Evaluation of MODIS and VIIRS cloud-gap-filled snow-cover products for production of an Earth science data record

Abstract. MODerate resolution Imaging Spectroradiometer (MODIS) cryosphere products have been available since 2000 – following the 1999 launch of the Terra MODIS and the 2002 launch of the Aqua MODIS – and include global snow-cover extent (SCE) (swath, daily, and 8 d composites) at 500 m and ∼5 km spatial resolutions. These products are used extensively in hydrological modeling and climate studies. Reprocessing of the complete snow-cover data record, from Collection 5 (C5) to Collection 6 (C6) and Collection 6.1 (C6.1), has provided improvements in the MODIS product suite. Suomi National Polar-orbiting Partnership (S-NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) Collection 1 (C1) snow-cover products at a 375 m spatial resolution have been available since 2011 and are currently being reprocessed for Collection 2 (C2). Both the MODIS C6.1 and the VIIRS C2 products will be available for download from the National Snow and Ice Data Center beginning in early 2020 with the complete time series available in 2020. To address the need for a cloud-reduced or cloud-free daily SCE product for both MODIS and VIIRS, a daily cloud-gap-filled (CGF) snow-cover algorithm was developed for MODIS C6.1 and VIIRS C2 processing. MOD10A1F (Terra) and MYD10A1F (Aqua) are daily, 500 m resolution CGF SCE map products from MODIS. VNP10A1F is the daily, 375 m resolution CGF SCE map product from VIIRS. These CGF products include quality-assurance data such as cloud-persistence statistics showing the age of the observation in each pixel. The objective of this paper is to introduce the new MODIS and VIIRS standard CGF daily SCE products and to provide a preliminary evaluation of uncertainties in the gap-filling methodology so that the products can be used as the basis for a moderate-resolution Earth science data record (ESDR) of SCE. Time series of the MODIS and VIIRS CGF products have been developed and evaluated at selected study sites in the US and southern Canada. Observed differences, although small, are largely attributed to cloud masking and differences in the time of day of image acquisition. A nearly 3-month time-series comparison of Terra MODIS and S-NPP VIIRS CGF snow-cover maps for a large study area covering all or parts of 11 states in the western US and part of southwestern Canada reveals excellent correspondence between the Terra MODIS and S-NPP VIIRS products, with a mean difference of 11 070 km2, which is ∼0.45 % of the study area. According to our preliminary validation of the Terra and Aqua MODIS CGF SCE products in the western US study area, we found higher accuracy of the Terra product compared with the Aqua product. The MODIS CGF SCE data record beginning in 2000 has been extended into the VIIRS era, which should last at least through the early 2030s.

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Continuity of MODIS and VIIRS Snow Cover Extent Data Products for Development of an Earth Science Data Record

Remote Sensing ◽

10.3390/rs12223781 ◽

2020 ◽

Vol 12 (22) ◽

pp. 3781

Author(s):

George Riggs ◽

Dorothy Hall

Keyword(s):

Snow Cover ◽

Infrared Imaging ◽

Earth Science ◽

Satellite System ◽

Science Data ◽

Snow Cover Extent ◽

Data Record ◽

Data Products ◽

Moderate Resolution ◽

Earth Science Data

An Earth Observing System global snow cover extent data products record at moderate spatial resolution (375–500 m) began in February 2000 with the Moderate-resolution Imaging Spectroradiometer (MODIS) instrument onboard the Terra satellite. The record continued with the Aqua MODIS in July 2002, the Suomi-National Polar Platform (S-NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) in January 2012 and continues with the Joint Polar Satellite System-1 (JPSS-1) VIIRS, launched in November of 2017. The objective of this work is to develop a snow cover extent Earth Science Data Record (ESDR) using different satellites, sensors and algorithms. There are many issues to understand when data from different algorithms and sensors are used over a decade-scale time period to create a continuous dataset. Issues may also arise with sensor degradation and even differences in sensor band locations. In this paper we describe development of an ESDR derived from existing MODIS and VIIRS data products and demonstrate continuity among the products. The MODIS and VIIRS snow cover detection algorithms produce very similar daily snow cover maps, with 90–97% agreement in snow cover extent (SCE) in different landscapes. Differences in SCE between products ranged from 2–15% and are attributable to convolved factors of viewing geometry, pixel spread across a scan and time of observation. Compared at a common grid size of 1 km, there is a mean of 95% agreement in SCE and a difference range of 1–10% between the MODIS and VIIRS SCE maps. Mapping sensor observations to a coarser resolution grid reduces the effect of the factors convolved in the 500 m tile to tile comparisons. We conclude that the MODIS and VIIRS SCE data products are reliable constituents of a moderate-resolution ESDR.

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Quantifying and attributing changes in tropospheric ozone from a combination of satellite measurements and models

10.5194/egusphere-egu2020-21123 ◽

2020 ◽

Author(s):

Jessica Neu ◽

Kazuyuki Miyazaki ◽

Kevin Bowman ◽

Gregory Osterman

Keyword(s):

Time Series ◽

Tropospheric Ozone ◽

Earth Science ◽

Satellite Measurements ◽

Science Data ◽

The Past ◽

Long Term Changes ◽

Earth Science Data

<p>Given the importance of tropospheric ozone as a greenhouse gas and a hazardous pollutant that impacts human health and ecosystems, it is critical to quantify and understand long-term changes in its abundance.&#160; Satellite records are beginning to approach the length needed to assess variability and trends in tropospheric ozone, yet an intercomparison of time series from different instruments shows substantial differences in the net change in ozone over the past decade.&#160; We discuss our efforts to produce Earth Science Data Records of tropospheric ozone and quantify uncertainties and biases in these records.&#160; We also discuss the role of changes in the magnitude and distribution of precursor emissions and in downward transport of ozone from the stratosphere in determining tropospheric ozone abundances over the past 15 years.</p>

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MODIS Cloud-Gap Filled Snow-Cover Products: Advantages and Uncertainties

10.5194/hess-2019-123 ◽

2019 ◽

Cited By ~ 3

Author(s):

Dorothy K. Hall ◽

George A. Riggs ◽

Nicolo E. DiGirolamo ◽

Miguel O. Román

Keyword(s):

United States ◽

Snow Cover ◽

Environmental Science ◽

Hydrological Modeling ◽

Infrared Imaging ◽

Regional Climate ◽

Science Data ◽

Snow Cover Extent ◽

Data Record ◽

Terra Modis

Abstract. MODerate resolution Imaging Spectroradiometer (MODIS) cryosphere products that have been available since the launch of the Terra MODIS in 2000 and the Aqua MODIS in 2002 include snow-cover extent (swath, daily and eight-day composites) and daily snow albedo. These products are used in hydrological modeling and studies of local and regional climate, and are increasingly being used to study regional hydrological and climatological changes over time. Reprocessing of the complete snow-cover data record, from Collection 5 (C5) to Collection 6 (C6) and Collection 6.1 (C6.1), has led to improvements in the MODIS product suite. Suomi National Polar-orbiting Partnership (S-NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) Collection 1 (C1) snow-cover products have been available since 2011, and are currently being reprocessed for Collection 2 (C2). To address the need for a cloud-reduced or cloud-free daily snow product for both MODIS and VIIRS, a new daily cloud-gap filled snow-cover product was developed for MODIS C6.1 and VIIRS C2 processing. MOD10A1F (Terra) and MYD10A1F (Aqua) are daily, 500-m resolution cloud-gap filled (CGF) snow-cover map products from MODIS. VNP10A1F is the 375-m resolution CGF snow map from VIIRS. The CGF maps provide daily cloud-free snow maps, along with cloud-persistence maps showing the age of the snow or non-snow observation in each pixel. Work is ongoing to evaluate and document uncertainties in the MODIS and VIIRS standard daily CGF snow-cover products. Analysis of the MOD/MYD10A1F products for study areas in the western United States shows excellent results in terms of accuracy of snow-cover mapping. When there are frequent clear-sky episodes, MODIS is able to capture enough clear views of the surface to produce accurate snow-cover information and snow maps. Even in the extensively-cloud-covered northeastern United States during winter months, snow maps from MODIS CGF products are useful, though the snow maps are likely to miss some snow, particularly during the spring snowmelt period when snow may fall and melt within a day or two, before the clouds clear from the storm that deposited the snow. Comparisons between the Terra and Aqua CGF snow maps have revealed differences that are related to cloud masking in the two algorithms. We conclude that the MODIS Terra CGF is the more accurate MODIS snow-cover product, and should therefore be the basis of an Environmental Science Data Record that will extend the CGF data record from the Terra MODIS beginning in 2000 through the VIIRS era, at least through the early 2030s.

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Global River Radar Altimetry Time Series (GRRATS): new river elevation earth science data records for the hydrologic community

Earth System Science Data ◽

10.5194/essd-12-137-2020 ◽

2020 ◽

Vol 12 (1) ◽

pp. 137-150 ◽

Cited By ~ 5

Author(s):

Stephen Coss ◽

Michael Durand ◽

Yuchan Yi ◽

Yuanyuan Jia ◽

Qi Guo ◽

...

Keyword(s):

Time Series ◽

Earth Science ◽

Altimeter Data ◽

Radar Altimeter ◽

Science Data ◽

Radar Altimetry ◽

In Situ Data ◽

Virtual Station ◽

Earth Science Data

Abstract. The capabilities of radar altimetry to measure inland water bodies are well established, and several river altimetry datasets are available. Here we produced a globally distributed dataset, the Global River Radar Altimeter Time Series (GRRATS), using Envisat and Ocean Surface Topography Mission (OSTM)/Jason-2 radar altimeter data spanning the time period 2002–2016. We developed a method that runs unsupervised, without requiring parameterization at the measurement location, dubbed virtual station (VS) level, and applied it to all altimeter crossings of ocean-draining rivers with widths >900 m (>34 % of the global drainage area). We evaluated every VS, either quantitatively for VS locations where in situ gages are available or qualitatively using a grade system. We processed nearly 1.5 million altimeter measurements from 1478 VSs. After quality control, the final product contained 810 403 measurements distributed over 932 VSs located on 39 rivers. Available in situ data allowed quantitative evaluation of 389 VSs on 12 rivers. The median standard deviation of river elevation error is 0.93 m, Nash–Sutcliffe efficiency is 0.75, and correlation coefficient is 0.9. GRRATS is a consistent, well-documented dataset with a user-friendly data visualization portal, freely available for use by the global scientific community. Data are available at https://doi.org/10.5067/PSGRA-SA2V1 (Coss et al., 2016).

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The Dark Target Algorithm for Observing the Global Aerosol System: Past, Present, and Future

Remote Sensing ◽

10.3390/rs12182900 ◽

2020 ◽

Vol 12 (18) ◽

pp. 2900 ◽

Cited By ~ 1

Author(s):

Lorraine A. Remer ◽

Robert C. Levy ◽

Shana Mattoo ◽

Didier Tanré ◽

Pawan Gupta ◽

...

Keyword(s):

Earth Science ◽

Surface Reflectance ◽

Climate Data ◽

Data Record ◽

Moderate Resolution ◽

Resolution Imaging ◽

Terra Modis ◽

Climate Data Record ◽

New Instruments ◽

Existing Data

The Dark Target aerosol algorithm was developed to exploit the information content available from the observations of Moderate-Resolution Imaging Spectroradiometers (MODIS), to better characterize the global aerosol system. The algorithm is based on measurements of the light scattered by aerosols toward a space-borne sensor against the backdrop of relatively dark Earth scenes, thus giving rise to the name “Dark Target”. Development required nearly a decade of research that included application of MODIS airborne simulators to provide test beds for proto-algorithms and analysis of existing data to form realistic assumptions to constrain surface reflectance and aerosol optical properties. This research in itself played a significant role in expanding our understanding of aerosol properties, even before Terra MODIS launch. Contributing to that understanding were the observations and retrievals of the growing Aerosol Robotic Network (AERONET) of sun-sky radiometers, which has walked hand-in-hand with MODIS and the development of other aerosol algorithms, providing validation of the satellite-retrieved products after launch. The MODIS Dark Target products prompted advances in Earth science and applications across subdisciplines such as climate, transport of aerosols, air quality, and data assimilation systems. Then, as the Terra and Aqua MODIS sensors aged, the challenge was to monitor the effects of calibration drifts on the aerosol products and to differentiate physical trends in the aerosol system from artefacts introduced by instrument characterization. Our intention is to continue to adapt and apply the well-vetted Dark Target algorithms to new instruments, including both polar-orbiting and geosynchronous sensors. The goal is to produce an uninterrupted time series of an aerosol climate data record that begins at the dawn of the 21st century and continues indefinitely into the future.

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Global River Radar Altimetry Time Series (GRRATS): New River Elevation Earth Science Data Records for the Hydrologic Community

10.5194/essd-2019-84 ◽

2019 ◽

Author(s):

Stephen Coss ◽

Michael Durand ◽

Yuchan Yi ◽

Yuanyuan Jia ◽

Qi Guo ◽

...

Keyword(s):

Time Series ◽

Earth Science ◽

Altimeter Data ◽

Radar Altimeter ◽

Science Data ◽

Radar Altimetry ◽

In Situ Data ◽

Virtual Station ◽

Earth Science Data

Abstract. The capabilities of radar altimetry to measure inland water bodies are well established and several river altimetry datasets are available. Here we produced a globally-distributed dataset, the Global River Radar Altimeter Time Series (GRRATS), using Envisat and Ocean Surface Topography Mission (OSTM)/Jason-2 radar altimeter data spanning the time period 2002–2016. We developed a method that runs unsupervised, without requiring parameterization at the measurement location, dubbed virtual station (VS) level and applied it to all altimeter crossings of ocean draining rivers with widths > 900 m (> 34 % of global drainage area). We evaluated every VS, either quantitatively for VS where in-situ gages are available, or qualitatively using a grade system. We processed nearly 1.5 million altimeter measurements from 1,478 VS. After quality control, the final product contained 810,403 measurements distributed over 932 VS located on 39 rivers. Available in-situ data allowed quantitative evaluation of 389 VS on 12 rivers. Median standard deviation of river elevation error is 0.93 m, Nash-Sutcliffe efficiency is 0.75, and correlation coefficient is 0.9. GRRATS is a consistent, well-documented dataset with a user-friendly data visualization portal, freely available for use by the global scientific community. Data are available at DOI 10.5067/PSGRA-SA2V1 (Durand et al., 2016).

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