# Correction of Directional Effects in VEGETATION NDVI Time-Series
## 4 Results
Unlike, anomalies calculated from MVC NDVI (see the bottom panel in Figure 3), the NDVI anomaly evolution derived from the BRDF-adjusted NDVI dataset does not go through the anomaly regions rapidly. As a result, the bottom panel of Figure 4 shows an anomaly signal with variability timescales, and an overall trend that seems to be consistent with seasonal variations expected for a vegetated site. More details on the spatial and temporal consistency of the NDVI anomalies derived from the presented methodology can be found in Section 6 of its Copernicus Global Land quality assessment report [39]. Figure 3: Time series of NDVI (top panel) and NDVI anomalies (bottom panel) for a pixel near Bukedea (lat = 1.25\({}^{\circ}\), lon = 34.08\({}^{\circ}\)), Uganda. Data is extracted from the CGLS Collection 1 km NDVI Version 2 product. The NDVI temporal evolution for 2019 and long-term (1998–2020) average NDVI profile are shown in the top panel. NDVI anomalies over 2019 are computed via a z-score. Positive (negative) anomalies denote whether an NDVI values is above (below) the long-term NDVI average. The absolute value of the NDVI anomaly indicates how many standard deviations the NDVI value departs from the long-term NDVI average. Colored bands in the bottom panel denote vegetation growth status and are defined as follows: normal([\(-\)1, 1]; white), warning ([\(\pm\)1, \(\pm\)1.5]; yellow), favorable ([1.5, 2]; light green), unfavorable ([\(-\)1.5, \(-\)2]; light red), very favorable (\(>\)2; dark green) and very unfavorable (\(-\)2\(<\); dark red). Gaps in Time-Series occur due to the lack of clear data for that dekad. To go further in the appraisal of the methodology, two additional sites (Belmanip 151, Belmanip 332) were considered to be they show marked NDVI seasonal patterns. MVC and BRDF-adjusted NDVI time-series for a pixel near Belmanip 151 site (Tanzania) are shown in Figure 5. The uncertainties associated with the BRDF-adjusted NDVI are not shown to avoid a cluttered plot. From Figure 5, NDVI time-series from both datasets show an overall agreement in terms of phenological variations with no noticeable lag among time-series. However, it can be noticed that during periods where high NDVI levels are observed, very rapid variations are noticeable on the Belmanip 151 MVC NDVI profile. The impact of directional effects on NDVI time-series is revealed by noise on the datasets [40], which leads to spurious deviations for a timescale less than the characteristic timescale of the vegetation activity. The rapid variability on NDVI time-series inferred by the BRDF effect is even more conspicuous at the end of the growing canopy periods [16] when NDVI reaches a peak. The lack of high-frequency variations in BRDF-adjusted NDVI Time-Series, especially in the summertime, demonstrates that directional effects have been properly removed. MVC NDVI and BRDF-adjusted NDVI time-series for Belmanip 332 site (India) are shown in Figure 6 with the same line and color code as Figure 5. The NDVI time-series of Belmanip 332 site show a strong seasonal component which is characteristic of an area that is cultivated twice a year. From a visual inspection of Figure 6, it can be gleaned that the BRDF-adjusted NDVI dataset does not lag the directional NDVI time-series. The BRDF-adjusted NDVI time-series, from Belmanip 151 and Belmanip 332 sites, follow the overall long-term phenological variations seen in MVC NDVI. From the examples above, it is noticeable that the onset of the growing season in BRDF-adjusted NDVI time-series is affected by the process of data accumulation. Since no significant delay on the NDVI time-series is being introduced by the BRDF adjustment, the agreement between both NDVI datasets, in regard to the temporal evolution, adds confidence on the adaptive window method that is proposed here (see Section 3.2.2). This latter acts for avoiding abrupt temporal  
Figure 4: Same as in Figure 3 but Time-Series are extracted from BRDF-adjusted NDVI datasets.