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Prediction of apple first flowering date using daily land surface temperature spatio-temporal reconstruction and machine learning

文献类型： 外文期刊

作者： Liu, Miao 1 ; Zhu, Yaohui 1 ; Yang, Hao 1 ; Pu, Ruiliang 4 ; Qiu, Chunxia 2 ; Zhao, Fa 1 ; Han, Shaoyu 1 ; Xu, Weimeng 1 ; Meng, Yang 1 ; Long, Huiling 1 ; Yang, Guijun 1 ;

作者机构： 1.Beijing Acad Agr & Forestry Sci, Informat Technol Res Ctr, Key Lab Quantitat Remote Sensing Agr, Minist Agr & Rural Affairs, Beijing 100097, Peoples R China

2.Xian Univ Sci & Technol, Coll Geomat, Xian 710054, Peoples R China

3.Natl Engn Res Ctr Informat Technol Agr, Beijing 100097, Peoples R China

4.Univ S Florida, Sch Geosci, Tampa, FL 33620 USA

5.Changan Univ, Coll Geol Engn & Geomat, Xian 710054, Peoples R China

关键词： Apple; First flowering date; Land surface temperature; Reconstruction; Phenology model

期刊名称：COMPUTERS AND ELECTRONICS IN AGRICULTURE （ 影响因子：6.757； 五年影响因子：6.817 ）

ISSN： 0168-1699

年卷期： 2022 年 202 卷

页码：

收录情况： SCI

摘要： The first flowering date (FFD) is a critical phenological parameter closely related to the apple yield, so the ac-curate prediction of the FFD is important for precise orchard production management. Existing methods to predict the FFD are mostly based on air temperature (Ta) measured at meteorological stations, but to great differences in meteorological variations and the ecological conditions, these methods cannot present the dif-ferences of FFD under complex meteorological conditions and provide spatially continuous FFD information at the level of a region. Therefore, we propose a method to predict spatially continuous apple FFD from remote sensing land surface temperature (LST) based on flowering prediction model. Firstly, the missing LST data were reconstructed by using spatio-temporal reconstruction (STR) approach developed. Next, new air temperature (NAT) data were generated by using the daily Ta estimation (DTE) model and the reconstructed LST. Finally, apple FFD was predicted by the NAT data and the apple flowering prediction model established based on random forest (RF) algorithm and the phenology sequential model, and the prediction accuracy was verified by com-parison with the independently measured apple FFD. The LST reconstructed by using the STR approach has mean absolute error (MAE) ranging from 0.51 to 0.68 degrees C, and root mean square error (RMSE) ranging from 1.07 to 1.21 degrees C. The MAE between the NAT data and the High-Resolution Land Surface Data Assimilation System (HR-CLDAS) meteorological data ranges from 2.15 to 3.23 degrees C, and the RMSE ranges from 2.81 to 4.27 degrees C. In addition, the determination coefficient (R2) and RMSE between the predicted and measured FFD is 0.72 and 2.96 days, respectively. These results demonstrate that the developed method maximizes the potential of MODIS LST in predicting spatially continuous apple FFD, which is valuable for flower and fruit thinning, to defend against frost disasters, and in general for refined orchard production management.

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