TY - JOUR
T1 - Satellite solar-induced chlorophyll fluorescence and near-infrared reflectance capture complementary aspects of dryland vegetation productivity dynamics
AU - Wang, Xian
AU - Biederman, Joel A.
AU - Knowles, John F.
AU - Scott, Russell L.
AU - Turner, Alexander J.
AU - Dannenberg, Matthew P.
AU - Köhler, Philipp
AU - Frankenberg, Christian
AU - Litvak, Marcy E.
AU - Flerchinger, Gerald N.
AU - Law, Beverly E.
AU - Kwon, Hyojung
AU - Reed, Sasha C.
AU - Parton, William J.
AU - Barron-Gafford, Greg A.
AU - Smith, William K.
N1 - Funding Information:
We thank Brady W. Allred for providing constructive feedback on an early version of this manuscript. X.W. acknowledges funding from NASA Future Investigators (grant 80NSSC19K1335 ). W.K.S. acknowledges support from NASA CCS (grant 80NSSC21K1709 ). W.K.S. and M.P.D. acknowledge support from NASA SMAP (grant 80NSSC20K1805 ). W.K.S. and S.C.R. acknowledge support from the US Geological Survey Community for Data Integration (cooperativer agreement G19AC00424 ) and the Department of Defense SERDP (grant RC18-1322 ). W.K.S. and W.J.P acknowledge support from the United States Department of Agriculture CDI (cooperative agreement 58-3050-9-013 and 58-0111-17-013 ). MODIS surface reflectance ( MCD43A4 ) data used here are available via https://lpdaac.usgs.gov/products/mcd43a4v006/ . TROPOMI SIF data used here are available via ftp://fluo.gps.caltech.edu/data/tropomi/ungridded/ . Eddy covariance half-hour observations of the net ecosystem exchange of CO 2 are available via ameriflux.lbl.gov through funding from the U.S. Department of Energy's Office of Science. Vegetation fractional cover data are available via https://rangelands.app/ . Any use of trade, firm, or product names is for descriptive purposes only and does not imply endorsement by the U.S. Government.
Funding Information:
We thank Brady W. Allred for providing constructive feedback on an early version of this manuscript. X.W. acknowledges funding from NASA Future Investigators (grant 80NSSC19K1335). W.K.S. acknowledges support from NASA CCS (grant 80NSSC21K1709). W.K.S. and M.P.D. acknowledge support from NASA SMAP (grant 80NSSC20K1805). W.K.S. and S.C.R. acknowledge support from the US Geological Survey Community for Data Integration (cooperativer agreement G19AC00424) and the Department of Defense SERDP (grant RC18-1322). W.K.S. and W.J.P acknowledge support from the United States Department of Agriculture CDI (cooperative agreement 58-3050-9-013 and 58-0111-17-013). MODIS surface reflectance (MCD43A4) data used here are available via https://lpdaac.usgs.gov/products/mcd43a4v006/. TROPOMI SIF data used here are available via ftp://fluo.gps.caltech.edu/data/tropomi/ungridded/. Eddy covariance half-hour observations of the net ecosystem exchange of CO2 are available via ameriflux.lbl.gov through funding from the U.S. Department of Energy's Office of Science. Vegetation fractional cover data are available via https://rangelands.app/. 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 Elsevier Inc.
PY - 2022/3/1
Y1 - 2022/3/1
N2 - Mounting evidence indicates dryland ecosystems play an important role in driving the interannual variability and trend of the terrestrial carbon sink. Nevertheless, our understanding of the seasonal dynamics of dryland ecosystem carbon uptake through photosynthesis [gross primary productivity (GPP)] remains relatively limited due in part to the limited availability of long-term data and unique challenges associated with satellite remote sensing across dryland ecosystems. Here, we comprehensively evaluated longstanding and emerging satellite vegetation proxies in their ability to capture seasonal dryland GPP dynamics. Specifically, we evaluated: 1) reflectance-based proxies normalized difference vegetation index (NDVI), soil adjusted vegetation index (SAVI), near infrared reflectance index (NIRv), and kernel NDVI (kNDVI) from the MODerate resolution Imaging Spectroradiometer (MODIS); and 2) newly available physiologically-based proxy solar-induced chlorophyll fluorescence (SIF) from the TROPOspheric Monitoring Instrument (TROPOMI). As a performance benchmark, we used GPP estimates from a robust network of 21 western United States eddy covariance tower sites that span representative gradients in dryland ecosystem climate and functional composition. We found that NIRv and SIF were the best performing GPP proxies and captured complementary aspects of seasonal GPP dynamics across dryland ecosystem types. NIRv offered better performance than the other proxies across relatively low-productivity, sparsely non-evergreen vegetated sites (R2 = 0.59 ± 0.13); whereas SIF best captured seasonal dynamics across relatively high-productivity sites, including evergreen-dominated sites (R2 = 0.74 ± 0.07). Notably, across grass-dominated sites, all reflectance-based proxies (NDVI, SAVI, NIRv and kNDVI) showed significant seasonal bias (hysteresis) that strengthened with the total fraction of woody vegetation cover, likely due to seasonal patterns in woody vegetation reflectance that are unrelated to or decoupled from GPP. Future efforts to fully integrate the complementary strengths of NIRv and SIF could significantly improve our understanding and representation of dryland GPP dynamics in satellite-based models.
AB - Mounting evidence indicates dryland ecosystems play an important role in driving the interannual variability and trend of the terrestrial carbon sink. Nevertheless, our understanding of the seasonal dynamics of dryland ecosystem carbon uptake through photosynthesis [gross primary productivity (GPP)] remains relatively limited due in part to the limited availability of long-term data and unique challenges associated with satellite remote sensing across dryland ecosystems. Here, we comprehensively evaluated longstanding and emerging satellite vegetation proxies in their ability to capture seasonal dryland GPP dynamics. Specifically, we evaluated: 1) reflectance-based proxies normalized difference vegetation index (NDVI), soil adjusted vegetation index (SAVI), near infrared reflectance index (NIRv), and kernel NDVI (kNDVI) from the MODerate resolution Imaging Spectroradiometer (MODIS); and 2) newly available physiologically-based proxy solar-induced chlorophyll fluorescence (SIF) from the TROPOspheric Monitoring Instrument (TROPOMI). As a performance benchmark, we used GPP estimates from a robust network of 21 western United States eddy covariance tower sites that span representative gradients in dryland ecosystem climate and functional composition. We found that NIRv and SIF were the best performing GPP proxies and captured complementary aspects of seasonal GPP dynamics across dryland ecosystem types. NIRv offered better performance than the other proxies across relatively low-productivity, sparsely non-evergreen vegetated sites (R2 = 0.59 ± 0.13); whereas SIF best captured seasonal dynamics across relatively high-productivity sites, including evergreen-dominated sites (R2 = 0.74 ± 0.07). Notably, across grass-dominated sites, all reflectance-based proxies (NDVI, SAVI, NIRv and kNDVI) showed significant seasonal bias (hysteresis) that strengthened with the total fraction of woody vegetation cover, likely due to seasonal patterns in woody vegetation reflectance that are unrelated to or decoupled from GPP. Future efforts to fully integrate the complementary strengths of NIRv and SIF could significantly improve our understanding and representation of dryland GPP dynamics in satellite-based models.
KW - Dryland heterogeneity
KW - Gross primary productivity
KW - Near-infrared reflectance
KW - Remote sensing
KW - Solar-induced fluorescence
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U2 - 10.1016/j.rse.2021.112858
DO - 10.1016/j.rse.2021.112858
M3 - Article
AN - SCOPUS:85121963201
SN - 0034-4257
VL - 270
JO - Remote Sensing of Environment
JF - Remote Sensing of Environment
M1 - 112858
ER -