Optimal Temporal Window Selection for Winter Wheat and Rapeseed Mapping with Sentinel-2 Images: A Case Study of Zhongxiang in China
Abstract
:1. Introduction
- (1)
- The variable importance (VI) is calculated from the random forest (RF) framework, using the mean decrease accuracy (MDA) method, to assess the importance of the spectral-temporal features at different image acquisition times.
- (2)
- An evaluation method is proposed which comprehensively considers the spatial accuracy and statistical accuracy, which ensures that the final mapping results have valuable application significance.
- (3)
- As a representative of the typical cloudy and rainy weather in south-central China in winter, Zhongxiang City is taken as the study area to provide a reference for the selection of the optimal temporal window for crop mapping under the conditions of limited remote sensing imagery in south-central China in winter.
2. Study Area and Datasets
2.1. Study Area
2.2. Reference Data
2.3. Sentinel-2 Data Collection and Preprocessing
3. Method
3.1. Random Forest Classifier
3.2. Selecting the Optimal Temporal Window
3.3. Validation
3.3.1. Sample-Based Accuracy Assessment
3.3.2. Statistics-Based Area Accuracy Assessment
4. Results and Analysis
4.1. Classification Results
4.2. Optimal Temporal Window Analysis
5. Conclusions
Author Contributions
Funding
Acknowledgments
Conflicts of Interest
References
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Class Name | Training | Validation |
---|---|---|
Winter wheat | 1919 | 720 |
Rapeseed | 1395 | 466 |
Others | 4883 | 1890 |
Crop Type | Year | |
---|---|---|
2017 | 2016 | |
Winter wheat | 37,184.00 ha | 36,184.00 ha |
Rapeseed | 29,495.26 ha | 30,001.53 ha |
Image Acquisition Date | Mark |
---|---|
30 October 2017 | A |
9 December 2017 | B |
24 December 2017 | C |
3 April 2018 | D |
8 April 2018 | E |
18 April 2018 | F |
Ranking | Temporal Images | OA | Kappa | |||
---|---|---|---|---|---|---|
1 | DEF | 0.935 | 0.914 | 0.061 | 0.061 | 0.060 |
2 | ABCDEF | 0.949 | 0.933 | 0.121 | 0.013 | 0.072 |
3 | ABDEF | 0.943 | 0.929 | 0.108 | 0.047 | 0.081 |
4 | CDF | 0.942 | 0.923 | 0.147 | 0.037 | 0.097 |
5 | BE | 0.935 | 0.915 | 0.176 | 0.002 | 0.097 |
6 | DF | 0.934 | 0.914 | 0.108 | 0.087 | 0.098 |
7 | CDEF | 0.944 | 0.927 | 0.154 | 0.033 | 0.099 |
8 | EF | 0.933 | 0.913 | 0.004 | 0.229 | 0.104 |
9 | ABCF | 0.951 | 0.935 | 0.144 | 0.058 | 0.105 |
10 | AE | 0.928 | 0.906 | 0.175 | 0.025 | 0.108 |
Image Acquisition Date Mark | Frequency of Occurrence |
---|---|
A | 3 |
B | 4 |
C | 4 |
D | 6 |
E | 7 |
F | 8 |
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Meng, S.; Zhong, Y.; Luo, C.; Hu, X.; Wang, X.; Huang, S. Optimal Temporal Window Selection for Winter Wheat and Rapeseed Mapping with Sentinel-2 Images: A Case Study of Zhongxiang in China. Remote Sens. 2020, 12, 226. https://doi.org/10.3390/rs12020226
Meng S, Zhong Y, Luo C, Hu X, Wang X, Huang S. Optimal Temporal Window Selection for Winter Wheat and Rapeseed Mapping with Sentinel-2 Images: A Case Study of Zhongxiang in China. Remote Sensing. 2020; 12(2):226. https://doi.org/10.3390/rs12020226
Chicago/Turabian StyleMeng, Shiyao, Yanfei Zhong, Chang Luo, Xin Hu, Xinyu Wang, and Shengxiang Huang. 2020. "Optimal Temporal Window Selection for Winter Wheat and Rapeseed Mapping with Sentinel-2 Images: A Case Study of Zhongxiang in China" Remote Sensing 12, no. 2: 226. https://doi.org/10.3390/rs12020226