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

农业大数据学报 ›› 2021, Vol. 3 ›› Issue (3): 33-44.doi: 10.19788/j.issn.2096-6369.210304

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

阿依吐拉·买买提祖农null¹(), 帅艳菊², 魏浩东³, 何真⁴, 肖沁茜², 胡琼⁴, 徐保东³, 游良志⁵, 曹凑贵², 凌霖⁶()

^1. 华中农业大学植物科学技术学院/宏观农业研究院，武汉 430070
^2. 华中农业大学植物科学技术学院/农业农村部长江中游作物生理生态与耕作重点实验室，武汉 430070
^3. 华中农业大学资源与环境学院/宏观农业研究院，武汉 430070
^4. 华中师范大学城市与环境科学学院/湖北省地理过程分析与模拟重点实验室，武汉 430079
^5. 国际食物政策研究所，美国华盛顿 20005
^6. 上海海关学院检验检疫技术交流部，上海 201204

收稿日期:2021-06-10 出版日期:2021-09-26 发布日期:2021-12-22
通讯作者: 凌霖 E-mail:m.ayitula@webmail.hzau.edu.cn;liz@hzau.edu.cn
作者简介:阿依吐拉·买买提祖农，女，硕士，研究方向：种植制度评价; E-mail: m.ayitula@webmail.hzau.edu.cn
基金资助:
大田油料作物机械化优质高产品种筛选评价技术研究及示范(2020YFD1000901)

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. 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 E-mail:m.ayitula@webmail.hzau.edu.cn;liz@hzau.edu.cn

摘要/Abstract

摘要： 目的

本研究旨在获得潜江市不同稻作模式温室气体排放和固碳情况，以评价不同稻作模式的绿色发展潜力。

方法

首先，利用随机森林对遥感影像进行分类，获得了潜江市各稻作模式分布数据，结合气象、土壤、作物管理数据库，利用校正和验证后的DNDC模型进行区域模拟，获得潜江市甲烷（CH₄）和氧化亚氮（N₂O）两种温室气体排放量及土壤有机碳变化量（dSOC）。其次，在DNDC模型中设置情景分析，假设目前稻虾模式由不同稻作模式变迁而来，利用相关指标的变化评价稻虾模式在潜江地区的绿色发展潜力。

结果

各项指标表明，校正后DNDC模型对CH₄和N₂O模拟效果良好。2019年潜江市每1 km²范围内主要稻作模式CH₄和N₂O排放量及全年dSOC总量变化区间分别为0.40kg～64043.34 kg，0.002kg～227.08 kg和0.18 kg C～35835.27 kg C。潜江市全年单位面积CH₄和N₂O排放量均表现为稻虾模式最小，分别为394.50kg·hm^-2，1.43kg·hm^-2，单位面积dSOC表现为稻虾模式最大为274.30 kg C·hm^-2，稻闲模式最小，为204.95 kg C·hm^-2。当潜江市稻虾模式转变为其他主要模式后，其周年CH₄排放总量增加2.31%~11.25%， N₂O排放总量增加11.49%~67.09%，dSOC减少9.95%~22.81%。

结论

本研究中，稻麦模式表现为CH₄排放最大，稻油模式的N₂O排放最大，两者固碳能力中等；稻闲模式由于只有一季种植，温室气体排放小于稻旱轮作模式，但其固碳能力较差；稻虾模式的减排和固碳能力相较于稻闲与稻旱轮作模式强，具有更高的绿色发展潜力，但其仍具有减排空间。

关键词: DNDC模型, 遥感影像识别, 稻虾模式, 温室气体, 有机碳积累, 随机森林, 减排

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

中图分类号:

P407.8

阿依吐拉·买买提祖农null, 帅艳菊, 魏浩东, 何真, 肖沁茜, 胡琼, 徐保东, 游良志, 曹凑贵, 凌霖. 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[J]. 农业大数据学报, 2021, 3(3): 33-44.

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

[1]	吴蕾,梁晓贺,乌吉斯古楞,王瑞. 基于词向量的检索扩展方法与农业领域实证[J]. 农业大数据学报, 2019, 1(2): 114-120.
[2]	张晨阳, 杨雪冰, 张文生. 气象大数据超短临精准降水机器学习与典型应用[J]. 农业大数据学报, 2019, 1(1): 78-87.

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