The emergence of deep learning, big data and cloud computing technologies has solved the problems of tedious image downloading, storage and integration, and time-consuming data computing in the traditional remote sensing image processing. Google Earth Engine (GEE) is provided by Google, a cloud-based geospatial processing platform for analyzing planetary-scale geospatial data [34]. The platform has access to most satellite data and provides tools to process and analyze the data online. Based on the GEE platform, vegetation index data such as NDVI and EVI can be downloaded directly. It is also possible to perform wave synthesis calculations, normalization and other operations. Generally, when selecting crop-growth-related feature variables, only the correlation between regional yield and each feature is considered. However, there are differences in land use, soil type and cover within the region. Such differences are the heterogeneity and complexity in spatial distribution within the region, i.e., spatial heterogeneity. Spatial heterogeneity is an important research theory in the field of ecology, and has gradually been applied to the field of human-economic geography [35]. Spatial heterogeneity can, to some extent, affect the correlation between crop yield and various types of characteristic data, which in turn affects the final prediction. Currently, the effect of spatial heterogeneity has been less considered in the existing studies in the field of crop yield. Through the previous scholars' studies, we can learn that deep learning models have greater advantages over traditional linear regression methods in dealing with complex nonlinear relationships. Moreover, deep learning models perform better than machine learning models when dealing with a larger number of datasets. Among the many modeling approaches in deep learning, CNN and LSTM are widely used in the field of crop yield prediction. Many studies have shown that the CNN-LSTM hybrid model approach has higher accuracy in solving crop yield prediction problems. For the county-level rice data in Hubei Province, China, which has a long time interval and a large amount of data, we chose the deep learning model as the prediction model for prediction. To evaluate the accuracy results of the hybrid CNN-LSTM model on the rice yield prediction problem and to verify its prediction performance, experiments were conducted. In this paper, the prediction ability of the CNN-LSTM hybrid model was tested based on county-level rice yield in Hubei Province, China, and the CNN model and ConvLSTM model were used as comparison experiments. The GEE platform was utilized to download satellite remote sensing information. The images were synthesized and processed to convert them into normalized histograms. As input data for training the model, vegetation indices, climatic indicators and custom parameters indicating spatial heterogeneity were used. Rice production data of Hubei Province from 2000-2019 were used for validation. The main objectives of this study are: (1) Exploring a satisfactory method for county-level rice yield forecasting. To verify the prediction ability of the CNN-LSTM model, the CNN model and the ConvLSTM model are used for comparison. (2) Discuss the effect of spatial heterogeneity on the results of rice yield prediction. We add a custom dummy variable and evaluate whether it can improve the final prediction accuracy.