The Vegetation Drought Response Index (VegDRI): A New Drought Monitoring Approach for Vegetation

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1 The Vegetatin Drught Respnse Index (VegDRI): A New Drught Mnitring Apprach fr Vegetatin Brian D. Wardlw 1, Tsegaye Tadesse 1, Jesslyn F. Brwn 2, and Yingxin Gu 2 1 Natinal Drught Mitigatin Center (NDMC), University f Nebraska-Lincln 2 U.S. Gelgical Survey (USGS), Earth Resurces Observatin and Science (EROS) Center Wrk perfrmed under USGS cntract 03CRCN Intrductin Remte sensing has prven useful fr large-area vegetatin mnitring given the synptic cverage, high tempral repeat cycle, and cntinuus, mderate reslutin bservatins f satellitebased sensrs. In particular, time-series nrmalized difference vegetatin index (NDVI) data frm the glbal imager, the Advanced Very High Reslutin Radimeter (AVHRR), has been widely used fr vegetatin and ecsystem mnitring (Tucker et al., 1985; Reed et al., 1994; Jakubauskas et al., 2002). Analysis f time-series NDVI data and NDVI-derived metrics have been an effective means fr identifying vegetatin cnditin anmalies (e.g., apparent declines in vegetatin health). Operatinal effrts such as the Green Reprt ( RangeView ( and the U.S. Frest Service s Wildland Fire Assessment System ( prvide NDVI-derived prducts that describe the percentage r deviatin f current vegetatin cnditins frm the nrmal cnditins expressed histrically in the NDVI data. The Vegetatin Cnditin Index (VCI) (Kgan, 1990), which is based n a transfrmatin f the AVHRR NDVI data, and the Temperature Cnditin Index (TCI), which is calculated frm AVHRR s thermal data (Kgan, 1995), are peratinally prduced and have been cmmnly used fr natinal- t glbal-scale drught mnitring (Liu and Kgan, 1996; Kgan, 1997; Unganai and Kgan, 1998). Althugh these numerus peratinal prducts have been useful fr vegetatin mnitring, they are limited fr effectively characterizing the impact f drught n vegetatin because the anmalies caused by drught stress cannt be discriminated frm anmalies prduced by ther envirnmental causes f vegetatin stress (e.g., flding, fire, pest infestatin, and hail damage) and anthrpgenic drivers (e.g., land cver/land use cnversin). Additinal infrmatin is required t discriminate the drught-impacted areas frm lcatins where the vegetatin is being influenced by these ther envirnmental and anthrpgenic factrs. Traditinally, climate-based drught indicatrs such as the Palmer Drught Severity Index (PDSI) and Standardized Precipitatin Index (SPI) have been used fr drught mnitring. Hwever, climatebased drught mnitring appraches have a limited spatial precisin at which drught patterns can be mapped because the indices are calculated frm pint-based meterlgical measurements cllected at weather statin lcatins. In additin, weather statins are scarce in remte areas and nt unifrmly distributed. As a result, climate-based drught index maps depict brad-scale drught patterns that are prduced frm pint-based data using statistical-based spatial interplatin techniques, and the level f spatial detail in thse patterns is highly dependent n the density and distributin f weather statins. Therefre, the spatial detail in climate-based drught index maps is limited due t the dependence n uneven and sparse weather statin distributins, which limits drught planning and mnitring activities in areas nt well cvered by weather statins. The Vegetatin Drught Respnse Index (VegDRI) is a new drught mnitring tl that integrates satellite-based NDVI bservatins, climate-based drught index data, and several biphysical characteristics f the envirnment t prduce an indicatr that expresses the level f drught stress n vegetatin. VegDRI integrates cncepts frm bth the remte sensing-based NDVI and the climatebased drught index appraches t prduce 1-km reslutin maps that characterize the intensity and spatial pattern f drught-induced vegetatin stress ver large areas. In the VegDRI apprach, the 1-km NDVI images prvide detailed spatial patterns f vegetatin cnditins, which are analyzed in cmbinatin with dryness infrmatin represented in the climate-based drught index data t identify and characterize the intensity and spatial extent f drught cnditins. Biphysical parameters such as the

2 land cver type, sil available water hlding capacity, irrigatin status, and eclgical setting f an area are als analyzed because these envirnmental characteristics can influence specific climate-vegetatin interactins. VegDRI was develped in a cllabrative research effrt between the Natinal Drught Mitigatin Center (NDMC) and USGS Earth Resurces Observatin and Science (EROS) Center and is designed t be a near-real time, natinal drught mnitring tl fr the cnterminus United States. The 1-km VegDRI maps depict mre spatially detailed, drught-specific infrmatin related t vegetatin than traditinal drught mnitring tls. In additin, this infrmatin is prvided n relevant spatial and tempral scales t decisin makers wrking at the lcal t natinal level. 2. VegDRI Methdlgy and Data Inputs 2.1 Overview f VegDRI VegDRI characterizes the level f drught stress n vegetatin by integrating tw satellite-based NDVI metrics, tw climate-based drught indices, and five biphysical characteristics f the envirnment (listed in Table 1 and discussed in Sectin 2.2). The VegDRI methdlgy cnsists f three main steps: 1) prcessing, summarizatin, and rganizatin f the data fr each input variable int a database (Sectin 2.3), 2) develpment f empirically derived VegDRI mdels fr three seasnal phases (spring, summer, and fall) (Table 2) by applying a classificatin and regressin tree (CART) analysis technique t the histrical infrmatin in the database (Sectin 2.4), and 3) applicatin f the seasnal mdels t gespatial data t prduce a 1-km VegDRI map (Sectin 2.5). VegDRI maps (Figure 1) are currently prduced ver a 15-state regin f the central United States in an peratinal, near-real time fashin. VegDRI cntains seven categries reflecting varying levels f drught-induced vegetatin stress that are based n the PDSI classificatin scheme (Palmer, 1965). The maps als include three additinal classes that depict areas ver which VegDRI values cannt be calculated. These additinal classes include water, ut f seasn (i.e., time when the vegetatin is nt phtsynthetically active fr a lcatin), and n seasn (i.e., lcatins such as the suthwestern United States where n detectable vegetatin respnse was detected in the histrical AVHRR NDVI data). 2.2 VegDRI Data Inputs Satellite-Based NDVI-Related Variables A 17-year time series ( ) f biweekly cmpsited, 1-km AVHRR NDVI data (Eidenshink, 2006) was used t calculate the tw vegetatin-related metrics, percent average seasnal greenness (PASG), and start f seasn anmaly (SOSA) fr inclusin int the VegDRI mdel. Prir t the calculatin f these metrics, a weighted least squares regressin technique (Swets et al., 1999) was applied t the NDVI time series t minimize nn-vegetatin-related nise and ther artifacts (e.g., cluds and variable illuminatin and viewing angles) that are cmmnly fund in the 14-day NDVI cmpsites (Ls et al., 1994). PASG is a biweekly measure that reflects hw the vegetatin cnditins fr a specific perid during the grwing seasn cmpare t histrical average cnditins fr that same perid ver the 17-year AVHRR histrical recrd. PASG is calculated in fur steps: 1) the histrical median start (SOST) and end f seasn (EOST) was identified fr each pixel using a delayed r mving windw averaging technique (Reed et al., 1994); 2) the seasnal greenness (SG) (i.e., NDVI value accumulated frm the SOST up t the current biweekly perid in the grwing seasn) was calculated fr each biweekly perid until the EOST was reached (see Brwn et al. (in press) fr mre details abut the SG calculatins); 3) perid-specific mean SG values were then calculated frm the SG values f the individual years; and 4) fr each year, the perid-specific SG value at the pixel level was divided by mean SG fr that perid t prduce the histrical time series f PASG values.

3 SOSA: The SOSA metric represents the departure f the SOST day f year (DOY) frm the histrical median SOST DOY fr each pixel and was included in the VegDRI mdel t accunt fr the different timings f emergence f varius natural and agricultural vegetatin types as well as land cver change, all f which can influence the seasnal vegetatin perfrmance (and thus PASG) recrded in the satellite bservatins. The SOSA als allws a key distinctin t be made between areas having a lw early seasn PASG due t envirnmental-related vegetatin stress versus nnenvirnmental related factrs such as crp rtatins r ther land cver changes that are unrelated t drught stress. The SOSA was calculated at the pixel level fr each year in the perid f recrd by subtracting the SOST DOY fr a specific year frm the median SOST DOY ver the 17-year perid Climate-Based Drught Index Variables Tw climate-based drught indices, the Standardized Precipitatin Index (SPI) and the selfcalibrated Palmer Drught Severity Index (PDSI), were incrprated int the VegDRI mdel. A 17-year time series f data fr bth indices was generated fr specific weather statin lcatins frm the Natinal Agricultural Decisin Supprt System (NADSS). The indices values were calculated n the same biweekly time-step t be cnsistent with the cmpsiting perid length f the AVHRR NDVI data. Rigrus quality cntrl was applied during weather statin selectin t retain nly weather statins that were currently active, had a sufficient histrical recrd length (e.g., minimum f 30 years f precipitatin data), cntained minimal missing data (e.g., less than 10 percent f all bservatins missing), and were nt lcated in urban areas r adjacent t large water bdies. SPI: The SPI is designed t quantify the precipitatin anmaly fr a specific time perid (e.g., previus 1, 3, 5, r 12 mnths) based n the lng-term precipitatin recrd ver that same time perid (McKee et al., 1995). The SPI is calculated by fitting the lng-term precipitatin recrd ver a specific time step t a prbability distributin, which is then transfrmed t a gamma distributin. Then, the mean SPI fr a specific lcatin and time perid is set t zer. The psitive and negative SPI values represent mre and less precipitatin cmpared t histrical mean precipitatin, respectively (Edwards and McKee, 1997). The SPI was used as a measure f dryness in VegDRI and a single SPI interval was selected fr each seasnal VegDRI mdel based n statistical testing f SPI intervals ranging frm 1 t 52 weeks. A 52-week SPI was fund t prvide the best predictive VegDRI accuracy fr the spring and summer mdels, and a 40-week SPI perfrmed best fr the fall mdel. PDSI: A self-calibrated PDSI (Wells et al., 2004), which reflects hw sil misture cnditins cmpare t nrmal cnditins using a supply-and-demand mdel f a water balance equatin, was selected as the dependent variable fr the VegDRI mdel. This index was an apprpriate dependent variable fr VegDRI because the PDSI accunts fr the effects f precipitatin and temperature, bth f which strngly influence the drught stress n vegetatin. In additin, the PDSI is a wellrecgnized drught metric and the PDSI-like drught scale implemented fr VegDRI is familiar t the drught cmmunity Static Biphysical Variables Several static biphysical variables describing varius envirnmental characteristics that influence drught stress n vegetatin were integrated int VegDRI: Land Cver Type (LC): A 1-km land cver map was generated frm the 30-m USGS Natinal Land Cver Dataset (NLCD 2001) (Hmer et al., 2004) using a znal majrity functin. A land cver cmpnent was included t better parameterize the VegDRI mdel t different NDVI signals and climate-vegetatin respnses exhibited by different land cver types. Sil available water capacity (AWC): AWC was extracted frm the STATSGO database fr each STATSGO sil map unit (USDA, 1994), which was then cnverted t a 1-km grid. The AWC variable was used t represent the variability amng sils t hld misture and make it available t plants.

4 Ecregin Type (ECO): A 1-km ecregin grid based n the Omernik Level III ecregin data (Omernik, 1987) was used t prvide a gegraphic framewrk that accunts fr the cnsiderable abitic (e.g., climate, gelgy, elevatin, and hydrlgy) and bitic (e.g., plant characteristics) variability exhibited acrss the United States, which can influence basic climate cnditins and the level f drught stress n vegetatin. Percent Irrigated Agriculture (IrrAg): A 1-km percent irrigated agriculture map was derived frm a 250-m irrigated lands map based n three data surces: satellite-derived vegetatin index (VI) data, USDA cunty-level irrigatin summary statistics fr 2002, and general land cver infrmatin. The satellite data, cllected by the Mderate Reslutin Imaging Spectrradimeter (MODIS) abard the Earth Observing System Terra platfrm (Justice and Twnshend, 2002), prvided a surce fr a measurement f the annual peak f grwing seasn prductivity at a 250- m spatial reslutin. The peak VI was calculated fr 2002 fr the derivatin f irrigated areas. In an autmated classificatin envirnment (ArcView Avenue), the 2002 cunty statistics (i.e,, number f irrigated acres) prvided the criteria fr dynamically identifying and selecting the number f cells with the highest annual peak MODIS VI within the apprpriate land cver categries. The land cver infrmatin, prvided by the 2001 Natinal Land Cver Database (Hmer et al., 2004), guided the mdel s that land cver categries ther than pasture/hay r cultivated crps were eliminated frm cnsideratin. Discriminatin between irrigated and nnirrigated areas is essential in the VegDRI mdels because rainfed vegetatin has greater sensitivity and respnse t drught than vegetatin receiving targeted water applicatins. Elevatin (ELEV): A 1-km digital elevatin grid extracted frm GTOPO30 (Gesch et al., 1999) was included t distinguish elevatinal differences fr a specific land cver and/r sil type in the VegDRI mdel, which can result in differing levels f sensitivity t drught stress amng lcatins with similar land cver and/r sils. 2.3 Database Develpment A database f the satellite, climate, and biphysical data discussed abve was assembled fr 1,342 weather statin lcatins ver 15 states (nte: an additinal 564 statins will be added fr the western expansin in 2008) fr VegDRI mdel develpment. Biweekly histrical SPI and self-calibrated PDSI data were calculated and sequentially rdered fr each statin. Fr the variables that were in a gridded frmat, summary statistics were calculated fr a 3-by-3 pixel windw centered n each statin lcatin. The average value fr cntinuus variables (e.g., mean AWC) and the dminant (r majrity) class fr the categrical variables (e.g., majrity land cver type) were calculated frm within the windw and added t the database fr each statin. Biweekly histrical PASG values were temprally stacked in the same manner as the climate data, and a single SOSA value per year was used fr each statin. A single, cnstant value was used fr each f the static biphysical recrds acrss the 17 years in the database. The recrds in the database were then subdivided int the three seasnal phases (Table 2) fr which VegDRI mdels were develped. 2.4 Mdel Generatin and Implementatin A cmmercial CART algrithm called Cubist (Rulequest, 2007) was used t analyze the histrical data and generate three seasnal, rule-based piecewise linear regressin mdels (nte: rules and instances ptins were used). The rule sets frm the apprpriate seasnal Cubist mdel were then applied t the gridded image input data using MapCubist sftware, which was develped at the USGS EROS Center, t prduce the 1-km VegDRI maps. Fr the SPI, which was acquired as pint-based data fr individual weather statins, 1-km grids had t be generated fr the biweekly perids using an inverse distance weighting interplatin apprach. Fr near-real time, peratinal VegDRI map prductin, the climate-based indices and the NDVI-derived metrics are updated fr the previus tw-week perid and the apprpriate seasnal mdel is applied t this infrmatin alng with the static biphysical variables t generate a new VegDRI map within 24 hurs f the last data acquisitin. Further infrmatin abut the specific inputs and methds fr the VegDRI discussed in Sectin 2 is prvided in Brwn et al. (in press).

5 3. Results and Prducts The 1-km reslutin f the VegDRI maps allws mre detailed spatial patterns and lcal variability in drught cnditins t be mapped and mnitred than the current state-f-the-art drught mnitring tl, the U.S. Drught Mnitr (USDM), as shwn in the July 2007 example fr Nebraska (Figure 2). The USDM and VegDRI maps bth characterize similar, brad-scale drught patterns acrss Nebraska, but the VegDRI map depicts cnsiderably mre substate t subcunty variatins in the drught cnditins. This result is best demnstrated in the panhandle regin f western Nebraska, where the USDM classifies several cunties int a single drught categry while VegDRI detects areas with differing levels f drught severity within each cunty. VegDRI maps have been peratinally prduced and updated n a biweekly cycle during the grwing seasn (May Octber) fr a 15-state regin f the central United States fr 2006 and 2007 (Figure 3). Gegraphic expansin f VegDRI acrss the western United States is planned fr the spring f 2008 and cmplete cverage f the cnterminus United States is targeted fr Table 3 summarizes the VegDRI prduct characteristics. Wrk is als currently under way t imprve the tempral reslutin f the VegDRI by mving t weekly updates f the maps. In additin, the develpment f a retrspective time series f VegDRI maps dating back t 1989 fr the cnterminus United States is scheduled. VegDRI maps and summary infrmatin can be accessed by the general public via the Wrld Wide Web frm tw lcatins. Drught Mnitring viewer ( hsted by the USGS, prvides an interactive envirnment where the VegDRI maps can be viewed in cmbinatin with a variety f gespatial infrmatin layers (e.g., plitical bundaries, rads, hydrgraphy, land cver, NDVI-derived metrics, and gridded precipitatin prducts). VegDRI Web page ( hsted by the NDMC, prvides a series f quick view VegDRI maps (at multiple spatial scales and fr specific land cver types) and accmpanying drught area statistics tables. Change maps, time-series animatins f VegDRI maps, and a histrical archive f VegDRI are als available. Currently, VegDRI data are nt available fr dwnlad t the general public. The data will be made available fr distributin nce the evaluatin f VegDRI s accuracy is cmpleted and the index s strengths and weaknesses can be characterized. In 2009, we plan t distribute VegDRI data in a GISready frmat (GeTiff) prjected in the Lambert Azimuthal Equal Area prjectin. VegDRI evaluatin results have been prmising and suggest this new index hlds cnsiderable ptential fr large-area drught mnitring. T date, three types f evaluatin have been cnducted. 1) K-fld crss-validatin (using hldut years): A 17-fld crss-validatin (Khavi, 1995) fr each seasnal mdel revealed a relative high predictive accuracy indicated by the mean crrelatin cefficient (r ±1 standard deviatin) f the mdel-predicted VegDRI versus the bserved VegDRI acrss each f 17 mdels tested based n a different hldut year. The crss-validatin results are r = 0.81±0.02, 0.86±0.01, 0.88±0.01 fr the spring, summer, and fall VegDRI mdels, respectively. Fr further infrmatin n the results f the crss-validatin, see Brwn et al. (in press). 2) Spatial pattern analysis: The VegDRI maps were cmpared with ther cmmnly used drught index maps such as the USDM t determine the level f spatial agreement between their brad-scale drught cnditin patterns. Althugh this apprach is qualitative, the cmparisn with an independent data surce such as the USDM allws the relative accuracy f the VegDRI maps t be assessed at a general scale and majr areas f disagreement t be identified fr clser evaluatin. Fr example, in Figure 2, a similar brad-scale drught signal was detected fr western Nebraska in bth VegDRI and USDM maps, as well as the early-stage drying cnditins ver eastern Nebraska.

6 3) Expert feedback: A netwrk f expert evaluatrs that includes state climatlgists, USDM authrs, rangeland/crp experts, and agricultural prducers frm acrss the United States are evaluating the accuracy f the VegDRI maps thrughut the grwing seasn fr their lcal area and prviding bth qualitative and quantitative feedback. Expert evaluatin f the VegDRI maps began in 2007 and the results are currently being summarized. Further quantitative cmparisns f VegDRI t biphysical vegetatin measurements (e.g., bimass and leaf area index) frm varius clip plt datasets, sil misture measurements frm state Mesnets and ther netwrks, and USDA crp yield data are planned. 4. Future Wrk Several research initiatives are under way r planned t enhance the current VegDRI mdel, including: A transitin t time-series 1-km NDVI data frm MODIS is planned t capitalize n the high-quality data stream prvided by this new glbal imager. Research is being cnducted t crsswalk AVHRR and MODIS NDVI data and develp a cmparable intersensr NDVI time series. Once an intersensr NDVI time series is cmpleted, VegDRI will use the histrical time series f NDVI bservatins frm AVHRR fr mdel develpment and apply the mdels t crsswalked MODIS NDVI data fr near-real time prductin f VegDRI maps. The mdels will als be applied t the 250- m MODIS NDVI data in an effrt t develp a higher reslutin VegDRI prduct. The incrpratin f a temperature cmpnent int the VegDRI mdel will als be explred thrugh the use f thermal imagery. New indices derived frm satellite-based thermal bservatins, such as the evaprative stress index (ESI) (Andersn et al., 2007), will be tested. Higher reslutin sil infrmatin frm the Sil Survey Gegraphic (SSURGO) Database will als be evaluated in an effrt t better parameterize the spatial variability and characteristics f sils in the VegDRI mdel. Alternative tempral mdeling windws (e.g., multiple perid, mving windws) are being tested t further enhance the current VegDRI mdels and make the apprach mre flexible as VegDRI prductin expands t ther areas f the United States. 5. Summary VegDRI is a new, natinal-level drught mnitring tl that prvides near-real time, 1-km reslutin maps that depict the gegraphic extent and severity f drught cnditins n vegetatin. The integratin f 1-km vegetatin cnditin bservatins, derived frm AVHRR NDVI data, with climatebased drught index data and ther biphysical infrmatin in the VegDRI mdel enables higher reslutin drught mnitring infrmatin t be generated. The imprved, 1-km reslutin f VegDRI prvides drught infrmatin at a mre relevant spatial scale fr lcal-scale planning, mitigatin, and respnse activities than the cmmn drught indicatrs that are currently being used. As a result, the VegDRI culd be used by a brad user cmmunity that includes agricultural prducers, drught and natural resurce specialists, plicy makers, and ther stakehlders t make mre infrmed decisins at natinal, reginal, state, and cunty levels. 6. References Andersn, M.C., J.M. Nrman, J.R. Mecikalski, J.A. Otkin, and W.P. Kustas, A climatlgical study f evaptranspiratin and misture stress acrss the cntinental United States based n thermal remte sensing: surface misture climatlgy, Jurnal f Gephysical Research, 112, D11112, di: /2006JD Brwn, J.F., B.D. Wardlw, T. Tadesse, M.J. Hayes, and B.C. Reed, in press. The vegetatin drught respnse index (VegDRI): a new integrated apprach fr mnitring drught stress in vegetatin, GIScience and Remte Sensing.

7 Edwards, D. C. and T. B. McKee, Characteristics f 20th century drught in the United States at multiple time scales, Climatlgy Reprt Number 97 2, Clrad State University, Frt Cllins, Clrad. Eidenshink, J.C., A 16-year time series f 1 km AVHRR satellite data f the cnterminus United States and Alaska, Phtgrammetric Engineering & Remte Sensing, 72: Gesch, D.B., K.L. Verdin, and S.K. Greenlee, New land surface digital elevatin mdel cvers the earth: Es, Transactins, American Gephysical Unin, 80(6): Hmer, C., C. Huang, L. Yang, B. Wylie, and M. Can, Develpment f a 2001 natinal land-cver database fr the United States, Phtgrammetric Engineering and Remte Sensing, 70(7): Jakubauskas, M. E., D.L. Petersn, J.H. Kastens, and D. R. Legates, Time series remte sensing f landscape-vegetatin interactins in the suthern Great Plains, Phtgrammetric Engineering and Remte Sensing, 68: Justice, C.O., and J.R.G. Twnshend, Special issue n the Mderate Reslutin Imaging Spectrradimeter (MODIS): A new generatin f land surface mnitring. Remte Sens. Envirn., 83, 1-2. Kgan, F.N., Remte sensing f weather impacts n vegetatin in nn-hmgeneus areas, Internatinal Jurnal f Remte Sensing, 11(8): Kgan, F.N., Drughts f the late 1980s in the United States as derived frm NOAA plar-rbiting satellite data, Bulletin f the American Meterlgical Sciety, 76(5): Kgan, F.N., Glbal drught watch frm space, Bulletin f the American Meterlgical Sciety, 78(4): Khavi, R., A study f crss-validatin and btstrap fr accuracy estimatin and mdel selectin, In Prceedings f the 14th Internatinal Jint Cnference n Artificial Intelligence, Liu, W.T., and F.N. Kgan, Mnitring reginal drught using the vegetatin cnditin index, Internatinal Jurnal f Remte Sensing, 17(14): Ls, S.O., C.O. Justice, and C.J. Tucker, A glbal 1 by 1 NDVI data set fr climate studies derived frm the GIMMS cntinental NDVI data, Internatinal Jurnal f Remte Sensing, 15(17): McKee, T.B., N.J. Desken, and J. Kleist, Drught mnitring with multiple time scales, Preprints, 9th Cnference n Applied Climatlgy, January 15 20, Dallas, Texas, pp Omernik, J.M., Ecregins f the cnterminus United States, Annals f the Assciatin f American Gegraphers, 77(1): Palmer, W. C., Meterlgical Drught. Research Paper N. 45, U.S. Department f Cmmerce Weather Bureau, Washingtn, D.C. 58 pp. Reed, B.C., J.F. Brwn, D. VanderZee, T.R. Lveland, J.W. Merchant, and D. O. Ohlen, Measuring phenlgical variability frm satellite imagery, Jurnal f Vegetatin Science, 5: Rulequest, An verview f cubist, Rulequest Web site, URL: (last date accessed December 10, 2007).

8 Swets, D.L., B.C. Reed, J.R. Rwland, and S.E. Mark, A weighted least-squares apprach t tempral smthing f NDVI, Prceedings f the 1999 ASPRS Annual Cnference, frm Image t Infrmatin, Prtland, Oregn, May 17-21, 1999, Bethesda, Maryland, American Sciety fr Phtgrammetry and Remte Sensing, CD-ROM, 1 disc. Tucker, C.J., J.R.G. Twnshend, and T.E. Gff, African land cver classificatin using satellite data, Science, 9227(4685): Unganai, L.S., and F.N. Kgan, Drught mnitring and crn yield estimatin in suthern Africa frm AVHRR data, Remte Sensing f the Envirnment, 63: USDA (U.S. Department f Agriculture), Natural Resurce Cnservatin Service, 1994, State Sil Gegraphic (STATSGO) Data Base: Data Use Infrmatin, Sil Cnservatin Service, Miscellaneus Publicatin Number 1492, 113 pp. Wells, N., S. Gddard, and M.J. Hayes, A self-calibrating palmer drught severity index, Jurnal f Climate, 17(12):

9 Figure 1. Operatinal VegDRI prduct fr Octber 8, 2007, fr a 15-state regin f the central United States.

10 (a) (b) Figure 2. (a) VegDRI fr July 30, 2007 and (b) the U.S. Drught Mnitr fr July 31, (Nte: White areas indicate n drught in Figure 2b.)

11 Figure 3. Map f gegraphic expansin schedule fr peratinal VegDRI prductin.

12 Table 1. Data inputs fr the VegDRI mdel Data Set Name Type Acrnym Surce Tempral Frequency Frmat Reference(s) Standardized Precipitatin Index Climate SPI ACIS/NADSS biweekly Ascii (at sites) and 1 km raster surface Edwards and McKee, 1997 Palmer Drught Severity Index (Self-calibrated) Percent f Average Seasnal Greenness Climate PDSI ACIS/NADSS biweekly Ascii (at sites) Palmer, 1965 Wells et al., 2004 Satellite PASG AVHRR NDVI biweekly 1 km raster Brwn et al., in press Start f Seasn Anmaly Satellite SOSA AVHRR NDVI annual 1 km raster Brwn et al., in press Land Cver Biphysical LC Natinal Land Cver Database static 1 km raster Hmer et al., 2004 Sil Available Water Capacity Biphysical AWC STATSGO static 1 km raster USDA, 1994 Irrigated Agriculture Biphysical IrrAg USGS EROS / NDMC static 1 km raster Eclgical Regins Biphysical ECO EPA Ecregins static 1 km raster Omernik, 1987 Elevatin Biphysical ELEV GTOPO30 static 1 km raster Gesch et al., 1999

13 Table 2. Seasnal VegDRI mdeling phases Phase Start Date End Date General Grwth Stage Spring mid-april mid-june Emergence, early grwth Summer mid-june end f August Maturity, peak grwth Fall end f August early Octber Senescence, harvest Table 3. VegDRI prduct characteristics. Mnitring target Surce instrument, missin, prcessing chain Prduct descriptin Spatial reslutins Gegraphic map prjectin Vegetatin stress (drught-caused) AVHRR instrument NOAA-11, -14, -16, -17 missins VegDRI prcessing chain Gridded index mdeled after the Palmer Drught Severity Index 1,000-m Lambert Azimuthal Equal Area File frmat Getiff (planned in 2009) Gegraphic extent 7 states in states in states in states in 2009 Prduct frequency 14-day (in near real time during ) 7-day (2009 frward planned) Prduct delay (r Latency) ~ 24 hurs frm last bservatin Perid f recrd Gaps (r time-series hetergeneity) Prduct access (annymus FTP) Cmpatibility with cmmnly used sftware Relevant citatins 1989-current (histrical reprcessing fr 22 states planned in 2008) Nne ftp prduct distributin planned in 2009 Yes Brwn et al., in press.