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Extraction of planting areas of main crops based on sparse representation of time-series leaf area index

  • role： First author第一作者
  • Affiliation：

    College of Information and Electrical Engineering, China Agricultural University, Beijing 100083, China

    Key Laboratory of Remote Sensing for Agri-Hazards, Ministry of Agriculture and Rural Affairs, Beijing 100083, China

  • Email：wangpx@cau.edu.cn
  • Introduction：王鹏新，1965年生，男，教授，研究方向为定量遥感及其农业应用。E-mail：wangpx@cau.edu.cn
WANG Pengxin12,  
  • Affiliation：

    College of Information and Electrical Engineering, China Agricultural University, Beijing 100083, China

    Key Laboratory of Remote Sensing for Agri-Hazards, Ministry of Agriculture and Rural Affairs, Beijing 100083, China

XUN Lan12,  
  • role： Corresponding author通信作者
  • Affiliation：

    College of Information and Electrical Engineering, China Agricultural University, Beijing 100083, China

    Key Laboratory of Remote Sensing for Agri-Hazards, Ministry of Agriculture and Rural Affairs, Beijing 100083, China

  • Email：lilixch@cau.edu.cn
  • Introduction：李俐，1976年生，女，副教授，研究方向为微波遥感及其农业应用。E-mail：lilixch@cau.edu.cn
LI Li12*,  
  • Affiliation：

    College of Information and Electrical Engineering, China Agricultural University, Beijing 100083, China

    Key Laboratory of Remote Sensing for Agri-Hazards, Ministry of Agriculture and Rural Affairs, Beijing 100083, China

WANG Lei12,  
  • Affiliation：

    College of Information and Electrical Engineering, China Agricultural University, Beijing 100083, China

    Key Laboratory of Remote Sensing for Agri-Hazards, Ministry of Agriculture and Rural Affairs, Beijing 100083, China

KONG Qingling12

Resümee

Crop mapping is an important component of agriculture monitoring. Accurate information on crop area coverage is vital for food security and the agricultural industry, and the demand for timely crop mapping is high. Previous research indicated that remote sensing technology is a practical and feasible method for agricultural crop area extraction. In this study, the north area of the Yellow River in the North China Plain is chosen as the study area, where the main crops are winter wheat, maize, cotton, and soybean. To obtain the distribution information of crops, the yearly four-day composite MODIS time-series Leaf Area Index (LAI) with 500 m spatial resolution is collected. A total of 92 MODIS LAI images obtained yearly from 2007 to 2016 are used to build time-series LAI curves. To avoid the edge effect of the time-series LAI caused by the Savitzky–Golay filter, the last two phases of LAI images in the last year and the first two phases of LAI images in the next year are added to build the time-series LAI in a year. The Savitzky–Golay filter is then applied on the yearly time-series LAI pixel by pixel to minimize effects of anomalous values caused by atmospheric haze, cloud contamination, and so on. Fourier transform method based on reconstructed LAI is further employed to extract the key parameters. The 11 parameters, including the amplitudes of 0–5 terms and the phases of 1–5 terms, are taken as the features for crop identification. The training samples and verification samples of various crops are obtained through ground investigation and Google Earth images. On the basis of the training samples of various crops, online dictionary learning algorithm is applied to construct the dictionary used to identify the crops. With the dictionary, the orthogonal matching pursuit algorithm is further applied on samples under testing to obtain the sparse representation coefficient. Then the crops are identified according to the minimum reconstruction error, which can be calculated by the dictionary and the coefficient. Therefore, the areas planting winter wheat, spring maize, summer maize, cotton, and orchard from 2007 to 2016 are extracted in the study area. Lastly, the accuracy of the identification results is evaluated yearly by a confusion matrix. Results show that the reconstructed time-series LAI curves are smooth and consistent with crop growth and development characteristics. Overall identification accuracy reaches 77.97% with a Kappa coefficient of 0.74 from 2007 to 2016. User accuracies for individual crops are as follows: winter wheat and summer maize, 90.60%; spring maize, 73.40%; early summer maize, 81.80%; cotton, 69.40%; and orchard, 81.60%. Annual overall accuracies from 2007 to 2016 range between 70.57% and 83.71% and Kappa coefficients range from 0.66 to 0.81. In conclusion, combining the harmonic characteristics of the time-series LAI with the sparse representation can effectively identify the areas for planting different crops. The approach developed in this study is feasible for extracting information on main crop distribution in the study area.

Schlüsselwort

remote sensing;leaf area index;sparse representation;Savitzky-Golay filter;North China Plain;crops;identification

References

  1. 1.
    Arvor D, Jonathan M, Meirelles M S P, Dubreuil V and Durieux L. 2011. Classification of MODIS EVI time series for crop mapping in the state of Mato Grosso, Brazil. International Journal of Remote Sensing, 32(22): 7847–7871
  2. 2.
    Belgiu M and 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
  3. 3.
    Candes E J and Wakin M B. 2008. An introduction to compressive sampling. IEEE Signal Processing Magazine, 25(2): 21–30
  4. 4.
    Chen J, Jönsson P, Tamura M, Gu Z H, Matsushita B and Eklundh L. 2004. A simple method for reconstructing a high-quality NDVI time-series data set based on the savitzky–golay filter. Remote Sensing of Environment, 91(3/4): 332–344
  5. 5.
    Chen Y, Nasrabadi N M and Tran T D. 2011. Hyperspectral image classification using dictionary-based sparse representation. IEEE Transactions on Geoscience and Remote Sensing, 49(10): 3973–3985
  6. 6.
    Conrad C, Colditz R, Dech S, Klein D and Vlek P L G. 2011. Temporal segmentation of MODIS time series for improving crop classification in Central Asian irrigation systems. International Journal of Remote Sensing, 32(23): 8763–8778
  7. 7.
    Efron B, Hastie T, Johnstone I and Tibshirani R. 2004. Least angle regression. The Annals of Statistics, 32(2): 407–499
  8. 8.
    Fang M R and Tian Q J. 2004. The study of land type classification technology based on time-series LAI of MODIS. Remote Sensing for Land and Resources, 16(3): 5–7, 22
  9. 9.
    Jia K, Liang S L, Wei X Q, Yao Y J, Su Y R, Jiang B and Wang X X. 2014. Land cover classification of Landsat data with phenological features extracted from time series MODIS NDVI data. Remote Sensing, 6(11): 11518–11532
  10. 10.
    Liang S Z, Xing Q G, Shi P and Zhou D. 2011. Harmonic analysis on NDVI time series of typical land covers in Shandong Province. Chinese Journal of Ecology, 30(1): 59–65
  11. 11.
    Liu S, Zhu Y J and Xue L. 2015. Remote sensing image super-resolution method using sparse representation and classified texture patches. Geomatics and Information Science of Wuhan University, 40(5): 578–582
  12. 12.
    Mairal J, Bach F, Ponce J and Sapiro G. 2010. Online learning for matrix factorization and sparse coding. The Journal of Machine Learning Research, 11: 19–60
  13. 13.
    Mallat S G and Zhang Z F. 1993. Matching pursuits with time-frequency dictionaries. IEEE Transactions on Signal Processing, 41(12): 3397–3415
  14. 14.
    Massey R, Sankey T T, Congalton R G, Yadav K, Thenkabail P S, Ozdogan M and Meador A J S. 2017. MODIS phenology-derived, multi-year distribution of conterminous U.S. crop types. Remote Sensing of Environment, 198: 490–503
  15. 15.
    Mo X G, Liu S X, Lin Z H and Qiu J X. 2011. Patterns of evapotranspiration and GPP and their responses to climate variations over the North China Plain. Acta Geographica Sinica, 66(5): 589–598
  16. 16.
    Pan Y Z, Li L, Zhang J S, Liang S L, Zhu X F and 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
  17. 17.
    Savitzky A and Golay M J E. 1964. Smoothing and differentiation of data by simplified least squares procedures. Analytical Chemistry, 36(8): 1627–1639
  18. 18.
    Skakun S, Franch B, Vermote E, Roger J C, Becker-Reshef I, Justice C and Kussul N. 2017. Early season large-area winter crop mapping using MODIS NDVI data, growing degree days information and a Gaussian mixture model. Remote Sensing of Environment, 195: 244–258
  19. 19.
    Song L, Cheng Y M and Zhao Y Q. 2012. Hyper-spectrum classification based on sparse representation model and auto-regressive model. Acta Optica Sinica, 32(3): 0330003
  20. 20.
    Song X F and Jiao L C. 2012. Classification of hyperspectral remote sensing image based on sparse representation and spectral information. Journal of Electronics and Information Technology, 34(2): 268–272
  21. 21.
    Tang P Q, Wu W B, Yao Y M and Yang P. 2011. New method for extracting multiple cropping index of North China Plain based on wavelet transform. Transactions of the CSAE, 27(7): 220–225
  22. 22.
    Tang X Q, Liu Y Z and Chen J L. 2016. Improvement of remote sensing image classification method based on sparse representation. Computer Engineering, 42(3): 254–258, 265
  23. 23.
    Tropp J A and Gilbert A C. 2007. Signal recovery from random measurements via orthogonal matching pursuit. IEEE Transactions on Information Theory, 53(12): 4655–4666
  24. 24.
    Wang P X, Xun L, Li L, Xie Y and Wang L. 2017. Extraction of planting areas of main crops based on Fourier transformed characteristics of time series leaf area index products. Transactions of the Chinese Society of Agricultural Engineering, 33(21): 207–215
  25. 25.
    Wang X, Li X B, Tan M H and Xin L J. 2015. Remote sensing monitoring of changes in winter wheat area in North China Plain from 2001 to 2011. Transactions of the Chinese Society of Agricultural Engineering, 31(8): 190–199
  26. 26.
    Wardlow B D and Egbert S L. 2008. Large-area crop mapping using time-series MODIS 250 m NDVI data: an assessment for the U. S. Central Great Plains. Remote Sensing of Environment, 112(3): 1096–1116
  27. 27.
    Whelen T and Siqueira P. 2017. Use of time-series L-band UAVSAR data for the classification of agricultural fields in the San Joaquin Valley. Remote Sensing of Environment, 193: 216–224
  28. 28.
    Wright J, Yang A Y, Ganesh A, Sastry S S and Ma Y. 2009. Robust face recognition via sparse representation. IEEE Transactions on Pattern Analysis and Machine Intelligence, 31(2): 210–227
  29. 29.
    Wu W B, Yang P, Tang H J, Zhou Q B, Ryosuke S and Zhang L. 2009. Comparison of two fitting methods of NDVI time series datasets. Transactions of the CSAE, 25(11): 183–188
  30. 30.
    Xia Q, Xing S, Ma D Y, Mo D L, Li P C and Ge Z X. 2016. An improved K-SVD-based denoising method for remote sensing satellite images. Journal of Remote Sensing, 20(3): 441–449
  31. 31.
    Yin W, Li Y X, Zhou Z M and Liu S Q. 2013. Remote sensing image fusion based on sparse representation. Acta Optica Sinica, 33(4): 0428003

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