Reflectance based vicarious calibration of HySIS sensors and spectral stability study over pseudo-invariant sites

Mahalingam, S.; Srinivas, P V V S; Devi, Priya K.; Sita, D.; Das, Soumen; Leela, T. Sushma; Venkataraman, V.

doi:10.1109/tengarss48957.2019.8976044

Cited by 11 publications

(7 citation statements)

References 10 publications

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“…It is important to consider the increasing availability of hyperspectral satellite data: (a) Italy launched the PRecursore IperSpettrale della Missione Applicativa (PRISMA) Earth Observation satellite in 2019 [21]; (b) China launched the GaoFen-5 (GF-5) in 2018 [22]; (c) Japan launched the Hyperspectral Imager Suite (HISUI) hyperspectral satellite sensor in 2019 [23]; (d) India launched the ISRO's Hyperspectral Imaging Satellite (HYSIS) hyperspectral satellite in 2018 [24]; and (e) Germany launched the DLR Earth Sensing Imaging Spectrometer (DESIS) hyperspectral instrument in 2018 on board the International Space Station (ISS) and is planning to launch the Environmental Mapping and Analysis Program (EnMAP) hyperspectral satellite in 2022 [25,26]. Moreover, NASA and ESA are cooperating for two new hyperspectral missions, i.e., SBG-NASA and CHIME-ESA [27].…”

Section: Introductionmentioning

confidence: 99%

Sino–EU Earth Observation Data to Support the Monitoring and Management of Agricultural Resources

et al. 2021

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Novel approaches and algorithms to estimate crop physiological processes from Earth Observation (EO) data are essential to develop more sustainable management practices in agricultural systems. Within this context, this paper presents the results of different research activities carried out within the ESA-MOST Dragon 4 programme. The paper encompasses two research avenues: (a) the retrieval of biophysical variables of crops and yield prediction; and (b) food security related to different crop management strategies. Concerning the retrieval of variables, results show that LAI, derived by radiative transfer model (RTM) inversion, when assimilated into a crop growth model (i.e., SAFY) provides a way to assess yields with a higher accuracy with respect to open loop model runs: 1.14 t·ha−1 vs 4.42 t·ha−1 RMSE for assimilation and open loop, respectively. Concerning food security, results show that different pathogens could be detected by remote sensing satellite data. A k coefficient higher than 0.84 was achieved for yellow rust, thus assuring a monitoring accuracy, and for the diseased samples k was higher than 0.87. Concerning permanent crops, neural network (NN) algorithms allow classification of the Pseudomonas syringae pathogen on kiwi orchards with an overall accuracy higher than 91%.

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Section: Introductionmentioning

confidence: 99%

Sino–EU Earth Observation Data to Support the Monitoring and Management of Agricultural Resources

et al. 2021

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“…Due to the limitations of imaging cameras, there exists a trade-off between spectral and spatial resolution, leading to hyperspectral images often having a lower spatial resolution, especially in the majority of hyperspectral data bands concentrated in the visible light range (400–1000 nm). Hyperspectral imaging, with its high spectral resolution, finds extensive applications in fields such as facial recognition [ 2 ], medical diagnostics [ 3 ], geological exploration [ 4 ], resource and environmental surveys [ 5 , 6 , 7 ], and agricultural monitoring [ 8 , 9 , 10 , 11 ].…”

Section: Introductionmentioning

confidence: 99%

Comparative Analysis of GF-5 and Sentinel-2A Fusion Methods for Lithological Classification: The Tuanjie Peak, Xinjiang Case Study

Chi,

Zhang,

Jin

et al. 2024

Sensors

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This study investigates the application of hyperspectral image space–spectral fusion technology in lithologic classification, using data from China’s GF-5 and Europe’s Sentinel-2A. The research focuses on the southern region of Tuanjie Peak in the Western Kunlun Range, comparing five space–spectral fusion methods: GSA, SFIM, CNMF, HySure, and NonRegSRNet. To comprehensively evaluate the effectiveness and applicability of these fusion methods, the study conducts a comprehensive assessment from three aspects: evaluation of fusion effects, lithologic classification experiments, and field validation. In the evaluation of fusion effects, the study uses an index analysis and comparison of spectral curves before and after fusion, concluding that the GSA fusion method performs the best. For lithologic classification, the Random Forest (RF) classification method is used, training with both area and point samples. The classification results from area sample training show significantly higher overall accuracy compared to point samples, aligning well with 1:50,000 scale geological maps. In field validation, the study employs on-site verification combined with microscopic identification and comparison of images with actual spectral fusion, finding that the classification results for the five lithologies are essentially consistent with field validation results. The “GSA+RF” method combination established in this paper, based on data from GF-5 and Sentinel-2A satellites, can provide technical support for lithological classification in similar high-altitude regions.

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“…There have been about two dozen spaceborne hyperspectral imaging sensors [3], starting with the Earth Observing-1 (EO-1) and Project for On-Board Autonomy 1 (PROBA-1) missions in the early 2000s [4,5]. Further hyperspectral earth observation satellites continued to demonstrate the usefulness of hyperspectral data and many more are planned [6][7][8][9][10][11][12][13]. However, the infrequent nature of their observations limits the use of hyperspectral sensors in applications with strong temporal requirements, such as disaster monitoring or the study of continuous ecosystem dynamics.…”

Section: Introductionmentioning

confidence: 99%

Robust and Reconfigurable On-Board Processing for a Hyperspectral Imaging Small Satellite

et al. 2023

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Hyperspectral imaging is a powerful remote sensing technology, but its use in space is limited by the large volume of data it produces, which leads to a downlink bottleneck. Therefore, most payloads to date have been oriented towards demonstrating the scientific usefulness of hyperspectral data sporadically over diverse areas rather than detailed monitoring of spatio-spectral dynamics. The key to overcoming the data bandwidth limitation is to process the data on-board the satellite prior to downlink. In this article, the design, implementation, and in-flight demonstration of the on-board processing pipeline of the HYPSO-1 cube-satellite are presented. The pipeline provides not only flexible image processing but also reliability and resilience, characterized by robust booting and updating procedures. The processing time and compression rate of the simplest pipeline, which includes capturing, binning, and compressing the image, are analyzed in detail. Based on these analyses, the implications of the pipeline performance on HYPSO-1’s mission are discussed.

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Reflectance based vicarious calibration of HySIS sensors and spectral stability study over pseudo-invariant sites

Cited by 11 publications

References 10 publications

Sino–EU Earth Observation Data to Support the Monitoring and Management of Agricultural Resources

Sino–EU Earth Observation Data to Support the Monitoring and Management of Agricultural Resources

Comparative Analysis of GF-5 and Sentinel-2A Fusion Methods for Lithological Classification: The Tuanjie Peak, Xinjiang Case Study

Robust and Reconfigurable On-Board Processing for a Hyperspectral Imaging Small Satellite

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