سابِق|تال

Analysis of the influence of key-phase growth-environment-landscape features on the accuracy of county-level winter wheat yield estimation in Hebei Province

  • role： First author第一作者
  • Affiliation：

    Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100101

    University of Chinese Academy of Sciences, Beijing 100049, China

  • Email：zhujiong@aircas.ac.cn
  • Introduction： 朱炯，研究方向为农业遥感估产。E-mail：zhujiong@aircas.ac.cn
ZHU Jiong12,  
  • Affiliation：

    Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100101

DU Xin1,  
  • role： Corresponding author通信作者
  • Affiliation：

    Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100101

  • Email：liqz@aircas.ac.cn
  • Introduction： 李强子，研究方向为农业遥感。E-mail：liqz@aircas.ac.cn
LI Qiangzi1*,  
  • Affiliation：

    Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100101

ZHANG Yuan1,  
  • Affiliation：

    Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100101

WANG Hongyan1,  
  • Affiliation：

    Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100101

    University of Chinese Academy of Sciences, Beijing 100049, China

ZHAO Yuncong12

ملخص

Accurate and rapid regional-scale crop yield estimation can provide effective data support for the formulation of national food security policies. Compared with complex mechanism models, sampling statistical surveys and empirical models based on multi-source data have better reliability and operability for county-level or city-level yield estimation. Previous studies have proposed many factors related to winter wheat yield, but systematic research on the selection and analysis of multi-source factors is lacking. On the basis of remote sensing, meteorological, and statistical data, this study systematically explored the influence of key-phase growth-environment-landscape features on winter wheat yield estimation at the county-level and determined the best time phase and characteristic parameters.The considered features included crop condition, environmental forcing (e.g., precipitation, light, and temperature), and farmland landscape. The key phases were the key periods of winter wheat yield formation (P1—P5), which were extracted from the NDVI curve of the crop growth process. Random Forest (RF) regression models were developed using different combinations of phases and features to simulate statistical wheat yield data and evaluate the importance of different combinations via an accuracy assessment. The performance of the models built from each layer combination was compared using the Mean Relative Error (MRE), Root Mean-Squared Error (RMSE), Normalized Root Mean-Squared Error (NRMSE), and coefficient of determination (R2). Data on years 2014—2017 were used to build the models, and 2018 data were utilized for validation.Results showed that P2, P3, and P4 resulted in higher accuracy than P1 and P5 in terms of single phases. The model accuracy using multi-phase features was higher than that of using single phases, and the combination of P2 and P4 was the best. Among all the features, crop growth features had the greatest impact on yield estimation accuracy, whereas the addition of environmental forcing factors (e.g., water, light, and temperature) did not significantly improve the accuracy. The addition of farmland landscape features could effectively improve the accuracy of yield estimation. Moreover, five important features (PROP, NDVI_P2, B2_P2, ED, and B1_P4) were selected, and a yield estimation model was established to obtain the county-level yield of winter wheat in Hebei Province. The MRE of wheat yield estimation at the county level in 2018 was as low as 2.85%, and the RMSE, NRMSE, and R2 were 253.25 kg/ha, 4.09%, and 0.83, respectively.Conclusion Multi-phase performance is better than single-phase performance. Combining crop growth features with farmland landscape features (RMSE of 247.79 kg/ha) provides more accurate estimates than using crop growth features alone (RMSE of 295.95 kg/ha). Furthermore, the RF model produces good yield estimation results. This study provides insights into and new methods for nationwide estimation of winter wheat yield at the county level.

مفهوم

remote sensing;yield estimation;winter wheat;statistical data;NDVI;Random Forest;Hebei Province

References

  1. 1.
    Balaghi R, Tychon B, Eerens H and Jlibene M . 2008. Empirical regression models using NDVI, rainfall and temperature data for the early prediction of wheat grain yields in Morocco. International Journal of Applied Earth Observation and Geoinformation, 10(4): 438-452
  2. 2.
    Bolton D K and Friedl M A . 2013. Forecasting crop yield using remotely sensed vegetation indices and crop phenology metrics. Agricultural and Forest Meteorology, 173: 74-84
  3. 3.
    Breiman L . 2001. Random forests. Machine Learning, 45(1): 5-32
  4. 4.
    Cheng Z Q and Meng J H . 2015. Research advances and perspectives on crop yield estimation models. Chinese Journal of Eco-Agriculture, 23(4): 402-415
  5. 5.
    De Wit A J W and Van Diepen C A . 2007. Crop model data assimilation with the Ensemble Kalman filter for improving regional crop yield forecasts. Agricultural and Forest Meteorology, 146(1/2): 38-56
  6. 6.
    Doraiswamy P . 2004. Crop condition and yield simulations using Landsat and MODIS. Remote Sensing of Environment, 92(4): 548-559
  7. 7.
    Dorigo W A, Zurita-Milla R, De Wit A J W, Brazile J, Singh R and Schaepman M E . 2007. A review on reflective remote sensing and data assimilation techniques for enhanced agroecosystem modeling. International Journal of Applied Earth Observation and Geoinformation, 9(2): 165-193
  8. 8.
    Erickson J . 1984. The LACIE experiment in satellite aided monitoring of global crop production//Woodwell G M, ed. SCOPE 23: The Role of Terrestrial Vegetation in the Global Carbon Cycle. New York: John Wiley and Sons
  9. 9.
    Everingham Y, Sexton J, Skocaj D and Inman-Bamber G . 2016. Accurate prediction of sugarcane yield using a random forest algorithm. Agronomy for Sustainable Development, 36(2): 27
  10. 10.
    Ge Y J, Zhao Y L and Ren H Y . 2020. The effect of farmland fragmentation on the intensity of farmland in different utilization ways. Advances in Earth Science, 35(2): 180-188
  11. 11.
    Holzworth D P, Snow V, Janssen S, Athanasiadis I N, Donatelli M, Hoogenboom G, White J W and Thorburn P . 2015. Agricultural production systems modelling and software: current status and future prospects. Environmental Modelling and Software, 72: 276-286
  12. 12.
    Huang J X, Li X L, Liu D Y, Ma H Y, Tian L Y and Su W . 2015. Comparison of winter wheat yield estimation by sequential assimilation of different spatio-temporal resolution remotely sensed LAI datasets. Transactions of the Chinese Society for Agricultural Machinery, 46(1): 240-248
  13. 13.
    Ines A V M, Das N N, Hansen J W and Njoku E G . 2013. Assimilation of remotely sensed soil moisture and vegetation with a crop simulation model for maize yield prediction. Remote Sensing of Environment, 138: 149-164
  14. 14.
    Jeong J H, Resop J P, Mueller N D, Fleisher D H, Yun K, Butler E E, Timlin D J, Shim K M, Gerber J S, Reddy V R and Kim S H . 2016. Random forests for global and regional crop yield predictions. PLoS One, 11(6): e0156571
  15. 15.
    Ji F J, Meng J H and Fang H T . 2020. Study on soybean yield estimation using the CoupledCASA and WOFOST model. Remote Sensing Technology and Application, 35(2): 406-415
  16. 16.
    Jiang Z W, Chen Z X, Ren J Q and Zhou Q B . 2012. Estimation of crop yield using CERES-Wheat model based on particle filter data assimilation method. Transactions of the Chinese Society of Agricultural Engineering, 28(14): 138-146
  17. 17.
    Jin X L, Kumar L, Li Z H, Feng H K, Xu X G, Yang G J and Wang J H . 2018. A review of data assimilation of remote sensing and crop models. European Journal of Agronomy, 92: 141-152
  18. 18.
    Kogan F, Kussul N, Adamenko T, Skakun S, Kravchenko O, Kryvobok O, Shelestov A, Kolotii A, Kussul O and Lavrenyuk A . 2013. Winter wheat yield forecasting in Ukraine based on Earth observation, meteorological data and biophysical models. International Journal of Applied Earth Observation and Geoinformation, 23: 192-203
  19. 19.
    Lewis J E, Rowland J and Nadeau A . 1998. Estimating maize production in Kenya using NDVI: some statistical considerations. International Journal of Remote Sensing, 19(13): 2609-2617
  20. 20.
    Li J L and Liu C . 2005. Research on NDVI seasonal dynamics of Agri-ecosystem on south Coast of Hangzhouwan bay using MODIS data. Geography and Geo-Information Science, 21(3): 30-34
  21. 21.
    Liu J M, He X T, Wang P X and Huang J X . 2019. Early prediction of winter wheat yield with long time series meteorological data and random forest method. Transactions of the Chinese Society of Agricultural Engineering, 35(6): 158-166
  22. 22.
    Liu Z Z, Zhang X W, Chen Y S, Zhang C C, Qin F and Zeng H W . 2017. Remote sensing estimation of biomass in winter wheat based on CASA model at region scale. Transactions of the Chinese Society of Agricultural Engineering, 33(4): 225-233
  23. 23.
    Lobell D B . 2013. The use of satellite data for crop yield gap analysis. Field Crops Research, 143: 56-64
  24. 24.
    Lobell D B, Asner G P, Ortiz-Monasterio J I and Benning T L . 2003. Remote sensing of regional crop production in the Yaqui Valley, Mexico: estimates and uncertainties. Agriculture, Ecosystems and Environment, 94(2): 205-220
  25. 25.
    Lobell D B, Thau D, Seifert C, Engle E and Little B . 2015. A scalable satellite-based crop yield mapper. Remote Sensing of Environment, 164: 324-333
  26. 26.
    Maselli F, Romanelli S, Bottai L and Maracchi G . 2000. Processing of GAC NDVI data for yield forecasting in the Sahelian region. International Journal of Remote Sensing, 21(18): 3509-3523
  27. 27.
    Nilsson H E . 1995. Remote sensing and image analysis in plant pathology. Canadian Journal of Plant Pathology, 17(2): 154-166
  28. 28.
    Pachavo G and Murwira A . 2014. Remote sensing net primary productivity (NPP) estimation with the aid of GIS modelled shortwave radiation (SWR) in a Southern African Savanna. International Journal of Applied Earth Observation and Geoinformation, 30: 217-226
  29. 29.
    Qian Y L, Hou Y Y, Yan H, Mao L X, Wu M X and He Y B . 2012. Global crop growth condition monitoring and yield trend prediction with remote sensing. Transactions of the Chinese Society of Agricultural Engineering, 28(13): 166-171, 298
  30. 30.
    Quan W T, Zhou H, Li H M, Yi W J and Li H L . 2015. Remote Recognition and Growth Monitoring of Winter Wheat in Key Stages Based on S-G Filter in Guanzhong Region. Chinese Journal of Agrometeorology, 36(1): 93-99
  31. 31.
    Rasmussen M S . 1992. Assessment of millet yields and production in northern Burkina Faso using integrated NDVI from the AVHRR. International Journal of Remote Sensing, 13(18): 3431-3442
  32. 32.
    Ren J Q, Chen Z X, Zhou Q B, Liu J and Tang H J . 2015. MODIS vegetation index data used for estimating corn yield in USA. Journal of Remote Sensing, 19(4): 568-577
  33. 33.
    Rural Social and Economic Investigation Department of National Bureau of Statistics . 2020. China Statistical Yearbook (County-Level)-2019. Beijing: China Statistics Press
  34. 34.
    Tatsumi K, Yamashiki Y, Torres M A C and Taipe C L R . 2015. Crop classification of upland fields using Random forest of time-series Landsat 7 ETM+ data. Computers and Electronics in Agriculture, 115: 171-179
  35. 35.
    Tennakoon S B, Murty V V N and Eiumnoh A . 1992. Estimation of cropped area and grain yield of rice using remote sensing data. International Journal of Remote Sensing, 13(3): 427-439
  36. 36.
    Thenkabail P S, Ward A D and Lyon J G . 1994. LANDSAT-5 thematic mapper models of soybean and corn crop characteristics. International Journal of Remote Sensing, 15(1): 49-61
  37. 37.
    Wang F L, Wang F M, Hu J H, Xie L L and Xie J K . 2020. Estimating and Mapping Rice Yield Using UAV-Hyperspectral Imager based Relative Spectral Variates. Remote Sensing Technology and Application, 35(2): 458-468
  38. 38.
    Wang P X, Qi X, Li L, Wang L and Xu L X . 2019. Estimation of maize yield based on random forest regression. Transactions of the Chinese Society for Agricultural Machinery, 50(7): 237-245
  39. 39.
    Wang P X, Sun H T, Wang L, Xie Y, Zhang S Y and Li L . 2016. Winter wheat yield estimation based on 4D Variational assimilation method and remotely sensed vegetation temperature condition index. Transactions of the Chinese Society for Agricultural Machinery, 47(3): 263-271
  40. 40.
    Wang R Z, Jiang H, Jin J X and Cheng M . 2018. Respond of winter wheat phenology to climate change and its effect on yield in Huang-Huai-Hai area. Jiangsu Agricultural Sciences, 46(22): 71-75
  41. 41.
    West H, Quinn N and Horswell M . 2019. Remote sensing for drought monitoring and impact assessment: Progress, past challenges and future opportunities. Remote Sensing of Environment, 232: 111291
  42. 42.
    Wiegand C, Shibayama M, Yamagata Y and Akiyama T . 1989. Spectral observations for estimating the growth and yield of rice. Japanese Journal of Crop Science, 58(4): 673-683
  43. 43.
    Wu B F . 2004. China crop watch system with remote sensing. Journal of Remote Sensing, 8(6): 481-497
  44. 44.
    Xin Q C, Gong P, Yu C Q, Yu L, Broich M, Suyker A E and Myneni R B . 2013. A production efficiency model-based method for satellite estimates of corn and soybean yields in the Midwestern US. Remote Sensing, 5(11): 5926-5943
  45. 45.
    Xu J W, Mei X R, Ju H, Li Y C, Liu Q and Yang J Y . 2014. Simulation of winter wheat yield in response to irrigation level at critical growing stages in the Huang-Huai-Hai plain. Acta Agronomica Sinica, 40(8): 1485-1492
  46. 46.
    Yang H, Chen E X, Li Z Y, Zhao C J, Yang G J, Pignatti S, Casa R and Zhao L . 2015. Wheat lodging monitoring using polarimetric index from RADARSAT-2 data. International Journal of Applied Earth Observation and Geoinformation, 34: 157-166
  47. 47.
    Zhang J Y, Fu J, Yang K J, Wang Y F, Wang Z H and Wei J P . 2015. Effects of different plant patterns and planting density on yield and photosynthetic characteristics of maize. Journal of Anhui Agricultural Sciences, 43(23): 29-32, 93
  48. 48.
    Zhao H, Liu Y C, Wu D and Han Z Z . 2020. Analysis of irrigation water requirement characteristics for wheat-maize continuous cropping in the five counties of Hebei Province. South-to-North Water Transfers and Water Science and Technology, 18(4): 185-194
  49. 49.
    Zhao J W, Wang X P, Du X Y and Shang Z Y . 2016. Spatial structure and control factors of winter wheat production in Hebei province. Chinese Journal of Eco-Agriculture, 24(12): 1683-1692
  50. 50.
    Zhu W X, Li S J, Zhang X B, Li Y and Sun Z G . 2018. Estimation of winter wheat yield using optimal vegetation indices from unmanned aerial vehicle remote sensing. Transactions of the Chinese Society of Agricultural Engineering, 34(11): 78-86

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