GACNPs: Fine-Grained Drought Monitoring Using Remote Sensing Data Based on Conditional Neural Processes

Due to the constraints posed by the complexity and vast amounts of geospatial data, achieving fine-grained drought geospatial interpolation using sparse observation points has always been a challenging problem. Therefore, to solve the problem, this letter proposes a new model called geographical attention conditional neural processes (GACNPs) that extends spatial multiattention conditional neural processes (SMACNPs). GACNPs as a type of probabilistic model inherit the flexibility of neural networks in parameterizing stochastic processes. We design a geographic information embedding module by measuring the geographic information correlation between known observation points and target points, thereby better capturing the complexities in spatial data. Considering that real-world data distributions often do not conform to Gaussian distributions, we propose a nonlinear mapping (NLM) module to handle those more complex distributions that cannot be expressed by Gaussian assumptions, such as multimodal distributions. We utilize MODIS satellite to obtain remote sensing indices with 1-km spatial resolution, and integrating these with meteorological drought indices derived from ground weather stations for multisource data fusion, we conducted an experimental analysis of drought disaster situation in Yunnan Province. Our study demonstrates that the use of GACNPs outperforms traditional methods and other neural network approaches in terms of spatial interpolation accuracy. This improvement facilitates more precise meteorological drought monitoring tasks.