Crop stubble retention categories recognition from multi-modal remote sensing imageries: a spatiotemporal cube approach

The degradation of croplands due to traditional tillage practices is a significant challenge for sustainable agriculture. Crop stubble retention (CSR), as a key conservation tillage method, is crucial for evaluating cropland sustainability, and the efficient acquisition of CSR category information directly affects tillage strategy optimization. However, existing studies often overlook the acquisition of fine CSR category information. This study proposes a method for CSR category recognition based on spatiotemporal cubes and multi-modal remote sensing data. Taking Lishu County, Jilin Province, China, as an example, we constructed a unified standard spatiotemporal dataset using the Raster Dataset Clean & Reconstitution Multi-Grid (RDCRMG) architecture and developed a spatiotemporal cube-based computation framework. Combined with the Random Forest model, we evaluated the effectiveness of the method by comparing single-modal, multi-modal, and optimal percentile-featured multi-modal remote sensing data. The proposed method achieved an overall accuracy of 86.1% and a Kappa coefficient of 0.79 when using multi-modal data with optimal percentile features. Our results demonstrate that this method significantly improves the accuracy of CSR category recognition, providing precise spatial information and technical support for conservation tillage and sustainable cropland management.