SPATIO-TEMPORAL DYNAMICS OF SURFACE WETNESS CONDITIONS USING REMOTE SENSING DATA

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1 SPATIO-TEMPORAL DYNAMICS OF SURFACE WETNESS CONDITIONS USING REMOTE SENSING DATA M. Shammi Akther and Quazi K. Haan* Department of Geomatic Engineering, Univerity of Calgary, 2500 Univerity Dr NW, Calgary, Alberta, Canada, T2N1N4; (makther, Earth Obervation for a Changing World - Temporal Iue KEY WORDS: oil moiture, temperature vegetation wetne index, potential urface temperature, normalized difference vegetation index, MODIS ABSTRACT: Soil moiture i an important eco-hydrological variable that influence crop/foret productivity, drought, foret fire and inect outbreak among other. In thi paper, our objective i to predict patio-temporal dynamic over northern portion of the Canadian Province of Alberta. We implemented a remote ening-baed method, uch a, temperature vegetation wetne index [TVWI: a function of potential urface temperature and normalized difference vegetation index (NDVI)] during the month of May-September in We ued MODIS-baed urface reflectance to calculate NDVI at 250 m and alo urface temperature at 1 km in the formulation of TVWI. Our analyi revealed that a deviation between TVWI and ground-baed meaurement of volumetric oil moiture wa within ±20% for ignificant amount of time (i.e., 75-85%). 1. INTRODUCTION Soil moiture (SM) i an important eco-hydrological variable that influence crop/foret productivity, drought, foret fire and inect outbreak among other. A number of indirect method (e.g., neutron cattering, time domain reflectometry, and frequency domain reflectormetry, among other) are traditionally ued to meaure SM. Thee method are, in general, capable of producing accurate meaurement. A thee method produce point type meaurement, we need to employ other method to addre patial variability. In thi context, remote ening-baed method can be effective in predicting both of the patial and temporal dynamic of the SM (e.g., Haan et al., 2007; Patel et al., 2009). One of the mot commonly ued remote ening-baed method i the integration of vegetation index [e.g., normalized difference vegetation index (NDVI)] and urface temperature (T S ) (Nemani and Running, 1989; Moran et al., 1994; Lambin and Ehrlich, 1996; Li et al., 2008; Tang et al., 2009; Petropoulo et al., 2010) in predicting urface moiture to infer SM. However, Carlon (2007) noted that thi approach wa not applicable over topographically variable terrain. To addre thi particular limitation, Haan et al. (2007) propoed a modification in T S to convert into potential urface temperature (θ S ) and then combined with NDVI. Thi method i termed a temperature vegetation wetne index (TVWI). In thi paper, our objective i to: (i) implement the TVWI method to delineate urface wetne condition in the topographically-variable Canadian Province of Alberta; and (ii) ae it ability to capture ground-baed meaurement of SM. 2. METHODS 2.1 Study Area and Data Requirement Figure 1 how the extent of our tudy area, which i the northern portion of Alberta. Thi area i having a variable topography. Climatically, Alberta experience temperature variation from -8 C in the outh to -24 C in the north during January; and from 20 C in the outh to 16 C in the north in July (Government of Alberta; lat viited April. 2010). In thi tudy, we ued 8-day compoite of urface reflectance for red ( nm) and near infrared (NIR: nm ) pectral band at 250 m patial reolution and T S image at 1 km patial reolution during May-September period in Thee data were acquired by MODIS enor of Terra atellite and freely available from NASA. Table 1 how the epoch over which each of the 8-day average data were collected. We alo collected digital elevation model (DEM) at 250-m reolution generated from 3-arc econd reolution height point-data from NASA Shuttle Radar Topography Miion archive. Thi DEM wa ued to eliminate the topographic influence on T S. In order to etablih relation between TVWI and SM, we acquired ground-baed volumetric oil moiture (VSM) of 100 cm depth at 14 tation during the ame period of atellite data (ee Figure 1 for the location information). The ground-baed SM meaurement were acquired uing Theta Probe type ML2x and made available to u by the Agriculture and Agri Food Canada.). * Correponding author.

2 R / C p po θ = T (3) p Where p (in kpa) = atmopheric preure, z (in m)= elevation, T S = urface temperature (in K), p o = average preure at mean ea level (101.3 kpa), R= ga contant (287 J kg-1 K-1), c p =pecific heat capacity of air (~1004 J kg- 1 K-1) θ S (in K)=Surface potential temperature Epoch No. Day of Year Date in 2006 Figure 1: The extent of the tudy area with the gray atellite image in the background along with the boundary of the Province of Alberta. The white hollow circle are repreenting the location where the ground-baed oil moiture data were acquired. 2.2 Mathematical Model We calculated NDVI a a function of red and NIR reflectance uing the following equation: Where NDVI ρ NIR red = (1) ρ NIR ρ + ρ ρ = Surface reflectance value for the red and NIR band. The converion of T S into θ S wa performed in two tep [Haan et al., 2007]: (i) atmopheric preure at each pixel wa calculated a a function of elevation; and (ii) θ S then wa calculated a a function of atmopheric preure. The expreion are a follow: red May-08 May May-16 May May-24 May May-01 Jun Jun-09 Jun Jun-17 Jun Jun-25 Jun Jun-03 Jul Jul-11 Jul Jul-19 Jul Jul-27 Jul Jul-04 Aug Aug-12 Aug Aug-20 Aug Aug-28 Aug Aug.-05 Sep Sep-13 Sep Sep- 21 Sep Sep.- 29 Sep Sep.- 07 Oct. Table 1: Decription of time period when the MODIS-baed data were acquired. We then generated the catter plot of θ S -NDVI for each of the epoch a decribed in Table 1. In general, we oberved trapezoidal hape a hown in Figure 2. In Figure 2, the dry edge (θ dry ) would be the edge where θ S would be highet in relation to NDVI; and reflect the unavailability of water for upporting evapotranpiration (i.e., TVWI=0). The wet edge (θwet), on the other hand, would be the edge where θ S would be lowet in relation to NDVI; and reflect the unretricted amount of water for upporting evapotranpiration (i.e., TVWI=1). Mathematically, TVWI wa calculated a follow: θ dry θ TVWI = (4) θ θ dry wet z p = (2) 293

3 Figure 2: Schematic diagram illutrating the calculation of TVWI a a function of θ S and NDVI (after Haan et al., 2007). 2.3 Comparion between TVWI and SM Data In thi tudy, we generated 20 TVWI map during Uing thee map, we extracted temporal dynamic of TVWI at all of the 14 ground tation (ee Figure 3 for location). Then, we calculated an average TVWI value for each of the epoch. In a imilar way, we averaged the ground-baed meaurement of VSM for the epoch of interet. Finally, we compared VSM value with the TVWI value in term of deviation [i.e., (VSM-TVWI)*100/VSM]. 3. RESULTS AND DISCUSSION Figure 3 how an example catter plot between θ S -NDVI for the epoch 5 (i.e., DOY in between ). In thi tudy, we conidered a contant value of θ wet (=275 K) like Haan et al. (2007). Additionally, Table 2 how the value of θ dry1, θ dry2 (i.e., both the lope and intercept) for each of epoch. Figure 4 how the comparion between ground-baed meaurement of VSM and TVWI. It revealed that the deviation wa within ±20% for 75% of the time (ee Figure 4a). The ±20% deviation wa acceptable in comparing environmental variable becaue of given variability within the natural ytem (Haan et al., 2010). Our analyi alo revealed that one of the ground tation data (i.e., Atmore) wa cauing relatively large deviation. Upon excluding thi tation, we oberved that the deviation wa within ±20% for 85% of the time (ee Figure 4b). We generated an average TVWI Map for the month May- September in 2006 (ee Figure 5). Figure 5 alo how the relative frequency of the TVWI-value. It revealed that approximately 99.27% of the time TVWI-value fell within the range between We oberved that the TVWI had an increaing trend in the northward direction. Figure 3: Example of catter plot of θ S v. NDVI and the correponding dry and wet edge during the epoch 5. θ dry1 θ dry2 Epoch Slope Intercept Table 2: Value of dryline1 (θ dry1 ) and lope and intercept of the dryline2 (θ dry2 ) over the tudy period. 4. CONCLUSION In thi paper, we demontrated a remote ening-baed method to delineate patio-temporal variability of urface wetne condition uing TVWI. Our preliminary analyi revealed that TVWI would be effective in capturing SM. Thu, we need to analye more data in order to enhance our confidence in uing TVWI to predict SM.

4 Figure 4: The deviation between the averaged ground-baed meaurement of VSM and TVWI at (a) 14 tation; (b) 13 tation. At each of the epoch, the average value of VSM and TVWI at all of the tation were compared. Reference Carlon, T,, An overview of the "triangle method" for etimating urface evapotranpiration and oil moiture from atellite imagery. Senor, 7, pp Haan, Q.K.; Bourque, C.P.-A; Meng, F.-R.; and Cox, R.M, 2007a. A wetne index uing terrain-corrected urface temperature and normalized difference vegetation index derived from tandard MODIS product: an evaluation of it ue in a humid foret-dominated region of eatern Canada. Senor, 7, pp Haan, Q.K.; and Bourque, C.P.-A; Meng., Spatial enhancement of MODIS-baed image of leaf area index: application to the boreal foret region of northern Alberta, Canada. Remote Sen, 2, pp Lambin, E.F.; Ehrlich, D, The urface temperature - vegetation index pace for land cover and land-cover change analyi. Int. J. Remote Sen, 17, pp Li, Z.; Wang, Y.; Zhoub, Q.; Wu, J.; Peng, J.; Chang, H., Spatiotemporal variability of land urface moiture baed on vegetation and temperature characteritic in northern Shaanxi Loe Plateau, China. J. Arid Environ., 72, pp Moran, M.S.; Peter-Lidard, C.D.; Watt, J.M.; and McElroy, J., Etimating oil moiture at the waterhed cale with atellite-baed radar and land urface model. Can. J. Remote Sen., 30, pp Figure 5: Spatial dynamic of averaged TVWI and it relative frequency ditribution. Nemani, R.; Running, S Etimation of regional urface reitance to evapotranpiration from NDVI and thermal-ir AVHRR data. J. Appl. Meteor., 28, pp Patel, N. R.; Anapahha, R.; Kumar, S.; Saha, S.K; Dadhwal,

5 V.K., Aeing potential of MODIS derived temperature/vegetation condition index (TVDI) to infer oil moiture tatu. Int. J. Remote Sen.,, 30, pp Petropoulo, G.; Carlon, T.N.; Wooter, M.J. and Ilam, S., A review of T/VI remote ening baed method for the retrieval of land urface energy fluxe and oil urface moiture. Prog. Phy. Geography, 33(2), pp Tang, R.; Li, Z. L.; Tang, B., An application of the T VI triangle method with enhanced edge determination for evapotranpiration etimation from MODIS data in arid and emi-arid region: Implementation and validation. Remote Sen. Environ., 114, pp ACKNOWLEDGEMENTS Thi tudy wa partially funded by (i) Univerity Reearch Grant Committee (URGC) Reearch Grant from Univerity of Calgary; and (ii) tart-up reearch grant from Department of Geomatic Engineering and Schulich School of Engineering at the Univerity of Calgary; to Dr. Q. Haan. We would like to acknowledge funding to M. Akther from Univerity of Calgary for a Faculty of Graduate Studie Scholarhip. We alo would like to acknowledge (i) NASA for providing the MODIS data free of charge; and (ii) Agriculture and Agri-Food Canada for providing ground-baed volumetric oil moiture data.