| 基于遥感和AquaCrop作物模型的多同化算法比较 |
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| 引用本文： | 邢会敏,李振海,徐新刚,冯海宽,杨贵军,陈召霞.基于遥感和AquaCrop作物模型的多同化算法比较[J].农业工程学报,2017,33(13):183-192. |
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| 作者姓名： | 邢会敏 李振海 徐新刚 冯海宽 杨贵军 陈召霞 |
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| 作者单位： | 1. 北京农业信息技术研究中心,北京 100097;国家农业信息化工程技术研究中心,北京 100097;农业部农业信息技术重点实验室,北京 100097;北京市农业物联网工程技术研究中心,北京 100097;商丘师范学院测绘与规划学院,商丘 476000;2. 北京农业信息技术研究中心,北京 100097;国家农业信息化工程技术研究中心,北京 100097;农业部农业信息技术重点实验室,北京 100097;北京市农业物联网工程技术研究中心,北京 100097 |
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| 基金项目： | 国家自然科学基金（41571416，41601346）；北京市农林科学院创新能力建设专项（KJCX20150409）；北京市自然科学基金（4152019） |
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| 摘 要： | 为了研究不同数据同化方法在AquaCrop(FAO Crop model to simulate yield response to water)模型模拟作物地上生物量(above ground biomass,AGB)、冠层覆盖度(canopy cover,CC)和产量过程的效率,以冬小麦为研究对象,利用2012-2013、2013-2014和2014-2015年冬小麦田间试验数据,将标定的Aqua Crop生长模型与遥感光谱信息相结合开展同化技术分析,应用粒子群优化(particle swarm optimization,PSO)、模拟退火(simulated annealing,SA)和复合型混合演化(shuffled complex evolution,SCE-UA)3种数据同化算法,以不同生育期、不同水分处理和不同氮肥水平的AGB和CC为双变量开展多同化算法的模拟分析,对3种数据同化算法的运算效率和同化结果进行对比分析。结果表明:1)3种数据同化算法达到的应度值0.26时,SCE-UA同化算法用时最少(833 s),SA数据同化算法用时最多(1433 s),表明SCE-UA同化算法效率最优,SA数据同化算法效率最低;2)不同生育期的同化结果,AGB的同化精度随着生育期的推进而降低,AGB的模拟值在拔节期和挑旗期高于实测值,被高估,在开花期和灌浆期被低估,总的AGB被低估;CC在拔节期和挑旗期被低估,在开花期和灌浆期被高估,总的CC被低估;3)不同水分处理的同化结果,AGB普遍被低估,CC在雨养(W0)条件下被高估,在正常灌溉(W1)和过量灌溉(W2)条件下被低估;产量均被低估;4)不同氮肥水平,AGB的模拟精度随着施N量的增加而降低,并且普遍被低估,CC普遍被高估,产量均被低估。以上结果表明,PSO、SA和SCE-UA 3种数据同化算法均能有效模拟冬小麦的AGB、CC和产量,其中SCE-UA数据同化算法无论在运算效率还是同化结果的精度上均优于PSO和SA数据同化算法。
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| 关 键 词： | 遥感 作物 模型 AquaCrop模型 数据同化 生物量 冠层覆盖度 |
| 收稿时间： | 2017/1/13 0:00:00 |
| 修稿时间： | 2017/6/23 0:00:00 |
Multi-assimilation methods based on AquaCrop model and remote sensing data |
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| Xing Huimin,Li Zhenhai,Xu Xingang,Feng Haikuan,Yang Guijun and Chen Zhaoxia.Multi-assimilation methods based on AquaCrop model and remote sensing data[J].Transactions of the Chinese Society of Agricultural Engineering,2017,33(13):183-192. |
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| Authors: | Xing Huimin Li Zhenhai Xu Xingang Feng Haikuan Yang Guijun and Chen Zhaoxia |
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| Institution: | 1. Beijing Research Center for Information Technology in Agriculture, Beijing 100097, China; 2. National Engineering Research Center for information Technology in Agriculture, Beijing 100097; 3. Key Laboratory of Agri-informaties, Ministry of Agriculture, Beijing 100097, China; 4. Beijing Engineering Research Center of Agricultural Internet of Things, Beijing 100097, China; 5. Shangqiu Normal University, Department of Surveying and Planning, Shangqiu 476000, China;,1. Beijing Research Center for Information Technology in Agriculture, Beijing 100097, China; 2. National Engineering Research Center for information Technology in Agriculture, Beijing 100097; 3. Key Laboratory of Agri-informaties, Ministry of Agriculture, Beijing 100097, China; 4. Beijing Engineering Research Center of Agricultural Internet of Things, Beijing 100097, China;,1. Beijing Research Center for Information Technology in Agriculture, Beijing 100097, China; 2. National Engineering Research Center for information Technology in Agriculture, Beijing 100097; 3. Key Laboratory of Agri-informaties, Ministry of Agriculture, Beijing 100097, China; 4. Beijing Engineering Research Center of Agricultural Internet of Things, Beijing 100097, China;,1. Beijing Research Center for Information Technology in Agriculture, Beijing 100097, China; 2. National Engineering Research Center for information Technology in Agriculture, Beijing 100097; 3. Key Laboratory of Agri-informaties, Ministry of Agriculture, Beijing 100097, China; 4. Beijing Engineering Research Center of Agricultural Internet of Things, Beijing 100097, China;,1. Beijing Research Center for Information Technology in Agriculture, Beijing 100097, China; 2. National Engineering Research Center for information Technology in Agriculture, Beijing 100097; 3. Key Laboratory of Agri-informaties, Ministry of Agriculture, Beijing 100097, China; 4. Beijing Engineering Research Center of Agricultural Internet of Things, Beijing 100097, China; and 1. Beijing Research Center for Information Technology in Agriculture, Beijing 100097, China; 2. National Engineering Research Center for information Technology in Agriculture, Beijing 100097; 3. Key Laboratory of Agri-informaties, Ministry of Agriculture, Beijing 100097, China; 4. Beijing Engineering Research Center of Agricultural Internet of Things, Beijing 100097, China; |
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| Abstract: | Data assimilation has been known a promising approach for crop growth processing monitoring and yield estimation. In order to analyze the assimilation accuracy and efficiency of above ground biomass (AGB), canopy cover (CC) and yield of crop using AquaCrop crop growth model, which is an FAO crop model to simulate yield response to water, the field experiments were conducted during the 2012?2013, 2013?2014, and 2014?2015 winter wheat growing seasons at the National Precision Agriculture Demonstration Research Base in Beijing, China, and data were obtained in the jointing stage, flag leaf stage, anthesis stage and filling stage, respectively. Combined with ground remote sensing data, the AGB, CC and yield were simulated using the AquaCrop model under different water and nitrogen treatments with 3 data assimilation algorithms, which included particle swarm optimization (PSO), simulated annealing (SA) and shuffled complex evolution (SCE-UA). Both AGB and CC were used as state variables in the 3 algorithms. The computation efficiency and assimilation results of these algorithms were compared. The results showed as follows: 1) Among the 3 assimilation algorithms, the efficiency of SCE-UA assimilation algorithm was the highest (833 s), while the SA was the lowest (1433 s). 2) Under different growth stages, the accuracy of AGB reduced with the growth of winter wheat. The simulation values of AGB were underestimated in jointing and flag leaf stages while overestimated in anthesis and filling stages. The simulation values of CC were overestimated in the jointing and flag leaf stages while underestimated in anthesis and filling stages. The ranges of RMSE (root mean square error), consistency indexand MBEbetween the assimilated and measured AGB and CC by SCE-UA method were 0.57-1.92 t/hm2, 0.90-1.00 and -11.8%-18.5%, and 6.6%-12.1%, 0.94-1.00 and -3.7%-9.5%, respectively. 3) Under 3 different water treatments (W0 (rainfed), W1 (normal irrigation) and W2 (over irrigation)), the AGB and yield values were underestimated. The CC values were overestimated under W0 treatment and underestimated under W1 and W2 treatments. The ranges of RMSE, consistency index and MBEbetween the assimilated and measured AGB, CC and yield by SCE-UA method were 0.93-1.43 t/hm2, 0.98?1.00 and -12.5%?1.3%, and 8.0%?15.5%, 0.89?1.00 and 0.3%?18.4%, and 0.64?0.85 t/hm2, 0.84?0.97 and -12.5%?-5.7%, respectively. 4) Under 4 nitrogen fertilizer treatments (N1 (no fertilization), N2 (half fertilization), N3 (normal fertilization) and N4 (over fertilization)), the AGB values reduced with the increasing of the amount of nitrogen fertilizer, the CC values were overestimated and the yield values were underestimated. The ranges of RMSE, consistency index and MBEbetween the assimilated and measured AGB, CC and yield by SCE-UA method were 0.98-1.69 t/hm2, 0.98?1.00 and -10.1%?6.0%, 8.9%?11.4%, 0.97-1.00 and -0.8%?8.7%, 0.60?0.96 t/hm2, 0.90?1.00 and -9.6%?-2.6%, respectively. For the 3 assimilation algorithms, the SCE-UA algorithm produced a higher estimation accuracy for the total AGB, CC and yield than the PSO algorithm and SA algorithm. The results indicate that the PSO, SA and SCE-UA can well simulate winter wheat AGB, CC and yield, and amongst them the SCE-UA algorithm performs the best in terms of computation efficiency and accuracy. |
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| Keywords: | remote sensing crops models AquaCrop model data assimilation aboveground biomass canopy cover SCE-UA |
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