Vegetation Characterization through the Use of Precipitation-Affected SAR Signals
Abstract
:1. Introduction
2. Study Area and Data Description
2.1. Study Area
2.2. Study Data
2.2.1. Sentinel-1 SAR
2.2.2. Precipitation Condition
- All Sentinel-1 pixels within a GPM resolution cell were labeled as ‘affected by precipitation’ if GPM measured more than 10 mm of precipitation for that GPM cell and the eight adjacent GPM cells. This should furthermore hold for the day preceding the Sentinel-1 acquisition, as well as for the day of acquisition.
- All Sentinel-1 pixels within a GPM resolution cell were labeled as ‘not affected by precipitation’ if GPM measured zero precipitation for that GPM cell and the eight adjacent GPM cells. This should furthermore hold for all three days preceding the Sentinel-1 acquisition, as well as for the day of acquisition.
2.2.3. State of Vegetation
- NV: No to marginal Vegetation (indicatively 0 m–0.1 m high): states of annual crop associated with bare ground and the germination stage.
- LV: Low Vegetation (indicatively 0.1 m–1 m high): states of sugarcane associated with early growth stages and grasslands.
- MV: Medium Vegetation (indicatively 2 m–4 m high): states of sugarcane associated with the maturation and senescence stages.
- HV: High Vegetation (indicatively 5–30 m high): forest.
2.2.4. Soil Type
3. Methodology
3.1. Feature Extraction
3.2. Configurations and Precipitation Information Scenarios
3.3. Parametrization of Distributions and Classification
- Overall accuracy: percentage of total number of correctly classified samples with respect to all classified samples.
- Producer’s accuracy: fraction of correctly classified samples with respect to all samples of the truth, which is directly related to the omission error and can hence be interpreted as the accuracy from the classification map maker’s perspective.
- User’s accuracy: fraction of correctly classified samples with respect to all samples classified as this class, which is directly related to the commission error and can hence be interpreted as the accuracy from the classification map user’s perspective.
3.4. Hellinger Distances
4. Results and Discussion
4.1. Hellinger Distances
- Generally, the PDFs of P and P2P are less discriminative than the PDFs of the other scenarios. When precipitation occurs, the backscatter of all vegetation states approximate the backscatter of HV, as was also illustrated by Figure 4. For both P and P2P, the distance between NV and HV is smaller than the distances between the other vegetation states and HV. This illustrates the severe effect of precipitation on backscatter when the ground is practically bare, seriously complicating accurate discrimination under such conditions. In addition, the distances between the vegetation states is generally smaller for P than for P2P, implying that the incorporation of two consecutive precipitation-affected acquisitions improves the discriminatory power as compared to a single precipitation-affected acquisition.
- The distances for the scenarios with no information (None) and NP are similar to each other due to the relative underrepresentation of samples associated with scenario P (also see Table 3). As for the precipitation condition pairs, NP2P and NP2NP are similar to each other, but differ from P2NP. The latter mainly has larger differences between the PDF of NV and the PDFs of other vegetated states (LV, MV and HV). These latter states also show smaller differences between each other. This may be explained by P2NP causing higher remaining moisture content in vegetation (i.e., for LV MV and HV) after the first precipitation event as compared to the remaining moisture in soil when NV due to faster evaporation for bare ground.
- Overall, there is relatively high confusion between LV and MV and to a lesser extent between LV and NV. This can be ascribed to the broad range of grasslands in the LV class and the confusion of higher grasslands with bushland; also see Section 2.2.3.
4.2. Classification Results with Precipitation Conditions
4.3. Effect of Incidence Angle on Classification Results
4.4. Effect of Soil Type Information on Classification Results
5. Conclusions
Author Contributions
Funding
Acknowledgments
Conflicts of Interest
References
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Land Cover | Coverage |
---|---|
Sugarcane | 19% |
Annual crop | 4% |
Grassland | 33% |
Mid-vegetation | 15% |
Native forest | 23% |
Production forest | 1% |
Water | 3% |
Built-up | 2% |
Scenario | Precipitation Information | Occurrence | Variate Analysis |
---|---|---|---|
None | None | 100% (100%) | Uni () |
NP | Non-Precipitated | 18% (73%) | Uni () |
P | Precipitated | 6% (27%) | Uni () |
P2NP | Precipitated to Non-Precipitated | 0.9% (12%) | Bi (, ) |
NP2P | Non-Precipitated to Precipitated | 0.8% (10%) | Bi (, ) |
P2P | Precipitated to Precipitated | 0.7% (9%) | Bi (, ) |
NP2NP | Non-Precipitated to Non-Precipitated | 5% (69%) | Bi (, ) |
Scenario | NV | LV | MV | HV |
---|---|---|---|---|
None | 376,809 | 2,885,966 | 210,071 | 4,078,977 |
NP | 352,150 | 2,177,407 | 180,758 | 3,260,595 |
P | 24,659 | 708,559 | 29,313 | 818,382 |
P2NP | 12,093 | 416,469 | 8469 | 489,782 |
NP2P | 70,982 | 283,664 | 4452 | 284,219 |
P2P | 12,566 | 292,090 | 20,844 | 328,600 |
NP2NP | 281,168 | 1,893,743 | 176,306 | 2,976,376 |
Configuration | Incidence Angle | Soil Types |
---|---|---|
1 | All | All |
2 | 29.1°–35.9° | All |
3 | 39.2°–46.0° | All |
4 | All | Oxisols |
5 | All | Ultisols |
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Molijn, R.A.; Iannini, L.; López Dekker, P.; Magalhães, P.S.G.; Hanssen, R.F. Vegetation Characterization through the Use of Precipitation-Affected SAR Signals. Remote Sens. 2018, 10, 1647. https://doi.org/10.3390/rs10101647
Molijn RA, Iannini L, López Dekker P, Magalhães PSG, Hanssen RF. Vegetation Characterization through the Use of Precipitation-Affected SAR Signals. Remote Sensing. 2018; 10(10):1647. https://doi.org/10.3390/rs10101647
Chicago/Turabian StyleMolijn, Ramses A., Lorenzo Iannini, Paco López Dekker, Paulo S.G. Magalhães, and Ramon F. Hanssen. 2018. "Vegetation Characterization through the Use of Precipitation-Affected SAR Signals" Remote Sensing 10, no. 10: 1647. https://doi.org/10.3390/rs10101647