Study On Apple First Flowering Date Prediction Based On Collaboration Of Remote Sensing Land Surface Temperatured And Meteorological Data

The first flowering date(FFD)of apple is an important parameter for apple growth and development,and is also a key phenology period closely related to apple yield.Accurate prediction of apple FFD is of great importance to the fine production management such as flowering thinning,frost disaster prevention in orchards.Most of the existing prediction methods are based on meteorological station data,it is difficult to obtain spatially continuous information on the FFD because of the sparse distribution of ground meteorological stations.At the same time,most of the apple growing areas are located in warm temperate regions,and the topography is mostly hilly and mountainous,so the spatial representativeness of point meteorological station data will reduce the accuracy of FFD prediction for the whole region.Aiming at the above problems,this paper takes the apple dominant growing areas in Luochuan,as the study area,and proposes a method to predict the apple first flowering date based on high spatial-temporal remote sensing land surface temperature data,it can obtain spatially continuous information on FFD in complex meteorological and topographic environments.The main research contents and conclusions of this thesis are as follows.(1)Study on the construction of prediction model for the first flowering date driven by daily air temperature.In view of the current situation that existing flowering phenology models are limited by the temporal resolution of hourly air temperature data in predicting the FFD,based on the gridded meteorological data from October 1,2019 to April 30,2020 and the flowering phenology sequential model,the sensitivity relationships between daily chill and heat accumulation and daily maximum temperature(Tamax),daily minimum temperature(Tamin),and daily average temperature(Taavg)were explored firstly,and then constructed a prediction model for the first flowering date driven by daily air temperature using random forest algorithm.The results show that the model constructed using three groups of daily temperature characteristic factors(Tamax+Taavg,Tamin+Taavg and Tamax+Tamin+Taavg)have better results in estimating daily chill and heat accumulation,and the highest model accuracy was obtained for the combination of Tamax+Tamin+Taavg daily temperature characteristic factors.When Tamax+Tamin+Taavg is used to predict the apple FFD,the R2 and RMSE between the predicted and measured values are 0.92 and 3.44 days,respectively.The model constructed in this study can effectively convert the input air temperature data from hourly scale to daily scale,which has good application value and potential in the subsequent work of apple FFD prediction.(2)Study on the spatio-temporal reconstruction algorithm of land surface temperature.In order to avoid the problem of spatio-temporal discontinuity of land surface temperature data due to missing original data caused by clouds and rain,a Spatio-Temperal Reconstruction(STR)algorithm is proposed in this study.Firstly,a preliminary land surface temperature reconstruction result was generated by calculating the spatial distance factor and temporal similarity factor between effective land surface temperature pixel pairs,and then a spatiotemporal seamless land surface temperature dataset was obtained in the study area based on Harmonic Analysis of Time Series(HANTS).The results show that the STR algorithm can effectively fill in the land surface temperature at different missing rates,and the reconstructed land surface temperature have high spatial continuity with the original land surface temperature.By simulating the missing original land surface temperature,the RMSE between the reconstructed daytime,nighttime and daily average land surface temperature and the original land surface temperature using the STR algorithm are 1.21℃,1.10℃ and 1.07℃,respectively.The above results show that the method can reconstruct daily land surface temperature data with spatial and temporal missing rates,and at the same time can ensure the spatial and temporal continuity of the reconstructed data.(3)Study on apple first flowering date prediction based on reconstructed land surface temperature.Using the original land surface temperature,gridded meteorological data,elevation,latitude and longitude as variables,a daily air temperature estimation model was constructed using the random forest algorithm.Next,the reconstructed land surface temperature dataset was combined to generate a high spatial and temporal continuous daily air temperature dataset containing Tamax,Tamin and Taavg in the study area from October 1,2019 to April 30,2020.Then the prediction model for the first flowering date was used to predicting the apple FFD based on the daily air temperature dataset.The results show that the R2 between the predicted and measured values of apple FFD is 0.72 and the RMSE is 2.96 days,which indicates that the accuracy of the FFD prediction by this method is comparable to the result by using the traditional sequential model based on real hourly air temperature data driven.By combining the land surface temperature,meteorological data and the phenology sequential model,in response to the problem that it is difficult to obtain spatially continuous phenological information in the traditional method of predicting the apple first flowering date based on meteorological station data,this study proposes a method to predict regional scale apple first flowering date prediction based on collaboration of remote sensing land surface temperature and meteorological data,which can provide a reference value for future study of fruit tree phenological period prediction under remote sensing quantification.And the method proposed in this study also provides an effective support to obtain the spatial distribution of the apple FFD,which has important application value for orchard refinement production management.