Accurate daily surface soil moisture (SSM) mapping at high spatial resolution (e.g., 30 m) remains challenging due to individual satellite sensor limitations. Although passive microwave sensors provide frequent coarse-resolution observations and synthetic aperture radar (SAR) offers high-resolution data intermittently, achieving both simultaneously requires sensor synergy. This paper introduces the microwave–optical multi-stage synergistic downscaling framework (MMSDF) to generate daily 30 m SSM products. The framework integrates SMAP L4 (9 km), MODIS data (500 m–1 km), harmonized Landsat Sentinel-2 (HLS, 30 m), radiometric terrain corrected Sentinel-1 (RTC-S1, 30 m), and auxiliary geographic data. It comprises three stages: (1) downscaling SMAP L4 to 1 km via random forest; (2) calibrating Sentinel-1 water cloud model (WCM) using intermediate 1 km SSM to retrieve 30 m SSM without in situ calibration; and (3) fusing daily 1 km SSM and intermittent 30 m WCM-derived retrievals using the spatial–temporal fusion model (ESTARFM) to generate seamless daily 30 m SSM maps. Validation against in situ measurements from 16 sites in Hunan Province, China (summer 2024) yielded R of 0.54 and RMSE of 0.045 ${\mathrm{cm}}^3/{\mathrm{cm}}^3$. Results demonstrate the framework’s capability to synergize multi-source data for high-resolution daily SSM estimates valuable for hydrological and agricultural applications.

Scalable and interpretable change monitoring systems are increasingly critical for earth observation. Existing supervised change detection (CD) methods rely heavily on labeled data and suffer from poor generalization across sensors, resolutions, and regions. Unsupervised and recent zero-shot approaches alleviate annotation needs but remain limited by vague semantic guidance, fragmented object boundaries, and naive fusion of heterogeneous cues, resulting in low interpretability and unreliable discrimination. To bridge this gap, we propose TDCD, a triadic fusion framework towards task-driven earth monitoring, which unifies task-aware prompting, object-level cascade, and adaptive fusion for zero-shot land-cover change detection. TDCD first leverages LLM-generated prompts to achieve task-driven semantic (TDS) discrimination without fine-tuning. It then refines change evidence through a bidirectional object cascade (BOC) mechanism that enforces object-level coherence. Finally, an adaptive decision fusion (ADF) module integrates spatio-temporal and semantic streams, emphasizing reliable signals across heterogeneous cues. Evaluated on three diverse benchmark datasets, TDCD consistently outperforms eight existing unsupervised and zero-shot methods, achieving an F1-score improvement of up to 57.75% while providing detailed from–to change categories. Its robust performance across sensors and regions confirms strong generalization and practical utility for scalable and task-aware monitoring from remote sensing imagery. Codes will be available at https://github.com/StephenApX/TDCD