Alternative climate products, such as gauge-based gridded data, ground-based weather
radar, satellite precipitation and climate reanalysis products, are being increasingly applied …

Crop yield prediction is extremely challenging due to its dependence on multiple factors
such as crop genotype, environmental factors, management practices, and their interactions …

This study investigates whether coupling crop modeling and machine learning (ML)
improves corn yield predictions in the US Corn Belt. The main objectives are to explore …

Rainfall–runoff modelling is one of the key challenges in the field of hydrology. Various
approaches exist, ranging from physically based over conceptual to fully data-driven …

This study reports a new and significantly enhanced analysis of US flood hazard at 30 m
spatial resolution. Specific improvements include updated hydrography data, new methods …

Non-predictive or inaccurate weather forecasting can severely impact the community of
users such as farmers. Numerical weather prediction models run in major weather …

Access to daily high-resolution gridded surface weather data based on direct observations
and over long time periods is essential for many studies and applications including …

Yield prediction is of great significance for yield map**, crop market planning, crop
insurance, and harvest management. Remote sensing is becoming increasingly important in …

Crop type map** at the field level is necessary for a variety of applications in agricultural
monitoring and food security. As remote sensing imagery continues to increase in spatial …

Vegetation phenology controls the seasonality of many ecosystem processes, as well as
numerous biosphere-atmosphere feedbacks. Phenology is also highly sensitive to climate …