# Correction of Directional Effects in VEGETATION NDVI Time-Series
## 6 Conclusions  
An implementation of a methodology aimed to remove directional effects from the long-term VEGETATION NDVI Time-Series has been presented here with a detailed description of the core processing stages of BRDF correction and computation of a BRDF-adjusted NDVI. In addition, VEGETATION data for subregions in Eastern Africa, Western Europe and Northern India have been used to evaluate the expected BRDF correction performance on NDVI Time-Series. For each case, the impact of the BRDF correction on NDVI Time-Series is evaluated in taking the reduction of statistical noise as criterion. These examples show that an overall noise reduction of \(\sim 30\%\) in NDVI Time-Series is achieved after applying the BRDF correction. This estimate is consistent with previous works [40; 43] that used the noise reduction in NDVI (or reflectance) time-series to assess the performance of the BRDF correction. Thus, the noise reduction in the processed BRDF-adjusted NDVI time-series suggests that the high-frequency noise component identified in MVC NDVI time-series is likely associated with directional effects. Moreover, it can be significantly diminished by the BRDF correction described here.  
Since the methodology presented can successfully mitigate directional effects introduced by the very large swaths of the VEGETATION series of sensors and is computationally efficient, it was adapted to process the whole VEGETATION NDVI data archive (March 1998-June 2020) at a global scale. A full validation of this BRDF-adjusted NDVI dataset will be presented in a forthcoming article based on the Copernicus Global Land Service validation report of the methodology here presented [39]. The archival value of the long-term NDVI Time-Series dataset after BRDF correction allows a seamless extension of the ongoing vegetation activity monitoring by the Copernicus Sentinel 3 satellite constellation.  
[PERSON] and [PERSON] developed the overall methodology. [PERSON] conducted the prototype development and analysis. [PERSON] and [PERSON] prepared the manuscript. [PERSON] and [PERSON] implemented and verified the atmospheric correction. [PERSON] and [PERSON] built the operational chain to perform the atmospheric correction. [PERSON] was the Copernicus Global Land Service manager within this project. [PERSON] contributed with the derivation of the spectral correction coefficients. [PERSON] performed checks during the tuning phase of the algorithm. All authors reviewed this manuscript and provided comments and suggestions. All authors have read and agreed to the published version of the manuscript.  
The research leading to the current version of the product has received funding from various European Commission Research and Technical Development programs. The product is based on PROBA-V data provided by Belgian Science Policy Office (BELSPO) and distributed by VITO. The authors would like to thank the Belgian Science Policy Office(BelSPO) for their continuous commitment and financial support of the VEGETATION program. This product was generated by the Global component of the Land Service of Copernicus, the Earth Observation program of the European Commission.  
We acknowledge the three anonymous referees for a careful reading of the manuscript and comments that improved this article. [PERSON] would like to thank the Mission Exploitation Platform (MEP--[[https://proba-v-mep.esa.int/](https://proba-v-mep.esa.int/)]([https://proba-v-mep.esa.int/](https://proba-v-mep.esa.int/)), accessed on 15 March 2021) team--specially to [PERSON], [PERSON] and [PERSON]--for providing valuable technical support during the prototype development phase. NumPy [46], SciPy [47] and Matplotlib [48] have been used in the prototyping and development of this processor.  
The authors declare no conflict of interest.