# Crop Yield Prediction Based on Agrometeorological Indexes and Remote Sensing Data
## 3 Methodology  
### Input Variable Calculation  
#### 3.1.1 Calculation of Technical Yield  
Grain yield is affected by many factors, including natural factors and nonnatural factors. Climate is the most important natural factor, as it is constantly fluctuating. Nonnatural factors such as cultivation techniques and field management strategies have improved over time. Therefore, the annual crop yield per unit area (\(Y\)), which is defined as the average amount of agricultural products harvested per unit of land area, can be divided into two parts: climatic yield (\(Y_{w}\)) and trend yield (\(Y_{t}\)) (Equation (1)). Climatic yield is determined  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **Data** & **Data Source** & **Spatial Resolution** & **Temporal Resolution** \\ \hline \multirow{6}{*}{**Meteorological** ** reanalysis data**} & \multicolumn{3}{c}{ERA5 dataset of the European Center for Medium-Term Weather Forecast (ECMWF 2017)} \\  & \multicolumn{1}{c}{Total} & \multicolumn{1}{c}{European Center for Medium-Term Weather Forecast (ECMWF 2017)} & \multirow{3}{*}{0.25\({}^{\circ}\)} & \multirow{3}{*}{Monthly} \\  & \multicolumn{1}{c}{} & & & \\  & \multicolumn{1}{c}{} & & & \\  & \multicolumn{1}{c}{} & & & \\  & \multicolumn{1}{c}{} & & & \\  & \multicolumn{1}{c}{} & & & \\  & \multicolumn{1}{c}{} & & & \\  & \multicolumn{1}{c}{} & & & \\  & \multicolumn{1}{c}{} & & & \\  & \multicolumn{1}{c}{} & & & \\  & \multicolumn{1}{c}{} & & & \\  & \multicolumn{1}{c}{} & & & \\  & \multicolumn{1}{c}{} & & & \\  & \multicolumn{1}{c}{} & & & \\  & \multicolumn{1}{c}{} & & & \\  & \multicolumn{1}{c}{} & & & \\  & \multicolumn{1}{c}{} & & & \\  & \multicolumn{1}{c}{} & & & \\  & \multicolumn{1}{c}{} & & & \\  & \multicolumn{1}{c}{} & & & \\  & \multicolumn{1}{c}{} & & & \\ \end{tabular}
\end{table}
Table 1: Data sources and characteristics.  
by short-term climate variations, while the trend yield, which is also referred to as the technical yield, is influenced by long-term factors.  
\[Y=Y_{t}+Y_{w} \tag{1}\]  
There are many methods used to calculate the technical yield [52]. The most commonly used methods include the moving average method, HP filtering method [53], and exponential smoothing method [54]. We used these three methods to separate the technical yield from the historical actual yield and then used the historical actual yield minus the technical yield to calculate the climate yield. We calculated the correlation between the climate yield derived from the three methods and the area of cultivated land suffered by disaster. The climate yield with the highest correlation and the corresponding technical yield were selected as the final calculation results. The correlation here was calculated using the gray correlation analysis. Gray correlation analysis judges the correlation degree between variables according to the degree of geometric similarity of the time series curve, focuses on the consistency of the change trends among variables and is not limited by the size and distribution of samples [55]. Through the above method, based on municipal-level corn yield data of Jilin Province from 1996 to 2018 and Liaoning Province from 1997 to 2018, the technical yield and climate yield of the corresponding period were obtained.