This paper presents two new state-of-the-art Spatial Rain Field Interpolation Convolutional Neural Networks (SRFICNNs), referred to as LD (Learned Deviation) and LI (Learned Interpolation) models, for predicting the point rain rate at finer spatial scales. The main contribution is the successful introduction of the prior-art deep learning technique into high resolution rainfall rate prediction with significant improvement in accuracy. This is very important for the effective implementation of fade mitigation techniques for both terrestrial and satellite networks. The comparison of the models’ performances with ground truth (radar measurements) shows that the proposed models give an excellent mean square error (MSE) and Structural SIMilarity (SSIM) in rainfall fields reconstruction if the network depth falls in the range of 15~25 weight layers. The final model uses 20 layers for high resolution point rain rates prediction. Further study shows that the LD model offers a faster convergence and yields a more accurate rain rate prediction. In particular, this paper compares the rain rate exceedance distribution and Log-Normality property from the model estimates with values calculated from measured data. Results show that the LD model gives a highly accurate estimates of these two indices with corresponding Root Mean Square (RMS) error of 5.1709 × 10 ^-4 and 0.0013, respectively.

With China’s urbanization process and changes in rural family structures, the abandonment of farmland in scattered villages within hilly mountainous regions is becoming an increasingly serious issue, restricting the improvement of land use efficiency. This study analyzes the basic characteristics and variations in abandoned farmland by conducting surveys and interviews with peasants in a scattered village in southern China. Using the Heckman two-stage model, we perform empirical analysis on the factors influencing farmland abandonment, addressing potential sample selection bias. The findings show the following: peasants with better health and higher education levels are more likely to transition to non-agricultural occupations which contributes to an increased abandonment of farmland. However, larger and more integrated land parcels, along with favorable farming conditions, help reduce abandonment. Additionally, rural land transfer and agricultural subsidies are important factors that enhance farmland utilization and mitigate abandonment. These results provide a reference for addressing the abandonment of farmland and improving both the farming environment and social policies in rural villages.