The Empirical Cloud Mask Algorithm (ECMA)

Published: 17 October 2018| Version 1 | DOI: 10.17632/92dpg5xvr2.1
Fahad Alawadi


NASA’s atmospheric science team using MODIS, have developed their own standard cloud mask product that can be used to detect the presence of clouds over a given area. The cloud mask product however, has several significant drawbacks. For example, the mask is incapable of discriminating effectively between heavy aerosols (dust) and clouds that appear over both land and water. This shortcoming is commonly attributed to its dependency on static thresholds. Moreover, it cannot be generated in near-real-time, due to the absence of specific ancillary data required for its generation. Due to these shortcomings, a single mathematical formula -The Empirical Cloud Mask Algorithm (ECMA) – has been devised which is able to bypass these constraints of dependency upon auxiliary data or thresholds. The EMCA is composed from a total of eight of MODIS bands (469 nm, 555 nm, 645 nm, 859 nm, 1380 nm, 2130 nm, 11 μm and 12 μm). Currently, the ECMA has a simple yes/no output which corresponds to cloudy or non-cloudy pixels in a day-only MODIS scene. Therefore, further research remains pending to expand its applicability over night passes. Although, the current ECMA expression is developed based on the MODIS bands, its applicability using different sensors using different spectral and thermal bands is also open for investigation. Finally, the ECMA can be viewed as the dynamic mathematical-code representation of day-only clouds observed in MODIS, whose nature is equally dynamic and can thus explain its success.


Steps to reproduce

1-Have your Level2 HDF MODIS file ready that includes the required six at-sensor Rayleigh-corrected reflectance bands (469 nm, 555 nm, 645 nm, 859 nm, 1380 nm, 2130 nm) and the two Brightness Temperature bands (thermal bands) at 11 μm and 12 μm 2- Open your file using the software SeaDAS 3-Copy the algorithm and paste it into "create a band from the math expression" tab


PAAET College of Basic Education


Remote Sensing, Environment (Patient Social Context)