Abstract

Land surface reflectance measurements from the VEGETATION program (SPOT-4, SPOT-5 and PROBA-V satellites) have led to the acquisition of consistent time-series of Normalized Difference Vegetation Index (NDVI) at a global scale. The wide imaging swath (>2000 km) of the family of VEGETATION space-borne sensors ensures a daily coverage of the Earth at the expense of a varying observation and illumination geometries between successive orbit overpasses for a given target located on the ground. Such angular variations infer saw-like patterns on time-series of reflectance and NDVI. The presence of directional effects is not a real issue provided that they can be properly removed, which supposes an appropriate BRDF (Bidirectional Reflectance Distribution Function) sampling as offered by the VEGETATION program. An anisotropy correction supports a better analysis of the temporal shapes and spatial patterns of land surface reflectance values and vegetation indices such as NDVI. Herein we describe a BRDF correction methodology, for the purpose of the Copernicus Global Land Service framework, which includes notably an adaptive data accumulation window and provides uncertainties associated with the NDVI computed with normalized reflectance. Assessing the general performance of the methodology in comparing time-series between normalized and directional NDVI reveals a significant removal of the high-frequency noise due to directional effects. The proposed methodology is computationally efficient to operate at a global scale to deliver a BRDF-corrected NDVI product based on long-term Time-Series of VEGETATION sensor and its follow-on with the Copernicus Sentinel-3 satellite constellation.

Highlights

  • The normalized difference vegetation index (NDVI) is the most widespread vegetation index [1,2]

  • A notable example of directional effects on MVC NDVI time-series is a site located near Bukedea, Uganda

  • Despite the viewing angle constraints implemented in the synthesis of MVC NDVI product, directional effects on the 2019 NDVI time-series can be identified as saw-like patterns

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Summary

Introduction

The normalized difference vegetation index (NDVI) is the most widespread vegetation index [1,2]. It is defined as the difference—normalized by the sum—between the absorption of radiation in the RED spectral region, mainly caused by chlorophyll pigments, and the reflectance in the NIR (near-infrared) spectral region as a result of canopy structure. Since soil and snow reflectance spectral values do not show a clear spectral difference between these bands [3], NDVI was deemed relevant to highlight and monitor vegetated surfaces. NDVI shows a significant relationship with fractional vegetation cover and leaf area index (LAI) [4].

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