Abstract
Abstract. Data from the DLR Earth Sensing Imaging Spectrometer (DESIS), mounted on the International Space Station (ISS), were used to develop and test algorithms for remotely retrieving ecosystem productivity. Twenty DESIS images were used from three widely separated forested study sites representing deciduous and conifer forests. Gross primary production (GPP) values from eddy covariance flux towers at the sites were matched with DESIS spectral reflectances collected on the same days. Multiple algorithms were successful relating spectral reflectance with GPP, including: spectral vegetation indices (SVI) sensitive to chlorophyll content, SVI used in a photosynthetic light-use efficiency model framework, spectral shape characteristics through spectral derivatives and absorption feature analysis, and statistical models leading to multiband hyperspectral indices from partial least squares regression. Successful algorithms were able to achieve R2 better than 0.7 using a diverse set of observations combining data from different sites from multiple years and at multiple times during the year. The demonstrated robustness of the algorithms provides some confidence in using DESIS imagery to map spatial patterns of GPP.
Highlights
Forests provide important ecological and economic services including climate change mitigation by removing CO2 from the atmosphere through photosynthesis and storing carbon in biomass and soils
We evaluated multiple approaches testing how well various values calculated from the DLR Earth Sensing Imaging Spectrometer (DESIS) reflectance were related to the midday Gross primary production (GPP) based on the coefficient of determination (R2) of linear regressions between the variables
Photochemical Reflectance Index (PRI), is able to separate out the variability in Light Use Efficiency (LUE) that determines GPP for those summertime deciduous observations (Fig. 4)
Summary
Forests provide important ecological and economic services including climate change mitigation by removing CO2 from the atmosphere through photosynthesis and storing carbon in biomass and soils. Climate change and anthropogenic activities have significantly affected forest ecosystem function and productivity by imposing novel combinations of multiple stresses which act to alter plant biochemistry and traits. Remote sensing has the potential to provide critical actionable information for forest managers. The physiological condition of leaves is related to the contents of photosynthetic and photoprotective leaf pigments which can be inferred from spectral reflectance and are related to forest productivity. Approaches using the spectral information should be seamlessly applicable at different times of year and across different locations and forest types. Successful approaches must be both sensitive to changes in productivity and insensitive to confounding factors, such as forest type, background reflectance, and shadowing. We wish to test these approaches across multiple sites and at different times of the year and across multiple years
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