ESTIMATION OF ACTUAL EVAPOTRANSPIRATION USING SURFACE ENERGY BALANCE APPROACH AND LANDSAT-8 IMAGES OF SEMARANG AREA, CENTRAL JAVA

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1 ESTIMATION OF ACTUAL EVAPOTRANSPIRATION USING SURFACE ENERGY BALANCE APPROACH AND LANDSAT-8 IMAGES OF SEMARANG AREA, CENTRAL JAVA Khalifah Insan Nur Rahmi 1 and Projo Danoedoro 1 1 Center for Remote Sensing (PUSPICS), Faculty of Geography, Universitas Gadjah Mada, Yogyakarta, Indonesia khalifah.insan.nr@gmail.com, projo.danoedoro@geo.ugm.ac.id KEY WORDS: Actual evapotranspiration, Landsat 8, split window algorithm, SEBS, Energy Balance. Water is an indispensable requirement for every creature on the earth's surface. Water is closely related to the hydrological cycle, one of the processes that occurs during that cycle is evapotranspiration both actually and potentially. However, information in terms of spatial distribution of actual evapotranspiration is very rare, particularly in Indonesia. Landsat-8 satellite data offers opportunity in coping with this need, since the image dataset is available in both reflective and thermal spectral bands. Those bands can be generated to land-cover and temperature information, which are important to surface energy balance computation including evapotranspiration. The purposes of this study were (1) to understand the capability of Landsat 8 for deriving actual evapotranspiration (ETa) parameters estimation, (2) to know its accuracy according to data obtained from meteorological and climatology stations, and (3) to determine the spatial distribution of ETa based on land cover information. The ETa can be extracted from remotely sensed images using Surface Energy Balance System (SEBS) algorithms. ETa estimation made use of SEBS algorithm comprising several parameters, i.e. net radiation (Rn), which is proportional to the amount of soil surface heat flux (G0), sensible heat flux (H) and latent heat flux (λe). ETa is part of the λe which is calculated based on SEBS algorithms. The parameters required for SEBS include albedo, emissivity, land surface temperature, NDVI, vegetation fraction, LAI, surface roughness momentum transfer (Z0m), canopy height, and elevation represented by digital elevation model (DEM). Each of these parameters serves to establish the elements of energy balance of Rn, G0, H, or λe. The land surface temperature was computed on a pixel basis using split-window algorithm. The results showed that all parameters have good accuracies in comparison with the reference data to built SEBS algorithm. ETa accuracy results referring to the data from meteorological dan climatology stations showed standard error of estimates of 0.99 mm/day, 2.18 mm/day, and 2.66 mm /day at 3 different station locations. The highest ETa value was located in the objects of body of water, i.e. at 9.6 mm/day; while the lowest one was located in the objects of zinc roof, i.e. at 5.6 mm/day. This study demonstrated the advantages of spatial data like Landsat-8 satellite images and DEM over meteorological station data, particularly in modelling the spatial distribution of ETa in a relatively small area, which could not be done using data obtained from meteorological stations. 1. INTRODUCTION Evapotranspiration (ET) is a combination of two processes in the hydrological cycle, i.e. evaporation and transpiration. The evaporation is a process of changing liquid water into water vapor occurring in ground water, water bodies or other inanimate objects, while transpiration is a water evaporation process that occurs in living organisms, especially plants (Asdak, 1995). There are two types of ET, i.e. potential ET (ETp) and actual ET (ETa). ETp is evapotranspiration that occurs in the optimal condition of the land surface of vegetation, soil, and water; while ETa is evapotranspiration occuring in a certain time and area. ETa is mostly influenced by land cover types, so that. actual evapotranspiration is one of the most important parameter of water loss that can be estimated from each land cover type. Therefore, ETa can be used as a basis for water resource management involving water input data. However, information in terms of spatial distribution of actual evapotranspiration is very rare, particularly in Indonesia. Previous studies showed that the actual evapotranspiration could be estimated spatially using remotely sensed imagery. For example, Su (2002) built Surface Energy Balance System (SEBS) methodology to extract ETa from remotely sensed data. His work was then further developed by Jia (2011), Kurkura (2011), and Rwasoka (2011) using MODIS imagery with low spatial resolution. Research of Wondimagegn (2006) derived ETa from Landsat 7 ETM+ to estimates summer time in the Netherlands. It is very interesting to note that the use of such methods were not widely applied in Indonesia. In addition, the advent of Landsat 8 OLI system offers spectral bands that are not the same as the ones available in previous satellite systems, particularly in visible and thermal infrared regions. Therefore, there is a need to evaluate the capability of Landsat 8 OLI for estimating evapotranspiration in Indonesia. Based on the aforementioned background, the objectives of this research were to understand the capability of Landsat 8 as a newest generation of Landsat system, and to know its accuracy according to data obtained from meteorological and climatology stations in the Semarang area, Central Java as study area, and (c) to determine the spatial distribution

2 of ETa based on land cover information. This study was conducted in 3 watersheds, comprising Mangkang Timur, Garang, and Kanal Timur watersheds, which are located in Semarang area, Central Java. The location of 3 watersheds is in 110º º28 30 E and 6º57 0 7º11 0 S. This area has an extent of km 2 or 336,675 pixels of Landsat 8 (Figure 1). 2. MATERIALS AND METHODS 2.1 Materials Figure 1. Landsat 8 imagery of the study area The data used in this study were: (a) primary data from satellite imagery, and (b) secondary data from meteorological and climatology stations. Satellite images used in this study were Landsat 8, MODIS, and SRTM. Secondary data that used in this study were evaporation, actual water vapor pressure, saturated water vapor pressure, air temperature, solar radiation, and wind velocity, which came from 3 meteorological offices, i.e. Climatology Station, Meteorology Station, and Maritime Meteorology Station. All of the data recorded in dry seasons of 2013 and ILWIS software was used for modeling the evapotranspiration by integrating image processing of satellite data and raster-based GIS analyses. Indonesian topographic map (RBI) at 1: scales was also used to support geometric correction and guidance during the fieldwork. 2.2 Methods In order to evaluate the capability of the Landsat 8 OLI imagery, 10 datasets containing 10 dates of recording during dry season periods of 2013 and 2014 were used. The purpose of these 10 datasets was to check its consistency. ETa derivation using SEBS algorithm developed from energy balance model (Jia, 2009). This model is commonly written as: Rn = G0 + H + λe (1) where Rn is the net radiation (Wm -2 ), G0 is the soil surface heat flux (Wm -2 ), H is the turbulent latent heat flux (Wm -2 ) and λe is the latent heat of vaporation (Jkg -1 ). Eq. 1 elaborated into: ( ) (2)

3 where α is the land surface albedo, R swd is the downward solar radiation flux (Wm -2 ), R lwd is the downward long wave radiation flux (Wm -2 ), ε is the emissivity of the surface, σ is Stefan-Boltzmann constant (5.678 x 10-8 Wm -2 K -4 ). ( ) ( ) (3) where fc is vegetation fraction, г c = 0.05 for full vegetation canopy, г c = for bare soil. * ( ) ( ) ( )+ (4) (5) (6) (7) (( ) ) ( ) (8) * ( ) ( ) ( )+ (9) ( ) (10) where is potential air and surface temperature, z is elevation reference, u* is friction velocity, ρ air density, k = 0.4 is Von Kaman constant, do is difference elevation, Z0m is surface roughness length for momentum transfer, Z0h is surface roughness length for heat transfer, and is stability correction function for momentum and transfer energy for sensible heat. L is Obukhov length, g is gravitation acceleration, is virtual potential temperature near surface (Brutsaert in Wondimagegn, 2006) where Λ is evaporative fraction. (11) ETa is a part of λe which, is calculated based on SEBS algorithm. Parameters which are needed to build SEBS are: albedo, emissivity, land surface temperature (LST), vegetation density in terms of Normalized Difference Vegetation Index (NDVI), vegetation fraction, Leaf Area Index (LAI), surface roughness length for transfer momentum (Z0m), canopy height, and Digital Elevation Model (DEM). Each parameter needed to build element of energy balance which are Rn, G0, H, and λe. With remote sensing methods, the following section describes each parameter: 1. Emissivity Image of emissivity was extracted from multispectral of Landsat 8. Land cover map was derived using multispectral classification of Landsat 8, i.e. by applying maximum likelihood algorithm. Land cover types of the study area was classified into 7 objects (Table 1). Based on the land cover classes, emissivity value of each pixel was specified. Table 1. Emissivity of each land cover Land cover Emissivity Water body (TA) 0,98 Covered crown vegetation (VBR) 0,99 Uncovered crown vegetation (VBT) 0,96 Dry soil (TK) 0,92 Wet soil (TB) 0,95 Aspalt (A) 0,96 Galvanized object (S) 0,90 Source: Danoedoro, 2012 and Sutanto, 1994

4 2. Albedo Albedo map was derived from Landsat 8 multispectral image according to Smith (2010) algorithm: (12) where ρ 2, ρ 4, ρ 5, ρ 6, ρ 7 are blue, red, near infrared, middle infrared and far infrared bands of multispectral Landsat 8 respectively. 3. LST (Land Surface Temperatue) LST was derived from bands 10 and 11 of Landsat 8 using Split Windows Algorithm (SWA) (Rozenstein, 2013). This algorithm has 3 parameters of brightness temperature from band 10 and 11, atmospheric transmission that is extracted from MODIS imagery, and emissivity which have been extracted previously (parameter 1). 4. NDVI NDVI was extracted from multispectral Landsat 8, and was calculated using the following equation: where ρ NIR is near infrared band of Landsat 8 (band 5), while ρ RED is red band of Landsat 8 (band 4). (13) 5. Vegetation fraction Vegetation fraction map was derived from NDVI image using the following equation: ( ) (14) where NDVImax is maximum pixel value and NDVImin is minimum pixel value of NDVI image. 6. LAI LAI map was extracted using Reduce Simple Ratio (RSR) algorithm (Schiffman, et. al., 2008) based on the following equation: (15) (16) where ρ MIR is middle infrared band of Landsat 8 (band 6) 7. Z0m Image of Z0m was extracted from NDVI image using the following equation (Jia, 2002): ( ) (17) 8. Canopy height Map of canopy height derived from NDVI imagery with following equation (Wondimagegn, 2006): (18) 9. DEM DEM was delivered by SRTM-1 which has 30 m x 30 m spatial resolution. The DEM shows elevation above sea level value in each pixel. Based on the parameters that have been transformed into maps described above, the ETa could then be computed spatially. Previous research by Wondimagegn (2006), Jia (2009), Kurkura (2011), and Rwasoka (2012) calculated ETa value (mm/d) using equation: (19)

5 where is daily evaporative fraction (mm/d), Rn is net radiation (Wm -2 ), G0 is soil surface heat flux (Wm -2 ), is the latent heat of water taken as 2.47 x 106 (J/kg), and is the density of water (kg/m 3 ). 3. RESULTS AND DISCUSSION There were several differences in accuracy values achieved by the mapping of each parameter. We used different data reference to assess the accuracy of each parameter. Firstly, accuracy assessment of emissivity map made use of image-based land cover map, by which the land cover map showed 94.28% overall accuracy. This assessment was based on confusion matrix analysis. Emissivity and land cover maps are shown in Fig. 2. Secondly, accuracy assesment of albedo map was based on data reference obtained from previous study. Albedo map (Fig. 3) has five out of six objects that are in accordance with reference data (Table 2). Thirdly, LST map derived from Landsat 8 s bands 10 and 11 using Split Window Algorithm was checked using meteorological data. Standard error of estimate (SEE) of the LST map was 1.79 o C, compared to LSTs calculated by 3 meteorology and climatology offices in the study area. Fourthly, the parameters 4, 5, and 6 are NDVI, vegetation fraction, and LAI showing the vegetation conditions of the study area. Accuracy asessment of these parameters used vegetation density measurement in the field. Field observation data showed that vegetation densities were equal to NDVI values. The resultant maps of these parameters are presented in Fig. 5, 6, and 7. Figure 2. Land cover and emissivity imagery Figure 5. NDVI imagery and its statistics Figure 3. Albedo imagery and its statistics Figure 6. Vegetation fraction imagery and its statistics Figure 4. LST imagery and its histograms Figure 7. LAI imagery and its statistics

6 Table 2. Albedo values resulted from image processing and reference data Land Cover Image Processing Reference Accordance TK v VBT x A v TA v S VBR v TB v Source: Processing, 2015 and Oke, 1992, Ahrens, 2006 The seventh parameter was surface roughness length for transfer momentum (Z0m) (Fig. 8), which was assessed with respect to each land cover type using reference data from previous study. The result showed that all objects were in accordance with reference data (Table 3). The eighth parameter, i.e. canopy height map showed in Fig. 9, was checked for accuracy assessment by field measurement using abney level, in order to obtain information on the height of vegetation canopy. The result gave SEE value of 1.8 m. The last parameter used in this model was DEM from SRTM-1 imagery (Fig. 10). Pixel values showing surface elevation were by elevation point data from the Indonesia Geospatial Information Agency (BIG). There were 570 elevation points involved in the accuracy assessment. It resulted SEE of m. Figure 8. Z0m imagery and its statistics Figure 10. Distribution of elevation points in DEM imagery and its statistics Table 3 Surface roughness Values of the Image Processing and Reference LC Image Processing Reference Data Accordance TK v VBT v A v TA v S v VBR v TB v Source: Processing, 2015 and Wieringa, 1993 Figure 9. Canopy height imagery and its statistics Once the parameters had been checked for accuracy assessment, the ETa estimation model using SEBS algorithm applied. Accuracy assessment of the computed ETa was carried out using advection aridity method. It was developed based on FAO s Penman-Monteith method. The advection aridity method made use of meteorology and climatology data as mention previously in page 2. Result obtained from actual evapotranspiration calculation using advection aridity method (ET AA ) showed in Table 4, 5, and 6 respectively, in which each ET AA represents one meteorology station. The table also shows evaporation data (E) for comparison with ETa obtained using SEBS algorithm (ETa SEBS ). The coordinate location of Climatology Station is X= E and Y= N (UTM zone=49m). In this location, value of ETa SEBS is 9 12 mm/d and value of ET AA is 6 9 mm/d. There is a difference between value of ETa obtained from SEBS estimation and the data reference (ET AA ). The difference existed because there were inaccuracies in the

7 process of building up the parameters, as mentioned previously. As a result, all these inaccuracies accumulated in the resultant estimates of ETa SEBS, which was modeled using multiple maps. In addition, there was a difference in data types between data reference and ETa SEBS which contribute to the distortion of the SEBS estimate. The reference used point data, while ETa SEBS delivered map with 30 x 30 m pixel size. The locations of the other stations are in X=431824E and Y= N (UTM zone=49m) and X= Eand Y= N (UTM zone=49m) for Meteorology Station and Maritime Meteorology Station offices. SEE resulted from accuracy asessment of each station is presented in Table 7. The table shows that Climatology Station has the best accordance with the ETa SEBS, followed by Meteorology Station and Meteorology Maritime Station offices respectively. The SEE value showing the differences between ETa SEBS and ET AA were 0.99 mm/d, 2.18 mm/d, and 2.65 mm/d for Climatology Station, Meteorology Station and Meteorology Maritime Station offices respectively. The Climatology Station office has most complete tools for measuring the meteorological data, so that the relatively small difference between its ET AA and the ETaSEBS in terms of SEE could be considered as a basis for comparison between SEBS model and ground station model, rather than using the other two stations. However, it should also be noted that the E calculated from the station covers both evaporation and transpiration, so that ETa SEBS was found bigger than E of the ground station. Table 4. E, ET AA, and ETa SEBS (mm/d) values of Climatology Station of Semarang Date E ET AA ETa SEBS 24-Jun-13 (mm/d) Aug Sep Sep Oct May Aug Aug Oct Nov Source: Processing, 2015 Table 5. E, ET AA, and ETa SEBS (mm/d) values of Meteorology Station of Semarang Date E ET AA ETa SEBS 24-Jun Aug Sep Sep Oct May Aug Aug Oct Nov Source: Processing, 2015 Table 6. E, ET AA, and ETa SEBS (mm/d) values of Meteorology Maritime Station of Semarang Date E ET AA ETa SEBS 24-Jun Aug Sep Sep Oct May Aug Aug Oct Nov Source: Processing, 2015 Table 7. SEE estimations between ETa SEBS and ET AA and ETaSEBS and E (mm/d) in each station. Stations SEE SEBS-AA SEE SEBS-E Climatology Station Meteorology Station Meteorology Maritim Station Source: Processing, 2015 In the image of 24 June 2013, the highest ETA was water body, i.e. 9.6 mm/d. Meanwhile, the lowest ETa was galvanized object of 7.2 mm/d. Seven out of 10 resultant ETa images showed water bodies contributed the highest ETa than the other objects. Similar to the galvanized objects, which have seven out of 10 images that showed lowest ETa. This condition happened because actual evaporation is a process of changing liquid water into water vapor from land surface into atmosphere. Evaporation coming from any object needs supply of water, and it will be bigger if the object contains more water. On the other hand, the capability of object to absorb Rn and G0 would influence the value of ETa as well.

8 Ten maps showing spatial distribution of ETa is presented in Fig. 15. These are estimates based on 10 different dates of recording. Water body objects in the study area such as reservoirs, sea, fishponds, rivers, and tidal floodsv have high ETa values, and they are presented in orange-red colors. For examples, Marina beach and Garang river have orange-red colors on all images. Vegetation covers that have significant contribution to the transpiration process ranks second to the water bodies. Based on these maps, it can be concluded that evaporation played more important role in the estimation of ETa than transpiration. Dense vegetation canopy has a higher ETa than less dense vegetation canopy. The Fig. 15 showed that the dense vegetation canopy near the peak of Mt. Ungaran has darker colors than the other vegetated areas. Meanwhile, galvanized object, asphalt, and dry soils which are impervious surface have low ETa values 4. CONCLUSION Accuracy assessment of nine parameters showed that they are good enough to build the SEBS algorithm as a basis for ETA estimate. The ETa resulted from SEBS algorithm has accuracy of 0.99 mm/d in comparison with the Climatology Station of Semarang, 2.18 mm/d as compared to Meteorology Station of Semarang, and 2.66 mm/d as compared to the Meteorology Maritime Station of Semarang. Those three references were based on calculation of secondary data using Advection Aridity method. Water bodies have highest ETa value of 9.6 mm/d, while the galvanized objects have the lowest ETa value of 5.6 mm/d. High ETa values were found in south and north of the study areas, i.e. Java Sea and Mt. Ungaran forest. Low ETa values were found in the middle part of the study area, i.e. Impervious surface such as: galvanized objects, asphalt, and dry soils. REFERENCES Asdak, Chay Hidrologi dan Pengelolaan DAS. Yogyakarta: Gadjah Mada University Press. Choundhury, B.J. dan J.L. Montheith A Four Layer Model for the Heat Budget of Homogenous Land Surfaces. Quarterly Journal Roy. Meteorology Society, 114, Danoedoro, Projo Pengantar Penginderaan Jauh Digital. Yogyakarta: Penerbit Andi. Wondimagegn, Hailegiorgis S Remote Sensing Analysis of Summer Time Evapotranspiration using SEBS Algorithm. Thesis Enschede: ITC. Jensen, J.R Introductory Digital Image Processing, A Remote Sensing Perspective, 3rd Edition. Sydney: Pearson Prentice Hall. Jia, I., dkk Regional Estimation of Daily to Annual Regional Evapotranspiration with MODIS data in the Yellow River Data Wetland. Hydrology and Earth System Sciences. 13, Kurkura, Mussa Water Balance of Upper Awash Basin based on Satellite-derived Data (Remote Sensing). Thesis. Addis Ababa: Addis Ababa Institute of Technology. Lillesand, Thomas M., dan R.W. Kiefer Remote Sensing and Image Interpretation Second Edition. New York: John Wiley and Sons. Rozenstein, Offer, dkk Derivation of Land Surface Temperature for Landsat-8 TIRS Using a Split Window Algorithm. Sensor 2014, 14, Rwasoka, D.T., dkk Estimation of Actual Evapotranspiration Using the Surface Energy Balance System (SEBS) Algorithm in the Upper Manyame catchment in Zimbabwe. Journal of Physics and Chemistry of the Earth 36, Schiffman, B., et. al Estimation of LAI through The Acquisition of Ground Truth Data in Yesemitte National Park. ASPRS 2008 Annual Conference. Portland, Oregon, April 28 May 2, 2008 Su, Z. B., The Surface Energy Balance System (SEBS) for Estimation of Turbulent Heat Fluxes. Hydrology and Earth System Sciencess. 6, 1, Sutanto Penginderaan Jauh Jilid II. Yogyakarta: Gadjah Mada University Press. Teixeira, A.H.C., Measurement and Modelling of Evapotranspiration to Assess Agricultural Water Productivity in Basins with Changing Landuse Patterns. Thesis. Wageningen: Wageningen University. Wieringa, Jon Representative Roughness Parameters for Homogeneous Terrain. Boundary Layer Meteorology. 63, Smith, R.B The Heat Budget of Earth s Surface Decuded From Space. diakses 21 Juni 2014.

9 Figure 15. Map of Actual Evapotranspiration in Semarang Area, Central Java