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Estimation of maize residue cover on the basis of SAR and optical remote sensing image

  • role： First author第一作者
  • Affiliation：

    Key Laboratory of Arable Land Conservation in North China, Ministry of Agriculture and Rural Affairs, College of Land Science and Technology, China Agricultural University ,Beijing 100193, China

  • Email：Liuzhiyu_cau@163.com
  • Introduction：刘之榆，1994年生，男，硕士研究生，研究方向为土地利用与信息技术及农业遥感。E-mail：Liuzhiyu_cau@163.com
LIU Zhiyu,  
  • role： Corresponding author通信作者
  • Affiliation：

    Key Laboratory of Arable Land Conservation in North China, Ministry of Agriculture and Rural Affairs, College of Land Science and Technology, China Agricultural University ,Beijing 100193, China

  • Email：lzh@cau.edu.cn
  • Introduction：刘忠，1970年生，男，副教授，研究方向为土地利用与信息技术及农业遥感。E-mail：lzh@cau.edu.cn
LIU Zhong*,  
  • Affiliation：

    Key Laboratory of Arable Land Conservation in North China, Ministry of Agriculture and Rural Affairs, College of Land Science and Technology, China Agricultural University ,Beijing 100193, China

WAN Wei,  
  • Affiliation：

    Key Laboratory of Arable Land Conservation in North China, Ministry of Agriculture and Rural Affairs, College of Land Science and Technology, China Agricultural University ,Beijing 100193, China

HUANG Jinyu,  
  • Affiliation：

    Key Laboratory of Arable Land Conservation in North China, Ministry of Agriculture and Rural Affairs, College of Land Science and Technology, China Agricultural University ,Beijing 100193, China

WANG Jiaying,  
  • Affiliation：

    Key Laboratory of Arable Land Conservation in North China, Ministry of Agriculture and Rural Affairs, College of Land Science and Technology, China Agricultural University ,Beijing 100193, China

ZHENG Mandi

resumen

Crop residue is the remaining stems, leaves, and fruit pods in the field after crop harvest. Crop residue plays an important role in the farmland ecosystem. Remote sensing technology has advantages in time and space, and it has become the main method to estimate Crop Residue Cover (CRC). Using remote sensing technology to estimate CRC can obtain information about ground CRC quickly in a large scale, which is of great significance to the promotion of conservation tillage. On the basis of a Sentinel-1 SAR image and a Sentinel-2 optical image, radar index and optical remote sensing index were constructed, respectively. The autumn and spring field sample data in 2018 and 2019 in Lishu County, Jilin Province were combined. The correlation of the remote sensing index and the maize residue cover was explored, and the method of soil texture zoning modeling was adopted to reduce the influence of surface background factors on the estimation of CRC. To further improve the estimation accuracy of maize residue cover, the radar index and optical remote sensing index were combined. Moreover, the optimal subset regression and soil texture zoning were used to establish the maize residue cover estimation model, and the estimation mapping of maize residue cover in the study area was then completed. Results show that: soil texture zoning modeling can effectively solve the problem of soil heterogeneity, thus improving the accuracy of inversion. The performance of each remote sensing index in autumn high coverage period in 2018 is better than that in spring low coverage period in 2019. The STI and NDTI index have strong stability and the best performance in optical remote sensing index. R2 is 0.701 and 0.697, respectively; whereas in the radar index, the correlation between γVH0 based on cosine correction method and CRC measured is the highest, and R2 is 0.564. The combination of radar index and optical remote sensing index can effectively improve the accuracy of CRC estimation. The regression model based on the combined index has the best performance with the method of optimal subset regression and soil texture zoning. The R2 of the model is 0.799, and the RMSE is 13.67%, which show high accuracy. Therefore, the proposed method improves the accuracy of CRC estimation.

palabra clave

microwave remote sensing;straw;coverage;maize;SAR;optimal subset regression

References

  1. 1.
    Aase J K and Tanaka D L. 1991. Reflectances from four wheat residue cover densities as influenced by three soil backgrounds. Agronomy Journal, 83(4): 753-757 [DOI: 08300040020x]
  2. 2.
    Biard F, Bannari A and Bonn F. 1995. SACRI (Soil Adjusted Corn Residue Index): un indice utilisant le proche et le moyen infrarouge pour la détection de résidus de culture de maïs//Proceedings of the 17ème Symposium Canadien Sur La Télédétection. Saskatoon: 413-419
  3. 3.
    Blanco-Canqui H and Lal R. 2007. Soil structure and organic carbon relationships following 10 years of wheat straw management in no-till. Soil and Tillage Research, 95(1/2): 240-254
  4. 4.
    Brisco B, Brown R J, Snider B, Sofko G J, Koehler J A and Wacker A G. 1991. Tillage effects on the radar backscattering coefficient of grain stubble fields. International Journal of Remote Sensing, 12(11): 2283-2298
  5. 5.
    Cai W T, Zhao S H, Wang Y M, Peng F C, Heo J and Duan Z. 2019. Estimation of winter wheat residue coverage using optical and SAR remote sensing images. Remote Sensing, 11(10): 1163
  6. 6.
    Cai W T, Zhao S H, Zhang Z H, Peng F C and Xu J J. 2018. Comparison of different crop residue indices for estimating crop residue cover using field observation data//Proceedings of the 7th International Conference on Agro-Geoinformatics. Hangzhou: IEEE: 1-4
  7. 7.
    Causarano H J, Doraiswamy P C, McCarty G W, Hatfield J L, Milak S and Stern A J. 2008. EPIC modeling of soil organic carbon sequestration in croplands of Iowa. Journal of Environmental Quality, 37(4): 1345-1353
  8. 8.
    CTIC. 2016. Tillage Type Definitions. West Lafayette: Conservation Technology Information Center
  9. 9.
    Daughtry C S T. 2001. Discriminating crop residues from soil by shortwave infrared reflectance. Agronomy Journal, 93(1): 125-131
  10. 10.
    Daughtry C S T, Graham M W, Stern A J, Quemada M, Hively W D and Russ A L. 2018. Landsat-8 and worldview-3 data for assessing crop residue cover//Proceedings of the IGARSS 2018-2018 IEEE International Geoscience and Remote Sensing Symposium. Valencia: IEEE
  11. 11.
    Daughtry C S T, Hunt E R, Doraiswamy III P C and McMurtrey J E. 2005. Remote sensing the spatial distribution of crop residues. Agronomy Journal, 97(3): 864-871
  12. 12.
    Daughtry C S T, Hunt E R and McMurtrey III J E. 2004. Assessing crop residue cover using shortwave infrared reflectance. Remote Sensing of Environment, 90(1): 126-134
  13. 13.
    Daughtry C S T, Serbin G, Reeves III J B, Doraiswamy P C and Hunt E R. 2010. Spectral reflectance of wheat residue during decomposition and remotely sensed estimates of residue cover. Remote Sensing, 2(2): 416-431
  14. 14.
    Gelder B K, Kaleita A L and Cruse R M. 2009. Estimating mean field residue cover on Midwestern soils using satellite imagery. Agronomy Journal, 101(3): 635-643
  15. 15.
    He M M, Guo Q, Li A, Chen J, Chen B and Feng X X. 2018. Automatic fast feature-level image registration for high-resolution remote sensing images. Journal of Remote Sensing, 22(2): 277-292
  16. 16.
    Huang J Y, Liu Z, Wan W, Liu Z Y, Wang J Y and Wang S. 2020. Remote sensing retrieval of maize residue cover on soil heterogeneous background. Chinese Journal of Applied Ecology, 31(2): 474-482
  17. 17.
    Jacinthe P A and Lal R. 2009. Tillage effects on carbon sequestration and microbial biomass in reclaimed farmland soils of southwestern Indiana. Soil Science Society of America Journal, 73(2): 605-613
  18. 18.
    Jin X L, Ma J H, Wen Z D and Song K S. 2015. Estimation of maize residue cover using Landsat-8 OLI image spectral information and textural features. Remote Sensing, 7(11): 14559-14575
  19. 19.
    Kong Q L, Li L, Xu K H and Zhu D H. 2017. Monitoring crop residue area in northeast of China based on Sentinel-1A data. Transactions of the Chinese Society for Agricultural Machinery, 48(S1): 284-289
  20. 20.
    Lal R, Kimble J M, Follett R F and Cole C V. 1999. The Potential of U.S. Cropland to Sequester Carbon and Mitigate the Greenhouse Effect. Boca Raton: Lewis Publishers: 128
  21. 21.
    Lei B Y. 2015. Estimation of Winter Wheat Residue Biomass Based on Full Polarimetric Radar Data. Nanjing: Nanjing University
  22. 22.
    Li M Z, Yu X T, Gao Y K and Fan W Y. 2018. Remote sensing quantification on forest biomass based on SAR polarization decomposition and Landsat data. Journal of Beijing Forestry University, 40(2): 1-10
  23. 23.
    Li Z T. 2016. Elimination of the Impact of Soil on the Estimation of Residue Cover by Remote Sensing Based on External Parameter Orthogonization. Beijing: University of Chinese Academy of Sciences
  24. 24.
    Li Z T, Wang C K, Pan X Z, Liu Y, Li Y L and Shi R J. 2016. Estimation of wheat residue cover using simulated Landsat-8 OLI datas. Transactions of the Chinese Society of Agricultural Engineering, 32(S1): 145-152
  25. 25.
    McNairn H, Boisvert J B, Major D J, Gwyn Q H J, Brown R J and Smith A M. 1996. Identification of agricultural tillage practices from C-band radar backscatter. Canadian Journal of Remote Sensing, 22(2): 154-162
  26. 26.
    McNairn H, Duguay C, Boisvert J, Huffman E and Brisco B. 2001. Defining the sensitivity of multi-frequency and multi-polarized radar backscatter to post-harvest crop residue. Canadian Journal of Remote Sensing, 27(3): 247-263
  27. 27.
    McNairn H, Duguay C, Brisco B and Pultz T J. 2002. The effect of soil and crop residue characteristics on polarimetric radar response. Remote Sensing of Environment, 80(2): 308-320
  28. 28.
    McNairn H and Protz R. 1993. Mapping corn residue cover on agricultural fields in Oxford County, Ontario, using thematic mapper. Canadian Journal of Remote Sensing, 19(2): 152-159
  29. 29.
    McNairn H, Wood D, Gwyn Q H J, Brown R J and Charbonneau F. 1998. Mapping tillage and crop residue management practices with RADARSAT. Canadian Journal of Remote Sensing, 24(1): 28-35
  30. 30.
    Nagler P L, Daughtry C S T and Goward S N. 2000. Plant litter and soil reflectance. Remote Sensing of Environment, 71(2): 207-215
  31. 31.
    Nagler P L, Inoue Y, Glenn E P, Russ A L and Daughtry C S T. 2003. Cellulose absorption index (CAI) to quantify mixed soil-plant litter scenes. Remote Sensing of Environment, 87(2/3): 310-325
  32. 32.
    Narayanan R M, Mielke L N and Dalton J P. 1992. Crop residue cover estimation using radar techniques. Applied Engineering in Agriculture, 8(6): 863-869
  33. 33.
    Qi J G, Marsett R, Heilman P, Bieden-Bender S, Moran S, Goodrich D and Weltz M. 2002. RANGES improves satellite‐based information and land cover assessments in southwest United States. EOS, Transactions American Geophysical Union, 83(51): 601-606
  34. 34.
    Rundquist D and Streck N A. 2002. Estimating residual wheat dry matter from remote sensing measurements. Photogrammetric Engineering and Remote Sensing, 68(11): 1193-1202
  35. 35.
    Serbin G, Hunt E R, Daughtry C S T, McCarty G W and Doraiswamy P C. 2009. An improved ASTER index for remote sensing of crop residue. Remote Sensing, 1(4): 971-991
  36. 36.
    Sharma A R, Kharol S K, Badarinath K V S and Singh D. 2010. Impact of agriculture crop residue burning on atmospheric aerosol loading-a study over Punjab State, India. Annales Geophysicae, 28(2): 367-379
  37. 37.
    Smith A M and Major D J. 1996. Radar backscatter and crop residues. Canadian Journal of Remote Sensing, 22(3): 243-247
  38. 38.
    Ulaby F T and Dobson M C. 1989. Handbook of Radar Scattering Statistics for Terrain. Norwood: Artech House
  39. 39.
    van Deventer A P, Ward A D, Gowda P H and Lyon J G. 1997. Using thematic mapper data to identify contrasting soil plains and tillage practices. Photogrammetric Engineering and Remote Sensing, 63(1): 87-93
  40. 40.
    Verhulst N, Nelissen V, Jespers N, Haven H, Sayre K D, Raes D, Deckers J and Govaerts B. 2011. Soil water content, maize yield and its stability as affected by tillage and crop residue management in rainfed semi-arid highlands. Plant and Soil, 344(1/2): 73-85
  41. 41.
    Wang C K. 2013. Estimation Crop Residue Cover by Remote Sensing and Correction of Moisture Effects on Estimates. Beijing: University of Chinese Academy of Sciences
  42. 42.
    Wilhelm W W, Johnson J M F, Hatfield J L, Voorhees W B and Linden D R. 2004. Crop and soil productivity response to corn residue removal: a literature review. Agronomy Journal, 96(1): 1-17
  43. 43.
    Wollenhaupt N C and Pingry J. 1991. Estimating Residue using the Line-Transect Method. Madison: University of Wisconsin
  44. 44.
    Yuan C Z, Zhou C T and Wang D T. 2018. Application of SENTINEL-1 TOOLBOX software in InSAR data processing. Journal of Geomatics, 43(3): 108-111
  45. 45.
    Zhang M, Li Q Z, Meng J H and Wu B F. 2011. Review of crop residue fractional cover monitoring with remote sensing. Spectroscopy and Spectral Analysis, 31(12): 3200-3205
  46. 46.
    Zhang M, Meng J H, Dong T F, Wu B F and Sun H J. 2012. Spectral responses analysis of soybean residues. Journal of Remote Sensing, 16(6): 1115-1129
  47. 47.
    Zhang Y F, Li H W, He J, Wang Q J, Li W Y, Cheng W Z and Zhang X Y. 2015. Effects of maize straw mulching on runoff and sediment process of slope. Transactions of the Chinese Society of Agricultural Engineering, 31(7): 118-124
  48. 48.
    Zheng B J, Campbell J B and de Beurs K M. 2012. Remote sensing of crop residue cover using multi-temporal Landsat imagery. Remote Sensing of Environment, 117: 177-183

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