基于遥感识别与DNDC模型的不同稻作模式评价——以潜江市为例

doi:10.19788/j.issn.2096-6369.210304

Abstract

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.

Method

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.

Results

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.

Conclusion

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

Ayitula Maimaitizunong, Shuai Yanju, Haodong Wei, Zhen He, Qinxi Xiao, Qiong Hu, Baodong Xu, Liangzhi You, Cougui Cao, Lin Ling. Evaluation of Green Development of Rice-Based Cropping Systems Using Remote Sensing Data and the DNDC Model: Case Study of Qianjiang City[J].Journal of Agricultural Big Data, 2021, 3(3): 33-44.

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序号	地点	经纬度	均温和降水量	模式	处理	数据来源
Ⅰ	枣阳	32°10′N，112°10′ E	15.5℃； 500~1000mm	稻麦模式（RW）	N0C0、N0C1、N0C2、N1C0、N1C1、N1C2、N2C0、N2C1、N2C2	Hu et al., 2019^[25]
Ⅱ	武穴	29°51′N，115°33′ E	17.8℃; 1361mm	稻麦模式(RW)	CTNS、CTS、 NTNS、NTS	Zhang et al., 2015^[26]
Ⅲ	武穴	30°01′N，115°74′ E	17.5℃； 1132~1742mm	稻油模式(RR)	CF、FWI、RFL	Xu et al., 2016^[27]
Ⅳ	潜江	30°39′N，113°11′ E	16.1℃; 1100mm	稻虾模式(RC)&稻闲模式(RF)	SF、SNF、NS、 NSNF、S、NS	孙自川, 2018^[28]
Ⅴ	武汉	30°30′N，114°20′E	23.6℃； 906.8mm	双季稻模式(DR)	NCNT	张浩然，2019^[29]
Ⅵ	荆州	30°21′N，112°09′E	24.6℃； 734.9mm	双季稻模式(DR)	U+CI、U+SWD、US+SWD、CRU+CI、CRU+SWD、CRUS+SWD	李如楠等，2020^[30]
Ⅶ	监利	30°03′N，113°47′E	16.2℃； 1100~1300mm	稻虾模式 (RC)&稻闲模式(RF)	RC-NN、RC-QN、 RC-CN、RF-CN	帅艳菊，2021^[31]

作物参数 Crop parameters	默认值Default values			校准值Calibration values
作物参数 Crop parameters	水稻	小麦	油菜	一季中稻	早稻	晚稻	小麦	油菜
籽粒最大生物量 Max biomass production of grain (kg C/hm2/yr)	3377.58	3120.1	1295.82	3468	2960	3136	2300	1100
籽粒生物量占比 Biomass fraction of grain	0.41	0.41	0.23	0.43	0.44	0.41	0.41	0.25
籽粒碳氮比 Biomass C/N ratio of grain	45	40	20	45	43	48	40	20
成熟积温 Thermal degree days for maturity(℃)	2000	1300	1800	2300	2200	2000	1300	1800
需水量 Water demand (g water/g DM)	508	200	300	600	550	580	450	400
氮固定指数 N fixation index (crop N/N from soil)	1.05	1	1	1.05	1	1	1	1
最适温度 Optimum temperature (℃)	25	22	25	30	27	28	22	24

模式	面积（hm²）	均量			总量
模式	面积（hm²）	CH₄(kg·hm^-2)	N₂O(kg·hm^-2)	dSOC(kg C·hm^-2)	CH₄(×10³ t)	N₂O(t)	dSOC(×10³t C)
RC	58395.64	394.50	1.43	274.30	23.04	83.64	16.02
RF	22313.36	407.35	1.45	204.95	9.09	32.43	4.57
RW	25793.52	470.60	1.62	223.57	12.14	41.76	5.77
RR	10930.95	436.48	1.95	235.23	4.77	21.31	2.57
汇总	117433.47	--	--	--	49.04	179.14	28.93

转变模式	变化量			变化百分比
转变模式	CH₄（t）	N₂O（t）	dSOC（t C）	CH₄（%）	N₂O（ %）	dSOC（%）
RF	532.95	9.61	-3653.25	2.31	11.49	-22.81
RW	2591.00	38.55	-1593.47	11.25	46.09	-9.95
RR	1581.74	56.11	-2123.38	6.87	67.09	-13.26

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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

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