of on-orbit operation.
IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING
(2022)
Article
Geochemistry & Geophysics
Chao Niu, Kun Tan, Xue Wang, Bo Han, Shule Ge, Peijun Du, Feng Wang
Summary: This article describes the use of a cross-calibration method to calibrate the ZY1-02D hyperspectral imager, as the laboratory and vicarious calibration methods were not accurate after the satellite launch. By comparing with other calibration methods and conducting validation experiments, it is shown that the cross-calibration provides high-accuracy and stable radiation performance, and it is applicable to different ground features.
IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING
(2022)
Article
Forestry
Ville Karjalainen, Timo Tokola, Jukka Malinen
Summary: This study examines the possibility of predicting the stoniness of topsoil using geophysical data and soil type information. The results show the potential of using gamma-ray and soil type data for estimating topsoil stoniness.
CANADIAN JOURNAL OF FOREST RESEARCH
(2022)
Article
Geography, Physical
Parvez Rana, Benoit St-Onge, Jean-Francois Prieur, Brindusa Cristina Budei, Anne Tolvanen, Timo Tokola
Summary: This study evaluates the effectiveness of three standardization approaches for individual tree species classification using airborne laser scanning (ALS) feature values. The findings show that feature standardization significantly improves the performance of the classification models, especially for the disjoint areas and global models. Intensity features show the largest variation between different areas and should be normalized for transferring local models. However, for partially overlapping areas, normalization is not necessary due to similar ALS settings.
ISPRS JOURNAL OF PHOTOGRAMMETRY AND REMOTE SENSING
(2022)
Article
Environmental Sciences
Luiz E. Christovam, Milton H. Shimabukuro, Maria de Lourdes B. T. Galo, Eija Honkavaara
Summary: This article proposes an improved approach for processing satellite image time series, aiming to enhance crop monitoring. By adding a Multi-Layer Perceptron loss function, the generative model is able to generate more realistic synthetic pixels, improving crop type mapping. Experimental results show that the proposed approach performs better in synthetic pixels and semantic segmentation compared to the original method.
Article
Environmental Sciences
Samuli Junttila, Roope Nasi, Niko Koivumaki, Mohammad Imangholiloo, Ninni Saarinen, Juha Raisio, Markus Holopainen, Hannu Hyyppa, Juha Hyyppa, Paeivi Lyytikainen-Saarenmaa, Mikko Vastaranta, Eija Honkavaara
Summary: Climate change is causing increased reproduction of pest insects, resulting in global tree mortality. It is therefore crucial to have early information on pest infestation to mitigate the damage. This study successfully classified trees in decline due to European spruce bark beetle infestation using multispectral unmanned aerial vehicle imagery. The results indicate that fall provides the most accurate classification results.
Article
Environmental Sciences
Kirsi Karila, Raquel Alves Oliveira, Johannes Ek, Jere Kaivosoja, Niko Koivumaki, Panu Korhonen, Oiva Niemelainen, Laura Nyholm, Roope Nasi, Ilkka Polonen, Eija Honkavaara
Summary: The objective of this study was to explore the potential of using drone images and various neural network architectures for measuring the parameters of silage grass, and comparing the results with other methods. The findings showed that neural networks outperformed random forest in most cases, and RGB data performed well in certain parameters, while hyperspectral images showed advantages in other parameters.
Article
Agronomy
Raquel Alves Oliveira, Jose Marcato Junior, Celso Soares Costa, Roope Nasi, Niko Koivumaki, Oiva Niemelainen, Jere Kaivosoja, Laura Nyholm, Hemerson Pistori, Eija Honkavaara
Summary: In this study, low-cost RGB images captured by a UAV were used along with convolutional neural networks to estimate dry matter yield and nitrogen concentration of grass swards. The results demonstrate that this approach is a promising and effective tool for practical applications.
Article
Forestry
Noora Tienaho, Tuomas Yrttimaa, Ville Kankare, Mikko Vastaranta, Ville Luoma, Eija Honkavaara, Niko Koivumaki, Saija Huuskonen, Jari Hynynen, Markus Holopainen, Juha Hyyppa, Ninni Saarinen
Summary: The structural complexity of trees is important for ecological processes and ecosystem services. In this study, the fractal-based box dimension (D-b) was used to assess the structural complexity of Scots pine trees using point cloud data from terrestrial laser scanning (TLS) and aerial imagery from an unmanned aerial vehicle (UAV). The results showed significant differences between the D-b values measured by TLS and UAV, with UAV measurements being higher and having a wider range. The differences were explained by variations in point density, distribution, tree heights, and the number of boxes in the D-b method. Despite these differences, there was still a consistent correlation between TLS and UAV measurements, with a correlation coefficient of 75%.
Review
Energy & Fuels
Olli-Jussi Korpinen, Mika Aalto, K. C. Raghu, Timo Tokola, Tapio Ranta
Summary: In this paper, we reviewed 94 publications that focused on analyzing and optimizing energy biomass supply chains using spatial data. The study found that there has been an increase in the use of geographical information systems in this field, along with a diversity of methods, objectives, and data sources. However, case studies with spatial data from multiple countries were scarce in the reviewed papers. The paper also calls for the development of a standard way of reporting geographical contents to improve the comprehension and reproducibility of research in this field.
Article
Remote Sensing
Anand George, Niko Koivumaeki, Teemu Hakala, Juha Suomalainen, Eija Honkavaara
Summary: This study implemented and assessed a redundant positioning system for high flying altitude drones based on visual-inertial odometry (VIO). The performance of various implementations was studied, and stereo-VIO provided the best results. The stereo baseline of 30 cm was most optimal for flight altitudes of 40-60 m, with a positioning accuracy of 2.186 m for an 800 m-long trajectory. The research results are important for the increasing use of autonomous drones and beyond visual line-of-sight flying.
Article
Forestry
Rorai Pereira Martins-Neto, Antonio Maria Garcia Tommaselli, Nilton Nobuhiro Imai, Eija Honkavaara, Milto Miltiadou, Erika Akemi Saito Moriya, Hassan Camil David
Summary: This study explores the use of different combinations of UAV hyperspectral data and LiDAR metrics to classify tree species in a degraded Brazilian Atlantic Forest remnant. By combining spectral data with geometric information from LiDAR, the classification accuracy was improved in a complex tropical forest.
Article
Environmental Sciences
Jaakko Oivukkamaki, Jon Atherton, Shan Xu, Anu Riikonen, Chao Zhang, Teemu Hakala, Eija Honkavaara, Albert Porcar-Castell
Summary: The potential of chlorophyll fluorescence and photoprotection-based indices for the detection of a wide range of nutrient contents in vegetation was investigated. Leaf-level observations showed that the relationships between these indices and foliar nutrient contents were influenced by leaf chlorophyll contents and leaf morphology. Canopy-level observations further revealed that spectral indices were also influenced by canopy structure, affecting their capacity to detect foliar nutrient contents.
Article
Agronomy
Roope Nasi, Hannu Mikkola, Eija Honkavaara, Niko Koivumaki, Raquel A. Oliveira, Pirjo Peltonen-Sainio, Niila-Sakari Keijala, Mikael Anakkala, Laura Alakukku, Laura Alakukku
Summary: Crop growth within agricultural parcels can be uneven, even with even management. Aerial images can determine vegetation presence and variability, but the reasons for uneven growth are less studied. This study evaluated the relationship between drone image data and field/soil quality indicators. The results showed that soil/field indicators can effectively explain spatial variability in drone images, which can be utilized for cultivation planning and field parcel evaluation.
Article
Agronomy
Erika Akemi Saito Moriya, Nilton Nobuhiro Imai, Antonio Maria Garcia Tommaselli, Eija Honkavaara, David Luciano Rosalen
Summary: This study used vegetation indices and hyperspectral remote sensing technology to successfully detect the areas affected by sugarcane mosaic disease, providing an effective tool for crop disease monitoring.
Article
Environmental Sciences
Mohammad Imangholiloo, Ville Luoma, Markus Holopainen, Mikko Vastaranta, Antti Makelainen, Niko Koivumaki, Eija Honkavaara, Ehsan Khoramshahi
Summary: Tree species information is crucial for forest management, especially in seedling stands. This study proposes a pre-processing technique based on canopy threshold to improve seedling classification, and compares the accuracy of convolutional neural network (CNN) and random forest (RF) methods. It also demonstrates that fusing vegetation indices with multispectral data enhances the classification accuracy.
Article
Environmental Sciences
Ehsan Khoramshahi, Roope Naesi, Stefan Rua, Raquel A. A. Oliveira, Axel Paivansalo, Oiva Niemelainen, Markku Niskanen, Eija Honkavaara
Summary: This article explores the use of drone techniques to identify alien barleys in oat fields. By employing a machine learning approach and drone images, the study successfully detects and localizes barley plants, providing a useful method for modern grain production industries.
Article
Environmental Sciences
Ruonan Chen, Liangyun Liu, Xinjie Liu, Zhunqiao Liu, Lianhong Gu, Uwe Rascher
Summary: This study presents methods to accurately estimate sub-daily GPP from SIF in evergreen needleleaf forests and demonstrates that the interactions among light, canopy structure, and leaf physiology regulate the SIF-GPP relationship at the canopy scale.
REMOTE SENSING OF ENVIRONMENT
(2024)
Article
Environmental Sciences
Daniel L. Goldberg, Madankui Tao, Gaige Hunter Kerr, Siqi Ma, Daniel Q. Tong, Arlene M. Fiore, Angela F. Dickens, Zachariah E. Adelman, Susan C. Anenberg
Summary: A novel method is applied in this study to directly use satellite data to evaluate the spatial patterns of urban NOx emissions inventories. The results show that the 108 spatial surrogates used by NEMO are generally appropriate, but there may be underestimation in areas with dense intermodal facilities and overestimation in wealthy communities.
REMOTE SENSING OF ENVIRONMENT
(2024)
Article
Environmental Sciences
Zhuoyue Hu, Xiaoyan Li, Liyuan Li, Xiaofeng Su, Lin Yang, Yong Zhang, Xingjian Hu, Chun Lin, Yujun Tang, Jian Hao, Xiaojin Sun, Fansheng Chen
Summary: This paper proposes a whisk-broom imaging method using a long-linear-array detector and high-precision scanning mirror to achieve high-resolution and wide-swath thermal infrared data. The method has been implemented in the SDGs satellite and has shown promising test results.
REMOTE SENSING OF ENVIRONMENT
(2024)
Article
Environmental Sciences
Dandan Wang, Leiqiu Hu, James A. Voogt, Yunhao Chen, Ji Zhou, Gaijing Chang, Jinling Quan, Wenfeng Zhan, Zhizhong Kang
Summary: This study evaluates different schemes for determining model coefficients to quantify and correct the anisotropic impact from remote sensing LST for urban applications. The schemes have consistent results and accurately estimate parameter values, facilitating the broadening of parametric models.
REMOTE SENSING OF ENVIRONMENT
(2024)
Article
Environmental Sciences
Jamie Tolan, Hung - Yang, Benjamin Nosarzewski, Guillaume Couairon, Huy V. Vo, John Brandt, Justine Spore, Sayantan Majumdar, Daniel Haziza, Janaki Vamaraju, Theo Moutakanni, Piotr Bojanowski, Tracy Johns, Brian White, Tobias Tiecke, Camille Couprie
Summary: Vegetation structure mapping is crucial for understanding the global carbon cycle and monitoring nature-based approaches to climate adaptation and mitigation. This study presents the first high-resolution canopy height maps for California and Sao Paulo, achieved through the use of very high resolution satellite imagery and aerial lidar data. The maps provide valuable tools for forest structure assessment and land use monitoring.
REMOTE SENSING OF ENVIRONMENT
(2024)
Article
Environmental Sciences
Regina Eckert, Steffen Mauceri, David R. Thompson, Jay E. Fahlen, Philip G. Brodrick
Summary: In this paper, a mathematical framework is proposed to improve the retrieval of surface reflectance and atmospheric parameters by leveraging the expected spatial smoothness of the atmosphere. Experimental results show that this framework can reduce the surface reflectance retrieval error and surface-related biases.
REMOTE SENSING OF ENVIRONMENT
(2024)
Article
Environmental Sciences
Chongya Jiang, Kaiyu Guan, Yizhi Huang, Maxwell Jong
Summary: This study presents the Field Rover method, which uses vehicle-mounted cameras to collect ground truth data on crop harvesting status. The machine learning approach and remote sensing technology are employed to upscale the results to a regional scale. The accuracy of the remote sensing method in predicting crop harvesting dates is validated through comparison with satellite data.
REMOTE SENSING OF ENVIRONMENT
(2024)
Article
Environmental Sciences
Oksana V. Lunina, Anton A. Gladkov, Alexey V. Bochalgin
Summary: In this study, an unmanned aerial vehicle (UAV) was used to detect and map surface discontinuities with displacements of a few centimeters, indicating the presence of initial geological deformations. The study found that sediments of alluvial fans are susceptible to various tectonic and exogenous deformational processes, and the interpretation of ultra-high resolution UAV images can help recognize low-amplitude brittle deformations at an early stage. UAV surveys are critical for discerning neotectonic activity and its related hazards over short observation periods.
REMOTE SENSING OF ENVIRONMENT
(2024)
Article
Environmental Sciences
Feng Zhao, Weiwei Ma, Jun Zhao, Yiqing Guo, Mateen Tariq, Juan Li
Summary: This study presents a data-driven approach to reconstruct the terrestrial SIF spectrum using measurements from the TROPOMI instrument on Sentinel-5 precursor mission. The reconstructed SIF spectrum shows improved spatiotemporal distributions and demonstrates consistency with other datasets, indicating its potential for better understanding of the ecosystem function.
REMOTE SENSING OF ENVIRONMENT
(2024)
Article
Environmental Sciences
Stephen Stehman, John E. Wagner
Summary: This article investigates optimal sample allocation in stratified random sampling for estimation of accuracy and proportion of area in applications where the target class is rare. The study finds that precision of estimated accuracy has a stronger impact on sample allocation than estimation of proportion of area, and the trade-offs among these estimates become more pronounced as the target class becomes rarer. The results provide quantitative evidence to guide sample allocation decisions in specific applications.
REMOTE SENSING OF ENVIRONMENT
(2024)
Article
Environmental Sciences
Jingyao Zheng, Tianjie Zhao, Haishen Lu, Defu Zou, Nemesio Rodriguez-Fernandez, Arnaud Mialon, Philippe Richaume, Jianshe Xiao, Jun Ma, Lei Fan, Peilin Song, Yonghua Zhu, Rui Li, Panpan Yao, Qingqing Yang, Shaojie Du, Zhen Wang, Zhiqing Peng, Yuyang Xiong, Zanpin Xing, Lin Zhao, Yann Kerr, Jiancheng Shi
Summary: Soil moisture and freeze/thaw (F/T) play a crucial role in water and heat exchanges at the land-atmosphere interface. This study reports the establishment of a wireless sensor network for soil moisture and temperature over the permafrost region of Tibetan Plateau. Satellite-based surface soil moisture (SSM) and F/T products were evaluated using ground-based measurements. The results show the reliability of L-band passive microwave SSM and F/T products, while existing F/T products display earlier freezing and later thawing, leading to unsatisfactory accuracy.
REMOTE SENSING OF ENVIRONMENT
(2024)