Data and code

1. Soil and biomass carbon stocks of Canada

Download here Forest carbon stock and uncertainty maps of Canada (kg/m², Years= 2015-2019) and Soil carbon stock and uncertainty maps of Canada (kg/m² -0-30cm and 0-1m soil depth). Reference “Sothe, C., Gonsamo, A., Arabian, J., Kurz, W. A., Finkelstein, S. A., & Snider, J. (2022). Large soil carbon storage in terrestrial ecosystems of Canada. Global Biogeochemical Cycles, 36(2), e2021GB007213.” Download here soil carbon concentration (g/kg) in Canada at 6 soil depths: 0cm, 5cm, 15cm, 30cm, 60cm and 100cm. Reference Sothe, C., Gonsamo, A., Arabian, J., & Snider, J. (2022). Large scale mapping of soil organic carbon concentration with 3D machine learning and satellite observations. Geoderma, 405, 115402.

2. Circumpolar vegetation dynamics (CVD) product

Phenology index (PI)  based vegetation dynamics product, comprising start (SOS), end (EOS), length of growing season (LOS), and growing season integrated annual normalized difference vegetation index (NDVI), specifically designed for the entire circumpolar north (> 45^oN) using SPOT VGT data starting from 1999.
“Gonsamo A. & J.M. Chen (2015).  Circumpolar vegetation dynamics product for global change study. Remote Sensing of Environment, 182, 13–26.” Download the CVD product

3. 250m gross and net primary productivity of Canada’s landmass

Canada’s gross primary productivity (GPP) and net primary productivity (NPP) simulated using boreal ecosystem productivity simulator (BEPS) at 250 m spatial resolution with improved input parameter and driver fields and phenology and nutrient release parameterization schemes. The daily GPP and NPP are simulated over Canada at 250 m spatial resolution, the highest resolution simulation yet for the country or any other comparable region. Total NPP (GPP) for Canada’s land area was 1.27 (2.68) Pg C for 2008, with forests contributing 1.02 (2.2) Pg C.
“Gonsamo, A., J.M. Chen, D.T. Price, W.A. Kurz, J. Liu, C. Boisvenue, R.A. Hember, C. Wu, K.-h. Chang (2013). Improved assessment of gross and net primary productivity of Canada’s landmass. Journal of Geophysical Research Biogeosciences, 118, 1546–1560.” Download link

4. Every 10-day 250m leaf area index of Canada’s landmass

The University of Toronto (UofT) LAI system was improved (v2) including enhanced spatial resolution (250 m) by considering an improved land cover map, local topography, clumping index, and background reflectance variations in order to produce canopy LAI time series. The 10-day data for entire Canada and the ENVI header files can be downloaded here (see below). “Gonsamo, A., J.M. Chen (2014). Improved LAI algorithm implementation to MODIS data by incorporating background, topography, and foliage clumping information. IEEE Transactions on Geoscience and Remote Sensing, 52, pp. 1076–1088.”
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5. Continuous observation of leaf area index at Fluxnet-Canada sites

Continuous observation of leaf area index (LAI) is needed in order to interpret and model carbon, water and energy fluxes measured at Fluxnet tower sites. Although remote sensing LAI products can be used in regional and global scale modelling with reasonable performance, the site level modelling of ecophysiological processes needs more accurate LAI time series than those provided by global LAI products.  Here we apply a semi-empirical approach using satellite measured modified soil-adjusted vegetation index (MSAVI) and sparsely sampled LAI time series measurements at 7 Canadian Carbon Program (CCP) flux tower sites to produce continuous observations of site level LAI. The LAI time series is for 2000-present. “Gonsamo A, Chen JM (2014). Continuous observation of leaf area index at Fluxnet-Canada sites. Agricultural and Forest Meteorology 189: 168-174.”
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6. Spectral Response Cross Calibration Equations for 21 Satellite Sensors

Global and regional vegetation assessment strategies often rely on the combined use of multisensor satellite data. Variations in spectral response function (SRF) which characterizes the sensitivity of each spectral band have been recognized as one of the most important sources of uncertainty for the use of multisensor data. Here, we provide the SRF cross calibration equations for 21 Earth observation satellite sensors and their cross-sensor corrections for red, near infrared (NIR), and shortwave infrared (SWIR) reflectances, and normalized difference vegetation index (NDVI) aimed at global vegetation monitoring.  The training data set to derive the SRF cross-sensor correction coefficients were generated from the state-of-the-art radiative transfer models.  Our approach includes a polynomial regression and spectral curve information generated from a training data set representing a wide dynamics of vegetation distributions to minimize land cover specific SRF cross-sensor correction coefficient variations. Variations in processing strategies, non spectral differences, and algorithm preferences among sensor systems and data streams hinder cross-sensor spectra and NDVI comparability and continuity. The SRF cross-sensor correction approach provided here, however, can be used for studies aiming at large-scale vegetation monitoring with acceptable accuracy. “Gonsamo A., and J.M. Chen (2013).  Spectral response function comparability among 21 satellite sensors for vegetation monitoring. IEEE Transactions on Geoscience and Remote Sensing, 51, 1319-1335.”
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Copyright (C) “1982-2014 by Jean-Michel Walter”
Contributor: Alemu Gonsamo

CIMES is distributed under the terms of the GNU General Public License.

Citation: Gonsamo A, Walter J-M and Pellikka P (2011) CIMES: A package of programs for determining canopy geometry and solar radiation regimes through hemispherical photographs. Computers and Electronics in Agriculture 79, 207-215.

Click here for license file

CIMES-FISHEYE IS ALSO HOSTED HERE

Source codes will be provided upon request (see below for email addresses)