Evaluation and comparison of MAIAC, DT and DB aerosol products over China

Abstract. A new Multiangle Implementation of Atmospheric Correction (MAIAC) algorithm has been applied in Moderate Resolution Imaging Spectroradiometer (MODIS) sensor and recently provides globally high spatial resolution Aerosol Optical Depth (AOD) products at 1 km. Meanwhile, several improvements are modified in classical Dark Target (DT) and Deep Blue (DB) aerosol retrieval algorithms in MODIS collection 6.1 products. However, validation and comparison for MAIAC, DT and DB algorithms is still lacking in China. In this paper, a comprehensive assessment and comparison of AOD products at 550 nm wavelength based three aerosol retrieval algorithms in MODIS sensor using ground-truth measurements from Aerosol Robotic Network (AERONET) sites over China during 2000 to 2017 is presented. In general, after quality assurance (QA) filter, the coefficient of determination (R2=0.854), correlation coefficient (R=0.929), root-mean-square error (RMSE=0.178), mean bias (Bias=0.019) and the fraction fall within expected error (Within_EE=67.10 %, EE=±(0.05+0.15×AOD)) results for MAIAC algorithm show better accuracy than those from DT and DB algorithms. While the R2, R, RMSE, Bias and Within_EE of DT algorithm are 0.817, 0.930, 0.192, 0.077, 55.36 %, respectively, those corresponding statistics for DB algorithm are 0.827, 0.921, 0.190, 0.018, 63.32 %. Moreover, the spatiotemporal completeness for MAIAC (29.69 %) product is also better than DT (8.00 %) and DB (19.50 %) products after QA filter. In addition, the land type dependence characteristic, view geometry dependence, spatiotemporal retrieval accuracy and spatial variation pattern difference for three products are also analyzed in details.

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Evaluation and comparison of multiangle implementation of the atmospheric correction algorithm, Dark Target, and Deep Blue aerosol products over China

Atmospheric Chemistry and Physics ◽

10.5194/acp-19-8243-2019 ◽

2019 ◽

Vol 19 (12) ◽

pp. 8243-8268 ◽

Cited By ~ 12

Author(s):

Ning Liu ◽

Bin Zou ◽

Huihui Feng ◽

Wei Wang ◽

Yuqi Tang ◽

...

Keyword(s):

Zenith Angle ◽

Atmospheric Correction ◽

Ground Truth ◽

The North ◽

Deep Blue ◽

Aerosol Retrieval ◽

Angle Scattering ◽

Retrieval Algorithms ◽

Moderate Resolution Imaging Spectroradiometer ◽

Land Types

Abstract. A new multiangle implementation of the atmospheric correction (MAIAC) algorithm has been applied in the Moderate Resolution Imaging Spectroradiometer (MODIS) sensor and has recently provided globally high-spatial-resolution aerosol optical depth (AOD) products at 1 km. Moreover, several improvements have been modified in the classical Dark Target (DT) and Deep Blue (DB) aerosol retrieval algorithms in MODIS Collection 6.1 products. Thus, validation and comparison of the MAIAC, DT, and DB algorithms are urgent in China. In this paper, we present a comprehensive assessment and comparison of AOD products at a 550 nm wavelength based on three aerosol retrieval algorithms in the MODIS sensor using ground-truth measurements from AErosol RObotic NETwork (AERONET) sites over China from 2000 to 2017. In general, MAIAC products achieved better accuracy than DT and DB products in the overall validation and accuracy improvement of DB products after the QA filter, demonstrating the highest values among the three products. In addition, the DT algorithms had higher aerosol retrievals in cropland, forest, and ocean land types than the other two products, and the MAIAC algorithms were more accurate in grassland, built-up, unoccupied, and mixed land types among the three products. In the geometry dependency analysis, the solar zenith angle, scattering angle, and relative azimuth angle, excluding the view zenith angle, significantly affected the performance of the three aerosol retrieval algorithms. The three products showed different accuracies with varying regions and seasons. Similar spatial patterns were found for the three products, but the MAIAC retrievals were smaller in the North China Plain and higher in Yunnan Province compared with the DT and DB retrievals before the QA filter. After the QA filter, the DB retrievals were significantly lower than the MAIAC retrievals in south China. Moreover, the spatiotemporal completeness of the MAIAC product was also better than the DT and DB products.

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250-m Aerosol Retrieval from FY-3 Satellite in Guangzhou

Remote Sensing ◽

10.3390/rs13050920 ◽

2021 ◽

Vol 13 (5) ◽

pp. 920

Author(s):

Zhongting Wang ◽

Ruru Deng ◽

Pengfei Ma ◽

Yuhuan Zhang ◽

Yeheng Liang ◽

...

Keyword(s):

Mixing Model ◽

Blue Band ◽

Aerosol Retrieval ◽

Aerosol Distribution ◽

Medium Resolution ◽

Fine Spatial Resolution ◽

Yearly Average ◽

Moderate Resolution ◽

Resolution Imaging ◽

Moderate Resolution Imaging Spectroradiometer

Aerosol distribution with fine spatial resolution is crucial for atmospheric environmental management. This paper proposes an improved algorithm of aerosol retrieval from 250-m Medium Resolution Spectral Image (MERSI) data of Chinese FY-3 satellites. A mixing model of soil and vegetation was used to calculate the parameters of the algorithm from moderate-resolution imaging spectroradiometer (MODIS) reflectance products in 500-m resolution. The mixing model was used to determine surface reflectance in blue band, and the 250-m aerosol optical depth (AOD) was retrieved through removing surface contributions from MERSI data over Guangzhou. The algorithm was used to monitor two pollution episodes in Guangzhou in 2015, and the results displayed an AOD spatial distribution with 250-m resolution. Compared with the yearly average of MODIS aerosol products in 2015, the 250-m resolution AOD derived from the MERSI data exhibited great potential for identifying air pollution sources. Daily AODs derived from MERSI data were compared with ground results from CE318 measurements. The results revealed a correlation coefficient between the AODs from MERSI and those from the ground measurements of approximately 0.85, and approximately 68% results were within expected error range of ±(0.05 + 15%τ).

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Evaluation of NASA Deep Blue/SOAR aerosol retrieval algorithms applied to AVHRR measurements

Journal of Geophysical Research Atmospheres ◽

10.1002/2017jd026934 ◽

2017 ◽

Vol 122 (18) ◽

pp. 9945-9967 ◽

Cited By ~ 21

Author(s):

A. M. Sayer ◽

N. C. Hsu ◽

J. Lee ◽

N. Carletta ◽

S.-H. Chen ◽

...

Keyword(s):

Deep Blue ◽

Aerosol Retrieval ◽

Retrieval Algorithms

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Development of a remote sensing-based rice yield forecasting model

Spanish Journal of Agricultural Research ◽

10.5424/sjar/2016143-8347 ◽

2016 ◽

Vol 14 (3) ◽

pp. e0907 ◽

Cited By ~ 14

Author(s):

Mostafa K. Mosleh ◽

Quazi K. Hassan ◽

Ehsan H. Chowdhury

Keyword(s):

Remote Sensing ◽

Vegetation Index ◽

Normalized Difference Vegetation Index ◽

Rice Yield ◽

Decision Makers ◽

Coefficient Of Determination ◽

Moderate Resolution ◽

Resolution Imaging ◽

Moderate Resolution Imaging Spectroradiometer ◽

Boro Rice

This study aimed to develop a remote sensing-based method for forecasting rice yield by considering vegetation greenness conditions during initial and peak greenness stages of the crop; and implemented for “boro” rice in Bangladeshi context. In this research, we used Moderate Resolution Imaging Spectroradiometer (MODIS)-derived two 16-day composite of normalized difference vegetation index (NDVI) images at 250 m spatial resolution acquired during the initial (January 1 to January 16) and peak greenness (March 23/24 to April 6/7 depending on leap year) stages in conjunction with secondary datasets (i.e., boro suitability map, and ground-based information) during 2007-2012 period. The method consisted of two components: (i) developing a model for delineating area under rice cultivation before harvesting; and (ii) forecasting rice yield as a function of NDVI. Our results demonstrated strong agreements between the model (i.e., MODIS-based) and ground-based area estimates during 2010-2012 period, i.e., coefficient of determination (R2); root mean square error (RMSE); and relative error (RE) in between 0.93 to 0.95; 30,519 to 37,451 ha; and ±10% respectively at the 23 district-levels. We also found good agreements between forecasted (i.e., MODIS-based) and ground-based yields during 2010-2012 period (R2 between 0.76 and 0.86; RMSE between 0.21 and 0.29 Mton/ha, and RE between -5.45% and 6.65%) at the 23 district-levels. We believe that our developments of forecasting the boro rice yield would be useful for the decision makers in addressing food security in Bangladesh.

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Comparison of the cloud top heights retrieved from MODIS and AHI satellite data with ground-based Ka-band radar

10.5194/amt-2019-276 ◽

2019 ◽

Author(s):

Juan Huo ◽

Daren Lu ◽

Shu Duan ◽

Yongheng Bi ◽

Bo Liu

Keyword(s):

Satellite Data ◽

Radar Data ◽

Retrieval Algorithm ◽

Ka Band ◽

Retrieval Accuracy ◽

Retrieval Algorithms ◽

Moderate Resolution ◽

Resolution Imaging ◽

Radar Measurements ◽

Moderate Resolution Imaging Spectroradiometer

Abstract. To better understand the accuracy of cloud top heights (CTHs) derived from passive satellite data, ground-based Ka-band radar measurements from 2016 and 2017 in Beijing were compared with CTH data inferred from the Moderate Resolution Imaging Spectroradiometer (MODIS) and the Advanced Himawari Imager (AHI). Relative to the radar CTHs, the MODIS CTHs were found to be underestimated by −1.10 ± 2.53 km and 49 % of CTH differences were within 1.0 km. Like the MODIS results, the AHI CTHs were underestimated by −1.10 ± 2.27 km and 42 % were within 1.0 km. Both the MODIS and AHI retrieval accuracy depended strongly on the cloud depth (CD). Large differences were mainly occurring for the retrieval of thin clouds of CD 1 km, the CTH difference decreased to −0.48 ± 1.70 km for MODIS and to −0.76 ± 1.63 km for AHI. MODIS CTHs greater than 6 km showed better agreement with the radar data than those less than 4 km. Statistical analysis showed that the average AHI CTHs were lower than the average MODIS CTHs by −0.64 ± 2.36 km. The monthly accuracy of both retrieval algorithms was studied and it was found that the AHI retrieval algorithm had the largest bias in winter while the MODIS retrieval algorithm had the lowest accuracy in spring.

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Evaluation of the Multi-Angle Implementation of Atmospheric Correction (MAIAC) Aerosol Algorithm for Himawari-8 Data

Remote Sensing ◽

10.3390/rs11232771 ◽

2019 ◽

Vol 11 (23) ◽

pp. 2771 ◽

Cited By ~ 2

Author(s):

Lu She ◽

Hankui Zhang ◽

Weile Wang ◽

Yujie Wang ◽

Yun Shi

Keyword(s):

Mean Squared Error ◽

Atmospheric Correction ◽

Remote Sensing Data ◽

Japan Meteorological Agency ◽

Absolute Bias ◽

Moderate Resolution ◽

Resolution Imaging ◽

Moderate Resolution Imaging Spectroradiometer ◽

Highly Correlated ◽

New Generation

Himawari-8, operated by the Japan Meteorological Agency (JMA), is a new generation geostationary satellite that provides remote sensing data to retrieve atmospheric aerosol optical depth (AOD) at high spatial (1 km) and high temporal (10 min) resolutions. The Geostationary- National Aeronautics and Space Administration (NASA) Earth exchange (GeoNEX) project recently adapted the multiangle implementation of atmospheric correction (MAIAC) algorithm, originally developed for joint retrieval of AOD and surface anisotropic reflectance with the moderate resolution imaging spectroradiometer (MODIS) data, to generate Earth monitoring products from the latest geostationary satellites including Himawari-8. This study evaluated the GeoNEX Himawari-8 ~1 km MAIAC AOD retrieved over all the aerosol robotic network (AERONET) sites between 6°N–30°N and 91°E–127°E. The corresponding JMA Himawari-8 AOD products were also evaluated for comparison. We only used cloud-free and the best quality satellite AOD retrievals and compiled a total of 16,532 MAIAC-AERONET and 21,737 JMA-AERONET contemporaneous pairs of AOD values for 2017. Statistical analyses showed that both MAIAC and JMA data are highly correlated with AERONET AOD, with the correlation coefficient (R) of ~0.77, and the root mean squared error (RMSE) of ~0.16. The absolute bias of MAIAC AOD (0.02 overestimation) appears smaller than that of the JMA AOD (0.05 underestimation). In comparison with the JMA data, the time series of MAIAC AOD were more consistent with AERONET AOD values and better capture the diurnal variations of the latter. The dependence of MAIAC AOD bias on scattering angles is also discussed.

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Validation and Comparison of MODIS C6.1 and C6 Aerosol Products over Beijing, China

Remote Sensing ◽

10.3390/rs10122021 ◽

2018 ◽

Vol 10 (12) ◽

pp. 2021 ◽

Cited By ~ 17

Author(s):

Xinpeng Tian ◽

Qiang Liu ◽

Xiuhong Li ◽

Jing Wei

Keyword(s):

Optical Properties ◽

Urban Areas ◽

Surface Characteristics ◽

Complex Surface ◽

Deep Blue ◽

Spatial Coverage ◽

Moderate Resolution ◽

Moderate Resolution Imaging Spectroradiometer ◽

Beijing China ◽

Better Than

The operational Moderate Resolution Imaging Spectroradiometer (MODIS) Aerosol Products (APs) have provided long-term and wide-spatial-coverage aerosol optical properties across the globe, such as aerosol optical depth (AOD). However, the performance of the latest Collection 6.1 (C6.1) of MODIS APs is still unclear over urban areas that feature complex surface characteristics and aerosol models. The aim of this study was to validate and compare the performance of the MODIS C6.1 and C6 APs (MxD04, x = O for Terra, x = Y for Aqua) over Beijing, China. The results of the Dark Target (DT) and Deep Blue (DB) algorithms were validated against Aerosol Robotic Network (AERONET) ground-based observations at local sites. The retrieval uncertainties and accuracies were evaluated using the expected error (EE: ±0.05 + 15%) and the root-mean-square error (RMSE). It was found that the MODIS C6.1 DT products performed better than the C6 DT products, with a greater percentage (by about 13%–14%) of the retrievals falling within the EE. However, the DT retrievals collected from two collections were significantly overestimated in the Beijing region, with more than 64% and 48% of the samples falling above the EE for the Terra and Aqua satellites, respectively. The MODIS C6.1 DB products performed similarly to the C6 DB products, with 70%–73% of the retrievals matching within the EE and estimation uncertainties. Moreover, the DB algorithm performed much better than DT algorithm over urban areas, especially in winter where abundant missing pixels were found in DT products. To investigate the effects of factors on AOD retrievals, the variability in the assumed surface reflectance and the main optical properties applied in DT and DB algorithms are also analyzed.

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SST Comparison of AVHRR and MODIS Time Series in the Western Mediterranean Sea

Remote Sensing ◽

10.3390/rs12142241 ◽

2020 ◽

Vol 12 (14) ◽

pp. 2241

Author(s):

María José López García

Keyword(s):

Time Series ◽

Regional Scale ◽

Atmospheric Correction ◽

Western Mediterranean ◽

Western Mediterranean Sea ◽

Temporal And Spatial Variability ◽

Moderate Resolution ◽

Long Term Trends ◽

Moderate Resolution Imaging Spectroradiometer ◽

Development And Validation

Sea Surface Temperature (SST) is a key parameter for understanding atmospheric and oceanic processes. Since the late 1980s, infrared satellite images have been used to complement in situ records for studying the temporal and spatial variability of SST. The Advanced Very High Resolution Radiometer (AVHRR) of the National Oceanic and Atmospheric Administration (NOAA)’s satellite was the first sensor successfully used to compute SST following the development and validation of the atmospheric correction algorithm known as “split-window”. More recently, the MODerate-resolution Imaging Spectroradiometer (MODIS) on board the National Aeronautics and Space Administration (NASA)’s Terra and Aqua satellites, launched in 1999 and 2002, respectively, also provides SST products which can be combined with AVHRR series to complete the analysis of time series. This paper presents a comparison of the monthly SST data derived from both sensors, AVHRR and MODIS, in a series of ten years (2000–2009) in the Western Mediterranean basins. The results showed a high correlation (R2 = 0.99) between the sensors when averaged values at the regional scale were compared. SST obtained from AVHRR were slightly higher (+0.18 °C ± 0.2 °C, on average) than SST from MODIS. The series were most similar during winter and spring (+0.09 °C ± 0.1 °C for January to May) with a greater difference from June to December (+0.24 °C ± 0.2 °C). The comparative analysis showed that the two sensors can be used jointly to estimate long-term trends at the regional scale.

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Hourly PM2.5 Estimates from a Geostationary Satellite Based on an Ensemble Learning Algorithm and Their Spatiotemporal Patterns over Central East China

Remote Sensing ◽

10.3390/rs11182120 ◽

2019 ◽

Vol 11 (18) ◽

pp. 2120 ◽

Cited By ~ 2

Author(s):

Jianjun Liu ◽

Fuzhong Weng ◽

Zhanqing Li ◽

Maureen C. Cribb

Keyword(s):

Ensemble Learning ◽

Learning Algorithm ◽

Fine Particulate Matter ◽

East China ◽

Coefficient Of Determination ◽

Spatial Agglomeration ◽

Moderate Resolution ◽

Ensemble Learning Algorithm ◽

Moderate Resolution Imaging Spectroradiometer ◽

Polar Orbiting Satellite

Satellite-derived aerosol optical depths (AODs) have been widely used to estimate surface fine particulate matter (PM2.5) concentrations over areas that do not have PM2.5 monitoring sites. To date, most studies have focused on estimating daily PM2.5 concentrations using polar-orbiting satellite data (e.g., from the Moderate Resolution Imaging Spectroradiometer), which are inadequate for understanding the evolution of PM2.5 distributions. This study estimates hourly PM2.5 concentrations from Himawari AOD and meteorological parameters using an ensemble learning model. We analyzed the spatial agglomeration patterns of the estimated PM2.5 concentrations over central East China. The estimated PM2.5 concentrations agree well with ground-based data with an overall cross-validated coefficient of determination of 0.86 and a root-mean-square error of 17.3 μg m−3. Satellite-estimated PM2.5 concentrations over central East China display a north-to-south decreasing gradient with the highest concentration in winter and the lowest concentration in summer. Diurnally, concentrations are higher in the morning and lower in the afternoon. PM2.5 concentrations exhibit a significant spatial agglomeration effect in central East China. The errors in AOD do not necessarily affect the retrieval accuracy of PM2.5 proportionally, especially if the error is systematic. High-frequency spatiotemporal PM2.5 variations can improve our understanding of the formation and transportation processes of regional pollution episodes.

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Validation and Accuracy Assessment of MODIS C6.1 Aerosol Products over the Heavy Aerosol Loading Area

Atmosphere ◽

10.3390/atmos10090548 ◽

2019 ◽

Vol 10 (9) ◽

pp. 548 ◽

Cited By ~ 7

Author(s):

Xinpeng Tian ◽

Zhiqiang Gao

Keyword(s):

Urban Areas ◽

Vegetation Index ◽

Normalized Difference Vegetation Index ◽

Accuracy Assessment ◽

Aerosol Loading ◽

Deep Blue ◽

Moderate Resolution ◽

Moderate Resolution Imaging Spectroradiometer ◽

Terra Modis ◽

Reflecting Surface

The aim of this study is to evaluate the accuracy of MODerate resolution Imaging Spectroradiometer (MODIS) aerosol optical depth (AOD) products over heavy aerosol loading areas. For this analysis, the Terra-MODIS Collection 6.1 (C6.1) Dark Target (DT), Deep Blue (DB) and the combined DT/DB AOD products for the years 2000–2016 are used. These products are validated using AErosol RObotic NETwork (AERONET) data from twenty-three ground sites situated in high aerosol loading areas and with available measurements at least 500 days. The results show that the numbers of collections (N) of DB and DT/DB retrievals were much higher than that of DT, which was mainly caused by unavailable retrieval of DT in bright reflecting surface and heavy pollution conditions. The percentage falling within the expected error (PWE) of the DT retrievals (45.6%) is lower than that for the DB (53.4%) and DT/DB (53.1%) retrievals. The DB retrievals have 5.3% less average overestimation, and 25.7% higher match ratio than DT/DB retrievals. It is found that the current merged aerosol algorithm will miss some cases if it is determined only on the basis of normalized difference vegetation index. As the AOD increases, the value of PWE of the three products decreases significantly; the undervaluation is suppressed, and the overestimation is aggravated. The retrieval accuracy shows distinct seasonality: the PWE is largest in autumn or winter, and smallest in summer. The most severe overestimation and underestimation occurred in the summer. Moreover, the DT, DB and DT/DB products over different land cover types still exhibit obvious deviations. In urban areas, the PWE of DB product (52.6%) is higher than for the DT/DB (46.3%) and DT (25.2%) products. The DT retrievals perform poorly over the barren or sparsely vegetated area (N = 52). However, the performance of three products is similar over vegetated area. On the whole, the DB product performs better than the DT product over the heavy aerosol loading area.

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