Article
Environmental Sciences
Aisheng Wu, Xiaoxiong Xiong, Amit Angal, Qiaozhen Mu, Sherry Li
Summary: This study assesses the radiometric calibration stability and consistency of the Terra and Aqua MODIS sensors. The results show that both sensors have remained stable within 1% over their mission periods, and the consistency of radiometric calibration varies depending on the measurement method used.
Article
Environmental Sciences
Zhenzhen Cui, Chao Ma, Hao Zhang, Yonghong Hu, Lin Yan, Changyong Dou, Xiao-Ming Li
Summary: The Sustainable Development Science Satellite 1 (SDGSAT-1) carries a multispectral imager (MII) that is used for detailed terrestrial change detection and coastal monitoring. A vicarious radiometric calibration experiment was conducted to calibrate the MII using different methods and measurements. The calibrated MII images were compared with Landsat-8 Operational Land Imager (OLI) and Sentinel-2A MultiSpectral Instrument (MSI) images, and the differences were within acceptable limits. The findings support the application of SDGSAT-1 data.
Article
Geochemistry & Geophysics
Zhenqiang Qin, Xian Li, Yanfeng Gu
Summary: In this article, a physics-based method is proposed to estimate and compensate illumination for the radiometric correction of UAV multispectral images (MSIs). The proposed method builds an illumination estimation model based on intraimage and interimage consistency hypotheses, and utilizes the physical imaging principle to alleviate the influence of varying illumination. Numerical experiments on three UAV datasets demonstrate that the proposed method outperforms current methods and significantly reduces the normalized root mean square error (NRMSE).
IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING
(2022)
Article
Geochemistry & Geophysics
Wanshan Peng, Yan Gong, Shenghui Fang, Yongjun Zhang, Jadunandan Dash, Jie Ren, Jiacai Mo
Summary: This article proposes a radiometric block adjustment model considering the vignetting effect and the light-dark differences between images. The method requires only a small number of calibration samples, reducing the complexity of the experiment. The results show that this method can compensate for vignetting to some extent and improve the radiometric consistency of the dataset.
IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING
(2023)
Article
Environmental Sciences
Giuseppe Zibordi, Ewa Kwiatkowska, Frederic Melin, Marco Talone, Ilaria Cazzaniga, David Dessailly, Juan Ignacio Gossn
Summary: This study summarizes the evaluation of Ocean Color Radiometry (OCR) data from the Ocean and Land Color Instruments (OLCI) onboard the Copernicus Sentinel-3 satellites. The evaluation results show discrepancies and differences in OLCI-A and OLCI-B data across different water types and viewing angles. The study calls for further development of atmospheric correction capability to improve the accuracy and reliability of the data.
REMOTE SENSING OF ENVIRONMENT
(2022)
Article
Environmental Sciences
Per-Ola Olsson, Ashish Vivekar, Karl Adler, Virginia E. Garcia Millan, Alexander Koc, Marwan Alamrani, Lars Eklundh
Summary: This study evaluated the accuracy of the Parrot Sequoia camera and sunshine sensor, revealing the impact of camera temperature sensitivity and atmospheric influence on images. Recommendations include preheating the camera and capturing images of reflectance calibration panels to compensate for atmospheric effects. The research also highlighted the significant influence of the sunshine sensor orientation, suggesting the use of smoothing functions for data processing. The developed workflow showed high correlation with data from a handheld spectroradiometer for vegetation index calculation.
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
Engineering, Aerospace
Elahe Moradi, Alireza Sharifi
Summary: Radiometric calibration is a method of estimating the reflection of a target from measured input radiation. This study focused on radiometrically calibrating three spectral bands of Sentinel-2A using Landsat-8 OLI data for comparison. The study found that corrected data from Landsat-8 OLI had more valid results than data corrected and sharpened with panchromatic data.
AIRCRAFT ENGINEERING AND AEROSPACE TECHNOLOGY
(2021)
Article
Environmental Sciences
Aisheng Wu, Xiaoxiong Xiong, Rajendra Bhatt, Conor Haney, David R. Doelling, Amit Angal, Qiaozhen Mu
Summary: This study assessed the radiometric stability and calibration consistency of the SNPP and NOAA20 VIIRS reflective solar bands, indicating that the reflectances of both sensors are stable within 1% over their mission periods. NOAA20 VIIRS reflectances are systematically lower than SNPP by 2 to 4%, with some short wavelength bands showing up to 7% difference.
Article
Engineering, Electrical & Electronic
Yin Zhang, Qingwu Hu, Hailong Li, Jiayuan Li, Tiancheng Liu, Yuting Chen, Mingyao Ai, Jianye Dong
Summary: In this article, a BP neural network-based radiometric correction method considering optimal parameters was proposed. The best combination of input parameters and hidden layer node number was selected through comparison. The accuracy and robustness of the BP neural network with optimal parameters were verified.
IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING
(2023)
Article
Geochemistry & Geophysics
Jiale Jiang, Qiaofeng Zhang, Wenhui Wang, Yapeng Wu, Hengbiao Zheng, Xia Yao, Yan Zhu, Weixing Cao, Tao Cheng
Summary: In this study, a relative radiometric correction method for multiflight UAV images based on concurrent satellite imagery is proposed. The method shows better consistency and accuracy compared to traditional methods, and demonstrates stronger robustness and adaptability.
IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING
(2022)
Article
Environmental Sciences
Qiang Zhang, Yongguang Zhao, Lei Zhang, Jiaqi Wu, Wan Li, Jun Yan, Xiaohua Jiang, Zhiyu Yan, Jing Zhao
Summary: The stability and accuracy of on-orbit radiometric calibration are crucial for the quantitative application of satellite hyperspectral data. The Zhuhai-1 micro-nano satellite constellation consists of eight hyperspectral satellite missions, each equipped with the Orbita Hyperspectral Sensor (OHS) featuring a gradient filter spectroscopic design. Since OHSs do not have on-board calibration devices, it is difficult to accurately calibrate them for all integration stages. Ensuring radiometric consistency among different OHSs within the constellation is of utmost importance. To address these challenges, an on-orbit radiometric calibration model was developed considering all integration stages and utilizing the BOA reflectance and atmosphere parameters from the CEOS RadCalNet. This study analyzes the radiometric stability and consistency of calibration results for four RadCalNet sites, and evaluates the on-orbit radiometric performance of OHSs using site-synchronous surface reflectance measurements.
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
Environmental Sciences
Mahesh Shrestha, Joshua Mann, Emily Maddox, Terry Robbins, Jeffrey Irwin, Travis Kropuenske, Dennis Helder
Summary: This paper presents a dual-spectrometer approach to improve the accuracy of surface reflectance measurement and demonstrates its effectiveness through field measurement data.
Article
Environmental Sciences
Jingming Su, Fuqi Si, Minjie Zhao, Haijin Zhou, Yan Hong
Summary: To evaluate the radiometric calibration performance of EMI-2, the UV2 and VIS1 bands of EMI-2 were cross-calibrated with the corresponding bands of TROPOMI over Dome C. Spectral adjustment factors (SAF) were derived from the solar spectrum measured by the sensor to minimize uncertainties caused by different spectral response functions (SRF), and a correction method based on the radiative transfer model (RTM) SCIATRAN was used to suppress unaccounted angular dependence of atmospheric scattering. The TOA reflectance ratio between EMI-2 measurements and TROPOMI showed flat characteristics and strong correlation, indicating that the radiometric calibration of EMI-2 is within the relative accuracy requirement.
Review
Environmental Sciences
Jere Kaivosoja, Juho Hautsalo, Jaakko Heikkinen, Lea Hiltunen, Pentti Ruuttunen, Roope Nasi, Oiva Niemelainen, Madis Lemsalu, Eija Honkavaara, Jukka Salonen
Summary: This review highlights the lack of quantitative and repeatable reference data measurement solutions in the areas of mapping pests, weeds, and diseases using UAV imaging in precision farming applications. It points out that the majority of studies rely on subjective visual observations for reference data, which can be difficult to capture and may not be reliable.
Article
Environmental Sciences
Juha Suomalainen, Raquel A. Oliveira, Teemu Hakala, Niko Koivumaki, Lauri Markelin, Roope Nasi, Eija Honkavaara
Summary: This study introduces the application of drones in environmental monitoring and the development of a workflow for direct reflectance transformation, with improved accuracy of reflectance factors through effective radiometric calibration and atmospheric correction methods. Experimental tests demonstrate high accuracy of the workflow, suitable for tasks such as forest monitoring, large-scale autonomous mapping, and real-time applications.
REMOTE SENSING OF ENVIRONMENT
(2021)
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%.
Article
Environmental Sciences
Heini Kanerva, Eija Honkavaara, Roope Nasi, Teemu Hakala, Samuli Junttila, Kirsi Karila, Niko Koivumaki, Raquel Alves Oliveira, Mikko Pelto-Arvo, Ilkka Polonen, Johanna Tuviala, Madeleine Ostersund, Paivi Lyytikainen-Saarenmaa
Summary: Various stresses are causing forest health decline globally. The European spruce bark beetle is a major biotic stress agent in Europe. Remote sensing using unmanned aerial systems and machine learning techniques can provide a powerful tool for monitoring forest health. This study investigated the performance of a deep one-stage object detection neural network in detecting damage by the bark beetle in spruce trees using RGB images captured by drones. The network was able to detect and classify visually symptomatic spruce trees, but had difficulty distinguishing between healthy trees and trees with stem symptoms and green crowns.
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.