## 2 Related Works  
The research of crop yield prediction is mainly based on two kinds of data. One is statistical data (locally sensed data), mainly genetic characteristics (data from seeds (with a tendency to improve year by year)), soil conditions, environmental factors that changes in time and space (weather, etc. ), planting density, management specifications (irrigation conditions, etc.). The other is remote sensing data, such as multispectral remote sensing images. Currently, leading crop yield prediction techniques mostly rely on locally sensed data since the locally sensed data can provide detailed information [13]. But locally sensed data are expensive to collect, often noisy, and extremely difficult to scale. Remote sensing data, on the other hand, is a cheap and globally accessible resource that is more suitable for surveying large areas and monitoring national and regional agricultural changes. Thus, crop yield prediction based on remote sensing data is a hot research topic. Currently, the deep learning-based method is the predominant method in the crop prediction task since it has shown excellent performance in many fields. There are many papers based on deep-learning approaches. [PERSON] et al. from Stanford University published \"Deep Gaussian Process for Crop Yield Prediction Based on Remote Sensing Data\" in 2017 [1], which won the Best Student Paper Award in the Computational Sustainability Track of AAAI 2017, and the work also won the Best Big Data Solution in the World Bank Big Data Innovation Challenge. They used multispectral remote sensing images (nine bands) from MODIS satellites [6; 14] to estimate soybean and corn yields in 11 U.S. states. Their contributions to the technical solution are threefold. Firstly, the raw data are embedded using a histogram. The embedding method is motivated by the author's belief that the location of spectral information is not related to yield estimation. However, the method in this paper also has some problems: The spectral information of different bands at the same location should be correlated with each other, which is equivalent to a feature vector, but the paper constructs separate histograms for different spectral bands and then stitches these histograms together, which separates the correlation between different spectral information at the same location. Secondly, the Deep Gaussian Process is combined with a neural network. The motivation for this approach is that yields should obey the continuity assumption. The reason is that the remote sensing data alone cannot cover all the factors that affect crop yield. Moreover, these factors, such as land fertility, should have continuity in space and time. [PERSON] et al. proposed integrating the continuity assumption of yield into a neural network using the Deep Gaussian Process. Finally, two neural networks based on LSTM and CNN were used to estimate the yield, respectively. [PERSON] et al. of Stanford University conducted corn and soybean yield estimation on remotely sensed images from Argentina and Brazil based on the method of [PERSON] et al. mentioned above [7]. They also used the MODIS self-contained Land cover mask to determine the planting areas of maize and soybean. Since the MODIS Land cover mask does not distinguish between crop types, they used the setting that all cropland in the major crop production areas is planted with this type of crop. For example, the arable land with the highest soybean production in Argentina's provinces is all considered soybean growing areas. [PERSON] of the University of Amsterdam proposed using 3D convolution to estimate crop yields on remotely sensed images [5]. They replicated the work of [PERSON] et al. at Stanford University and found that the yield estimation results are prone to high prediction for low yields and low prediction for high yields. We speculate that this may be related to the uneven labeling in the dataset. The labels in the dataset become approximately normally distributed, with most values concentrated around the mean.