Yong Xie

The Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) Vertical Feature Mask (VFM) product reports the scene classification globally. However, misclassifications have been observed because of labelling dense dust aerosols as clouds. Both CALIPSO and MODerate resolution Imaging Spectroradiometer (MODIS) aboard the Aqua spacecraft are operating in the A-train orbit with similar local equatorial crossing times. A novel approach to improve the scene classification by integrating the CALIPSO VFM product and measurements of MODIS thermal bands is proposed. According to the infrared (IR) split window technique, the brightness temperature difference (BTD) between MODIS 12 and 11 μm (BTD (12, 11 μm)) is calculated for separating dust aerosol and cloud. Generally, the BTD (12, 11 μm) value is positive for dust aerosol and negative for cloud. After spatial collocation, the comparison between measurements of the two sensors is performed. If a cloud layer in VFM product has positive MODIS BTD (12, 11 μm) value in the same area, the misclassification is identified and thus corrected to improve the VFM product.

In the eastern United States large amounts of smoke emitted from both wildfires and prescribed fires affect the regional air quality and long‐term climate and may have an impact on public health. Satellite remote sensing is an effective approach for detecting and monitoring the smoke plume. The spectral characteristics of smoke plume are measurably different from those of other cover types, such as vegetation, cloud, snow, and so on. A multi‐threshold method has been developed for detecting smoke plumes with eight MODIS spectral bands based on the analysis of spectral characteristics of different cover types. A series of tests are applied to all pixels in one granule (5‐min measurements) to filter out non‐smoke pixels step by step with water masking. At each step, specific thresholds are utilized. The results have been validated with true color images for a number of cases from different areas and time, showing that the algorithm works well except for a few missing or incorrect identified smoke pixels.