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Using satellite surface temperature data to monitor urban heat island

Meister et al. | Feb 13, 2026

Using satellite surface temperature data to monitor urban heat island
Image credit: Meister, Horvath, and Brown de Colstoun

This manuscript investigates the urban heat island (UHI) effect by utilizing two satellite datasets: Landsat (high spatial resolution, lower temporal resolution) and MODIS (lower spatial resolution, high temporal resolution). The authors hypothesized that Landsat would provide better spatial detail, while MODIS would better capture temporal variations. Their analysis in the Washington D.C.–Baltimore region supports these hypotheses, demonstrating that Landsat offers finer spatial details, whereas MODIS provides more consistent seasonal patterns and better detects heatwave frequencies.

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Impact of Silverado Fire on soil carbon

Choi et al. | Feb 20, 2025

Impact of Silverado Fire on soil carbon

Soil stores three times more carbon than the atmosphere, making small changes in its storage and release crucial for carbon cycling and climate models. This study examined the impact of the 2020 California Silverado Fire on pyrogenic carbon (PyC) deposits using nitrogen and carbon isotopes as proxies. While the results showed significant variability in δ¹⁵N, δ¹³C, total carbon, and total nitrogen across sites, they did not support the hypothesis that wildfire increases δ¹⁵N while keeping δ¹³C constant, emphasizing the need for location-based controls when using δ¹⁵N to track PyC.

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The impact of attending a more selective college on future income

Ho et al. | Oct 16, 2024

The impact of attending a more selective college on future income

Debates around legacy preferences, recruited athletes, and affirmative action in U.S. college admissions often focus on the belief that graduating from a more selective institution leads to higher future earnings. The study hypothesized a positive correlation between college selectivity and future income due to enhanced resources and opportunities.

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Vineyard vigilance: Harnessing deep learning for grapevine disease detection

Mandal et al. | Aug 21, 2024

Vineyard vigilance: Harnessing deep learning for grapevine disease detection

Globally, the cultivation of 77.8 million tons of grapes each year underscores their significance in both diets and agriculture. However, grapevines face mounting threats from diseases such as black rot, Esca, and leaf blight. Traditional detection methods often lag, leading to reduced yields and poor fruit quality. To address this, authors used machine learning, specifically deep learning with Convolutional Neural Networks (CNNs), to enhance disease detection.

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