A Method to Improve MODIS AOD Values: Application to South America

Lanzaco, Bethania Luz; Olcese, Luis E.; Palancar, Gustavo Gerardo; Toselli, Beatriz M.

doi:10.4209/aaqr.2015.05.0375

Cited by 20 publications

(7 citation statements)

References 37 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…For these cases, it has been recommended to use the DB algorithm [18]. Recent works have evaluated the applicability of MODIS algorithms in various regions of the world, such as the semi-arid region of the USA [19], huge variety of sites in China [16,20], South America [21,22], Saudi Arabia [23], Peru [24], and globally [11,[25][26][27].…”

Section: Introductionmentioning

confidence: 99%

Evaluation of MODIS Dark Target AOD Product with 3 and 10 km Resolution in Amazonia

et al. 2022

View full text Add to dashboard Cite

The techniques and analyses employed by remote sensing provide key information about atmospheric particle properties at regional and global scales. However, limitations in optical spectral models used to represent the different types of aerosols in the atmosphere and their effects (direct and indirect) are still one of the major causes of sources of uncertainties and substantial impacts in climate prediction. There are no studies yet in South America, especially in the Amazon Basin, that have evaluated the advantages, disadvantages, inconsistencies, applicability, and suitability of the MODIS sensor (Moderate Resolution Imaging Spectroradiometer) destined for monitoring the ambient aerosol optical thickness over rivers and continents. In this study, the results of the DT (Dark Target) algorithm for products with 3 km and 10 km resolutions were systematically evaluated for six sites in the Amazon rainforest. The comparisons between the products were carried out with the AERONET (Aerosol Robotic Network) measurements, which were used as reference. Statistical parameters between AERONET vs. MODIS were also evaluated based on biomass burning records in the site regions. Here, the DT 10 km product showed satisfactory performance for the Amazon region, with observations between the expected error (EE) limits above 66%, in addition to R > 0.8 and RMSE < 0.3. However, the regional analysis for the two sites in the central and southern regions of the Amazon basin did not have the same performance, where the results showed an EE of 24 and 47%, respectively. The DT 3 km product did not perform well in any site, with an EE below 50%. Both products overestimated the AOD, but the 3 km product overestimated it approximately four times more due to its algorithm setup. Thus, we recommend the 10 km product for general analysis in Amazonia. Regional biomass burning records showed a direct relationship with the AERONET vs. MODIS DT with overestimation of both products. All variations between products and sites were justified based on the difficulty of retrieving surface reflectance and the model selected for local aerosols. Improvements in the optical spectral model currently implemented in the algorithms, with more realistic representations of the main types of the aerosol present in the Amazon Basin, may contribute to better performance among the evaluated products.

show abstract

Section: Introductionmentioning

confidence: 99%

Evaluation of MODIS Dark Target AOD Product with 3 and 10 km Resolution in Amazonia

et al. 2022

View full text Add to dashboard Cite

show abstract

“…Machine learning methods satisfy these requirements and have been widely adopted in the aerosol science. For instance, (Lanzaco et al, 2016a) used machine learning techniques to correct the AOD value at 550nm, resulting in a significant improvement in the AOD value obtained from satellite data. (Palancar et al, 2016) used a similar method to obtain AOD values of 340 nm from MODIS and calculated the aerosol radiative forcing in the UV-B region, and the obtained values were consistent with those obtained using AERONET AOD (AODA) as input.…”

Section: Introductionmentioning

confidence: 99%

Machine Learning Based Bias Correction for Modis Aerosol Optical Depth in Beijing

Fan

Wang

Chen

et al. 2023

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

View full text Add to dashboard Cite

Abstract. Aerosol refers to suspensions of small solid and liquid particles in the atmosphere. Although the content of aerosol in the atmosphere is small, it plays a crucial role in atmospheric and the climatic processes, making it essential to monitor. In areas with poor aerosol characteristics, satellite-based aerosol optical depth (AOD) values often differ from ground-based AOD values measured by instruments like AERONET. The use of 3km DT, 10km DT and 10km DTB algorithms in Beijing area has led to significant overestimation of AOD values, highlighting the need for improvement. This paper proposes the use of machine learning techniques, specifically support vector regression (SVR) and artificial neural network (ANN), to correct the deviation of AOD data. Our approach leverages ground-based monitoring data, meteorological reanalysis data and satellite products to train the models. Our results show that the ANN model outperforms the SVR model achieving R2, RMSE and Slope values of 0.88, 0.12 and 0.97, respectively, when applied to nearly two decades of data from 2001 to 2019. This study significantly improves the accuracy of MODIS AOD values, reducing overestimation and bringing them closer to ground-based AOD values measured by AERONET. Our findings have important applications in climate research and environmental monitoring.

show abstract

“…Topography has been reported to be correlated negatively with the spatial pattern of AOD in several studies because of its strong relation with aerosol emissions and particle accumulation [19,41,42,43]. Meteorological variables, such as precipitation [19,44,45,46], wind speed [45,47,48], temperature [46,49,50,51], relative humidity [45,52] and planetary boundary layer height (PBLH) [53,54] play important roles in the diffusion, dilution, and accumulation of aerosol particles. The effect of vegetation on AOD varies in different areas of China.…”

Section: Introductionmentioning

confidence: 99%

Spatiotemporal Variability and Influencing Factors of Aerosol Optical Depth over the Pan Yangtze River Delta during the 2014–2017 Period

Cheng

Chen

et al. 2019

IJERPH

View full text Add to dashboard Cite

Large amounts of aerosol particles suspended in the atmosphere pose a serious challenge to the climate and human health. In this study, we produced a dataset through merging the Moderate Resolution Imaging Spectrometers (MODIS) Collection 6.1 3-km resolution Dark Target aerosol optical depth (DT AOD) with the 10-km resolution Deep Blue aerosol optical depth (DB AOD) data by linear regression and made use of it to unravel the spatiotemporal characteristics of aerosols over the Pan Yangtze River Delta (PYRD) region from 2014 to 2017. Then, the geographical detector method and multiple linear regression analysis were employed to investigate the contributions of influencing factors. Results indicate that: (1) compared to the original Terra DT and Aqua DT AOD data, the average daily spatial coverage of the merged AOD data increased by 94% and 132%, respectively; (2) the values of four-year average AOD were high in the north-east and low in the south-west of the PYRD; (3) the annual average AOD showed a decreasing trend from 2014 to 2017 while the seasonal average AOD reached its maximum in spring; and that (4) Digital Elevation Model (DEM) and slope contributed most to the spatial distribution of AOD, followed by precipitation and population density. Our study highlights the spatiotemporal variability of aerosol optical depth and the contributions of different factors over this large geographical area in the four-year period, and can, therefore, provide useful insights into the air pollution control for decision makers.

show abstract

A Method to Improve MODIS AOD Values: Application to South America

Cited by 20 publications

References 37 publications

Evaluation of MODIS Dark Target AOD Product with 3 and 10 km Resolution in Amazonia

Evaluation of MODIS Dark Target AOD Product with 3 and 10 km Resolution in Amazonia

Machine Learning Based Bias Correction for Modis Aerosol Optical Depth in Beijing

Spatiotemporal Variability and Influencing Factors of Aerosol Optical Depth over the Pan Yangtze River Delta during the 2014–2017 Period

Contact Info

Product

Resources

About