End-to-End Predictive Network for Accurate Early Crop Planting Area Estimation

Early crop planting area estimation is crucial for achieving effective government resource allocation, optimizing resource distribution planning, and preparation related to food security. Utilizing remote sensing images during the crop growth period for crop planting area estimation has garnered increasing attention. However, area estimation from remote sensing often lags in obtaining image data. Moreover, this method is also influenced by the quality of remote sensing image data and segmentation accuracy. This paper proposes a new method for early area estimation based on multi-year land cover data using a three-dimensional convolutional end-to-end network. This method eliminates the impact of the intermediate process of image segmentation accuracy on area estimation. Additionally, multi-subimage technology is employed to solve the issue of inconsistent input sample size, and label distribution smoothing technology is used to tackle the problem of unbalanced sample distribution. The proposed method was evaluated on U.S. corn and soybean datasets. In comparison to baseline methods, the method achieved relative errors of 0.67% for corn and 3.72% for soybeans at the national level in the United States in 2021. This demonstrates the effectiveness of the proposed method and the potential for early decision-making support. This approach offers a new perspective for area estimation, significantly advancing the timing of planting area prediction and enhancing the accuracy of early area estimation, providing actionable insights for decision-making and resource management.