Aerosol Indices Derived from MODIS Data for Indicating Aerosol-Induced Air Pollution

Abstract

Aerosol optical depth (AOD) is a critical variable in estimating aerosol concentration in the atmosphere, evaluating severity of atmospheric pollution, and studying their impact on climate. With the assistance of the 6S radiative transfer model, we simulated apparent reflectancein relation to AOD in each Moderate Resolution Imaging Spectroradiometer (MODIS) waveband in this study. The closeness of the relationship was used to identify the most and least sensitive MODIS wavebands. These two bands were then used to construct three aerosol indices (difference, ratio, and normalized difference) for estimating AOD quickly and effectively. The three indices were correlated, respectively, with in situ measured AOD at the Aerosol Robotic NETwork (AERONET) Lake Taihu, Beijing, and Xianghe stations. It is found that apparent reflectance of the blue waveband (band 3) is the most sensitive to AOD while the mid-infrared wavelength (band 7) is the least sensitive. The difference aerosol index is the most accurate in indicating aerosol-induced atmospheric pollution with a correlation coefficient of 0.585, 0.860, 0.685, and 0.333 at the Lake Taihu station, 0.721, 0.839, 0.795, and 0.629 at the Beijing station, and 0.778, 0.782, 0.837, and 0.643 at the Xianghe station in spring, summer, autumn and OPEN ACCESSRemote Sens. 2014, 6 1588 winter, respectively. It is concluded that the newly proposed difference aerosol index can be used effectively to study the level of aerosol-induced air pollution from MODIS satellite imagery with relative ease.

Mapping Aquatic Vegetation in a Large, Shallow Eutrophic Lake: A Frequency-Based Approach Using Multiple Years of MODIS Data

“…Second, one species of submerged aquatic vegetation was difficult to detect in our study: Potamogeton malaianus. Water strongly absorbs electromagnetic radiation in the optical region, resulting in significant damping of the radiometric signal and thereby posing difficulties in satellite remote sensing of submerged aquatic vegetation [50]. Future attempts to accurately address Potamogeton malaianus should be used to develop more accurate estimates of the aquatic vegetation distribution in Lake Taihu.…”

Assessment of Remote Sensing Data to Model PM10 Estimation in Cities with a Low Number of Air Quality Stations: A Case of Study in Quito, Ecuador

“…Moreover, these geographical variables are not frequently updated by government institutions. In the case of remote sensing data, the predictors most commonly used in LUR models to retrieve PM10 are aerosol optical depth (AOD) and normalized difference vegetation index (NDVI) from moderate-resolution imaging spectroradiometer (MODIS) products [17][18][19][20]. MODIS products have a low spatial resolution that limits their application in medium or small cities [21][22][23], but they are an efficient alternative to retrieve pollutants in regional (large cities/regions) or national (countries) areas.…”

A New Neighboring Pixels Method for Reducing Aerosol Effects on the NDVI Images

“…The apparent reflectance (also called top-of-atmosphere reflectance) is more sensitive to AOD in the blue (0.47 µm) and red band (0.65 µm) and is less sensitive in the SWIR band (2.13 µm). They were thus used to construct aerosol indices [25]. Aerosol resistant VIs such as AFRI and ARVI and aerosol retrieval algorithms of the DT method were all skillfully developed based on the polarization of aerosol effects on different bands and have achieved very good results.…”