The Universal Normalized Vegetation Index (UNVI) is an improved Vegetation Index (VI) based on the Universal Pattern Decomposition Method (UPDM). However

UPDM-based UNVI involves the calculation of a complex coefficient matrix

which is inconvenient for users. We reformulated the computation of the coefficients of the UPDM without changing its main mathematical formulation to generalize the UNVI in a user friendly manner. We derived new matrices and developed a UNVI software using IDL to facilitate the convenient calculation of the UNVI based on data from MODIS and Lands at TM

ETM

and OLI satellite sensors. Vegetation information derived from satellite data is highly significant to the operational monitoring of the Earth’s land cover. VIs are determined traditionally by calculating directly the algebraic combinations of the reflectance at different bands

that is

from the visible to the SWIR spectral range. These VIs (e.g.

NDVI and EVI) were calculated by limited reflectance bands and might cause loss of information. All available data are considered as input variables in UPDM-based UNVI in the calculation of VIs. We provided the code and the coefficient matrices in this study to make UNVI usable for all users and multiple sensors. For the UPDM-based UNVI

the spectrum of each pixel is expressed as the linear sum of three fixed standard spectral patterns (i.e.

water

vegetation

and soil)

along with a supplementary one (i.e.

yellow leaves)

associated with particular objects found on land. The goal of UPDM is to transform the reflectance values of the n bands of a target pixel into three standard coefficients

along with a supplementary one

using standard spectral decomposition patterns. We derived the matrices to facilitate the convenient calculation of the UNVI based on data from the MODIS and Lands at TM

ETM

and OLI satellite sensors. We also provided the software and coefficient matrices of UNVI. We assessed the capabilities of the UNVI to evaluate the Gross Primary Production (GPP) of vegetation compared with the GPP data derived from the flux tower sites. The GPP estimated by UNVI used in this model was GPP∝PAR×VI×VI. The GPP estimated by UNVI has a higher correlation with the GPP obtained from the flux sites. The

R

2

between the GPP from the flux sites and that estimated by UNVI is above 0.79 for the mixed forest and deciduous broad-leaved forest vegetation types. This result is consistent with the strong correlation between UNVI and vegetation physicochemical parameters. Thus

UNVI could be applied in estimating vegetation GPP. In this study

we reformulated the computation of the coefficients of UPDM without changing its main mathematical formulation and provided the index

which was termed UNVI. We also derived new matrices to facilitate the convenient calculation of the UNVI based on data from MODIS and the Landsat-TM

ETM

and OLI satellite sensors. The UNVI could be obtained directly by multiplying the coefficient matrix M and the surface reflectance

which would result in a user friendly computation of UNVI. We developed a software using IDL to facilitate the calculation of the UNVI from different remote sensing images. We applied UNVI in the GPP estimation to introduce the operation of the UNVI software. The results show that the UNVI-based GPP estimation has a high correlation with the GPP obtained from the flux sites

with coefficient

R

2

above 0.79. Thus

UNVI can be used for vegetation monitoring. The UNVI software provides the important technical support for studies and applications for the remote sensing inversion of vegetation physicochemical parameters and estimation of vegetation GPP.