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首页> 外文期刊>International journal of remote sensing >Prediction of rice crop yield using MODIS EVI-LAI data in the Mekong Delta, Vietnam
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Prediction of rice crop yield using MODIS EVI-LAI data in the Mekong Delta, Vietnam

机译：使用MODIS EVI-LAI数据预测越南湄公河三角洲的水稻作物产量

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摘要

Predicting rice crop yield at the regional scale is important for production estimates that ensure food security for a country. This study aimed to develop an approach for rice crop yield prediction in the Vietnamese Mekong Delta using the Moderate Resolution Imaging Spectroradiometer (MODIS) enhanced vegetation index (EVI) and leaf area index (LAI). Data processing consisted of four main steps: (1) constructing time-series vegetation indices, (2) noise filtering of time-series data using the empirical mode decomposition (EMD), (3) establishment of crop yield models, and (4) model validation. The results indicated that the quadratic model using two variables (EVI and LAI) produced more accurate results than other models (i.e. linear, interaction, pure quadratic, and quadratic with a single variable). The highest correlation coefficients obtained at the ripening period for the spring-winter and autumn-summer crops were 0.70 and 0.74, respectively. The robustness of the established models was evaluated by comparisons between the predicted yields and crop yield statistics for 10 sampling districts in 2006 and 2007. The comparisons revealed satisfactory results for both years, especially for the spring-winter crop. In 2006, the root mean squared error (RMSE), mean absolute error (MAE), and mean bias error (MBE) for the spring-winter crop were 10.18%, 8.44% and 0.9%, respectively, while the values for the autumn-summer crop were 17.65%, 14.06%, and 3.52%, respectively. In 2007, the spring-winter crop also yielded better results (RMSE = 10.56%, MAE = 9.14%, MBE = 3.68%) compared with the autumn-summer crop (RMSE = 17%, MAE = 12.69%, MBE = 2.31%). This study demonstrates the merit of using MODIS data for regional rice crop yield prediction in the Mekong Delta before the harvest period. The methods used in this study could be transferable to other regions around the world.
机译：预测区域范围内的稻谷作物单产对于确保国家粮食安全的产量估算很重要。这项研究旨在开发一种使用中等分辨率成像光谱仪（MODIS）增强的植被指数（EVI）和叶面积指数（LAI）的越南湄公河三角洲稻米产量预测方法。数据处理包括四个主要步骤：（1）建立时间序列植被指数；（2）使用经验模式分解（EMD）对时间序列数据进行噪声过滤；（3）建立农作物产量模型；以及（4）模型验证。结果表明，使用两个变量（EVI和LAI）的二次模型比其他模型（即线性，交互，纯二次和具有单个变量的二次模型）产生的结果更准确。春，秋两季作物在成熟期获得的最高相关系数分别为0.70和0.74。通过比较2006年和2007年10个采样区的预测产量与作物单产统计数据，评估了建立的模型的鲁棒性。这些比较显示了这两年都令人满意的结果，尤其是对于春冬季作物。 2006年，春冬季作物的均方根误差（RMSE），平均绝对误差（MAE）和平均偏差误差（MBE）分别为10.18％，8.44％和0.9％，而秋季为夏季作物分别为17.65％，14.06％和3.52％。与秋夏作物（RMSE = 17％，MAE = 12.69％，MBE = 2.31％）相比，2007年春冬作物的收成也更好（RMSE = 10.56％，MAE = 9.14％，MBE = 3.68％） ）。这项研究证明了使用MODIS数据进行湄公河三角洲收获前区域水稻产量预测的优点。本研究中使用的方法可能会转移到世界其他地区。

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  • 来源
    《International journal of remote sensing》 |2013年第20期|7275-7292|共18页
  • 作者

    N.T. Son; C.F. Chen; C.R. Chen; L.Y. Chang; H.N. Due; L.D. Nguyen;

  • 作者单位

    Centre for Space and Remote Sensing Research, National Central University, Jhongli City, Taoyuan County 32001, Taiwan;

    Centre for Space and Remote Sensing Research, National Central University, Jhongli City, Taoyuan County 32001, Taiwan,Department of Civil Engineering, National Central University, Jhongli City, Taoyuan County 32001, Taiwan;

    Department of Civil Engineering, National Central University, Jhongli City, Taoyuan County 32001, Taiwan;

    Department of Civil Engineering, National Central University, Jhongli City, Taoyuan County 32001, Taiwan;

    Faculty of Agriculture and Natural Resources, An Giang University, 25 Vo Thi Sau St., LongXuyen City, Vietnam;

    GIS and Remote Sensing Research Centre, Vietnamese Academy of Science and Technology, 01 Mac Dinh Chi St., District 1, Ho Chi Minh City, Vietnam;

  • 收录信息 美国《科学引文索引》(SCI);美国《工程索引》(EI);
  • 原文格式 PDF
  • 正文语种 eng
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