MRTS-Boosting: A Quality-Aware Multivariate Time Series Classification Framework for Robust Rice Detection Under Cloud Contamination

Accurate rice detection is essential for food security, sustainable agriculture, and environmental monitoring. Satellite time series observations provide scalable capabilities for rice detection; however, their application in tropical regions is challenged by persistent cloud contamination, asynchronous crop development cycles, and temporal misalignment among multisensor observations, which reduce classification reliability. This study introduces Multivariate Robust Time Series Boosting (MRTS-Boosting), a quality-aware framework for multivariate time series classification (TSC) designed to improve robustness under noisy and irregular observational conditions. The framework integrates quality-weighted feature construction, joint extraction of full-series and interval-based temporal features, and a flexible multivariate formulation that accommodates heterogeneous satellite inputs without strict temporal alignment. Performance was evaluated using synthetic datasets with controlled cloud contamination, 103 benchmark datasets from the University of California, Riverside (UCR) TSC Archive, and 3261 real-world rice field observations from Indonesia. Comparisons were conducted against representative whole-series, interval-based, shapelet-based, kernel-based, and ensemble classifiers. MRTS-Boosting achieved up to 87% accuracy under severe cloud contamination, an average rank of 2.7 on noise-augmented UCR datasets, and 93% accuracy with Cohen’s kappa of 0.76 for Indonesian rice detection, while maintaining moderate computational cost. These results demonstrate that MRTS-Boosting provides a robust, scalable, and computationally efficient framework for satellite-based rice detection. The framework remains competitive in univariate settings while benefiting from multisensor integration, indicating that performance gains arise from both methodological design and the effective use of heterogeneous data. MRTS-Boosting is therefore well-suited for precision agriculture applications under challenging observational conditions.