A New Approach for Prediction of Foliar Dust in a Coal Mining Region and Its Impacts on Vegetation Physiological Processes Using Multi‐Source Satellite Data Sets.

Estimating foliar dust (FD) is essential in understanding the complex interaction between FD, vegetation, and the environment. The elevated FD has a significant impacts on vegetation physiological processes. The present study aims to explore the potential of multi‐sensor optical satellite data sets (e.g., Landsat‐8, 9; Sentinel‐2B, and PlanetScope) in conjunction with in situ data sets for FD estimation over the Jharsuguda coal mining region in Eastern India. The efficacy of different spectral bands and various radiometric indices (RIs) was tested using linear regression models for FD estimation. Furthermore, the study attempts to quantify the impacts of FD on vegetation's physiological processes (e.g., carbon uptake, transpiration, water use efficiency, leaf temperature) through proxy data sets. The key findings of the study uncovered sensor‐specific and common trends in vegetation spectral profiles under varying FD concentrations. A saturation threshold was observed around 50 g/m2 of FD concentration, beyond which additional FD concentration exhibited limited impact on spectral reflectance. On the other hand, the assessment of FD estimation models revealed distinct performances and shared trends across various satellite sensors. Notably, near‐infrared and shortwave infrared‐1 bands, along with certain RIs, such as the Global Environmental Monitoring Index and the Non‐Linear Index, emerged as pivotal for accurate FD estimation. Besides, the study results revealed that vegetation‐associated carbon uptake experienced a ∼2 to 3 gC reduction for every additional gram of FD per square meter. Moreover, the vegetation transpiration reduction per unit of FD ranged from approximately 0.0005 to 0.0006 mm/m2/day, highlighting a moderate impact on transpiration levels. These findings aid a significant evidence base to our understanding of FD's impact on vegetation physiological processes. Plain Language Summary: Estimating foliar dust (FD) cover is essential to understand its impact on vegetation and the environment. Elevated FD levels can reduce photosynthesis, hinder carbon sequestration, and affect transpiration, thereby altering plant health and ecosystem functions. Accurate FD estimation helps in assessing these ecological impacts and developing strategies to mitigate adverse effects. Furthermore, FD can affect remote sensing‐based analysis by altering the spectral reflectance properties of vegetation, potentially leading to inaccuracies in vegetation‐related studies (e.g., health assessment, productivity estimation, species identification, etc.). By understanding these effects, the study shall aid in refining remote sensing models for more precise vegetation analysis. So, the present study utilizes multi‐sensor optical satellite data sets (Landsat‐8, Landsat‐9, Sentinel‐2B, and PlanetScope) along with in situ data to estimate FD in the Jharsuguda coal mining region in Eastern India. Vegetation's spectral profiles under dusty and non‐dusty conditions are also studied. This study also quantifies the impacts of FD on vegetation physiological processes, providing critical insights into the ecological consequences of dust accumulation on plant surfaces. Key Points: Demonstrated an approach to predict foliar dust concentration from satellite dataEvaluated the efficacy of spectral bands of 4 satellite sensors and 26 radiometric indicesQuantified the effect of foliar dust on vegetation physiological processes [ABSTRACT FROM AUTHOR]