Abstract
Abstract
Artificial light at night (ALAN) is increasingly recognized as an emerging environmental stressor with potential implications for agricultural systems, particularly for photoperiod-sensitive crops such as rice. However, large-scale and spatially explicit assessments of potential ALAN exposure in agricultural landscapes remain limited. In this study, we present a reproducible geospatial framework for screening potential ALAN exposure in rice-growing landscapes by integrating high-resolution rice cultivation maps and road-network-based proxies representing public lighting infrastructure, with calibrated Visible Infrared Imaging Radiometer Suite (VIIRS) nighttime radiance data providing a common spatial reference. To evaluate the sensitivity of road-based estimates, multiple road-network scenarios and nominal road-proximity masks were incorporated into the analysis, and rice–road intersections were aggregated to a common VIIRS-scale grid for national comparisons. The framework was demonstrated through a nationwide case study in Thailand, one of the world’s major rice-producing countries. Our results show that national estimates are highly sensitive to road-network selection and that local-access roads contribute substantially to the final proximity estimates. Under the complete road-network scenario, estimated rice–road proximity areas increased from 11 175 km
2
(11.80%) to 16 697 km
2
(17.63%) for major-rice systems and from 1 527 km
2
(10.12%) to 2 598 km
2
(17.20%) for minor-rice systems when moving from one- to two-pixel proximity masks. These findings demonstrate that road proximity should be interpreted as a structural proxy rather than direct evidence of ALAN exposure or agricultural impacts. Consequently, the proposed approach should be regarded as a first-order screening tool rather than a direct assessment of crop damage or yield loss. Overall, this study provides a transparent, reproducible, and transferable approach for screening potential ALAN exposure in agricultural systems and highlights the importance of considering nighttime lighting as an emerging environmental factor in agroecosystems. The proposed framework can support future field-validation studies, light-aware agricultural management, and environmental monitoring in rapidly developing regions.