Preprints
https://doi.org/10.5194/essd-2022-166
https://doi.org/10.5194/essd-2022-166
 
18 Aug 2022
18 Aug 2022
Status: this preprint is currently under review for the journal ESSD.

AnisoVeg: Anisotropy and Nadir-normalized MODIS MAIAC datasets for satellite vegetation studies in South America

Ricardo Dalagnol1,2,3, Lênio Soares Galvão3, Fabien Hubert Wagner1,2, Yhasmin Mendes Moura4,5, Nathan Gonçalves6, Yujie Wang7,8, Alexei Lyapustin7, Yan Yang1, Sassan Saatchi1,2, and Luiz Eduardo Oliveira Cruz Aragão3,9 Ricardo Dalagnol et al.
  • 1Center for Tropical Research, Institute of the Environment and Sustainability, University of California, Los Angeles, Los Angeles, CA 90095, USA
  • 2NASA-Jet Propulsion Laboratory, California Institute of Technology, Pasadena, CA 91109, USA
  • 3Earth Observation and Geoinformatics Division, National Institute for Space Research-INPE, São José dos Campos, SP, 12227-010, Brazil
  • 4Institute of Geography and Geoecology, Karlsruhe Institute of Technology, Karlsruhe, Germany
  • 5Centre for Landscape and Climate Research, School of Geography, Geology, and the Environment, University of Leicester, Leicester, UK
  • 6Michigan State University, Department of Forestry, College of Agriculture & Natural Resources, East Lansing, MI, USA
  • 7NASA Goddard Space Flight Center, Greenbelt, MD, United States
  • 8Joint Center for Earth Systems Technology, University of Maryland Baltimore County, 1000 Hilltop Circle, Baltimore, MD
  • 9Geography, College of Life and Environmental Sciences, University of Exeter, Exeter EX44RJ, UK

Abstract. The AnisoVeg product consists of monthly 1-km composites of anisotropy (ANI) and nadir-normalized (NAD) surface reflectance layers obtained from the Moderate Resolution Imaging Spectroradiometer (MODIS) sensor over the entire South America. The satellite data were pre-processed using the Multi-Angle Implementation Atmospheric Correction (MAIAC). The AnisoVeg product spans 22 years of observations (2000 to 2021) and includes the reflectance of MODIS bands 1 to 8 and two vegetation indices (VIs): Normalized Difference Vegetation Index (NDVI) and Enhanced Vegetation Index (EVI). While the NAD layers reduce the data variability added by bidirectional effects on the reflectance and VI time series, the unique ANI layers allow the use of this multi-angular data variability as a source of information for vegetation studies. The AnisoVeg product has been generated using daily MODIS MAIAC data from both Terra and Aqua satellites, normalized for a fixed solar zenith angle (SZA = 45°), modelled for three sensor view directions (nadir, forward, and backward scattering), and aggregated to monthly composites. The anisotropy was calculated by the subtraction of modelled backward and forward scattering surface reflectance. The release of the ANI data for open usage is novel, as well as the NAD data at an advance processing level. We demonstrate the use of such data for vegetation studies using three types of forests in eastern Amazon with distinct gradients of vegetation structure and aboveground biomass (AGB). The gradient of AGB was positively associated with ANI, while NAD values were related to different canopy structural characteristics. This was further illustrated by the strong and significant relationship between EVIANI and forest height observations from the Global Ecosystem Dynamics Investigation (GEDI) LiDAR sensor considering a simple linear model (R2 = 0.55). Overall, the time series of the AnisoVeg product (NAD and ANI) provide distinct information for various applications aiming at understanding vegetation structure, dynamics, and disturbance patterns. All data, processing codes and results are made publicly available to enable research and the extension of AnisoVeg products for other regions outside the South America. The code can be found at https://doi.org/10.5281/zenodo.6561351 (Dalagnol and Wagner, 2022), EVIANI and EVINAD can be found as assets in the Google Earth Engine (GEE) (described in the data availability section), and the full dataset is available at the open repository <https://doi.org/10.5281/zenodo.3878879> (Dalagnol et al., 2022).

Ricardo Dalagnol et al.

Status: open (until 21 Oct 2022)

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on essd-2022-166', Bruce Nelson, 13 Sep 2022 reply
    • AC1: 'Reply on RC1', Ricardo Dal Agnol da Silva, 22 Sep 2022 reply

Ricardo Dalagnol et al.

Data sets

AnisoVeg: Anisotropy and Nadir-normalized MODIS MAIAC datasets for satellite vegetation studies in South America Ricardo Dalagnol, Lênio Soares Galvão, Fabien Hubert Wagner, Yhasmin Mendes de Moura, Nathan Gonçalves, Yujie Wang, Alexei Lyapustin, Yan Yang, Sassan Saatchi, Luiz Eduardo Oliveira e Cruz de Aragão https://doi.org/10.5281/zenodo.3878879

Model code and software

maiac_processing: Script and functions to process daily MODIS/MAIAC data to BRDF-corrected 16-day and monthly mosaic composites. Ricardo Dalagnol, Fabien Wagner https://github.com/ricds/maiac_processing

Ricardo Dalagnol et al.

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Short summary
The AnisoVeg dataset brings 22-years of monthly satellite data from the Moderate Resolution Imaging Spectroradiometer (MODIS) sensor for South America at 1-km resolution aimed for vegetation applications. It has nadir-normalized data which is the most traditional approach to correct satellite data, but also unique anisotropy data with strong biophysical meaning, explaining 55 % of Amazonian Forest height. We expect this dataset to help large-scale estimates of vegetation biomass and carbon.