Evaluation of remote sensing based terrestrial productivity from MODIS using regional tower eddy flux network observations

By F. Heinsch, Maosheng Zhao, S. Running, J. Kimball, R. Nemani, Kenneth J. Davis, P. Bolstad, Bruce Douglas Cook, A. Desai, D. Ricciuto and 16 more. IEEE Transactions on Geoscience and Remote Sensing, 2006

701 citations recorded by the source corpus.

Subfields: Ecology, Global and Planetary Change

View Paper: https://www.semanticscholar.org/paper/fdb3636a3fe45a0823f7569785d72839d5a1e7c7

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In Ecology

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Citations in the corpus

Shifting plant phenology in response to global change. (2007, methodology): “For instance, comparison of the timing of ecosystem production as measured by the satellite MODIS sensor versus ground-based eddy-covariance metrics from a network of North America sites (AmeriFlux), showed generally good correlations [ 81 ].”
‘Breathing’ of the terrestrial biosphere: lessons learned from a global network of carbon dioxide flux measurement systems (2008, methodology): “In addition, this step was required to make the networks a tool for validating estimates of terrestrial carbon exchange derived from the MODIS sensor on the TERRA and AQUA satellites (Running et al. 1999; Sims et al. 2005; Heinsch et al. 2006).”
Assessing the accuracy of blending Landsat–MODIS surface reflectances in two landscapes with contrasting spatial and temporal dynamics: A framework for algorithm selection (2013, methodology): “To overcome this constraint, many monitoring systems combine: (i) remotely sensed data with dynamic physically-based models (model–data fusion; e.g., Heinsch et al., 2006; Renzullo et al., 2008; van Dijk & Renzullo, 2011); or (ii) two or more remotely sensed data sources with complementary data…”
Deriving a light use efficiency model from eddy covariance flux data for predicting daily gross primary production across biomes (2007, methodology): “The GPP algorithm was tested with flux datasets from a range of biomes (Heinsch et al., 2006).”
Relationship Between Remotely-sensed Vegetation Indices, Canopy Attributes and Plant Physiological Processes: What Vegetation Indices Can and Cannot Tell Us About the Landscape (2008, background): “These models can be validated and improved by combining them with flux tower data [62-64].”
Testing LiDAR models of fractional cover across multiple forest ecozones (2009, background): “Despite their use for pixel validation, the problems associated with local radiation measurements are two-fold: 1) they are time consuming and difficult to obtain over large spatial areas representative of remote sensing pixels; and 2) it is often expensive to obtain measurements several times throu”
Climate change mitigation: a spatial analysis of global land suitability for Clean Development Mechanism afforestation and reforestation (2008, methodology): “Heinsch et al. (2005) have shown good correlation (r = 0.”
Linking chlorophyll a fluorescence to photosynthesis for remote sensing applications: mechanisms and challenges. (2014, methodology): “The question of whether SIF is a measure for LUE is difficult to answer.”