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Conference Paper

Simulation of Spectral Disturbance Effects for Improvement of Soil Property Estimation

Authors
/persons/resource/milewski

Milewski,  Robert
1.4 Remote Sensing, 1.0 Geodesy, Departments, GFZ Publication Database, Deutsches GeoForschungsZentrum;

/persons/resource/asmaa

Abdelbaki,  Asmaa
1.4 Remote Sensing, 1.0 Geodesy, Departments, GFZ Publication Database, Deutsches GeoForschungsZentrum;

/persons/resource/chabri

Chabrillat,  S.
1.4 Remote Sensing, 1.0 Geodesy, Departments, GFZ Publication Database, Deutsches GeoForschungsZentrum;

Tziolas,  Nikolaos
External Organizations;

Van Wesemael,  Bas
External Organizations;

Jacquemoud,  Stephane
External Organizations;

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Citation

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


Cite as: https://gfzpublic.gfz-potsdam.de/pubman/item/item_5025736
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.