Building a consistent medium resolution satellite data set using moderate resolution imaging spectroradiometer products as reference

2010 ◽  
Vol 4 (1) ◽  
pp. 043526 ◽  
Author(s):  
Jeffrey G. Masek
Keyword(s):  
Satellite Data ◽  
Data Set ◽  
Medium Resolution ◽  
Moderate Resolution ◽  
Resolution Imaging ◽  
Moderate Resolution Imaging Spectroradiometer

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

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).

scholarly journals Investigating the frequency and interannual variability in global above-cloud aerosol characteristics with CALIOP and OMI

2016 ◽  
Vol 16 (1) ◽  
pp. 47-69 ◽  
Author(s):  
R. Alfaro-Contreras ◽  
J. Zhang ◽  
J. R. Campbell ◽  
J. S. Reid
Keyword(s):  
Interannual Variability ◽  
Orthogonal Polarization ◽  
Cloud Detection ◽  
Ozone Monitoring Instrument ◽  
Data Set ◽  
Moderate Resolution ◽  
Resolution Imaging ◽  
Moderate Resolution Imaging Spectroradiometer ◽  
First Time ◽  
Aerosol Index

Abstract. Seven and a half years (June 2006 to November 2013) of Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) aerosol and cloud layer products are compared with collocated Ozone Monitoring Instrument (OMI) aerosol index (AI) data and Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) cloud products in order to investigate variability in estimates of biannual and monthly above-cloud aerosol (ACA) events globally. The active- (CALIOP) and passive-based (OMI-MODIS) techniques have their advantages and caveats for ACA detection, and thus both are used to derive a thorough and robust comparison of daytime cloudy-sky ACA distribution and climatology. For the first time, baseline above-cloud aerosol optical depth (ACAOD) and AI thresholds are derived and examined (AI  =  1.0, ACAOD  =  0.015) for each sensor. Both OMI-MODIS and CALIOP-based daytime spatial distributions of ACA events show similar patterns during both study periods (December–May) and (June–November). Divergence exists in some regions, however, such as Southeast Asia during June through November, where daytime cloudy-sky ACA frequencies of up to 10 % are found from CALIOP yet are non-existent from the OMI-based method. Conversely, annual cloudy-sky ACA frequencies of 20–30 % are reported over northern Africa from the OMI-based method yet are largely undetected by the CALIOP-based method. Using a collocated OMI-MODIS-CALIOP data set, our study suggests that the cloudy-sky ACA frequency differences between the OMI-MODIS- and CALIOP-based methods are mostly due to differences in cloud detection capability between MODIS and CALIOP as well as QA flags used. An increasing interannual variability of  ∼  0.3–0.4 % per year (since 2009) in global monthly cloudy-sky ACA daytime frequency of occurrence is found using the OMI-MODIS-based method. Yet, CALIOP-based global daytime ACA frequencies exhibit a near-zero interannual variability. Further analysis suggests that the OMI-derived interannual variability in cloudy-sky ACA frequency may be affected by OMI row anomalies in later years. A few regions are found to have increasing slopes in interannual variability in cloudy-sky ACA frequency, including the Middle East and India. Regions with slightly negative slopes of the interannual variability in cloudy-sky ACA frequencies are found over South America and China, while remaining regions in the study show nearly zero change in ACA frequencies over time. The interannual variability in ACA frequency is not, however, statistically significant on both global and regional scales, given the relatively limited sample sizes. A longer data record of ACA events is needed in order to establish significant trends of ACA frequency regionally and globally.

scholarly journals Evaluation of MODIS Water Vapour Products over ALGERIA using Radiosonde Data

2021 ◽  
Vol 44 ◽  
Author(s):  
Houaria Namaoui ◽  
Salem Kahlouche ◽  
Ahmed Hafidh Belbachir
Keyword(s):  
Water Vapour ◽  
Satellite Data ◽  
Correlation Coefficients ◽  
Precipitable Water ◽  
Radiosonde Data ◽  
Precipitable Water Vapour ◽  
Total Precipitable Water ◽  
Moderate Resolution ◽  
Resolution Imaging ◽  
Moderate Resolution Imaging Spectroradiometer

Remote sensing of atmospheric water vapour using GNSS and Satellite data has become an efficient tool in meteorology and climate research. Many satellite data have been increasingly used to measure the content of water vapour in the atmosphere and to characterize its temporal and spatial variations. In this paper, we have used observations from radiosonde data collected from three stations (Algiers, Bechar and Tamanrasset) in Algeria from January to December 2012 to evaluate Moderate Resolution Imaging Spectroradiometer (MODIS) total precipitable water vapour (PWV) products. Results show strong agreement between the total precipitable water contents estimated based on radiosondes observations and the ones measured by the sensor MODIS with the correlation coefficients in the range 0.69 to 0.95 and a mean bias, which does not exceed 1.5.  

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

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.

scholarly journals Evaluation of the Simard et al. 2011 Global Canopy Height Map in Boreal Forests

2020 ◽  
Vol 12 (7) ◽  
pp. 1114
Author(s):  
Wei Yang ◽  
Akihiko Kondoh
Keyword(s):  
Boreal Forests ◽  
Forest Canopy ◽  
Spatial Separation ◽  
Canopy Height ◽  
Tree Cover ◽  
Data Sets ◽  
Data Set ◽  
Moderate Resolution ◽  
Resolution Imaging ◽  
Moderate Resolution Imaging Spectroradiometer

Light detection and ranging (LiDAR) provides a state-of-the-art technique for measuring forest canopy height. Nevertheless, it may miss some forests due to its spatial separation of individual spots. A number of efforts have been made to overcome the limitation of global LiDAR datasets to generate wall-to-wall canopy height products, among which a global satellite product produced by Simard et al. (2011) (henceforth, the Simard-map) has been the most widely applied. However, the accuracy of the Simard-map is uncertain in boreal forests, which play important roles in the terrestrial carbon cycle and are encountering more extensive climate changes than the global average. In this letter, we evaluated the Simard-map in boreal forests through a literature review of field canopy height. Our comparison shows that the Simard-map yielded a significant correlation with the field canopy height (R2 = 0.68 and p < 0.001). However, remarkable biases were observed with the root mean square error (RMSE), regression slope, and intercept of 6.88 m, 0.448, and 10.429, respectively. Interestingly, we found that the evaluation results showed an identical trend with a validation of moderate-resolution imaging spectroradiometer (MODIS) tree-cover product (MOD44B) in boreal forests, which was used as a crucial input data set for generating the Simard-map. That is, both the Simard-map and MOD44B yielded an overestimation (underestimation) in the lower (upper) tails of the scatterplots between the field and satellite data sets. This indicates that the MOD44B product is the likely source of error for the estimation biases of the Simard-map. Finally, a field calibration was performed to improve the Simard-map in boreal forests by compensating for the estimation biases and discarding non-forest areas, which provided a more reliable canopy height product for future applications.

scholarly journals The Collection 6 MODIS aerosol products over land and ocean

2013 ◽  
Vol 6 (11) ◽  
pp. 2989-3034 ◽  
Author(s):  
R. C. Levy ◽  
S. Mattoo ◽  
L. A. Munchak ◽  
L. A. Remer ◽  
A. M. Sayer ◽  
...  
Keyword(s):  
Arid Land ◽  
Data Set ◽  
Deep Blue ◽  
New Information ◽  
Spatial Coverage ◽  
Moderate Resolution ◽  
Resolution Imaging ◽  
Moderate Resolution Imaging Spectroradiometer ◽  
Land Surfaces ◽  
Cloud Mask

Abstract. The twin Moderate resolution Imaging Spectroradiometer (MODIS) sensors have been flying on Terra since 2000 and Aqua since 2002, creating an extensive data set of global Earth observations. Here, we introduce the Collection 6 (C6) algorithm to retrieve aerosol optical depth (AOD) and aerosol size parameters from MODIS-observed spectral reflectance. While not a major overhaul from the previous Collection 5 (C5) version, there are enough changes that there are significant impacts to the products and their interpretation. The C6 aerosol data set will be created from three separate retrieval algorithms that operate over different surface types. These are the two "Dark Target" (DT) algorithms for retrieving (1) over ocean (dark in visible and longer wavelengths) and (2) over vegetated/dark-soiled land (dark in the visible), plus the "Deep Blue" (DB) algorithm developed originally for retrieving (3) over desert/arid land (bright in the visible). Here, we focus on DT-ocean and DT-land (#1 and #2). We have updated assumptions for central wavelengths, Rayleigh optical depths and gas (H2O, O3, CO2, etc.) absorption corrections, while relaxing the solar zenith angle limit (up to ≤ 84°) to increase poleward coverage. For DT-land, we have updated the cloud mask to allow heavy smoke retrievals, fine-tuned the assignments for aerosol type as function of season/location, corrected bugs in the Quality Assurance (QA) logic, and added diagnostic parameters such topographic altitude. For DT-ocean, improvements include a revised cloud mask for thin-cirrus detection, inclusion of wind speed dependence on the surface reflectance, updates to logic of QA Confidence flag (QAC) assignment, and additions of important diagnostic information. At the same time, we quantified how "upstream" changes to instrument calibration, land/sea masking and cloud masking will also impact the statistics of global AOD, and affect Terra and Aqua differently. For Aqua, all changes will result in reduced global AOD (by 0.02) over ocean and increased AOD (by 0.02) over land, along with changes in spatial coverage. We compared preliminary data to surface-based sun photometer data, and show that C6 should improve upon C5. C6 will include a merged DT/DB product over semi-arid land surfaces for reduced-gap coverage and better visualization, and new information about clouds in the aerosol field. Responding to the needs of the air quality community, in addition to the standard 10 km product, C6 will include a global (DT-land and DT-ocean) aerosol product at 3 km resolution.

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

2020 ◽  
Vol 13 (1) ◽  
pp. 1-11 ◽  
Author(s):  
Juan Huo ◽  
Daren Lu ◽  
Shu Duan ◽  
Yongheng Bi ◽  
Bo Liu
Keyword(s):  
Satellite Data ◽  
Cloud Base ◽  
Ka Band ◽  
Retrieval Accuracy ◽  
Moderate Resolution ◽  
Resolution Imaging ◽  
Radar Measurements ◽  
The Mean ◽  
Moderate Resolution Imaging Spectroradiometer ◽  
The Difference

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 are 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 are found to be underestimated by−1.10 ± 2.53 km on average and 49 % of CTH differences are within 1.0 km. The AHI CTHs are underestimated by −1.10 ± 2.27 km and 42 % are within 1.0 km. Both the MODIS and AHI CTH retrieval accuracy depends strongly on the cloud depth (CD). Large differences are mainly due to the retrieval of thin clouds of CD <1 km, especially when the cloud base height is higher than 4 km. For clouds with CD >1 km, the mean CTH difference decreases to -0.48±1.70 km for MODIS and to -0.76±1.63 km for AHI. It is found that MODIS CTHs with higher values (i.e. >6 km) show smaller discrepancy with radar CTH than those MODIS CTHs with lower values (i.e. <4 km). Statistical analysis illustrate that the CTH difference between the two satellite instruments is lower than the difference between the satellite instrument and the ground-based Ka-band radar. The monthly accuracy of both CTH retrieval algorithms is investigated and it is found that summer has the smallest retrieval difference.

scholarly journals A NEW SPATIAL AND TEMPORAL FUSION MODEL

2016 ◽  
Vol III-7 ◽  
pp. 203-206
Author(s):  
Jing Wang ◽  
Bo Huang
Keyword(s):  
Temporal Resolution ◽  
Visual Analysis ◽  
High Temporal Resolution ◽  
Fusion Model ◽  
Data Set ◽  
Regression Error ◽  
Moderate Resolution ◽  
Resolution Imaging ◽  
Temporal Model ◽  
Moderate Resolution Imaging Spectroradiometer

As Moderate Resolution Imaging Spectroradiometer (MODIS) and Enhanced Thematic Mapper Plus (ETM+) has a tradeoff between the high temporal resolution and high spatial resolution, this paper proposed a spatial and temporal model with auto-regression error correction (AREC) method to blend the two types of images in order to obtain the composed image with both high spatial and temporal resolution. Experiments and validation were conducted on a data set located in Shenzhen, China and compared with Spatial and Temporal Adaptive Reflectance Fusion Model (STARFM) in several objective indexes and visual analysis. It was found that AREC could effectively predict the land cover changes and the fusion results had better performances versus the ones of STARFM.

scholarly journals A NEW SPATIAL AND TEMPORAL FUSION MODEL

2016 ◽  
Vol III-7 ◽  
pp. 203-206
Author(s):  
Jing Wang ◽  
Bo Huang
Keyword(s):  
Temporal Resolution ◽  
Visual Analysis ◽  
High Temporal Resolution ◽  
Fusion Model ◽  
Data Set ◽  
Regression Error ◽  
Moderate Resolution ◽  
Resolution Imaging ◽  
Temporal Model ◽  
Moderate Resolution Imaging Spectroradiometer

As Moderate Resolution Imaging Spectroradiometer (MODIS) and Enhanced Thematic Mapper Plus (ETM+) has a tradeoff between the high temporal resolution and high spatial resolution, this paper proposed a spatial and temporal model with auto-regression error correction (AREC) method to blend the two types of images in order to obtain the composed image with both high spatial and temporal resolution. Experiments and validation were conducted on a data set located in Shenzhen, China and compared with Spatial and Temporal Adaptive Reflectance Fusion Model (STARFM) in several objective indexes and visual analysis. It was found that AREC could effectively predict the land cover changes and the fusion results had better performances versus the ones of STARFM.

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