| WOFOST模型与遥感数据同化的土壤速效养分反演 |
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| 引用本文： | 蒙继华,程志强,王一明.WOFOST模型与遥感数据同化的土壤速效养分反演[J].遥感学报,2018,22(4):546-558. |
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| 作者姓名： | 蒙继华 程志强 王一明 |
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| 作者单位： | 中国科学院遥感与数字地球研究所数字地球重点实验室;中国科学院大学 |
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| 基金项目： | 国家自然科学基金（编号：41171331，41010118）；中国科学院科技服务网络计划（编号：KFJ-EW-STS-069）；国家高技术研究发展计划（863计划）（编号：2013AA12A302） |
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| 摘 要： | 土壤速效养分是作物生长的必要条件,合理控制土壤速效养分含量对粮食增产、农民增收以及环境保护都有重要意义。随着现代农业技术的发展,可以通过变量施肥将土壤速效养分含量控制在最佳状态,这也对土壤养分的获取精度提出了更高的要求。当前的主要土壤速效养分遥感监测方法在监测精度、稳定性、成本控制和可推广性依然存在一定不足,甚至限制对变量施肥的指导作用。本文针对传统土壤速效养分估算方法的不足,提出了利用作物模型与时间序列遥感数据相结合实现耕层土壤速效养分反演的新思路,该思路以养分缺失引起的作物长势参数的变化为切入点,在数据同化算法设计和养分模块优化改造的基础上,利用作物长势参数遥感监测结果与模型模拟结果的差异设计了土壤速效养分反演算法,实现速效养分含量信息的有效获取。设计地面观测实验并利用地面观测数据对反演精度进行评价,结果表明该方法可以对土壤中的速效养分进行实时、高精度的稳定反演,3种主要的速效养分速效氮、有效磷和速效钾的R2分别达到了0.68、0.74和0.52,平均相对误差分别为7.45%、6.17%和9.97%。
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| 关 键 词： | WOFOST模型 数据同化 遥感 反演 土壤速效养分 |
| 收稿时间： | 2016/11/24 0:00:00 |
Simulating soil available nutrients by a new method based on WOFOST model and remote sensing assimilation |
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| MENG Jihu,CHENG Zhiqiang and WANG Yiming.Simulating soil available nutrients by a new method based on WOFOST model and remote sensing assimilation[J].Journal of Remote Sensing,2018,22(4):546-558. |
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| Authors: | MENG Jihu CHENG Zhiqiang and WANG Yiming |
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| Institution: | Key Laboratory of Digital Earth, Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing 100101, China,Key Laboratory of Digital Earth, Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing 100101, China;University of Chinese Academy of Sciences, Beijing 100049, China and Key Laboratory of Digital Earth, Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing 100101, China;University of Chinese Academy of Sciences, Beijing 100049, China |
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| Abstract: | Soil available nutrients are important for crop growth and yield accumulation. The improvement of yield and protection of the environment can be achieved by maintaining soil available nutrients at an optimal level. Currently, more than half of the available nutrients come from fertilization in a modern farm management. Appropriate fertilization can control these nutrients at an appropriate level. The precondition of fertilization optimization is acquiring the status of soil available nutrients. We proposed a new method for simulating the available nutrients to address the abovementioned issue. On the basis of the advantages of a crop model in simulating crop growth accurately and steadily, WOFOST crop model was selected as the primary model to simulate a nutrient-limited crop growth. The key parameters were calibrated through a documentary method, farm data collection, field observation, and Remote Sensing estimation prior to applying the WOFOST model. Moreover, necessary model optimizations, such as changing the structure of the WOFOST and adding a new algorithm to the soil nutrient module, were implemented. The EnKF method was selected for assimilating time-series HJ-1 A/B data into the WOFOST to realize the simulation at field and pixel scales. On the basis of the model calibration, optimization, and assimilation method construction, the simulation method for soil available nutrients was established. In this study, the theoretical basis of this new method was analyzed, and several necessary analyses were conducted to check the feasibility of the WOFOST in estimating the soil available nutrients. A lookup table method was used to realize the model simulation in a reverse order. The contents of the soil available nutrients were simulated as the output data by taking a Leaf Area Index (LAI) that was estimated from time-series HJ-CCD data as the input. This method was applied in the Shuangshan Farm in 2014. The available nitrogen (N), phosphorus (P), and potassium (K) in the maize fields were simulated. The time of the end of SAN simulation sage (ESS) can influence the simulation results; thus, we varied such time from 173 to 273 with 10 steps and repeatedly operated using the WOFOST model to obtain different simulation results of various method application times. Furthermore, we conducted several field campaigns to obtain the observation data of the soil nutrients. These data were used to analyze the precision. Results showed that the optimal time of the ESS for N, P, and K are different, and the highest R2 values are 0.68, 0.74, and 0.52, correspondingly. The average relative errors are 7.45%, 6.17%, and 9.97% respectively. This new method can reliably simulate the status of the soil available nutrients in terms of prediction accuracy, stability, and application value. |
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| Keywords: | WOFOST model assimilation method remote sensing simulation algorithm soil available nutrients |
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