Long-term (2013–2022) mapping of winter wheat in the North China Plain using Landsat data: classification with optimal zoning strategy

Figures & data

Figure 1. Study area (a), Köppen climate zoning (b) and wheat planting zoning (c).

Table 1. Spectral indices used for classification input.

Index		Expression
NDVI	Normalized Difference Vegetation Index	$\frac{{\mathit{\rho }}_{\mathit{NIR}}-{\rho }_{\mathit{RED}}}{{\rho }_{\mathit{NIR}}+{\rho }_{\mathit{RED}}}$
NDPI (Chen et al., 2019; Wang et al., 2017)	Normalized Difference Phenology Index	$\frac{{\mathit{\rho }}_{\mathit{NIR}}-\left(\mathit{\alpha }\times {\rho }_{\mathit{RED}}+\left(1-\mathit{\alpha }\right)\times {\rho }_{\mathit{SWIR}1}\right)}{{\rho }_{\mathit{NIR}}+\left(\mathit{\alpha }\times {\rho }_{\mathit{RED}}+\left(1-\mathit{\alpha }\right)\times {\rho }_{\mathit{SWIR}1}\right)}$ (α = 0.74)
EVI	Enhanced Vegetation Index	$2.5\times \frac{{\mathit{\rho }}_{\mathit{NIR}}-{\rho }_{\mathit{RED}}}{{\rho }_{\mathit{NIR}}+6{\rho }_{\mathit{RED}}-7.5{\rho }_{\mathit{BLUE}}+1}$
LSWI (Xiao et al., 2002, 2004)	Land Surface Water Index	$\frac{{\mathit{\rho }}_{\mathit{NIR}}-{\rho }_{\mathit{SWIR}1}}{{\rho }_{\mathit{NIR}}+{\rho }_{\mathit{SWIR}1}}$
DYI (Zang et al., 2020)	Differential Yellow Index	${\rho }_{\mathit{GREEN}}-{\rho }_{\mathit{BLUE}}$

Figure 2. Distribution of field survey samples.

Table 2. Information of field survey samples.

Sowing Year	Sampling Time	Sample size		Sample Type	Equivalized number of 30m pixels		Sample Sources
		Winter wheat	Non-winter wheat		Winter wheat	Non-winter wheat
2015	/	12	/	Point	12	/	Qiu et al. (2022)
2016	/	95	/	Point	95	/	Qiu et al. (2022)
2017	2018.06	633	368	Point	633	368	GPS field survey
2018	2018.12	45	48	Polygon	2457	1323	UAV Interpretation
2019	2020.03	102	121	Polygon	3394	7144	UAV Interpretation
2020	/	28	/	Point	28	/	Qiu et al. (2022)
2021	/	18	/	Point	18	/	Qiu et al. (2022)
2022	2023.03–05	343	244	Point	343	244	GPS field survey

Table 3. Crop mapping products used for sample generation.

Sowing Year	Product Type	Spatial Resolution (m)	Data source	Officially reported accuracy (OA)
2017	Winter wheat map	30	Mapped by the CBAH (Qiu et al., 2017) method with the Sentinel-2 data from Sep. 2017 to Jun. 2018	87.9%*
	Winter wheat map	30	30 m winter wheat distribution map of China for four years (2016–2019) (Dong et al., 2020)	89.9%
	Winter wheat map	20	Winter wheat map in Northern China (2017–2018) (Dong et al., 2020)	89.9%
	Rapeseed map	20	China rapeseed map (2017–2021) (Zang et al., 2023)	94.9%

*This accuracy is reported in Dong et al. (2020).

Figure 3. The workflow of the proposed winter wheat mapping approach ((a) Sample Generation; (b) Optimal Zoning; (c) Mapping and Validation).

Figure 4. Landsat-8 satellite-based normalized vegetation index (NDVI) time-series variation curves for winter wheat and rapeseed.

Figure 5. Optimal zoning results and the distribution of training samples in each subzone.

Figure 6. (a) The splitting and the merging process of the study area. (b) The change of classification accuracy (OA) during the splitting and merging process.

Figure 7. (a) Produced 30 m winter wheat map in 2022; (b) change trend of winter wheat from 2013 to 2022 at 5-km grid. The urban boundary is accessed from He et al. (2019).

Figure 8. Spatial details of winter wheat maps in different years.

Table 4. The accuracy indices of the winter wheat maps from 2015 to 2023.

Sowing Year	PA	UA	OA	Kappa
2015	75.0%	/	/	/
2016	87.4%	/	/	/
2017	93.5%	93.7%	92.4%	82.6%
2018	97.0%	94.3%	94.3%	87.3%
2019	96.4%	89.1%	92.3%	84.7%
2020	89.3%	/	/	/
2021	83.3%	/	/	/
2022	89.8%	85.6%	85.2%	69.2%
Average	89.0%	90.7%	91.1%	81.0%

Figure 9. The comparison of the mapped area of winter wheat with the census area at the province level during 2013–2020.

Table 5. Accuracies assessment based on Sentinel-2 derived maps.

Reference map	Sowing year	OA
TWDTW (Dong et al., 2020)	2016–2018	80.4%
PTDTW (Dong et al., 2020)	2017	87.9%
CBAH (Qiu et al., 2017)	2017	89.3%
ATDG (Yang et al., 2023)	2021–2022	85.0%

Figure 10. Details of winter wheat maps obtained using the optimal zoning method and other methods for the years 2017(a) and 2022(b).

Table 6. Accuracy assessment of the classification results based on different zoning methods. Bold font and underlining indicate the highest and second highest accuracy, respectively.

Sowing year	Mean OA ± Standard derivation
	Non-zoning	Climate zoning	Wheat planting zoning	Optimal zoning
2017	93.0%±0.2%	92.4%±0.2%	92.1%±0.3%	92.4%±0.5%
2018	94.6%±0.2%	94.1%±0.1%	92.8%±0.1%	94.4%±0.2%
2019	77.9%±0.3%	86.1%±1.7%	85.8%±1.0%	92.5%±1.0%
2022	84.0%±0.2%	84.1%±0.4%	85.2%±0.3%	85.4%±0.3%
Average	87.4%±0.2%	89.2%±0.6%	89.0%±0.4%	91.2%±0.5%

Table 7. Accuracy improvements of the optimal zoning compared to non-zoning in different subzones.

Zone	Optimal Zoning	Non-zoning	Improved OA	Number of samples
A	94.1%	94.4%	–0. 3%	3673
B	93.7%	93.9%	–0. 2%	1108
C	100%	100%	0	24
D	73.0%	67.5%	5.5%	5170
E	100%	100%	0	2
F	87.1%	83.0%	4.1%	5929
Average	91.3%	89.8%	1.5%

Figure 11. Different spatial patterns of different zoning results: optimal zoning (a), Köppen climate zoning (b) and wheat planting zoning (c).

Figure 12. Optimal zoning results based on different CCI thresholds: 0.95 (a), 0.97 (b) and 0.99 (c).

Table 8. Comparison of classification result accuracy of different CCI thresholds. Bold font indicates that accuracy ranks first, and underlining indicates that accuracy ranks second.

Sowing year	Mean OA ± Standard derivation
	0.99	0.97	0.95
2017	92.2% ± 1.0%	92.4% ± 0.5%	91.8% ± 1.0%
2018	92.4% ± 0.8%	94.4% ± 0.2%	93.9% ± 0.3%
2019	90.8% ± 1.0%	92.5% ± 1.0%	86.7% ± 0.7%
2022	85.0% ± 1.0%	85.4% ± 0.3%	84.7% ± 1.0%
average	90.1% ± 1.0%	91.2% ± 0.5%	89.3% ± 0.8%

Chen, X., Guo, Z., Chen, J., Yang, W., Yao, Y., Zhang, C., & Cao, X. (2019). Replacing the red band with the red-SWIR band (0.74 ρ red+ 0.26 ρ swir) can reduce the sensitivity of vegetation indices to soil background. Remote Sensing, 11(7), 851. https://doi.org/10.3390/rs11070851

 Web of Science ®Google Scholar

Wang, C., Chen, J., Wu, J., Tang, Y., Shi, P., Black, T. A., & Zhu, K. (2017). A snow-free vegetation index for improved monitoring of vegetation spring green-up date in deciduous ecosystems. Remote Sensing of Environment, 196, 1–12. https://doi.org/10.1016/j.rse.2017.04.031

 Web of Science ®Google Scholar

Xiao, X., Boles, S., Frolking, S., Salas, W., Moore Iii, B., Li, C., & Zhao, R. (2002). Observation of flooding and rice transplanting of paddy rice fields at the site to landscape scales in China using VEGETATION sensor data. International Journal of Remote Sensing, 23(15), 3009–3022. https://doi.org/10.1080/01431160110107734

 Web of Science ®Google Scholar

Xiao, X., Zhang, Q., Braswell, B., Urbanski, S., Boles, S., Wofsy, S., & Ojima, D. (2004). Modeling gross primary production of temperate deciduous broadleaf forest using satellite images and climate data. Remote Sensing of Environment, 91(2), 256–270. https://doi.org/10.1016/j.rse.2004.03.010

 Web of Science ®Google Scholar

Zang, Y., Chen, X., Chen, J., Tian, Y., Shi, Y., Cao, X., & Cui, X. (2020). Remote sensing index for mapping canola flowers using MODIS data. Remote Sensing, 12(23), 3912. https://doi.org/10.3390/rs12233912

 Web of Science ®Google Scholar

Qiu, B., Hu, X., Chen, C., Tang, Z., Yang, P., Zhu, X., & Jian, Z. (2022). Maps of cropping patterns in China during 2015–2021. Scientific Data, 9(1), 479. https://doi.org/10.1038/s41597-022-01589-8

 PubMed Web of Science ®Google Scholar

Qiu, B., Luo, Y., Tang, Z., Chen, C., Lu, D., Huang, H., & Xu, W. (2017). Winter wheat mapping combining variations before and after estimated heading dates. ISPRS Journal of Photogrammetry & Remote Sensing, 123, 35–46. https://doi.org/10.1016/j.isprsjprs.2016.09.016

 Web of Science ®Google Scholar

Dong, J., Fu, Y., Wang, J., Tian, H., Fu, S., Niu, Z., & Yuan, W. (2020). Early-season mapping of winter wheat in China based on Landsat and Sentinel images. Earth System Science Data, 12(4), 3081–3095. https://doi.org/10.5194/essd-12-3081-2020

 Web of Science ®Google Scholar

Dong, Q., Chen, X., Chen, J., Zhang, C., Liu, L., Cao, X., & Cui, X. (2020). Mapping winter wheat in North China using Sentinel 2A/B data: A method based on phenology-time weighted dynamic time warping. Remote Sensing, 12(8), 1274. https://doi.org/10.3390/rs12081274

 Web of Science ®Google Scholar

Zang, Y., Qiu, Y., Chen, X., Chen, J., Yang, W., Liu, Y., & Cao, X. (2023). Mapping rapeseed in China during 2017–2021 using Sentinel data: An automated approach integrating rule-based sample generation and a one-class classifier (RSG-OC). GIScience & Remote Sensing, 60(1), 2163576. https://doi.org/10.1080/15481603.2022.2163576

 Web of Science ®Google Scholar

He, C., Liu, Z., Gou, S., Zhang, Q., Zhang, J., & Xu, L. (2019). Detecting global urban expansion over the last three decades using a fully convolutional network. Environmental Research Letters, 14(3), 034008. https://doi.org/10.1088/1748-9326/aaf936

 Web of Science ®Google Scholar

Yang, G., Li, X., Liu, P., Yao, X., Zhu, Y., Cao, W., & Cheng, T. (2023). Automated in-season mapping of winter wheat in China with training data generation and model transfer. ISPRS Journal of Photogrammetry & Remote Sensing, 202, 422–438. https://doi.org/10.1016/j.isprsjprs.2023.07.004

 Web of Science ®Google Scholar

Data availability statement

The Landsat-8 data used in this paper are available in GEE. The produced wheat maps are shared in GEE (https://code.earthengine.google.com/0bedafeeb6899c8a92e23a4e64782206?noload=1) and Zenodo (https://doi.org/10.5281/zenodo.10522027).

The validation data in this study are available from the corresponding author, X.H.Chen, upon reasonable request.