agricultural applications of tempotempo.si.edu/presentations/june2018/26-zoogman tempo 2018.pdf ·...
TRANSCRIPT
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Agricultural Applications of TEMPO
Peter Zoogman
Minerva Schools at KGI
Marena Lin Harvard University
Chris Chan Miller
Harvard-Smithsonian Center for Astrophysics
TEMPO
Science Team Meeting June 7, 2018
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Overview and Opportunities
� Yield gaps requires measures on both the size of the gaps and
underlying causes
� Growing challenges with changes in climate, more frequent extreme events, and increasing background ozone
� These challenges demand better information sources on plant
and crop health to inform best practices
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Broadband Methods Response of ecosystems to climate: Mean growing length derived from MODIS vegetation index (2001-2006)
Ganguly et al. [2010]
This type of information is critical for agricultural decisions (e.g. maximizing yield)
Lobell et al. [2012]
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Importance of temporal resolution
Lin et al. [in prep]
Aggregation in standard MODIS product results in temporal and magnitude errors in vegetation indices
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Importance of temporal resolution
Lin et al. [in prep]
Blue pixels (high observation count) show smooth evolution of ground cover characteristics.
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Importance of temporal resolution
Lin et al. [in prep]
Coloring by year demonstrates ability to compare inter-annual changes. These may be due to human decisions on planting or harvesting and/or environmental conditions.
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TEMPO in context
Modified from Ghulam et al. [2015]
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TEMPO in context
Modified from Ghulam et al. [2015]
NDVI = (NIR – Red) / (NIR + Red)
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TEMPO in context
Modified from Ghulam et al. [2015]
NDVI = (NIR – Red) / (NIR + Red) NBR= (NIR – SWIR) / (NIR + SWIR)
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TEMPO in context
Modified from Ghulam et al. [2015]
NDVI = (NIR – Red) / (NIR + Red) NBR= (NIR – SWIR) / (NIR + SWIR) TEMPO bands see chlorophyll absorption/reflection and significant portion of red edge
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Visible Vegetation Indices NDVI
Visible Atmospherically Resistant Index
NBR
Triangular Greenness Index
Visible indices of vegetation from MODIS (May 27, 2018) display similar spatial distribution to traditional IR indices
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Spectrally resolved indicators
TGI is an approximation of chlorophyll reflectance that we could quantify more precisely with TEMPO à more efficient fertilization
Hunt et al. [2013]
Joiner et al. [2016] Solar Induced Fluorescence measurements can be used for studies primary productivity, carbon uptake, and drought responses
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Observing elevated background ozone
Assimilation of surface measurements does not fully correct bias or spatial pattern Adding TEMPO observations fully corrects bias and captures most of the distribution
[Zoogman et al. 2014]
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Observing Ozone Effects
Ghulam et al. [2015] Fishman et al. [2014]
Ozone damaged crops exhibit significantly modified visible reflectance spectra
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Future Directions � Ongoing activities to utilize existing platforms for agriculture
applications, including both NIR measurements and Vis (e.g. MODIS, GOME-2, TROPOMI)
� Exploit available TEMPO-like data to develop and refine vegetation indices � Synthetic TEMPO data � GEO-TASO
� Investigate power of TEMPO to quantify effect of atmospheric
composition on crops (and the effects of crops on composition!)