Exploring viirs continuity with modis in an expedited capability for monitoring drought-related vegetation conditions

Trenton D. Benedict; Jesslyn F. Brown; Stephen P. Boyte; Daniel M. Howard; Brian A. Fuchs; Brian D. Wardlow; Tsegaye Tadesse; Kirk A. Evenson

doi:10.3390/rs13061210

Exploring viirs continuity with modis in an expedited capability for monitoring drought-related vegetation conditions

Trenton D. Benedict, Jesslyn F. Brown, Stephen P. Boyte, Daniel M. Howard, Brian A. Fuchs, Brian D. Wardlow, Tsegaye Tadesse, Kirk A. Evenson

Daugherty Water for Food Global Institute

Research output: Contribution to journal › Article › peer-review

4 Scopus citations

Abstract

Vegetation has been effectively monitored using remote sensing time-series vegetation index (VI) data for several decades. Drought monitoring has been a common application with algorithms tuned to capturing anomalous temporal and spatial vegetation patterns. Drought stress models, such as the Vegetation Drought Response Index (VegDRI), often use VIs like the Normalized Difference Vegetation Index (NDVI). The EROS expedited Moderate Resolution Imaging Spectrora-diometer (eMODIS)-based, 7-day NDVI composites are integral to the VegDRI. As MODIS satellite platforms (Terra and Aqua) approach mission end, the Visible Infrared Imaging Radiometer Suite (VIIRS) presents an alternate NDVI source, with daily collection, similar band passes, and moderate spatial resolution. This study provides a statistical comparison between EROS expedited VIIRS (eVIIRS) 375-m and eMODIS 250-m and tests the suitability of replacing MODIS NDVI with VIIRS NDVI for drought monitoring and vegetation anomaly detection. For continuity with MODIS NDVI, we calculated a geometric mean regression adjustment algorithm using 375-m resolution for an eMODIS-like NDVI (eVIIRS’) eVIIRS’ = 0.9887 × eVIIRS − 0.0398. The resulting statistical comparisons (eVIIRS’ vs. eMODIS NDVI) showed correlations consistently greater than 0.84 throughout the three years studied. The eVIIRS’ VegDRI results characterized similar drought patterns and hotspots to the eMODIS-based VegDRI, with near zero bias.

Original language	English (US)
Article number	1210
Journal	Remote Sensing
Volume	13
Issue number	6
DOIs	https://doi.org/10.3390/rs13061210
State	Published - Mar 2 2021

Keywords

EMODIS
EVIIRS
Geometric mean regression
Moderate resolution imaging spectro-radiometer
Normalized difference vegetation index
Remote sensing satellites
VegDRI
Vegetation drought response index
Visible infrared imaging radiometer suite

ASJC Scopus subject areas

Earth and Planetary Sciences(all)

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10.3390/rs13061210

Cite this

@article{ce01d5cf4ec54c0385ed91bf09e55680,

title = "Exploring viirs continuity with modis in an expedited capability for monitoring drought-related vegetation conditions",

abstract = "Vegetation has been effectively monitored using remote sensing time-series vegetation index (VI) data for several decades. Drought monitoring has been a common application with algorithms tuned to capturing anomalous temporal and spatial vegetation patterns. Drought stress models, such as the Vegetation Drought Response Index (VegDRI), often use VIs like the Normalized Difference Vegetation Index (NDVI). The EROS expedited Moderate Resolution Imaging Spectrora-diometer (eMODIS)-based, 7-day NDVI composites are integral to the VegDRI. As MODIS satellite platforms (Terra and Aqua) approach mission end, the Visible Infrared Imaging Radiometer Suite (VIIRS) presents an alternate NDVI source, with daily collection, similar band passes, and moderate spatial resolution. This study provides a statistical comparison between EROS expedited VIIRS (eVIIRS) 375-m and eMODIS 250-m and tests the suitability of replacing MODIS NDVI with VIIRS NDVI for drought monitoring and vegetation anomaly detection. For continuity with MODIS NDVI, we calculated a geometric mean regression adjustment algorithm using 375-m resolution for an eMODIS-like NDVI (eVIIRS{\textquoteright}) eVIIRS{\textquoteright} = 0.9887 × eVIIRS − 0.0398. The resulting statistical comparisons (eVIIRS{\textquoteright} vs. eMODIS NDVI) showed correlations consistently greater than 0.84 throughout the three years studied. The eVIIRS{\textquoteright} VegDRI results characterized similar drought patterns and hotspots to the eMODIS-based VegDRI, with near zero bias.",

keywords = "EMODIS, EVIIRS, Geometric mean regression, Moderate resolution imaging spectro-radiometer, Normalized difference vegetation index, Remote sensing satellites, VegDRI, Vegetation drought response index, Visible infrared imaging radiometer suite",

author = "Benedict, {Trenton D.} and Brown, {Jesslyn F.} and Boyte, {Stephen P.} and Howard, {Daniel M.} and Fuchs, {Brian A.} and Wardlow, {Brian D.} and Tsegaye Tadesse and Evenson, {Kirk A.}",

note = "Funding Information: This research was funded by the U. S. Geological Survey National Land Imaging Program. Support for T. Benedict, J. Brown, S. Boyte, K. Evenson, and D. Howard was provided by funding from the U.S. Department of the Interior, U. S. Geological Survey, National Land Imaging Program under the Core Science Systems Mission Area. The authors also wish to thank M. Rigge for his review, and the reviewers of the manuscript for their helpful suggestions. Any use of trade, firm, or product names is for descriptive purposes only and does not imply endorsement by the U.S. Government. Publisher Copyright: {\textcopyright} 2021 by the authors. Licensee MDPI, Basel, Switzerland.",

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month = mar,

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doi = "10.3390/rs13061210",

language = "English (US)",

volume = "13",

journal = "Remote Sensing",

issn = "2072-4292",

publisher = "Multidisciplinary Digital Publishing Institute (MDPI)",

number = "6",

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T1 - Exploring viirs continuity with modis in an expedited capability for monitoring drought-related vegetation conditions

AU - Benedict, Trenton D.

AU - Brown, Jesslyn F.

AU - Boyte, Stephen P.

AU - Howard, Daniel M.

AU - Fuchs, Brian A.

AU - Wardlow, Brian D.

AU - Tadesse, Tsegaye

AU - Evenson, Kirk A.

N1 - Funding Information: This research was funded by the U. S. Geological Survey National Land Imaging Program. Support for T. Benedict, J. Brown, S. Boyte, K. Evenson, and D. Howard was provided by funding from the U.S. Department of the Interior, U. S. Geological Survey, National Land Imaging Program under the Core Science Systems Mission Area. The authors also wish to thank M. Rigge for his review, and the reviewers of the manuscript for their helpful suggestions. Any use of trade, firm, or product names is for descriptive purposes only and does not imply endorsement by the U.S. Government. Publisher Copyright: © 2021 by the authors. Licensee MDPI, Basel, Switzerland.

PY - 2021/3/2

Y1 - 2021/3/2

N2 - Vegetation has been effectively monitored using remote sensing time-series vegetation index (VI) data for several decades. Drought monitoring has been a common application with algorithms tuned to capturing anomalous temporal and spatial vegetation patterns. Drought stress models, such as the Vegetation Drought Response Index (VegDRI), often use VIs like the Normalized Difference Vegetation Index (NDVI). The EROS expedited Moderate Resolution Imaging Spectrora-diometer (eMODIS)-based, 7-day NDVI composites are integral to the VegDRI. As MODIS satellite platforms (Terra and Aqua) approach mission end, the Visible Infrared Imaging Radiometer Suite (VIIRS) presents an alternate NDVI source, with daily collection, similar band passes, and moderate spatial resolution. This study provides a statistical comparison between EROS expedited VIIRS (eVIIRS) 375-m and eMODIS 250-m and tests the suitability of replacing MODIS NDVI with VIIRS NDVI for drought monitoring and vegetation anomaly detection. For continuity with MODIS NDVI, we calculated a geometric mean regression adjustment algorithm using 375-m resolution for an eMODIS-like NDVI (eVIIRS’) eVIIRS’ = 0.9887 × eVIIRS − 0.0398. The resulting statistical comparisons (eVIIRS’ vs. eMODIS NDVI) showed correlations consistently greater than 0.84 throughout the three years studied. The eVIIRS’ VegDRI results characterized similar drought patterns and hotspots to the eMODIS-based VegDRI, with near zero bias.

AB - Vegetation has been effectively monitored using remote sensing time-series vegetation index (VI) data for several decades. Drought monitoring has been a common application with algorithms tuned to capturing anomalous temporal and spatial vegetation patterns. Drought stress models, such as the Vegetation Drought Response Index (VegDRI), often use VIs like the Normalized Difference Vegetation Index (NDVI). The EROS expedited Moderate Resolution Imaging Spectrora-diometer (eMODIS)-based, 7-day NDVI composites are integral to the VegDRI. As MODIS satellite platforms (Terra and Aqua) approach mission end, the Visible Infrared Imaging Radiometer Suite (VIIRS) presents an alternate NDVI source, with daily collection, similar band passes, and moderate spatial resolution. This study provides a statistical comparison between EROS expedited VIIRS (eVIIRS) 375-m and eMODIS 250-m and tests the suitability of replacing MODIS NDVI with VIIRS NDVI for drought monitoring and vegetation anomaly detection. For continuity with MODIS NDVI, we calculated a geometric mean regression adjustment algorithm using 375-m resolution for an eMODIS-like NDVI (eVIIRS’) eVIIRS’ = 0.9887 × eVIIRS − 0.0398. The resulting statistical comparisons (eVIIRS’ vs. eMODIS NDVI) showed correlations consistently greater than 0.84 throughout the three years studied. The eVIIRS’ VegDRI results characterized similar drought patterns and hotspots to the eMODIS-based VegDRI, with near zero bias.

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KW - EVIIRS

KW - Geometric mean regression

KW - Moderate resolution imaging spectro-radiometer

KW - Normalized difference vegetation index

KW - Remote sensing satellites

KW - VegDRI

KW - Vegetation drought response index

KW - Visible infrared imaging radiometer suite

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DO - 10.3390/rs13061210

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JO - Remote Sensing

JF - Remote Sensing

IS - 6

M1 - 1210

ER -

Exploring viirs continuity with modis in an expedited capability for monitoring drought-related vegetation conditions

Abstract

Keywords

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