vegetation-conversion 1.0.1

Vegetation Conversion Python Utilities

vegetation-conversion

Vegetation Conversion Python Utilities.

This package provides utilities for converting vegetation indices used in PT-JPL workflows.

Gregory H. Halverson (they/them)
[e-mail]
NASA Jet Propulsion Laboratory 329G

Installation

pip install vegetation-conversion

Usage

Import this package as vegetation_conversion:

import vegetation_conversion

1. SAVI_from_NDVI(NDVI)

• Description: Converts NDVI to a SAVI proxy using an empirical linear transformation.
• Scientific basis:
  • SAVI was originally developed to reduce soil background influence in sparse vegetation by introducing a soil adjustment factor, $L$, in: $$ SAVI = \frac{(NIR - Red)(1 + L)}{NIR + Red + L} $$
  • In workflows where only NDVI is available, PT-JPL implementations commonly use a linear NDVI-to-SAVI approximation to maintain compatibility with downstream canopy/energy-partitioning steps.
  • This package preserves the PT-JPL parity mapping: $$ SAVI = 0.45 \cdot NDVI + 0.132 $$
  • This mapping is not a universal physical replacement for the original SAVI expression; it is an empirically tuned conversion used for model continuity.
• Parameters:
  • NDVI: Normalized Difference Vegetation Index as numpy.ndarray or rasters.Raster.
• Returns:
  • SAVI proxy with same container type (numpy.ndarray or rasters.Raster).
• Citation: Huete (1988).

2. fAPAR_from_SAVI(SAVI)

• Description: Estimates the fraction of absorbed photosynthetically active radiation (fAPAR) from SAVI.
• Scientific basis:
  • fAPAR represents the fraction of incoming PAR absorbed by green vegetation canopy and is a key state variable controlling photosynthesis, transpiration, and coupled land-atmosphere fluxes.
  • Empirical remote-sensing studies have shown approximately linear relationships between vegetation indices and canopy radiation absorption metrics over practical operating ranges.
  • This package uses the PT-JPL parity relation: $$ fAPAR = 1.3632 \cdot SAVI - 0.048 $$
  • Output is physically bounded by clipping to $[0, 1]$, where 0 denotes no absorbed PAR and 1 denotes full absorption.
• Parameters:
  • SAVI: Soil-Adjusted Vegetation Index as numpy.ndarray or rasters.Raster.
• Returns:
  • fAPAR in $[0, 1]$ with same container type (numpy.ndarray or rasters.Raster).
• Citation: Fisher et al. (2008).

3. fIPAR_from_NDVI(NDVI)

• Description: Estimates the fraction of intercepted photosynthetically active radiation (fIPAR) from NDVI.
• Scientific basis:
  • fIPAR characterizes canopy interception of incoming PAR and is frequently used in ecosystem productivity and evapotranspiration partitioning frameworks.
  • The PT-JPL-compatible mapping first constrains NDVI to plausible bounds and then applies a linear offset: $$ fIPAR = clip(clip(NDVI, 0, 1) - 0.05, 0, 1) $$
  • The inner clipping limits NDVI to standard normalized range; subtracting 0.05 applies an empirical threshold for low-vegetation/background conditions; the outer clipping enforces physical limits for a fraction.
• Parameters:
  • NDVI: Normalized Difference Vegetation Index as numpy.ndarray or rasters.Raster.
• Returns:
  • fIPAR in $[0, 1]$ with same container type (numpy.ndarray or rasters.Raster).
• Citation: Gower et al. (1999).

References

• Fisher, J. B., et al. (2008). The land surface water and energy budget of the western United States based on MODIS satellite data. Water Resources Research, 44(9), W09422. https://doi.org/10.1029/2007WR006057
• Gower, S. T., Kucharik, C. J., & Norman, J. M. (1999). Direct and indirect estimation of leaf area index, fAPAR, and net primary production of terrestrial ecosystems. Remote Sensing of Environment, 70(1), 29-51. https://doi.org/10.1016/S0034-4257(99)00056-7
• Huete, A. R. (1988). A soil-adjusted vegetation index (SAVI). Remote Sensing of Environment, 25(3), 295-309. https://doi.org/10.1016/0034-4257(88)90106-X