[1] Many regions of the world are dependent on snow cover for frost protection and summer water supplies. These same regions are predominantly forested, with forests highly vulnerable to change. Here we combine a meta-analysis of observational studies across the globe with modeling to show that in regions with average December-January-February (DJF) temperatures greater than −1°C, forest cover reduces snow duration by 1–2 weeks compared to adjacent open areas. This occurs because the dominant effect of forest cover shifts from slowing snowmelt by shading the snow and blocking the wind to accelerating snowmelt from increasing longwave radiation. In many locations, midwinter melt removes forest snow before solar radiation is great enough for forest shading to matter, and with warming temperatures, midwinter melt is likely to become more widespread. This temperature-effect in forest-snow-climate interactions must be considered in representations of the combined ecohydrological system and can be used advantageously in forest management strategies.

Integrating Artificial Intelligence (AI) techniques with remote sensing holds great potential for revolutionizing data analysis and applications in many domains of Earth sciences. This review paper synthesizes the existing literature on AI applications in remote sensing, consolidating and analyzing AI methodologies, outcomes, and limitations. The primary objectives are to identify research gaps, assess the effectiveness of AI approaches in practice, and highlight emerging trends and challenges. We explore diverse applications of AI in remote sensing, including image classification, land cover mapping, object detection, change detection, hyperspectral and radar data analysis, and data fusion. We present an overview of the remote sensing technologies, methods employed, and relevant use cases. We further explore challenges associated with practical AI in remote sensing, such as data quality and availability, model uncertainty and interpretability, and integration with domain expertise as well as potential solutions, advancements, and future directions. We provide a comprehensive overview for researchers, practitioners, and decision makers, informing future research and applications at the exciting intersection of AI and remote sensing.

Many applications require estimation of reference evapotranspiration (ETo) in areas where meteorological measurements are limited. Previous studies have shown that simple evapotranspiration models based on radiation and temperature perform relatively well in humid climates but underpredict ETo in drier and windier climates. In this paper, estimates of ETo based on existing simple models were compared with ETo calculated with the more comprehensive Penman-Monteith equation using meteorological measurements at 106 locations in the contiguous United States for a range of climates. Results showed that the simpler models were closest to the more comprehensive model at sites where the annual mean relative humidity (RH) was approximately 70% and annual 2-m wind speed (U) was less than 2 m·s−1. Equations for adjusting the model coefficients were developed based on annual averages of RH [or vapor pressure deficit (VPD)] and U to improve the performance of these models for drier and windier sites. Publicly...