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Abstract Ecosystems provide social, ecological and economic benefits and are linked through the landscape. Landscape degradation and loss of biodiversity undermine ecosystem functioning especially in vital ecosystems such as the Mara Basin in Africa. Holistically monitored and assessed landscape provide values to support conservation strategy which could enhance sustainable resource utilisation. The study relied on high resolution 10-m Sentinel 2A, Landsat 30-m and SEBAL to generate biophysical parameters for valuation in the landscapes. A household survey was used to derive forest benefits to local livelihoods. The well conserved Trans Mara forest provided high ecological benefits but few social benefits to the local community. Tea buffers have proven to be an effective conservancy strategy for the forest ecosystem. The long term carbon-based benefits in conservation areas of rural Africa is hindered by communities' high dependency on firewood and charcoal energy. The land policy on limited livestock access to the pastoralists in the conservancies should be based on the livestock carrying capacity of the lands. Further, the valuation maps can be used for land use planning where critical wildlife protection areas, movement corridors and mixed use zones of high livestock value can be identified. The holistic assessment of value of African landscape based on key functionalities and linked spatially over the landscape using remotely sensed data is indeed crucial to improve decision making and conservation strategy towards sustainable ecosystem management. Highlights • Conservation buffers provide sustainable protection to vital landscape ecosystems. • Ecological ecosystem values should be balanced with social and economic benefits. • Carbon-based benefits cannot be sustained with current energy uses in rural Africa. • Holistic assessment of landscape values can aid land use and conservation policy. [ABSTRACT FROM AUTHOR]

The objective of this study was to assess the accuracy, spatial variation and potential value of remote sensing (RS) estimates of evapotranspiration (ET) and biomass production for irrigated sugarcane in Mpumalanga, South Africa. Weekly ET and biomass production were estimated from RS data from 2011 to 2013 using the Surface Energy Balance Algorithm for Land (SEBAL). Ground estimates of canopy interception of photosynthetically active radiation (FPAR) and aerial biomass were compared to RS estimates. ET was estimated with a surface renewal (SR) system in one field. Evaporation coefficient (Kc) values were calculated from ET and reference grass evaporation. Remote sensing FPAR and biomass estimates compared well with field measurements (R2 = 0.89 and 0.78). SEBAL ET estimates exceeded SR estimates by 5 mm/week, while full canopy Kc values for SEBAL compared better with literature values than with SR Kc values. SEBAL estimates of ET and biomass were regarded as reliable. Considerable spatial variation was observed in seasonal RS ET (1 034 ± 223 mm), biomass (45 ± 17 t/ha) and biomass water use efficiency (WUEBIO, defined as dry biomass produced per unit of ET) (4.1 ± 1.0 kg/m3). About 32% of sugarcane fields had values below economic thresholds, indicating an opportunity to increase productivity. Actual yields correlated well with WUEBIO values, suggesting that this may be used for monitoring crop performance and identifying areas that require remedial treatment. [ABSTRACT FROM AUTHOR]

Accurately measuring regional evapotranspiration (ET) is of great significance for studying global climate change, regional hydrological cycles, and surface energy balance. However, estimating regional ET from mixed vegetation types is still challenging. In this study, the Surface Energy Balance Algorithm for Land (SEBAL) and the Surface Energy Balance System (SEBS) models were applied to estimate surface ET in a small agricultural watershed. Landsat8 satellite images were used as input data to the single-source models. The two models were validated at single point and ecosystem scales. The results showed that both models overestimated ET observations in paddy fields and orange groves but underestimated them in dry farmland. The error was mainly caused by the heterogeneity of the mixed pixels. The linear spectral mixture model and a set of equations were introduced to reduce the simulation error. The revised results showed that the relative precision of SEBAL was improved by 9.87% and 10.06%, respectively. This research is expected to provide new ideas for future development of accurate remote-sensing ET estimations on heterogeneous surfaces. [ABSTRACT FROM AUTHOR]

The enlarged demand of water due to agriculture expansion, increasing industrial and domestic needs, compounded with climate change is causing scarcity of water globally and Pakistan is also facing same conditions. Due to limited availability of water, it is necessary to adopt global best practices being employed for sustainable agricultural water management. Remote sensing techniques are being intensively used around the globe with confidence to monitor the crops growth and crop water use. The aim of the research is to develop crop water consumption monitoring system at the outlet level to regulate irrigation water distribution at farm level. Surface Energy Balance Algorithm for Land (SEBAL) is used to develop pilot case study for year 2017-18 at a distributary canal command level named 5R-Yousafwala located in Sahiwal District. Crop classification based on Landsat-8 imagery was provided by Punjab Irrigation Department. Landsat-8 and Sentinel-2 images were acquired for NDVI monitoring. Metrological data or Penman Monteith based evapotranspiration calculation was acquired from Global Land Data Assimilation System (GLDAS). Analogue cadastral maps of outlet command areas were converted to digital georeferenced format. The provided crop classification was verified after discussion with farmers during detailed field survey. In Rabi season of 2017-18, mean evapotranspiration (ET) actual was estimated about 352 mm, while in Kharif season of 2018, it was estimated as 425 mm. The crop water deficit was estimated as 36 % in rabi season and 30 % in kharif season. Irrigation share was only 37 % in Rabi season and 48% in Kharif season. Rainfall contributed 09 % in Rabi season and 30% in Kharif season. Groundwater was paramount contributor with 54 % share in rabi season while in Kharif season it contributed relatively minor share of 22%. The ET potential and ET actual fluctuate in all outlet commands due to the diverse crop cover and economic capacity to pump groundwater. The results could be proved very useful for irrigation department to monitor and reform the irrigation supplies at particular outlets by observing the crop demand, weather conditions and crop stage instead of monitoring outlet discharge only. [ABSTRACT FROM AUTHOR]

O principal objetivo deste estudo foi analisar de forma comparativa a estimativa do saldo de radiação à superfície (Rn) a partir de imagens orbitais com dados de superfície medidos, bem como os componentes do Rn, para dois diferentes tipos de cobertura vegetal (pastagem e floresta) sob condições atmosféricas tropical-úmida. Este estudo foi realizado no estado de Rondônia, no noroeste do Brasil. Para realizar as análises através de técnicas de sensoriamento remoto foram utilizadas sete imagens orbitais do sensor MODIS a bordo do satélite Aqua, para a aplicação do algoritmo e os dados de superfície medidos por duas torres micrometeorológicas, sendo instaladas em área de pastagem e de floresta. Os principais resultados obtidos a partir das imagens MODIS mostram erro percentual (EP) máximos e mínimos coerentes para o Rn de 3% e 0,02%, para a área de floresta e para a área de pastagem apresentou valores de EP de 10% e 0,7%. Esses resultados mostram que o sensoriamento remoto orbital é uma ferramenta importante e eficaz em estudos hidrológicos e ambientais.

As técnicas de sensoriamento remoto têm sido muito promissoras para o desenvolvimento de medidas mais confiáveis e economicamente viáveis da produção vegetal em larga escala. O algoritmo SEBAL (Surface Energy Balance Algorithm for Land) tem como vantagem a obtenção de parâmetros biofísicos usando imagens de satélite e poucos dados observacionais. Este trabalho objetivou estimar a produtividade da cana-de-açúcar por meio da aplicação do algoritmo SEBAL e de imagens Landsat 5 TM. O estudo foi realizado em plantios de cana-de-açúcar da fazenda Boa Fé, localizada no Triângulo Mineiro, município de Conquista, Minas Gerais. A metodologia utilizada apresentou variação de desempenho nas estimativas de produtividade da cana-de-açúcar de cada gleba, provavelmente em decorrência de influências da dimensão dos talhões e da resolução espacial da imagem, e de variedades e épocas de plantio e colheita da cultura. No entanto, os resultados apontam que a metodologia tem potencial para ser aplicada em áreas extensas com limitada disponibilidade de dados meteorológicos.

The Surface Energy Balance Algorithm for Land (SEBAL) is widely used to estimate actual evapotranspiration (ETa). One major limitation of the SEBAL model is the subjectiveness in selecting extreme cold/hot pixels. In the present study, the SEBAL model is modified by determining the extreme cold/hot status, based on the theoretical trapezoidal relationship between land surface temperature (Ts) and Enhanced Vegetation Index (EVI), which is established for each pixel. In this way, the dependence of SEBAL model on the existence of extreme cold/hot status and the subjectiveness in selecting cold/hot pixels with SEBAL model are eliminated. The performance of the classical SEBAL model and the modified version, T-SEBAL, are compared for estimating ETa for a semi-arid catchment, and the result showed that the accuracy of ETa estimation is improved by the T-SEBAL model compared with the classical SEBAL model.

A evapotranspiração (ET) é uma das principais componentes do ciclo hidrológico. Nesse contexto, este trabalho teve como objetivo analisar o comportamento espaço-temporal do Balanço de Energia e da Evapotranspiração Real Diária na Área de Preservação Ambiental da Ilha de Santa Rita, Alagoas, Brasil, tomando como subsídio as possíveis alterações no uso e cobertura do solo. A metodologia utilizada envolve o uso do algoritmo SEBAL, imagens do satélite Landsat 5 - TM e dados complementares de estação meteorológica localizada na área de estudo. A estimativa diária de ET24h real variou de 0 a 1 mm dia-1 no solo exposto e na cidade; a 6 mm dia-1, em corpos d'água e mangue, com um valor médio de 3,78 mm dia-1, 3,45 mm dia-1 e 4,75 mm dia-1, nos dias 03/09/2003, 26/08/2006 e 17/03/2011, respectivamente, em toda a área estudada. Os resultados por meio do algoritmo SEBAL (SURFACE ENERGY BALANCE ALGORITHMS FOR LAND) podem ser utilizados em condições climáticas semelhantes às da Área de Preservação Ilha de Santa Rita/Alagoas. As distribuições de cada componente do balanço de energia: saldo de radiação, fluxo de calor no solo, sensível e latente, apresentaram influência significativa nos diferentes tipos de uso e cobertura do solo. Além disso, constataram-se diferenças evidentes entre os fluxos em zona rural e área urbana. A abordagem utilizada é muito adequada para uma exploração real de dados de satélite, podendo ser útil para estimar um número de parâmetros no contínuo solo-planta-atmosfera.

As trocas de energia na interface solo-planta-atmosfera, por meio das componentes do balanço de radiação (Rn) e dos fluxos de calor no solo (G), sensível (H) e latente (LE), são essenciais para a modelagem climática e hidrológica que, por sua vez, afetam toda a biosfera. Dessa forma, esta pesquisa teve por objetivos: (1) estimar e comparar o comportamento dos componentes do balanço de energia, usando o algoritmo SEBAL - Surface Energy Balance Algorithm for Land, em diferentes tipos de uso e cobertura do solo e (2) validar o algoritmo. Para isso, foram usadas três imagens TM - Landsat 5 de órbita e ponto 214/066, com datas de passagem em 26 de agosto de 2006, 6 de setembro de 2010 e 28 de janeiro de 2011, todas recortadas para enquadrar o litoral sul de Pernambuco. Os resultados mostraram a capacidade do SEBAL de estimar a variabilidade espacial dos diferentes componentes do balanço de energia, com capacidade de distinguir diferentes ocupações do solo. O cômputo do balanço de energia possibilita observar as formas diferenciadas do uso da energia pelos diferentes tipos de cobertura do solo e verificar aqueles que propiciam melhor conforto térmico. As estimativas da evapotranspiração obtidas pelo algoritmo SEBAL apresentaram uma boa concordância com os resultados obtidos pelo método da FAO-Penman-Monteith demonstrando, dessa forma, a potencialidade da abordagem metodológica escolhida.

The surface energy balance algorithm for land (SEBAL) has been demonstrated to provide accurate estimates of crop evapotranspiration (ET) and yield at different spatial scales even under highly heterogeneous conditions. However, validation of the SEBAL using in-field direct and indirect measurements of plant water status is a necessary step before deploying the algorithm as an irrigation scheduling tool. To this end, a study was conducted in a maize field located near the Venice Lagoon area in Italy. The experimental area was irrigated using a 274 m long variable rate irrigation (VRI) system with 25-m sections. Three irrigation management zones (IMZs; high, medium and low irrigation requirement zones) were defined combining soil texture and normalized difference vegetation index (NDVI) data. Soil moisture sensors were installed in the different IMZs and used to schedule irrigation. In addition, SEBAL-based actual evapotranspiration (ETr) and biomass estimates were calculated throughout the season. VRI management allowed crop water demand to be matched, saving up to 42 mm (−16%) of water when compared to uniform irrigation rates. The high irrigation amounts applied during the growing season to avoid water stress resulted in no significant differences among the IMZs. SEBAL-based biomass estimates agreed with in-season measurements at 72, 105 and 112 days after planting (DAP; r2 = 0.87). Seasonal ET matched the spatial variability observed in the measured yield map at harvest. Moreover, the SEBAL-derived yield map largely agreed with the measured yield map with relative errors of 0.3% among the IMZs and of 1% (0.21 t ha−1) for the whole field. While the FAO method-based stress coefficient (Ks) never dropped below the optimum condition (Ks = 1) for all the IMZs and the uniform zone, SEBAL Ks was sensitive to changes in water status and remained below 1 during most of the growing season. Using SEBAL to capture the daily spatial variation in crop water needs and growth would enable the definition of transient, dynamic IMZs. This allows farmers to apply proper irrigation amounts increasing water use efficiency.

Evapotranspiration (ET) plays a crucial role in integrated water resources planning, development and management, especially in tropical and arid regions. Determining ET is not straightforward due to the heterogeneity and complexity found in real-world hydrological basins. This situation is often compounded in regions with limited hydro-meteorological data that are facing rapid development of irrigated agriculture. Remote sensing (RS) techniques have proven useful in this regard. In this study, we compared the daily actual ET estimates derived from 3 remotely-sensed surface energy balance (SEB) models, namely, the Surface Energy Balance Algorithm for Land (SEBAL) model, the Operational Simplified Surface Energy Balance (SSEBop) model, and the Simplified Surface Balance Index (S-SEBI) model. These products were generated using the Moderate Resolution Imaging Spectroradiometer (MODIS) satellite imagery for a total of 44 satellite overpasses in 2005, 2010, and 2015 in the heterogeneous, highly-utilized, rapidly-developing and data-limited Kilombero Valley (KV) river basin in Tanzania, eastern Africa. Our results revealed that the SEBAL model had a relatively high ET compared to other models and the SSEBop model had relatively low ET compared to the other models. In addition, we found that the S-SEBI model had a statistically similar ET as the ensemble mean of all models. Further comparison of SEB models’ ET estimates across different land cover classes and different spatial scales revealed that almost all models’ ET estimates were statistically comparable (based on the Wilcoxon’s test and the Levene’s test at a 95% confidence level), which implies fidelity between and reliability of the ET estimates. Moreover, all SEB models managed to capture the two spatially-distinct ET regimes in KV: the stable/permanent ET regime on the mountainous parts of the KV and the seasonally varied ET over the floodplain which contains a Ramsar site (Kilombero Valley Floodplain). Our results have the potential to be used in hydrological modelling to explore and develop integrated water resources management in the valley. We believe that our approach can be applied elsewhere in the world especially where observed meteorological variables are limited.

This study aims to investigate the effects of land use/cover change (LUCC) and climate change on wetland evapotranspiration (ET), and to identify the importance of the main effect factors in the spatiotemporal dynamics of ET. In the wetland of Liaohe River Delta, China, the ET of eight growing seasons during 1985−2017 was estimated using the surface energy balance algorithm for land (SEBAL) model with Landsat and meteorological data. Results show that the average relative error of regional ET estimated by the SEBAL model is 9.01%, and the correlation coefficient between measured and estimated values is 0.61, which indicates that the estimated values are reliable. This study observed significant spatial and temporal variations in ET across the region of interest. The distribution of the average and relative change rate of daily ET in the study area showed bimodal characteristics, that is, the lowest trough occurred in 2005, whereas crests occurred in 1989 and 2014. Simultaneously, the daily ET varied with the land use/cover area. Regional daily ET displays highly heterogeneous spatial distribution, that is, the ET of different land uses/cover types in descending order is as follows: water body, wetland vegetation, non-wetland vegetation, and non-vegetation (except water area). Therefore, the spatial pattern of ET is relevant to the land use/cover types to some extent. In addition, the temporal variation of wetland ET is closely related to landscape transformation and meteorological factor change. A strong correlation was found between ET and the weighted values of meteorological factors, with a correlation coefficient of 0.69. Meanwhile, the annual fluctuations of daily ET and the weighted values were relatively similar. Therefore, the findings highlight the importance of using cheap and readily available remote sensing data for estimating and mapping the variations in ET in coastal wetland.

This study presents an approach that integrates remote sensing evapotranspiration into multi-objective calibration (i.e., runoff and evapotranspiration) of a fully distributed hydrological model, namely a distributed hydrology⁻soil⁻vegetation model (DHSVM). Because of the lack of a calibration module in the DHSVM, a multi-objective calibration module using ε-dominance non-dominated sorted genetic algorithm II (ε-NSGAII) and based on parallel computing of a Linux cluster for the DHSVM (εP-DHSVM) is developed. The module with DHSVM is applied to a humid river basin located in the mid-west of Zhejiang Province, east China. The results show that runoff is simulated well in single objective calibration, whereas evapotranspiration is not. By considering more variables in multi-objective calibration, DHSVM provides more reasonable simulation for both runoff (NS: 0.74% and PBIAS: 10.5%) and evapotranspiration (NS: 0.76% and PBIAS: 8.6%) and great reduction of equifinality, which illustrates the effect of remote sensing evapotranspiration integration in the calibration of hydrological models.

Scarcity of information on the water productivity of different water, land, and other ecosystems in Africa, hampers the optimal allocation of the limited water resources. This study presents an innovative method to quantify the spatial variability of biomass production, crop yield, and economic water productivity, in a data scarce landscape of the Pangani Basin. For the first time, gross return from carbon credits and other ecosystem services are considered, in the concept of Economic Water Productivity. The analysis relied on the MODIS satellite data of 250 m and eight-day resolutions, and the SEBAL model, utilizing Monteith’s framework for ecological production. In agriculture, irrigated sugarcane and rice achieved the highest water productivities in both biophysical and economic values. Rainfed and supplementary irrigated banana and maize productivities were significantly lower than the potential values, reflecting a wide spatial variability. In natural landscapes, forest and wetland showed the highest biomass production. However, the transition to economic productivity was low but showed the potential to increase significantly when non-market goods and services were considered. Spatially explicit information, from both biophysical and economic water productivity, provides a holistic outlook of the socio-environmental and the economic water values of a land-use activity, and can identify areas for improvement, and trade-offs in river basins.

A period of excess precipitation since 1993 in the Devils Lake basin in northeastern North Dakota has caused extensive flooding of agricultural land and has raised the question of whether irrigation of agricultural crops to increase evapotranspiration (ET) might be an effective way to remove water from the basin. The objectives of this study were to compare ET estimates derived from application of the Mapping ET at High Resolution with Internalized Calibration (METRIC) algorithm for North Dakota conditions (METRICND) under irrigated and rainfed conditions and to assess the potential for irrigation to increase crop ET as a flood mitigation strategy. Weather data, land use maps, and Landsat 5 Thematic Mapper imagery from 2006, 2007, and 2008 were used as inputs to the METRICND model. The ET for irrigated crops (ETIrrigated) was estimated at five test sites from the Devils Lake Basin Water Utilization Test Project (DLBWUTP). The ET for the predominantly rainfed study area (ETRainfed) was estimated using land use maps to identify locations of the same crops as were present on the test sites. The METRICND model was compared to ET values derived from an eddy covariance (EC) system for approximately two months in 2007 at an irrigated alfalfa test site in the DLBWUTP; the mean absolute error between METRICND and the EC system for the comparison period was 0.51 mm d-1. Linear regression of ET (in mm) for the test sites and the larger study area yielded ETIrrigated = 1.23 × ETRainfed + 4.77 with R² = 0.96, and a t-statistic indicated that the slope was greater than 0 at p = 0.001, indicating the potential for increased ET under irrigation. However, addition of large volumes of irrigation water to the predominantly poorly drained soils in the basin will cause waterlogging and trafficability problems. Installation of subsurface drainage may help alleviate waterlogging, improve crop productivity, and increase ET, but subsurface drainage brings its own complications of disposal of the drained water, salinity of the drainage effluent, and possible sodicity problems on some soils. [ABSTRACT FROM AUTHOR]

Water allocation to crops, and especially to the most water intensive ones, has always been of great importance in agricultural processes. Deficit or excessive irrigation could create either crop health-related problems or water over-consumption, respectively. The latter could lead to groundwater depletion and deterioration of its quality through deep percolation of agrichemical residuals. In this context, and under the current conditions where Cyprus is facing effects of possible climate changes, the purpose of this study seeks to estimate the needed crop water requirements of the past (1995-2004) and the corresponding ones of the present (2005-2015) in order to test if there were any significant changes regarding the crop water requirements of the most water-intensive trees in Cyprus. The Mediterranean region has been identified as the region that will suffer the most from variations of climate. Thus the paper refers to effects of these variations on crop evapotranspiration (ETc) using remotely-sensed data from Landsat TM/ETM+/OLI employing a sound methodology used worldwide, the Surface Energy Balance Algorithm for Land (SEBAL). Though the general feeling is that of changes on climate will consequently affect ETc, our results indicate that there is no significant effect of climate variation on crop evapotranspiration, despite the fact that some climatic factors have changed. Applying Student's t-test, the mean values for the most water-intensive trees in Cyprus of the 1994-2004 decade have shown no statistical difference from the mean values of 2005-2015 for all the cases, concluding that the climate change taking place in the past decades in Cyprus have either not affected the crop evapotranspiration or the crops have managed to adapt to the new environmental conditions through time. [ABSTRACT FROM AUTHOR]

The aim of this study was to determine the accuracy estimation of actual evapotranspiration by using Surface Energy Balance Algorithm for Land (SEBAL) in comparison with FAO-Penman-Monteith method in Landsat and MODIS images in Malayer County of Iran. In this study, Landsat 8 and MODIS images of 2013 were used. Result showed that there are not many differences between SEBAL and FAOPenman- Monteith methods in estimation of evapotranspiration. According to the aim of the study, result showed that MODIS sensor with high accuracy have calculated evapotranspiration more accurately than Landsat 8 by RMSE = 1.004 and MBE = 0.0033 respectively in surface temperature estimation which has great effect on estimation of the net radiation flux, soil heat flux and the sensible heat flux. On the other hand, according to low spatial resolution of MODIS sensor, we can conclude that this sensor has less value for zoning of evapotranspiration than Landsat 8 image according to the area topography and heterogeneous land uses. [ABSTRACT FROM AUTHOR]

In typical surface energy balance (SEB) models such as surface energy balance for land (SEBAL) and mapping evapotranspiration (ET) at high resolution with internalized calibration (METRIC), calibration of sensible heat flux ( H ) requires identification of endmember (i.e. hot and cold) pixels. Such pixel selection is typically done manually, which makes it labor intensive to apply SEBAL or METRIC over large spatial areas or long time series. In this paper, we introduce a new automated approach that uses an exhaustive search algorithm (ESA) to identify endmember pixels for use in these models. The fully automated models were applied on 134 near cloud-free Landsat images with each image covering one of four flux measurement sites covering a distinct land cover type in humid Florida or relatively drier Oklahoma. Observed land surface temperatures ( T s ) at automatically identified hot and cold pixels were within 0.25% of manually identified pixels (both coefficient of determination, R 2 , and Nash-Sutcliffe efficiency, NSE, ≥ 0.98, and root mean squared error, RMSE, ≤ 1.31 K). The new fully automated model performed better and demonstrated better consistency than an existing semi-automated method that used a statistical approach to subset coldest and hottest pixels within an image. Daily ET estimates derived from the automated SEBAL and METRIC models were in good agreement with their manual counterparts (e.g., NSE ≥ 0.94 and RMSE ≤ 0.35 mm day − 1 ). Automated and manual pixel selection resulted in similar estimates of observed ET across all sites. The proposed automated pixel selection approach greatly reduces time demands (e.g. approximately one image per hour vs. hundreds of image per hour) for applying SEBAL and METRIC and allows for their more widespread and frequent use. This automation can also reduce potential bias introduced by an inexperienced operator and extends the domain of the models to new users. [ABSTRACT FROM AUTHOR]

Robust evapotranspiration (ET) models are required to predict water usage in a variety of terrestrial ecosystems under different geographical and agrometeorological conditions. As a result, several remote sensing-based surface energy balance (SEB) models have been developed to estimate ET over large regions. However, comparison of the performance of several SEB models at the same site is limited. In addition, none of the SEB models have been evaluated for their ability to predict ET in rain-fed high biomass sorghum grown for biofuel production. In this paper, we evaluated the performance of five widely used single-source SEB models, namely Surface Energy Balance Algorithm for Land (SEBAL), Mapping ET with Internalized Calibration (METRIC), Surface Energy Balance System (SEBS), Simplified Surface Energy Balance Index (S-SEBI), and operational Simplified Surface Energy Balance (SSEBop), for estimating ET over a high biomass sorghum field during the 2012 and 2013 growing seasons. The predicted ET values were compared against eddy covariance (EC) measured ET (ET EC ) for 19 cloud-free Landsat image. In general, S-SEBI, SEBAL, and SEBS performed reasonably well for the study period, while METRIC and SSEBop performed poorly. All SEB models substantially overestimated ET under extremely dry conditions as they underestimated sensible heat ( H ) and overestimated latent heat ( LE ) fluxes under dry conditions during the partitioning of available energy. METRIC, SEBAL, and SEBS overestimated LE regardless of wet or dry periods. Consequently, predicted seasonal cumulative ET by METRIC, SEBAL, and SEBS were higher than seasonal cumulative ET EC in both seasons. In contrast, S-SEBI and SSEBop substantially underestimated ET under too wet conditions, and predicted seasonal cumulative ET by S-SEBI and SSEBop were lower than seasonal cumulative ET EC in the relatively wetter 2013 growing season. Our results indicate the necessity of inclusion of soil moisture or plant water stress component in SEB models for the improvement of their performance, especially under too dry or wet environments. [ABSTRACT FROM AUTHOR]

Data limitations on groundwater (GW) recharge over large areas are still a challenge for efficient water resource management, especially in semi-arid regions. Thus, this study seeks to integrate hydrological cycle variables from satellite imagery to estimate the spatial distribution of GW recharge in the Ipanema river basin (IRB), which is located in the State of Pernambuco in Northeast Brazil. Remote sensing data, including monthly maps (2011–2012) of rainfall, runoff and evapotranspiration, are used as input for the water balance method within Geographic Information Systems (GIS). Rainfall data are derived from the TRMM Multi-satellite Precipitation Analysis (TMPA) Version 7 (3B43V7) product and present the same monthly average temporal distributions from 15 rain gauges that are distributed over the study area (r = 0.93 and MAE = 12.7 mm), with annual average estimates of 894.3 (2011) and 300.7 mm (2012). The runoff from the Natural Resources Conservation Service (NRCS) method, which is based on regional soil information and Thematic Mapper (TM) sensor image, represents 29% of the TMPA rainfall that was observed across two years of study. Actual evapotranspiration data, which were provided by the SEBAL application of MODIS images, present annual averages of 1213 (2011) and 1067 (2012) mm. The water balance results reveal a large inter-annual difference in the IRB GW recharge, which is characterized by different rainfall regimes, with averages of 30.4 (2011) and 4.7 (2012) mm year −1 . These recharges were mainly observed between January and July in regions with alluvial sediments and highly permeable soils. The GW recharge approach with remote sensing is compared to the WTF (Water Table Fluctuation) method, which is used in an area of alluvium in the IRB. The estimates from these two methods exhibit reliable annual agreement, with average values of 154.6 (WTF) and 124.6 (water balance) mm in 2011. These values correspond to 14.89 and 13.53% of the rainfall that was recorded at the rain gauges and the TMPA, respectively. Only the WTF method indicates a very low recharge of 15.9 mm for the second year. The values in this paper provide reliable insight regarding the use of remotely sensed data to evaluate the rates of alluvial GW recharge in regions where the potential runoff cannot be disregarded from WB equation and must be calculated spatially. [ABSTRACT FROM AUTHOR]

In this study, the Surface Energy Balance Algorithm for Land (SEBAL) was used to compute the surface albedo, vegetation indices (NDVI, SAVI and LAI), surface temperature, soil heat flux and evapotranspiration (ET) over two contrasting years (dry and wet) in the Brígida River basin, a semi-arid region of north-eastern Brazil. The actual ET was computed during satellite overpass and was integrated for 24 h on a pixel-by-pixel basis for the daily ET distribution. Due to the topographic effects, an attempt was also made to incorporate DEM information to estimate the net radiation. The land cover types identified in the watershed are cropland, bare land, dense canopy, grassland, and caatinga vegetation. In order to study the variation among the biophysical parameters and ET, two-way analysis of variance (ANOVA) was used. The ET calculated by SEBAL ranged between 2.46 and 6.87 mm/day for the dry year (1990) and 1.31 and 6.84 mm/day for the wet year (2009) for the river basin. The results showed that a reduction in vegetation cover is evident in the temporal and spatial analysis over the studied periods in the region and that these facts influence the values of the energy balance and ET. The results showed significant differences in the variables of land cover type and year at the probability level of 0.05 for all land cover types. [ABSTRACT FROM AUTHOR]

Estimating the water budgets of large basins is a challenge because of the lack of data and information. It becomes more complicated in endorheic basins that consist of separate land and water phases. The application of remotely-sensed data is one solution in this regard. The present study addresses this issue and develops a modeling framework to evaluate a water budget based on remotely-sensed data for endorheic basins. To explore the methodology, Lake Urmia basin was selected as a case study. The lake water level has declined steeply since 1995 and stakeholders have agreed to allocate 3100 MCM of water per year to the lake. This makes it necessary to monitor river inflow into the lake to fulfill the agreement. Gauging stations have been employed around the lake, but they could not account for shortages such as water uptake below the stations. To do this, separate water budgets for the water body and the land were required. More specifically, it was necessary to estimate actual evapotranspiration ( ET ) from freshwater ( E ) and saltwater ( E ) estimated using the SEBAL model. Different methods were applied to estimate soil moisture, groundwater exploitation, and surface-groundwater inflow into the lake. A comparison of the observed and estimated amounts showed good agreement. For instance, the coefficient of determination for the observed/reported and estimated ET and E were 0.83 and 0.84, respectively. The average annual inflow was estimated to be 2.2 BCM/year for 2002-2008 using the RS model, which is about 84 % of the total inflow from the last recording stations before the lake and shows influence of water exploitation after these stations. Future study should focus on increasing temporal and spatial resolution of the method [ABSTRACT FROM AUTHOR]

Evapotranspiration (ET) is an important expenditure in water and energy balances, especially on cold and high-altitude land surfaces. Daily ET of the upper reach of the Shule River Basin was estimated using Landsat 5 TM data and the Surface Energy Balance Algorithm for Land (SEBAL) model. Based on observations made at the Suli station, the algorithms of land surface temperature and soil heat flux in SEBAL were modified. Land surface temperature was retrieved and compared with ground truth via three methods: the radiative transfer equation method, the mono-window algorithm, and the single-channel method. We selected the best of these methods, mono-window algorithm, for estimating ET. The average error of daily ET estimated by the modified SEBAL model and measured by the eddy covariance system was 16.4%, with a root-mean-square error of 0.52 mm d−1. The estimated ET means were 3.09, 2.48, and 1.48 mm d−1 on June 9 (DOY 160), June 25 (DOY 176), and July 27 (DOY 208) of the year 2010, respectively. The average estimated ET on the glacier surface of all days was more than 3 mm d−1, a measurement that is difficult to capture in-situ and has rarely been reported. This study will improve the understanding of water balance in cold, high-altitude regions. Copyright © 2016 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

The applicability of a land surface temperature (LST)-evapotranspiration (ET) regression model to estimate ET fraction (ETrF) was tested in the temperate maritime climate of Central Ireland. In this study, the Mapping ET at high Resolution and with Internalized Calibration (METRIC) model was applied to calculate evapotranspiration from a mixed land cover area in Central Ireland. The ETrF values estimated on a pixel-by-pixel basis using two different surface roughness maps derived from two different estimates of Leaf Area Index (LAI) showed no significant differences. The METRIC based ETrF values from a Landsat Enhanced Thematic Mapper Plus (ETM+) image from 12 July 2013 were in similar range with those obtained from a regression model using land surface temperature (LST)-ET relationship derived from a Landsat 5 Thematic Mapper (TM) image of 17 July 2006. Results indicated that ETrF values could be estimated by using LST values from Landsat TM, even when stress conditions were prevailing in the region. [ABSTRACT FROM AUTHOR]

Groundwater markets improve the agricultural economy by transferring water entitlements from low-efficient users to high-efficient ones to maximize productivity. Aiming at developing an efficient groundwater market, the environmental effects of the market mechanism should be assessed, and a reliable method for monitoring water consumption needs to be employed. Toward this end, this paper proposes three annual smart groundwater market mechanisms to maximize water net benefits, minimize groundwater withdrawal, and precisely measure water consumption in agricultural fields. To guarantee the aquifer's safe yield in each mechanism, a groundwater simulation model (i.e., Groundwater Modeling System (GMS)) is used to control groundwater table drawdown at the end of the planning horizon. In addition, the fields' evapotranspiration (ET) is estimated using Surface Energy Balance Algorithm for Land (SEBAL) and Mapping Evapo Transpiration at high Resolution with Internalized Calibration (METRIC) algorithm to measure the net groundwater consumption during the market. In this regard, we evaluated the algorithms' performances using observed data from a local lysimeter. They are applied to the Nough plain in Iran to assess the effectiveness of the proposed market framework. The findings illustrate their efficiency in recovering approximately 80% (23.33 million cubic meters (MCM)) of groundwater loss due to overexploitation in the study area and increasing the users' annual benefits by 10.6% compared to the non-market condition. In addition, results imply that the METRIC model approximates daily crop ET with a higher accuracy level than the SEBAL model with RMSE, MAE, and Percentage Error of 0.37 mm/day, 0.32 mm/day, and 14.92%, respectively. This research revealed that the proposed market framework is a powerful tool for reallocating water entitlements and increasing water productivity in arid and semi-arid regions. • A groundwater market is proposed to promote economic and environmental situation. • The uncertainty of available water is incorporated in the proposed methodolgoy • Designed market mechanisms recover nearly 85% of groundwater loss in the study area. • The market framework raises the economic benefit by 128.7 billion rials annually. • METRIC algorithm approximates daily crop evapotranspiration with RMSE of 0.37 mm. [ABSTRACT FROM AUTHOR]

Evapotranspiration (ET) is expected to increase by a considerable amount because of the impact of future temperature increase. Nowadays, the daily to seasonal ET maps can be used to provide information for a sustainable and adaptive watershed eco-environment. This study attempts to estimate the spatial ET of South Korea (99,900 km2), located within the latitudes of 33°06'N to 43°01'N and the longitudes of 124°04'E to 131°05'E, on a daily basis. The satellite-based image-processing model Surface Energy Balance Algorithms for Land (SEBAL) was adopted and modified to generate the spatial ET data. The SEBAL was calibrated using two years (2012-2013) of measured ETs by an eddy covariance (EC) flux tower at three locations (two in a mixed forest area and one in a rice paddy area). The primary inputs for the model were land surface temperature/emissivity (LST/E), the Normalized Distribution Vegetation Index (NDVI), albedo (Ab) from a Terra Moderate-resolution Imaging Spectroradiometer (MODIS) satellite, a digital elevation model, and wind speed and solar radiation (Rs) from 76 ground-based weather stations. When LST data were unavailable because of clouds and/or snow, the bias-corrected ground temperature measured at the weather stations was used. The NDVI and Ab were used as the monthly average value to maintain relatively stable values rather than using the original time interval data. The determination coefficient (R2) between SEBAL and the flux tower ET was 0.45-0.54 for the two mixed forest towers and 0.79 for the rice paddy tower reflecting the known characteristics of closed and open space ET estimation. The spatial distribution of SEBAL showed that the spatial ET reflected the geographical characteristics, revealing that the ET of lowland areas was higher than that of highland areas. [ABSTRACT FROM AUTHOR]

Understanding the spatio-temporal variation of actual evapotranspiration (ET a ) over irrigated agricultural areas helps to manage water. Many remote sensing (RS) methods have been developed recently to estimate ET a . Nevertheless, selecting the suitable method is still a challenge. The objective of this research is to evaluate four RS methods for estimating ET a over the large-scale Gezira Scheme of Sudan. Three methods, namely Surface Energy Balance Algorithm for Land (SEBAL), Mapping EvapoTranspiration at High Resolution with Internalized Calibration (METRIC) and Simplified Surface Energy Balance (SSEB), were applied using Landsat images. Another RS dataset, i.e. ET a product from Moderate Resolution Imaging Spectroradiometer (MODIS) satellite (MOD16A2), was also used in the comparison. The ET a methods were validated using field-scale water balance (WB) estimates during the 2004 crop season. Furthermore, the present study suggested the use of Normalized Difference Vegetation Index (NDVI) and integrated NDVI (iNDVI) as an approach to assessing the performance of the ET a estimates from RS on daily and seasonal scales, respectively. Several performance measures were evaluated, and a score was given for each method in order to extract the most valuable information for water management out of four different methods. Based on ranking criteria, SSEB gave the best performance followed by SEBAL. METRIC is not suitable for estimating ET a where ground-data are limited. MOD16A2 was less sensitive to detection of ET a at field level because of pixel resolution, but it had high score at the scheme level. All methods showed similar spatial patterns of seasonal ET a , with a decreasing ET a gradient from southeast to northeast of the scheme. In conclusion, SSEB and MOD16A2 are seen as suitable operational ET a methods on the regional scale as both methods require less application time compared to the other complex models. SSEB has the advantage of estimating ET a on field scale and on daily basis. The results of this study contribute to improving the understanding of performance of such methods in large irrigation schemes in arid climate zones. [ABSTRACT FROM AUTHOR]

Field-based soil moisture measurements are cumbersome. Thus, remote sensing techniques are needed because allows field and landscape-scale mapping of soil moisture depth-averaged through the root zone of existing vegetation. The objective of the study was to evaluate the accuracy of an empirical relationship to calculate soil moisture from remote sensing data of irrigated soils of the Apodi Plateau, in the Brazilian semiarid region. The empirical relationship had previously been tested for irrigated soils in Mexico, Egypt, and Pakistan, with promising results. In this study, the relationship was evaluated from experimental data collected from a cotton field. The experiment was carried out in an area of 5 ha with irrigated cotton. The energy balance and evaporative fraction (Λ) were measured by the Bowen ratio method. Soil moisture (θ) data were collected using a PR2 - Profile Probe (Delta-T Devices Ltd). The empirical relationship was tested using experimentally collected Λ and θ values and was applied using the Λ values obtained from the Surface Energy Balance Algorithm for Land (SEBAL) and three TM - Landsat 5 images. There was a close correlation between measured and estimated θ values (p

Neste trabalho buscou-se determinar o saldo de radiação (Rn) instantâneo na região semiárida que abrange o entorno do reservatório de Itaparica - PE, Brasil, utilizando o algoritmo SEBAL e 13 imagens do satélite Landsat 5 - TM. O recorte selecionado para o estudo cobre além do próprio reservatório, o perímetro irrigado de Apolônio Sales, a área urbana do município de Petrolândia, bem como áreas de solo exposto e de vegetação nativa. Determinou-se neste trabalho albedo da superfície e índices de vegetação para cada pixel. A radiação de onda longa emitida foi obtida em função da emissividade e da temperatura de superfície de cada pixel, a radiação de onda longa incidente da atmosfera foi calculada em função da emissividade atmosférica e da temperatura do ar medida em estação meteorológica, e a radiação de onda curta incidente foi calculada em condição de céu claro. Com base nestes dados, foi calculado então Rn para cada uma das 13 imagens, cujo valor médio variou entre 423,98 e 608,79 W/m² para os dias 31/07/2003 e 05/04/2007 respectivamente. Esta variação se mostrou fortemente influenciada pela vegetação, sendo os maiores valores apresentados em áreas de maior cobertura vegetal e os menores valores nas áreas de solo exposto.