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

References

• Asgarian, A., Soffianian, A., & Pourmanafi, S. (2016). Crop type mapping in a highly fragmented and heterogeneous agricultural landscape: A case of central Iran using multi-temporal Landsat 8 imagery. Computers Electronics in Agriculture, 127, 531–540. https://doi.org/10.1016/j.compag.2016.07.019
   Web of Science ®Google Scholar
• Belgiu, M., Bijker, W., Csillik, O., & Stein, A. (2021). Phenology-based sample generation for supervised crop type classification. International Journal of Applied Earth Observation and Geoinformation, 95, 102264. https://doi.org/10.1016/j.jag.2020.102264
   Web of Science ®Google Scholar
• Belgiu, M., & Csillik, O. (2018). Sentinel-2 cropland mapping using pixel-based and object-based time-weighted dynamic time warping analysis. Remote Sensing of Environment, 204, 509–523. https://doi.org/10.1016/j.rse.2017.10.005
   Web of Science ®Google Scholar
• Blickensdörfer, L., Schwieder, M., Pflugmacher, D., Nendel, C., Erasmi, S., & Hostert, P. (2022). Mapping of crop types and crop sequences with combined time series of Sentinel-1, Sentinel-2 and Landsat 8 data for Germany. Remote Sensing of Environment, 269, 112831. https://doi.org/10.1016/j.rse.2021.112831
   Web of Science ®Google Scholar
• Cai, Z. G. (2010). 中国小麦种植区划研究 [Study on Chinese wheat planting regionalization]. Journal of Triticeae Crops, 30(5), 886–895.
   Google Scholar
• 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
• Dong, Q., Chen, X., Chen, J., Yin, D., Zhang, C., Xu, F., & Stein, A. (2022). Bias of area counted from sub-pixel map: Origin and correction. Science of Remote Sensing, 6, 100069. https://doi.org/10.1016/j.srs.2022.100069
   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
• 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
• Fan, L., Chen, S., Liang, S., Sun, X., Chen, H., You, L., & Yang, P. (2020). Assessing long-term spatial movement of wheat area across China. Agricultural Systems, 185, 102933. https://doi.org/10.1016/j.agsy.2020.102933
   Web of Science ®Google Scholar
• FAO. (2014). FAO statistical yearbook 2014, Asia and the Pacific, food and agriculture. (pp. 71–99).
   Google Scholar
• Gadiraju, K. K., & Vatsavai, R. R. (2020). Comparative analysis of deep transfer learning performance on crop classification. Proceedings of the 9th ACM SIGSPATIAL International Workshop on Analytics for Big Geospatial Data, Article 1. https://doi.org/10.1145/3423336.3431369
   Google Scholar
• Gu, Z., Chen, J., Chen, Y., Qiu, Y., Zhu, X., & Chen, X. (2023). Agri-Fuse: A novel spatiotemporal fusion method designed for agricultural scenarios with diverse phenological changes. Remote Sensing of Environment, 299, 113874. https://doi.org/10.1016/j.rse.2023.113874
   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
• He, T., Xie, C., Liu, Q., Guan, S., & Liu, G. (2019). Evaluation and comparison of random forest and A-LSTM networks for large-scale winter wheat identification. Remote Sensing, 11(14), 1665. https://doi.org/10.3390/rs11141665
   Web of Science ®Google Scholar
• Horowitz, S. L., & Pavlidis, T. (1976). Picture segmentation by a tree traversal algorithm. Journal of the ACM, 23(2), 368–388. https://doi.org/10.1145/321941.321956
   Web of Science ®Google Scholar
• Huang, X., Song, Y., Yang, J., Wang, W., Ren, H., Dong, M., & Li, J. (2022). Toward accurate mapping of 30-m time-series global impervious surface area (GISA). International Journal of Applied Earth Observation and Geoinformation, 109, 102787. https://doi.org/10.1016/j.jag.2022.102787
   Web of Science ®Google Scholar
• Jin, S., Homer, C., Yang, L., Danielson, P., Dewitz, J., Li, C., & Howard, D. (2019). Overall methodology design for the United States national land cover database 2016 products. Remote Sensing, 11(24), 2971. https://doi.org/10.3390/rs11242971
   Web of Science ®Google Scholar
• Liang, S., & Wang, J. (2019). Advanced remote sensing: Terrestrial information extraction and applications. pp. 871–914 https://doi.org/10.1016/B978-0-12-815826-5.00024-6. https://public.ebookcentral.proquest.com/choice/publicfullrecord.aspx?p=5986168
   Google Scholar
• Li, M., Feng, X., & Belgiu, M. (2024). Mapping tobacco planting areas in smallholder farmlands using Phenological-Spatial-Temporal LSTM from time-series Sentinel-1 SAR images. International Journal of Applied Earth Observation and Geoinformation, 129, 103826. https://doi.org/10.1016/j.jag.2024.103826
   Web of Science ®Google Scholar
• Li, S., Li, F., Gao, M., Li, Z., Leng, P., Duan, S., & Ren, J. (2021). A new method for winter wheat mapping based on spectral reconstruction technology. Remote Sensing, 13(9), 1810. https://doi.org/10.3390/rs13091810
   Web of Science ®Google Scholar
• Liu, T., & Chen, X. (2018). Application of deep learning in globeland30-2010 product refinement. The International Archives of the Photogrammetry. Remote Sensing Spatial Information Sciences, 42, 1111–1116. https://doi.org/10.5194/isprs-archives-XLII-3-1111-2018
   Google Scholar
• Liu, J., Feng, Q., Gong, J., Zhou, J., Liang, J., & Li, Y. (2018). Winter wheat mapping using a random forest classifier combined with multi-temporal and multi-sensor data. International Journal of Digital Earth, 11(8), 783–802. https://doi.org/10.1080/17538947.2017.1356388
   Web of Science ®Google Scholar
• Liu, Y., Zhang, H., Zhang, M., Cui, Z., Lei, K., Zhang, J., & Ji, P. (2022). Vietnam wetland cover map: Using hydro-periods Sentinel-2 images and google earth engine to explore the mapping method of tropical wetland. International Journal of Applied Earth Observation and Geoinformation, 115, 103122. https://doi.org/10.1016/j.jag.2022.103122
   Web of Science ®Google Scholar
• Li, C., Xian, G., Zhou, Q., & Pengra, B. W. (2021). A novel automatic phenology learning (APL) method of training sample selection using multiple datasets for time-series land cover mapping. Remote Sensing of Environment, 266, 112670. https://doi.org/10.1016/j.rse.2021.112670
   Web of Science ®Google Scholar
• Li, R., Xu, M., Chen, Z., Gao, B., Cai, J., Shen, F., & Chen, D. (2021). Phenology-based classification of crop species and rotation types using fused MODIS and Landsat data: The comparison of a random-forest-based model and a decision-rule-based model. Soil Tillage Research, 206, 104838. https://doi.org/10.1016/j.still.2020.104838
   Web of Science ®Google Scholar
• Massey, R., Sankey, T. T., Congalton, R. G., Yadav, K., Thenkabail, P. S., Ozdogan, M., & Meador, A. J. S. (2017). MODIS phenology-derived, multi-year distribution of conterminous U.S. crop types. Remote Sensing of Environment, 198, 490–503. https://doi.org/10.1016/j.rse.2017.06.033
   Web of Science ®Google Scholar
• Maus, V., Cmara, G., Cartaxo, R., Sanchez, A., Ramos, F. M., & Queiroz, G. R. D. (2016). A time-weighted dynamic time warping method for land-use and land-cover mapping. IEEE Journal of Selected Topics in Applied Earth ObservationsRemote Sensing, 9(8), 3729–3739. https://doi.org/10.1109/JSTARS.2016.2517118
   Web of Science ®Google Scholar
• Nabil, M., Zhang, M., Wu, B., Bofana, J., & Elnashar, A. (2022). Constructing a 30 m African cropland layer for 2016 by integrating multiple remote sensing, crowdsourced, and auxiliary datasets. Big Earth Data, 6(1), 54–76. https://doi.org/10.1080/20964471.2021.1914400
   Web of Science ®Google Scholar
• Nasrallah, A., Baghdadi, N., Mhawej, M., Faour, G., Darwish, T., Belhouchette, H., & Darwich, S. (2018). A novel approach for mapping wheat areas using high resolution Sentinel-2 images. Sensors, 18(7), 2089. https://doi.org/10.3390/s18072089
   PubMed Web of Science ®Google Scholar
• Olofsson, P., Foody, G. M., Herold, M., Stehman, S. V., Woodcock, C. E., & Wulder, M. A. (2014). Good practices for estimating area and assessing accuracy of land change. Remote Sensing of Environment, 148, 42–57. https://doi.org/10.1016/j.rse.2014.02.015
   Web of Science ®Google Scholar
• Ozdogan, M., & Woodcock, C. E. (2006). Resolution dependent errors in remote sensing of cultivated areas. Remote Sensing of Environment, 103(2), 203–217. https://doi.org/10.1016/j.rse.2006.04.004
   Web of Science ®Google Scholar
• Pan, Y., Li, L., Zhang, J., Liang, S., Zhu, X., & Sulla-Menashe, D. (2012). Winter wheat area estimation from MODIS-EVI time series data using the crop proportion phenology index. Remote Sensing of Environment, 119, 232–242. https://doi.org/10.1016/j.rse.2011.10.011
   Web of Science ®Google Scholar
• Peel, M. C., Finlayson, B. L., & McMahon, T. A. (2007). Updated world map of the Köppen-Geiger climate classification. Hydrology Earth System Sciences, 11(5), 1633–1644. https://doi.org/10.5194/hess-11-1633-2007
   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., Li, W., Tang, Z., Chen, C., & Qi, W. (2015). Mapping paddy rice areas based on vegetation phenology and surface moisture conditions. Ecological Indicators, 56, 79–86. https://doi.org/10.1016/j.ecolind.2015.03.039
   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
• Qu, C., Li, P., & Zhang, C. (2021). A spectral index for winter wheat mapping using multi-temporal Landsat NDVI data of key growth stages. ISPRS Journal of Photogrammetry & Remote Sensing, 175, 431–447. https://doi.org/10.1016/j.isprsjprs.2021.03.015
   Web of Science ®Google Scholar
• Shen, R., Dong, J., Yuan, W., Han, W., Ye, T., & Zhao, W. (2022). A 30 m resolution distribution map of maize for China based on Landsat and Sentinel images. Journal of Remote Sensing, 5, 1–12. https://doi.org/10.34133/2022/9846712
   Google Scholar
• Skakun, S., Vermote, E., Roger, J. C., & Franch, B. (2017). Combined use of Landsat-8 and Sentinel-2A images for winter crop mapping and winter wheat yield assessment at regional scale. AIMS Geosciences, 3(2), 163. https://doi.org/10.3934/geosci.2017.2.163
   PubMed Web of Science ®Google Scholar
• Song, Y., & Wang, J. (2019). Mapping winter wheat planting area and monitoring its phenology using Sentinel-1 backscatter time series. Remote Sensing, 11(4), 449. https://doi.org/10.3390/rs11040449
   Web of Science ®Google Scholar
• Stehman, S. V., & Foody, G. M. (2019). Key issues in rigorous accuracy assessment of land cover products. Remote Sensing of Environment, 231, 111199. https://doi.org/10.1016/j.rse.2019.05.018
   Web of Science ®Google Scholar
• Sulla-Menashe, D., Friedl, M. A., & Woodcock, C. E. (2016). Sources of bias and variability in long-term Landsat time series over Canadian boreal forests. Remote Sensing of Environment, 177, 206–219. https://doi.org/10.1016/j.rse.2016.02.041
   Web of Science ®Google Scholar
• Sun, H., Xu, A., Lin, H., Zhang, L., & Mei, Y. (2012). Winter wheat mapping using temporal signatures of MODIS vegetation index data. International Journal of Remote Sensing, 33(16), 5026–5042. https://doi.org/10.1080/01431161.2012.657366
   Web of Science ®Google Scholar
• Tao, J.-B., Wu, W.-B., Zhou, Y., Wang, Y., & Jiang, Y. (2017). Mapping winter wheat using phenological feature of peak before winter on the North China Plain based on time-series MODIS data. Journal of Integrative Agriculture, 16(2), 348–359. https://doi.org/10.1016/S2095-3119(15)61304-1
   Web of Science ®Google Scholar
• Teillet, P., Barker, J., Markham, B., Irish, R., Fedosejevs, G., & Storey, J. (2001). Radiometric cross-calibration of the Landsat-7 ETM+ and Landsat-5 TM sensors based on tandem data sets. Remote Sensing of Environment, 78(1–2), 39–54. https://doi.org/10.1016/S0034-4257(01)00248-6
   Web of Science ®Google Scholar
• Tian, H., Wang, Y., Chen, T., Zhang, L., & Qin, Y. (2021). Early-season mapping of winter crops using Sentinel-2 optical imagery. Remote Sensing, 13(19), 3822. https://doi.org/10.3390/rs13193822
   Web of Science ®Google Scholar
• Tran, K. H., Zhang, H. K., McMaine, J. T., Zhang, X., & Luo, D. (2022). 10 m crop type mapping using Sentinel-2 reflectance and 30 m cropland data layer product. International Journal of Applied Earth Observation and Geoinformation, 107, 102692. https://doi.org/10.1016/j.jag.2022.102692
   Web of Science ®Google Scholar
• Wang, S., Azzari, G., & Lobell, D. B. (2019). Crop type mapping without field-level labels: Random forest transfer and unsupervised clustering techniques. Remote Sensing of Environment, 222, 303–317. https://doi.org/10.1016/j.rse.2018.12.026
   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
• Wang, Y., Zhang, Z., Feng, L., Ma, Y., & Du, Q. (2021). A new attention-based CNN approach for crop mapping using time series Sentinel-2 images. Computers and Electronics in Agriculture, 184, 106090. https://doi.org/10.1016/j.compag.2021.106090
   Web of Science ®Google Scholar
• Wardlow, B. D., Egbert, S. L., & Kastens, J. H. (2007). Analysis of time-series MODIS 250 m vegetation index data for crop classification in the U.S. Central Great Plains. Remote Sensing of Environment, 108(3), 290–310. https://doi.org/10.1016/j.rse.2006.11.021
   Web of Science ®Google Scholar
• Weiss, M., Jacob, F., & Duveiller, G. (2020). Remote sensing for agricultural applications: A meta-review. Remote Sensing of Environment, 236, 111402. https://doi.org/10.1016/j.rse.2019.111402
   Web of Science ®Google Scholar
• Werner, J. P. S., Belgiu, M., Bueno, I. T., Dos Reis, A. A., Toro, A. P. S. G. D., Antunes, J. F. G., Figueiredo, G. K. D. A., Lamparelli, R. A. C., Magalhães, P. S. G., Coutinho, A. C., Esquerdo, J. C. D. M., & Figueiredo, G. K. D. A. (2024). Mapping integrated crop–livestock systems using fused Sentinel-2 and PlanetScope time series and deep learning. Remote Sensing, 16(8), 1421. https://doi.org/10.3390/rs16081421
   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., Boles, S., Liu, J., Zhuang, D., Frolking, S., Li, C., & Moore, B. (2005). Mapping paddy rice agriculture in southern China using multi-temporal MODIS images. Remote Sensing of Environment, 95(4), 480–492. https://doi.org/10.1016/j.rse.2004.12.009
   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
• Xing, H., Hou, D., Wang, S., Yu, M., & Meng, F. (2021). O-LCMapping: A Google Earth Engine-based web toolkit for supporting online land cover classification. Earth Science Informatics, 14(1), 529–541. https://doi.org/10.1007/s12145-020-00562-6
   Web of Science ®Google Scholar
• Xuan, F., Dong, Y., Li, J., Li, X., Su, W., Huang, X., & Zhang, Y. (2023). Mapping crop type in Northeast China during 2013–2021 using automatic sampling and tile-based image classification. International Journal of Applied Earth Observation and Geoinformation, 117, 103178. https://doi.org/10.1016/j.jag.2022.103178
   Web of Science ®Google Scholar
• Xu, F., Li, Z., Zhang, S., Huang, N., Quan, Z., Zhang, W., & Prishchepov, A. V. (2020). Mapping winter wheat with combinations of temporally aggregated Sentinel-2 and Landsat-8 data in Shandong province, China. Remote Sensing, 12(12), 2065. https://doi.org/10.3390/rs12122065
   Web of Science ®Google Scholar
• Xu, S., Zhu, X., Chen, J., Zhu, X., Duan, M., Qiu, B., & Cao, R. (2023). A robust index to extract paddy fields in cloudy regions from SAR time series. Remote Sensing of Environment, 285, 113374. https://doi.org/10.1016/j.rse.2022.113374
   Web of Science ®Google Scholar
• Yang, D. (2021). 安徽首个“旱改水”稻田喜获丰收 [Anhui’s first “drought to water” paddy field has achieved a bumper harvest]. https://www.moa.gov.cn/xw/qg/202109/t20210930_6378693.htm
   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
• Yang, Y., Tao, B., Ren, W., Zourarakis, D. P., Masri, B. E., Sun, Z., & Tian, Q. (2019). An improved approach considering intraclass variability for mapping winter wheat using multitemporal MODIS EVI images. Remote Sensing, 11(10), 1191. https://doi.org/10.3390/rs11101191
   Web of Science ®Google Scholar
• Yang, G., Yu, W., Yao, X., Zheng, H., Cao, Q., Zhu, Y., & Cheng, T. (2021). AGTOC: A novel approach to winter wheat mapping by automatic generation of training samples and one-class classification on Google Earth Engine. International Journal of Applied Earth Observation and Geoinformation, 102, 102446. https://doi.org/10.1016/j.jag.2021.102446
   Web of Science ®Google Scholar
• Yu, Z., Tian, Q., Pan, Q., Yu, S., Wang, D., Duan, C., & Niu, Y. (2002). Theory and practice of ultra-high-yield cultivation of winter wheat in Huanghuai wheat area. The Crop Journal, 28(5), 577–585.
   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
• 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
• Zhang, W., Brandt, M., Prishchepov, A. V., Li, Z., & Fensholt, R. (2021). Mapping the dynamics of winter wheat in the North China Plain from dense Landsat time series (1999 to 2019). Remote Sensing, 13(6), 1170. https://doi.org/10.3390/rs13061170
   Web of Science ®Google Scholar
• Zhang, D., Fang, S., She, B., Zhang, H., Jin, N., Xia, H., & Ding, Y. (2019). Winter wheat mapping based on Sentinel-2 data in heterogeneous planting conditions. Remote Sensing, 11(22), 2647. https://doi.org/10.3390/rs11222647
   Web of Science ®Google Scholar
• Zhang, X., Qiu, F., & Qin, F. (2019). Identification and mapping of winter wheat by integrating temporal change information and Kullback–Leibler divergence. International Journal of Applied Earth Observation Geoinformation, 76, 26–39. https://doi.org/10.1016/j.jag.2018.11.002
   Web of Science ®Google Scholar
• Zhao, G., Chang, X., Wang, D., Tao, Z., Wang, Y., Yang, Y., & Zhu, Y. (2018). General situation and development of wheat production. Crops, 18(4), 1–7.
   Google Scholar
• Zhou, Z.-H. (2016). 机器学习 [Machine learning]. Tsinghua University Press.
   Google Scholar
• Zhu, H., Li, B., & Wang, Z. (2021). 旱地改水田 产量翻一番 湖北4年“旱改水”6.5万亩 [Conversion of dry land into paddy fields, doubling the output of Hubei Province’s “Drought to Water” in 4 years has been 65,000 mu]. https://zrzyt.hubei.gov.cn/bmdt/ztzl/yggdbhjyjyyd/mtbdyggd/202101/t20210106_3201229.shtml
   Google Scholar
• Zhu, H., Zou, H., Yang, W., & Wang, B. (2021). 湖北基本形成数量、质量、生态一体耕地保护格局 [Hubei has basically formed a pattern of cultivated land protection integrating quantity, quality and ecology]. https://www.hubei.gov.cn/hbfb/bmdt/202101/t20210121_3298458.shtml
   Google Scholar