Calibration of the DSCOVR EPIC visible and NIR channels using MODIS Terra and Aqua data and EPIC lunar observations

Abstract. The unique position of the Deep Space Climate Observatory (DSCOVR) Earth Polychromatic Imaging Camera (EPIC) at the Lagrange 1 point makes an important addition to the data from currently operating low Earth orbit observing instruments. EPIC instrument does not have an onboard calibration facility. One approach to its calibration is to compare EPIC observations to the measurements from polar-orbiting radiometers. Moderate Resolution Imaging Spectroradiometer (MODIS) is a natural choice for such comparison due to its well-established calibration record and wide use in remote sensing. We use MODIS Aqua and Terra L1B 1 km reflectances to infer calibration coefficients for four EPIC visible and NIR channels: 443, 551, 680 and 780 nm. MODIS and EPIC measurements made between June 2015 and 2016 are employed for comparison. We first identify favorable MODIS pixels with scattering angle matching temporarily collocated EPIC observations. Each EPIC pixel is then spatially collocated to a subset of the favorable MODIS pixels within 25 km radius. Standard deviation of the selected MODIS pixels as well as of the adjacent EPIC pixels is used to find the most homogeneous scenes. These scenes are then used to determine calibration coefficients using a linear regression between EPIC counts s−1 and reflectances in the close MODIS spectral channels. We present thus inferred EPIC calibration coefficients and discuss sources of uncertainties. The lunar EPIC observations are used to calibrate EPIC O2 absorbing channels (688 and 764 nm), assuming that there is a small difference between moon reflectances separated by ∼ 10 nm in wavelength and provided the calibration factors of the red (680 nm) and NIR (780 nm) are known from comparison between EPIC and MODIS.

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Calibration of the DSCOVR EPIC visible and NIR channels using MODIS and EPIC lunar observations

10.5194/amt-2017-222 ◽

2017 ◽

Author(s):

Igor V. Geogdzhayev ◽

Alexander Marshak

Keyword(s):

Near Ir ◽

Natural Choice ◽

Imaging Camera ◽

Lunar Observations ◽

Moderate Resolution ◽

Important Addition ◽

Resolution Imaging ◽

Moderate Resolution Imaging Spectroradiometer ◽

Spectral Channels ◽

Calibration Coefficients

Abstract. The unique position of the Deep Space Climate Observatory (DSCOVR) Earth Polychromatic Imaging Camera (EPIC) at the Lagrange 1 point makes an important addition to the data from currently operating low orbit Earth observing instruments. EPIC instrument does not have an onboard calibration facility. One approach to its calibration is to compare EPIC observations to the measurements from polar orbiting radiometers. Moderate Resolution Imaging Spectroradiometer (MODIS) is a natural choice for such comparison due to its well-established calibration record and wide use in remote sensing. We use MODIS Aqua and Terra L1B 1 km reflectances to infer calibration coefficients for four EPIC visible and NIR channels: 443 nm, 551 nm, 680 nm and 780 nm. MODIS and EPIC measurements made between June 2015 and June 2016 are employed for comparison. We first identify favorable MODIS pixels with scattering angle matching temporarily collocated EPIC observations. Each EPIC pixel is then spatially collocated to a subset of the favorable MODIS pixels within 25 km radius. Standard deviation of the selected MODIS pixels as well as of the adjacent EPIC pixels is used to find the most homogeneous scenes. These scenes are then used to determine calibration coefficients using a linear regression between EPIC counts and reflectances in the close MODIS spectral channels. We present thus inferred EPIC calibration coefficients and discuss sources of uncertainties. The Lunar EPIC observations are used to calibrate EPIC O2 absorbing channels (688 nm and 764 nm) assuming that there is a small difference between moon reflectances separated by 10 nm difference in wavelength and the calibration factors of the red (680 nm) and near-IR (780 nm) are known from comparison between EPIC and MODIS.

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Development and Demonstration of a Method for GEO-to-LEO NDVI Transformation

Remote Sensing ◽

10.3390/rs13204085 ◽

2021 ◽

Vol 13 (20) ◽

pp. 4085

Author(s):

Kenta Obata ◽

Kenta Taniguchi ◽

Masayuki Matsuoka ◽

Hiroki Yoshioka

Keyword(s):

Vegetation Index ◽

Normalized Difference Vegetation Index ◽

Middle Latitude ◽

Transformation Method ◽

Low Earth Orbit ◽

Linear Mixture Model ◽

Moderate Resolution ◽

Resolution Imaging ◽

Moderate Resolution Imaging Spectroradiometer ◽

Index Time Series

This study presents a new method that mitigates biases between the normalized difference vegetation index (NDVI) from geostationary (GEO) and low Earth orbit (LEO) satellites for Earth observation. The method geometrically and spectrally transforms GEO NDVI into LEO-compatible GEO NDVI, in which GEO’s off-nadir view is adjusted to a near-nadir view. First, a GEO-to-LEO NDVI transformation equation is derived using a linear mixture model of anisotropic vegetation and nonvegetation endmember spectra. The coefficients of the derived equation are a function of the endmember spectra of two sensors. The resultant equation is used to develop an NDVI transformation method in which endmember spectra are automatically computed from each sensor’s data independently and are combined to compute the coefficients. Importantly, this method does not require regression analysis using two-sensor NDVI data. The method is demonstrated using Himawari 8 Advanced Himawari Imager (AHI) data at off-nadir view and Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) data at near-nadir view in middle latitude. The results show that the magnitudes of the averaged NDVI biases between AHI and MODIS for five test sites (0.016–0.026) were reduced after the transformation (<0.01). These findings indicate that the proposed method facilitates the combination of GEO and LEO NDVIs to provide NDVIs with smaller differences, except for cases in which the fraction of vegetation cover (FVC) depends on the view angle. Further investigations should be conducted to reduce the remaining errors in the transformation and to explore the feasibility of using the proposed method to predict near-real-time and near-nadir LEO vegetation index time series using GEO data.

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Calibration of the DSCOVR EPIC Visible and NIR Channels using Multiple LEO Radiometers

Frontiers in Remote Sensing ◽

10.3389/frsen.2021.671933 ◽

2021 ◽

Vol 2 ◽

Author(s):

Igor V. Geogdzhayev ◽

Alexander Marshak ◽

Mikhail Alexandrov

Keyword(s):

Near Infrared ◽

Infrared Imaging ◽

Statistical Tests ◽

Calibration Procedure ◽

Low Earth Orbit ◽

Earth Orbit ◽

Full Dataset ◽

Moderate Resolution ◽

Moderate Resolution Imaging Spectroradiometer ◽

Calibration Coefficients

The first five years of operation of the Deep Space Climate Observatory (DSCOVR) Earth Polychromatic Imaging Camera (EPIC) at the Lagrange one point have produced results that uniquely complement the data from currently operating low orbit Earth-observing instruments. In this paper we describe an updated unified approach to EPIC calibration. In this approach, calibration coefficients and their trends were obtained by comparing EPIC observations to the measurements from polar orbiting radiometers. In this study L1B reflectances from Moderate Resolution Imaging Spectroradiometer (MODIS) onboard the Aqua and Terra satellites, Multi-angle Imaging Spectroradiometer (MISR) onboard Terra and Visible Infrared Imaging Radiometer (VIIRS) onboard the Suomi National Polar-orbiting Partnership (Suomi NPP) spacecraft were used to infer calibration coefficients for four EPIC visible and near-infrared channels: 443 nm, 551 nm, 680 nm, and 780 nm. EPIC Version three measurements made between June 2015 and August 2020 were used for comparison. The calibration procedure identifies the most homogeneous low Earth orbit radiometer scenes matching scattering angles that are temporarily and spatially collocated with EPIC observations. These scenes are used to determine reflectance to count (R/C) ratios in spectrally analogous channels. Seasonal average R/C ratios were analyzed to obtain EPIC calibration gains and trends. The trends for the full dataset period are not statistically significant except in the 443 nm channel. No significant changes in calibration were found after the instrument’s exit from safe hold in March 2020. The R/C ratios were also used to determine the differences in EPIC gains resulting from separate calibrations: against MODIS Aqua or Terra, as well as against forward or aftward MISR cameras. Statistical tests indicate that the differences between the two datasets are not significant except in the 780 nm channels where Aqua-derived coefficients may be around 2% lower compared to Terra. The dependence of EPIC calibration gains on the instrument scattering angle and on DSCOVR-Earth distance were investigated. Lastly, model Low Earth Orbit (LEO) reflectances calculated to match the EPIC viewing geometry were employed to study how EPIC calibration coefficients depend on EPIC-LEO viewing geometry differences. The effect of LEO and EPIC angular mismatch on calibration was shown to be small.

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Limb Correction of MODIS and VIIRS Infrared Channels for the Improved Interpretation of RGB Composites

Journal of Atmospheric and Oceanic Technology ◽

10.1175/jtech-d-15-0245.1 ◽

2016 ◽

Vol 33 (5) ◽

pp. 1073-1087 ◽

Cited By ~ 7

Author(s):

Nicholas J. Elmer ◽

Emily Berndt ◽

Gary J. Jedlovec

Keyword(s):

Situational Awareness ◽

Infrared Imaging ◽

Temporal Frequency ◽

Multiple Sensors ◽

Limb Effect ◽

Moderate Resolution ◽

Multiple Case ◽

Resolution Imaging ◽

Moderate Resolution Imaging Spectroradiometer ◽

Spectral Channels

AbstractRed–green–blue (RGB) composite imagery combines information from several spectral channels into one image to aid in the operational analysis of atmospheric processes. However, infrared channels are adversely affected by the limb effect, the result of an increase in optical pathlength of the absorbing atmosphere between the satellite and the earth as viewing zenith angle increases. This study develops a technique to quickly correct for limb effects in both clear and cloudy regions using latitudinally and seasonally varying limb correction coefficients for real-time applications. These limb correction coefficients account for the increase in optical pathlength in order to produce limb-corrected RGB composites. The improved functionality of limb-corrected RGB composites is demonstrated by multiple case studies of Air Mass and Dust RGB composites using Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) and Suomi–National Polar-Orbiting Partnership (SNPP) Visible Infrared Imaging Radiometer Suite (VIIRS) imagery. However, the limb correction can be applied to any polar-orbiting sensor infrared channels, provided the proper limb correction coefficients are calculated. Corrected RGB composites provide multiple advantages over uncorrected RGB composites, including increased confidence in the interpretation of RGB features, improved situational awareness for operational forecasters, and the ability to use RGB composites from multiple sensors jointly to increase the temporal frequency of observations.

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Influence of Straw Burning on Urban Air Pollutant Concentrations in Northeast China

International Journal of Environmental Research and Public Health ◽

10.3390/ijerph16081379 ◽

2019 ◽

Vol 16 (8) ◽

pp. 1379 ◽

Cited By ~ 4

Author(s):

Zhenzhen Wang ◽

Jianjun Zhao ◽

Jiawen Xu ◽

Mingrui Jia ◽

Han Li ◽

...

Keyword(s):

Northeast China ◽

Arable Land ◽

Pollution Index ◽

Air Pollutant ◽

Thermal Anomalies ◽

Pollutant Concentrations ◽

Moderate Resolution ◽

Resolution Imaging ◽

Straw Burning ◽

Moderate Resolution Imaging Spectroradiometer

Northeast China is China’s primary grain production base. A large amount of crop straw is incinerated every spring and autumn, which greatly impacts air quality. To study the degree of influence of straw burning on urban pollutant concentrations, this study used The Moderate-Resolution Imaging Spectroradiometer/Terra Thermal Anomalies & Fire Daily L3 Global 1 km V006 (MOD14A1) and The Moderate-Resolution Imaging Spectroradiometer/Aqua Thermal Anomalies and Fire Daily L3 Global 1 km V006 (MYD14A1) data from 2015 to 2017 to extract fire spot data on arable land burning and to study the spatial distribution characteristics of straw burning on urban pollutant concentrations, temporal variation characteristics and impact thresholds. The results show that straw burning in Northeast China is concentrated in spring and autumn; the seasonal spatial distributions of PM2.5, PM10 andAir Quality Index (AQI) in 41 cities or regions in Northeast China correspond to the seasonal variation of fire spots; and pollutants appear in the peak periods of fire spots. In areas where the concentration coefficient of rice or corn is greater than 1, the number of fire spots has a strong correlation with the urban pollution index. The correlation coefficient R between the number of burned fire spots and the pollutant concentration has a certain relationship with the urban distribution. Cities are aggregated in geospatial space with different R values.

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A Climatological Assessment of Intense Desert Dust Episodes over the Broader Mediterranean Basin Based on Satellite Data

Remote Sensing ◽

10.3390/rs13152895 ◽

2021 ◽

Vol 13 (15) ◽

pp. 2895

Author(s):

Maria Gavrouzou ◽

Nikolaos Hatzianastassiou ◽

Antonis Gkikas ◽

Christos J. Lolis ◽

Nikolaos Mihalopoulos

Keyword(s):

Mediterranean Basin ◽

Eastern Mediterranean ◽

Mean Value ◽

Early Spring ◽

Dust Aerosol ◽

Ozone Monitoring Instrument ◽

Central Mediterranean ◽

Moderate Resolution ◽

Resolution Imaging ◽

Moderate Resolution Imaging Spectroradiometer

A satellite algorithm able to identify Dust Aerosols (DA) is applied for a climatological investigation of Dust Aerosol Episodes (DAEs) over the greater Mediterranean Basin (MB), one of the most climatologically sensitive regions of the globe. The algorithm first distinguishes DA among other aerosol types (such as Sea Salt and Biomass Burning) by applying threshold values on key aerosol optical properties describing their loading, size and absorptivity, namely Aerosol Optical Depth (AOD), Aerosol Index (AI) and Ångström Exponent (α). The algorithm operates on a daily and 1° × 1° geographical cell basis over the 15-year period 2005–2019. Daily gridded spectral AOD data are taken from Moderate Resolution Imaging Spectroradiometer (MODIS) Aqua Collection 6.1, and are used to calculate the α data, which are then introduced into the algorithm, while AI data are obtained by the Ozone Monitoring Instrument (OMI) -Aura- Near-UV aerosol product OMAERUV dataset. The algorithm determines the occurrence of Dust Aerosol Episode Days (DAEDs), whenever high loads of DA (higher than their climatological mean value plus two/four standard deviations for strong/extreme DAEDs) exist over extended areas (more than 30 pixels or 300,000 km2). The identified DAEDs are finally grouped into Dust Aerosol Episode Cases (DAECs), consisting of at least one DAED. According to the algorithm results, 166 (116 strong and 50 extreme) DAEDs occurred over the MB during the study period. DAEDs are observed mostly in spring (47%) and summer (38%), with strong DAEDs occurring primarily in spring and summer and extreme ones in spring. Decreasing, but not statistically significant, trends of the frequency, spatial extent and intensity of DAECs are revealed. Moreover, a total number of 98 DAECs was found, primarily in spring (46 DAECs) and secondarily in summer (36 DAECs). The seasonal distribution of the frequency of DAECs varies geographically, being highest in early spring over the eastern Mediterranean, in late spring over the central Mediterranean and in summer over the western MB.

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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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New generation geostationary satellite observations support seasonality in greenness of the Amazon evergreen forests

Nature Communications ◽

10.1038/s41467-021-20994-y ◽

2021 ◽

Vol 12 (1) ◽

Author(s):

Hirofumi Hashimoto ◽

Weile Wang ◽

Jennifer L. Dungan ◽

Shuang Li ◽

Andrew R. Michaelis ◽

...

Keyword(s):

Cloud Cover ◽

Seasonal Patterns ◽

Optical Remote Sensing ◽

Amazon Forest ◽

Evergreen Forests ◽

Geostationary Satellites ◽

Moderate Resolution ◽

Resolution Imaging ◽

Moderate Resolution Imaging Spectroradiometer ◽

New Generation

AbstractAssessing the seasonal patterns of the Amazon rainforests has been difficult because of the paucity of ground observations and persistent cloud cover over these forests obscuring optical remote sensing observations. Here, we use data from a new generation of geostationary satellites that carry the Advanced Baseline Imager (ABI) to study the Amazon canopy. ABI is similar to the widely used polar orbiting sensor, the Moderate Resolution Imaging Spectroradiometer (MODIS), but provides observations every 10–15 min. Our analysis of NDVI data collected over the Amazon during 2018–19 shows that ABI provides 21–35 times more cloud-free observations in a month than MODIS. The analyses show statistically significant changes in seasonality over 85% of Amazon forest pixels, an area about three times greater than previously reported using MODIS data. Though additional work is needed in converting the observed changes in seasonality into meaningful changes in canopy dynamics, our results highlight the potential of the new generation geostationary satellites to help us better understand tropical ecosystems, which has been a challenge with only polar orbiting satellites.

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Real-Time Detection of Daytime and Night-Time Fire Hotspots from Geostationary Satellites

Remote Sensing ◽

10.3390/rs13091627 ◽

2021 ◽

Vol 13 (9) ◽

pp. 1627

Author(s):

Chermelle B. Engel ◽

Simon D. Jones ◽

Karin J. Reinke

Keyword(s):

Real Time ◽

Infrared Imaging ◽

Night Time ◽

Clear Link ◽

Radiative Power ◽

Biogeographical Region ◽

Moderate Resolution ◽

Resolution Imaging ◽

Moderate Resolution Imaging Spectroradiometer ◽

Fire Radiative Power

This paper introduces an enhanced version of the Biogeographical Region and Individual Geostationary HHMMSS Threshold (BRIGHT) algorithm. The algorithm runs in real-time and operates over 24 h to include both daytime and night-time detections. The algorithm was executed and tested on 12 months of Himawari-8 data from 1 April 2019 to 31 March 2020, for every valid 10-min observation. The resulting hotspots were compared to those from the Visible Infrared Imaging Radiometer Suite (VIIRS) and the Moderate Resolution Imaging Spectroradiometer (MODIS). The modified BRIGHT hotspots matched with fire detections in VIIRS 96% and MODIS 95% of the time. The number of VIIRS and MODIS hotspots with matches in the coincident modified BRIGHT dataset was lower (at 33% and 46%, respectively). This paper demonstrates a clear link between the number of VIIRS and MODIS hotspots with matches and the minimum fire radiative power considered.

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Consequences of the 2019 Greenland Ice Sheet Melt Episode on Albedo

Remote Sensing ◽

10.3390/rs13020227 ◽

2021 ◽

Vol 13 (2) ◽

pp. 227

Author(s):

Arthur Elmes ◽

Charlotte Levy ◽

Angela Erb ◽

Dorothy K. Hall ◽

Ted A. Scambos ◽

...

Keyword(s):

Radiative Forcing ◽

Surface Albedo ◽

Ice Sheet ◽

Greenland Ice Sheet ◽

Surface Energy Budget ◽

Landsat 8 ◽

Moderate Resolution ◽

Resolution Imaging ◽

Moderate Resolution Imaging Spectroradiometer ◽

Seasonal Snow Cover

In mid-June 2019, the Greenland ice sheet (GrIS) experienced an extreme early-season melt event. This, coupled with an earlier-than-average melt onset and low prior winter snowfall over western Greenland, led to a rapid decrease in surface albedo and greater solar energy absorption over the melt season. The 2019 melt season resulted in significantly more melt than other recent years, even compared to exceptional melt years previously identified in the moderate-resolution imaging spectroradiometer (MODIS) record. The increased solar radiation absorbance in 2019 warmed the surface and increased the rate of meltwater production. We use two decades of satellite-derived albedo from the MODIS MCD43 record to show a significant and extended decrease in albedo in Greenland during 2019. This decrease, early in the melt season and continuing during peak summer insolation, caused increased radiative forcing of the ice sheet of 2.33 Wm−2 for 2019. Radiative forcing is strongly influenced by the dramatic seasonal differences in surface albedo experienced by any location experiencing persistent and seasonal snow-cover. We also illustrate the utility of the newly developed Landsat-8 albedo product for better capturing the detailed spatial heterogeneity of the landscape, leading to a more refined representation of the surface energy budget. While the MCD43 data accurately capture the albedo for a given 500 m pixel, the higher spatial resolution 30 m Landsat-8 albedos more fully represent the detailed landscape variations.

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