Prediction of Common Surface Soil Properties Based on Vis-NIR Airborne and Simulated EnMAP Imaging Spectroscopy Data: Prediction Accuracy and Influence of Spatial Resolution
Abstract
:1. Introduction
2. Materials and Methods
2.1. Test Sites and Datasets
2.1.1. Cabo de Gata-Nijar Test Site for Iron Oxide and Clay Prediction
2.1.2. Luxembourg Test Site for SOC Prediction
2.1.3. Simulation of EnMAP Spaceborne Hyperspectral Images
2.2. Methods
2.2.1. Quantitative Soil Prediction Models
2.2.2. Pre-Processing
2.2.3. Model Building and Evaluation
2.2.4. Dataset Separation for Calibration and Validation
2.2.5. Model Performance Assessment
- T1
- the hyperspectral data uncertainty;
- T2
- the model coefficients uncertainty;
- T3
- the laboratory reference measurement uncertainty and sampling errors; and
- T4
- the dependency between the spectral and model uncertainties.
2.2.6. Spatial Structure Analysis and Influence of Sensor Resolution
3. Results and Discussion
3.1. Quantitative Soil Prediction Models
3.2. Spatial Structure Analysis and Influence of Sensor Resolution
4. Conclusions
- Although the slight decrease in prediction model performance, the spatial distribution of the soil properties is in general coherent between the simulated EnMAP and the airborne mapping.
- The variance contributor analysis and semivariograms show a highlighted importance of resolution adapted sampling strategies for the simulated EnMAP case. Adapting to this can potentially increase the performance of future multivariate models.
- The analyses of the variograms show that spatial structures predicted based on simulated EnMAP are well representative of the predicted spatial structures based on the airborne imagery with systematically lower calculated semivariance (averaging effect). The differences between EnMAP and airborne mapping are associated with heterogeneous areas where much finer detail and local variations are present in airborne soil maps and mixed pixels at EnMAP scale cannot represent variations at very small scale.
- The shape of the semivariograms is coherent with local conditions for SOC and clay (crop fields, and geomorphic unit).
- The automatic PLS procedure included in the EnMAP-Box is adequate to derive good soil prediction models which perform in an expected range (with RPIQ > 2.2 for the airborne data) and might be suitable for model building in an operational environment as long as adequate ground truth data are available.
- This paper was a first example concerning case studies from two different soil environments using semi-operational multivariate statistics for the quantitative prediction of soil properties using simulated EnMAP satellite imagery. In general, this work demonstrates the high potential of upcoming spaceborne hyperspectral missions for soil science studies but has also shown the need for future adapted strategies to cope with the lower spatial resolution. Nevertheless, compared with airborne soil maps at much finer scale, simulated EnMAP images at 30 m scale with good spectral resolution and estimated signal-to-noise ratio similar to sensor tests were able to deliver regional soil maps that are coherent with previous analyses in the region.
- Other factors that influence the prediction accuracy (e.g., spectral noise like atmosphere, surface roughness, sensor noise and illumination) are inherently included in error measures used and should be considered. We carried out a variance analysis to at least distinguish between modeling and data errors. The analysis showed that around 70%–80% of the variance of the results is due to uncertainties in the spectral data itself.
- Further work should focus on the strategy to cope with degraded satellite signal compared to airborne hyperspectral imagery including field effects and the larger spatial resolution by developing adapted ground sampling strategies for independent validation of the soil models. In particular, more developments are needed on the methodological approaches to check the suitability of current and future improved soil algorithms for global soil mapping, and look at the availability of adequate methodologies for soil model building using appropriate databases for model calibration. One main avenue of research concerns the use of recently available regional and global soil spectral databases to calibrate the soil spectral models and further develop the capabilities for operational quantitative soil mapping from space.
Acknowledgments
Author Contributions
Conflicts of Interest
References
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Soil Database Available | Selected HyMap/AHS | Selected EnMAP | |
---|---|---|---|
Iron Oxide | 51 | 23/20 | 23/20 |
Clay | 51 | 25/18 | 22/17 |
SOC | 81 | 46/31 | 46/31 |
Iron Oxide | Clay | SOC | |
---|---|---|---|
HyMap/AHS | 14.3 (3.7, 41.5) | 20.1 (7.7, 65.3) | 14.6 (8.9, 42.6) |
EnMAP | 14.3 (3.7, 41.2) | 21.9 (7.9, 65.1) | 14.9 (8.2, 41.1) |
(a) Airborne HyMap/AHS | ||||
R2 | RMSE * | RPD | RPIQ | |
Iron Oxide | 0.66 | 4.7 | 1.7 | 2.3 |
Clay | 0.64 | 2.4 | 1.7 | 2.2 |
SOC | 0.74 | 2.2 | 1.9 | 2.9 |
(b) Spaceborne EnMAP | ||||
R2 | RMSE * | RPD | RPIQ | |
Iron Oxide | 0.6 | 5 | 1.6 | 2.2 |
Clay | 0.53 | 2.6 | 1.5 | 1.4 |
SOC | 0.67 | 2.8 | 1.7 | 2.2 |
(a) HyMap/AHS | ||||
T1 | T2 | T3 | T4 | |
Iron Oxide | 0.83 | 0.05 | 0.04 | 0.09 |
Clay | 0.76 | 0.06 | 0.09 | 0.10 |
SOC | 0.80 | 0.05 | 0.04 | 0.11 |
(b) EnMAP | ||||
T1 | T2 | T3 | T4 | |
Iron Oxide | 0.75 | 0.05 | 0.08 | 0.12 |
Clay | 0.70 | 0.06 | 0.13 | 0.11 |
SOC | 0.72 | 0.60 | 0.09 | 0.13 |
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Steinberg, A.; Chabrillat, S.; Stevens, A.; Segl, K.; Foerster, S. Prediction of Common Surface Soil Properties Based on Vis-NIR Airborne and Simulated EnMAP Imaging Spectroscopy Data: Prediction Accuracy and Influence of Spatial Resolution. Remote Sens. 2016, 8, 613. https://doi.org/10.3390/rs8070613
Steinberg A, Chabrillat S, Stevens A, Segl K, Foerster S. Prediction of Common Surface Soil Properties Based on Vis-NIR Airborne and Simulated EnMAP Imaging Spectroscopy Data: Prediction Accuracy and Influence of Spatial Resolution. Remote Sensing. 2016; 8(7):613. https://doi.org/10.3390/rs8070613
Chicago/Turabian StyleSteinberg, Andreas, Sabine Chabrillat, Antoine Stevens, Karl Segl, and Saskia Foerster. 2016. "Prediction of Common Surface Soil Properties Based on Vis-NIR Airborne and Simulated EnMAP Imaging Spectroscopy Data: Prediction Accuracy and Influence of Spatial Resolution" Remote Sensing 8, no. 7: 613. https://doi.org/10.3390/rs8070613