GCDI: A Composite Drought Index on a 1-km Grid for China via Multi-Source Geo-Aware Feature Learning

Reliable drought assessment requires spatially continuous drought information, whereas station-based drought indices are spatially discrete and many existing gridded products are available at relatively coarse spatial scales. To address this issue, this study generates a monthly Geographically Weighted Composite Drought Index, termed GCDI-1, on a 1-km output grid over China. A geographically weighted SPEI-related auxiliary variable, termed GeoSPEI-1, is constructed from neighboring station-based SPEI-1 values to incorporate neighboring-station drought information into drought index generation. GeoSPEI-1 is jointly used with multi-source remote sensing and reanalysis variables as input to a geographically weighted feature-learning model, termed GeoCSL, which combines convolutional neural network, self-attention, and long short-term memory components to process the multi-source inputs. Input-combination experiments showed that GeoSPEI-1 was the primary contributor among the evaluated input variables within GeoCSL, while the multi-source environmental variables provided additional complementary information. Ten-fold cross-validation using the 2001–2010 training samples yielded an RMSE of 0.546 and an MAE of 0.407 for GeoCSL against station-based SPEI-1. Station-referenced evaluation using the temporally held-out 2011–2012 data further showed that GCDI-1 achieved an RMSE of 0.556 and an MAE of 0.410, compared with 0.562 and 0.421 for leave-one-station-out Kriging and 0.754 and 0.577 for SPEIbase-1, respectively. These results indicate that incorporating geographically weighted neighboring-station information together with multi-source environmental variables can support the generation of spatially continuous drought information and the use of GCDI-1 as a supplementary gridded product for regional drought monitoring and assessment in China.