**4. Discussion**

#### *4.1. Trends in EVI and the Hydrological Variables*

Some of the observed trends in vegetation greenness in this study are consistent with those reported by more recent regional and continental studies. For example, the positive trend in vegetation greenness observed in most parts of the Sahel has also been reported in recent studies by Leroux et al. [37], which examined vegetation changes in the Sahel between 2000 and 2015, and by Ugbaje et al. [12], which investigated the variability of vegetation productivity across Africa between 2000 and 2014. Additionally, the hotspot of declining vegetation greenness observed in southwestern Niger is consistent with findings from many studies (e.g., [37,38]). This hotspot has been dubbed "a Sahelian exception" because it stands in contrast to the dominant Sahelian greening [37]. Likewise, the negative trend in greenness over Zambia is in line with the reported decline in vegetation productivity by Ugbaje et al. [12] for the period between 2000 and 2014. Similarly, the clusters of a negative trend in vegetation greenness in parts of Somalia, Kenya, and Tanzania correspond to those reported by [39]

over the period 2000 to 2010. This indicates that vegetation activity in this region has continued to decline even after 2010, up to 2015, as reported here.

Similar to the observed vegetation greenness trends, some of the trajectories of hydrological variables are in line with those of other studies. For instance, the positive trend in soil moisture and precipitation in southern Chad and South Sudan was also reported in a study by Huber et al. [9] which analyzed vegetation greenness dynamics in relation to water availability in the Sahel over the period of 1982 to 2007. Similarly, the general positive trend in soil moisture in southern Africa is in agreemen<sup>t</sup> with the improvement in soil water content for the period of 1993 to 2012, observed by Wei et al. [40]. Thus, our results indicate that this positive trend in soil moisture and precipitation persisted up to 2015. Also, the observed decline in precipitation and TWSA in parts of the Congo basin has also been reported by Zhou et al. [41] for the period covering 2000 to 2012.

However, the results here show a few notable areas of di fferences in trend directions from what is reported by other studies. For example, in Ugbaje et al. [12], vegetation productivity in Angola was largely stable, which is in contrast with a dominant positive trend in vegetation greenness observed in this study (Figure 2). Similarly, contrary to Zhou et al. [41] who observed a corresponding decrease in EVI following a decline in water availability in the Congo, here, EVI for most parts of the basin was either stable or showed a positive trend (Figure 2). This may be due to improvement in non-water related constraints like decreasing cloud cover and the concomitant increase in solar radiation [42]. Overall, the di fferences in the trajectories of vegetation dynamics between these studies and those in this study may be linked to the contrast in the time interval considered for the trend analyses.

#### *4.2. Spatiotemporal Variation of Vegetation Greenness in Relation to Water Availability*

Unlike many studies that assessed the relationship of vegetation greenness and water availability using one or more hydrological variables from remote sensing (e.g., [3,6,43]), this is the first study, to the best of our knowledge, that has analyzed vegetation greenness dynamics in relation to precipitation, soil moisture, and TWSA within a multivariate framework. The RF modeling results of water availability were generally strongly coupled to vegetation greenness when the cyclic seasonal variation in the analyzed time series is not removed. This is generally expected for Africa as the vegetation phenological cycle is primarily driven by the wet–dry season cycle. However, the moderate relationship observed in the semi-arid areas like in the Horn of Africa and the northern Sahel can be explained by their erratic precipitation regime.

On the flip side, when the monthly means were removed from the time series, the strong relationships between vegetation greenness and water availability was not as widespread across Africa. Most notably, the observed relationship was weak in the savanna belt stretching from the West coast of Africa to the east coast of Eretria and Djibouti. This result implies that the predominantly positive trend in greenness anomalies observed in the region may be driven by factors other than water availability. Such factors may include atmospheric fertilization, agricultural intensification including the use of improved and high yielding crop cultivars, a fforestation, and a positive trend in the growing season length [44–46]. However, in eastern and southern Africa, south of Zambia, vegetation greenness anomaly dynamics was more linked to anomalies in water availability compared to the relationship observed in West Africa across to the east coast of Eretria and Djibouti. The relatively high proportion of grass cover in most parts of eastern and southern Africa (see Figure 1) may, in part, explain this di fference in regional response to water availability. Because of their shallow rooting system, grasses are generally more sensitive to fluctuations in near-surface soil moisture availability than most woody plants [47], as can be seen from the relatively strong relationship obtained for the grassland cover type (Figure 4B). Nonetheless, the sensitivity of grassland linked to moisture anomalies is also a reflection of the increasing frequency and severity of water-related extreme events such as drought and flooding in eastern and southern Africa [48,49]. Therefore, the dynamics in water availability related to anomalies in one or more of the hydrological variables (soil moisture, TWSA, and rainfall) was to a large extent the principal driver of trends in vegetation greenness anomalies observed in eastern and southern Africa.

#### *4.3. The Relative Roles of Precipitation, Soil Moisture and TWSA in Driving Vegetation Greenness*

The dominance of soil moisture as the first ranked predictor of vegetation greenness dynamics in substantial parts of Africa from the two RF models (original or monthly anomalies data) (Figure 5) can be attributed to soil moisture being a better integral of the e ffects of topography and energy on plant available water than precipitation and TWSA. This also confirms soil moisture as a vital component of the hydrological cycle better linked with the vegetation phenological cycle [8]. However, there are locations where precipitation and/or TWSA were better indicators of plant available water than soil moisture. For example, with the original data, precipitation outranked soil moisture (and TSWA) as a predictor of vegetation greenness dynamics in south-eastern Africa which contrasts with south-western Africa where soil moisture exercised dominant control (Figure 5A). The better performance of precipitation in south-eastern Africa can be attributed to the strong influence of the adjacent Indian Ocean and ENSO events on the ecohydrological regime of the area [50].

Although precipitation is considered the primary climatic driver of vegetation phenology [8], here monthly anomalies of precipitation were not as important as anomalies in soil moisture and TWSA in explaining anomalies in vegetation canopy greening across Africa. This result is similar to the findings in other studies that reported a better correlation between satellite-observed soil moisture and NDVI anomalies (e.g., [4]) and between GRACE TWSA and NDVI anomalies (e.g., [6]). This result, therefore, is in line with the understanding that the amount of water stored in the soil rather than the amount of precipitation received determines the survival of plants to extreme events like drought.

Regardless of whether the original or anomalies time series was used for modeling, vegetation greenness generally responded quicker (0–1 months) to changes in soil moisture and TWSA than to precipitation (1–2 months). This is plausible especially if we consider precipitation as the source and soil moisture and TWSA as the conduit of plant-available water. This premise is also supported by the results of Chen et al. [4] and Yang et al. [6] which indicated a generally strong correlation between vegetation greenness anomalies and soil moisture and TWSA anomalies over Australia at 0 to 1 months of vegetation delayed response. On the global scale, Xie et al. [51] also observed a vegetation greenness delayed response of 0- to 1-month to the TWSA. Additionally, Yang et al. [6] and Gessner et al. [3] found NDVI to be strongly correlated with precipitation at time lags upwards of a month.

#### **5. Conclusions and Outlook**

Remote sensing observations of EVI, soil moisture, TWSA, and station-satellite blend precipitation products were used to assess the impact/influence of the hydrological controls of vegetation greenness dynamics over Africa for the period between 2003 and 2015. By using a multivariate approach with the distribution-free RF algorithm, the relationships between these hydrological variables and vegetation greenness, with and without monthly anomalies, were assessed. The advantage of this approach is that it allows for the modeling of complex interactions between vegetation greenness and the hydrological variables, including lag e ffects. This is a more robust way to assess the relative importance of the hydrological variables and their lag e ffects on vegetation response to water availability.

In most parts of Africa, the RF model with the seasonal component present in the time series (original data) generally performed better than the model driven by monthly anomalies of the variables. These results indicate that water availability is a better driver of vegetation phenology than anomalous vegetation greenness trends in most parts of Africa. This contrast in model performance is particularly striking across West Africa and eastward to Eretria and Djibouti. With regards to the relative importance of the three hydrological variables to vegetation greenness dynamics, soil moisture was the only variable that consistently performed with both time series types and was ranked as the first important variable in more than 50% of the pixels examined. Nonetheless, precipitation and TWSA had significant roles in controlling vegetation greenness with the original and monthly anomaly time series, respectively. Of particular note is the strong predictive power of precipitation in the eastern flank of south-eastern Africa, which is a demonstration of the strong influence of the adjacent Indian Ocean on the vegetation dynamics of the region. In terms of the response of vegetation greenness to changes in the hydrological

measure, soil moisture, and TWSA were generally concurrent or led vegetation by 1 month, whereas precipitation led vegetation by 1–2 months. This demonstrates that soil moisture and TWSA are direct indicators of plant-available water than precipitation.

However, a key point to note is that soil moisture may have masked the strength of TWSA in predicting vegetation greenness response to water since TWSA is a measure of water stored from the surface to the boundary between the earth's crust and the mantle, which is inclusive of soil moisture. The soil moisture product used in this study represents measurements not deeper than 10 cm. This relationship between soil moisture and TWSA explains why the total area where soil moisture was important rapidly declined with a concomitant increase in the total area of TWSA influence across the variable of importance ranking (Figure 6). Thus, there is the need to further partition GRACE TWSA into the surface and groundwater components. However, because the current microwave sensors measure only soil moisture at the top few centimeters, TWSA supplements these measurements in areas where the root zone is deeper. Another possibility is to jointly assimilate the SMOS near-surface soil moisture observations and TWSA into a hydrological model to better approximate the root zone soil moisture, as demonstrated recently by Tian et al. [47], providing a better indicator of plant-available water and better vegetation response.

All in all, our results illustrate the usefulness of remote sensing soil moisture and TWSA as complementary data to precipitation in assessing and monitoring vegetation greenness dynamics, despite their relatively low spatial resolution (>0.25◦). This is especially important in Africa, where there is a dearth of in situ precipitation and soil moisture observations. Furthermore, the relationships observed across the vegetation types in this study can be used in benchmarking coupled vegetation-climate models. The generally low correlation between vegetation greenness anomalies and the hydrological variables anomalies observed over most of Africa (Figure 3B) can be improved if additional hydroclimatic variables such as vapor pressure deficit and evapotranspiration, as well as root zone soil moisture estimates, are incorporated to the RF model.

**Author Contributions:** Conceptualization, S.U.U. and T.F.A.B.; methodology, S.U.U. and T.F.A.B.; formal analysis, S.U.U.; software, S.U.U.; writing—original draft preparation, S.U.U.; writing—review and editing, T.F.A.B.; supervision, T.F.A.B. All authors have read and agreed to the published version of the manuscript.

**Funding:** This research received no external funding.

**Conflicts of Interest:** The authors declare no conflict of interest.
