Crop Classification Using Multi-Temporal Sentinel-2 Data in the Shiyang River Basin of China
Abstract
:1. Introduction
- (1)
- explore what degree of accuracy can be achieved for crop mapping when using the multi-temporal and multi-spectral Sentinel-2 images and random forest model in the Shiyang River Basin;
- (2)
- identify the influence of the spectral and temporal information of Sentinel-2 on crop classification and the suitable feature selection strategies;
- (3)
- explore how early in the growing season the crops could be classified with an acceptable accuracy.
2. Study Area and Data
2.1. Study Area
2.2. Data
2.2.1. Remote Sensing Data
2.2.2. Ground Truth Dataset
2.2.3. Pre-Extraction of Cropland
3. Crop Classification Methods
3.1. Crop Classification Model and the Accuracy Assessment
3.2. Experiment Design
3.2.1. Classification Using Single Band
3.2.2. Classification Using Multi-Spectral Bands
3.2.3. Selection of the Optimal Temporal Window
3.2.4. Early Identification of Crops
4. Results
4.1. Crop Classification Accuracy Using Single Band
4.2. Crop Classification Accuracy Using Multi-Spectral Bands
4.2.1. Spectral Combinations on Single Date
4.2.2. Spectral Combinations with Multi-Temporal Information
4.3. Selecting Optimal Temporal Window
4.4. Early Identification of Crop Types
4.5. Basin-Scale Crop Classification Mapping
5. Discussion
5.1. The Impact of Multi-Spectral Information on Crop Classification
5.2. The Impact of Temporal Information on Crop Classification
5.3. The Selection Strategy of Spectral-Temporal Features
5.4. Limitations
6. Conclusions
- (1)
- Reasonable choice of spectral band combinations can effectively improve the crop classification accuracy. The RE-1 and SWIR-1 bands of Sentinel-2 are more efficient in identifying crops than other bands in the Shiyang River Basin.
- (2)
- In single-date crop classification, images from the middle growth periods are most pivotal for crop classification.
- (3)
- Images including the early, mid and late stages of the growing season are indispensable for achieving optimal performance in crop classification. In this study, four images from the key temporal window can get the best trade-off among the classification accuracy and number of images to be used.
- (4)
- Sentinel-2 data in combination with the RF method have the potential for the early detection of crops. In the Shiyang River Basin, the time of in-season classification could be advanced in late July (DOY210) with the overall accuracy reaching 0.9. Wheat could be identified accurately as early as in mid-June (one month before harvest). Alfalfa could be mapped as early as in mid-June (the first harvest). Sunflower, melon2, fennel and corn could be recognized as early as early August (one month before harvest).
Author Contributions
Funding
Conflicts of Interest
References
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Crop Type | Code | Sowing Window | Peak Greenness | Harvest Window |
---|---|---|---|---|
Wheat | WH | Early April | Mid-June | Mid-July |
Corn | CO | Late April | Mid-July | Mid-September |
Melon1 | M1 | Late April | Mid-July | Late August |
Melon2 | M2 | May | Early August | September |
Fennel | FN | Late April | Mid-July | Late August |
Sunflower | SF | Late April | July | Late August |
Sunflower & Melon | SM | April–May | Early August | September |
Alfalfa | AL | Three harvests from March to October |
Acquisition Date | Day of Year (DOY) | Number of Images |
---|---|---|
23 April 2019 | 113 | 11 |
13 May 2019 | 133 | 11 |
28 May 2019 | 148 | 11 |
17 June 2019 | 168 | 11 |
22 July 2019 | 203 | 11 |
01 August 2019 | 213 | 11 |
06 August 2019 | 218 | 11 |
1 August 2019 | 223 | 11 |
31 August 2019 | 243 | 11 |
05 September 2019 | 248 | 11 |
20 September 2019 | 263 | 11 |
Total | 121 |
Bands | Name | Central Wavelength (nm) | Band Width | Spatial Resolution (nm) |
---|---|---|---|---|
2 | Blue | 490 | 65 | 10 |
3 | Green | 560 | 35 | 10 |
4 | Red | 665 | 30 | 10 |
5 | RE-1 | 705 | 15 | 20 |
6 | RE-2 | 740 | 15 | 20 |
7 | RE-3 | 783 | 20 | 20 |
8 | NIR | 842 | 115 | 10 |
11 | SWIR-1 | 1610 | 90 | 20 |
12 | SWIR-2 | 2190 | 180 | 20 |
Class | Training Fields | Testing Fields | Training Pixels | Testing Pixels |
---|---|---|---|---|
Wheat | 20 | 9 | 1973 | 663 |
Corn | 40 | 22 | 2118 | 740 |
Melon1 | 18 | 7 | 943 | 140 |
Melon2 | 19 | 8 | 916 | 372 |
Fennel | 20 | 10 | 954 | 595 |
Sunflower | 23 | 16 | 1494 | 1028 |
Sunflower and Melon | 16 | 10 | 813 | 378 |
Alfalfa | 20 | 10 | 1900 | 1009 |
DOY | Spectral Bands and Overall Accuracy (OA) of Best Combinations | |||||||
---|---|---|---|---|---|---|---|---|
C2Bs | OA | C3Bs | OA | C4Bs | OA | C5Bs | OA | |
203 | B5, B11 | 0.71 | B4, B5, B11 | 0.83 | B4, B6, B11, B12 | 0.85 | B4, B6, B8, B11, B12 | 0.86 |
213 | B5, B11 | 0.74 | B5, B11, B12 | 0.83 | B4, B5, B8, B11 | 0.85 | B4, B5, B8, B11, B12 | 0.86 |
218 | B5, B11 | 0.71 | B5, B6, B11 | 0.81 | B5, B8, B11, B12 | 0.84 | B2, B5, B8, B11, B12 | 0.85 |
223 | B5, B11 | 0.73 | B5, B6, B11 | 0.80 | B5, B8, B11, B12 | 0.83 | B4, B5, B8, B11, B12 | 0.84 |
Combinations | Band of Sentinel-2 | Overall Accuracy |
---|---|---|
C2Bs | RE-1, SWIR-1 | 0.94 |
C3Bs | RE-1, NIR, SWIR-1 | 0.95 |
C4Bs | GREEN, RE-1, NIR, SWIR-1 | 0.95 |
C5Bs | BLUE, Red, RE-1, NIR, SWIR-1 | 0.95 |
C6Bs | BLUE, GREEN, Red, RE-1, NIR, SWIR-1 | 0.95 |
C7Bs | All bands without Red and SWIR-2 | 0.95 |
C8Bs | All bands without Red | 0.95 |
C9Bs | All bands | 0.95 |
Combinations | DOY | Overall Accuracy |
---|---|---|
C2Ds | 213, 243 | 0.92 |
C3Ds | 148, 213, 248 | 0.94 |
C4Ds | 148, 168, 213, 243 | 0.95 |
C5Ds | 148, 168, 203, 213, 243 | 0.95 |
C6Ds | 148, 168, 203, 213, 218, 248 | 0.95 |
C7Ds | 133, 148, 168, 203, 213, 223, 248 | 0.95 |
C8Ds | 133, 148, 168, 203, 213, 218, 223, 248 | 0.95 |
C9Ds | 113, 148, 168, 203, 213, 218, 223, 248, 263 | 0.95 |
C10Ds | 113, 148, 168, 203, 213, 218, 223, 243, 248, 263 | 0.95 |
C11Ds | 113, 133, 148, 168, 203, 213, 218, 223, 243, 248, 263 | 0.95 |
Actual Types | Classified | Total | PA | |||||||
---|---|---|---|---|---|---|---|---|---|---|
M1 | FN | SF | SM | AL | M2 | WH | CO | |||
M1 | 115 | 2 | 1 | 2 | 1 | 19 | 140 | 0.82 | ||
FN | 1 | 523 | 56 | 1 | 14 | 595 | 0.88 | |||
SF | 19 | 13 | 979 | 1 | 16 | 1028 | 0.95 | |||
SM | 1 | 354 | 5 | 7 | 11 | 378 | 0.94 | |||
AL | 1 | 2 | 999 | 3 | 1 | 3 | 1009 | 0.99 | ||
M2 | 47 | 1 | 322 | 0 | 2 | 372 | 0.87 | |||
WH | 4 | 1 | 658 | 663 | 0.99 | |||||
CO | 1 | 5 | 15 | 41 | 2 | 4 | 672 | 740 | 0.90 | |
Total | 137 | 548 | 1100 | 398 | 1007 | 331 | 667 | 737 | 4925 | |
UA | 0.84 | 0.95 | 0.89 | 0.89 | 0.99 | 0.97 | 0.99 | 0.91 | ||
OA = 0.94 | Kappa = 0.93 |
Actual Types | Classified | Total | PA | |||||||
---|---|---|---|---|---|---|---|---|---|---|
M1 | FN | SF | SM | AL | M2 | WH | CO | |||
M1 | 120 | 5 | 15 | 140 | 0.86 | |||||
FN | 0 | 561 | 20 | 14 | 595 | 0.94 | ||||
SF | 26 | 10 | 985 | 7 | 1028 | 0.96 | ||||
SM | 357 | 11 | 2 | 8 | 378 | 0.94 | ||||
AL | 2 | 1 | 999 | 2 | 0 | 5 | 1009 | 0.99 | ||
M2 | 1 | 51 | 0 | 320 | 0 | 372 | 0.86 | |||
WH | 4 | 659 | 663 | 0.99 | ||||||
CO | 1 | 1 | 12 | 49 | 1 | 2 | 674 | 740 | 0.91 | |
Total | 147 | 577 | 1070 | 407 | 1011 | 329 | 661 | 723 | 4925 | |
UA | 0.82 | 0.97 | 0.92 | 0.88 | 0.99 | 0.97 | 0.99 | 0.93 | ||
OA = 0.95 | Kappa = 0.94 |
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Yi, Z.; Jia, L.; Chen, Q. Crop Classification Using Multi-Temporal Sentinel-2 Data in the Shiyang River Basin of China. Remote Sens. 2020, 12, 4052. https://doi.org/10.3390/rs12244052
Yi Z, Jia L, Chen Q. Crop Classification Using Multi-Temporal Sentinel-2 Data in the Shiyang River Basin of China. Remote Sensing. 2020; 12(24):4052. https://doi.org/10.3390/rs12244052
Chicago/Turabian StyleYi, Zhiwei, Li Jia, and Qiting Chen. 2020. "Crop Classification Using Multi-Temporal Sentinel-2 Data in the Shiyang River Basin of China" Remote Sensing 12, no. 24: 4052. https://doi.org/10.3390/rs12244052