Transplanting Date Estimation Using Sentinel-1 Satellite Data for Paddy Rice Damage Assessment in Indonesia
Abstract
:1. Introduction
2. Materials and Methods
2.1. Study Area
2.2. Methodology
2.2.1. Overview
2.2.2. Data Used
2.2.3. Preprocessing
- Orbit file: Obtain an accurate orbit state vector of the satellite.
- Subset: Clip the region of interest (East longitude 107.201–107.367°, South latitude 6.760–6.910°).
- Calibration: Convert digital value to BSC, σ0, where the area normalization is aligned with ground range plane; or β0, where the area normalization is aligned with the slant range.
- Terrain flattening: Convert β0 to γ0 (where the area normalization is aligned with a plane perpendicular to a slant range).
- Speckle filter: Apply a speckle filter (default: Lee filter with a size of 3 × 3).
- Terrain correction: Perform geometric correction using a digital elevation model (SRTM 3Sec) and interpolate on a Universal Transverse Mercator (UTM) grid at 10 m intervals including reference points (N 9,236,000 m, E 743,800 m) by bilinear interpolation.
- Logarithmic conversion: Convert BSC to a decibel value.
2.2.4. Signal Search
2.2.5. Signal Synthesis
2.2.6. Pixel-by-Pixel Transplanting Date Estimation
2.2.7. Field-by-Field Transplanting Date Estimation
2.2.8. Optimization of Transplanting Date Estimation Method
2.2.9. Examination of Validity
3. Results and Discussion
3.1. Optimization of Transplanting Date Estimation Method
- A: Smoothening in the time direction had a strong improvement effect of approximately 0.7 days on the STD. It is considered that this is because the noise is reduced by smoothening; however, it is also considered that the restriction of the upper limit value of the BSC increases owing to the upward shifting of the BSC at the local minimum by smoothening. The stronger the smoothening, the smaller the STD tends to be. However, if the smoothening is very strong, the distortion of the time-series data becomes larger and the preliminary estimation is delayed from approaching the final estimation. Therefore, an intermediate strength, psm = 0.01 is adopted here.
- B: The spread width in the space direction showed a strong improvement effect of approximately 0.4 days on the STD. The wider the spread, the smaller the STD tended to be; however, this result may be influenced by the assumption at the test site that same block has same transplanting dates. Since such an assumption is not always correct outside the test site, an intermediate value of σl = 30 m is adopted for the spatial spread width.
- C: Moderate improvement effect of approximately 0.2 days was observed in the STD using the speckle filter. Here, the Lee filter, which gave the smallest STD, was adopted.
- D: The STD increased significantly (approximately 0.9 days) when only the data with an incident angle of 32° was used. When the data with all incident angles were used, the incident angle correction had a weak improvement effect of approximately 0.02 days on the STD. Here, a method of using data with all incident angles after performing incident angle correction was adopted.
- E: The upper limit of the BSC at the time of transplantation had an effect of approximately 0.2 days on STD. In principle, the smaller the upper limit value is, the smaller the STD becomes. However, if the upper limit value is significantly small, a signal below the upper limit value may not be found at the time of transplantation and the transplanting date may not be identified. Therefore, an intermediate value of vth = −13 dB is adopted in this study. For setting no. 11 with vth = −15 dB, there were two fields for which the transplanting dates could not be identified. Here, just one upper limit is set; however, it is also possible to set two upper limits and apply the second upper limit (vth = −13 dB) to the fields where the transplanting dates cannot be identified by the first upper limit (vth = −15 dB).
- F: By averaging the BSC around the transplanting date, there was a weak improvement effect of approximately 0.01 days on the STD. There was almost no difference in the STD when comparing the cases where the average period was ±10 days and ±20 days; however, ±20 days was adopted as the average period because a longer period makes it easier to determine the latest transplanting date when performing preliminary estimation.
- G: For the BSC, σ0 was used because the STD was small; however, the difference between the STDs of σ0 and γ0 is not significant (approximately 0.003 days). It is noted that in the case of a paddy field near a steep slope, such as Bali island, using γ0 may improve the estimation accuracy.
- H: As for the field average, the STD was smaller by approximately 0.04 days in the case of averaging the transplanting date estimated for pixels within the field than in the case of estimating the transplanting date using the BSC averaged for each field. In this study, the method of averaging the estimated transplanting date for pixels in the field by weighting the synthesized signal intensity was adopted since the STD was the smallest. Although not adopted here, the method using the BSC averaged for each field is faster and has acceptable accuracy; hence, it is a useful method when it is required to reduce the calculation time.
3.2. Examination of Validity
3.2.1. Comparison with NDVI
3.2.2. Comparison with Block Value
3.2.3. Difference between the Preliminary and Final Estimations
4. Conclusions
Author Contributions
Funding
Acknowledgments
Conflicts of Interest
References
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Block. | Date | Block | Date |
---|---|---|---|
1A | 2019/05/05 | 8B | 2019/05/22 |
1B | 2019/05/15 | 9A | 2019/05/23 |
2A | 2019/05/15 | 9B | 2019/05/20 |
2B | 2019/05/08 | 10A | 2019/05/05 |
3A | 2019/05/01 | 10B | 2019/05/05 |
3B | 2019/05/01 | 11A | 2019/05/02 |
4A | 2019/04/28 | 11B | 2019/05/15 |
4B | 2019/04/28 | 12 | 2019/05/20 |
5 | 2019/05/19 | 13 | 2019/05/12 |
6 | 2019/05/08 | 14A | 2019/05/08 |
7A | 2019/05/05 | 14B | 2019/05/12 |
7B | 2019/05/12 | 15 | 2019/05/12 |
8A | 2019/05/22 |
Option | Condition | Settings |
---|---|---|
A | Smoothing in the time direction | Smoothing parameter psm = 0.01 |
B | Spread width in the spatial direction | Gaussian spatial spread width σl = 30 m |
C | Speckle filter | Use Lee filter |
D | Incident angle | All incident angles are used, with incident angle correction |
E | BSC upper limit at the time of transplantation | BSC upper limit vth = −13 dB |
F | BSC averaging period at the time of transplantation | The BSC at the time of transplantation is obtained by averaging the BSC in the period from −20 days to +20 days around the date of the local minimum of the time-series data. |
G | Backscattering coefficient | Use σ0 for BSC |
H | Field average | The transplanting date estimation is performed for each pixel first, and the transplanting date estimated for the pixels inside the polygon surrounding the field is averaged with the synthesized signal intensity as a weight. |
Option | No. | Settings |
---|---|---|
A | 1 | Without smoothing in the time direction (psm= 1) |
2 | Weak time smoothing (psm= 0.05) | |
3 | Strong time smoothing (psm = 0.001) | |
B | 4 | Without spread in the spatial direction (σl = 0.1 m) |
5 | Wide spatial spread (σl = 60 m) | |
C | 6 | No speckle filter |
7 | Use gamma map for speckle filter | |
D | 8 | Use all incident angle data and do not correct the incident angle dependence |
9 | Use only data with an incident angle of 32 ° | |
E | 10 | Large upper limit of BSC (vth = −11 dB) |
11 | Small upper limit of BSC (vth = −15 dB) | |
F | 12 | Do not average BSC (Use the local minimum of BSC) |
13 | Short average period for BSC (tij − 10 days ~ tij + 10 days) | |
G | 14 | Use γ0 for BSC (without correction for incident angle dependence) |
15 | Use γ0 for BSC (with correction for incident angle dependence) | |
H | 16 | The transplanting date estimation is performed for each pixel first, and the transplanting date estimated for the pixels inside the polygon surrounding the field is averaged with the overlapping area of the pixel and the polygon as a weight. |
17 | The transplanting date estimation is performed for each pixel first, and the transplanting date estimated for the pixels inside the polygon surrounding the field is averaged with the overlapping area of the pixel and the polygon times the synthesized signal intensity as a weight. | |
18 | The transplanting date estimation is performed for each field using field average BSC, which is obtained by averaging the BSCs of the pixels inside the polygon surrounding the field with the overlapping area of the pixel and the polygon as a weight. The signal synthesis was performed for the neighboring field. |
Option | Condition | No. | AVG | STD | AVG dif. | STD dif. |
---|---|---|---|---|---|---|
- | Default | 0 | 7.77 | 5.63 | +0.00 | +0.00 |
A | Smoothing in the time direction | 1 | 8.52 | 6.38 | +0.75 | +0.74 |
2 | 7.04 | 5.87 | −0.73 | +0.23 | ||
3 | 8.79 | 5.44 | +1.02 | −0.19 | ||
B | Spread width in the spatial direction | 4 | 7.98 | 6.05 | +0.20 | +0.42 |
5 | 7.73 | 5.57 | −0.04 | −0.07 | ||
C | Speckle filter | 6 | 7.69 | 5.77 | −0.08 | +0.13 |
7 | 7.69 | 5.79 | −0.08 | +0.15 | ||
D | Incident angle | 8 | 7.71 | 5.65 | −0.06 | +0.02 |
9 | 7.47 | 6.54 | −0.30 | +0.90 | ||
E | BSC upper limit at the time of transplantation | 10 | 7.69 | 5.81 | −0.08 | +0.18 |
11 | 7.70 | 5.56 | −0.07 | −0.07 | ||
F | BSC average period at the time of transplantation | 12 | 7.74 | 5.64 | −0.03 | +0.01 |
13 | 7.76 | 5.63 | −0.02 | +0.00 | ||
G | Backscattering coefficient | 14 | 7.72 | 5.66 | −0.05 | +0.02 |
15 | 7.75 | 5.64 | −0.02 | +0.00 | ||
H | Field average | 16 | 7.78 | 5.68 | +0.01 | +0.04 |
17 | 7.78 | 5.67 | +0.01 | +0.04 | ||
18 | 7.83 | 5.64 | +0.06 | +0.01 |
Transplanting Date Search Period | Average (day) | Standard Deviation (day) |
---|---|---|
2018/03/01–2018/06/01 | 0.61 | 5.39 |
2018/11/01–2019/02/01 | 0.96 | 6.92 |
2019/03/15–2019/06/15 | −1.23 | 5.63 |
2019/12/01–2020/02/15 | −1.43 | 5.09 |
All of the above four periods | −0.33 | 5.93 |
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Manago, N.; Hongo, C.; Sofue, Y.; Sigit, G.; Utoyo, B. Transplanting Date Estimation Using Sentinel-1 Satellite Data for Paddy Rice Damage Assessment in Indonesia. Agriculture 2020, 10, 625. https://doi.org/10.3390/agriculture10120625
Manago N, Hongo C, Sofue Y, Sigit G, Utoyo B. Transplanting Date Estimation Using Sentinel-1 Satellite Data for Paddy Rice Damage Assessment in Indonesia. Agriculture. 2020; 10(12):625. https://doi.org/10.3390/agriculture10120625
Chicago/Turabian StyleManago, Naohiro, Chiharu Hongo, Yuki Sofue, Gunardi Sigit, and Budi Utoyo. 2020. "Transplanting Date Estimation Using Sentinel-1 Satellite Data for Paddy Rice Damage Assessment in Indonesia" Agriculture 10, no. 12: 625. https://doi.org/10.3390/agriculture10120625