A Comparison of Multi-Angle Implementation of Atmospheric Correction and MOD09 Daily Surface Reflectance Products From MODIS

Abstract

This study presents the first systematic comparison of MAIAC Collection 6 MCD19A1 daily surface reflectance (SR) product with standard MODIS SR (MOD/MYD09). The study was limited to four tiles located in mid-Atlantic United States (H11V05), Canada (H12V03), central Amazon (H11V09), and North-Eastern China (H27V05) and used over 5000 MODIS granules in 2018. Overall, there is a remarkable agreement between the best quality pixels of the two products, in particular in the Red and NIR bands. Over selected tiles, the evaluation found that MAIAC provides from 4 to 25% more high-quality retrievals than MOD09 annually, with the largest difference in tropical regions, confirming results of the previous studies. The comparison of spectral characteristics showed a systematic MAIAC-MOD09 difference increasing from NIR to Blue, typical of biases of a Lambertian assumption in MOD09 algorithm. Over the North-Eastern China, MCD19A1 SR is found more stable at wide range of aerosol optical depth (AOD) variations, whereas MOD09 SR shows a consistent positive bias increasing with AOD and at shorter wavelengths. The observed SR differences can be attributed to differences in cloud detection, aerosol retrieval and in atmospheric correction which is performed using an accurate BRDF-coupled radiative transfer model in MAIAC and a Lambertian surface model in MOD09. While this study is not representative of the global performance because of its limited geographical coverage, it should help the land community to better understand the differences between the two products.

Calibration of Maxar Constellation Over Libya-4 Site Using MAIAC Technique

“…MAIAC is an interdisciplinary algorithm providing high accuracy cloud/cloud shadow [24], [25] and snow detection [26], column water vapor (CWV) from MODIS near-IR bands 17−19, aerosol optical depth (AOD) and atmospherically corrected spectral surface reflectance and BRDF [27], [28]. MAIAC atmospheric properties and BRDF are reported at 1 km spatial resolution on global Sinusoidal grid in daily products MCD19A2 and MCD19A3, respectively.…”

“…Both MAIAC AOD and CWV have an expected uncertainty of 10-15% [18], [29]- [32] based on regional and global validation against AERONET measurements [33]. A comparison of MAIAC surface reflectance (SR) with standard MODIS SR product MOD09 [34] showed that MAIAC provides from 5% to 25% more high quality retrievals with improved accuracy in particular at the shortwave Blue-Green region [27]. For this work, we used MAIAC cloud/cloud shadow mask, AOD, CWV, and surface BRDF model parameters.…”

Using deep ensemble forest for high-resolution mapping of PM2.5 from MODIS MAIAC AOD in Tehran, Iran

“…However, both algorithms lead to AOD products with a resolution of 10 or 3 km, which are not desirable to monitor the variation of PM2.5 concentration in urban areas. Recently, the Multi-Angle Implementation of Atmospheric Correction (MAIAC) algorithm has been developed to retrieve AOD data from satellite observations, which has provided an opportunity to estimate AOD at a higher resolution (1 km) Lyapustin, Zhao, and Wang (2021). Several studies have demonstrated the potential of MAIAC AOD data for high resolution estimation of PM2.5 concentration Di et al (2016); Xiao et al (2017).…”

“…In this way, the advantages of decision tree ensemble approaches and deep neural networks are combined in dealing with complex regression problems Zhou and Feng (2017). Lyapustin et al (2021); ? Considering the aforementioned properties of deep ensemble forest, the main objective of this paper is to assess the potential of deep ensemble forest for estimating PM2.5 concentration from MODIS AOD (1 km MAIAC), weather data, and PM2.5 observations collected at air quality monitoring stations over Tehran city.…”

AnisoVeg: anisotropy and nadir-normalized MODIS multi-angle implementation atmospheric correction (MAIAC) datasets for satellite vegetation studies in South America

“…It offers substantial improvement over conventional algorithms by mitigating atmospheric interference and advancing the accuracy of surface reflectance over tropical vegetation by a factor of 3 to 10 (Hilker et al, 2012). Due to the improvements in cloud detection, aerosol retrieval and atmospheric correction, the MAIAC algorithm provides from 4 to 25% more high-quality retrievals than the traditional MOD09 product, with the largest estimate being observed for tropical regions (Lyapustin et al, 2021). Studies have used MODIS MAIAC observations with nadir-normalized geometry to assess Amazonian forests' structure, functioning, and impacts of environmental and climate change (Hilker et al, 2014;Wagner et al, 2017;Anderson et al, 2018;Dalagnol et al, 2018;Fonseca et al, 2019;Bontempo et al, 2020;Gonçalves et al, 2020;Zhang et al, 2021).…”