Rapid and Accurate Mapping of Winter Wheat Distribution in Jiaozuo City Using Sentinel-2 and Google Earth Engine

Haofeng Luo

doi:10.54097/ttcn1622

Authors

Haofeng Luo

DOI:

https://doi.org/10.54097/ttcn1622

Keywords:

Winter wheat distribution; Sentinel-2; Remote sensing cloud computing; Accuracy; Classifier.

Abstract

Rapid and accurate mapping using remote sensing and cloud computing is crucial for food security. This study employs Google Earth Engine (GEE) and Sentinel-2A multispectral imagery to analyze the distribution of winter wheat in Jiaozuo City from 2019 to 2021 using the one-class support vector machine (OCSVM) classification method. The results show high classification accuracy, with an overall accuracy of 0.952, a Kappa coefficient of 0.952, a producer's accuracy of 0.953, and a consumer's accuracy of 0.978, validating the classification effectiveness. This method is efficient, easy to implement, and suitable for large-scale crop mapping, providing valuable support for sustainable agriculture and policy-making.

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References

[1] FAO. The future of food and agriculture – Trends and challenges. Rome: Food and Agriculture Organization of the United Nations, 2017.

[2] Gorelick, N., Hancher, M., Dixon, M., et al. Google Earth Engine: Planetary-scale geospatial analysis for everyone. Remote Sensing of Environment, 2017, 202: 18-27.

[3] Cai Y, Guan K, Peng J, et al. A high-performance and in-season classification system of field-level crop types using time-series Landsat data and a machine learning approach. Remote Sensing of Environment, 2018, 210: 35-47.

[4] Zhu, Z., Wulder, M.A., Roy, D.P., et al. Benefits of the free and open Landsat data policy. Remote Sensing of Environment, 2019, 224: 382-385.

[5] Yin, Jie, et al. "A comparative study on wheat identification and growth monitoring based on multi-source remote sensing data."Remote Sensing Technology and Application36.2 (2021): 332-341.

[6] Liang, Z. H. A. O., et al. "Large-scale winter wheat extraction based on GEE platform and automatic statistical allocation algorithm." HUBEI AGRICULTURAL SCIENCES 61.19 (2022): 132.

[7] Cai, Wenxin, Jinyan Tian, Xiaojuan Li, Lin Zhu, and Beibei Chen. 2022. "A New Multiple Phenological Spectral Feature for Mapping Winter Wheat" Remote Sensing14, no. 18: 4529.

[8] Zhang, H.; Du, H.; Zhang, C.; Zhang, L. An automated early-season method to map winter wheat using time-series Sentinel-2 data: A case study of Shandong, China.Comput. Electron. Agric.2021,182, 105962.

[9] Thenkabail, P.S., et al. An Automated Methodology for Mapping Global Croplands and Cropland Subtypes Using Landsat and MODIS Data. Remote Sensing of Environment, 2015, 162: 25-45.

[10] Xiao, X., et al. A Time-Series MODIS-NDVI Approach for Mapping Winter Wheat in China. Remote Sensing of Environment, 2005, 98: 441-457.

[11] Zhang, M., et al. Mapping Winter Wheat in China Using MODIS Time-Series Data. Remote Sensing of Environment, 2008, 112: 135-148.

[12] Atzberger, C. Advances in Remote Sensing of Agriculture: Context Description and Classification. Remote Sensing of Environment, 2013, 130: 85-110.

[13] Huete, A.R., et al. Overview of the Radiometric and Biophysical Performance of the MODIS Vegetation Indices. Remote Sensing of Environment, 2002, 83: 195-213.

[14] Gitelson, A.A., et al. Vegetation Index-Based Assessment of Winter Wheat Canopy Chlorophyll Content at the Plot Scale. Remote Sensing of Environment, 2002, 82: 70-85.

[15] Tucker, C.J. Red and Photographic Infrared Linear Combinations for Monitoring Vegetation. Remote Sensing of Environment, 1979, 8: 127-150.

[16] Huete, A.R., et al. A Soil-Adjusted Vegetation Index (SAVI) to Eliminate the Need for a Bare Soil Line. Remote Sensing of Environment, 1985, 18: 29-50.

[17] Jiang, Z., et al. Development of the Enhanced Vegetation Index. Remote Sensing of Environment, 2000, 73: 164-177.

[18] Meroni, M., et al. A Critical Analysis of the Use of Normalized Difference Vegetation Index (NDVI) for Crop Monitoring. Remote Sensing of Environment, 2004, 92: 429-441.

[19] Qi, J., et al. A Vegetation Index for the Remote Assessment of Plant Canopy Chlorophyll Content. Remote Sensing of Environment, 1994, 48: 43-54.

[20] Serra, P., et al. Mapping Winter Wheat in Spain Using MODIS Time-Series Data. Remote Sensing of Environment, 2007, 107: 345-358.