AUTHOR=Zhang Ying , Lin Xinhao , Hei Junmiao , Wang Yixiao , Zhang Ang 

TITLE=Multiscale remote sensing methods for monitoring wetland ecosystem dynamics and crop development

JOURNAL=Frontiers in Environmental Science

VOLUME=Volume 13 - 2025

YEAR=2025

URL=https://www.frontiersin.org/journals/environmental-science/articles/10.3389/fenvs.2025.1626025

DOI=10.3389/fenvs.2025.1626025

ISSN=2296-665X

ABSTRACT=IntroductionUnderstanding the interplay between wetland ecosystems and agricultural crop development is vital for sustainable water and food resource management amid climate variability. Emerging technologies in artificial intelligence (AI) and big data analytics now offer powerful tools to integrate multiscale remote sensing with ecosystem modeling.MethodsThis study introduces a unified framework that combines remote sensing and AI-driven inference to monitor the spatiotemporal dynamics of wetland hydrology and crop phenology, aligning with modern approaches to integrated resource management. Traditional remote sensing methods often struggle to capture the temporal variability and complex dependencies in such ecosystems due to their reliance on static thresholds and single-modality data. To overcome these limitations, we propose an AI-enhanced methodology comprising two modules: the Graph-Augmented Attention Recommendation Network (GAARN) and the Multi-Perspective Preference Distillation (MPPD) strategy. GAARN fuses structural insights from environmental graphs with temporal patterns via attention-based encoders, enabling detailed mapping of land-water-vegetation transitions. MPPD incorporates semantic knowledge from ecological ontologies, meteorological data, and crop taxonomies to guide learning through consistency regularization and contrastive embedding alignment.ResultsOur dual-module framework offers robust interpretation of sparse observations, adaptive forecasting under climate variability, and scalable modeling of wetland-crop interactions. Validation across diverse agroecological zones reveals superior performance over conventional baselines in predicting vegetative indices, water extent changes, and crop growth stages.DiscussionThese results highlight the potential of our framework for advancing precision agriculture, wetland monitoring, and climate-resilient policy-making.