Structural complexity biases vegetation greenness measures

Identifying influence factors and thresholds of the next day's pollen concentration in different seasons using interpretable machine learning

The prevalence of pollen allergies is a pressing global issue, with projections suggesting that half of the world's population will be affected by 2050 according to the estimation of the World Health Organization (WHO). Accurately forecasting pollen allergy risks requires identifying key factors and their thresholds for aerosol pollen. To address this, we developed a technical framework combining advanced machine learning and SHapley Additive exPlanations (SHAP) technology, focusing on Beijing. By analyzing meteorological data and vegetation phenology, we identified the factors influencing next-day's pollen concentration (NDP) in Beijing and their thresholds. Our results highlight vegetation phenology data from Synthetic Aperture Radar (SAR), temperature, wind speed, and atmospheric pressure as crucial factors in spring. In contrast, the Normalized Difference Vegetation Index (NDVI), air temperature, and wind speed are significant in autumn. Leveraging SHAP technology, we established season-specific thresholds for these factors. Our study not only confirms previous research but also unveils seasonal variations in the relationship between radar-derived vegetation phenology data and NDP. Additionally, we observe seasonal fluctuations in the influence patterns and threshold values of daily air temperatures on NDP. These insights are pivotal for improving pollen concentration prediction accuracy and managing allergic risks effectively.

Thermal, water, and land cover factors led to contrasting urban and rural vegetation resilience to extreme hot months

With continuing global warming and urbanization, it is increasingly important to understand the resilience of urban vegetation to extreme high temperatures, but few studies have examined urban vegetation at large scale or both concurrent and delayed responses. In this study, we performed an urban-rural comparison using the Enhanced Vegetation Index and months that exceed the historical 90th percentile in mean temperature (referred to as "hot months") across 85 major cities in the contiguous United States. We found that hot months initially enhanced vegetation greenness but could cause a decline afterwards, especially for persistent (≥4 months) and intense (≥+2 °C) episodes in summer. The urban responses were more positive than rural in the western United States or in winter, but more negative during spring-autumn in the eastern United States. The east-west difference can be attributed to the higher optimal growth temperatures and lower water stress levels of the western urban vegetation than the rural. The urban responses also had smaller magnitudes than the rural responses, especially in deciduous forest biomes, and least in evergreen forest biomes. Within each biome, analysis at 1 km pixel level showed that impervious fraction and vegetation cover, local urban heat island intensity, and water stress were the key drivers of urban-rural differences. These findings advance our understanding of how prolonged exposure to warm extremes, particularly within urban environments, affects vegetation greenness and vitality. Urban planners and ecosystem managers should prioritize the long and intense events and the key drivers in fostering urban vegetation resilience to heat waves.

A 30-m annual corn residue coverage dataset from 2013 to 2021 in Northeast China

Crop residue cover plays a key role in the protection of black soil by covering the soil in the non-growing season against wind erosion and chopping for returning to the soil to increase organic matter in the future. Although there are some studies that have mapped the crop residue coverage by remote sensing technique, the results are mainly on a small scale, limiting the generalizability of the results. In this study, we present a novel corn residue coverage (CRC) dataset for Northeast China spanning the years 2013-2021. The aim of our dataset is to provide a basis to describe and monitor CRC for black soil protection. The accuracy of our estimation results was validated against previous studies and measured data, demonstrating high accuracy with a coefficient of determination (R2) of 0.7304 and root mean square error (RMSE) of 0.1247 between estimated and measured CRC in field campaigns. In addition, it is the first of its kind to offer the longest time series, enhancing its significance in long-term monitoring and analysis.