A Novel Time-Aware Siamese Network for Crop Classification using Spectrotemporal Signatures Derived from Multi-Dimensional Data Format

Abstract

Crop classification is critical for precision agriculture, food security and policymaking. While the fusion of multi-sensor data and deep learning holds significant potential for enhancing crop classification, the application of deep learning-based spectral matching techniques in this domain remains underexplored. To address this gap, this study proposes a novel Time-Aware Siamese Network (TASN) for Spectrotemporal Signature (STS) matching, focusing on kharif crops across three districts in Punjab, Pakistan. The research integrates fusion of Landsat-8/9 and Sentinel-2 data to generate a High-Spatial and High-Temporal (HSHT) dataset, determine optimal temporal lengths for STS extraction using Jeffries-Matusita (JM) distance analysis, applies smoothing techniques to reduce noise in STS, and evaluates TASN’s transfer learning capability for cross-regional crop classification. Results demonstrate that the HSHT dataset significantly enhances temporal resolution, enabling precise crop monitoring. The TASN model achieves 95% accuracy with the Universal Normalized Vegetation Index (UNVI), 92% with NDVI and 91% with EVI. Transfer learning experiments reveal robust adaptability, applying STS of one region to others, TASN attains 98% accuracy for rice classification in Hafizabad using UNVI and 81% to 87% accuracy for multi-crop classification in Lodhran. These findings highlight the efficacy of spectral matching for scalable, transferable crop classification. The study advances agricultural remote seeing by introducing a deep learning framework for STS matching and demonstrating its utility across diverse regions. By combining multi-sensor fusion and TASN’s transferability, this study offers a practical tool for policymakers and farmers, supporting scalable crop monitoring and food security initiatives.