Estimating reference evapotranspiration using remote sensing and ...

Applied Geography 31 (2011) 251e258

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Estimating reference evapotranspiration using remote sensing and empirical models in a region with limited ground data availability in Kenya Eduardo Eiji Maeda a, *, David A. Wiberg b, Petri K.E. Pellikka a a b

Department of Geosciences and Geography, University of Helsinki, Gustaf Hällströmin katu 2, 00014, Helsinki, Finland Land Use Change and Agriculture Program at the International Institute for Applied Systems Analysis (IIASA), Laxenburg, Austria

a b s t r a c t Keywords: Reference evapotranspiration Remote sensing Empirical models Taita Hills Kenya

Evapotranspiration is an important component of the hydrological cycle and its accurate quantification is crucial for the design, operation and management of irrigation systems. However, the lack of meteorological data from ground stations is a clear barrier to the proper management of water resources in poor countries, increasing the risks of water scarcity and water conflicts. In the presented study, three temperature based ET models are evaluated in the Taita Hills, Kenya, which is a particularly important region from the environmental conservation point of view. The Hargreaves, the Thornthwaite and the BlaneyeCriddle are the three tested methods, given that these are the most recommended approaches when only air temperature data are available. Land surface temperature data, retrieved from the MODIS/ Terra sensor are evaluated as an alternative input for the models. One weather station with complete climate datasets is used to calibrate the selected model using the FAO-56 PenmaneMonteith method as a reference. The results indicate that the Hargreaves model is the most appropriate for this particular study area, with an average RMSE of 0.47 mm d1, and a correlation coefficient of 0.67. The MODIS LST product was satisfactorily incorporated into the Hargreaves model achieving results that are consistent with studies reported in the literature using air temperature data collected in ground stations. Ó 2010 Elsevier Ltd. All rights reserved.

Introduction Although global withdrawals of water resources are still below the critical limit, more than two billion people live in highly waterstressed areas due to the uneven distribution of this resource in time and space (Oki & Kanae, 2006). Simulated scenarios indicate that up to 59% of the world population will face some sort of water shortage by 2050 (Rockstrom et al., 2009). In Kenya, currently over 55% of the rural population do not have access to quality drinkable water (FAO, 2005). In such regions, the accurate assessment of water demand and distribution is crucial to improve water management and avoid scarcity. Currently roughly 70% of freshwater withdraws are used for agriculture (FAO, 2005). In most sub-Saharan African countries agriculture is the main economic activity, representing about 40% of their gross domestic product (Barrios, Ouattara, & Strobl, 2008) and it is estimated that between the years 1975 and 2000 the agricultural areas increased by 57% in sub-Saharan Africa (Brink & Eva, 2009). Consequently, a careful control of the water used for

* Corresponding author. Tel.: þ358 44 2082876. E-mail address: eduardo.maeda@helsinki.fi (E.E. Maeda). 0143-6228/$ e see front matter Ó 2010 Elsevier Ltd. All rights reserved. doi:10.1016/j.apgeog.2010.05.011

irrigation is a key aspect to be considered in order to ensure a proper distribution of the available resources between residential, industrial and agricultural use. Several studies have shown that careful irrigation management can considerably improve crops’ water use efficiency without causing yield reduction (Du, Kang, Sun, Zhang, & Zhang, 2010, Hassanli, Ahmadirad, & Beecham, 2010). One of the fundamental requirements to estimating the amount of water necessary for optimal agricultural production is to effectively understand the relationships between climatic conditions and evapotranspiration (ET). ET is defined as the combination of two separate processes, in which water is lost on the one hand from the soil surface by evaporation and on the other hand from the crop by transpiration (Allen, Pereira, Raes, & Smith, 1998). Reliable estimates of ET are essential to identify temporal variations on irrigation requirements, improve water resource allocation and evaluate the effect of land use and management changes on the water balance (Ortega-Farias, Irmak, & Cuenca, 2009). Quantification of ET is a basic component for the design, operation and management of irrigation systems. Given the fact that the direct measurement of ET is a difficult task, the development of hydrometeorological models to estimate “reference ET” (ETo) resulted in important contributions for irrigation management at global, regional and local scales. ETo is

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defined as the ET rate from a reference surface, where the reference surface is a hypothetical grass with specific and well-known characteristics (Allen et al., 1998). The concept of ETo was introduced to study the evaporative demand of the atmosphere independently of crop type, crop phenology and management practices. A great variety of ETo models have been developed during the past decades. ETo models are typically based on physical principles, empirical equations or even a combination of physical and empirical approaches. Empirically based methods are established grounded on observations and statistical analysis, and usually are appropriate for a specific region or climatic condition (e.g. Fooladmand & Ahmadi, 2009; Ahmadi & Fooladmand, 2008; Gavilán, Lorite, Tornero, & Berengena, 2006). On the other hand, physically based models aim to simulate fundamental principles such as energy balance and mass transfer. Although some disadvantages and problems were reported, physically based models have proven to perform well in a great variety of climatic conditions (e.g. Liu & Luo, 2010; Villa Nova, Pereira, & Shock, 2007; Cancela, Cuesta, Neira, & Pereira, 2006). Nevertheless, complex models developed to simulate the physical processes involved in the ET frequently have many variables and require a large amount of input data. For instance, solar radiation, relative humidity and wind speed are some of the variables usually necessary to estimate ETo using physically based models. Assembling and maintaining meteorological stations capable of measuring such variables is very costly. In many developing and poor countries, meteorological stations are often insufficient to acquire the information necessary to represent the spatialetemporal variation of ETo. As a result, the irrigation management in such areas is not optimal, increasing the risks of water scarcity and water conflicts. Hence, intense efforts are necessary to search for alternative methods and datasets to be used in poor regions highly affected by hunger and water scarcity. The combination of ET models with remote sensing data provides a feasible alternative to obtain temporally and spatially continuous information about biophysical variables. According to Wagner, Kunstmann, Bárdossy, Conrad, and Colditz. (2008), in poorly gauged catchments, remote sensing data can significantly improve the availability of necessary information, for instance albedo, leaf area index and Land Surface Temperature (LST) (Wan, 2008). This study evaluated three empirical ETo models in a region particularly important from the environmental conservation point of view in Southeast-Kenya, where intense agricultural expansion is taking place. In order to overcome the low availability of meteorological data, LST data acquired by the MODIS/Terra sensor were tested as an alternative input for the evaluated models. Study area The Taita Hills are the northernmost part of the Eastern Arc Mountains of Kenya and Tanzania, situated in the middle of the Tsavo plains of the Taita-Taveta District in the Coast Province, Kenya (Fig. 1). The population of the whole Taita-Taveta district has grown from 90 146 (1962) persons to over 300 000 (Republic of Kenya, 2001). The indigenous cloud forests have suffered substantial loss and degradation for several centuries as they have been converted to agriculture, because of the abundant rainfall (annual 1100 mm) and rich soils (cambisols and humic nitosols) that provide good conditions for agricultural production. Approximately half of the cloud forests have been cleared for agricultural lands and plantation forests since 1955 (Pellikka, Lötjönen, Siljander, & Lens, 2009). During the last 2 decades, the agricultural lands have increased especially in the surrounding foothills and lowlands (Clark & Pellikka, 2009).

Located in the inter-tropical convergence zone, the area has a bimodal rainfall pattern, the long rains occurring in MarcheMay and short rains in NovembereDecember. The agriculture in the hills is intensive small-scale subsistence farming. In the lower highland zone and in upper midland zone, the typical crops are maize, beans, peas, potatoes, cabbages, tomatoes, cassava and banana. In the slopes and lower parts of the hills with average annual rainfall between 600 and 900 mm, early maturing maize species and sorghum and millet species are cultivated. In the lower midland zones with average rainfall between 500 and 700 mm, dryland maize types and onions are cultivated, among others. The two growing seasons, totaling to 150e170 days, coincide with the long and the short rains (Jaetzold & Schmidt, 1983). The land is prepared during the dry season, and the crops are seeded prior to the short rains and long rains. Harvesting takes place after the end of the rainy seasons (Gachimbi et al., 2005). The hills are surrounded by Tsavo national parks and sisal plantations leaving only little land for agriculture to spread over the plains. Moreover, the Eastern Arc Mountains maintain some of the richest concentration of endemic animals and plants on Earth, and thus it is considered one of the 25 world’s biodiversity hotspots (Myers, Mittermeier, Mittermeier, Fonseca, & Kent, 2000). Although only a small fraction of the indigenous cloud forests is preserved in the Taita Hills, it continues to have an outstanding diversity of flora and fauna and high level of endemism. Hence, the area is considered to have high scientific interest and there is a high potential for succeeding in the connectivity development and community based natural resource management (Himberg, Pellikka, & Luukkanen, 2009). Material and methods Many empirically and physically based ETo models have been developed during the past decades, varying in complexity and data requirements. Generally, complex physically based models incorporate a more comprehensive set of variables and parameters, which allows the model to perform well in a greater variety of climatic conditions. Unfortunately, such methods demand very detailed meteorological data, which are frequently missing from meteorological stations (Jabloun & Sahli, 2008). Moreover, setting up new stations capable to record these data is in general expensive. To overcome this problem, three empirical ETo models that require only air temperature data were evaluated, namely the Hargreaves, the Thornthwaite, and the BlaneyeCriddle methods. Each of these models, as well as the approach used for calibration and error assessment, are described below. Reference evapotranspiration (ETo) models A) Hargreaves The Hargreaves method was developed by Hargreaves and Samani (1985), using eight years of daily lysimeter data from Davis, California, and tested in different locations such as Australia, Haiti and Bangladesh. Since then, the method has been successfully applied worldwide (e.g. Gavilán et al., 2006). The Hargreaves equation requires only daily mean, maximum and minimum air temperature and extraterrestrial radiation. The equation can be written as:

ETo ¼ 0:0023 RA ððTmax Tmin ÞÞ0:5 ðTmean þ 17:8Þ

(1)

where ETo is the reference evapotranspiration given in mm day1; RA is the extraterrestrial radiation; Tmean is mean temperature; Tmin is the minimum temperature and Tmax is the maximum temperature.


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Fig. 1. Geographic location of the Taita Hills. The upper-right corner of the figure shows the Digital Elevation Model of the study area and the approximate location of the main towns.

B) Thornthwaite The Thornthwaite method (Thornthwaite, 1948) is based on an empirical relationship between ETo and mean air temperature. The value of ETo for a standard month of 30 days, as a function of the monthly average temperature is given by the following equations:

Tmean a ETo ¼ 16 10 I

I ¼

12 1;514 X ti i¼1

5

a ¼ 67; 5:108 I 3 7; 71:106 I 2 þ 0; 01791I þ 0; 492

(2)

(3)

(4)

where I is the thermal index imposed by the local normal climatic temperature regime. C) BlaneyeCriddle The BlaneyeCriddle equation (Blaney & Criddle, 1962) is another early empirical model developed to estimate ETo. This model was developed for the arid western portion of the United States and it was demonstrated to provide accurate estimates of ETo under these conditions. Although it was developed some decades ago, this method is still successfully applied in many water management studies (e.g. Fooladmand & Ahmadi, 2009). The BlaneyeCriddle equation is given by (Doorenbos & Pruitt, 1977):

ETo ¼ p ð0:46 Tmean þ 8:13ÞÞ

(5)

where p is the mean daily percentage of annual daytime hours for different latitudes. Input data The standard approach for modelling ETo is to use weather data retrieve from ground meteorological stations as input for the models. Nevertheless, in poor and developing countries, the available data from ground stations is frequently insufficient to represent the spatial distribution of ET at detailed spatial scales. This

drawback is also observed even when only basic measurements, such as air temperature, are required. In the present study, remote sensing data obtained by the Moderate Resolution Imaging Spectroradiometer (MODIS) were used as input to the ETo models. The MODIS sensor (Justice et al., 2002) was launched in 1999 and 2002 on board of the Terra and Aqua satellites, respectively. It can provide images in 36 different spectral bands, with spatial resolutions of 250, 500 and 1000 m, depending on the spectral band. The United States National Aeronautics and Space Administration (NASA), responsible for processing and distributing the MODIS sensor data, makes available a large variety of products for Land, Ocean and Atmospheric uses. The present study made use of the MOD11A2 product, which offers daytime and nighttime Land Surface Temperature (LST) data stored on a 1-km Sinusoidal grid as the average values of clear-sky LSTs during an 8-day period (Wang et al., 2005). The MODIS LST represents the radiometric temperature related to the thermal infrared (TIR) radiation emitted from the land surface observed by an instantaneous MODIS observation (Wan, 2008). The daytime LST corresponds to measurements acquired around 10:30 am, while nighttime LST records are acquired around 22:30 pm (local solar time). The MOD11A2 products are validated over a range of representative conditions, meaning that the product uncertainties are well defined and has been satisfactorily used in a large variety of scientific studies. In total, 368 LST images (8-day composite), corresponding to the entire MOD11A2 product dataset from the years 2001e2008, were downloaded from the Land Processes Distributed Active Archive Center (LP DAAC). The images were reprojected to the UTM coordinate system (datum WGS84) using the software MODIS Reprojection Tool (MRT). The temperature values, which are originally in Kelvin, were transformed to degrees Celsius in order to fit the models, and the 8 days composite images compiled into monthly averages. All image processing procedures and calculations were carried out using the software MATLABÔ. In order to clearly distinguish the approach used in this study, when LST data is used in replacement of air temperature data from ground stations, the Hargreaves, the Thornthwaite, and the BlaneyeCriddle models will be hereafter denominated as HargreavesLST, Thornthwaite-LST, and BlaneyeCriddle-LST, respectively.

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Calibration and error assessment Many studies have been reported during the last years applying the Hargreaves, the Thornthwaite and the BlaneyeCriddle models (Gavilán et al., 2006, Ahmadi & Fooladmand, 2008, Fooladmand & Ahmadi, 2009). The results of such studies clearly mention that although those models were effective in estimating ETo, the empirical nature of these methods makes necessary rigorous local calibration. Hence, the empirical equations were calibrated using as reference the FAO PenmaneMonteith (FAO-PM) method. The FAO-PM method is recommended as the standard ETo method and has been accepted by the scientific community as the most precise one for its good results when compared with other equations in different regions worldwide (Cai, Liu, Lei, & Pereira, 2007; Jabloun & Sahli, 2008). Although the FAO-PM method also carries intrinsic uncertainties and errors, it has been proved to behave well under a variety of climatic conditions, and for this reason the use of such method to calibrate or validate empirical equations has been widely recommended (Allen et al., 1998; Itenfisu, Elliott, Allen, & Walter, 2003; Gavilán et al., 2006).The FAO-PM equation is given by (Allen et al., 1998):

EToR ¼

900 u ðe e Þ 0:408DðRn GÞ þ gTþ273 a 2 s D þ gð1 þ 0:34u2 Þ

(6)

where EToR represents the ETo used as reference in this study; Rn is the net radiation at the crop surface [MJ m2 day1]; G is the soil heat flux density [MJ m-2 day1]; T is the mean daily air temperature at 2 m height [ C]; u2 is the wind speed at 2 m height [m s1]; es is saturation vapour pressure [kPa]; ea is actual vapour pressure [kPa]; (es e ea) is the saturation vapour pressure deficit [kPa]; D is the slope vapour pressure curve [kPa C1]; g is the psychrometric constant [kPa C1]. The meteorological data necessary for the FAO-PM equations were obtained from a synoptic station placed at Voi town and operated by the Kenya meteorological department. The station is located at an altitude of 597 m above sea level, longitude 38.34 E, and latitude 3.24 S. ETo values were also calculated for this exact point using the empirical models and MODIS LST data. The calibration parameters were defined using the following equation (Allen et al., 1998):

ETocal ¼ a þ b,EToLST

(7)

where ETocal represents the calibrated ETo values, in which the calibration parameters a and b are determined by regression analysis using as reference the FAO-PM method; ETolst is the ETo values estimated using the empirical models and MODIS LST as input. The models were compared using standard statistics and linear regression analysis (Douglas, Jacobs, Sumner, & Ray, 2009). Root Mean Squared Error (RMSE) and Mean Absolute Error (MAE) were computed using the equations described below:

RMSE ¼

MAE ¼

n 1X ðETocal EToR Þ2 n 1

n 1X jETocal EToR j n 1

Fig. 2. Monthly averages of maximum, minimum and mean air temperature measured at Voi weather station and monthly averages of day, night and mean Land Surface Temperatures (LST) retrieved from MODIS sensor (2001e2008).

records obtained by the MODIS sensor is presented in Fig. 2. The graphic shows the monthly averages from 2001 to 2008. The LST data were acquired from one point with the same latitude and longitude as the ground weather station. A close fit between the minimum air temperature and the night LST is observed. However, seasonal variations are noticed in the comparison between the maximum air temperatures and day LST. The results obtained in the evaluation of the ETo models are summarized in Table 1. As described in Eqs. (8) and (9), the RMSE and the MAE were used to quantify the differences between the ETo estimated using the reference method (FAO-PM) and the estimates obtained using the empirical models parameterized using MODIS LST data. The global average RMSE and MAE are fairly homogeneous for each of the evaluated models. The RMSE ranged from 0.47 mm day1, with the Hargreaves-LST model, to 0.53 mm day1, with the BlaneyeCriddle-LST model. The MAE achieved similar figures, ranging from 0.39 mm day1, with the Hargreaves-LST model, to 0.46 mm day1, with the BlaneyeCriddle-LST model. The monthly errors obtained by the tested models, in comparison with the reference method, are presented in Fig. 3. Although the monthly performance of the models is uniform between March and July, important differences are observed in January, February and between August and December (Fig. 3). In particular for the BlaneyeCriddle-LST model, it is possible to notice a clear increase of the RMSE and MAE in January, November and December. This fact indicates that the BlaneyeCriddle-LST model is likely more sensitive to the differences observed between air temperature and LST during these months (Fig. 2). In other words, the BlaneyeCriddle-LST model performed better in months when air temperature are closely related to LST, but had its performance reduced in months when air temperature and LST are less correlated. On the other hand, the Hargreaves-LST model was more efficient in

!0:5 (8)

Table 1 Summary of the results obtained from the models’ error analysis and linear regression analysis.

(9)

Results A comparison between the air temperature records measured at Voi weather station and the Land Surface Temperature (LST)

Correlation coefficient (R) RMSE (mm day1) MAE (mm day1) Calibration parameter (a) Calibration parameter (b)

HargreavesLST

ThornthwaiteLST

BlaneyeCriddleLST

0.67

0.66

0.55

0.47 0.39 3.221

0.49 0.42 3.507

0.53 0.46 1.980

0.497

0.543

1.379


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Fig. 3. Root Mean Squared Error (RMSE) and Mean Absolute Error (MAE) obtained using Eqs. (8) and (9). The errors are used to quantify the differences between the ETo estimated using the reference method (FAO-PM) and the estimates obtained using the empirical models parameterized using MODIS LST data.

minimizing the effects of the differences observed between air temperature and LST during November, December and January. The model performed well during these months, retrieving RMSEs of 0.51, 0.61 and 0.51 mm day1, respectively. Fig. 4 shows the monthly average values of ETo estimates, using as baseline the period from 2001 to 2008. The figure illustrates the profiles obtained by the reference method that used datasets obtained form a ground station, and for the three evaluated models, which used as input MODIS LST records from the same geographic location as the ground station. The profile of the curve formed by the Hargreaves-LST model estimates closely follow the profile obtained using the reference method during almost the entire year. The worst fit observed for this model occurs in March, when Hargreaves-LST underestimates the ETo in comparison with the reference method. In contrast, the profiles obtained by the Thornthwaite -LST and the BlaneyeCriddle-LST models show clear differences in comparison with the reference method during the year. In particular, a large overestimations are observed during December and January, and underestimations between June and September. The fitted regression lines obtained in the regression analysis performed using the reference and the evaluated models are displayed in Fig. 5. The Hargreaves-LST model achieved the best fit, resulting in a correlation coefficient of 0.67. On the other hand, the worst correlation is observed for the BlaneyeCriddle-LST model

(0.55). Moreover, the linear regression graphic (Fig. 5) clearly shows that the BlaneyeCriddle-LST model has problems representing ETo values below 4 mm d1 and above 6 mm d1, indicating that this model is not able to properly represent the seasonal range of ETo in this region. Considering the results achieved in this study and comparisons with previous research, it is concluded that the BlaneyeCriddle-LST model is not appropriated for this region when using the proposed methodology. On the other hand, the Hargreaves-LST model achieved the best results in the linear regression and in the analysis of errors, which are also consistent with results obtained in previous studies. Hence, it feasible to assert that this method is considered most appropriate for applying the proposed methodology in this study area. The Hargreaves model with its respective calibration parameters and the input data acquired by the MODIS sensor, was applied to represent the spatio-temporal distribution of the ETo in the study area. Fig. 6 illustrates intra-annual changes in ETo in the Taita Hills using average LST records from 2001 to 2008. The figure shows that the ETo varies significantly between the lowlands ranging from 600 to 1000 m and hills between 1000 and 2200 m due to differences in land surface and atmospheric temperature. In addition, the yearly variation is strong both in the lowlands and in the hills. In general ETo is higher in the months September and October, and reaches the lowest values between April and June. An interesting ETo spatial pattern is observed in the northwest corner of the hills, which lay in a rainshadow region and thus receives less precipitation year around. Even though this area is located at 1600 m altitude, the ETo is lower when compared to other parts of the hills at a same altitude. This behaviour is better visualized in Fig. 7, which shows the ETo profile during May and October along two transects drawn in the study area. The correlation between ETo and altitude varies according to season and altitude range. ETo follows more closely changes in the altitude in October than in May. In May the variation ranges from 4.5 to 5.4, while in October the variation is from 5.3 to 6.9. Discussion

Fig. 4. Monthly average distribution of ETo estimated by the FAO-PM and compared with the values calculated by the calibrated Hargreaves, Thornthwaite and BlaneyeCriddle methods using MODIS land surface temperature data (baseline 2001e2008).

The seasonal differences observed between the MODIS/LST dataset and air temperature were already reported in literature. For instance, Mostovoy, King, Reddy, and Kakani (2005) observed good correlations between LST and max/min air temperatures during the winter season over Mississippi. Nevertheless, a poor correlation was observed in this same area during summer season. The authors suggest that changes in vegetation phenology during summer may

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Fig. 5. Fitted regression lines obtained from the regression analyses performed using the reference method (FAO-PM) and the calibrated Hargreaves-LST, Thornthwaite-LST and BlaneyeCriddle-LST methods.

reduce the correlation between LST and min/max air temperatures. Comparing MODIS day LST with maximum air temperature over different ecosystems in Africa Vancutsem, Ceccato, Dinku, and Connor (2010) state that, in addition to seasonality factors, the differences between day LST and Tmax may vary according to the ecosystems, the solar radiation, and cloud-cover. Regarding the reliability of the MODIS LST data, it is important to mention that this product was extensively tested using comparisons with in-situ values and radiance-based validation (Wan, Zhang, Zhang, & Li, 2002, 2004; Wan, 2008; Coll, Wan, &

Galve, 2009). The results of these tests indicate that in most cases the MODIS LST error is lower than 1 K. Hence, errors inherent to the air temperature records obtained by the ground weather station must also be considered as a cause for the discrepancies observed. Although the air temperature dataset used in this study was checked to eliminate missing and incorrect records, it is known that many sources of error can be identified in ground stations measurements. For instance, untrained operators and old and uncalibrated equipment are common causes of low quality measurements.

Fig. 6. Monthly average Reference Evapotranspiration (ETo) maps obtained using the Hargreaves model and average LST records from 2001 to 2008.


257

Fig. 7. Reference evapotranspiration profile for May and October along two transects crossing the study area.

The monthly RMSE and MAE obtained by the Hargreaves-LST and Thornthwaite-LST models were consistent with results observed in previous published researches. For instance, in a study case carried out in the south of Iran, Ahmadi and Fooladmand (2008) achieved RMSE lower than 1 mm day1 using the Thornthwaite equation, while in the present study the results ranged from 0.34 to 0.63 mm day1. The average RMSE obtained by the Hargreaves-LST model was 0.47 mm day1, while the monthly errors ranged from 0.37 to 0.62 mm day1. These results are compatible with the errors observed by Gavilán et al. (2006), which evaluated the Hargreaves equation under semiarid conditions in Southern Spain, finding RMSE ranging from 0.46 to 1.65 mm d1. The correlation coefficients obtained by the Hargreaves-LST and Thornthwaite-LST models are consistent with the results reported by Narongrit and Yasuoka (2003), which achieved R2 of 0.57 and 0.60 when comparing these respective models with the FAO-PM method. In contrast, the performance achieved by the BlaneyeCriddle model is considered below level when compared with previously published results. For instance, Fooladmand and Ahmadi (2009) found correlation coefficients up to 0.96 in the linear regression analysis between the BlaneyeCriddle and the FAO-PM methods applied in the south of Iran. It is worth mentioning that the results obtained by Ahmadi and Fooladmand (2008) and Gavilán et al. (2006) were achieved using weather data from ground stations as input for the evaluated models (i.e. Hargreaves, Thornthwaite) and the reference model (i.e FAO-PM). On the other hand, in the particular case of this study the evaluated models received as inputs MODIS-LST data and the results were compared with the FAO-PM model using weather data from a ground station. Hence, although these empirical ETo models were initially developed using air temperature, this study demonstrated that similar results can be achieved using LST as an alternative input in areas with limited ground data availability. The use of LST data retrieved for orbital sensors have the advantage of allowing the spatial analysis of ETo at higher spatial resolutions. Nevertheless, as it was demonstrated in Figs. 2, 3 and 4, the errors and accuracy of this approach may vary according to the season. The benefits of using a spatially-explicit approach is demonstrated in the maps presented in Fig. 6. The method allowed the identification of spatio-temporal variation at local scales, which would be technically impracticable using standard methods, considering that just one meteorological station is present in this region. Such characteristic can benefit the improvement of water management in the region and support policy decisions for land use allocation based on agro-climatic conditions. Additionally, spatially-explicit ETo models represent an essential component in applications such as drought monitoring (e.g. Boken, 2009), desertification risk assessment (e.g. Santini, Caccamo, Laurenti, Noce, & Valentini, 2010) and agricultural yield forecast.

In this context, the dilemma of reconciling economic and population growth with environmental sustainability is an undeniable issue currently faced in poor countries. The development of the agricultural sector is essential to combat food insecurity. Nevertheless, the expansion of croplands without logistic and technological planning is a severe threat to the environment. The indiscriminate use of water resources associated with agricultural expansion can rapidly lead to water scarcity and water use conflicts. The development of infra-structure for data collection and appropriate irrigation system technologies is a key priority for preventing these problems. However, in regions such as EasternAfrica, political and economic issues are frequently a barrier for such development. Hence, alternative methods and data sources are essential to create short-term solutions for environmental monitoring. Conclusions In the presented research, an alternative for estimating ETo was evaluated by using LST data from the MODIS sensor and empirical models. Based on the analysis of RMSE, MAE and linear regression analysis, the Hargreaves ETo model was selected as the most appropriate for this particular study area. Although some drawbacks are evident in using LST data as inputs for the ETo models, the MODIS LST product was satisfactorily incorporated into the Hargreaves model. After the calibration, this model retrieved an average RMSE close to 0.5 mm d1. This outcome is consistent with results obtained by previous studies reported in the literature using air temperature data collected by ground stations. Moreover, the errors and uncertainties identified in the use of remote sensing LST can be tolerated considering the reduced weather data collection network in this region. The MODIS sensor offers almost daily LST data, which can be easily downloaded from internet. Therefore, the methodology presented in this study can be considered a feasible alternative for estimating ETo in this region, without any additional costs. The operational use of such a method can potentially improve water distribution modelling, and finally allow an increased control of water use for irrigation. Nevertheless, further studies are necessary to expand this method for other regions in East-Africa. The different models should be tested for it suitability in different climate conditions over East-Africa and the spatial variability of the calibration parameters needs to be identified. Moreover, the method can be significantly improved by using low cost direct methods (e.g. lysimeters) to calibrate the empirical equations. Acknowledgements Part of this research was carried out during the Young Scientists Summer Program (YSSP) at the International Institute for Applied

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