Assessing agricultural drought in summer over Oklahoma Mesonet sites using the water-related vegetation index from MODIS

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1 DOI 1.17/s ORIGINAL PAPER Assessing griculturl drought in summer over Oklhom Mesonet sites using the wter-relted vegettion index from MODIS Rjen Bjgin 1 & Xingming Xio 1,2 & Jeffrey Bsr 3,4 & Prdeep Wgle 1 & Yuting Zhou 1 & Yo Zhng 1 & Hyden Mhn 3 Received: 11 Novemer 215 /Revised: 1 July 216 /Accepted: 18 July 216 # ISB 216 Astrct Agriculturl drought, common phenomenon in most prts of the world, is one of the most chllenging nturl hzrds to monitor effectively. Lnd surfce wter index (LSWI), clculted s normlized rtio etween ner infrred (NIR) nd short-wve infrred (SWIR), is sensitive to vegettion nd soil wter content. This study exmined the potentil of LSWI-sed, drought-monitoring lgorithm to ssess summer drought over 113 Oklhom Mesonet sttions comprising vrious lnd cover nd soil types in Oklhom. Drought durtion in yer ws determined y the numer of dys with LSWI < (DNLSWI) during summer months (June August). Summer rinfll nomlies nd LSWI nomlies followed similr sesonl dynmics nd showed strong correltions (r 2 =.62.73) during drought yers (21, 26, 211, nd 212). The DNLSWI trcked the est-west grdient of summer rinfll in Oklhom. Drought intensity incresed with incresing durtion of DNLSWI, nd the intensity incresed rpidly when DNLSWI ws more thn 48 dys. The comprison etween LSWI nd the US Drought Monitor Electronic supplementry mteril The online version of this rticle (doi:1.17/s ) contins supplementry mteril, which is ville to uthorized users. * Xingming Xio xingming.xio@ou.edu Deprtment of Microiology nd Plnt Biology, Center for Sptil Anlysis, University of Oklhom, 11 Dvid L. Boren Blvd, Normn, OK 7319, USA Ministry of Eduction Key Lortory for Biodiversity Science, nd Engineering, Institute of Biodiversity of Sciences, Fudn University, Shnghi 2433, Chin School of Meteorology, University of Oklhom, Normn, OK, USA Oklhom Climte Survey, Normn, OK, USA (USDM) showed strong liner negtive reltionship; i.e., higher drought intensity tends to hve lower LSWI vlues nd vice vers. However, the greement etween LSWIsed lgorithm nd USDM indictors vried sustntilly from 32 % (D 2 clss, moderte drought) to 77 % ( nd D clss, no drought) for different drought intensity clsses nd vried from 3 % (western Oklhom) to >8 % (estern Oklhom) cross regions. Our results illustrted tht drought intensity thresholds cn e estlished y counting DNLSWI (indys)ndusedssimplecomplementrytoolinseverl drought pplictions for semi-rid nd semi-humid regions of Oklhom. However, lrger discrepncies etween USDM nd the LSWI-sed lgorithm in rid regions of western Oklhom suggest the requirement of further djustment in the lgorithm for its ppliction in rid regions. Keywords Drought durtion. Drought intensity. Lnd surfce wter index. Summer drought Introduction Drought is recurrent nd inevitle thret in severl prts of the world (Hulse nd Escott 1986; Shhid nd Behrwn 28; Sönmezetl.25). Southern Gret Plins of the USA experience drought on vrying sptil nd temporl scles (Bsr et l. 213; Christin et l. 215). Drought is lso mong the most difficult of ll nturl hzrds to monitor effectively. Yet, the repeted occurrence of drought events hs highlighted the need to develop effective droughtmonitoring tools to ssess the impcts of this phenomenon. Reserch to retrieve lef wter content from the reflectnce cquired from stellite sensors hs progressed for more thn three decdes. Tucker 198 first suggested tht the 155

2 175-nm spectrl intervls were the est-suited nd in the 7 25-nm region for monitoring plnt cnopy wter sttus from spce. A numer of rodnd rtio nd comintion techniques using Themtic Mpper (TM) chnnel 4 (76 9 nm, ner infrred) nd TM chnnel 5 ( nm, shortwve infrred) were proposed for remote sensing of plnt wter sttus (Hunt et l. 1987; Jckson et l. 1983). The comintion of the ner infrred (NIR) nd short-wve infrred (SWIR) nds hs the potentil of retrieving vegettion cnopy wter content (Ceccto et l. 21, 22; Mki et l. 24). The wter-relted vegettion index computed from the comintion of NIR nd SWIR hs different nomencltures y different uthors. Go 1996 nd Chen et l. 25 referred it the normlized difference wter index (NDWI). Kimes et l used the term normlized difference infrred index (NDII). Similrly, Jurgens 1997 nd Xio et l. 22, clled the sme comintion of NIR nd SWIR nds s the lnd surfce wter index (LSWI). Despite known y different nmes, the fetures they hve in common is tht the NIR spectrl region serves s moisture reference nd nd the SWIR spectrl domin is used s the moisture-mesuring nd. The wter-relted vegettion index is mesurement of liquid wter in vegettion cnopies nd hence is sensitive to the totl mount of liquid wter contined in vegettion when the vegettion cover is high. Some recent studies (Bjgin et l. 215; Chndrsekr et l. 211; Wgle et l. 214) hve identified LSWI s n index in extrcting the vegettion wter sttus nd in drought detection. Becuse griculturl drought occurs due to lck of soil moisture nd the consequent wter stress in the vegettion, wter-sed index should lso e used long with the greenness-relted indices such s normlized difference vegettive index (NDVI) nd enhnced vegettive index (EVI) to develop systemtic nd effective method of griculture drought ssessment (Bjgin et l. 215; Chndrsekr et l. 211; Tinetl.213; Wgle et l. 214). The Moderte Resolution Imging Spectrometer (MODIS) sensor on ord the NASA Terr stellite pltform provides continuous dily oservtions of the lnd surfce. Our hypothesis is tht the wter-relted vegettion index LSWI computed from time series MODIS imges offers new nd improved cpcity for drought monitoring. In this study, we evluted the hypothesis over 113 Mesonet sites cross Oklhom under different lnd cover nd soil types. Also, the drought intensity clss clssified sed on LSWI vlues corresponding to US Drought Monitor (USDM) drought intensity clsses re further linked to the durtion of LSWI < (DNLSWI) to estlish certin threshold of DNLSWI (in dys) to define drought intensity clsses. Therefore, results from this study will help in improving the cpility of remote sensing vegettion drought monitoring y estlishing LSWI s complimentry tool to existing NDVI-sed drought products. Specificlly, we ddressed the following reserch questions: 1) Is le to cpture the drought events cross multiple sites over yers? 2) Is LSWI-sed drought-monitoring lgorithm developed for two tllgrss pririe sites (Bjgin et l. 215) pplicle to quntify drought intensity over 113 Mesonet sites comprising vrious lnd cover nd soil types in Oklhom? 3) Wht is the reltionship etween the DNLSWI nd drought intensity clssified y USDM? Mterils nd methods Dt Oklhom Mesonet sttions nd rinfll dt An extensive environmentl oservtion network is well estlished nd distriuted over Oklhom, known s the Oklhom Mesonet (Brock et l. 1995). The Oklhom Mesonet is network of 12 utomted sttions with t lest 1 in ech 77 counties of Oklhom. The Mesonet provides qulity-controlled mesurements of meteorologicl nd lndsurfce vriles such s precipittion, temperture, nd soil moisture t intervls spnning 5 3 min depending on the vriles ( In this study, we used 113 Mesonet sttions tht hve continuous mesurements of meteorologicl prmeters from 2 to 213. Retired nd replced Mesonet sttions were not considered ecuse site replcements were on different MODIS pixels. The loctions of the selected sites re presented in Fig. 1; iophysicl fetures re presented in Tle S1.In this study, we used the precipittion nd soil wter content (SWC) dt for three summer months (June August) nd clculted the rinfll nd SWC nomlies from the 14-yer men (2 213). Additionlly, the nomlies in rinfll clculted from 3-yer rinfll dt (climtologicl norml) from Coopertive Oserver Progrm (COOP, Ntionl Wether Service) sites were compred with the rinfll nomlies computed from 14-yer dt from Mesonet sttions, two from ech climte division of Oklhom. MODIS surfce reflectnce nd vegettion index dt The MODIS is n instrument on ord the NASA s Terr (EOS m) nd Aqu (EOS pm) spcecrft. This sensor provides simultneous oservtions of the tmosphere, terrestril surfce, nd ocens. The MODIS instrument hs temporl resolution of 1 to 2 dys with high rdiometric resolution imges (12 it). It collects dt for 36 spectrl nds, nd the following 7 of these nds re designted minly for lnd surfce nd vegettion studies: lue ( nm), green

3 Fig. 1 The loction nd distriution of the Mesonet sites (113 Mesonet sttions) in Oklhom, USA ( nm), red (62 67 nm), ner infrred (nir nm nd nir nm), nd shortwve infrred (swir nm nd swir nm; Lillesnd et l. 214). The 8-dy MODIS lnd surfce reflectnce product (MOD9A1) t 5-m sptil resolution ws used in this study. The MOD9A1 time series dtsets for individul Mesonet sites were downloded from the dt portl mnged y the Erth Oservtion nd Modeling Fcility t the University of Oklhom ( The geogrphic loctions of the Mesonet sites were used to retrieve MODIS dt t pixel level. For ech MODIS 8-dy composite, surfce reflectnce (ρ) vlues for visile, NIR, nd SWIR nds were used to clculte NDVI, EVI, nd LSWI s NDVI ¼ ρ NIR1 ρred ð1þ ρ NIR1 þ ρred ρ NIR1 ρred EVI ¼ ð2þ ρ NIR1 þ 6 ρred 7:5 ρlue þ 1 LSWI ¼ ρ NIR1 ρswir1 ð3þ ρ NIR1 þ ρswir1 USDM dt The USDM mp is weekly drought product developed y prtnership of vrious gencies including Ntionl Ocenic nd Atmospheric Administrtion (NOAA), the US Deprtment of Agriculture (USDA), nd the Ntionl Drought Mitigtion Center (NDMC) ( unl.edu/monitoringtools/usdroughtmonitor.spx). The USDM includes weekly ntionl mp displying dryness divided into five ctegories, or levels of intensities, from D to D 4, sed on percentile rnking of numerous indictors or indices (Svood et l. 22). The D levels re sed on lend of different indices including the Plmer drought index, CPC soil moisture model, US Geologicl Survey (USGS) weekly stremflow, stndrdized precipittion index (SPI), nd stellite vegettion helth index (Kogn 22;Kognetl.24). The D levels re leled y drought intensity or severity, with D 1 eing the lest intense nd D 4 the most intense. The D clssifiction or drought wtch res re normlly dry nd my e heding into drought or recovering from drought, ut conditions hve not yet returned to norml (Svood et l. 22). The USDM rchived weekly mps re ville t For this study, weekly USDM drought mps for June August (2 to 213) were provided y the NDMC in shpefile formt nd then rsterized to the 1-km ALEXI CONUS grid. Numericl vlues were ssigned to ech drought ctegory, with no drought conditions set to, normlly dry conditions (D ) to 1, moderte drought (D 1 ) to 2, severe drought (D 2 ) to 3, extreme drought (D 3 ) to 4, nd exceptionl drought (D 4 ) to 5.

4 Methods LSWI-sed griculturl drought-monitoring lgorithm The LSWI-sed lgorithm uses LSWI s n indictor to ssess griculturl drought in tllgrss pririe (Bjgin et l. 215). Generlly, green vegettion hs positive LSWI vlues (>) nd dry vegettion hs negtive LSWI vlues (<). Therefore, LSWI < during growing seson indictes drought in tllgrss pririe in Oklhom (Bjgin et l. 215; Wgle et l. 214). The durtion of LSWI < (DNLSWI) during the summer months (June August) ws used to estimte the drought durtion nd drought intensity. To illustrte the lgorithm t single site, the dynmics of rinfll nd LSWI in drought (26) nd pluvil yer (27) t Mren Mesonet sttion is presented in Fig. 2. The LSWI ws greter thn zero throughout the growing seson in 27 when ecosystem received well-distriuted rinfll, while the LSWI ws less thn zero for sustntil numer of dys in 26 due to rinfll ssocited with drought (Dong et l. 211). Therefore, we used DNLSWI during the summer months (June August) to reflect the durtion (length) of drought period s n lgorithm to ssess summer drought of the ecosystem. Anomly nlysis of summer rinfll nd LSWI Men LSWI ws computed for the summer months, nd nomlies were determined for ech sttion during drought yers (21, 26, 211, nd 212) from the 14-yer men (2 213). Similrly, summer rinfll nomlies were computed for ech sttion during drought yers sed on the 14- yer men. The similrity etween the nd summer rinfll nomly for ech sttion ws determined y evluting the correltion etween them. This method identified the sttions where LSWI nomlies followed the trends of summer rinfll nomlies, thus providing direct method to ssess ecosystem drought. Results Chrcteristics of summer rinfll over 113 Mesonet sites nd identifiction of drought yers sed on summer rinfll Figure 3 shows the ox plots of the totl summer rinfll tht occurred in ech yer over the 113 Mesonet sites. The dispersion in the rinfll mong the 113 sttions is compred for ech yer, nd the line in the ox represents the medin summer rinfll mount, which is equivlent to the 5th percentile of oservtions (113 sttions). The medin summer rinfll ws highest (455 mm) in 27, while the yers including 21, 26, 211, nd 212 hd reltively low medin rinfll. For exmple, 5 % of the oservtions were elow 111 mm of summer rinfll in 211, indicting dry conditions t more thn hlf of the Mesonet sttions nd ws consistent with significnt drought during the period (Hoerling et l. 213; Tdesse et l. 215). The nlysis of summer drought for ech yer (2 213) ws computed y clculting the verge summer rinfll from the14-yer verge. Precipittion vlues representing 5 nd 25 % of the long-term verge rinfll were clculted for ech sttion. These vlues were then deducted from the long-term verge t every sttion to otin vlues of 25 nd 5 % precipittion. If the nnul rinfll ws etween 25 nd 5 % deficiency, then it ws clssified s moderte drought. If Fig. 2 Sesonl dynmics nd internnul vritions of dily rinfll nd lnd surfce wter index (LSWI) in drought (26) nd pluvil (27) yers t Mren, Oklhom 1 8 LSWI Rinfll.4.2 Rinfll (mm) 6 4. LSWI /1/26 5/1/26 9/1/26 1/1/27 5/1/27 9/1/27 1/1/28 Dte

5 the nnul rinfll ws less thn the vlue of 5 % deficiency, then it ws clssified s severe drought. For exmple, t the Acme Mesonet sttion: Averge summer rinfll ð Þ ¼ 26 mm : 55%of verge summer rinfll ¼ 5%of 26 ¼ 13 mm: 25% of verge summer rinfll ¼ 25% of 26 ¼ 65 mm: 5 % deficiency ¼ ¼ 13 mm : 25 % deficiency ¼ ¼ 195 mm : Summer rinfll during 211 ¼ 83 mm : Thus, the summer rinfll t Acme in 211 ws less thn the clculted 5 % deficiency nd ws susequently clssified s severe drought. Bsed on nnul rinfll deficiency, the mjority of the sttions received less thn norml mounts of rinfll in 21, 26, 211, nd 212, wheres sttions received norml to ove norml rinfll in 24, 27, 28, nd 213 (Fig. 3). For exmple, in 211, drought occurred t nerly ll sttions, wherey 7 % of sttions included t lest the moderte drought clssifiction with 29 % of those clssified s severe. A frequency distriution ws completed for drought periods when compred with the totl period y computing totl summer rinfll (June August) for 1582 site-yers (14 yers 113 sites) of totl dt. The results displyed in Fig. 3c demonstrte tht drought site yers hve significnt right skew in distriution, wherey the summer rinfll rnged from 5 to 35 mm with the gretest numer flling within 15-mm in. Conversely, the frequency distriution for ll yers (drought plus norml) rnged from 5 to 5 mm with the highest numer flling within the 25-mm in. Figure 3d shows the nomlies in summer rinfll clculted from 3-yer rinfll dt (climtologicl norml) from COOP sites compred with the rinfll nomlies computed from 14-yer dt from Mesonet sttions, two from ech climte divisions of Oklhom. The correltion nlysis showed strong reltionship (r 2 =.91) etween the nomlies of rinfll otined from two dt sources, suggesting tht drought yers (21, 26, 211, nd 212) identified in our nlysis cn represent the climtic extremes of Oklhom in the lst decde sed on climtologicl norml perspective. The reltionship etween rinfll nomly nd LSWI nomly Once the drought yers were selected, the reltionship etween summer rinfll nomlies nd LSWI nomlies ws investigted. Figure 4 displys the LSWI nomlies nd summer rinfll nomlies for individul pixels over the 113 Mesonet sttions during drought yers (21, 26, 211, nd 212). Overll, the nomlous summer rinfll results in nomlous LSWI t most Mesonet Fig. 3 Summer rinfll cross 113 Mesonet sites during (). The solid lines in the ox represent the medin, nd the dots ove nd elow the ox represent the 95th nd 5th percentiles, respectively. Yerly summer drought nlysis y rinfll deficiency: percentge of the Mesonet sttions under three drought ctegories (severe, moderte, nd norml) for (). The frequency distriution of site-yer grouped under different summer rinfll regimes (c) for whole study period (2 213) nd for drought yers (21, 26, 211, nd 212). Correltion of rinfll nomlies clculted from 3- yer rinfll dt from Coopertive Oserver Progrm (COOP) nd 15-yer rinfll dt from Mesonet sttions (d) Summer Rinfll (mm) No. of site yers (2-213) c Yer ll yers drought yers Percentge of mesonet sttions Mesonet d y =.97 x R 2 =.91 Yer norml moderte severe Totl summer rinfll (mm) COOP

6 Rinfll nomly (mm) Rinfll nomly y =.7+.6 *x R2 = Rinfll nomly (mm) Rinfll nomly (mm) c y = * x R2= Rinfll nomly(mm) Rinfll nomly (mm) y =.1+.4 *x R2 =.73 Rinll nomly(mm) Sttion numer Rinfll nomly (mm) d Sttion numer y= * x -.15 R2 = Rinfll nomly (mm) Fig. 4 Dynmics of summer rinfll nd LSWI nomlies in drought yers 21, 26, c 211, nd d 212 t 113 Mesonet sttions. The inset grphs re the regression nlyses etween summer rinfll nd LSWI nomlies (n = 113) sttions during drought yers. As such, the nomlies in summer rinfll nd LSWI reveled strong reltionship etween rinfll nd vegettion wter content. For exmple, pixel-sed correltion nlyses etween summer rinfll nomlies nd LSWI nomlies re presented in Fig. 4 (inset grphs). For ll identified drought yers, strong reltionships (r 2 =.61.67) etween nomlies of summer rinfll nd nomlies of LSWI were identified. Although the mgnitudes of the nomlies of summer rinfll nd LSWI vried from yer to yer, the reltionship etween two prmeters ws consistently strong. The reltionship etween SWC nomly nd vegettion indices nomly Figure 5 presents the Person s correltion coefficients (r) etween SWC nomlies nd three vegettion nomlies (NDVI, EVI, nd LSWI). As expected, etter reltionship (r LSWI =.52) of SWC nomlies ws oserved with LSWI nomlies thn NDVI nomlies (r NDVI =.4) nd EVI nomlies (r EVI =.44). We exmined the correltion coefficients (r LSWI, r EVI, nd r NDVI ) for ll 113 Mesonet sttions. Figure 6 compres the r vlues derived for NDVI, EVI, nd LSWI nomlies with SWC nomlies. The nlysis showed the significnt difference etween r LSWI nd r NDVI nd r LSWI nd r EVI with p vlues less thn.1. As whole, there re significnt r vlues tht fll ove the 1:1 line towrds the r LSWI.Ther LSWI ws 25 nd 2 % higher thn r NDVI nd r EVI, respectively, suggesting LSWI s etter indictor of soil wter content s compred to NDVI nd EVI. The reltionship etween LSWI-sed drought durtion nd summer rinfll Figure 7 shows the sctter plot of DNLSWI versus totl summer rinfll cross 113 Mesonet sttions inned into 5-mm clsses. The result highlights tht LSWI ws highly sensitive to summer rinfll nd the DNLSWI rpidly decresed s the mount of rinfll incresed. Specificlly, the DNLSWI ws more thn 5 dys when summer rinfll ws less thn 15 mm, indicting wter stress (LSWI <) during ctive growing period of the vegettion. Conversely, the DNLSWI ws less thn 2 weeks when summer rinfll ws greter thn 4 mm. The longitudinl grdient of summer rinfll is widely recognized pttern in Oklhom, where the mount of rinfll decreses from est (men summer rinfll 3 mm) to west (men summer rinfll 15 mm; Fig. 8). To understnd the occurrence of drought cross the rinfll grdient of Oklhom, we counted totl DNLSWI during summer months (June August) from 2 to 213 for ll Oklhom Mesonet sttions. As expected, distinct incresing pttern of totl numer of DNLSWI ws oserved cross est-west grdient of Oklhom (Fig. 8), which ws opposite to the rinfll pttern. The sites towrds the est with greter mount of verge summer rinfll hd the lest DNLSWI, wheres generl increment of DNLSWI ws oserved with lesser precipittion s we moved from est to west.

7 Fig. 5 Correltion nlysis etween soil wter content (SWC) nomly nd vegettion index (VI) nomlies NDVI, EVI, nd c LSWI. Ech point represents the VI nomlies nd SWC nomly vlue for ech month of the summer from 2 to 213 NDVI nomly r =.4 r =.44 EVI nomly c r = Soil wter content (SWC) nomly Chrcteristics of DNLSWI nd USDM drought history (2 213) The pttern ssocited with DNLSWI for 113 Mesonet sttions during the study period (2 213) is presented s ox plots in Fig. 9. These plots reveled the distriution of DNLSWI mong the Mesonet sites within yer nd mong yers. The medin DNLSWI ws reltively greter during the drought yers (2 = 32 dys, 26 = 48 dys, 211 = 56 dys, nd 212 = 56 dys) thn non-drought yers. The distriution s well s the medin DNLSWI ws the lowest in 27, which ws pluvil yer nd the wettest summer on record in centrl Oklhom (Arndt et l. 29; Christin et l. 215;Dong et l. 211). Figure 9 shows the frequency distriution of the Mesonet sttions (113 sttions over 14 yers) with ssocited DNLSWI (113 sttions over 3 months) for the totl study period nd drought yers seprtely. The count ws highest for DNLSWI equl to 8 dys ecuse it is very common tht mjority of the sttions could hve LSWI elow zero for 8 dys over limited period during sesonl drying. However, the rtio of drought yers to ll yers incresed s the DNLSWI incresed, suggesting tht drought yers contriuted lrger counts for the higher DNLSWI (Fig. 9c). For exmple, rtio of.13 for DNLSWI equl to 8 dys mens only Fig. 6 Reltionship etween the vlues of correltion coefficients of VI nomlies nd SWC nomly. Ech point represents the correltion coefficient otined y plotting monthly nomly vlues for ech sttion correltion coefficient (LSWI Vs SWC) r =.85 slope =.75 p= < correltion coefficient (LSWI Vs SWC) r =.9 slope =.8 p= < correltion coefficient (NDVI Vs SWC) correltion coefficient (EVI Vs LSWI)

8 DNLSWI Summer rinfll (mm) Fig. 7 Reltionship etween summer rinfll nd durtion of LSWI <. Ech point is n verge for ll Mesonet sttions inned y 5 mm of summer rinfll 13 % of the totl counts were contriuted y the drought yers, while for DNLSWI equl to 64 dys, drought yers contriuted 63 % of the totl counts, suggesting higher DNLSWI during the drought yers. Figure 1 shows the weekly percentge of Oklhom Mesonet sites ffected y D to D 4 drought from 2 to 213. The drought periods spnning 26, 211, nd 212 were evident nd reched D 4 sttus for extended periods. The plot lso depicts the pluvil condition during 27 when D drought occurred in very limited temporl window. However, significnt res, especilly sites in western Oklhom where drought conditions persisted even though mjority of the stte yielded ove norml precipittion, showed higher-intensity summer drought in 213, which ws lso considered s n overll pluvil yer sed on totl yer rinfll. The reltionship etween LSWI-sed drought severity nd USDM drought intensity ctegories The LSWI vlues corresponding to its NDVI vlues for ech week sed on USDM weekly mp re plotted in Fig. 11. Results showed tht lrger negtive vlues of LSWI corresponded to higher drought intensity ctegories identified y USDM clsses (i.e., D 3 nd D 4 extreme nd exceptionl), while no drought nd normlly dry ctegories ( nd D ) corresponded to the lrger positive LSWI vlues. Further, moderte to severe drought ctegories (D 1 nd D 2 ) corresponded to intermedite LSWI vlues. Bsed on this LSWI-NDVI two-dimensionl sctter plot, we identified the rnge of LSWI vlues for ech drought ctegories used y USDM in Bjgin et l Due to the lrge numer of site yers nd mixture of lnd cover types, the groupings of drought intensity could not e visulized effectively within the rnge formulted on oservtions t two tllgrss pririe sites. However, the generl pttern tht higher drought intensity tends to hve lower LSWI vlues nd vice vers ws oserved for ll lnd cover types s well s grsslnds nd croplnds. Compred to ll lnd cover types nd croplnds, grsslnds showed etter reltionships to the drought intensity ctegories. To determine the greement etween LSWI-sed drought intensity clssifiction sed on the LSWI vlue rnge nd USDM drought ctegories (Tle 1), we computed the percentge of pixels tht fll within the defined LSWI vlue rnge Fig. 8 The performnce of LSWI to trck est-west rinfll grdient of Oklhom: verge summer rinfll grdient from est to west nd DNLSWI (totl numer of dys with LSWI < during summer months) from 2 to 213 for 113 Mesonet sttions rrnged y est-west geogrphicl loctions Averge summer rinfll (mm) No. of dys (LSWI < ) Longitudes

9 Fig. 9 Durtion of LSWI < (DNLSWI) cross 113 Mesonet sites during (). The solid lines in the ox represent the medin, nd the dots ove nd elow the ox represent the 95th nd 5th percentiles, respectively. The frequency distriution of the Mesonet sttions (113 sttions 14 yers) with ssocited DNLSWI for () nd the rtio of numer of sttions with drought yers to totl yers (drought nd norml) for respective DNLSWI ins (c) DNLSWI (dys) Frequency ll yers drought yers Yer Rtio c DNLSWI DNLSWI for the prticulr drought clss. The ssessment ws performed for different lnd cover types (ll lnd covers, grsslnds, nd croplnds; Fig. 12). Overll, the greement ws higher (>6 %) for low-intensity ( nd D ) nd highintensity (D 3 nd D 4 ) droughts (the two ends of drought clss), ut the intermedite drought intensity (D 1 nd D 2 ) hd reltively low greement. However, the reltionship ws slightly improved when computed for individul lnd cover types with grsslnds showing the est greement. Furthermore, we nlyzed the greement of the LSWI-sed drought clssifiction for nine climte divisions of Oklhom to further nlyze the sptil vriility of drought trcking y the LSWI-sed lgorithm (Fig. 12). The LSWI identifiction showed etter greement (>8 %) with USDM nd D (no dry nd normlly dry) clsses in the estern humid res, wheres the greement ws low (<3 %) for the sme drought clsses in the western rid res (pnhndle). However, the western region identified s severe to exceptionl drought (D 3 nd D 4 ) y USDM mtched very well with the new LSWI-sed clssifiction. For exmple, 91 % of the pixels were clssified s severe nd exceptionl droughts in the pnhndle region, wheres USDM lso identified the sme drought intensity. However, only 19 % of the low-intensity drought pixels mtched well with the lower-intensity drought clssifiction of USDM. The reltionship etween USDM drought intensity, DNLSWI, nd verge LSWI vlue is presented in Fig. 13. The generl oservtion ws tht drought intensity incresed Percent Coverge D D1 D2 D3 Fig. 1 Percent of Oklhom re covered y USDM drought designtion from 2 to 213. The designtions (no drought), D (normlly dry), D 1 (moderte drought), D 2 (severe drought), D 3 Dte (extreme drought), nd D 4 (exceptionl drought) re the drought intensity clsses defined y USDM (dt source: US Drought Monitor) D4

10 Fig. 11 Reltionship etween NDVI nd LSWI for individul pixels of the ll types (), grsslnds (), nd croplnds (c) of lnd cover sites for June August over 14-yer study period (2 213). Ech point in the plot represents the weekly oservtion of drought intensity designtion for the study re s determined from US Drought Monitor (USDM) drought mps ( edu/mpsanddt/) LSWI LSWI D D1 D2 D3 D c.2 LSWI NDVI s DNLSWI ecme longer. For short DNLSWI periods ( 24 dys), the drought impct ws shrp nd then plteued etween 24 nd 48 dys. As DNLSWI ecme lrger (>48 dys), the ddition of ech new dy resulted into lrger drought impcts identified s higher drought intensity clss y the USDM (Fig. 13). This reltionship ws further supported y the verge LSWI vlues which declined s DNLSWI incresed. The decresing pttern of verge LSWI ws lso persistent for the shorter DNLSWI ut declined shrply s the DNLSWI ws longer thn 5 6 dys. Tle 1 A summry of the USDM drought intensity clsses nd the LSWI-sed clsses USDM drought intensity clss Description LSWI D vlues non-drought LSWI >.1 D normlly dry LSWI >.1 D 1 drought-moderte < LSWI.1 D 2 drought-severe.1 < LSWI D 3 drought-extreme LSWI.1 D 4 drought-exceptionl LSWI.1 Source: Bjgin et l. (215) Discussion The correltion nlyses etween summer rinfll nomlies nd LSWI nomlies in drought yers reveled sensitivity of LSWI to summer rinfll vriility in Oklhom. Higher negtive nomlies in summer rinfll resulted in lrger decline in LSWI vlues, n indiction of drought-impcted vegettion (Bjgin et l. 215; Wgle et l. 214). Regrdless of different lnd cover nd soil types cross 113 Oklhom Mesonet sites, LSWI trcked droughts in mjority of the study sites. However, it over-clssified the low-intensity droughts in rid western regions of Oklhom. Given the nticipted future increse in precipittion vriility (Liu et l. 212; Zhng nd Nering 25), ecosystems in this region re expected to e prticulrly susceptile to droughts resulting lrge losses for food nd livestock industries. Our results suggested tht the ility of LSWI to trck the summer rinfll nomlies could e one of the importnt fetures to ssess nd trck griculturl droughts. Our finding on the performnce of LSWI to trck wter content of the ecosystem ws consistent with the results y Chndrsekr et l. 211, which demonstrted LSWI s potentil indictor of incresing wter content in the ecosystem following the onset of monsoon in Indi. Since commonly used NDVI nd EVI re not lwys good indictors of vegettion conditions especilly during dverse

11 Int J Biometeorol 9 Totl Grsslnd Tin et l. 213; Wgle et l. 214). The opposite longitudinl ptterns of DNLSWI nd summer rinfll suggested tht counting the DNLSWI (in dys) hs the ility in trcking the drought cross vrious Mesonet sites of Oklhom. The results illustrte tht LSWI cn e used s n effective tool to monitor dryness persisted in the diverse (lnd cover nd soil types) ecosystems in semi-rid nd semi-humid regions in estern nd centrl Oklhom. However, the sptil vriility of drought trcking ility ws oserved sed on drought intensity. In estern humid regions of Oklhom, oth USDM-D nd LSWI-D showed no drought ( drought clss) when verge summer rinfll ws ove 25 3 mm (Tle 2). However, in western dry region of Oklhom, USDM- nd LSWI-sed drought ctegories were different. For exmple, ove 15 mm of summer rinfll ws considered s no drought ctegories y USDM, ut LSWI showed severe drought ctegory (D3) with 15 3 mm of summer rinfll. The less greement etween our LSWI-sed nd USDM drought ctegories for the low drought intensity ctegories is ecuse of the fct tht dry res like pnhndle region of Oklhom hs higher negtive LSWI vlues, nd consequently, the LSWI-sed lgorithm showed higher drought severity. LSWI vlues re considered proxy of vegettion wter content nd re the physicl vlues, wheres USDM considered severl fctors including locl reports of drought conditions (such s reports from wter mngers nd residents) (Svood et l. 22). This mde USDM ssessment more loclly djusted despite of corse sptil resolution. One of the min resons ehind ttempting to estlish the reltionship etween summer rinfll nd LSWI ws to determine the hydrologicl sttus of the ecosystem. The totl mount of summer rinfll received y prticulr ecosystem in prticulr yer could e relted to DNLSWI, which in turn Croplnd Agreement (%) & D D1 D2 D3 & D4 Drought ctegories 1 9 nd D D2 D1 D3 nd D4 Avg 8 Agreement (%) NE EC SE CT NC SC WC SW PH Avg Climte Divisions Fig. 12 Agreement of the drought intensity clss to the LSWI-sed clssifiction dpted from Bjgin et l. (215) for different lnd cover nd climte divisions of Oklhom (NE northestern, EC est centrl, SE southestern, CT centrl, NC north centrl, SC south centrl, WC west centrl, SW southwestern, nd PH pnhndle) climtic conditions for vegettion growth (Gmon et l. 1995; Gmon et l. 1993), LSWI cn etter trck the droughtimpcted vegettion ecuse of its higher sensitivity to drought (Bjgin et l. 215; Chndrsekr et l. 211; 5. drought intensity LSWI verge Drought Intensity Fig. 13 Reltionship etween USDM-sed drought intensity clsses, DNLSWI (durtion of LSWI <) nd verge LSWI. The USDM drought intensity clsses, D, D1, D2, D3, nd D4 re set to, 1, 2, 3, 4, nd 5, respectively Durtion of LSWI < (DNLSWI) LSWI verge 1

12 Tle 2 USDM- nd LSWIsed drought clsses in estern nd western Oklhom inned y verge summer rinfll of the Mesonet sttions locted in the res Summer rin (mm) Estern OK Western OK USDM D clss LSWI D clss USDM D clss LSWI D clss ove cn e inferred in terms of drought intensity. Although our results showed smooth decresing trend of DNLSWI with incresing summer rinfll, site-specific reltionship could not e estlished (Bjgin et l. 215) ecuse verging multiple dt points produced smoother overll trend. Thus, dditionl experiments re needed to identify the threshold vlues for ech site with different soil nd crop types in the future. Rinfll expressed s percentge deprture from the long-term verge for given period is widely used index for drought monitoring, where monitoring other prmeters such s soil moisture or evpotrnspirtion re costly nd difficult (Nicholson 1989; Nicholson 2). With this pproch, where totl summer rinfll is inferred in terms of DNLSWI for ssessing drought is extremely vlule since LSWI is derived from stellite sensors. Therefore, it is very importnt to pply this informtion rendered from LSWI nd summer rinfll reltionship while developing drought-monitoring network for this region. Knowledge of LSWI-sed drought intensity could e criticl for ssessing drought with different prmeters like DNLSWI. Quntifying drought intensity in terms of LSWI nd defining threshold for ech USDM drought clss will e n importnt impliction for future drought-monitoring progrm. For exmple, secretril disster re determintion nd notifiction process depends on the USDM drought intensity clssifiction for designting ny geogrphicl unit s disster re (USDA-FASA, 215). The criteri used re the re should e under either D 3 or D 2 (t lest 8 consecutive weeks) drought clss. USDM drought clssifiction involves series of informtion for finding threshold, comprised of complex procedures s well s could hve limited sptil precision ecuse it relies on sptilly interpolted climte dt input (Tdesse et l. 215). Our results suggested tht this USDM drought intensity clss cn e linked with DNLSWI. The intersection of intensity curve nd LSWI vg curves in Fig. 13 estlished threshold point t which drought impcts incresed shrply s LSWI vg declined. This threshold vlue is etween the D 2 nd D 3 drought intensity clsses nd cn e inferred in terms of DNLSWI, which is pproximtely 6 62 dys. Mny gencies hve used USDM drought intensity clss thresholds to guide mesures in vriety of ssistnce progrms such s Livestock Forge Disster Progrm (LFP), Emergency Hying nd Grzing, Livestock Indemnity Progrm, Noninsured Crop Disster Assistnce Progrm (NAP), nd Crop Insurnce Bsics (Mlly et l. 213; Mizzell nd Lkshmi 23; Otkin et l. 215). Such ssistnce progrms cn lterntively input DNLSWI thresholds for simple nd esy opertions s well s for etter precision in terms of sptil resolution (5 m). However, vlidtion of this pproch of LSWI-sed thresholds for such kind of pplictions remins further reserch topic. The MODIS-derived, LSWI-sed drought ssessment lgorithm is simple nd hs higher sptil resolution ( 5). However, the LSWI-sed drought lgorithm cn hve limittion when the reflectnce from lnd surfce is impcted y cloud cover (Jensen 29). An pproprite gp-filling lgorithm cn crete continuous dtset, therey reducing the effect of unrelile oservtions, which is needed for mking the drought-monitoring lgorithm roust. Another limittion is the threshold vlues used in the lgorithm. We used LSWI < during the growing seson s the indictor of griculturl drought in tllgrss pririe sed on clirtion mde on two study sites (Bjgin et l. 215). Although the lgorithm showed good greement in most of the Mesonet sites, the DNLSWI clerly over-clssified D nd D 1 drought conditions in the rid regions of Oklhom. This is ecuse these regions receive less rinfll thn the semi-rid to semi-humid regions of estern Oklhom, where the lgorithm ws originlly clirted. This result suggests tht it is necessry to further refine the LSWI-sed lgorithm to etter represent drought severity in rid western regions of Oklhom. One of the possile djustments could e the LSWI threshold vlues for the rid region considering more negtive mgnitudes of the LSWI vlues in rid regions. This djustment could reduce the discrepncies oserved etween the LSWI nd USDM drought clssifiction especilly for lower drought

13 intensity resulted from the lrger negtive vlues of LSWI, common feture of rid region. Conclusions Results of LSWI nlysis for the period of for 113 Mesonet sttions cross Oklhom reveled vlule informtion within the context of drought trcking. A strong correltion nd dynmics etween LSWI nomlies nd summer rinfll nomlies comprises fct tht LSWI is sensitive to rinfll vritions nd cn e used s n indictor of drought occurrence in n ecosystem. It is then deduced tht DNLSWI hd the close ssocition with the vegettion condition under rinfll vritions. Pixel-sed drought intensity clssifiction hs een tested to vlidte the LSWI-sed drought clss for different lnd cover nd soil types. Despite reltively lower degree of greement for the intermedite drought clsses, the LSWI-sed drought intensity clss ws relile for low- nd high-intensity clsses defined y USDM. There ws longitudinl sensitivity for low-intensity droughts etween estern nd western Oklhom s shown y lower greement of D nd D 1 drought with USDM in pnhndle region (western Oklhom). The drought ssessment t lrger scle could e mde more effective y incorporting informtion nd fetures of LSWI such s DNLSWI from site level to regionl scle with further improvement for rid regions, where lrger negtive LSWI vlues re common. The nlogy of DNLSWI to USDM drought intensity clss could e mde complement in current drought-monitoring progrm nd lgorithms. Results lso demonstrted tht y counting the numer of DNLSWI, drought intensity thresholds cn e estlished nd used s simple complementry tool in severl pplictions. Acknowledgments This study ws supported in prt y reserch grnt (Project No ) through the USDA Ntionl Institute for Food nd Agriculture (NIFA) s Agriculture nd Food Reserch Inititive (AFRI), Regionl Approched for Adpttion nd Mitigtion of Climte Vriility nd Chnge grnt (IIA ), NOAA Climte Office s Sectorl Applictions Reserch Progrm (SRP) grnt NA13AR431122, nd Oklhom s txpyers fund for the Oklhom Mesonet through the Oklhom Stte Regents for Higher Eduction nd the Oklhom Deprtment of Pulic Sfety. We would lso like to cknowledge the Ntionl Drought Mitigtion Center t the University of Nersk-Lincoln, the US Deprtment of Agriculture, nd the Ntionl Ocenic nd Atmospheric Administrtion for the dtset. References Arndt DS, Bsr JB, McPherson RA, Illston BG, McMnus GD, Demko DB (29) Oservtions of the overlnd reintensifiction of tropicl storm Erin (27). Bull Am Meteorol Soc 9: Bjgin R, Xio X, Wgle P, Bsr J, Zhou Y (215) Sensitivity nlysis of vegettion indices to drought over two tllgrss pririe sites. ISPRS J Photogrmm Remote Sens 18: Bsr JB, Myourn JN, Peirno CM, Tte JE, Brown PJ, Hoey JD, Smith BR (213) Drought nd ssocited impcts in the Gret Plins of the United Sttes review. Int J Geosci 4:72 Brock FV, Crwford KC, Elliott RL, Cuperus GW, Stdler SJ, Johnson HL, Eilts MD (1995) The Oklhom Mesonet: technicl overview. JAtmosOcenTechnol12:5 19 Ceccto P, Flsse S, Gregoire J-M (22) Designing spectrl index to estimte vegettion wter content from remote sensing dt: prt 2. Vlidtion nd pplictions. Remote Sens Environ 82: Ceccto P, Flsse S, Trntol S, Jcquemoud S, Grégoire J-M (21) Detecting vegettion lef wter content using reflectnce in the opticl domin. Remote Sens Environ 77:22 33 Chndrsekr K, Si MS, Beher G (211) Assessment of erly seson griculturl drought through lnd surfce wter index (LSWI) nd soil wter lnce model. ISPRS-Interntionl Archives of the Photogrmmetry, Remote Sensing nd Sptil Informtion Sciences 382:5 55 Christin J, Christin K, Bsr JB (215) Drought nd pluvil dipole events within the gret plins of the United Sttes. J Appl Meteorol Climtol 54: Dong X et l. (211) Investigtion of the 26 drought nd 27 flood extremes t the Southern Gret Plins through n integrtive nlysis of oservtions. J Geophys Res Atmos 116(D3). doi:1.129/21 JD14776 Gmon JA et l. (1995) Reltionships etween NDVI, cnopy structure, nd photosynthesis in three Clifornin vegettion types. Ecol Appl:28 41 Gmon JA, Field CB, Roerts DA, Ustin SL, Vlentini R (1993) Functionl ptterns in n nnul grsslnd during n AVIRIS overflight. Remote Sens Environ 44: Hoerling M et l. (213) Antomy of n extreme event. J Clim 26: Hulse JH, Escott VJ (1986) Drought inevitle nd unpredictle the pttern nd consequences of recurrent drought interdisciplinry. Science Reviews 11: Hunt ER, Rock BN, Noel PS (1987) Mesurement of lef reltive wter content y infrred reflectnce. Remote Sens Environ 22: Jckson R, Slter P, Pinter P (1983) Discrimintion of growth nd wter stress in whet y vrious vegettion indices through cler nd turid tmospheres. Remote Sens Environ 13: Jensen JR (29) Remote sensing of the environment: n Erth resource perspective. Person Eduction, New Delhi Kogn F (22) World droughts in the new millennium from AVHRRsed vegettion helth indices. Eos Trnsctions Americn Geophysicl Union 83: Kogn F, Strk R, Gitelson A, Jrglsikhn L, Dugrjv C, Tsooj S (24) Derivtion of psture iomss in Mongoli from AVHRRsed vegettion helth indices interntionl. Journl of Remote Sensing 25: Lillesnd T, Kiefer RW, Chipmn J (214) Remote sensing nd imge interprettion. Wiley, New York Liu L, Hong Y, Bednrczyk CN, Yong B, Shfer MA, Riley R, Hocker JE (212) Hydro-climtologicl drought nlyses nd projections using meteorologicl nd hydrologicl drought indices: cse study in Blue River sin. Oklhom Wter Resources Mngement 26: Mki M, Ishihr M, Tmur M (24) Estimtion of lef wter sttus to monitor the risk of forest fires y using remotely sensed dt. Remote Sens Environ 9: Mlly G, Zho L, Song X, Niyogi D, Govindrju R (213) 212 Midwest drought in the United Sttes. J Hydrol Eng 18:

14 Mizzell HP, Lkshmi V (23) Integrtion of science nd policy during the evolution of South Crolin s drought progrm wter: science, policy, nd mngement: chllenges nd opportunities Nicholson SE (1989) Long-term chnges in Africn rinfll. Wether 44: Nicholson SE (2) The nture of rinfll vriility over Afric on time scles of decdes to milleni. Glo Plnet Chng 26: Otkin JA, Shfer M, Svood M, Wrdlow B, Anderson MC, Hin C, Bsr J (215) Fcilitting the use of drought erly wrning informtion through interctions with griculturl stkeholders. Bull Am Meteorol Soc 96: Shhid S, Behrwn H (28) Drought risk ssessment in the western prt of Bngldesh. Nt Hzrds 46: Sönmez FK, Koemuescue AU, Erkn A, Turgu E (25) An nlysis of sptil nd temporl dimension of drought vulnerility in Turkey using the stndrdized precipittion index. Nt Hzrds 35: Svood M et l. (22) The drought monitor. Bull Am Meteorol Soc 83: Tdesse T, Wrdlow BD, Brown JF, Svood MD, Hyes MJ, Fuchs B, Gutzmer D (215) Assessing the vegettion condition impcts of the 211 drought cross the US southern Gret Plins using the vegettion drought response index (VegDRI). J Appl Meteorol Climtol 54: Tin Y, Zhou L, Romnov P, Yu B, Ek M 213 Comprison of Amzon nd centrl Afric tropicl vegettion dynmics using SEVIRI dt from 29 to 211. In: EGU Generl Assemly Conference Astrcts. p 6535 United Sttes Deprtment of griculture frm service gency (USDA-FSA) (215) Noninsured crop cisster ssistnce progrm (NAP). p_14813v1.pdf. Acessed July 216 Wgle P et l. (214) Sensitivity of vegettion indices nd gross primry production of tllgrss pririe to severe drought. Remote Sens Environ 152:1 14 Zhng X, Nering M (25) Impct of climte chnge on soil erosion, runoff, nd whet productivity in centrl Oklhom. Cten 61: