Guo Qile, Li Junling, Guo Peng. Extraction of peanut planting area based on dual-temporal remote sensing features of crops. J Appl Meteor Sci, 2022, 33(2): 218-230. DOI:  10.11898/1001-7313.20220208.
Citation: Guo Qile, Li Junling, Guo Peng. Extraction of peanut planting area based on dual-temporal remote sensing features of crops. J Appl Meteor Sci, 2022, 33(2): 218-230. DOI:  10.11898/1001-7313.20220208.

Extraction of Peanut Planting Area Based on Dual-temporal Remote Sensing Features of Crops

DOI: 10.11898/1001-7313.20220208
  • Received Date: 2021-11-25
  • Rev Recd Date: 2022-01-11
  • Publish Date: 2022-03-31
  • Various high-resolution satellite remote sensing platforms and sensors have been established recently, but there are still many challenges for crop type fine classification. New methods are needed for fine classification of crop types and extraction of peanut planting area using high-resolution optical remote sensing data. Using panchromatic and multi-spectral (PMS) data of GF-1 and GF-6 in the middle and late stages of crop growth, 40 classification features are constructed from the perspective of spectrum, texture, and temporal variation of remote sensing image by means of spatio-temporal fusion and spectral mathematical transformation.15 features are selected by ReliefF-Pearson method. To compare the effects of different feature types on classification, the selected features are combined into four feature spaces, and the separability between crops in these feature spaces is measured by Jeffries-Matusita (J-M) distance. Using maximum likelihood classification, support vector machine and random forest as classifiers, crop classification tests of four feature space schemes are carried out, and different classification results are evaluated from the perspective of remote sensing classification accuracy and landscape analysis. Compared with the original image, in four feature spaces, the J-M distance between peanut and other crops is close to the saturation value of 2.0, and their separability is significantly enhanced. For all test results, the overall accuracy of crop classification is more than 78.0%, the Kappa coefficient of crop classification is more than 0.7, and the user's accuracy and producer's accuracy of peanut are both more than 79.0%. Compared with the three classifiers, Random Forest has the highest classification accuracy, and the average values of the overall accuracy and Kappa coefficient, peanut user's accuracy and producer's accuracy of its four schemes are 91.49%, 0.89, 94.45% and 93.30%, respectively. In addition, combining dual-temporal remote sensing data, texture and vegetation index features, as well as their temporal changes, can effectively improve the classification accuracy. It shows that adding the texture feature can improve the accuracy better than the vegetation index features. The landscape shape analysis of the classification results also shows that the overall crop classification and peanut, their patch shapes are close to the reality, and random forest is the best classifier. Its mean shape indices of crop landscape-level and peanut class-level are only 1.333 and 1.270, and the corresponding mean patch fractal dimension indices are only 1.127 and 1.110. An optimal dual temporal remote sensing crop classification model is proposed. Using this model, summer peanut planting area extraction and area measurement are carried out in four main peanut producing counties in Huang-Huai-Hai Region. Compared with the statistical data, the determination coefficient is 0.98 and the relative error of area calculation is ±16.25%, showing a good application prospect.
  • Fig. 1  Typical study area and experimental measurements

    (the square denotes training sample, the circle denotes test sample)

    Fig. 2  Weight and ranking of features

    Fig. 3  Pearson correlation coefficient between features

    Fig. 4  Classification accuracy of different schemes

    Fig. 5  Landscape metrics of different schemes

    Fig. 6  Recognition results of peanut planting area by C-DRSC and spatial distribution of regional proportion of statistical area

    Fig. 7  Experimental effect analysis of peanut planting area measured by remote sensing

    Table  1  Experimental measurements for each crop type

    作物 训练样本 测试样本
    地块 像素 地块 像素
    玉米 49 59539 45 55786
    花生 79 212440 86 241625
    水稻 45 100386 43 94936
    大豆 30 27994 29 26862
    DownLoad: Download CSV

    Table  2  Information of selected features

    编码 特征参量 类型
    S1~S4 2020-08-01时相的红、绿、蓝和近红外波段 光谱特征
    S5~S8 2020-08-15时相的红、绿、蓝和近红外波段
    V1 2020-08-01时相的归一化植被指数 植被指数特征
    V2 2020-08-01时相的绿度总和指数
    V3 2020-08-01时相的比值植被指数
    V4 2020-08-01和2020-08-15两时相归一化植被指数差
    T1 2020-08-01时相纹理平均值 纹理特征
    T2 2020-08-15时相纹理平均值
    T3 2020-08-15和2020-08-01两时相纹理平均值差
    DownLoad: Download CSV

    Table  3  The J-M distances between peanut and the following crop in different feature spaces

    作物 原始影像 构造的特征空间
    2020-08-01 2020-08-15 A B C D
    大豆 1.9199 1.4455 1.9527 1.9564 1.9967 1.9970
    水稻 1.9669 1.4921 1.9998 1.9999 2.0000 2.0000
    玉米 1.7635 1.9935 1.8510 1.8616 1.9916 1.9922
    DownLoad: Download CSV
  • [1]
    Tang H J, Zhou Q B, Liu J, et al. Wheat Mapping Using High Resolution Remote Sensing Data. Beijing: Science Press, 2016.
    Luo J N, Liu L W. Research and implementation of remote sensing big data distributed technology. J Appl Meteor Sci, 2017, 28(5): 621-631. doi:  10.11898/1001-7313.20170510
    Ye Q L, Xu D P, Zhang D. Remote sensing image classification based on deep learning features and support vector machine. Journal of Forestry Engineering, 2019, 4(2): 125-131.
    Kuang Q M, Yu T Z. Review of leveraging AI for exploitation of satellite observations. Advances in Meteorological Science and Technology, 2020, 10(3): 21-29. doi:  10.3969/j.issn.2095-1973.2020.03.004
    Sun J, Cao Z, Li H, et al. Application of artificial intelligence technology to numerical weather prediction. J Appl Meteor Sci, 2021, 32(1): 1-11. doi:  10.11898/1001-7313.20210101
    Zhang P, Hu S G. Fine crop classification by remote sensing in complex planting area based on field parcel. Transactions of the CSAE, 2019, 35(20): 125-134. doi:  10.11975/j.issn.1002-6819.2019.20.016
    Jin Z Q, Wang X M, Bao Y S, et al. Squall line identification method based on convolution neural network. J Appl Meteor Sci, 2021, 32(5): 580-591. doi:  10.11898/1001-7313.20210506
    Li Y, Chen H L. Review of machine learning approaches for modern agrometeorology. J Appl Meteor Sci, 2020, 31(3): 257-266. doi:  10.11898/1001-7313.20200301
    Deng J Z, Liu Q D, Wang C W, et al. Crop classification based on multitemporal Sentinel-2 satellite imagery. Guangdong Agricultural Sciences, 2020, 47(4): 129-138.
    Liu J, Wang L M, Teng F, et al. Impact of red-edge waveband of RapidEye satellite on estimation accuracy of crop planting area. Transactions of the CSAE, 2016, 32(13): 140-148. doi:  10.11975/j.issn.1002-6819.2016.13.020
    Wei P F, Xu X G, Yang G J, et al. Remote sensing classification of crops based on the change characteristics of multi-phase vegetation index. Journal of Agricultural Science and Technology, 2019, 21(2): 54-61.
    Zhang Y, Yang W, Sun Y, et al. Fusion of multispectral aerial imagery and vegetation indices for machine learning-based ground classification. Remote Sensing, 2021, 13(8): 1411. doi:  10.3390/rs13081411
    Liu E H, Zhou G S, Zhou L, et al. Remote sensing inversion of leaf and canopy water content in different growth stages of summer maize. J Appl Meteor Sci, 2020, 31(1): 52-62. doi:  10.11898/1001-7313.20200105
    Yang L, Han L J, Song J L, et al. Monitoring and evaluation of high temperature and heat damage of summer maize based on remote sensing data. J Appl Meteor Sci, 2020, 31(6): 749-758. doi:  10.11898/1001-7313.20200610
    Zhang Y G, Tao Y X, Luo X B, et al. Hyperspectral image classification based on feature importance. Infrared Technology, 2020, 42(12): 1185-1191.
    Yang S T, Gu L J, Li X F, et al. Crop classification method based on optimal feature selection and hybrid CNN-RF networks for multi-temporal remote sensing imagery. Remote Sensing, 2020, 12(19): 3119. doi:  10.3390/rs12193119
    Orynbaikyzy A, Gessner U, Mack B, et al. Crop type classification using fusion of Sentinel-1 and Sentinel-2 data: Assessing the impact of feature selection, optical data availability, and parcel sizes on the accuracies. Remote Sensing, 2020, 12(17): 2779. doi:  10.3390/rs12172779
    Deng L Y, Shen Z F, Ke Y M, et al. Winter wheat planting area extraction using multi-temporal remote sensing images based on field parcel. Transactions of the CSAE, 2018, 34(21): 157-164. doi:  10.11975/j.issn.1002-6819.2018.21.019
    Ma Q R, Zuo X, Hu C D, et al. Effects of waterlogging on photosynthetic characteristics and yield of summer peanut. J Appl Meteor Sci, 2021, 32(4): 479-490. doi:  10.11898/1001-7313.20210409
    Zhou S D, Jing L Y, Meng H K, et al. Analysis on spatial distribution evolution of main peanut production areas in China and its influencing factors. Journal of Agrotechnical Economics, 2018(3): 100-109.
    Wei S C, Li K W, Zhang J Q, et al. Hazard assessment of peanut drought and flood disasters in Huang-Huai-Hai Region. J Appl Meteor Sci, 2021, 32(5): 629-640. doi:  10.11898/1001-7313.20210510
    Cheng Z S, Li Y R, Xu G Z, et al. Present situation of peanut production, scientific research and industrialization development countermeasures in Hebei Province. Journal of Peanut Science, 2003, 32(Suppl I): 60-63.
    Zhou S, Han B, Li S, et al. Current situation and development countermeasure of peanut in Henan Province. Tianjin Agricultural Sciences, 2021, 27(8): 56-59. doi:  10.3969/j.issn.1006-6500.2021.08.011
    Zhu B C, Li Z G, Wang Y T, et al. Consideration on development of peanut industry in Henan-Investigation on the development of peanut industry in Zhengyang County, Zhumadian City. China Grain Economy, 2019(10): 52-55. doi:  10.3969/j.issn.1007-4821.2019.10.015
    Liu N, Xiong A Y, Zhang Q, et al. Development of basic dataset of severe convective weather for artificial intelligence training. J Appl Meteor Sci, 2021, 32(5): 530-541. doi:  10.11898/1001-7313.20210502
    Wang N, Li Q Z, Du X, et al. Identification of main crops based on the univariate feature selection in Subei. Journal of Remote Sensing, 2017, 21(4): 519-530.
    Chen Y W, Huang X M, Li Y, et al. Ensemble learning for bias correction of station temperature forecast based on ECMWF products. J Appl Meteor Sci, 2020, 31(4): 494-503. doi:  10.11898/1001-7313.20200411
    Han F, Yang L, Zhou C X, et al. An experimental study of the short-time heavy rainfall event forecast based on ensemble learning and sounding data. J Appl Meteor Sci, 2021, 32(2): 188-199. doi:  10.11898/1001-7313.20210205
    Kononenko I. Estimating Attributes: Analysis and Extensions of RELIEF//European Conference on Machine Learning, 1994: 171-182.
    Liu Y, Meng Q Y, Wang Y J, et al. A method for estimating impervious surface percentage based on feature optimization and SVM. Geography and Geo-Information Science, 2018, 34(1): 24-31. doi:  10.3969/j.issn.1672-0504.2018.01.005
    Tong Q X, Zhang B, Zheng L F. Hyperspectral Remote Sensing: Principles, Techniques, and Applications. Beijing: Higher Education Press, 2006.
    Geng X R. Target Detection and Classification for Hyperspectral Imagery. Beijing: University of Chinese Academy of Sciences(Institute of Remote Sensing Applications), 2005.
    Deng N Y, Tian Y J. A New Method in Data Mining: Support Vector Machine. Beijing: Science Press, 2004.
    Breiman L. Random forests. Machine Learning, 2001, 45(1): 5-32. doi:  10.1023/A:1010933404324
    Congalton R G. A review of assessing the accuracy of classifications of remotely sensed data. Remote Sensing of Environment, 1991, 37(1): 35-46. doi:  10.1016/0034-4257(91)90048-B
    Zhang Y S, Ouyang F, Yuan Z M. Change characteristics of landscape pattern in farmland ecosystems in North China. Ecological Science, 2018, 37(4): 114-122.
    Luo Z J, Zhao Y, Zhao J, et al. Defining of permanent basic farmland based on landscape pattern and spatial autocorrelation. Transactions of the Chinese Society for Agricultural Machinery, 2018, 49(10): 195-204. doi:  10.6041/j.issn.1000-1298.2018.10.022
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    • Received : 2021-11-25
    • Accepted : 2022-01-11
    • Published : 2022-03-31


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