Data Driven Corn Yield Estimation Methods And Spatiotemporal Characteristic Analysis

Data-driven models are practical approaches to conflate multi-source phenological,meteorological and remote sensing data for estimating large-scale corn yields and assessing the impact of meteorological factors,which are vital to ensuring food security.However,most corn yield estimation models are based on a fixed spatiotemporal scale,which cannot reflect the dynamic processes of corn growth and the interactions across time and space.The insufficiency of the model structure brings uncertainty to the large-scale application of data-driven yield estimation models.This paper aims to address the problems such as the insufficient high-resolution corn phenology information,the unclear influence of scale effect,the complexed spatiotemporal autocorrelation processes of yield-meteorological interactions,the lack of efficient framework for multi-source data fusion and yield estimation.Taking the U.S.corn belt as the research area,this research focused on extracting multi-scale corn phenology information using remote sensing data;evaluating the scale effect on regression models between yield and meteorological factors;constructing the non-stationary process between yield and meteorological factors based on Bayesian spatiotemporal structure;and developing a deep learning framework for yield estimation using multi-source data.Solutions for large-scale corn yield estimation and meteorological impact assessment are provided in this research.Here presents the main researches and conclusions in this work as following:（1）A refined shape model approach based on MODIS data is proposed for multi-scale（pixel-county-district-state）corn phenology retrieval.This approach uses the morphological characteristics of WDRVI time-series to estimate corn phenological dates for 6 key stages.Comparing with the traditional threshold method,the shape model method reduces the RMSE by 11.9 days on average at the state-level.At the agricultural district level,the shape model method has the lowest RMSE as 5.1 days for emergence dates detection.The highest RMSE is6.9 days for planting dates detection.At the county level,the spatial distribution of the detected corn phenology is correlated with the latitude,indicating that this method is eligible for large-scale corn phenology detection.（2）Developed and compared statistical models with various spatiotemporal resolutions to understanding the scale effect on corn models.Multilinear regression models are trained using cross-sectional datasets pooled at three spatial resolutions（state,district,county）with temperature and precipitation related predictors according to two temporal resolutions（growing season,growing phase）.The results show that when modeling at the agricultural area scale,the adjusted R² is 11.0%higher than that at the state and county scales on average.Sensitivity analysis of the response of corn yield to meteorological factors showed that the killing degree days（KDD）is the most important factor.In the southern part of the corn belt in the silking-dough period,one unit of increase in KDD could cause a yield loss of 20.14 kg·hm^-2.In the northern part of the belt,during the dough-dented period,one unit of increase in KDD could result in a yield loss of 21.72 kg·hm^-2.Excessive precipitation during the planting-silking period has a negative impact on the corn yield,and the yield could be reduced by an average of 2.19kg·hm^-2 for every 1 mm increase of precipitation.（3）A Bayesian hierarchical structure-based spatiotemporally varying coefficient（STVC）model was constructed to quantitatively analyze the heterogenous interaction structure between corn yield and meteorological factors across space and time.The model based on temporal and spatial non-stationary processes,and fits the local yield response to meteorological factors.The results show that the STVC can reduce the degree of spatial aggregation for estimated residuals.Compared with the other five structures of time-space effect models,the average RMSE is reduced by 35.02%,and the measure of spatial autocorrelation Moran’s I is reduced by an average of 0.05.Even in the years when meteorological disasters are severe,the impact of meteorological factors can still be effectively fitted by STVC model.The results show that the STVC model can fit the spatiotemporal correlation and non-stationary process of the impact of meteorological factors on yield,which is of reference significance for understanding the response of corn yield to meteorological factors.（4）A deep learning yield estimation framework that integrates phenological,meteorological,and remote sensing data is developed based on the long short-term memory neural network（LSTM）structure.Compared with methods using single meteorological or remote sensing data,the use of multi-source meteorological and remote sensing data can reduce 41.21%and 20.25%of RMSE in county-level corn yield estimation.According to the sensitivity analysis results of the changes in the number of training samples,the LSTM model can learn effective yield characteristics from the increasing sample size.The LSTM model can improve the feature extraction ability by increasing the sample size.Compare the training data with 1-year and 10-year samples,the RMSE of the LSTM model in the test year decreased from 1450 kg·hm^-2 to870 kg·hm^-2.The LSTM model also outperformed LASSO and random forest approaches in the drought year.According to the results of in-season yield prediction,the observations at the silking-dough period is the most critical for prediction accuracy.