Teleconnections and Interannual Transitions As Observed in African Vegetation: 2015–2017

remote sensing

Article Teleconnections and Interannual Transitions as Observed in African Vegetation: 2015–2017

Assaf Anyamba 1,*, Erin Glennie 2 ID and Jennifer Small 2

1 Goddard Earth Sciences Technology and Research (GESTAR)/Universities Space Research Association (USRA) at NASA/Goddard Space Flight Center, Greenbelt, ML 20771, USA; [email protected] 2 Science Systems and Applications Incorporated (SSAI) at NASA/Goddard Space Flight Center, Greenbelt, ML 20771, USA; [email protected] * Correspondence: [email protected]; Tel.: +301-614-6601

Received: 28 May 2018; Accepted: 27 June 2018; Published: 2 July 2018

Abstract: El Niño/Southern Oscillation (ENSO) teleconnections present a hemispheric dipole pattern in both rainfall and vegetation between eastern and southern Africa. We analyze precipitation and normalized difference vegetation index (NDVI) departures during the 2015–2017 ENSO cycle; with one of the strongest warm events (El Niño) on record followed by a short and weak cold event (La Niña). Typically, southern (eastern) Africa is associated with dry (wet) conditions during El Niño, and wet (dry) conditions during La Niña. In general, the temporal and spatial evolution of vegetation responses show the expected dipole pattern during the 2015–2016 El Niño and following 2016–2017 La Niña. However, in 2015–2016 the eastern African impacts were displaced to the west and south of the canonical pattern. Composites of seasonal vegetation anomalies highlight the magnitude and position of impacts. Further investigation through empirical orthogonal teleconnections and spatial correlation analysis conﬁrms the similar, but opposite, teleconnection impacts in eastern and southern Africa. The diametrically opposed patterns have particular implications for agricultural production and the availability of fodder and forage, especially in the pastoral communities of the two regions.

Keywords: normalized difference vegetation index (NDV)I; precipitation; El Niño/Southern Oscillation (ENSO); Indian Ocean Dipole (IOD); teleconnections; Africa

1. Introduction The El Niño Southern Oscillation (ENSO) is a coupled oceanic and atmospheric phenomenon that occurs at irregular intervals of two to seven years in the equatorial Pacific Ocean [1]. A warm event, or an El Niño, is associated with positive sea surface temperature (SST) anomalies in the eastern equatorial Pacific Ocean, negative SST anomalies in the western equatorial Pacific Ocean, and weak westerly trade winds. A cold event, or a La Niña, has a roughly opposite effect [1,2]. The dramatic change in ocean temperature disrupts atmospheric dynamics in this region and has downstream effects on global circulation patterns influencing temperature, winds and precipitation in various regions via teleconnection signals [2–6]. Southern Africa and eastern Africa are particularly attuned to the ENSO signal [7–10]. The two regions experience strong but opposite effects; during El Niño events southern Africa is typically warm and dry and eastern Africa is typically cool and wet [8]. The effects are largely reversed during La Niña events (Figure1). In both cases, peak vegetation impacts lag peak ENSO-related SST anomalies by one to two months [7,11].

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Figure 1. The map shows the ‘canonical’ vegetation re responsesponse patterns associated with the El Niño Southern Oscillation (ENSO), derived by correlating Niño 3.4 SST index with monthly anomalies of AVHRR NDVI3g data from 1981 to 2017.2017. The result shows a dipole pattern in vegetation response between eastern Africa (+) and southern Africa ( −).).

While ENSO is a driving force of interannual vari variabilityability in this region, the Indian Ocean Dipole (IOD), an oceanic and atmospheric oscillation in in the Indian Ocean basin, also exerts control on the climatic conditions [12,13]. [12,13]. The The IOD IOD occurs, occurs, on on aver average,age, every every three three to five five years beginning in boreal spring and fading by winter [14]. [14]. During a positive IOD, the Indian Ocean basin is warm in the west and cool in in the the east, east, leading leading to to increased increased convective convective activity activity over over eastern eastern Africa. Africa. A Anegative negative IOD IOD is associatedis associated with with dry dry conditions conditions in ineastern eastern Africa Africa [14]. [14 ].Whether Whether the the IOD IOD is is forced forced by by ENSO ENSO is is an ongoing subject of research. Numerous studies report that the IOD is an intrinsic mode of variability in the Indian Ocean [15–18]; [15–18]; however, severalseveral opposingopposing studiesstudies findfind thatthat thethe spatialspatial structurestructure and/orand/or temporal oscillation of the IOD is a mechanism of ENSO [19,20]. [19,20]. Regardless Regardless of the the dependence dependence of the two systems, systems, there there is is likely likely interaction interaction or or coupling coupling between between them them in in concurrent concurrent years years [18]. [18]. Generally, Generally, positive IOD events occur with positive ENSO events (El Niño), particularly if the ENSO signal is strong throughout the autumn preceding peak condit conditionsions [21]. [21]. Alternately, Alternately, negative IOD events are more likely likely to to coincide coincide with with negative negative ENSO ENSO even eventsts (La (La Niña). Niña). When When the thesystems systems are concurrent, are concurrent, one mayone mayheighten heighten or dampen or dampen the effects the effects of the of other the other over overeastern eastern Africa Africa depending depending on the on strength the strength and phaseand phase of the of thecycle cycle [13]. [13 Both]. Both impact impact the the hydrolog hydrologicic and and biospheric biospheric conditions conditions in in the the eastern eastern and southern regions of Africa. southern regions of Africa. The most recent ENSO developed from the end of 2014, aborted then strengthened in the spring The most recent ENSO developed from the end of 2014, aborted then strengthened in the spring and summer of 2015, finally peaking in December 2015 to January 2016. In terms of SST anomalies, it and summer of 2015, finally peaking in December 2015 to January 2016. In terms of SST anomalies, was one of the strongest on record, similar in magnitude to the 1997–1998 El Niño. Following this it was one of the strongest on record, similar in magnitude to the 1997–1998 El Niño. Following strong episode, the cycle transitioned in the fall of 2016 to a short and weak La Niña before conditions this strong episode, the cycle transitioned in the fall of 2016 to a short and weak La Niña before returned to neutral in February to March 2017. Some of the major El Niño impacts included record- conditions returned to neutral in February to March 2017. Some of the major El Niño impacts included breaking temperatures and droughts in the Amazon [22] and severe fires in Indonesia [23]. The IOD record-breaking temperatures and droughts in the Amazon [22] and severe fires in Indonesia [23]. followed a similar positive-to-negative cycle preceding ENSO, with positive SST anomalies across The IOD followed a similar positive-to-negative cycle preceding ENSO, with positive SST anomalies the entire Indian Ocean basin from August 2015 to April 2016, and negative anomalies in the across the entire Indian Ocean basin from August 2015 to April 2016, and negative anomalies in the northwest section of the basin from June to October 2016. northwest section of the basin from June to October 2016. ENSO is a major factor in vegetation dynamics in southern and eastern Africa [8–10,13,24]. Vegetation in semi-arid and arid environments is limited by water availability and is sensitive to

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ENSO is a major factor in vegetation dynamics in southern and eastern Africa [8–10,13,24]. Vegetation in semi-arid and arid environments is limited by water availability and is sensitive to interannual precipitation variability, particularly when precipitation is below average [25–29]. The normalized difference vegetation index (NDVI) is a useful proxy for the rainfall impacts associated with ENSO [9], and also provides insight into agricultural impacts [30,31]. Therefore, NDVI can enhance continuous monitoring because the data offers strong spatial coherence, temporal consistency, accessibility, and comparability across studies. In this manuscript we utilize monthly Moderate Resolution Imaging Spectroradiometer (MODIS) NDVI supported by Africa Rainfall Climatology (ARC) rainfall data to characterize vegetation dynamics in the two teleconnection regions in Africa during the 2015–2017 ENSO cycle. Speciﬁcally, we focus on the hemispheric dipole pattern in eastern and southern Africa and determine how this event compares to the canonical response. To place our ﬁndings in the context of previous events, we compare it to the last major ENSO of 1997–2000 using GIMMS NDVI3g data. We aim to build the record of documented ENSO effects in Africa and offer a framework for future monitoring and analysis. As the climate and the land surface both continue to evolve, it is critical to establish systematic methods to track the complex land surface impacts.

2. Materials and Methods

2.1. Data The MODIS vegetation index product (MOD13C2v6) provides a spatially and temporally consistent time series of NDVI data. The NDVI data are generated from MODIS surface reflectance data, which has been corrected for molecular scattering, ozone absorption and aerosols. The temporal compositing scheme reduces angular and sun-target-sensor variations. MODIS vegetation index products are produced every eight days, in square tiles with a 250 m, 500 m, or 1 km spatial resolution. The reduced resolution data in climate modeling grid (CMG) format, which is available monthly with global coverage at a 0.05 degree spatial resolution [32], is used in this analysis. Additionally, we incorporate the GIMMS NDVI3g version 1 dataset [33], the longest running series of NDVI data available, to compare the 2015–2017 ENSO to the 1997–2000 ENSO. This data is collected by Advanced Very High Resolution Radiometer (AVHRR) sensors and then composited and resampled to yield bi-monthly global NDVI images with a spatial resolution of 8 km. The entire series extends from 1981 to present. We compliment the analysis using the African Rainfall Climatology (ARC) rainfall dataset from the National Oceanic and Atmospheric Administration (NOAA)–Climate Prediction Center (CPC) archives [34,35]. The dataset is processed to support United States Agency for International Development–Famine Early Warning Systems Network (USAID/FEWSNET) operations. The ARC dataset is derived from several satellites and in situ sources including the polar orbiting Special Sensor Microwave/Imager and Advanced Microwave Sounding Unit sensors, infrared bands of the geostationary METEOSAT platforms, and rain gauge measurements from the Global Telecommunications System daily total rainfall product [35]. The oceanic Niño index (ONI) was used to define the period of the 2015–2017 ENSO event. The ONI is the standard metric used to define ENSO events, and classifies an El Niño episode as a period when the consecutive 3-month running mean sea surface temperature (SST) anomalies exceed +0.5 degrees Celsius. Periods qualify as La Niña episodes when average SST anomalies are below the −0.5 degree Celsius threshold. Extended reconstructed sea surface temperature data (ERSSTv4) anomalies, relative to a 30-year base period (1971–2000), are extracted from the Niño 3.4 region of the equatorial Pacific Ocean (5S–5N and 170–120W) and filtered to produce the ONI values. The original monthly Niño 3.4 SST anomalies were used for the correlation analysis. The data is compiled by the National Ocean and Atmospheric Administration Climate Prediction Center [36]. The dipole mode index (DMI), produced by the Japanese Agency for Marine-Earth Science Technology (JAMSTEC), is one index that is used to measure the IOD [37]. The DMI is a measure of Remote Sens. 2018, 10, 1038 4 of 16 theRemote anomalous Sens. 2018, 9 SST, x FOR gradient PEER REVIEW across the equatorial Indian Ocean, defined as the difference between4 of the 16 western Indian Ocean (60E–80E, 10S–10N) and eastern Indian Ocean (90E–110E, 10S–0). The SSTs are derived2.2. Methods from the NOAA OISST version 2 dataset.

2.2.2.2.1. Methods Seasonal Normalized Difference Vegetation Index (NDVI) and Precipitation Composite Analysis 2.2.1. Seasonal Normalized Difference Vegetation Index (NDVI) and Precipitation Composite Analysis An overview of the methods used in this analysis is presented in Figure 2. Standardized anomaliesAn overview are used of thethroughout methods usedthe inanalysis this analysis to isolate is presented the interannual in Figure 2variability. Standardized in NDVI anomalies and areprecipitation used throughout from the theseasonal analysis cycle. to isolateThe standardized the interannual anomalies variability were produced in NDVI andby calculating precipitation the fromclimatology, the seasonal or monthly cycle. Themean standardized over the entire anomalies time series, were and produced standard by calculatingdeviation of the each climatology, month in orthe monthly climatology. mean Next, over the the entire anomaly time series,images and are standard created deviationby subtracting of each the month climatology in the climatology. from the Next,original the values anomaly and images dividing are by created the standard by subtracting deviation. the climatologyThe result fromconveys the the original intensity values of andthe dividingdepartures by relative the standard to the normal deviation. range The of result variabilit conveysy in each the intensitypixel. Based of the on departuresthe ONI and relative the expected to the normalannual phase range oflocking variability of ENSO in each events pixel. [38,39], Based we on theselected ONI andApril the 2015 expected to May annual 2017 to phase capture locking the ofevolution ENSO eventsof the ENSO [38,39 ],cycle we selectedfrom the Aprildevelopment 2015 to Mayof El 2017Niño to to capturethe end theof La evolution Niña. While of the La ENSO Niña, cycleas defined from theby the development ONI, officially of El ended Niño to in the Dece endmber of La 2016, Niña. we While continued La Niña, the asanalysis deﬁned through by the ONI,May ofﬁcially2017 to endedaccommodate in December vegetation 2016, we response continued times. the analysis Three-month through cumulative May 2017 tocomposites accommodate of vegetationprecipitation response and NDVI times. at Three-monthpeak ENSO conditions cumulative illuminate composites the of magnitude precipitation and and distribution NDVI at peakof El ENSONiño and conditions La Niña illuminate impacts. the magnitude and distribution of El Niño and La Niña impacts.

Figure 2. Flowchart of data and methodsmethods used in this analysis.

2.2.2. Temporal Evolution of Impacts Analysis 2.2.2. Temporal Evolution of Impacts Analysis Monthly NDVI and precipitation anomalies were extracted from two agriculture and rangeland Monthly NDVI and precipitation anomalies were extracted from two agriculture and rangeland regions, one in Kenya and one in South Africa and Botswana (Figure 3). These regions were chosen regions, one in Kenya and one in South Africa and Botswana (Figure3). These regions were chosen to match regions defined by Anyamba et al. in 2002 [8] to examine the impacts of the 1997–2000 ENSO, to match regions deﬁned by Anyamba et al. in 2002 [8] to examine the impacts of the 1997–2000 therefore facilitating consistent comparison between the two events. Regression analyses were ENSO, therefore facilitating consistent comparison between the two events. Regression analyses performed between the regional NDVI and precipitation time series and teleconnection indices. Each were performed between the regional NDVI and precipitation time series and teleconnection indices. regression was run over the course of the 2015–2017 ENSO cycle to assess the immediate Each regression was run over the course of the 2015–2017 ENSO cycle to assess the immediate correspondence to the SST anomalies. Several lag-time adjustments allowed for determination of the correspondence to the SST anomalies. Several lag-time adjustments allowed for determination of the time at which ENSO exerts maximum influence on the rainfall and attendant vegetation. time at which ENSO exerts maximum inﬂuence on the rainfall and attendant vegetation.

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FigureFigure 3.3. Precipitation and normalized difference vegetationvegetation index (NDVI) data were extractedextracted inin EastEast Africa between between 35E–40E 35E–40E and and 2S–2N(A) 2S–2N(A) and and Sout Southh Africa Africa between between 19E–32E 19E–32E and and28S–20S(B), 28S–20S(B), and andanalyzed analyzed with with respect respect to the to thesea sea surface surface temperat temperatureure (SST) (SST) anomalies anomalies in in the the Niño Niño 3.4 3.4 region region of the PaciﬁcPacific toto assessassess thethe coherence,coherence, timing,timing, andand intensityintensity ofof teleconnectionteleconnection effects.effects.

2.2.3.2.2.3. Spatial Correlation AnalysisAnalysis AA multiple regression regression was was run run between between NDVI NDVI and and the the two two teleconnection teleconnection indices indices at multiple at multiple lag lagtimes times to characterize to characterize how howboth bothENSO ENSO and the and IOD the impact IOD impact land surface land surface dynamics dynamics in Africa. in Africa.The R- Thesquared R-squared values valuesquantify quantify the NDVI the variability NDVI variability explained explained by the joint by the teleconnection joint teleconnection influence. inﬂuence. Partial Partialcorrelation correlation coefficients coefﬁcients isolate isolate the contributions the contributions of ENSO of ENSO and andthe IOD the IOD to vegetation to vegetation variability variability in inAfrica. Africa. The The NDVI NDVI was was masked masked based based on a on precipitation a precipitation threshold threshold (lower (lower than than 150 150mm/year). mm/year). The Thecorrelation correlation statistics statistics are generated are generated on a on per a perpixel pixel basis basis across across the theentire entire continent. continent.

2.2.4.2.2.4. Empirical Orthogonal Teleconnection AnalysisAnalysis EmpiricalEmpirical orthogonalorthogonal teleconnection teleconnection (EOT) (EOT) analysis anal isysis a variantis a variant of empirical of empirical orthogonal orthogonal functions thatfunctions is useful that for is findinguseful for patterns finding that patterns are independent that are independent in either time in or either space time [40]. or Basic space EOT [40]. analysis, Basic whichEOT analysis, is orthogonal which is in orthogonal time, was usedin time, in was this used application. in this application. The method The reduces method the reduces time seriesthe time to representativeseries to representative components components by finding by locations finding lo thatcations explain that the explain maximum the maximum variability variability across the across map. Partthe map. one of Part EOT one analysis of EOT searches analysis for searches the ‘base for point’, the ‘base or pixel point’, that or has pixel the that highest has the combined highest correlation combined tocorrelation all other pixels.to all other Part pixels. two removes Part two patterns removes explained patterns by explained the base by point the throughbase point linear through regression, linear andregression, then the and analysis then the starts analysis over on starts the residualover on data.the residual For each data. iteration, For each the iteration, outputs are the a outputs time series are extracteda time series from extracted the base from point the and base a mappoint of and the a correlation map of the ofcorrelation all pixels of to all the pixels time series.to the time Using series. this method,Using this we method, found thatwe found the first that and the second first and EOTs seco werend EOTs highly were correlated highly correlated with the Niño3.4 with the and Niño3.4 DMI indices,and DMI respectively. indices, respectively.

3.3. Results

3.1.3.1. Seasonal NDVI and PrecipitationPrecipitation CompositeComposite Results EasternEastern Africa,Africa, particularlyparticularly Kenya, Uganda, andand southcentral Ethiopia had abnormallyabnormally low precipitationprecipitation inin September September and and October October 2015. As2015. predicted As predicted based on based past events, on past positive events, precipitation positive andprecipitation NDVI anomalies and NDVI became anomalies widespread became in widespre Novemberad in and November peaked in and January. peaked While in January. the direction While ofthe the direction regional of response the regional was consistentresponse withwas cons the canonicalistent with response, the canonical the spatial response, manifestation the spatial of themanifestation anomalies of was the rather anomalies dispersed. was rather In the dispersed. canonical In El the Niño canonical the greatest El Niño NDVI the greatest and precipitation NDVI and departuresprecipitation are departures concentrated are in concentrated north-east Kenya, in nort south-easth-east Kenya, Ethiopia, south-east and Somalia Ethiopia, (Figure and1). InSomalia 2015, (Figure 1). In 2015, the greatest precipitation anomalies shifted westwards to Uganda, south-western

Remote Sens. 2018, 10, 1038 6 of 16 Remote Sens. 2018, 9, x FOR PEER REVIEW 6 of 16 the greatest precipitation anomalies shifted westwards to Uganda, south-western Kenya, Tanzania, Kenya, Tanzania, and the northern border of Mozambique, with isolated in central Somalia and south and the northern border of Mozambique, with isolated in central Somalia and south eastern Ethiopia eastern Ethiopia (Figure 4A). The maximum rainfall departures were in the range of +1.0 to +3.0 (Figure4A). The maximum rainfall departures were in the range of +1.0 to +3.0 standardized deviations standardized deviations (150–400 mm). The NDVI anomalies followed the shift in precipitation, (150–400 mm). The NDVI anomalies followed the shift in precipitation, affecting the same regions affecting the same regions (Figure 5A). Greener-than-normal conditions continued through April, (Figure5A). Greener-than-normal conditions continued through April, when vegetation returned to when vegetation returned to neutral or slightly drier than average. neutral or slightly drier than average.

Figure 4. Standardized seasonal precipitation anomalies at peak El Niño conditions in (A) eastern Figure 4. Standardized seasonal precipitation anomalies at peak El Niño conditions in (A) eastern Africa from November 2015 to January 2016; and (B) southern Africa from December 2015 to February Africa from November 2015 to January 2016; and (B) southern Africa from December 2015 to February 2016. Precipitation anomalies during peak La Niña conditions are displayed in (C) eastern Africa from 2016. Precipitation anomalies during peak La Niña conditions are displayed in (C) eastern Africa from November 2016 to January 2017 in and (D) in southern Africa from December 2016 to February 2017. November 2016 to January 2017 in and (D) in southern Africa from December 2016 to February 2017.

InIn 2015,2015, southernsouthern AfricaAfrica exhibitedexhibited thethe drydry conditionsconditions commonlycommonly associatedassociated withwith thethe ElEl NiñoNiño phasephase ofof the the ENSO ENSO cycle. cycle. NegativeNegative NDVINDVI anomaliesanomalies beganbegan toto appearappear inin westernwestern andand centralcentral SouthSouth ◦ AfricaAfrica inin SeptemberSeptember andand by Novemb Novemberer virtually virtually all all of of the the land land areas areas below below 15°S 15 wereS were dominated dominated by bybelow-normal below-normal precipitation precipitation (Figure (Figure 4B)4B) and and NDVI NDVI (Figure 55B).B). ThroughoutThroughout the the event, event, the the dry dry conditionsconditions progressedprogressed from from west west to to east east across across the the region, region, with with the largest the largest NDVI NDVI departures departures (~ −2.5 (~standardized−2.5 standardized anomalies anomalies below below average) average) concentrated concentrated over over rangeland rangeland and and agricultural agricultural regions inin Botswana,Botswana, Mozambique Mozambique and and eastern eastern South South Africa. Africa. The precipitation The precipitation pattern ispattern similar, is with similar, maximum with departuresmaximum ofdepartures−2.0 standardized of −2.0 standardized deviations (~ deviations−300 mm) over(~−300 central mm) Mozambique.over central Mozambique. By February the By droughtFebruary conditions the drought began conditions to recede; began however, to recede; the vegetationhowever, the along vegetation the eastern along coast the waseastern slow coast to ◦ rebound.was slow The to rebound. drought wasThe drought mainly concentrated was mainly concentrated east of 20 E. east of 20°E.

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Figure 5. Standardized seasonal NDVI anomalies at peak El Niño conditions in (A) eastern Africa from Figure 5. Standardized seasonal NDVI anomalies at peak El Niño conditions in (A) eastern Africa November 2015 to January 2016; and (B) southern Africa from December 2015 to February 2016. NDVI from November 2015 to January 2016; and (B) southern Africa from December 2015 to February 2016. anomalies during peak La Niña conditions are displayed in (C) eastern Africa from November 2016 to NDVI anomalies during peak La Niña conditions are displayed in (C) eastern Africa from November January 2017 in and (D) in southern Africa from December 2016 to February 2017. 2016 to January 2017 in and (D) in southern Africa from December 2016 to February 2017.

InIn early early 2016, 2016, dry dry conditions conditions associated associated with with La La Niña Niña developed developed in in eastern eastern Africa Africa and and vegetation vegetation productivityproductivity quicklyquickly declined. declined. Widespread Widespread negative negative precipitation precipitation and vegetation and vegetation anomalies anomalies persisted persistedfrom October from October 2016 through 2016 throug Mayh 2017.May 2017. The The lowest lowest precipitation precipitation anomalies anomalies ofof approximately approximately − −1.51.5 standardized standardized anomalies anomalies occurred inin Kenya,Kenya, Tanzania, Tanzania, Uganda, Uganda, and and southern southern Somalia Somalia (Figure (Figure4C). − − 4C).Concurrent Concurrent NDVI NDVI has a similarhas a spatialsimilar distributionspatial distribution and departures and departures of about 1.0of toabout2.0 standardized−1.0 to −2.0 standardizedanomalies below anomalies average below (Figure average5C). (Figure 5C). DuringDuring the the same same period, period, southern southern Africa Africa expe experiencedrienced enhanced enhanced precipitation precipitation and and vegetation. vegetation. TheThe strong strong positive effects maymay havehave beenbeen a a response response to to the the combined combined inﬂuence influence of of the the relatively relatively weak weak La LaNiña Niña and and the warmthe warm pool ofpool water of water in the southernin the sout Indianhern OceanIndian (Figure Ocean 9). (Figure The precipitation 9). The precipitation (Figure4D) (Figureand NDVI 4D) (Figureand NDVI5D) impacts(Figure 5D) were impacts particularly were particularly strong in Botswana, strong in Zimbabwe, Botswana, andZimbabwe, central and centralnorthern and South northern Africa. South Maximum Africa. precipitation Maximum precipitation and NDVI departures and NDVI were departures both about were +2 both standardized about +2 standardizeddeviations above deviations average. above Although average. La Although Niña reached La Niña an early reached peak an in Octoberearly peak 2016, in October the NDVI 2016, and theprecipitation NDVI and anomalies precipitation did notanomalies fully develop did not until full Januaryy develop to February until January 2017. Theto February delayed vegetation2017. The delayedresponse vegetation may have response been due may to extended have been soil due moisture to extended recovery soil moisture time following recovery the time extremely following dry theconditions extremely in thedry previousconditions year in the [27, 41previous]. year [27,41].

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3.2.3.2. Temporal Temporal Evolutio Evolutionn of of Impacts Impacts Results Results ToTo facilitate facilitate comparisoncomparison betweenbetween the the 1997–2000 1997–2000 ENSO ENSO and 2015–2017and 2015–2017 ENSO ENSO the regions the extractedregions extractedin eastern in Africaeastern (35E–40E, Africa (35E–40E, 2S–2N) and2S–2N) southern and southern Africa (19E–32E, Africa (19E–32E, 28S–20S) 28S–20S) correspond correspond to the areas to theexamined areas examined in a previous in a previous assessment assessment of teleconnection of teleconnection effects in Africa effects [8 in]. WhileAfrica patterns [8]. While described patterns by describedAnyamba by et Anyamba al. [8] generally et al. [8] align generally with our align observations, with our thereobservations, are several there differences are several between differences the two betweenevents. the In the two 1997–1998 events. In Elthe Niño, 1997–1998 eastern El Africa Niño, moreeastern closely Africa reﬂected more closel they canonical reflected response,the canonical with response,positive extremeswith positive concentrated extremes in concentrated Kenya and Somalia.in Kenya NDVI and inSomalia. this region NDVI was in anomalouslythis region was high anomalouslyfor several months, high for in several contrast months, to the in short contrast and weak to the green short up and in weak this location green up in in November this location 2015 in to NovemberFebruary 2016.2015 to This February is partially 2016. explained This is partially by the spatial explained differences by the spatial in the responsedifferences patterns; in the response maximum patterns;departures maximum were located departures elsewhere were inlocated the later elsewhere event. Asin the conditions later event. transitioned As conditions to La transitioned Niña in 1999, toNDVI La Niña gradually in 1999, declined NDVI gradually and remained declined slightly and below remained average slightly from below September average 1999 from to February September 2001. 1999While to theFebruary more recent 2001. La While Niña the was more a briefer recent event, La theNiña negative was a NDVI briefer departures event, the were negative approximately NDVI departures10–15% greater were thanapproximately the 1999 episode. 10–15% greater than the 1999 episode. SouthernSouthern Africa Africa presented presented an an atypical atypical response response in in 1997, 1997, which which is is reflected reﬂected by by the the near-normal near-normal NDVINDVI throughout throughout mostmost ofof the the event. event. In fact,In fact, we notewe slightlynote slightly greener greener than average than average vegetation vegetation preceding precedingpeak El Niño peak conditions. El Niño conditions. The 2015 The event, 2015 however, event, however, presented presented a stronger a responsestronger withresponse NDVI with and NDVIprecipitation and precipitation departures departures of about of− 1.0about standardized −1.0 standardized anomaly anomaly (Figure (Figure6) in 2015. 6) in During2015. During La Niña, La Niña,the maximum the maximum NDVI NDVI departures departures of +1.5 of +1.5 standardized standardized anomalies anomalies in in both both 2000 2000 and and 2017 2017 occurred occurred in inJanuary January and and in in February, February, respectively. respectively.

FigureFigure 6. 6. NDVINDVI (green), (green), precipitation precipitation (blue), (blue), Niño Niño 3.4 3.4 (red (red line), line), and and the the dipole dipole mode mode index index (DMI) (DMI) (black(black line) line) are are compared compared from from January January 1997 1997 to to May May 2017 2017 in in eastern eastern Africa Africa (35E–40E, (35E–40E, 2S–2N) 2S–2N) and and southernsouthern Africa Africa (19E–32E, (19E–32E, 28S–20S) 28S–20S) in in the the top top fi ﬁgures.gures. Below, Below, the the time time series series from from 1997–2000 1997–2000 and and 2015–20172015–2017 ENSO ENSO cycles cycles are are extracted extracted to to sh showow the the patterns patterns in in greater greater detail. detail.

InIn both both teleconnection teleconnection regions,regions, thethe peak peak precipitation precipitation lags lags the the ENSO ENSO signal signal by zero by tozero two to months. two months.In eastern In Africa,eastern the Africa, maximum the maximum correlation correlati betweenon ENSObetween and ENSO precipitation and precipitation (r = 0.56, p <(r 0.005) = 0.56, occurs p < 0.005)without occurs any without lag adjustments. any lag adjustments. The strength The of the streng correlationth of the is correlation stable and signiﬁcantis stable and at lagsignificant intervals at of lagup intervals to four months. of up to In four southern months. Africa, In southern the maximum Africa, correlation the maximum (r = 0.42,correlationp < 0.05) (roccurs = 0.42, one p < month0.05) occursafter theone ENSO month signal, after and the is ENSO also signiﬁcant signal, and at twois also and significant three months at timetwo adjustments.and three months Based ontime the adjustments.correlation statisticsBased on at the several correlation lag times, statistics eastern at Africa several had lag a stronger,times, eastern more immediateAfrica had precipitationa stronger, moreimpact immediate than southern precipitation Africa impact in the most than recentsouthern ENSO Africa event. in the This most is partiallyrecent ENSO due toevent. East This Africa’s is partiallylocation due within to East the equatorialAfrica’s location plane within where ENSOthe equatorial impacts plane were where most pronounced ENSO impacts and were immediate. most pronouncedThe correlation and statisticsimmediate. are The summarized correlation in statistics Table1. are summarized in Table 1.

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Table 1. The correlation coefﬁcients (R), r-square values (r2), and p-values are presented for Niño 3.4 and precipitation and Niño 3.4 and NDVI during the study period of April 2015 to May 2017 in eastern and southern Africa. The statistics for each relationship are generated at multiple lag intervals. Signiﬁcant correlations (p < 0.05) are indicated by bold font.

Eastern Africa [35E–40E, 2S–2N] Southern Africa [19E–32E, 28S–20S] Precipitation NDVI Precipitation NDVI Lag R r2 p-Value R r2 p-Value R r2 p-Value R r2 p-Value No lag 0.56 0.31 <0.05 0.52 0.27 <0.05 0.37 0.14 >0.05 0.46 0.22 >0.05 1 month 0.54 0.29 <0.05 0.63 0.40 <0.05 0.42 0.18 <0.05 0.53 0.28 >0.05 2 month 0.54 0.29 <0.05 0.70 0.49 <0.05 0.41 0.17 <0.05 0.57 0.32 >0.05 3 month 0.53 0.28 <0.05 0.77 0.59 <0.05 0.42 0.18 <0.05 0.60 0.35 <0.05 4 month 0.47 0.22 <0.05 0.80 0.63 <0.05 0.37 0.14 >0.05 0.61 0.37 <0.05

Vegetation response to ENSO-induced precipitation anomalies in both regions takes approximately one to two months, and peak correlations occur at a lag of three to four months. The delay may be partially due to growth rates of different vegetation types but more importantly due to mediation between rainfall and vegetation growth via soil moisture [41]. Again, for the 2015–2017 period, ENSO had a stronger influence in eastern Africa than in southern Africa, with correlation coefficients of 0.80 and 0.61, respectively, when NDVI lags ENSO by four months. While the relationship between NDVI and Niño 3.4 is statistically significant at all lag intervals (zero to four months) in eastern Africa, the relationship is only significant for a vegetation lag of three or four months in southern Africa. In both regions, the relationship between NDVI and ENSO is substantially stronger than the relationship between precipitation and ENSO. A possible explanation for the difference is due to the randomness or day-to-day variability in rainfall over the season compared to the relatively stable and gradual growth or decline in vegetation over space and time.

3.3. Spatial Correlation Results The multiple regression between Niño 3.4, the DMI, and NDVI in Africa illuminated the independent impacts of each teleconnection on land surface dynamics in Africa (Figure7). In the partial correlation maps the green values indicate that the time-series of NDVI values at the pixel is positively correlated to Niño 3.4 (right) or the DMI (left). Red pixels indicate a negative relationship. As expected, Niño 3.4 was associated with a strong dipole pattern. This confirms that eastern African vegetation was positively correlated to ENSO in the 2015–2017 event and, therefore, has higher (lower) vegetation productivity in El Niño (La Niña). Although the coast of Kenya did not experience major vegetation impacts in the 2015–2017 event, the strong positive relationship apparent in the correlation analysis indicates the cycle was in sync even though the amplitude of the effect was not large. We see that the opposite pattern characterizes the response in southern Africa. However, in South Africa, we find an exception to the general pattern along the southern coast (Cape region). This ecologically and climatologically distinct region had a strong negative association with El Niño, which may be related to underlying differences in local topography and regional ocean influence [42]. The correlation between NDVI and the DMI presents a less cohesive spatial pattern. While eastern Africa generally has enhanced precipitation associated with the IOD, we see the region is dominated by a negative relationship. Instead, the positive impact migrated south to Zimbabwe and southern Mozambique. This pattern reflects the basin-wide warming that occurred in the Indian Ocean during the El Niño, and concentrated warm SST anomalies off the coast of southern Africa during the La Niña (Figure 9). Remote Sens. 2018, 10, 1038 10 of 16 Remote Sens. 2018, 9, x FOR PEER REVIEW 10 of 16

Figure 7. TheThe maps maps above above show show the the partial partial correlation correlation coefficients coefﬁcients between teleconnection indices and standardized NDVI anomalies from April 2015 to May 2017. (A) Shows the correlationcorrelation between the ENSO andand vegetationvegetation anomalies; anomalies; and and (B )(B shows) shows the the correlation correlation between between the Indian the Indian Ocean Ocean Dipole Dipole (IOD) vegetation(IOD) vegetation anomalies, anomalies, when thewhen IOD the precedes IOD precedes the NDVI the anomaliesNDVI anomalies by three by months. three months.

2 The highest R2 values are concentrated in southern AfricaAfrica and eastern Africa, indicating that changes inin easterneastern tropicaltropical PacificPacific Ocean Ocean SSTs SSTs explain explain a a substantial substantial amount amount of of vegetation vegetation variability variability in thesein these regions. regions. Specifically, Specifically, ENSO ENSO is most is most closely closel relatedy related to South to South Africa Africa (particularly (particularly the east the and east along and thealong southern the southern coast), coast), Somalia, Somalia, eastern eastern Ethiopia, Ethiopia and south-east, and south-east Kenya. DuringKenya. thisDuring period, this the period, IOD hadthe theIOD strongest had the influencestrongest on influence Botswana, on Zambia, Botswana, and Za Zimbabwe.mbia, and This Zimbabwe. finding was This rather finding unexpected was rather but mayunexpected be attributed but may to thebe attributed basin-wide to warming the basin-wi withinde warming the entire within Indian the Ocean. entire Indian Ocean.

3.4. Empirical Orthogonal TeleconnectionsTeleconnections (EOT)(EOT) ResultsResults The EOTEOT analysisanalysis successfully successfully distinguished distinguished the the impacts impacts of ENSO of ENSO and theand IOD the onIOD NDVI on NDVI in Africa. in TheAfrica. first The EOT, first which EOT, represents which represents 19.33% of19.33% the NDVI of the variability, NDVI variability, clearly corresponds clearly corresponds to ENSO to (r ENSO = 0.60, p(r< = 0.050.60,) (Figurep < 0.05)8A). (Figure The locus 8A). The of this locus relationship of this relationship was centered wasat centered a location at a in location Somalia in which Somalia is highlywhich is correlated highly correlated to other pixels.to other When pixels. this When time this series time is series correlated is correlated to all other to all pixels, other thepixels, dipole the patterndipole pattern between between eastern eastern and southern and southern Africa emergesAfrica emerges and is veryand similaris very tosimilar the canonical to the canonical pattern shownpattern inshown Figure in1 .Figure 1. The second EOT result, selected from a location in western Madagascar, was strongly correlated to allall otherother pixelspixels afterafter the the influence influence of of the the first first EOT EOT was was removed. removed. The The EOT EOT 2 represents 2 represents 10.50% 10.50% of the of NDVIthe NDVI variability variability and and is correlated is correlated to the to IODthe IOD signal signal (r = ( 0.70,r = 0.70,p < 0.05).p < 0.05). The The trend trend shows shows less less of a of lag a effectlag effect than than the NDVIthe NDVI pattern pattern associated associated with with ENSO, ENSO, perhaps perhaps due to due the to proximity the proximity of the Indianof the Indian Ocean. TheOcean. correlation The correlation between between each pixel each and pixel the trendand the presents trend apresents similar patterna similar to pattern that found to that in the found spatial in correlationthe spatial analysiscorrelation (Figure analysis8B). However,(Figure 8B). in theHowever, EOT 2 result in the the EOT negative 2 result impacts the negative are concentrated impacts are in centralconcentrated and southern in central Tanzania, and southern northern Tanzania, Mozambique, northern andMozambique, central South and Africa.central TheSouth eastern Africa. coast The ofeastern Kenya coast and of Madagascar Kenya and show Madagascar a more positiveshow a effectmore thanpositive in the effect spatial than correlation in the spatial result. correlation Overall, thisresult. pattern Overall, is rather this pattern diffuse is and rather not diffuse spatially and coherent. not spatially coherent.

Remote Sens. 2018, 10, 1038 11 of 16 Remote Sens. 2018, 9, x FOR PEER REVIEW 11 of 16

FigureFigure 8. 8. FiguresFigures AA and and B Brepresent represent the the first ﬁrst and and se secondcond results results of of the the empirical empirical orthogonal orthogonal teleconnections (EOT) analysis, respectively. In ﬁgure A, the NDVI pattern captured in EOT 1 and the teleconnections (EOT) analysis, respectively. In figure A, the NDVI pattern captured in EOT 1 and the ENSO signal are compared in the time series plot and the spatial correlation of pixels to the trend is ENSO signal are compared in the time series plot and the spatial correlation of pixels to the trend is represented in the map. Figure B displays the time series plot and correlation map for EOT 2, which represented in the map. Figure B displays the time series plot and correlation map for EOT 2, which corresponds to the IOD signal. corresponds to the IOD signal.

4.4. Discussion Discussion TheThe interannual interannual variability variability in in the the coupled coupled ocea oceann atmosphere atmosphere system system has has impacts impacts on on the the land land surfacesurface revealed revealed through through coherent coherent and and persistent departures in rainfall and vegetation.vegetation. More More importantly,importantly, this this translates translates into into significant significant soci socialal economic economic impacts. impacts. During During the the 2015–2017 2015–2017 ENSO ENSO suchsuch impacts impacts are are evident evident in in the the agricultural agricultural produc productiontion records records for for eastern eastern and and southern southern Africa Africa [43]. [43]. CornCorn yields yields in in Kenya Kenya were were 17% 17% above above average average in in 2015 2015 due due to to the the favorable favorable precipitation precipitation conditions conditions associatedassociated with ElEl Niño Niño [44 [44].]. The The following following growing grow seasonsing seasons in 2016 in and 2016 2017, and however, 2017, however, were extremely were extremelydry. Low cropdry. Low production, crop production, coupled with coupled high with food hi pricesgh food and prices heavy and livestock heavy losses livestock led tolosses acute led food to acuteinsecurity, food insecurity, disease outbreaks, disease outbreaks, and conflict and across conflic thet regionacross the [45 ].region South [45]. Africa South faced Africa dry conditionsfaced dry conditionsin the 2014–2015 in the 2014–2015 season, which season, exacerbated which exacerbate the droughtd the brought drought by brought El Niño by in El 2015–2016. Niño in 2015–2016. Although Althoughirrigated cornirrigated area hascorn more area than has doubledmore than between doubled 2000 between and 2013, 2000 water and stress 2013, led water to planting stress led delays to plantingand subsequent delays and reductions subsequent in corn reductions crop area in corn and production,crop area and with production, a total yield with of a 8.4 total million yield metricof 8.4 milliontons [46 metric]. Fortunately, tons [46]. the Fortunately, La Niña-induced the La Ni rainña-induced during the rain 2016–2017 during the growing 2016–2017 season growing mitigated season the mitigateddrought. the The drought. favorable The weather favorable conditions weather resulted conditions ina resulted record-breaking in a record-breaking corn crop of corn 16.4 crop million of 16.4metric million tons, metric doubling tons, that doubling of the previous that of the season previous [46]. season [46]. PastPast research research has has identified identified a a similar similar reversal reversal in in vegetation vegetation response response patterns patterns between between El El Niño Niño andand La La Niña Niña in in both both eastern eastern and and southern southern Africa Africa [8–10]. [8–10]. Parhi Parhi et et al. al. [10] [10 ]focus focus on on tropical tropical Africa, Africa, and and characterize modulations in tropical African rainfall in response to the growth and mature phases of El Niño. They find that impacts in eastern Africa are most intense during the short rainy season

Remote Sens. 2018, 10, 1038 12 of 16

Remote Sens. 2018, 9, x FOR PEER REVIEW 12 of 16 characterize modulations in tropical African rainfall in response to the growth and mature phases of(October–December) El Niño. They find and that manifest impacts as in changes eastern Africain freque arency, most rather intense than during intensity, the of short precipitation. rainy season In (October–December)an analysis of 30 years and of manifestNDVI data, as changes Philippon in frequency, et al. [9] find rather that than relative intensity, to other of precipitation. regions in Africa, In an analysiseastern and of 30southern years of Africa NDVI have data, the Philippon highest percen et al.tage [9] find of pixels that relative significantly to other related regions to ENSO in Africa, and easternthe highest and overall southern correlation Africa have values. the highestInterestingly, percentage they divide of pixels eastern significantly Africa into related two toregions ENSO with and thedistinct highest ENSO overall impacts: correlation northern values. eastern Interestingly, Africa, which they has divide a negative eastern correlation Africa into with two ENSO regions during with distinctthe summer ENSO rainfall impacts: season, northern and eastern equatorial Africa, east whichern hasAfrica, a negative which correlation presents witha strong ENSO positive during thecorrelation. summer rainfall season, and equatorial eastern Africa, which presents a strong positive correlation. Indian OceanOcean SSTsSSTs are are a a critical critical factor factor governing governing climate climate variability variability in Africain Africa and and play play a role a role in the in outcomethe outcome of ENSO of ENSO events. events. The IOD The was IOD positive was positi precedingve preceding peak El Niñopeak conditionsEl Niño conditions from November from toNovember January 2015,to January when 2015, it returned when it to returned a neutral to state.a neutral Although state. Although the DMI suggeststhe DMI suggests there was there a dipole was pattern,a dipole wepattern, observe we thatobserve the entirethat the Indian entire Ocean Indian basin Ocean was basin warm was from warm December from December 2015 to February 2015 to 2016February (Figure 20169). (Figure Furthermore, 9). Furthermore, the warm SST the anomalieswarm SST were anomalies not concentrated were not concentrated along the equator along and the closeequator to theand coast close of to eastern the coast Africa of eastern as in theAfrica case as of in the the 1997–1998 case of the El 1997–1998 Niño, and El the Niño, spatial and pattern the spatial was diffusepattern overall.was diffuse Compared overall. to Compared the strong to IOD the instro Decemberng IOD in 1997 December to February 1997 1998,to February the weak 1998, spatial the structureweak spatial of thestructure latest IODof the is latest especially IOD is striking. especial Wely striking. hypothesize We hypothesize that the El that Niño the event El Niño enhanced event SSTsenhanced across SSTs the Indianacross Oceanthe Indian basin, Ocean but the basin, sporadic but manifestationthe sporadic manifestation of warm anomalies of warm resulted anomalies in the south-westresulted in the shift south-west of effects in shift eastern of effects Africa. in Weeastern note Africa. that warm We SSTnote anomalies that warm in SST the anomalies southern Indianin the Oceansouthern in Indian December Ocean 2016 in toDecember February 2016 2017 to likely February amplified 2017 thelikely La amplified Niña effects the inLa southern Niña effects Africa. in Whilesouthern these Africa. events While provide these some events insight provide into some how SSTinsight anomalies into how influence SST anomalies African influence precipitation African and vegetation,precipitation analysis and vegetation, of a longer analysis time period of a islonger necessary time toperiod better is understand necessary to the better interactions understand between the ENSOinteractions and the between IOD and ENSO predict and the the outcomes. IOD and predict the outcomes.

Figure 9. Cumulative SST anomalies in the Pacific and Indian Ocean basins during three ENSO events. Figure 9. Cumulative SST anomalies in the Pacific and Indian Ocean basins during three ENSO events. (A) shows the distinct ‘warm tongue’ pattern that formed in the 1997 El Niño and corresponding (A) shows the distinct ‘warm tongue’ pattern that formed in the 1997 El Niño and corresponding dipole in the Indian Ocean; and (B) shows the reverse conditions that formed in the following La dipole in the Indian Ocean; and (B) shows the reverse conditions that formed in the following La Niña. (C) presents the relatively weak SST signal across the tropics that formed in the 2006 El Niño. Niña. (C) presents the relatively weak SST signal across the tropics that formed in the 2006 El Niño. The position of the SST departure is concentrated in the central Pacific Pacific and is detached from the South American coast;coast; TheThe followingfollowing LaLa NiñaNiña ( D(D)) showed showed a a strong strong cold cold signal, signal, also also reaching reaching to to the the center center of theof the Pacific; Pacific; (E) ( showsE) shows the latestthe latest El Niño El Niño SST distribution.SST distributi Whileon. While similar similar in intensity in intensity to the to 1997 the event, 1997 theevent, warm the warm anomalies anomalies have ahave larger a larger presence presence in the in eastern the eastern Pacific Pa basin,cific basin, and theand anomalies the anomalies are lessare attachedless attached to the to South the South American American coast. coast. Additionally, Addition theally, entire the Indianentire Indian Ocean basinOcean is basin flooded is flooded by a warm by signal;a warm ( Fsignal;) shows (F the) shows weak the negative weak negative SST anomalies SST anomalies in the 2016 in the La Niña.2016 La Niña.

The eastern or central position of SST anomalies in the Pacific Ocean basin is another critical factor in ENSO dynamics [47]. Although it is unclear whether the two types are mechanistically distinct or two manifestations of non-linear ENSO properties [48,49], the placement has implications

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The eastern or central position of SST anomalies in the Pacific Ocean basin is another critical factor in ENSO dynamics [47]. Although it is unclear whether the two types are mechanistically distinct or two manifestations of non-linear ENSO properties [48,49], the placement has implications for the timing, teleconnections, and Indian Ocean interaction [50]. Eastern Pacific (EP) ENSO events are considered the ‘canonical’ type and are more likely to result in strong impacts. The SSTs are concentrated along the South American coast and extend into the equatorial Pacific, driving basin-wide thermocline and surface wind oscillations. Alternately, Central Pacific (CP) ENSO events occur when the surface wind and SST anomalies are confined to the central Pacific, reducing the strength of teleconnection effects [50]. Interestingly, the 2015–2016 El Niño had characteristics of CP and EP events and is not easily classified as either. Warm SSTs were concentrated in the central Pacific to the western coast of South and Central America, where the warm water spreads north and south along the coast (Figure9). Although the warmest water was located in the central/eastern Pacific, rather than along the South American coast, there was a clear basin-wide effect. In general, the widespread shift in ocean temperature generated the expected teleconnection effects in Africa. Although the La Niña was weak and had a small SST footprint, we still observe the known vegetation and precipitation effects.

5. Conclusions The 2015–2017 ENSO episode clearly influenced climate and land surface response patterns in eastern and southern Africa. The impacts are evident in the precipitation, NDVI, and agricultural production records. Our results confirm that both regions are significantly correlated with equatorial Pacific SSTs and exhibit a clear dipole pattern that reverses between warm and cold ENSO events. Interestingly, precipitation—the variable most directly connected to global atmospheric and oceanic oscillations—had the weakest overall correlation to the teleconnection indices. The ENSO signal, with contributions from the IOD, more clearly influenced NDVI and the ultimate agricultural outcomes. The time-scale of vegetation responses, a process of biophysical changes in the plant, may be more in sync with the gradual fluctuations in SSTs associated with ENSO and IOD events than the relative randomness of precipitation over space and time. We suggest that NDVI can continue to be used as an integral part of the evaluation of ENSO impacts in Africa and similar semi-arid lands globally. While the familiar canonical patterns were observed, we find variations in the placement and magnitude of the precipitation and vegetation anomalies in the latest event. The greatest impacts in eastern Africa migrated westward and southward of the usual position and of the 1997–2000 event, and the positive La Niña impact in South Africa was especially strong. These subtle shifts are likely due to the specific properties of SST anomalies in the Pacific basin and interactions with Indian Ocean SST anomalies; however, attributing these observations to forces driving ENSO variations is outside of the scope of the current work. Several analytical techniques facilitated our exploration of the ENSO teleconnection effects, each with its own strengths and limitations. The seasonal composites provide an intuitive and informative snapshot of NDVI and precipitation conditions, but do not provide insight into the drivers of the observations. Time series correlation analysis demonstrates the magnitude and timing of correspondence between Niño 3.4, the DMI, precipitation, and NDVI. However, the regionally averaged signal used in the comparison obscures local patterns. Spatial correlation analysis fills this gap, and illuminates locations where the local pattern either agrees with, or differs from, the regional trend. EOT analysis distills the time series into the most representative patterns, specifically designed to investigate teleconnection impacts. However, the method is computationally intensive and yielded similar results to the spatial correlation analysis. Together the analyses are very useful for documenting the effects of ENSO and the IOD, which provides insight into variability in the teleconnections and informs future predictions. Additionally, well-documented effects can inform targeted mitigation and relief efforts, such as altering planting practices, formulating insurance coverages, planning emergency food assistance, and vaccinating Remote Sens. 2018, 10, 1038 14 of 16 animals. The availability of an 18-year MODIS time-series record provides opportunities to employ different analysis techniques, such as those used in this analysis, that are easily repeatable and could be used as a guideline for consistent monitoring. Our analysis and results show that the manifestations of ENSO and IOD impacts on the land surface can depart from the canonical pattern, and therefore it is critical to continue monitoring and documenting rainfall and vegetation extremes associated with ENSO and IOD events.

Author Contributions: Conceptualization, A.A.; Methodology, A.A. and E.G.; Validation, A.A., E.G., and J.S.; Formal Analysis, E.G.; Investigation, E.G.; Resources, A.A.; Data Curation, E.G.; Writing-Original Draft Preparation, E.G.; Writing-Review & Editing, A.A. and E.G.; Visualization, E.G. and J.S.; Supervision, A.A.; Project Administration, A.A.; Funding Acquisition, A.A. Funding: This research is supported by the United States Department of Agriculture Foreign Agricultural Service in support of the Global Agriculture Monitoring system (ttps://glam1.gsfc.nasa.gov/). Conﬂicts of Interest: The authors declare no conﬂicts of interest.

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