Details
| Original language | English |
|---|---|
| Title of host publication | IGARSS 2023 |
| Subtitle of host publication | 2023 IEEE International Geoscience and Remote Sensing Symposium |
| Publisher | Institute of Electrical and Electronics Engineers Inc. |
| Pages | 1257-1260 |
| Number of pages | 4 |
| ISBN (electronic) | 9798350320107 |
| ISBN (print) | 9798350331745 |
| Publication status | Published - 2023 |
| Event | 2023 IEEE International Geoscience and Remote Sensing Symposium - Pasadena, United States Duration: 16 Jul 2023 → 21 Jul 2023 |
Publication series
| Name | International Geoscience and Remote Sensing Symposium (IGARSS) |
|---|---|
| ISSN (Print) | 2153-6996 |
| ISSN (electronic) | 2153-7003 |
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.
Keywords
- crop residues, moisture, roughness, SOC, Soil spectral modelling
ASJC Scopus subject areas
- Computer Science(all)
- Computer Science Applications
- Earth and Planetary Sciences(all)
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IGARSS 2023 : 2023 IEEE International Geoscience and Remote Sensing Symposium. Institute of Electrical and Electronics Engineers Inc., 2023. p. 1257-1260 (International Geoscience and Remote Sensing Symposium (IGARSS)).
Research output: Chapter in book/report/conference proceeding › Conference contribution › Research › peer review
}
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 -