# Crop Yield Prediction Based on Agrometeorological Indexes and Remote Sensing Data
###### Abstract  
Timely and reliable estimations of crop yield are essential for crop management and successful food trade. In previous studies, remote sensing data or climate data are often used alone in statistical yield estimation models. In this study, we synthetically used agrometeorological indicators and remote sensing vegetation parameters to estimate maize yield in Jilin and Liaoning Provinces of China. We applied two methods to select input variables, used the random forest method to establish yield estimation models, and verified the accuracy of the models in three disaster years (1997, 2000, and 2001). The results show that the R\({}^{2}\) values of the eight yield estimation models established in the two provinces were all above 0.7, Lin's concordance correlation coefficients were all above 0.84, and the mean absolute relative errors were all below 0.14. The mean absolute relative error of the yield estimations in the three disaster years was 0.12 in Jilin Province and 0.13 in Liaoning Province. A model built using variables selected by a two-stage importance evaluation method can obtain a better accuracy with fewer variables. The final yield estimation model of Jilin province adopts eight independent variables, and the final yield estimation model of Liaoning Province adopts nine independent variables. Among the 11 adopted variables in two provinces, ATT (accumulated temperature above 10 \({}^{\circ}\)C) variables accounted for the highest proportion (54.54%). In addition, the GPP (gross primary production) anomaly in August, NDVI (Normalized Difference Vegetation Index) anomaly in August, and standardized precipitation index with a two-month scale in July were selected as important modeling variables by all methods in the two provinces. This study provides a reference method for the selection of modeling variables, and the results are helpful for understanding the impact of climate on potential yield. G 2021 132016 14 May  
production efficiency model and a crop growth model. The production efficiency model assumes that crop yields under nonstressed conditions correlate linearly with the amount of absorbed photosynthetically active radiation. This method first estimates the amount of crop aboveground dry matter using remotely sensed data and then converts it into crop yield. The crop growth model simulates physical crop growth processes and finally estimates the resulting yield. It is complex and requires a large amount of input data, such as soil, farmland management, and climate parameters. The uncertainty of the mechanism model is difficult to analyze [11]. The response of crops to extreme climate is not well reflected in the mechanism models [12; 13; 14; 15; 16; 17]. The data modelling method includes a statistical modelling and a machine learning method. The statistical model establishes a function relation between yield predictors and the resulting yield. It is often based on a series of assumptions, such as linear regression model assumptions. The machine learning method constructs an analysis system through data learning, which does not rely on explicit construction rules. It cannot give explicit expressions of the functional relationships between yield predictors and the resulting yield. From the perspective of data sources, the modelling method can be further divided into remote sensing data-based, meteorological data-based, and other data-based yield estimation models. The other data used in the modelling include soil characteristic parameters, production inputs (such as chemical fertilizer, agricultural machinery, etc. ), production conditions (such as irrigation), etc. [18; 19; 20; 21]. In yield estimation models based on remote sensing data, NDVI(Normalized Difference Vegetation Index) is the most commonly used remote sensing data, including the original NDVI, average or cumulative NDVI over a growth period [22], average or cumulative NDVI over the key growing stages [23; 24], etc.