Abstract
Identifying dust aerosols from passive satellite images is of great interest for many applications. In this study, we developed five different machine-learning (ML) based algorithms, including Logistic Regression, K Nearest Neighbor, Random Forest (RF), Feed Forward Neural Network (FFNN), and Convolutional Neural Network (CNN), to identify dust aerosols in the daytime satellite images from the Visible Infrared Imaging Radiometer Suite (VIIRS) under cloud-free conditions on a global scale. In order to train the ML algorithms, we collocated the state-of-the-art dust detection product from the Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) with the VIIRS observations along the CALIOP track. The 16 VIIRS M-band observations with the center wavelength ranging from deep blue to thermal infrared, together with solar-viewing geometries and pixel time and locations, are used as the predictor variables. Four different sets of training input data are constructed based on different combinations of VIIRS pixel and predictor variables. The validation and comparison results based on the collocated CALIOP data indicate that the FFNN method based on all available predictor variables is the best performing one among all methods. It has an averaged dust detection accuracy of about 81%, 89%, and 85% over land, ocean and whole globe, respectively, compared with collocated CALIOP. When applied to off-track VIIRS pixels, the FFNN method retrieves geographical distributions of dust that are in good agreement with on-track results as well as CALIOP statistics. For further evaluation, we compared our results based on the ML algorithms to NOAA’s Aerosol Detection Product (ADP), which is a product that classifies dust, smoke, and ash using physical-based methods. The comparison reveals both similarity and differences. Overall, this study demonstrates the great potential of ML methods for dust detection and proves that these methods can be trained on the CALIOP track and then applied to the whole granule of VIIRS granule.
Highlights
Mineral dust aerosols usually originate from the desert regions and can be transported to almost any part of the world [1,2]
The previous analyses are all based on the Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) “truth” data, which is on its narrow track
As seen in the figure, on-track and off-track feed forward neural network (FFNN) dust detection accuracies mostly have a 10% difference, which is comparable to the difference between FFNN and CALIOP
Summary
Mineral dust aerosols (hereafter dust for short) usually originate from the desert regions and can be transported to almost any part of the world [1,2]. Most of these algorithms are so-called “physicallybased” They rely on the physical intuitions of the developers to identify the radiative signatures (e.g., reflectance, color, and brightness temperature) of dust aerosols in a passive satellite image that are connected to the physical properties of dust (e.g., composition, size, shape, and temperature). Dust can reduce the brightness temperature of the scene and has a unique spectral signature [6] These radiative signatures of dust have been used independently or in combination in the previous studies to detect dust in a passive satellite image [7,8,9,10,11,12]. As a result of these problems, physically-based dust detection from passive satellite observations often misses dust layers that are either too thick or too thin, miss-identifies clouds as dust, and misses dust over the desert regions
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