Mapping of the winter crop planting areas in Huaihe River Basin based on Google Earth Engine

被引:0
|
作者
Pan L. [1 ]
Xia H. [1 ,2 ]
Wang R. [1 ]
Niu W. [1 ]
Tian H. [1 ]
Qin Y. [1 ,2 ]
机构
[1] College of Geography and Environmental Science, Henan University, Henan Key Laboratory of Earth System Observation and Modeling, Key Research Institute of Yellow River Civilization and Sustainable Development & Collaborative Innovation Center of Yellow Riv
[2] Key Laboratory of Geospatial Technology for the Middle and Lower Yellow River Regions (Henan University), Ministry of Education, Kaifeng
来源
Xia, Haoming (xiahm@vip.henu.edu.cn); Xia, Haoming (xiahm@vip.henu.edu.cn) | 1600年 / Chinese Society of Agricultural Engineering卷 / 37期
关键词
Crops; Google Earth Engine; Huaihe River Basin; Mapping; Planting area; Remote sensing;
D O I
10.11975/j.issn.1002-6819.2021.18.025
中图分类号
学科分类号
摘要
The winter crop has been one of the important crop types in China. Accurate and timely spatio-temporal distribution of planting area directly determines the grain output and economy, as well as the national food security. Taking the Huaihe River Basin as an example, this study aims to extract the planting areas of winter crops according to the phenology period using the Google Earth Engine cloud platform and the fusion of Landsat-7/8 and Sentinel-2A/B images. Firstly, a dataset of time-series images was constructed with a spatial resolution of 30 m. A CFMask algorithm was selected to preprocess the images, thereby calculating the Normalized Difference Vegetation Index (NDVI). More importantly, the maximum NDVI of all high-quality images within 10 days was used to obtain time-series data with equal time intervals. The linear interpolation was utilized to fill the pixels without high-quality images. Savitzky-Golay (S-G) filtering (a second-order filter with a moving window of 9 observations) was adopted to smooth the NDVI time series for the removal of noise. As such, a smoothed NDVI time series was obtained with a 10-day interval. Secondly, the peak growth, sowing, and harvest periods were determined to select sample points of winter crops with different spatial distributions, according to the NDVI time series. Subsequently, the winter crops were sowed in mid-late October, when the NDVI values were the lowest. The NDVI values gradually increased, after the emergence of seedlings in early November. The crops stopped growing in January during the overwintering period, where the NDVI stayed the same over the whole period. Furthermore, the NDVI resumed growing and gradually reached the peak growth period, when the winter crops turned green in February. After that, the NDVI reached the peak at the heading stage, and then gradually decreased. Correspondingly, the NDVI dropped to the bottom, when the harvest was over from the end of May to June. According to these characteristics in the process of winter crops growth, the peak growth period was determined from March 20, 2018, to April 20, 2018, the sowing period was determined from October 11, 2017, to November 10, 2018, and the harvest period was determined from May 20, 2018, to June 30, 2018. Particularly, the maximum NDVI was achieved in the peak growth period and the minimum and median of NDVI in the sowing and harvest period. Finally, the classification model of a decision tree was constructed, according to the NDVI boxplots of winter crops and non-winter crops at different time periods. The planting area map of winter crops was also generated for the Huaihe River Basin. The results showed that the planting area of winter crops was 8.762×106 hm2 in the Huaihe River Basin in 2018. Specifically, the user accuracy was 0.926, the producer accuracy was 0.970, the total accuracy was 0.958, and the Kappa coefficient was 0.912. Consequently, the large-scale planting area of winter crops was extracted accurately for the decision-making in similar areas. © 2021, Editorial Department of the Transactions of the Chinese Society of Agricultural Engineering. All right reserved.
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页码:211 / 218
页数:7
相关论文
共 31 条
  • [1] Franch B, Vermote E F, Becker-Reshef I, Et al., Improving the timeliness of winter wheat production forecast in the United States of America, Ukraine and China using MODIS data and NCAR growing degree day information, Remote Sensing of Environment, 161, pp. 131-148, (2015)
  • [2] Shao Y, Campbell J B, Taff G N, Et al., An analysis of cropland mask choice and ancillary data for annual corn yield forecasting using MODIS data, International Journal of Applied Earth Observation and Geoinformation, 38, pp. 78-87, (2015)
  • [3] Wang Limin, Liu Jia, Yang Fugang, Et al., Early recognition of winter wheat area based on GF-1 satellite, Transactions of the Chinese Society of Agricultural Engineering (Transactions of the CSAE), 31, 11, pp. 194-201, (2015)
  • [4] Hunt M L, Blackburn G A, Carrasco L, Et al., High resolutions wheat yield mapping using Sentinel-2[J/OL], Remote Sensing of Environment, 233, (2019)
  • [5] Jiao X F, Kovacs J, Shang J L, Et al., Object-oriented crop mapping and monitoring using multi-temporal polarimetric RADARSAT-2 data, ISPRS Journal of Photogrammetry and Remote Sensing, 96, pp. 38-46, (2014)
  • [6] Jin Z N, Azzari G, Lobell D., Improving the accuracy of satellite-based high-resolution yield estimation: A test of multiple scalable approaches, Agricultural and Forest Meteorology, 247, pp. 207-220, (2017)
  • [7] Jie Yi, Zhang Yongqing, Xun Lan, Et al., Crop classification based on multi-source remote sensing data fusion and LSTM algorithm, Transactions of the Chinese Society of Agricultural Engineering (Transactions of the CSAE), 35, 15, pp. 129-137, (2019)
  • [8] Liu L, Xiao X M, Qin Y W, Et al., Mapping cropping intensity in China using time series Landsat and Sentinel-2 images and Google Earth Engine[J/OL], Remote Sensing of Environment, 239, (2020)
  • [9] Liu C, Zhang Q, Tao S Q, Et al., A new framework to map fine resolution cropping intensity across the globe: Algorithm, validation, and implication[J/OL], Remote Sensing of Environment, 251, (2020)
  • [10] Tian H F, Huang N, Niu Z, Et al., Mapping winter crops in China with multi-source satellite imagery and phenology-based algorithm, Remote Sensing, 11, 7, pp. 820-843, (2019)