| 基于环境减灾卫星时序归一化植被指数的冬小麦产量估测 |
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| 引用本文： | 陈鹏飞,杨 飞,杜 佳.基于环境减灾卫星时序归一化植被指数的冬小麦产量估测[J].农业工程学报,2013,29(11):124-131. |
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| 作者姓名： | 陈鹏飞 杨 飞 杜 佳 |
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| 作者单位： | 1. 农业部农业信息技术重点实验室，北京 1000812. 中国科学院地理科学与资源研究所，资源与环境信息系统国家重点实验室，北京100101;2. 中国科学院地理科学与资源研究所，资源与环境信息系统国家重点实验室，北京100101;2. 中国科学院地理科学与资源研究所，资源与环境信息系统国家重点实验室，北京100101 |
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| 基金项目： | 农业部农业信息技术重点试验室开放基金（2011004）；中国科学院地理科学与资源研究所创新项目(201003002)；资源与环境信息系统国家重点实验室青年人才培养基金（O8R8B670KA）；国家自然科学基金(41071276)；广东省中科院全面战略合作项目（2012B091100256） |
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| 摘 要： | 依托国产环境减灾卫星构建作物归一化植被指数(NDVI)时序曲线,不但能提供与MODIS-NDVI、AVHRR-NDVI几乎相当的作物生长动态变化信息,还能提供更高分辨率的空间信息,将其应用于作物估产应更有优势。该研究以地处鲁西北平原的山东省禹城市为研究区,探讨基于环境减灾卫星影像构建冬小麦NDVI时序曲线,基于曲线特征参数,开展遥感估产的可行性。结果表明,可依赖环境减灾卫星遥感影像,重建冬小麦NDVI时序曲线,求算其生长季最大NDVI、返青期NDVI、生长季累积NDVI、营养生长期NDVI的变化速率、生殖生长期NDVI的变化速率等特征参数,建立可靠的估产模型。所建模型的建模决定系数为0.87,相对误差为5.02%;交叉检验决定系数为0.78,相对误差为6.87%。该研究可为基于遥感的作物估产提供参考。
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| 关 键 词： | 遥感 模型 估测 产量 时序NDVI 环境减灾卫星 冬小麦 |
| 收稿时间： | 2013/1/31 0:00:00 |
| 修稿时间： | 2013/5/21 0:00:00 |
Yield forecasting for winter wheat using time series NDVI from HJ satellite |
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| Chen Pengfei,Yang Fei and Du Jia.Yield forecasting for winter wheat using time series NDVI from HJ satellite[J].Transactions of the Chinese Society of Agricultural Engineering,2013,29(11):124-131. |
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| Authors: | Chen Pengfei Yang Fei and Du Jia |
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| Institution: | 1. Key Laboratory of Agricultural Information Technology, Ministry of Agriculture, Beijing 100081, China; 2. State Key lab of Resources and Environment Information System, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China) |
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| Abstract: | Abstract: A NDVI time series curve, proposed from high time resolution remote sensing images, contains rich information for crop yield forecasting. MODIS-NDVI and AVHRR-NDVI are normally used. However, the spatial resolution of MODIS and AVHRR are low. When they used for crop yield forecasting in China, the prediction accuracy will be reduced by a mixed pixel problem, as the farmland is small. China launched a HJ satellite constellation in 2009. The satellite constellation can provide an image with a time resolution of 2 days and a spatial resolution of 30 m. It would be helpful to make crop yield forecasting in field scale in China, based on the NDVI time series curve designed from higher spatial resolution HJ images. Taking Yucheng city as a research area, this study research the feasibility of proposing a NDVI time series curve using HJ satellite images and then making a winter wheat yield forecast using parameters extracted from the above curve. For this purpose, 11 images were acquired with a nearly 10 day interval from February 19th to June 5th, and the winter wheat yields of 12 sample sites were also measured during a field campaign in 2012. Firstly, quintic polynomial least square fitting was used to propose a NDVI time series curve using NDVI values extracted from the above images. The curve covered reviving, jointing, flowering, filling and mature stages of winter wheat. Secondly, parameters of a NDVI time series curve were calculated. They were maximum NDVI and accumulated NDVI during winter wheat growth stage, NDVI value at winter wheat reviving stage, NDVI change rate during winter wheat vegetation growth stage, and NDVI change rate during the winter wheat reproduction growth stage. Thirdly, using collected sample data, the yield prediction models were created, based on the above parameters respectively. Meanwhile, a single-phase image NDVI was also used to propose a yield prediction model, and it was compared to the above models, in order to show if a NDVI time series curve can provide more information than a single-phase image for yield prediction. At last, the multivariate yield prediction model was proposed based on the parameters of a NDVI time series curve using a stepwise multiple linear regression method, and was validated by a leave-one-cross-validation method. The results showed that the winter wheat NDVI series curve can be proposed by a HJ satellite image; A NDVI time series curve can provide more information for winter wheat yield prediction than a single phase image; The parameters extracted from a NDVI time series curve can be used to make a reliable winter wheat yield prediction model, and the designed multivariate yield prediction model has the determination coefficient (R2) value of 0.87 and relative error (RE) value of 5.02% at model calibration, and R2 value of 0.78 and RE value of 6.87% at model validation. |
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| Keywords: | remote sensing models forecasting yield time series NDVI HJ satellite winter wheat |
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