# Crop Yield Prediction Based on Agrometeorological Indexes and Remote Sensing Data
## 5 Discussion
It is applicable to yield estimation in both normal and disaster years. Our research has some limitations. First, the maize yield at municipal level was used for modeling because the time series yield data at county level is missing. Technical yield is one of the input variables of our modeling, and it has the greatest contribution to the accuracy of modeling. In order to calculate the technical yield, the yield data of long time series, usually longer than 30 years, is required. County level yield data can only be obtained for recent years, and there is an insufficiently long enough time series. However, our modeling strategy is also applicable to regions with long time series county scale data. Second, among the remote sensing parameters, we only tested NDVI and GPP. On the one hand, it is limited by article space. On the other hand, the vegetation indexes are highly correlated, and there is a certain degree of information redundancy among them. NDVI is the most widely used remote sensing parameter for yield estimation. Many other remote sensing parameters have been developed on the basis of NDVI. GPP is highly correlated with aboveground biomass and has a strong biophysics mechanism. Therefore, we chose NDVI and GPP to test. Our test results show that there is little difference between NDVI and GPP in the modeling accuracy. Finally, we recommend the use of NDVI in the establishment of a yield estimation model because it is easy to calculate. Third, technical yield is one of the input variables of modeling. The accuracy of the technical yield calculation has a certain influence on the accuracy of the subsequent yield estimation model. In order to reduce the uncertainty of the technical yield calculation results, we used three of the most popular methods to separate the technical yield from the historical actual yield. Corresponding to the three methods, we obtained three technical yield time series. Then, we chose the best result by calculating the correlation between the climate yield, which is the historical actual yield minus the technical yield, and the area of cultivated land suffered by disaster. Adverse climate conditions will lead to yield reduction, while favorable climate conditions will increase yield. There should be a good correlation between climate yield and the area of cultivated land suffered by disaster. Therefore, the climate yield with the highest correlation and the corresponding technical yield were regarded as the best calculation results. Although we chose the best technical yield calculation results by comparing the moving average method, HP filtering method, and exponential smoothing method, there are many other methods that can be used to calculate technical yield, and these methods can be tested in the future. Fourth, the agrometeorological indexes and remote sensing vegetation parameters used for model building in this study  
Figure 14: The climate yield in Jilin and Liaoning Provinces during the study period. are monthly scale data and calculated in a fixed time period (April to September). However, maize phenology varies among different regions and require different climate conditions at different growth phases. Dividing the growth season of maize into multiple phases and calculating agrometeorological indexes and remote sensing vegetation parameters for each phase can better capture the impact of climate conditions of different growth phases on maize yield. Fifth, a large quantity of remote sensing data, meteorological data, and statistical data with a long time series were used in this study. Collecting and preprocessing these data consume a great amount of computer storage and computation time. At present, some earth science data and analysis cloud computing platforms such as GEE provide a large number of high resolution remote sensing data, meteorological data, advanced computing functions, and flexible user interfaces. If the experimental process of this study were transferred to the cloud computing platform, the time cost and computer storage cost would be greatly reduced.