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  Simulation of Spectral Disturbance Effects for Improvement of Soil Property Estimation

Milewski, R., Abdelbaki, A., Chabrillat, S., Tziolas, N., Van Wesemael, B., Jacquemoud, S. (2023): Simulation of Spectral Disturbance Effects for Improvement of Soil Property Estimation - Proceedings, IGARSS 2023 - 2023 IEEE International Geoscience and Remote Sensing Symposium (Pasadena, CA, USA 2023).
https://doi.org/10.1109/IGARSS52108.2023.10282492

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 Creators:
Milewski, Robert1, Author              
Abdelbaki, Asmaa1, Author              
Chabrillat, S.1, Author              
Tziolas, Nikolaos2, Author
Van Wesemael, Bas2, Author
Jacquemoud, Stephane2, Author
Affiliations:
11.4 Remote Sensing, 1.0 Geodesy, Departments, GFZ Publication Database, Deutsches GeoForschungsZentrum, ou_146028              
2External Organizations, ou_persistent22              

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 Abstract: This study introduces the development of Spatially Upscaled Soil Spectral Libraries (SUSSL) approach to assess spectral disturbances caused by variations in surface conditions in remote sensing-based soil property prediction. The SUSSL incorporates realistic cropland reflectance scenarios using spectral modelling and aggregation techniques. By convoluting the spectral database to multispectral and hyperspectral satellite sensors, the sensitivity of spectral indices in retrieving undisturbed surface reflectance is evaluated. Preliminary findings indicate that the spectral disturbance effects significantly impact the accuracy of soil organic carbon (SOC) estimations, resulting in a noticeable loss compared to bare soil spectra. However, strict filtering criteria using spectral indices exhibit promise in enhancing SOC modelling performance, particularly for multispectral sensors. Hyperspectral sensors demonstrate higher baseline accuracies even in disturbed soil cases. This research highlights the importance of accounting for surface condition variations for reliable soil property mapping. Future work involves leveraging machine learning techniques on SUSSL data to improve prediction accuracy and spatial coverage of soil properties using Earth Observation data.

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 Dates: 2023
 Publication Status: Finally published
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 Rev. Type: -
 Identifiers: DOI: 10.1109/IGARSS52108.2023.10282492
GFZPOF: p4 T5 Future Landscapes
GFZPOFCCA: p4 CARF RemSens
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Title: IGARSS 2023 - 2023 IEEE International Geoscience and Remote Sensing Symposium
Place of Event: Pasadena, CA, USA
Start-/End Date: 2023

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Title: Proceedings
Source Genre: Proceedings
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Publ. Info: IEEE
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