Evaluating approaches relating ecosystem productivity with DESIS spectral information
Loading...
Author/Creator ORCID
Date
2022-02-11
Type of Work
Department
Program
Citation of Original Publication
Huemmrich, K. F., Campbell, P. E. K., Harding, D. J., Ranson, K. J., Wynne, R., Thomas, V., and Middleton, E. M.: EVALUATING APPROACHES RELATING ECOSYSTEM PRODUCTIVITY WITH DESIS SPECTRAL INFORMATION, Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLVI-1/W1-2021, 31–37, https://doi.org/10.5194/isprs-archives-XLVI-1-W1-2021-31-2022, 2022.
Rights
This work was written as part of one of the author's official duties as an Employee of the United States Government and is therefore a work of the United States Government. In accordance with 17 U.S.C. 105, no copyright protection is available for such works under U.S. Law.
Public Domain Mark 1.0
Public Domain Mark 1.0
Subjects
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.