Detection of Irrigated and Rainfed Crops in Temperate Areas Using Sentinel-1 and Sentinel-2 Time Series
Abstract
:1. Introduction
2. Study Site and Dataset
2.1. The Reference Dataset
2.2. Sentinel-2
2.3. Sentinel-1
2.4. Meteorological Data
3. Methods
3.1. Feature Computation
3.1.1. Optical Features
3.1.2. Radar Features
3.1.3. Cumulative Indices
3.2. Classification
3.3. Scenarios
- Scenario 1: with monthly cumulative SAR features only (VH, VV, VH/VV) referenced as “SAR only” in Figure 2,
- Scenario 2: with monthly cumulative Sentinel 2 features only (NDVI, NDRE, NDWI) referenced as “Optical only” in Figure 2,
- Scenario 3: with monthly cumulative optical and SAR features referenced as “Optical and SAR” in Figure 2,
- Scenario 4: scenario 3 with in addition cumulative rainfall referenced as “Optical, SAR and Rainfall” in Figure 2,
- Scenario 5: 10-day Optical, SAR features referenced as “10-day Optical and SAR” in Figure 2.
3.4. Validation
- Accuracy is the ratio between the correctly classified pixels and the sum of all pixels classified as this class, and
- Recall is the ratio between the correctly classified pixels and the total number of reference data pixels of that class.
3.5. Confidence Map
3.6. Postprocessing
4. Results
4.1. Performance of Each Scenario
4.2. Fscore Results
4.3. Analysis of Confusion Between Classes for Irrigated Crops
4.4. Confidence Map
4.5. Regional Statistics
5. Discussion
5.1. Optical or/and Radar Features
5.2. Impact of Cumulative Indices
5.3. Contribution of Rainfall Features
6. Conclusions
Author Contributions
Funding
Acknowledgments
Conflicts of Interest
References
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Growing Year | ||||||||||||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
Jan. | Feb. | Mar. | April | May | June | July | Aug. | Sept. | Oct. | Nov. | Dec. | |||||||
Maize | ||||||||||||||||||
Soybean | ||||||||||||||||||
Sunflower |
Class Label | Number of Plots | Total Area Sampled (ha) | Distribution (%) | RPG (%) | ||||
---|---|---|---|---|---|---|---|---|
2017 | 2018 | 2017 | 2018 | 2017 | 2018 | 2017 | 2018 | |
Maize irrigated | 526 | 639 | 943 | 727 | 60 | 48.1 | 82.8 | 84 |
Maize rainfed | 198 | 175 | 302 | 500 | 19.2 | 33.1 | ||
Soybean irrigated | 31 | 41 | 54 | 85 | 3.4 | 5.6 | 8.2 | 9.6 |
Soybean rainfed | 27 | 38 | 77 | 151 | 4.9 | 10 | ||
Sunflower | 50 | 49 | 120 | 40 | 7.6 | 2.7 | 8.6 | 5.5 |
Name | Description | Equation |
---|---|---|
NDVI | Normalized Difference Vegetation Index | (NIR − Red)/(NIR + Red) |
NDRE | Normalized Difference Red-Edge | (NIR − Red-Edge)/(NIR + Red-Edge) |
NDWI | Normalized Difference Water Index | (NIR − SWIR)/(NIR + SWIR) |
VV | Vertical—Vertical Polarisation | - |
VH | Vertical—Horizontal Polarisation | - |
VH/VV | Ratio | - |
ID | Scenario | Number of Features | |
---|---|---|---|
Cumulative | 1 | SAR only | 24 |
2 | Optical only | 24 | |
3 | Optical and SAR | 48 | |
4 | Optical, SAR and rainfall data | 56 | |
Not cumulative | 5 | 10-day Optical and SAR | 385 |
Class Label | 2017 | 2018 | ||
---|---|---|---|---|
Training | Validation | Training | Validation | |
Irrigated Maize | 10,000 | 51,731 | 10,000 | 33,651 |
Rainfed Maize | 10,000 | 12,606 | 10,000 | 24,899 |
Irrigated Soybean | 3388 | 2173 | 3844 | 4973 |
Rainfed Soybean | 3461 | 4437 | 7319 | 7464 |
Sunflower | 6502 | 4853 | 2173 | 1662 |
CPU Time (in Hours) | RAM (in GB) | |||||
---|---|---|---|---|---|---|
Scenario | ID | Nb. of Features | Model Learning | Classification | Model Learning | Classification |
SAR | 1 | 24 | 4.5 | 176 | 0.21 | 19 |
Optical | 2 | 24 | 2.2 | 150 | 0.14 | 19 |
Optical & SAR | 3 | 48 | 4.5 | 181 | 0.23 | 21 |
Optical SAR and Rainfall | 4 | 54 | 3.5 | 164 | 0.22 | 21 |
10-days Optical & SAR | 5 | 385 | 6.6 | 739 | 0.57 | 22 |
Class Label | 2017 | 2018 | ||
---|---|---|---|---|
Scenario 3 | Scenario 4 | Scenario 3 | Scenario 4 | |
Irrigated maize | ||||
Rainfed maize | ||||
Irrigated soybean | ||||
Rainfed soybean | ||||
Sunflower |
Class Label | 2017 | 2018 | ||||||||
---|---|---|---|---|---|---|---|---|---|---|
RPG | Scenario 4 | Diff. | Scenario 5 | Diff. | RPG | Scenario 4 | Diff. | Scenario 5 | Diff. | |
Maize | 20,987 | 21,479 | +2% | 20,601 | −2% | 20,242 | 20,695 | +2% | 20,149 | −1% |
Sunflower | 2210 | 1973 | −11% | 2183 | −1% | 1242 | 1131 | −9% | 1339 | +8% |
Soybean | 2301 | 1402 | −39% | 2445 | +6% | 2326 | 2001 | +2% | 2339 | 0% |
Total | 25,498 | 24,854 | −3% | 25,229 | −1% | 23,811 | 23,827 | 0% | 23,827 | 0% |
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Pageot, Y.; Baup, F.; Inglada, J.; Baghdadi, N.; Demarez, V. Detection of Irrigated and Rainfed Crops in Temperate Areas Using Sentinel-1 and Sentinel-2 Time Series. Remote Sens. 2020, 12, 3044. https://doi.org/10.3390/rs12183044
Pageot Y, Baup F, Inglada J, Baghdadi N, Demarez V. Detection of Irrigated and Rainfed Crops in Temperate Areas Using Sentinel-1 and Sentinel-2 Time Series. Remote Sensing. 2020; 12(18):3044. https://doi.org/10.3390/rs12183044
Chicago/Turabian StylePageot, Yann, Frédéric Baup, Jordi Inglada, Nicolas Baghdadi, and Valérie Demarez. 2020. "Detection of Irrigated and Rainfed Crops in Temperate Areas Using Sentinel-1 and Sentinel-2 Time Series" Remote Sensing 12, no. 18: 3044. https://doi.org/10.3390/rs12183044
APA StylePageot, Y., Baup, F., Inglada, J., Baghdadi, N., & Demarez, V. (2020). Detection of Irrigated and Rainfed Crops in Temperate Areas Using Sentinel-1 and Sentinel-2 Time Series. Remote Sensing, 12(18), 3044. https://doi.org/10.3390/rs12183044