Winter Wheat Maturity Prediction via Sentinel-2 MSI Images
Abstract
:1. Introduction
2. Study Area and Dataset
2.1. Study Area
2.1.1. North China Plain
2.1.2. Winter Wheat Planting Area Labels
- Six hundred wheat cropland sample points, with 300 points in each P1 and P2 study area (Figure 1b);
- One hundred and fifty waterbody sample points;
- One hundred and fifty road sample points;
- One hundred and fifty urban area sample points;
- One hundred and fifty green vegetation-covered sample points.
2.2. Remote Sensing Images
2.2.1. Sentinel-2 MSI
2.2.2. GF-1 WFV and HJ-2 CCD
2.3. Winter Wheat Maturity Data Collection
2.3.1. Harvester Work Record
2.3.2. Manual Labeling of Harvest on the Basis of Remote Sensing Images
3. Methods
3.1. Methodological Framework
- (i)
- Winter wheat planting area classification. In this study, winter wheat planting areas were extracted via a multigrowth-stage NDVI combined with threshold methods.
- (ii)
- Winter wheat maturity characteristic analysis. We conducted a comprehensive analysis, selecting VIs (such as NDVI, NDRE, NDII1, and NDII2) and thoroughly examine the winter wheat maturity characteristics and field harvest characteristics via boxplot analysis methods.
- (iii)
- Winter wheat maturity data prediction. On the basis of the selected valuable harvest VIs, a threshold method was used to determine maturity, which was combined with the information extracted from the winter wheat planting area to assess the harvest progress.
3.2. Vegetation Indices
3.3. Winter Wheat Spectral Feature Analysis and Extraction Techniques
3.3.1. Time Series Vegetation Index Feature Analysis
- (i)
- Roads, water bodies, and urban areas had NDVI values below 0.3, with minimal interannual variation.
- (ii)
- Cropland pixels were influenced by the natural environment, seasonal changes, and human cultivation. Wheat emerged at the end of October of the previous year, with the NDVI peaking at the end of April of the following year and decreasing to its lowest value by the end of May.
- (iii)
- In non-crop vegetation-covered areas, the NDVI values were influenced by the natural environment and seasonal changes. The NDVI values began to rise in March, peaked in June, decreased in November, and reached their lowest values by the end of December.
3.3.2. Boxplot
3.4. Maturity Prediction Model
- Strategy (1) used VIs from the emergence stage to the flowering stage (S1–S9, nine growth periods) for maturity prediction. Strategy (1) enabled the prediction of wheat maturity at the flowering stage (29 April 2023, Table 2). For maturity prediction via Strategy 1, each validation unit corresponded to 12 × 9 = 108 modeling values.
- Strategy (2) used VIs from the emergence stage to the filling stage (S1–S10, ten growth periods) for maturity prediction. Strategy (2) enabled the prediction of wheat maturity at the filling stage (14 May 2023, Table 2). For Strategy 2, each validation unit corresponded to 12 × 10 = 120 modeling values.
3.5. Performance Evaluation
3.5.1. Performance Evaluation of Winter Wheat Planting Areas Extraction
3.5.2. Performance Evaluation of Winter Wheat Maturity Prediction
4. Results
4.1. Analysis of Winter Wheat Maturation Features
4.2. Extraction of Winter Wheat Planting Areas
4.3. Winter Wheat Maturity Prediction
5. Analysis and Discussion
5.1. Winter Wheat Planting Area Extraction Model
5.2. Winter Wheat Maturity Prediction Model
5.3. Limitations and Uncertainty of This Work
6. Conclusions
- (i)
- Integration of the NDVI of the (a) jointing-heading and (b) maturation-harvesting stages can provide high-precision extraction of winter wheat planting areas.
- (ii)
- Utilizing the MSR model, RF model, and multitemporal multispectral remote sensing data enables high-performance prediction of winter wheat maturity. The proposed method offers the highest accuracy in predicting maturity at the winter wheat flowering stage, aiding in a timely and comprehensive understanding of the winter wheat maturity and in deploying large-scale harvesters within the region.
Author Contributions
Funding
Institutional Review Board Statement
Data Availability Statement
Conflicts of Interest
Appendix A
Strategies | Strategy (1) | Strategy (2) | Strategies | Strategy (1) | Strategy (2) | ||||||
---|---|---|---|---|---|---|---|---|---|---|---|
Models | MSR | PLSR | MSR | PLSR | Models | MSR | PLSR | MSR | PLSR | ||
Constants | 3.001 | 6.397 | 5.967 | 5.918 | - | - | - | - | - | ||
S1 | GNDVI | 0.000 | 0.280 | 0.000 | 0.436 | S6 | GNDVI | 0.000 | −0.821 | 0.000 | −1.069 |
NDVI | 0.000 | −0.504 | 5.765 | −0.567 | NDVI | 0.000 | −0.634 | 0.000 | −0.859 | ||
NDVI2 | 0.000 | −0.037 | 0.000 | −0.017 | NDVI2 | −13.718 | −0.488 | −18.668 | −0.630 | ||
RDVI | 0.000 | 0.288 | 0.000 | 0.436 | RDVI | 0.000 | −0.447 | 0.000 | −0.632 | ||
SAVI | 0.000 | 0.427 | 0.000 | 0.604 | SAVI | 0.000 | −0.396 | 0.000 | −0.565 | ||
OSAVI | 0.000 | −0.110 | 0.000 | −0.071 | OSAVI | 0.000 | −0.524 | 0.000 | −0.724 | ||
EVI | 0.000 | −0.993 | 0.000 | −0.722 | EVI | 52.601 | −0.587 | 0.000 | −1.081 | ||
CIRE | 0.000 | 0.244 | 0.000 | 0.359 | CIRE | 0.830 | −0.014 | 0.000 | 0.001 | ||
NDRE | 0.000 | −0.267 | 0.000 | −0.175 | NDRE | −25.486 | −0.485 | 0.000 | −0.630 | ||
NDII1 | −4.533 | −0.969 | 0.000 | −1.459 | NDII1 | 31.536 | −0.156 | 39.474 | −0.166 | ||
NDII2 | 0.000 | −1.114 | −7.869 | −1.454 | NDII2 | 0.000 | −0.471 | 0.000 | −0.593 | ||
NIRV | 0.000 | 2.172 | 0.000 | 2.775 | NIRV | 0.000 | −0.268 | 0.000 | −0.348 | ||
S2 | GNDVI | 0.000 | 0.691 | 0.000 | 0.882 | S7 | GNDVI | 16.028 | 1.263 | 15.582 | 1.554 |
NDVI | 0.000 | 0.204 | 0.000 | 0.226 | NDVI | 0.000 | 0.647 | 28.630 | 0.701 | ||
NDVI2 | 0.000 | 0.328 | 0.000 | 0.409 | NDVI2 | 0.000 | 0.976 | 0.000 | 1.181 | ||
RDVI | 0.000 | 0.375 | 0.000 | 0.394 | RDVI | 0.000 | 1.143 | 0.000 | 1.406 | ||
SAVI | 0.000 | 0.341 | 0.000 | 0.352 | SAVI | 0.000 | 1.077 | 0.000 | 1.322 | ||
OSAVI | 0.000 | 0.251 | 0.000 | 0.260 | OSAVI | 0.000 | 0.841 | 0.000 | 0.981 | ||
EVI | 0.000 | −0.122 | 0.000 | −0.382 | EVI | 0.000 | −0.500 | 0.000 | −1.103 | ||
CIRE | 0.000 | 0.207 | 0.000 | 0.269 | CIRE | 0.000 | 0.099 | 0.000 | 0.152 | ||
NDRE | 25.320 | 0.819 | 23.703 | 0.967 | NDRE | 0.000 | 0.819 | −43.237 | 1.000 | ||
NDII1 | 0.000 | 0.340 | 0.000 | 0.335 | NDII1 | 0.000 | 0.434 | 0.000 | 0.574 | ||
NDII2 | 0.000 | −0.026 | 0.000 | −0.076 | NDII2 | 0.000 | 0.191 | 0.000 | 0.199 | ||
NIRV | 0.000 | 0.806 | 0.000 | 0.934 | NIRV | 0.000 | 1.906 | 0.000 | 2.507 | ||
S3 | GNDVI | 0.000 | −0.579 | 0.000 | −0.639 | S8 | GNDVI | 0.000 | 0.895 | 0.000 | 1.243 |
NDVI | 0.000 | −0.512 | 0.000 | −0.606 | NDVI | 0.000 | −0.047 | 0.000 | 0.003 | ||
NDVI2 | 0.000 | −0.065 | 0.000 | −0.037 | NDVI2 | 0.000 | 0.548 | 0.000 | 0.774 | ||
RDVI | 0.000 | −0.245 | 0.000 | −0.307 | RDVI | 0.000 | 0.768 | 0.000 | 1.154 | ||
SAVI | 0.000 | −0.207 | 0.000 | −0.266 | SAVI | 0.000 | 0.735 | 0.000 | 1.099 | ||
OSAVI | 0.000 | −0.358 | 0.000 | −0.437 | OSAVI | 0.000 | 0.318 | 0.000 | 0.514 | ||
EVI | −77.441 | −1.976 | −70.848 | −2.540 | EVI | 0.000 | −2.677 | 0.000 | −2.833 | ||
CIRE | 0.000 | 0.038 | 0.000 | 0.043 | CIRE | 0.000 | 0.151 | 0.000 | 0.227 | ||
NDRE | 0.000 | −0.075 | 0.000 | −0.169 | NDRE | 0.000 | 1.068 | 22.872 | 1.459 | ||
NDII1 | 0.000 | −0.326 | 0.000 | −0.495 | NDII1 | 0.000 | 0.242 | −17.831 | 0.405 | ||
NDII2 | 8.013 | −0.518 | 0.000 | −0.675 | NDII2 | 0.000 | −0.082 | 0.000 | −0.060 | ||
NIRV | 0.000 | 0.341 | 0.000 | 0.429 | NIRV | 0.000 | 1.822 | 0.000 | 2.636 | ||
S4 | GNDVI | 0.000 | −0.077 | 0.000 | −0.026 | S9 | GNDVI | 0.000 | −1.204 | −14.801 | −1.119 |
NDVI | 0.000 | −0.481 | 0.000 | −0.509 | NDVI | 0.000 | −1.458 | 0.000 | −1.480 | ||
NDVI2 | 0.000 | −0.264 | 0.000 | −0.262 | NDVI2 | 0.000 | −0.931 | 0.000 | −0.838 | ||
RDVI | 0.000 | −0.368 | 0.000 | −0.395 | RDVI | 0.000 | −0.911 | 0.000 | −0.476 | ||
SAVI | 0.000 | −0.341 | 0.000 | −0.368 | SAVI | 0.000 | −0.788 | 0.000 | −0.350 | ||
OSAVI | 0.000 | −0.418 | 0.000 | −0.449 | OSAVI | 0.000 | −1.162 | 0.000 | −0.971 | ||
EVI | 0.000 | −2.165 | 0.000 | −2.393 | EVI | 0.000 | −1.059 | 0.000 | 0.699 | ||
CIRE | −1.000 | −0.113 | 0.000 | −0.157 | CIRE | 0.000 | 0.129 | 0.835 | 0.219 | ||
NDRE | 0.000 | −1.077 | 0.000 | −1.258 | NDRE | 0.000 | 0.251 | 0.000 | 0.761 | ||
NDII1 | 0.000 | −0.848 | 0.000 | −0.949 | NDII1 | 0.000 | 0.019 | 0.000 | 0.634 | ||
NDII2 | 0.000 | −0.763 | 0.000 | −0.860 | NDII2 | 0.000 | −0.415 | 0.000 | −0.095 | ||
NIRV | 0.000 | −0.056 | 0.000 | −0.040 | NIRV | −11.468 | −0.092 | 0.000 | 0.870 | ||
S5 | GNDVI | 0.000 | −0.680 | 0.000 | −0.830 | S10 | GNDVI | 0.000 | 0.180 | ||
NDVI | 0.000 | −0.767 | 0.000 | −0.962 | NDVI | 0.000 | −0.577 | ||||
NDVI2 | 0.000 | −0.589 | −7.182 | −0.760 | NDVI2 | 0.000 | 0.108 | ||||
RDVI | 0.000 | −0.608 | 0.000 | −0.761 | RDVI | 0.000 | 0.344 | ||||
SAVI | 0.000 | −0.544 | 0.000 | −0.684 | SAVI | 0.000 | 0.419 | ||||
OSAVI | 0.000 | −0.674 | 0.000 | −0.851 | OSAVI | 0.000 | −0.117 | ||||
EVI | 0.000 | −2.654 | 0.000 | −3.269 | EVI | 0.000 | −0.467 | ||||
CIRE | 0.000 | −0.101 | 0.000 | −0.128 | CIRE | 0.665 | 0.311 | ||||
NDRE | 0.000 | −0.999 | 0.000 | −1.214 | NDRE | 0.000 | 1.600 | ||||
NDII1 | 0.000 | −0.892 | 0.000 | −1.120 | NDII1 | 0.000 | 1.868 | ||||
NDII2 | −30.131 | −0.884 | −23.086 | −1.087 | NDII2 | 0.000 | 0.595 | ||||
NIRV | 23.287 | −0.320 | 32.302 | −0.378 | NIRV | 0.000 | 1.772 |
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Type | Label | Number | Description |
---|---|---|---|
winter wheat planting area | winter wheat cropland | 600 | winter wheat |
non-wheat planting area | waterbody | 150 | rivers and lakes |
road | 150 | cement and dirt roads | |
urban area | 150 | cities and towns (non-vegetation) | |
green vegetation-covered | 150 | non-wheat vegetation |
Abbr | Transit Time | Wheat Growth Stages | Abbr | Transit Time | Wheat Growth Stages |
---|---|---|---|---|---|
S1 | 2022-11-15 | emergence | S7 | 2023-03-28 2023-03-30 | flagging |
S2 | 2022-12-18 | overwintering 1 | S8 | 2023-04-09 | heading |
S3 | 2023-01-07 | overwintering 2 | S9 | 2023-04-16 2023-04-22 2023-04-24 2023-04-27 2023-04-29 | flowering |
S4 | 2023-01-29 | overwintering 3 | S10 | 2023-05-07 2023-05-14 | filling |
S5 | 2023-02-15 2023-02-26 | regreening | S11 | 2023-06-01 2023-06-06 2023-06-08 2023-06-13 | harvest |
S6 | 2023-03-10 2023-03-13 | jointing |
Group | Transit Time | Type |
---|---|---|
Group 1 | 2023-6-1 | Sentinel-2 MSI |
Group 2 | 2023-6-6 | Sentinel-2 MSI |
2023-6-8 | Sentinel-2 MSI | |
Group 3 | 2023-6-12 | HJ-2A CCD, GF1-WFV |
2023-6-13 | HJ-2B CCD, Sentinel-2 MSI |
VIs | Types | Calculation Method | Refs. |
---|---|---|---|
NDVI | multifunctional | (NIR-RED)/(NIR + RED) | [48] |
GNDVI | (NIR-GREEN)/(NIR + GREEN) | [49] | |
NDVI2 | NDVI × NDVI | - | |
RDVI | (NIR-RED)/((NIR + RED)0.5) | [50] | |
SAVI | 1.5 × (NIR-RED)/(NIR + RED + 0.5) | [51] | |
OSAVI | 1.16 × (NIR-RED)/(NIR + RED + 0.16) | [52] | |
NIRv | NIR × NDVI | [53] | |
EVI | 2.5 × (NIR-RED)/(NIR + 6 × NIR − 7.5 × BLUE + 1) | [54] | |
CIRE | chlorophyll | RE3/RE1 − 1 | [55] |
NDRE | (NIR–RE1)/(NIR + RE1) | [56] | |
NDII1 | leaf water | (NIR–SWIR1)/(NIR + SWIR1) | [57] |
NDII2 | (NIR–SWIR2)/(NIR + SWIR2) | [57] |
Confusion Matrix | Reference | |||
---|---|---|---|---|
Winter Wheat | Non-Winter Wheat | Total | ||
Map | Winter wheat | p11 | p12 | p1 |
Non-winter wheat | p21 | p22 | p2 | |
Total | p1 | p2 | 1 |
Confusion Matrix | Predicted Value | |||
---|---|---|---|---|
Winter Wheat | Non-winter Wheat | Total | ||
Actual value | Winter wheat | 550 | 50 | 600 |
Non-winter wheat | 2 | 598 | 600 | |
Total | 552 | 648 | 1200 |
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Yue, J.; Li, T.; Shen, J.; Wei, Y.; Xu, X.; Liu, Y.; Feng, H.; Ma, X.; Li, C.; Yang, G.; et al. Winter Wheat Maturity Prediction via Sentinel-2 MSI Images. Agriculture 2024, 14, 1368. https://doi.org/10.3390/agriculture14081368
Yue J, Li T, Shen J, Wei Y, Xu X, Liu Y, Feng H, Ma X, Li C, Yang G, et al. Winter Wheat Maturity Prediction via Sentinel-2 MSI Images. Agriculture. 2024; 14(8):1368. https://doi.org/10.3390/agriculture14081368
Chicago/Turabian StyleYue, Jibo, Ting Li, Jianing Shen, Yihao Wei, Xin Xu, Yang Liu, Haikuan Feng, Xinming Ma, Changchun Li, Guijun Yang, and et al. 2024. "Winter Wheat Maturity Prediction via Sentinel-2 MSI Images" Agriculture 14, no. 8: 1368. https://doi.org/10.3390/agriculture14081368