openEO

Calculate various biophysical parameters

Biophysical Parameters

Calculate various biophysical parameters for an area defined by a polygon. The result is a raster file containing the parameter values. A strict CloudMask is applied to avoid cloud contamination.                     The Leaf Area Index (LAI) is half the total area of a canopy’s green elements per unit of horizontal ground area. The satellite-derived value corresponds to the total green LAI of all the canopy layers, including the understory, which may represent a very significant contribution, particularly for forests. The LAI distributed by Terrascope is also known as GAI, which stands for Green Area Index and is related to the green part of the vegetation only (i.e. not only the leaves but excluding the non-green parts).

The Fraction of Absorbed Photosynthetic Active Radiation (fAPAR) quantifies the fraction of solar radiation absorbed by leaves for photosynthetic activity. It depends on the canopy structure, vegetation element optical properties, atmospheric conditions, and angular configuration.

The Fraction of Vegetation Coverage (fCOVER) corresponds to the fraction of ground covered by green vegetation. Practically, it quantifies the vegetation’s spatial extent.

The Canopy Water Content (CWC) is the water mass per unit ground area and is a key vegetation indicator in agriculture and forestry applications.

The Canopy Chlorophyll Content (CCC) is the total chlorophyll content per unit ground area in a contiguous group of plants. It is well suited for quantifying canopy level nitrogen content and gross primary production estimation.

Methodology

The methodology used to derive the biophysical parameters from Sentinel-2 is developed by INRA- EMMAH. The methodology was initially developed to generate biophysical products from SPOT- VEGETATION, ENVISAT-MERIS, SPOT-HRVIR, and LANDSAT-OLI sensors were later adapted for Sentinel-2. It mainly simulates a comprehensive database of canopy (TOC) reflectances based on observation of vegetation characteristics and illumination geometry. Neural networks are then trained to estimate a number of these canopy characteristics (BIOPARs) from the simulated TOC reflectances, along with set corresponding angles defining the observational configuration.

Quality

[RD1] reports RMSE values of 0.89 for LAI, 0.05 for FAPAR, 0.4 for FCOVER, 56 µg/cm2 for CCC and 0.03 g/cm2 for CWC, which demonstrate a good performance of the network. FAPAR and FCOVER show the best performance, with higher RMSE values for mid-range values of the product. LAI is well estimated to be up to values of LAI=6, and increasing uncertainties with LAI, and thus CCC and CWC are observed because of their dependency on LAI. Furthermore, the networks are unbiased between the BIOPAR variables, as expected.

Execution information

ParameterTypeDefault

spatial_extent (required)

Limits the data to process to the specified bounding box or polygons.\n\nFor raster data, the process loads the pixel into the data cube if the point at the pixel center intersects with the bounding box or any of the polygons (as defined in the Simple Features standard by the OGC).\nFor vector data, the process loads the geometry into the data cube if the geometry is fully within the bounding box or any of the polygons (as defined in the Simple Features standard by the OGC). Empty geometries may only be in the data cube if no spatial extent has been provided.\n\nEmpty geometries are ignored.\nSet this parameter to null to set no limit for the spatial extent.

temporal_extent (required)

Temporal extent specified as two-element array with start and end date/date-time.

array

biopar_type

BIOPAR type [FAPAR,LAI,FCOVER,CCC,CWC]

string
FAPAR

Contact

  • Jeroen Dries

    VITO

    Researcher

    principal investigator

  • VITO

    processor