Water quantity and quality in lakes are closely linked to the compounding effects of climate
change and human activities in their catchments, especially for lakes located in semi-arid …

Ongoing advances in satellite remote sensing data and machine learning methods have
enabled crop yield estimation at various spatial and temporal resolutions. While yield …

The crop leaf area index (LAI) and leaf chlorophyll content (LCC) are essential indicators
that reflect crop growth status, and their accurate estimation is helpful for agricultural …

Abstract Land Surface Temperature (LST) is a crucial parameter in studies of urban heat
islands, climate change, evapotranspiration, hydrological cycles, and vegetation monitoring …

Operational Earth observation missions, like the Sentinel-3 (S3) satellites, aim to provide
imagery for long-term environmental assessment to monitor and analyze vegetation …

Accurate estimation of crop leaf and canopy biochemical traits, such as leaf dry matter
content (Cm), leaf equivalent water thickness (Cw), leaf area index (LAI), dry leaf biomass …

Near-surface air temperature (NSAT) data is essential for climate analysis and applied
research in areas with sparse ground-based observations. In recent years, machine learning …

Due to the limited availability of in-situ observation data, most existing leaf area index (LAI)
inversion models do not fully leverage temporal information. Furthermore, the phenological …

Mid-ocean ridges are unique, tectonically active geographical units on Earth that profoundly
control the ocean environment and dynamics at the global scale. However, high-resolution …

Accurate and efficient estimation of biochemical traits, including leaf index area (LAI), leaf
chlorophyll content (LCC) and canopy chlorophyll content (CCC), is crucial for crop growth …