| 作物长势评估指数的设计与应用 |
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| 引用本文： | 张蕾,侯英雨,郑昌玲,刘维,何亮,郭安红,程路.作物长势评估指数的设计与应用[J].应用气象学报,2019,30(5):543-554. |
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| 作者姓名： | 张蕾 侯英雨 郑昌玲 刘维 何亮 郭安红 程路 |
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| 作者单位： | 国家气象中心, 北京 100081 |
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| 基金项目： | 公益性行业（气象）科研专项（GYHY201506001），国家重点研究发展计划（2017YFC1502402，2017YFD0300101） |
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| 摘 要： | 合理有效地开展作物长势评估，可以及时反映作物生长状况及其对天气气候条件的响应。由于WOFOST模型、ORYZA2000模型在模拟冬小麦、玉米和水稻生长发育过程具备较强机理性，研究基于2001年以来全国冬小麦、玉米、水稻主产区逐日模拟的作物发育进程、叶面积指数和地上总生物量，通过隶属函数构建评估指数，开展高时空分辨率的作物长势评估。结果表明:长势综合评估指数在作物生长前期以发育进程、叶面积指数和地上总生物量三要素加权集合表征，中后期以发育进程和地上总生物量与穗重相关性的加权集合表征；长势评估指数与常规地面观测和遥感长势监测一致性较好，可以反映天气气候条件影响。在作物生长季内，以日为单位构建了作物长势评估指数数据库；根据长势评估指数将作物长势分为长势好、长势偏好、长势持平、长势偏差、长势差，实现空间上的长势监测、对比；以空间集成的方式，开展省级作物长势对比分析；利用长势评估指数变化反映典型天气气候条件对作物生长发育的影响。上述基于作物模型的作物长势评估指数符合现代化农业气象科研与业务服务发展的需求。
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| 关 键 词： | 作物模型 冬小麦 玉米 水稻 长势评估 |
| 收稿时间： | 2019/5/10 0:00:00 |
| 修稿时间： | 2019/7/24 0:00:00 |
The Construction and Application of Assessing Index to Crop Growing Condition |
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| Zhang Lei,Hou Yingyu,Zheng Changling,Liu Wei,He Liang,Guo Anhong and Cheng Lu.The Construction and Application of Assessing Index to Crop Growing Condition[J].Quarterly Journal of Applied Meteorology,2019,30(5):543-554. |
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| Authors: | Zhang Lei Hou Yingyu Zheng Changling Liu Wei He Liang Guo Anhong and Cheng Lu |
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| Affiliation: | National Meteorological Center, Beijing 100081 |
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| Abstract: | It is generally accepted that crop growing assessment can reveal its temporary condition and response to weather and climate when crop growing condition is assessed in a reasonable and effective way. To address this, normal field observation and remote sensing monitoring are the major techniques to quantify the crop growing condition. However, limited by their time-efficiency and uncertainties in algorithm, there are some deficiencies within them. Crop model, as an alternative way, is proposed to detect crop growing condition, with the advantage of its better mechanism and timeliness. Currently, WOFOST and ORYZA2000 are widely used to simulate the growth of winter wheat, spring maize, summer maize, single-season rice and double-season rice. Derived from WOFOST and ORYZA2000, the daily outputs, i.e., the development stage, leaf area index and total aboveground production, are simulated from 2001. Three outputs are selected as impacted variables for crop growing, and quantified through membership function. In the initial stage of crop growth, development stage, leaf area index and total aboveground production generate comprehensive effect, and they are weighted aggregated to a integrated index by the respective weight of 0.3, 0.3 and 0.4 according to expert scoring method. In the later stage, development stage and total aboveground production are the major factors influencing crop condition, and they are aggregated to a integrated index by the weight derived from their relative relationship with dry weight of storage organs. The assessing index is keeping well with experimental observation and remote sensing monitoring, implying its effectivity in evaluation service. The crop growing condition is assessed on any day during the growing season, and the corresponding daily integrated index is built in datasets under Crop Growth Simulating and Monitoring System in China (CGMS-China). According to daily integrated index, crop growing condition is divided into 5 levels, i.e., better, good, normal, bad and worse. Based on CGMS-China, daily crop growing condition is illustrated in the spatial distribution, which can distinguish the regional or local distinction of condition. Moreover, assessing index is spatially aggregated to the index at the province scale, which is a base quantity for comparing the provincial crop growing condition, corresponding to the assessing scale of yield prediction in agrometeorological services. Under the typical weather conditions, assessing index is efficient in specific regions and even local stations. For example, impacts of high temperature and drought from 21 July to 10 August in 2018 are well performed in the assessing index change at spatial and temporal scales. The assessing index for crop growing assessment based on crop model can provide more accurate and quantifier outputs, fitting with the demand of modern agriculture and agrometeorological service. |
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| Keywords: | crop model winter wheat maize rice crop growing assessment |
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