Jujube yield prediction method combining Landsat 8 Vegetation Index and the phenological length

文献类型： 外文期刊

第一作者： Bai, Tiecheng

作者： Bai, Tiecheng;Mercatoris, Benoit;Chen, Youqi;Bai, Tiecheng;Zhang, Nannan

作者机构：

关键词： Remote sensing; Yield forecasting; Landsat 8; NDVI; Phenology length

期刊名称：COMPUTERS AND ELECTRONICS IN AGRICULTURE （ 影响因子：5.565； 五年影响因子：5.494 ）

ISSN： 0168-1699

年卷期： 2019 年 162 卷

页码：

收录情况： SCI

摘要： It is challenging to generate a time series of vegetation indices from moderate spatial resolution Landsat Thematic Mapper images (Landsat 8) for crop yield forecasting. In addition, crop yields are correlated with phenology information, especially the fruit filling days. The objectives of this study were to identify the phenology time for making a reliable jujube yield prediction, more importantly, explore an approach that used the length of phenology growth period to improve remotely sensed estimates of inter-annual variability for yields. The best time for making jujube yield prediction was found to be during the fruit filling period, showing higher correlation coefficient (r) between vegetation indices and yields. The average NDVI for 14th and 15th half-months represented a better performance for yield prediction, with a highest r value of 0.87 for NDVI, 0.82 for SAVI, 0.73 for NDWI and 0.73 for EVI, respectively. The potential of using Landsat-NDVI for jujube yield estimation, combined with the phenological length, was preliminarily proved based on 200 observations of individual jujube orchards, showing a validated R-2 of 0.85, 0.80 and 0.67, RMSE of 0.61, 0.78 and 0.85 t ha(-1) for 2013, 2014 and 2016, respectively. Furthermore, the phenological adjusted model was further evaluated by inter-annual official statistic data, with R-2 and RMSE values ranging from 0.38 to 0.53, and 0.31 to 0.47 t ha(-1), respectively. The proposed method showed better performance between years when the fruit filling days differed greatly than the leave-one-year-out method, which was verified to well fit to jujube yield monitoring and mapping two months before harvest.

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