Transactions Template JOURNAL OF ENGINEERING RESEARCH AND TECHNOLOGY, VOLUME 1, ISSUE 4, DECEMBER 2014 DIACHRONIC MONITORING OF SURFACE ENERGY FLUXES BY REMOTE DETECTION IN THE NORTHEN-EST OF NIGER W NATIONAL PARK 1, 2 Arouna Saley Hamidou, 1 Oumar Diop, and 1 Amadou Seidou Maiga. 1 Laboratory of Electronics, Computer, Telecommunication and Renewable Energy, Department SAT, Gaston Berger Uni- versity, P.O. Box 234, Saint-Louis, Senegal (French) 2 Department of Physics, Faculty of Science and Technology, University of Maradi, Niger (French) Abstract: The general objective of the present work is to contribute to a set up of an operational prototype of monitoring surface energy fluxes inside the Niger’s W Park, using Landsat data and few fields’ data. The model SEBAL/METRIC is used to estimate the main surface fluxes. The diachronic study of the obtained fluxes reveals constant daily mean values for a given season. During autumn 2002, the mean values of the daily evapotranspira- tion are almost 4mm/day. Humidity indicators are then deduced from the obtained fluxes. Their diachronic study permits to identify area with cold pixels as been less stressed than area having dry pixels. This study shows that Landsat imagery can be used, at a large scale, in monitoring the main biophysical processes occurring at the Soil-Vegetation-Atmosphere interface. Then; it allows identifying areas at risk, inside the Park, needing an ade- quate plan of management and conservation. Index Terms— Energy Fluxes; Remote Detection; Soil-Vegetation-Atmosphere interface; Diachronic I INTRODUCTION Numerous studies were interested these last decades in the processes of transfer of mass and energy at the ground level, through estimating and diachronic studying of surface ener- gy fluxes by remote detection. Such large-scale study is essential for a good understanding of physical processes occuring at the interface Soil-Vegetation-Atmosphere. It helps to better apprehend the combined impacts of natural variability of the climate and anthropogenic actions, observ- able these last decades at the global scale. Diachronic study of surface energy fluxes allows identification of degraded forest’s areas or areas subject to severe hydrous stress, as shown by earlier studies [1-2]. It helps also to prevent the risk of wild forest fires, since plant’s hydrous state is in- versely linked to inflammability of forest resources as shown by Viegas et al. [3]. It is therefore necessary to obtain at a large scale, reliable information on land surface energy fluxes and evapotranspiration. Many methods using remote detection data in calculating surface fluxes have been in focus as shown in earlier works [4-5-6-7]. The most used algorithms are: SEBAL, [8], TSEB,[9]), SEBI,[10] ), S- SEBI, [5]) ; SEBs, [11] and METRIC, [6]. In Niger, studies using remote data have been conducted in order to improve the management of the park, as shown in earlier studies [12-13-14-15]. Still, no study on surface en- ergy fluxes and their relationship with soil’s state has yet been conducted. It is so necessary to fill this gaps.The gen- eral objective of the present work is to contribute in the development of an operational prototype of monitoring those fluxes, using many Landsat data. This prototype is based on simplified procedures, to make it easy operational and re- producible for field’s managers in sahelian’s conditions, where field data are rare and inaccessible. II CHARACTEISTICS OF THE STUDY AREA The study area is located in Niger Republic (West Africa), Fig.1. It lays between longitudes 2°25'E and 2°45'E and AROUNA SALEY HAMIDOU, OUMAR DIOP, AND AMADOU SEIDOU MAIGA./ DIACHRONIC MONITORING OF SURFACE ENERGY FLUXES BY REMOTE DETECTION IN THE NORTHEN-EST OF NIGER W NATIONAL PARK 144 latitudes 12°25'N et 12°40'N. It covers a surface area of 63.000 ha. It is composed of a protected area in the south, inside the Park, and a non protected area in the North, out- side the park. The two areas are separated by a natural bor- der, the Niger River. Fig 1. Geographic position of the study’s area It is a tropical type with soudano-sahelian climatic system. Four types of geomorphologies are identified and mapped in the area: rocky plateaux, pediments and drains, battleships plateaux and the intermediary forms as shown by Benoit, [13]. III MATERIALS AND METHODS The data used in this study are from six Landsat TM and ETM+ detectors, path 192 row 051, acquired during autumn in Niger, with almost clear sky conditions where, one can minimise the effects of cloud on the reflectance detected by the satellite. Solar conditions, on the day of acquisition of each image are calculated in this study. The obtained values and the dates of acquisition are presented in Table1. They are used during atmospheric corrections of the reflectance detected by the satellite (using MODTRAN 4/FLAASH model according to Hoke, [16]) and during correction of the effects of relief on the reflectance (using a Digital Elevation Model of the study’s area). They are also used during the parameterisation of the surface energy balance equation, given as: RN + H + G + LE = 0 (1) Where, RN (W m-2): net incident solar radiation flux; H (W m-2): sensible heat flux; G (W m-2): soil heat flux; LE (W m- 2): latent heat flux. The raw Landsat images, used in this study, are of level 1 delivered by USGS, (UTM, and WGS 84 Zone 31). The pixel size is 30m x 30m.The supervised classification, by maximum likelihood method, is applied to classify each image. The use of this method is motivated by our well- known knowledge of the study’s area and because through experience, supervised classification becomes easier and more correct.Then, the six images were classified using this method. The results of this classification, for image acquired on 1st February 1990, are presented in FIg.2. Fig 2. Land use/occupation on 1st February 1990 The maps of this classification are indispensable at the time of executing SEBAL/ Metric, precisely while choosing the dry and cold pixels. These pixels (called anchor pixels) are pixels on which thermal gradient, dT and sensible heat flux- es, H are calculated. Luminances of optic domains (Visible, near and mean infrared) were converted into reflectances before mapping the surface energy fluxes. The obtained reflectances are then used to calculate the following inputs parameters: surface Albedo (α), Index of Vegetation (NDVI) and Surface Temperature (TS). The theoretical basis of mapping evapotranspiration from remote detection data are nowadays well documented [17-5-6]. Steps given by Allen et al, [6] were used in this study to map the surface energy fluxes and evapotranspiration. The basic Eq. (1) had been that of surface energy balance. Thus, the equivalent energy of evapotranspiration, LE has been estimated as a residual of Eq. (1), applied to each pixel. It is calculated according to: LE = RN - H - G (2) Where, RN is given by: RN = (1 - α) R global + R atm ↓ - R suf ↑ (3) With, R global : The incident global solar radiation, (W/m²) partially reflected by the surface in function of surface albe- do, R atm ↓: The incident atmospheric longwave radiation, (W/m²) and R suf ↑: Shortwave radiation emitted by earth’s surface, (W/m²). AROUNA SALEY HAMIDOU, OUMAR DIOP, AND AMADOU SEIDOU MAIGA./ DIACHRONIC MONITORING OF SURFACE ENERGY FLUXES BY REMOTE DETECTION IN THE NORTHEN-EST OF NIGER W NATIONAL PARK 145 145 H is the sensible heat flux, (W/m²) obtained by an iterative approach, from the aerodynamic equation, given by: H = (ρ air CpdT) / r ah (4) With: ρ air = air density in Kg m-3; Cp = 1004 J Kg-1K-1 (spe- cific capacity of air); dT (°K) = Thermal gradient of air (between Z1= 0.1 m and Z2 = 2 m above the ground), r ah = aerodynamic résistance to heat transfer in s m - ¹, be- tween tow nearest surfaces, separated by distance Z 2 -Z 1 . G (W/m²) is the soil conduction flux calculated according to Bastiaanssen, [17]: G = [(TS - 273.16) (0.0038 + 0.0074α) (1 - 0,98NDVI 4 )] RN (5) In Eq. (3) the surface albedo α is calculated according to Liang et al,: α = 0.356r 1 + 0.13r 3 + 0.373r 4 + 0.085r 5 + 0.072r 7 - 0.0018 (6) Where the r i is the reflectance in channels i (1; 3; 4; 5 et 7) of Landsat satellite, corrected from atmospheric and relief effects. These reflectances are deduced from the correspond- ing luminance L λi .The global solar radiance or incoming shortwave radiation is calculated using formula: R global = (Gcs × cosθ.img×τ sw ) / d² (7) With, Gcs = 1367 W m -2 (solar constant), cosθ.img (Integrate the solar declination; the latitude; the slope; the surface aspect angle and solar hour angle of our study area) is the spatial distribution of solar declination angle, d = relative mean distance between the earth and the sun; τ sw = transmis- sivity of the atmosphere, calculated in function of air effec- tive emissivity. The atmospheric radiation R atm ↓ is calculated according to the Stefan-Boltzmann’s formula: R atm ↓ = ε s ε a σT a 4 (8) With, ε s : The surface emissivity (it corresponds to the con- version factor of thermodynamic energy to radiative ener- gy), expressed in function of NDVI. ε a : Air effective emissivity ; σ : Boltzmann’s constant. The radiation emitted by the earth surface R suf ↑ is calculated according to Stefan-Boltzmann’s formula: R suf ↑ = ε s σ T 4 s (9) With Ts calculated from the radiative surface temperature T RS (Ts = (TRS/ ε s ) 4 i.e. by simple inversion of Stefan- Boltzmann’s Law). T RS is given by the following formula: T RS = K 2 /ln [(K 1 /r c(6) ) + 1] (10) K 1 and K 2 are specific constants of calibration for each type of Landsat satellite. The values of the constants are given inside the header files of each image, downloadable at the same time with the image. r c(6) is the real radiance emitted by the surface, corrected from the atmospheric and relief ef- fects. Calculation of H from formula (4) requires simultaneous existence of dry pixels and cold pixels on the site of study as shown by Allen et al, [6]. The supervised classification has permitted the identification of such pixels: dry pixels are rocky levelling and burned area and cold pixels are meadow and aquatic vegetation. To spatialize dT, we have first determined the values of H on dry pixels (H dry ) and after on cold pixels (H cold ). They obtained values are then used to estimate the thermal gradi- ent dT using an iterative process, starting by applying neu- tral stability conditions of the atmosphere, until obtainment of dT convergence after successive corrections of the at- mospheric stability, precisely on the aerodynamic resistance. The mapping of dT is made possible by assuming a linear relation with T S , according to Allen et al, [6]: dT = a - b T S (11) Where b and a, constants estimated on anchor pixels (dry/cold pixels), chosen on each image. The spatial distribution of dT is used in another iteration process from Eq. (4), thus allowing the mapping of H. The spatial distribution of the other instantaneous fluxes allows mapping the latent heat flux, H and then the instantaneous evapotranspiration ET inst witch is calculated according to the following equation: ETRday = FE *Rnday (12) Where, FE (in French) is the Fraction of Evaporation con- sidered constant for a given day, as suggested by Bas- tiaanssen et al, [4]: F E = LEinst. / (Rn-G) (13) LEinst is the instantaneous latent heat (LEinst) and (Rn-G) is the available energy at earth’s surface. Rnday is the net daily radiation given by: Rnday = (1 – α0)*Rgday- 110* τday (14) AROUNA SALEY HAMIDOU, OUMAR DIOP, AND AMADOU SEIDOU MAIGA./ DIACHRONIC MONITORING OF SURFACE ENERGY FLUXES BY REMOTE DETECTION IN THE NORTHEN-EST OF NIGER W NATIONAL PARK 146 Rgday: is the global daily radiation and τjour: daily transmis- sivity of atmosphere (expressed as function of sunstroke fraction n/N) given by: τday = 0.25 + 0.50 * n/N (15) Rgday is esteemed from the daily exo-atmospheric radiation Kexo and τday: Rgday = Kexo * τday (16) Known that, to evaporate 1Kg of water we need 2, 45*106 joules (latent heat of evaporation), we have ETRday in mm day -1calculated as: ETRday = ETR (joule) / (2, 45*106) (17) IV Results and discussion A. spatial and diachronic analyses of the inputs parameters and the obtained fluxes The inputs parameters of the model, i.e. surface temperature, surface Albedo and NDVI are estimated, in space at pixel scale and in time at the different dates of acquisition, Ta- ble1. This table and the figures of appendis A, (Fig1A, Fig2A, and Fig3A) show a very spatial variability of these inputs. This variability can be explained by the heterogene- ous character of the study’s area, observable in Fig.2. From Table1 we can observe that when the NDVI is high the corresponding temperature is low, vice versa. This result is general; a ground which vegetal cover increases sees its surface temperature decreasing. This could be due to the fact that the vegetation reduces the aerodynamic resistance of the evapotranspiration. A complementary study is necessary to be conducted in order to verify such hypothesis. Table 1: Values of the inputs for each day of image acquisi- tions Surface temperature is among the most key parameters that control the whole physical processes occurring at Sol- Vegetation-Atmosphere interface. It is therefore important to get more reliable information on this parameter. Thus, its distributions (spatial and temporal) were analysed. The spa- tial distribution shows that the surface temperature varies between 296.93°K and 328.75°K, Fig1A, with a mean value of 313.82°K. These values are in the same order of magni- tude as the ones obtained by remote detection in areas with almost the same type of climate as our study’s area, [20-21]. Minimum values correspond to cold pixels (water, meadow and aquatic vegetation) and high values to hot pixels (rocky levelling and burned area). The evolutions of surface tem- perature, in terms of vegetation abundance (through the vegetation index, NDVI) were also analysed. On 4th Octo- ber 1992, where the vegetation is abundant (mean NDVI =0.28, with maxima reaching up to 0.67) lowest surface temperature is obtained (TS = 307.11°K). This could be due to the fact that vegetation reduces the resistance of surface evapotranspiration, this induced diminish of surface temper- ature. The temporal comparison between the mean daily values of the evapotranspiration, on the following days: 04/10/1992, 30/11/1998, 02/02/2002, and 17/11/2002, shows that these values are practically constant (4mm/day), as shown in Ta- ble 2. Indeed, except precipitations and wind all the others biophysical parameters are generally constant for a given season, like in autumn, season during which the study’s images were acquired. On the other hand, values of the re- maining fluxes i.e. the sensible heat flux, H and the conduc- tion flux G, are varying both in space, figures of appendis B (Fig1B,Fig2B,Fig3B) and time, Table2, due to the variabil- ity of the phenomenon of convection. Table 2: Values of surface energy fluxes and evapotranspi- ration AROUNA SALEY HAMIDOU, OUMAR DIOP, AND AMADOU SEIDOU MAIGA./ DIACHRONIC MONITORING OF SURFACE ENERGY FLUXES BY REMOTE DETECTION IN THE NORTHEN-EST OF NIGER W NATIONAL PARK 147 147 B. characterizations on the soil’s state Before characterizing the soil’s state, the diagrams defined by the relation between TS and NDVI were used to locate the dry/cold pixels, using the triangle’s method, [2-19]. The relation between TS and albedo was then used to confirm the positions of such pixels. For the image, acquired on 5th February 2003, a threshold albedo of 0.2905 was obtained for corresponding TS of 313.6°K, fig.3. Fig 3. Surface temperature function of Albedo Cold pixels are pixels having cold temperatures with albedo lesser than threshold albedo. Dry pixels are pixels having high temperatures with albedo greater than threshold albedo. After locating the dry/cold pixels we have analyzed the spatial and temporal variability of the surface energy fluxes of two different areas extracted from the same image, those areas are named A and B: A has more cold pixels (well- watered and fully vegetated) than B and B has more dry pixels (almost bare soil not too much covered). Mean values of humidity indicators over area A, Table3a, shows highest values of evaporation fraction and daily evapotranspiration. On the other hand, these values are lowest over B, Table3b. This is explained by the fact that an increase of albedo in- duces diminish of energy absorbed by the surface and thus, lesser temperature; as regulation by latent heat flux is no more possible. Covered surfaces have the highest values of fraction of evaporation. This has grave consequences, ex- pressed as diminish of soil humidity and drainage of vegeta- tion, more marked in case of lack of water. During the months of February 1990 and 2003, humidity indicators of zone B have the lowest mean values compared to those of zone A, Tables3a and 3b. Hydrous stress is thus more marked over zone B. According to Thiery and al. [22], it was during the last 90’s and 2000’s decades that negatives impacts of climate changes are observed in sahelian’s re- gions. It is therefore highly probable that these observed lowest values are linked with the impacts of climate chang- es. Table 3a and 3b: Mean daily values of humidity indicators on zone A and on zone B Table 3a: Zone A Table 3b : Zone B V CONCLUSION This study has permitted a diachronic monitoring of the main surface energy fluxes and humidity indicators. Ob- tained maps have reflected the dynamic of the study’s area, for different inputs of the model used. The same dynamic was observed for the main resulting fluxes i.e. flux of con- duction, sensible heat flux and latent heat flux. The monitor- ing has allowed also the characterization of the soil’s state and identification of areas that can be subject to severe hy- drous stress. By lack of sufficient and pertinent field’s data we are not able to verify some hypothesis we made in this study. More images and field’s data are necessary to get AROUNA SALEY HAMIDOU, OUMAR DIOP, AND AMADOU SEIDOU MAIGA./ DIACHRONIC MONITORING OF SURFACE ENERGY FLUXES BY REMOTE DETECTION IN THE NORTHEN-EST OF NIGER W NATIONAL PARK 148 interpolated daily, monthly and seasonal values.But the obtained values are in the same order of magnitude as those encountered in the literature for regions having almost the same type of climatic characteristics as our study’s area. We are planning to conduct a campaign of field’s data collecting and a real time satellite image downloading, over a long period, through an important research project, in collabora- tion with some parteners. It will contribute in setting up the operational prototype of monitoring surface energy fluxes inside the Park. This will help to better apprehend, inside the Park, the various biophysical processes occurring at the sol- Vegetation- Atmosphere interface. Then; it allows identify- ing areas at risk, needing an adequate plan of management and conservation. VI RECOMMANDATIONS As shwon through this study, remote sensing can be a pow- erful mean of studying surface energy fluxes at a large scale. New generations of Satellites with a temporal resolution of 3 days, like Formosat and Venus, offer the possibilities to utilize the same approaches developed in this study. Many fied data are also necessary to validate the obtained surface energy fuxes by remote sensing. Then, to conduct a better diachronic monitoring of surface energy fluxes, at a large scale, we highly recommend conducting the folloving field’s works: 1. To realize a big campagne of field’s data collection, at pixel scale and at real time, corresponding to the passage of the satellite over the pixel; 2. To realize and test an operational prototype of con- tiuous mapping of surface energy fluxes, using sat- ellite’s informations; 3. To devellop algorthims permiting the interpolation of the surface energy fluxes at the day’s basis, sea- sonal’s basis and annual’s basis, between many dates of image’s acquisition. Taking in account the above three aspects in the monitoring processes is the only necessary condition for a better utiliza- tion of obtained surface energy fluxes in the hydrologicals and environnemental’s thematics. VII ACKNOWLEDGMENT The authors wish to thank Pr. Saadou Mahamane and Pr. Ali Mahamane, respectively rector and vice rector of the Uni- versity of Maradi, for their helpful contributions. This work was supported in part by the budget of the University of Maradi. REFERENCES [1] Hamimed A., Mederbal K., Khaldi A. " Utilisation des données satellitaires TM de Landsat pour le suivi de l’état hydrique d’un couvet végétal dans les conditions semi-arides en Algérie". Télédétection 2: 29-38, 2001. [2] Mehor. M, Hamimed. A, Khaldi. A, Seddini.A, Abdes- selam. B. "Spatialisation de la température et des flux énergétiques de surface à partir des données satel- litaires Landsat ETM+". Revue Française de photo- grammétrie et de télédétection, N°190, pp.15-17, 2008 [3] Viegas D.X., Viegas T.P., Ferreira A A.D. "Moisture content of fine forest fuels and fire occurrence in Por- tugal". The International Journal of Wild land Fire, vol. 2 (2): 69-85, 1992. [4] Bastiaanssen, W.G.M., M. Menenti, R.A. Feddes and A.A.M. Holtslag. "Remote sensing Surface Energy Balance Algorithm for Land (SEBAL): 1. Formula- tion". J. Hydrol., 212-213: 198-212, 1998. [5] Roerink, G.J., Z. Su and M. Menenti. S-SEBI. "A sim- ple remote sensing algorithm to estimate the surface energy balance". Phys. Chem. Earth, 2000 [6] Allen, R.G., T. Masahiro and T. Ricardo. "Satellite- based energy balance for mapping Evapotranspiration with internalised calibration (METRIC)-Model". J. Ir- rigat. Drain. Eng., 133(4): 395-406, 2007. [7] Hamimed, A., Z. Souidi and K. Mederbal. "Spatial évapotranspiration and surface energy fluxes from Landsat ETM + data: Application to a mountain forest region in 2009 Algeria". JAS AUF Alger, November, 2009. [8] Bastiaanssen, W.G.M. "Regionalization of surface fluxes densities and moisture indicators in composite terrain". Ph.D. Thesis, Agricultural University Wa- geningen, 273 p, 1995. [9] Norman, J.M., Kustas, W.P. and Humes, K.S. "Source approach for estimating soil and vegetation energy fluxes in observations of directional radiometric sur- face temperature". Agricultural and Forest Meteorolo- gy, 77:263-293, 1995. [10] Menenti, M.; Choudhury. "Parameterization of land surface evaporation by means of location dependent potential evaporation and surface temperature range". Proceedings of IAHS conference on Land Surface Pro- cesses, 1993. [11] Su, Z. "The Surface Energy Balance System (SEBS) for estimation of turbulent heat fluxes at scales ranging from a point to a continent". Hydrol. Earth Syst. Sci., 6(1): 85-99, 2002. [12] Couteron, P. "Reflection on spatial models for land Sudano-Sahelian area. DEA memory structures and spatial dynamics". University of Avignon, pp: 61, 1992. [13] Benoit, M. "Status and use of land on the outskirts of the National Park of "W" in Niger. Contribution to the study of natural and plant resources Tamou’s Town- ship Park and the "W". Office of Scientific and Tech- nical Research Overseas (ORSTOM) Niamey, Niger (In French), 1998. [14] Inoussa, M.M., A. Mahamane, C. Mbow, M. Saadou and B. Yvonne. "Spatio- temporal dynamics of wood- AROUNA SALEY HAMIDOU, OUMAR DIOP, AND AMADOU SEIDOU MAIGA./ DIACHRONIC MONITORING OF SURFACE ENERGY FLUXES BY REMOTE DETECTION IN THE NORTHEN-EST OF NIGER W NATIONAL PARK 149 149 land in the W National Park of Niger (West Africa) ". Drought, 2011. [15] Diouf. A. "Influence des régimes des feux d’aménagement sur la structure ligneuse des savanes Nord-soudaniennes dans le Parc du W (Sud Ouest NI- GER)". Thèse de doctorat, soutenue en 2013 à l’école inter facultaire de bios ingénieurs de l’Université Libre de Bruxelles, 2013. [16] Hoke.T. "MODTRAN4. Radiative transfer modelling for atmospheric correction". Proceeding of the Optical Spectroscopic Techniques and Instrumentation for At- mospheric and Space Research III, SPIE July 1999. [17] Bastiaanssen, W. "SEBAL-based sensible and latent heat fluxes in the irrigated Gediz Bassin, Turkey". J. Hydrol., 229(1-2): 87-100, 2000. [18] Liang, S., C.R. Shuey and C. Daughtry. "Narrowband to broad band conversions of land surface Albedo: II validation". Remote Sensing Env. 84: 25-41. Pënualas, 1993.The reflectance at 950-970 mm regions as an in- dicator of water status". Int. J. Remote Sensing, 14: 1887-1905, 2002. [19]Arouna, S.H., Oumar.D, Amadou.S.M. "A Spatial Analysis of Surface Energy Fluxes and evapotranspira- tion in the Northern-east of Niger W National Park". Research Journal of Environmental and Earth Science 5(3): 123-130, 2013 ISSN: 2041-0484; e-ISSN: 2041- 0492 © Maxwell Scientific Organization, 2013. [20] Bashir, M.A., Takeshi, H., Haruya,T., Abdelhadib, A. W., Akio, T. "The spatial analysis of surface tempera- ture and evapotranspiration for some land use/cover types in the Gezira area, Sudan". Research project sup- ported by the grants-in-aid (No.16405031), from the Japan Society for the promotion of Science, 2007. [21] Pënualas, C. "The reflectance at 950-970 mm regions as an indicator of water status".Int. J. Remote Sensing, 14: 1887-1905,1993. [22]Thierry, M., Erwann, F., Joan, B. "Evaluation des risques liés aux variationsSpatiotemporelles de la plu- viométrie au Sahel", 2007. ABBREVIATIONS LIST ETRday: daily evapotranspiration. ETM+: Enhanced Thematic Mapper plus. NDVI: Normalized Difference Vegetation Index. PNWN: Parc National du W du Niger (French). METRIC: Mapping evapotranspiration with high Resolu- tion and Internalized Calibration. SEBAL: Surface Energy Balance Algorithm for Land. SEBI: Surface Energy Balance Index. S-SEBI: Soil Surface Energy Balance Index SEBs: Soil Energy Balance System. TM: Thematic Mapper. TSEB: Two-Source Energy Balance algorithm.