1396 IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE …icdc.cen.uni-hamburg.de/fileadmin/user_upload/icdc... · National Natural Science Foundation from China under Grant 41171285, and

1396 IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING, VOL. 55, NO. 3, MARCH 2017

Compositing the Minimum NDVI for MODIS DataRonggao Liu, Member, IEEE

Abstract— The maximum and minimum normalized differencevegetation indexes (NDVIs) describe two extremes of vegetationgreenness during a predefined period. A maximum NDVI imagecan be composited easily via the direct selection of the maximumNDVI from multiple observations without the need to maskout cloud or snow. But, a minimum NDVI image cannot bebuilt in a similar manner. In this paper, an approach wasproposed to composite the minimum NDVI (the least vegetationgreenness) image. The minimum spectral index that consistsof the green (555 nm) and SWIR bands (2130 nm) fromMODIS data, which was named here as the Brown VegetationIndex (BVI), was taken as a proxy to composite the minimumvegetation NDVI. This composite method performs well on aglobal scale for the NDVIs that were derived from MODISland surface reflectance (MOD09A1) products. The BVI-basedminimum NDVI was compared with the direct selection of theminimum NDVI after excluding contaminated observations usinga refined cloud/snow mask. The comparison shows that the differ-ence for 97% of the minimum NDVI between the two approachesis within the range of ±0.1 NDVI unit. Various potential spectralindices for compositing the minimum NDVI were compared,which demonstrated the BVI-based approach was top rated.Several examples demonstrated that the composited minimumNDVI is valuable and effective for identifying evergreen forests,monsoon forests, and double cropping. The minimum NDVIcombined with the maximum NDVI would simplify the way todescribe intraannual vegetation changes.

Index Terms— Data processing, remote sensing, vegetationmapping.

I. INTRODUCTION

THE normalized difference vegetation index (NDVI),which is computed from the difference between the

reflectance of the near-infrared (NIR) and red bands, is anindicator of vegetation greenness [1], [2]. The NDVI plays animportant role in vegetation monitoring, which has been usedto directly monitor drought [3], to analyze the effect of climatechange on the land surface [4] and to extract phenologicalparameters from vegetation [5]. Additionally, this index is theprimary input for generating other geophysical products, suchas the leaf area index [6], evapotranspiration [7], and landcover classification [8].

Manuscript received August 24, 2015; revised December 14, 2015 andSeptember 17, 2016; accepted October 6, 2016. Date of publicationNovember 18, 2016; date of current version February 23, 2017. This workwas supported in part by the Key Research and Development Programs forGlobal Change and Adaptation under Grant 2016YFA0600201, in part by theNational Natural Science Foundation from China under Grant 41171285, andin part by the Carbon Project of the Chinese Academy of Sciences underGrant XDA05090303.

The author is with the State Key Laboratory of Resource and EnvironmentalInformation System, Institute of Geographical Sciences and Natural ResourcesResearch, Chinese Academy of Sciences, Beijing 100101, China, and alsowith the University of Chinese Academy of Sciences, Beijing 100049, China(e-mail: [email protected]).

Color versions of one or more of the figures in this paper are availableonline at http://ieeexplore.ieee.org.

Digital Object Identifier 10.1109/TGRS.2016.2623746

The daily NDVI generated from MODIS data can capturetemporal variations of vegetation. But, the daily NDVI seriesare highly duplicated and contaminated by clouds or snow.A new cloud-free NDVI is usually constructed for a prede-fined period via a composite procedure [9], [10]. The max-imum NDVI can be composited by directly selecting themaximum NDVI observation [9]. If the composite periodis short, the vegetation greenness can be considered to beunchanged so that the maximum NDVI is enough to char-acterize the vegetation status during this period. However,in some applications, to remove the contaminated observationseffectively or to congregate the large number of observa-tions to a few layers, a longer composite period should beapplied. In this case, both the maximum and minimum NDVIsare necessary to describe the two extremes of vegetationgreenness, because the vegetation greenness changes largely.For example, the maximum annual composite NDVI representsthe greenest status of vegetation during the entire year, whichcan differentiate the vegetated surface from the nonvegetatedsurface [11]. Similarly, the minimum annual composite NDVIrepresents the least greenness status of vegetation, whichis the vegetation growth baseline for extracting the phenol-ogy [5]. Unfortunately, it is not possible to build the mini-mum NDVI image via directly selecting the minimum NDVIfrom multiple observations, because the contamination coulddecrease the NDVI. Currently, to composite the minimumNDVI, the cloud mask is prerequisite [12]. But, the cloudmask is usually low confidence [13] or complex to gener-ate [14], which makes it difficult to composite the minimumNDVI.

In this paper, an index, named the Browning VegetationIndex (BVI), was proposed as a proxy to composite the mini-mum NDVI without the help of a cloud mask. The characteris-tics of the BVI-based minimum NDVI compositing approachwere analyzed. Several examples of applications of the mini-mum NDVI composite image were demonstrated. The remain-der of this paper was organized as follows. A description of theMOD09 observations and methods was provided in Section II.In Section III, the results of the analysis of the BVI-basedminimum NDVI characteristics were presented. In Section IV,several examples were demonstrated to show the usefulnessof the minimum NDVI composite data. A discussion of theresults was provided in Section V. Section VI concludesthis paper.

II. DATA AND METHODS

A. MOD09 Products

The eight-day MODIS land surface reflectance productsversion 5 (MOD09A1) were used to generate the NDVI

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LIU: COMPOSITING THE MINIMUM NDVI 1397

for the minimum NDVI composite. These data were down-loaded from ftp://ladsweb.nascom.nasa.gov. The MOD09A1provides eight-days reflectances at a 500-m resolution forseven bands (referred as B1∼B7): bands 1 (red: 620–670 nm), 2(NIR: 841–875 nm), 3 (Blue: 459–479 nm),4 (green: 545–565 nm), 5 (1230–1250 nm), 6 (1628–1652 nm), and 7 (SWIR: 2105–2155 nm). The MOD09A1 wascomposited from daily surface reflectance corrected for theeffects of atmospheric gases and aerosols [15]. The BVI wascomputed from the B4 and B7, and the NDVI was derivedfrom B1 and B2. The data include 46 periods per year.The solar zenith provided in the MOD09A1 was used todiscard the observations with high solar zenith (>80° ) inhigh latitude regions in winter. The accompanied cloud/snowflag in the MOD09A1 exists high uncertainties in someregions [16]. In this paper, a more reliable cloud/snow mask,which was generated using the time series information fromMOD09A1 [14], was used to exclude the contaminated obser-vations in data analysis.

B. BVI-Based Minimum NDVI Compositing Method

Healthy green vegetation strongly reflects green light.The green reflectance of vegetation during the growth stageshould be higher than that during the browning stage in theabsence of background soil contributions. Atmospheric andsnow effects can increase the green reflectance. Consequently,a lower green reflectance should correspond to a brownercontamination-free vegetation. However, the green reflectanceof exposed soil is usually much larger than that of vegetation.This means that the green is not enough to composite theminimum NDVI image unless the soil background effect wasremoved.

The spectral index BVI is defined as

BVI = B4 − B7

B4 + B7. (1)

As vegetation grows, B4 may increase or decrease, depend-ing on the contribution of background soil, but B7 shoulddecrease significantly, which results in an increase in theBVI. Snow increases B4 but decreases B7, which increasesthe BVI. The aerosol and cloud effect significantly increasesB4 and slightly increases B7, which also increases theBVI value. For contamination-free observations, the temporalshapes of the BVI and NDVI show almost parallel seasonalvariations [Fig. 1(a)]. But, the contamination effects on theNDVI and BVI are opposite: the contamination decreasesthe NDVI but increases the BVI. Provided that the timeseries observations from the same site were sorted by theBVI in ascending order, the sorted observations [derivedfrom the sequences of Fig. 1(a)] can be divided into threeportions [Fig. 1(b)]. The front portion contains contamination-free observations with a lower NDVI, the middle portionincludes clear-sky observations with a higher NDVI, and thecloudy/snow observations are concentrated in the back portion.The low NDVI of clear-sky observation corresponds to lowBVI while the low NDVI of contaminated observation corre-sponds to high BVI. Thus, the minimum BVI can be a possible

Fig. 1. Schematic for the BVI-based minimum NDVI composite.(a) Temporal profiles of NDVI and BVI. (b) NDVI series ascending orderby BVI.

proxy to composite the minimum NDVI from time seriesobservations.

Of course, the selection of the minimum BVI would notcorrespond exactly to the minimum NDVI. In the algorithm’simplementation, several of the smallest BVI observationswere selected as candidates. The true minimum NDVI asthen determined from all the candidate observations. If toofew observations were available, the observation with theminimum BVI was directly taken as the minimum NDVIobservation.

The implementation of compositing the minimum NDVI issimple.

1) The time series observations were sorted with theBVI.

2) If the compositing period is longer than 90 days, a min-imum of 15% of the BVI observations were taken ascandidates, and then the minimum NDVI observationwas selected from the candidates.

3) If the compositing period is shorter than 90 days,the observation with the minimum BVI was selecteddirectly.

C. Evaluating the Favorite of View Angle for the BVI-BasedMinimum NDVI Composite

The observational NDVI is affected by the bidirectionalproperties of the surface. The land surface in five consecutive


observational dates can be assumed to be statistically stableand the solar zenith is nearly invariant. The differences of theNDVI between these consecutive dates can mainly contributeto the view angle effect. To evaluate the view angle favorabilityfor the minimum BVI, five contamination-free observationsfrom consecutive observational dates at the same site weresorted in ascending order by their BVIs, and the rankedobservations were labeled as ranks 1–5. Then, the view zenithangles for each rank were counted for all global observationsfrom 2000 to 2011. The BVI-based minimum NDVI compositeis favored selecting the view angle that is distributed in rank 1.

D. Evaluating the Shadow Effect for the BVI-BasedMinimum NDVI Composite

The shadow decreases the surface reflectance of the visibleband. Without atmospheric correction, the shadow effect onthe visible band is less than on the SWIR band, because theatmospheric scattering for the visible bands is larger. Afteratmospheric correction, the scattering effect was removed sothat the shadow effect was similar in the visible and SWIRbands. Mathematically, the shadow effect decreases both thedenominator and numerator to increase the BVI. Therefore,theoretically, the BVI-based composite should be able toabstain from the shadow observations. Two steps were per-formed to test the shadow effect on the BVI-based composite.First, the observations at the same site for 2009 were sortedin ascending order by B1 to find the shadow observations.The first three observational reflectances were labeled as R1,R2, and R3. If R2 − R1 > R3 − R2 and R2 − R1 >0.005, the first ranked observation was labeled as a shadowobservation. To avoid the effect of overatmospheric correction,the observations that were labeled as thick aerosols in theMOD09A1 were excluded. Additionally, the water and per-manent snow sites were excluded. Then, all observations weresorted in ascending order via the BVI (referred as BVI series).The position of the shadow observation in the BVI series waslocated. The position frequency of the shadow observationin the BVI series was counted to evaluate the effect of theshadows. If the positions of the shadow observations wereconcentrated on the rear rank, it means that the BVI-basedcomposite discarded the shadow observations.

E. Tests of Various Spectral Indices for Compositing theMinimum NDVI

There are seven bands in the MODIS data that weredesigned to acquire land surface information. These bands maybe used alone or in combination to constitute various potentialindices to composite the minimum NDVI. The feasibilities ofthese potential indices were tested. Band 3 was not tested,because this band is used for atmospheric correction witha high uncertainty, and it is highly correlated with band 1.The benchmark for the minimum NDVI (NDVIMinMask) isthe direct minimum NDVI. The NDVI value correspondingto the minimum index (NDVIIndexComp) was considered to bethe result of this index-based composite approach. The mean

NDVI difference (MeanDiff) was calculated as

MeanDiff =

N∑

i=1(NDVIIndexComp − NDVIMinMask)

N. (2)

When the MeanDiff is closer to zero, the correspondingindex is better at selecting the minimum NDVI. Anothercriterion for evaluating the feasibilities of an index is the conta-mination of the composite data. If the selected observation waslabeled as cloud/snow by the refined cloud/snow mask [14],this selection was considered to be “contaminated.” The per-centage of contaminated observations for the composite resultswas counted for each index. If the percentage is lower, thisindex is better in selecting contamination-free observations.

III. RESULTS

A. Evaluation of the BVI’s Potential to Composite theMinimum NDVI Using Temporal Profilesof Observations

The temporal profiles of the time series of the NDVI and theNDVI corresponding to the five lowest BVI in each year wereused to illustrate the capability of the lowest BVI to selectthe contamination-free lowest NDVI. The time series NDVIvalues were extracted from the MOD09A1 at several sitesfrom 2000 to 2011. These sites cover several major land-covertypes, including deciduous forests, evergreen forests, grass,and crops.

These temporal profiles were presented in Fig. 2. Gener-ally, for deciduous vegetation, the selected minimum NDVIsconcentrate on the deciduous season. For tropical evergreenforests, the selected NDVIs are distributed randomly through-out the year without an obvious seasonal characteristic. Thesetemporal profiles show that a lower BVI corresponds to thelowest contamination-free NDVI observation for deciduousvegetation or to the contamination-free observations for trop-ical evergreen forests.

For the evergreen conifer forests in the boreal regions,the NDVI fluctuates by approximately 30% from 0.65 to 0.9due to the understory and mixed deciduous vegetation thatwithers in the winter. In spring, with snowmelt and forestgrowth, the NDVI increases. In summer, the NDVI reachesits maximum. In autumn, the understory vegetation withers,and the NDVI decreases. In winter, the vegetation informationis covered or contaminated by snow. During this vegetationgrowth cycle, the NDVI reaches the peak in summer andreaches the lowest in autumn or spring. From Fig. 2(a), mostof the selected observations for the five lowest BVIs wereobtained during the autumn or spring periods. The cloudand snow observations were excluded successfully from theselected observations.

For deciduous conifer forests in the boreal regions, both theunderstory vegetation and overstory vegetation withers in thewinter. The NDVI decreases by more than 30% from approx-imately 0.9 to 0.5. The maximum annual NDVI should belocated in summer, and the minimum annual vegetation NDVIshould appear in autumn or spring. As shown in Fig. 2(b),most of the selected observations for the five lowest BVIswere obtained in autumn or spring.


Fig. 2. Temporal profiles of NDVI and the NDVI selected from the five smallest BVI for time series observations from 2000 to 2011. (a) Evergreenneedleleaf forest (ENF, 106.48 E, 51.84 N). (b) Deciduous needleleaf forest (DNF, 104.45 E, 53.96 N). (c) Tropical forest (62.42 W, 2.54 S). (d) Monsoonforest (80.31 E, 22.03 N). (e) Deciduous broadleaf forest (EBF, 108.70 E, 35.27 N). (f) Grass (101.58 E, 34.89 N). (g) Monsoon crop (95.19 E, 21.28 N).(h) Double crop (115.25 E, 33.47 N).

For the tropical evergreen forests, the greenness of vege-tation remains nearly unchanged or features only very slightchanges. During the rainy period, a few clear-sky observationsare available. As shown in Fig. 2(c), the selected observationsfor the five lowest BVIs for tropical evergreen forests do notconcentrate during a specific period. However, the contami-nated observations can be effectively excluded.

For the monsoon forest in the Indian subcontinent, the rainyseason occurs annually in mid-April, and this instigates thebeginning of vegetation growth that promotes the rapid growthof the NDVI to its maximum value after a few days or approx-imately half a month. With the rainy season ending andthe dry season starting, the vegetation gradually withers andreaches a minimum value before the next rainy season begins.

During this cycle, the NDVI reaches its minimum before therainy season comes. The selected observations by the BVIin Fig. 2(d) capture these minimum annual NDVI correctly.The cloudy observations with the low NDVI values duringthe rainy period were successfully excluded.

For the temperate broadleaf deciduous forests, the leavesgrow in spring and wither in winter. In summer, the forestremains at a mature stage with a high NDVI for severalmonths. The minimum annual NDVI should appear in springbefore vegetation growth or in autumn after the leaves fall.As observed in Fig. 2(e), the lower NDVI observations can becorrectly selected.

For grass with significant seasonal variations, the temporalprofile is similar to that of a deciduous broadleaf forest.


Fig. 3. Global distribution of the BVI-based minimum NDVI minus thedirect minimum NDVI.

The maximum annual NDVI should occur at the end of sum-mer, and the minimum annual NDVI should occur during thewithered seasons of spring and winter. As shown in Fig. 2(f),the selected observations were ideally located during thisperiod as expected.

For crops in the monsoon regions, the minimum annualNDVI occurs in the drought period before the rainy seasonbegins. As shown in Fig. 2(g), the selected observations bythe five lowest BVIs were ideally located during this period.

Because the double crop was harvested twice in one year,there are two minima in the annual temporal NDVI profiles.As shown in Fig. 2(h), the selected observations of theminimum BVI perfectly match with the two harvest seasons.

These temporal profiles show that the lowest BVI caneffectively select the contamination-free observations duringthe vegetation withered season. For the tropical evergreenforest, although the BVI is not sensitive to leaf abscission,the contaminated observations were very effectively excluded.

B. Reliability of the BVI-Based Minimum NDVI Composite

The BVI-based minimum NDVI composite selects the min-imum NDVI observation without the help of the cloud mask.Another approach for the minimum NDVI composite is thedirect selection of the minimum NDVI observation usingrefined snow/cloud masks for MOD09A1 [14] to discard thecontaminated observations (hereafter referred to as the directminimum NDVI). The BVI-based minimum annual NDVI wascompared globally with the direct minimum annual NDVIfrom 2000 to 2011 to evaluate its reliability. Those pixels witha maximum annual NDVI of less than 0.5 were excluded fromthe count.

A total of 3.97×1010 observations were counted. The meandifference between the direct and the BVI-based minimumNDVI was 0.018 NDVI units. Fig. 3 shows the spatial differ-ence in the minimum annual NDVI for the two approaches,and approximately 30% of the observations are identical.The difference of 87% of the observations is within ±0.05,and 97% is within ±0.1. Generally, the BVI-based min-imum annual NDVI values are somewhat larger than thedirect minimum NDVI. For the tropical forests in Africa,

Fig. 4. Frequency of view zenith angle for the ranked BVI.

the BVI-based results are generally larger. In the Amazonianregions, the BVI-based results are generally lower than thedirect minimum NDVI. In other regions, the differencesbetween the two approaches are small, although the BVI-basedresults are generally slightly larger. Several factors maylead to the differences between the two approaches. First,any residual cloud, snow, or subpixel water decreases theNDVI. The contaminated observations were not thoroughlylabeled by the snow/cloud mask. However, the BVI-basedminimum NDVI can effectively exclude the contaminatedobservations with a higher BVI. In this case, the BVI-basedminimum NDVI should be larger. Second, the BVI is only aproxy but equivalent to the minimum NDVI. If the minimumNDVI does not fall within the candidates, the BVI-basedminimum NDVI would be somewhat larger. Contrarily, if thelow NDVI observations are overlabeled by the snow/cloudmask, the direct minimum NDVI would be higher than theBVI-based minimum NDVI. For the wetlands, the BVI-basedmethod selects the dry observations, which leads to a largerNDVI.

C. Favorite of View Angle for the BVI-Based MinimumNDVI Composite

Fig. 4 is a histogram of the angle distribution for eachrank during the 12 years on a global scale. When the rela-tive azimuth angle (solar azimuth minus sensor azimuth) isless than 0, the view zenith angle is set as negative. Thisfigure shows that a smaller BVI (rank 1) is favored overthe nadir view angle, because the frequency of rank 1 islarger than that of rank 5 in the nadir. The percentages of theobservations with a view zenith within ±30° for ranks 1–5are 81.08%, 81.01%, 79.70%, 78.48%, and 77.06%, respec-tively. A smaller BVI is somewhat favored over the backwarddirection (relative azimuth angle <0). The percentages fora view zenith that is less than −30° for ranks 1–5 are8.02%, 10.43%, 11.88%, 13.35%, and 15.03%, respectively.Additionally, the percentages for a view zenith that is largerthan 30° for ranks 1–5 are 12.46%, 10.05%, 9.88%, 9.67%,and 9.55%, respectively. The results show that the BVI-basedcomposite is somewhat favored over the forward direction. It is


Fig. 5. Frequency of shadow observation in the BVI series.

TABLE I

THE MEANDIFF AND THE CONTAMINATED PERCENTAGE FOR

THE MINIMUM COMPOSITE OF DIFFERENT INDEX

similar to the maximum NDVI composite, which favors theoff-nadir pixels with large forward-scatter view angles [17].

D. Effect of the Shadow for the BVI-BasedMinimum NDVI Composite

A total of 4.77×108 series were tested for a shadow effect,and 6.07×106 of these shadow observations were found to bea shadow. Fig. 5 shows the position frequency of the shadowobservations in the BVI series. As shown in the figure, exceptfor the first rank, the shadow position is mainly distributedin the rear parts. This means that the BVI-based compositepreferentially discards the shadow observations.

E. Tests of Various Spectral Indices for Compositing theMinimum NDVI

The MeanDiff and the contaminated percentage of eachindex-based composite were listed in Table I. As shown inthis table, the MeanDiff is high for B1, B1 combined withthe SWIR, B7, and B6, which suggests that these indicescannot select the minimum NDVI correctly. The minimumof these indices selects the green vegetation observations,because the growth of the vegetation decreases B1, B7,and B6. The MeanDiff for MinBVI, MinSI25, MinB02,MinSI26, MinB05, and MinSI27 is low; thus, these indices arepotentials for the minimum NDVI composite. The vegetationgrowth may increase the NIR reflectance so that the minimumNIR or the NIR-combined indices preferentially select theminimum NDVI.

However, the contamination for B2, B5, and their com-bined indices composites is high. Several factors negativelyimpair these approaches. Snowmelt water largely decreasesthe NIR reflectance due to the strong water absorption, causingsnow-contaminated observations to be selected, especially inthe boreal regions. Additionally, the NIR reflectance of a denseforest is sometimes higher than that of clouds, especially intropical evergreen forests, resulting in the selection of cloudyobservations. All indices that combine with band 2 or band 5,which are similar to band 2, favor the observation of thebrownest vegetation and cannot discard well the contaminatedobservations. In view of the MeanDiff and contamination,the MinBVI is the preferred index for the composite minimumNDVI among all of these indices.

IV. APPLICATIONS OF THE MINIMUM NDVI COMPOSITE

A. Minimum Annual NDVI and Its Applications

The maximum and minimum annual NDVIs represent twoextreme stages of vegetation growth. The former representsthe greenest vegetation status of one year, and the latterrepresents the surface during the dormant stage of vegetationin the absence of snow. Fig. 6 shows the maximum annualNDVI [Fig. 6(b)], the minimum annual NDVI [Fig. 6(c)],and the RGB color image (R: band 1, B: band 3, and G: band4) corresponding to the minimum annual NDVI [Fig. 6(a)]during 2011. The maximum annual NDVI was generatedby directly selecting the maximum NDVI from the observa-tions of the entire year. The permanent snow/ice and waterregions were composited using the minimum red bands (B1).As shown in Fig. 6(a), the cloud and snow contaminationswere effectively removed from the composite image via visualinterpretation. According to Fig. 6(b), the maximum annualNDVI shows a significant spatial variation and is closelyrelated to the surface type. The maximum annual NDVI islarger than 0.8 for the dense forests in the northern highlatitude regions north to 50 ° N and in the southeastern partof North America, eastern Asia, southeastern Asia, southernand central South America, and central Africa. The maxi-mum annual NDVI is approximately 0.4 for grass around thedesert regions and is approximately 0.6–0.8 for cropland. Themaximum annual NDVI is low in the nonvegetated regionswith NDVI values of approximately 0.1 in regions, such as


Fig. 6. Global annual NDVI composite and the evergreen vegetation distribution derived from the minimum and maximum annual NDVIs in 2011. (a) RGBcolor image (R: band 1, G: band 4, and B: band 3) based on the minimum annual BVI. (b) Maximum annual NDVI. (c) Minimum annual NDVI. (d) Differenceof maximum and minimum annual NDVIs. (e) Evergreen vegetation distribution derived from the maximum and minimum NDVI differences.

the polar region, high-altitude regions covered by permanentsnow/ice, and desert regions. For the savanna regions inAfrica and Australia, the maximum annual NDVI is approx-imately 0.25–0.40. From the maximum annual NDVI image,the vegetated regions and nonvegetated regions can be easilydistinguished.

According to Fig. 6(c), during the dormant stage of veg-etation, the minimum annual NDVI is approximately 0.2 inthe grass land, such as around the desert regions, and nearly0.35 in the crop land. For the tropical evergreen forests,the minimum annual NDVI is larger than 0.8. For theevergreen conifer forests, because the understory vegetationwithered during the dormant period, the minimum annualNDVI is approximately 30% lower than the maximum annualNDVI. For the deciduous forest, the minimum annual NDVI isapproximately 0.5 in the dormant stage. According to Fig. 6(b)and (c), the deciduous forests show a strong seasonal growth.The maximum annual NDVI is similar for both the evergreen

and deciduous forests, but the minimum annual NDVI forthe deciduous forests is low. The minimum and maximumannual NDVIs are low and similar in the desert regions. Thesesimilarities demonstrate that the BVI-based minimum NDVIcomposite method can select contamination-free observationsfor the bare land.

1) Application of the Minimum Annual NDVI for ExtractingEvergreen: Evergreen vegetation remains green through-out the year, which means that the minimum annualNDVI should be high and the difference between theminimum and maximum annual NDVIs should be low.In contrast, the greenness difference between the growthstage and the dormant stage is large for deciduousvegetation. It means that the minimum annual NDVIshould be low, and the amplitude between the maximumand minimum annual NDVIs should be high. Therefore,it is possible to distinguish the deciduous and ever-green forests by combining the minimum and maximum


Fig. 7. Spatial interannual variances of the minimum annual NDVIfrom 2000 to 2011.

annual NDVIs. Considering that the herbaceousvegetation in evergreen conifer forests is withered in thewinter, the minimum annual NDVI larger than 0.5 andthe difference between the maximum and minimumannual NDVIs less than 25% were taken as the indi-cators to determine the evergreen conifers. The mini-mum annual NDVI larger than 0.5 and the differencebetween the maximum and minimum annual NDVIsless than 15% were taken to determine as the evergreentropical vegetation. Fig. 6(d) shows the differences inthe maximum and minimum NDVIs. Fig. 6(e) showsthe distribution of the evergreen conifer forests andevergreen tropical forests, as inferred from the maximumand minimum NDVIs using the recommended threshold.

2) Interannual Change of the Minimum Annual NDVIand Its Application for Extracting the NDVI Baseline:The minimum annual NDVI is the baseline for analyzingthe NDVI time series. Typically, the minimum green-ness of vegetation should be similar in different years.However, the composited minimum annual NDVI maybe different from year to year due to the contaminationof seasonal clouds, snow, and water. The interannualmean variability of the minimum annual NDVI was cal-culated pixel-by-pixel for those pixels with a maximumannual NDVI that was larger than 0.5 from 2000 to 2011(Fig. 7). In the regions around the desert, the variabilityis approximately 0.02. For the croplands, the variabil-ity is approximately 0.04. The variability in deciduousregions is very low, especially in the midlatitude regionswith temperate forests, grasslands, and croplands, suchas in the grassland region of China and the croplands ofAmerica and Europe.

A large variability appears in the regions that are coveredby clouds or snow for a long period. In the cloudy regions,the number of clear observations is important for extractingthe land surface information [18]. In the tropical regions,clouds lead to high variances in the minimum annual NDVI,as observed in the tropical regions of Southeast Asia, the Ama-zon, and the region around the Gulf of Guinea of West Africa.In the region near the Gulf of Guinea in Africa, the largevariability is primarily from the humid climate. In this region,only a few clear-sky observations are available for an entireyear. In this case, the minimum annual NDVI is mainly deter-mined by the availability of clear-sky observations rather than

the vegetation status. Because the dates of available clear-skyobservations were different from year to year, the variabilityin this region is large. Another case is the Chengdu plain inChina in which the sky is almost always cloudy through-out the winter when the vegetation reaches the brownest.The minimum annual NDVI in this region is determined bythe available clear-sky observations, which vary from year toyear. Thus, the minimum annual NDVI shows a large inter-annual variance. In these cloud-covered regions, the climateis similar during the minimum NDVI period, and the inter-annual minimum annual NDVI fluctuation should be small.The large interannual variance of the minimum annual NDVIshould be attributed to the dates of the contamination-freeobservations.

In the high-latitude and high-altitude regions that are fre-quently covered by snow, such as boreal regions, the RockyMountains, Greenland, the Himalayan Mountains, and theAndes Mountains, the minimum annual NDVI would notcapture the minimum vegetation greenness. In the borealregions, snow falls in autumn when the vegetation may notreach the brownest, and vegetation may grow before the snowmelts completely. The minimum annual NDVI is determinedby the dates of snowfall and snowmelt, which vary from yearto year. Additionally, the period from brown vegetation towhen snow falls is very short that the clear-sky observationsare unavailable. In these regions, the temperature in winter isfar lower than what is required for leaf growth of deciduousvegetation. Thus, the interannual climate fluctuations hardlyaffect the vegetation greenness in the dormant stage. This sug-gests that the minimum annual NDVI should be similar fromyear to year. Therefore, a reliable baseline of the minimumannual NDVI can be determined from the values of severalyears.

B. Minimum NDVI for Identifying MonsoonDeciduous Forests

Monsoon forests are estimated to be over 40% in trop-ical forest areas, which become green in the wet seasonand defoliate in the dry season. Because the temperature isalways above the photosynthesis threshold, monsoon forestsare different from other deciduous forests regarding theirenergy balance and carbon and water cycles [19]. However,a monsoon forest is difficult to be correctly classified in mostglobal land cover products. In some regions, the defoliationperiod is less than one month or only several days, and amonsoon forest is usually misclassified as an evergreen forest.According to its unique characteristic, a monsoon forest canbe easily identified by its minimum NDVI during the dryseason (Fig. 8).

C. Minimum NDVI for Identifying Double Cropping

Multiple cropping, a rotation of two or more crops per yearin a unit cropland area, is an effective way for improvingcrop production to meet the requirements of an increasingpopulation. Understanding the extent and intensity of multiplecropping is important for monitoring food production [20].


Fig. 8. Detection of the monsoon forest using the maximum and minimum NDVIs. (a) RGB image composited by the minimum BVI (R: band 1, G: band 4,and B: band 3). (b) Maximum NDVI. (c) Minimum NDVI. (d) Evergreen and monsoon forest distribution.

Currently, cropping intensity is typically identified via a tem-poral profile analysis, which requires a cloud mask, time seriesinterpolation, and inflexion point identification [21]. However,accurate cloud detection is not easy, and the interpolationwould introduce other errors. A minimum NDVI compos-ite can greatly simplify this identification process. For theexamples in Fig. 9, the first cropping reaches the acme atapproximately day of the year (DOY) 141 and is harvestedat approximately DOY 161. The second cropping reaches itsacme at approximately DOY 225. For the double croppingregions, the NDVI value should be NDVI141 > NDVI161 <NDVI225. The NDVI141 is the minimum NDVI between theDOYs of 141 and 225. In contrast, for single cropping or forestregions, the temporal NDVI shows a monotone increase duringthe DOYs of 141–225. If the maximum NDVI during theDOYs of 105–141 and during the DOYs of 141–225, and theminimum NDVI during the DOYs 141–225 can be compos-ited, the double cropping can be easily identified using thesethree composite data. Fig. 9(a) shows the time series NDVIcurve for the double cropping during several years. Fig. 9(b)shows the maximum NDVI in the first cropping stage, andFig. 9(c) represents the minimum NDVI as the first croppingwas harvested, whereas Fig. 9(d) shows the maximum NDVI

during the second cropping. From Fig. 9(b)–(d), the doublecropping distribution [Fig. 9(e)] can be easily inferred.

V. DISCUSSION

The composited maximum NDVI, which represents thegreenest vegetation during the composite period, describesthe upper outlier of seasonal vegetation change. In contrast,the time series of the minimum NDVI composite can describethe lower outlier of change. Similar to the maximum NDVI,the time series of the minimum NDVI can also build an inde-pendent climate record data product. However, if the compos-ite period is short, the upper and lower outliers are overlappedso that the maximum NDVI is equivalent to the minimumNDVI. For a longer period, such as one month or one season,the minimum and maximum NDVIs are typically different.A combination of the minimum and maximum NDVIs wouldcondense the redundant and contaminated data to a few layersthat would simplify the description of intraannual vegetationchange. This would make it easy to identify double cropping,evergreen vegetation, and monsoon forests.

The maximum NDVI composite can be generated bydirectly selecting the maximum NDVI from multipleobservations without the help of a cloud/snow mask.


Fig. 9. Detection of the double cropping. (a) Time series NDVI. (b) Maximum NDVI-based composite RGB image (R: band 1, G: band 4, and B: band3) between DOY 41–161. (c) Minimum NDVI-based composite RGB image (R: band 1, G: band 4, and B: band 3) between DOY121–241. (d) MaximumNDVI-based composite RGB image (R: band 1, G: band 4, and B: band 3) between DOY 161–241. (e) Double cropping distribution, the yellow color isdouble crop, the green color is other vegetation, and the blue is water.

However, it is not possible to build the minimum NDVI imagein the same manner. The use of the lowest B2 or B5 maybe a potential approach for compositing the minimum NDVI.However, the strong water absorption contributing to theB2 and B5 bands makes these approaches globally unfeasible.Additionally, the B2 or B5 of some dense forests sometimesis higher than that of clouds, resulting in the misinterpretationof clouds as vegetation. Considering the ability to select thebrownest vegetation and the ability to remove the contamina-tion from clouds, snow, and water, the BVI-based approach isthe best choice to composite the minimum NDVI.

The BVI is a combination of the B4 and B7 bands. In thisformula, if the B4 and B7 are very low, a small noise inB4 and B7 would introduce a large uncertainty to the BVI.This would be significant for dense evergreen forest regions,which may partially explain that the BVI-based minimumNDVI composite cannot capture a small greenness fluctuationin dense evergreen forests. The aerosols would increase thevalue of the BVI so that selection of the minimum BVI wouldfavor the most clear-sky observations. Atmospheric correctionscan remove the aerosol effects. But, an atmospheric overcor-rection may decrease the BVI while increasing the NDVI,which would lead to a lower BVI that would correspond toa larger NDVI value. For those regions seasonally inundatedwith water, the BVI-based composite would avoid the waterobservations and favor dry observations. In the brownestseason of the wetlands, the exposed water would introduce alarge BVI, because the brownest vegetation observation would

not be selected. The subpixel residual cloud/snow increases theBVI, which benefits the BVI-based minimum NDVI compositeby automatically removing these contaminations. Because theresidual contaminations cannot usually be labeled correctlyby the cloud/snow mask, the BVI-based minimum NDVIcomposite is better than the direct minimum NDVI, becauseit has less residual contamination.

VI. CONCLUSION

A BVI-based minimum NDVI composite approach wasproposed. The BVI was demonstrated the best proxy forselecting the minimum NDVI observation while preventingatmospheric and snow contamination. This composite methodwas tested globally using a 12-year NDVI that is derived fromMODIS land surface reflectance products from 2000 to 2011.The temporal profiles of the time series observations for thesites showed that a low BVI corresponds well to a low NDVIcontamination-free observation. The minimum annual NDVIcomposite demonstrated that the BVI-based minimum NDVIcomposite approach performs well across various vegetationtypes. The mean difference between the BVI-based minimumNDVI and the direct minimum NDVI is 0.018 NDVI units.Approximately 30% of the results between the two approachesare identical, and the differences for 87% of the results arewithin ±0.05 NDVI units. The differences for 97% of theresults are within ±0.1 NDVI units. The BVI-based minimumNDVI composite somewhat favors the backward view angle.


Compared with other potential spectral indices for compositingthe minimum NDVI, the BVI-based approach is top rated. Sev-eral examples demonstrated that the minimum NDVI is effec-tive and valuable for identifying evergreen forests, monsoonforests, and double cropping. The minimum NDVI combinedwith the maximum NDVI constitutes a new climate recorddata product for describing intraannual vegetation changes.

ACKNOWLEDGMENT

The author would like to thank the MODIS Science Teamfor providing excellent and accessible data products thatmade this research possible. The author would also like tothank the anonymous reviewers for their insightful and criticalcomments, which make this paper be improved largely.

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Ronggao Liu (M’10) received the B.S. degreefrom the Chengdu College of Geology, Chengdu,China, in 1993, and the M.S. and Ph.D. degreesfrom the Institute of Geochemistry, Chinese Acad-emy of Science, Beijing, China, in 1996 and 2000,respectively.

He is currently a Professor with the Instituteof Geographical Sciences and Natural ResourcesResearch, Chinese Academy of Sciences, andthe Director of the Data Center for Resourcesand Environment, Chinese Academy of Sciences.

His research interests include cover algorithms for parameters retrieval anddevelopment of processing system for global long-term remote sensing data.

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