TY - GEN

T1 - Simulation of Spectral Disturbance Effects for Improvement of Soil Property Estimation

AU - Milewski, Robert

AU - Chabrillat, Sabine

AU - Tziolas, Nikolaos

AU - Van Wesemael, Bas

N1 - Funding Information: This research was performed within the framework of the WORLDSOILS application project (http://world-soils.com) funded by the European Space Agency's (ESA) Earth Observation Strategy 2040 within the EO Science for Society slice of the 5th EarthObservation Envelope Program. We thank the EnMAP science program funded by the German Federal Ministry of Economics and Technology and institutional support by the GFZ Potsdam for further promoting this work. We further thank Luis Guanter for MOTRAN modelling of shaded soil fraction, and Theres Küster for contributing with the HySimCaR model for crop simulations.

PY - 2023

Y1 - 2023

N2 - 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.

AB - 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.

KW - crop residues

KW - moisture

KW - roughness

KW - SOC

KW - Soil spectral modelling

UR - http://www.scopus.com/inward/record.url?scp=85178384808&partnerID=8YFLogxK

U2 - 10.1109/IGARSS52108.2023.10282492

DO - 10.1109/IGARSS52108.2023.10282492

M3 - Conference contribution

AN - SCOPUS:85178384808

SN - 9798350331745

T3 - International Geoscience and Remote Sensing Symposium (IGARSS)

SP - 1257

EP - 1260

BT - IGARSS 2023

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 2023 IEEE International Geoscience and Remote Sensing Symposium

Y2 - 16 July 2023 through 21 July 2023

ER -