Exploring TanDEM-X Interferometric Products for Crop-Type Mapping
Abstract
:1. Introduction
2. Material and Methods
2.1. Reference Data
2.2. TanDEM-X Data
2.2.1. Polarimetric Data
2.2.2. Single-Pass Interferometric Data
2.2.3. Repeat-Pass Interferometric Data
2.2.4. Interpretation of the Interferometric Coherences
- is the temporal decorrelation due to changes in the scene occurred during the acquisition times of both images. In single-pass interferometry this term can be neglected, i.e., .
- is the decorrelation due to the spatial baseline, also named as geometric decorrelation, which causes a wavenumber shift, i.e., a change in the band occupied by the range coordinate spectrum of both images [38]. This term is cancelled in the pre-processing by filtering the master and slave images to the common frequency band in the range dimension, as it is explained in Section 2.2.2. This filtering entails a loss of spatial resolution in the range coordinate, which may compromise the output product in applications in which very fine resolution needs to be maintained.
- is the coherence due to the vertical distribution of scattering properties of the scene, usually named as volume decorrelation because it is always present whenever there is vegetation volume in the scene.
- denotes the decorrelation due to thermal noise in the sensor, which depends on the signal-to-noise ratio (SNR) at each pixel. The decorrelation due to SNR can be estimated and compensated as explained in [3,35], but we decided not to compensate it to keep the data processing as simple as possible and because it would be only required in quantitative studies, e.g., vegetation height estimation.
- includes any decorrelation due to the signal processing steps, in which the most important is usually the one due to errors in the coregistration of the images. In our case we consider it is negligible, i.e., .
- is the loss of coherence due to the quantisation of the data with less bits than in the original raw data. Its effect is extensively discussed in [39]. Attending to the 8:3 block adaptive quantisation employed in the products (at both TanDEM-X and TerraSAR-X images) and the type of scene observed (agricultural crops), the average value of decorrelation is around 3.5 %, i.e., . This decorrelation term could be compensated for by dividing the measured coherence by this value, but it has not been done in this work because it will not affect the classification performance.
2.3. Classification Method and Evaluation
3. Results
3.1. Inspection of the Features
3.1.1. Images of Features
3.1.2. Time Series
3.2. Classification Results
3.2.1. Results at Pixel Level with HH and VV Channels
3.2.2. Results at Field Level with HH and VV Channels
3.2.3. Results at Pixel Level with Pauli Channels
3.2.4. Results at Field Level with Pauli Channels
4. Discussion
5. Conclusions
Author Contributions
Funding
Acknowledgments
Conflicts of Interest
References
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Date | Master/Slave | Incidence Angle (Degrees) | HoA (m) |
---|---|---|---|
4 June 2015 | TDX/TSX | 22.71 | 2.53 |
15 June 2015 | TDX/TSX | 22.71 | 2.53 |
26 June 2015 | TDX/TSX | 22.73 | 2.53 |
7 July 2015 | TDX/TSX | 22.73 | 2.54 |
18 July 2015 | TDX/TSX | 22.73 | 2.53 |
29 July 2015 | TDX/TSX | 22.74 | 2.53 |
9 August 2015 | TDX/TSX | 22.73 | 2.52 |
20 August 2015 | TDX/TSX | 22.73 | 2.53 |
31 August 2015 | TDX/TSX | 22.73 | 2.53 |
Master Date | Slave Date | HoA (m) | Baseline (m) |
---|---|---|---|
4 June 2015 | 15 June 2015 | 1526 | 5 |
15 June 2015 | 26 June 2015 | 40 | 168 |
26 June 2015 | 7 Jule 2015 | 47,730 | 1 |
7 July 2015 | 18 July 2015 | 450 | 16 |
18 July 2015 | 29 July 2015 | 69 | 97 |
29 July 2015 | 9 August 2015 | 101 | 61 |
9 August 2015 | 20 August 2015 | 164 | 41 |
20 August 2015 | 31 August 2015 | 127 | 53 |
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Busquier, M.; Lopez-Sanchez, J.M.; Mestre-Quereda, A.; Navarro, E.; González-Dugo, M.P.; Mateos, L. Exploring TanDEM-X Interferometric Products for Crop-Type Mapping. Remote Sens. 2020, 12, 1774. https://doi.org/10.3390/rs12111774
Busquier M, Lopez-Sanchez JM, Mestre-Quereda A, Navarro E, González-Dugo MP, Mateos L. Exploring TanDEM-X Interferometric Products for Crop-Type Mapping. Remote Sensing. 2020; 12(11):1774. https://doi.org/10.3390/rs12111774
Chicago/Turabian StyleBusquier, Mario, Juan M. Lopez-Sanchez, Alejandro Mestre-Quereda, Elena Navarro, María P. González-Dugo, and Luciano Mateos. 2020. "Exploring TanDEM-X Interferometric Products for Crop-Type Mapping" Remote Sensing 12, no. 11: 1774. https://doi.org/10.3390/rs12111774
APA StyleBusquier, M., Lopez-Sanchez, J. M., Mestre-Quereda, A., Navarro, E., González-Dugo, M. P., & Mateos, L. (2020). Exploring TanDEM-X Interferometric Products for Crop-Type Mapping. Remote Sensing, 12(11), 1774. https://doi.org/10.3390/rs12111774