Journal of Agricultural Big Data ›› 2021, Vol. 3 ›› Issue (3): 33-44.doi: 10.19788/j.issn.2096-6369.210304

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Evaluation of Green Development of Rice-Based Cropping Systems Using Remote Sensing Data and the DNDC Model: Case Study of Qianjiang City

Ayitula Maimaitizunong1(), Shuai Yanju2, Haodong Wei3, Zhen He4, Qinxi Xiao2, Qiong Hu4, Baodong Xu3, Liangzhi You5, Cougui Cao2, Lin Ling6()   

  1. 1. Macro Agriculture Research Institute/College of Plant Science and Technology, Huazhong Agricultural University, Wuhan 430070, China
    2. Ministry of Agriculture and Rural Affairs Key Laboratory of Crop Physiology, Ecology and Cultivation (The Middle Reaches of Yangtze River)/College of Plant Science and Technology, Huazhong Agricultural University, Wuhan 430070, China
    3. Macro Agriculture Research Institute/College of Resources &Environment, Huazhong Agricultural University, Wuhan 430070, China
    4. Key Laboratory for Geographical Process Analysis & Simulation of Hubei Province/College of Urban and Environmental Sciences, Central China Normal University, Wuhan 430079, China
    5. International Food Policy Research Institute, Washington D. C. 20005, USA
    6. Inspection and Quarantine Technology Communication Department, Shanghai Customs College, Shanghai 201204, China
  • Received:2021-06-10 Online:2021-09-26 Published:2021-12-22
  • Contact: Lin Ling;

Abstract: Objective

The purpose of this study was to estimate the greenhouse gas emission and carbon sequestration of different rice-based cropping systems in Qianjiang City, China, and to evaluate potential for their green development.


First, classified remote-sensing images were with the random forest method to map the distribution of rice cropping systems in Qianjiang City. Combined with meteorological, soil, and crop management datasets, a revised and validated DeNitrification–DeComposition (DNDC) model was used to conduct regional simulations. Estimates for methane (CH4) and nitrous oxide (N2O) emissions and changes in soil organic carbon (dSOC) in Qianjiang City were obtained. Second, scenario simulations were conducted in the DNDC model under the assumption that the current rice–crayfish system was evolved from different rice cropping systems, and changes in the related indicators were used to evaluate the green development potential of the systems.


All indicators showed that the validated DNDC model had good performance to simulate the effect on CH4 and N2O. In 2019, the CH4 and N2O emissions and the annual dSOC of the main rice cropping systems per km2 in Qianjiang City were 0.40–64,043.34 kg, 0.002–227.08 kg, and 0.18–5,835.27 kg C, respectively. The annual CH4 and N2O emissions per unit area in the rice–crayfish system were the lowest, at 394.50 kg·hm-2 and 1.43 kg·hm-2, respectively. The dSOC per unit area was the highest in the rice–crayfish system, at 274.30 kg C·hm-2, and that in the rice–fallow system was the lowest, at 204.95 kg C·hm-2. The annual total CH4 emission increased by 2.31%–11.25%, the total N2O emission increased by 11.49%–67.09%, and the dSOC decreased by 9.95%–22.81% when the rice–crayfish system was converted to other rice cropping systems in Qianjiang City.


In this study, the rice–wheat system showed the largest CH4 emission, and the rice–rape system showed the largest N2O emission, both of which had moderate carbon sequestration capacity. The greenhouse gas emission of the rice–fallow system is lower than that of the rice–dryland rotation system, but its carbon sequestration ability is poor. The rice–crayfish system has stronger emission reduction and carbon sequestration ability compared with the other rice-based systems, and has higher green development potential, though there is still potential for emission mitigation.

Key words: DNDC model, remote sensing recognition, rice cropping systems, greenhouse gases, carbon sequestration, random forest, emission reduction

CLC Number: 

  • P407.8