Surface Energy Balance Algorithm For Land Algorithm Research Articles

Spatiotemporal trends and evapotranspiration estimation using an improvised SEBAL convergence method for the semi-arid region of Western Rajasthan, India

Abstract The study demonstrates how to estimate evapotranspiration (ET) for the Western Rajasthan region of India utilizing remotely sensed images with the Surface Energy Balance Algorithm for Land (SEBAL). Landsat 8 and Moderate Resolution Imaging Spectroradiometer (MODIS) satellite inputs were used to compute seasonal and annual ET on the Google Earth Engine platform. The assessment utilizing the SEBAL algorithm, in combination with the Food and Agriculture Organization (FAO) Penman–Monteith (PM) and Hargreaves methods, demonstrates that SEBAL has adequate reliability for estimating ET for a spatially large extent in semi-arid regions when evaluated with the Hargreaves method. The values of R2, root-mean-square error (RMSE), and mean bias error (MBE) for FAO-PM were 0.63, 1.65 mm/day, and 1.28 mm/day, respectively. For the Hargreaves method, the values of R2, RMSE, and MBE were 0.96, 0.41 mm/day, and −0.31 mm/day, respectively. The study showed that the northern region witnessed the highest ET due to the availability of abundant surface water for irrigation. Overall, the results demonstrate the SEBAL model's effectiveness in estimating evapotranspiration. A downward trend in ET is observed in the region, mainly due to changing climatic conditions.

Sensitivity analysis of estimated evapotranspiration using soil moisture content, energy balance model of SEBAL algorithm

Abstract Evapotranspiration is the main factor of plant growth, which can be estimated using different methods. Accurate estimation of evaporation and transpiration helps in planning to meet the actual needs of the plant. In general, daily evapotranspiration depends on temperature, climate, soil moisture, plant, growth period, root length, and the texture of the root zone porous media. In this study, the moisture balance of the soil surface is simulated daily and the components of deep percolation, evaporation, irrigation, and transpiration are calculated. The obtained results have been compared with the output of the surface energy balance algorithm for land (SEBAL) algorithm to determine the energy balance on the earth's surface and to estimate evapotranspiration. Normalized objective function (NOF), Nash–Sutcliffe efficiency index, and mean absolute error were incorporated into the problem for evaluating the predicted values. The energy balance of the soil surface and the moisture balance of the root zone each have a different process in estimating the actual evaporation and transpiration, but the correlation between them was acceptable. The interval between the calculated values can be used as input data to determine the irrigation requirement.

Estimation of the Evapotranspiration of Irrigated Açaí (Euterpe oleracea M.), through the Surface Energy Balance Algorithm for Land—SEBAL, in Eastern Amazonia

The culture of açaí (Euterpe oleraceae M.), originating from floodplain areas, was planted on dry land in many properties in Pará, Brazil, making necessary the use of irrigation. To irrigate adequately with less waste, it is necessary that studies aim at increasing efficiency in the use of water in this sector, and one of the ways to do so is to estimate evapotranspiration (ET). The objective of this study was to estimate the actual daily evapotranspiration using the Surface Energy Balance Algorithm for Land (SEBAL) in eastern Amazonia. Six images from the Landsat 8 satellite were used, and the estimates of evapotranspiration with the SEBAL algorithm showed good agreement with the results obtained by the Bowen ratio method in the area of açaí planting, including the mean absolute error (MAE), mean relative error (MRE), root of mean square error (RMSE), and the concordance index (d index) equal to 0.45 mm day−1, 4.23%, 0.52 mm day−1, and 0.80, respectively. SEBAL showed the ability to distinguish the soil cover, demonstrating the sensitivity of the model, which provided the mapping of the components analyzed. The use of the algorithm helps in decision making regarding irrigation management and reducing costs and water losses.

Evapotranspiration estimation using SEBAL algorithm integrated with remote sensing and experimental methods

ABSTRACT Evapotranspiration is one of the most important elements of the hydrological cycle. Estimation of evapotranspiration is imperative for effective forest, irrigation, rangeland and water resources management as well as to increase yields and for better crop management. This study aims to evaluate the effectiveness of the Surface Energy Balance Algorithm for Land (SEBAL) in estimating evapotranspiration and crop coefficient of corn in the Mediterranean region of Adana province, Turkey. The Landsat 8 satellite images from March to September 2018 were used to acquire the coefficients of the respective bands. Then, the net radiation flux on the earth’s surface and the earth’s heat flux is obtained using incoming-outgoing radiation fluxes from albedo, surface emissivity coefficients, land surface temperature, and plant indicators. Next, the sensible heat flux is calculated by determining the hot and cold pixels under consideration via the atmospheric stability conditions. Finally, evapotranspiration maps are plotted. The crop coefficient of corn is also estimated with the respected maps being plotted. To validate the outcomes from the SEBAL algorithm, experimental methods were employed to calculate the evapotranspiration values and evaluated using suitable performance metrics. The results showed that the SEBAL generated evapotranspiration values are in high agreement with the FAO Penman-Monteith method registering the highest correlation (R = 0.91) and the lowest error (RMSE = 1.14). In addition, the SEBAL method registered the highest correlation values of 0.89, 0.87 and 0.68 with Turk, Makkink and Hargreaves experimental methods, respectively. Moreover, the crop coefficients estimated using SEBAL also manifested an acceptable correlation with all methods. The highest correlation value registered was with the FAO Penman-Monteith method (R = 0.98). The outcomes show that since the performance of the SEBAL algorithm in estimating the actual evapotranspiration and crop coefficient using Landsat 8 satellite images is acceptable, the SEBAL algorithm could be a very convenient method. Moreover, it could easily be assimilated into farming management systems and precision agriculture for better decision-making and higher yield.

Long-term monitoring of evapotranspiration using the SEBAL algorithm and Google Earth Engine cloud computing

Accurate estimation of evapotranspiration (ET) is essential for several applications in water resources management. ET models using remote sensing data have flourished in recent years allowing spatial and temporal assessments at unprecedented resolutions. This study presents geeSEBAL, a new tool for automated estimation of ET, based on the Surface Energy Balance Algorithm for Land (SEBAL) and a simplified version of the CIMEC (Calibration using Inverse Modeling at Extreme Conditions) process for the endmembers selection, developed within the Google Earth Engine (GEE) environment. The tool framework is introduced, and case studies across multiple biomes in Brazil are presented by comparing daily ET estimates with eddy covariance (EC) data from 10 flux towers. Based on 224 Landsat images using ERA5 Land as meteorological inputs, daily ET estimates of geeSEBAL yielded an average root mean squared difference (RMSD) of 0.67 mm day−1 when compared to EC data corrected for the energy balance closure. Additional analyses indicate a low geeSEBAL sensitivity to meteorological inputs, yielding an average RMSD of 0.71 mm day−1 when driven by in situ meteorological measurements. On the other hand, we found a higher sensitivity of the automated CIMEC algorithm to the selection of endmembers for internal calibration. For instance, by adjusting the endmembers percentiles to tropical biomes we found an error that was 36% lower compared to the standard CIMEC percentiles. Finally, we assessed the long-term effects (1984–2020) of land cover changes on surface energy fluxes and water use in agriculture for key areas in Brazil, from deforested areas in the Amazon to irrigated crops in the Pampas and Cerrado biomes. A comparison with a land surface temperature-based (SSEBop) and a vegetation-based (MOD16) model was also performed to assess relative advantages and disadvantages. This analysis showed that geeSEBAL has a significant potential for long-term assessment of ET in data-scarce areas, due to its lower sensitivity to meteorological inputs. geeSEBAL codes are written in Python and JavaScript and are freely available on GitHub (https://github.com/et-brasil/geesebal). geeSEBAL also includes a graphical user interface (https://etbrasil.org/geesebal), allowing important advances in water resources management at regional scales.

Evapotranspiration of banana using the SEBAL algorithm in an irrigated perimeter from the Northeastern Brazil

ABSTRACT The study aimed to estimate the evapotranspiration of banana (Musa spp.) in an irrigated perimeter of the municipality of Barbalha, CE, Brazil, using the Surface Energy Balance Algorithm for Land (SEBAL) model and to compare these results with those estimated using the Penman-Monteith method. Landsat-8 OLI/TIRS satellite images of May 22, 2016, August 10, 2016, and October 29, 2016 and data on temperature, relative humidity, wind speed and solar irradiance, obtained from an automatic weather station, installed close to the experimental area were used. The bands were stacked, and the stacked images were cut, then mathematical operations and evapotranspiration estimation were performed, whose actual daily banana values, estimated by the SEBAL algorithm, were 4.70; 5.00 and 6.50 mm, respectively, for May 22, August 10, and October 29, 2016. Comparing the daily ETr given by SEBAL with that obtained by the Penman-Monteith method, absolute errors of 0.26, 0.44, and 0.64 mm d-1 were observed for May 22, August 10, and October 29, 2016, respectively. These errors are within the ranges accepted in the literature.

Spatiotemporal assessment of actual evapotranspiration using satellite remote sensing technique in the Nile Delta, Egypt

Egypt's Nile delta area is a wealthy land for growing Egypt's principal crops such as rice. Being at the end of the Nile River the water shortage problem is very serious. Thus, water resource maintenance is necessary by tracking Actual crop evapotranspiration (ETC). The main aim of the recent study is to evaluate the ETC over the Mit-Yazid command area based on remote sensing methods daily and seasonally by Surface Energy Balance Algorithm for Land (SEBAL) technique using 18 Landsat 8 images during the 2017 and 2018 summer season. To evaluate the performance of SEBAL algorithm derived ETc (ETC-SEBAL) in the same conditions, Fao-Penman-Monteith method has been used, by applying several performance criteria. The results which revealed the acceptable accuracy of SEBAL algorithms with Landsat 8 images, are adequately accurate with an appropriate estimate of ETC's distribution across the study area with R2 = 0.843% and RMSE around 0.264 mm/day. Additionally, a field water equilibrium has been assessed using the ETC-SEBAL obtained, and the results show that the seasonal ETC was generality provided with water surface supply by (87,5%). Moreover, a new summer crop pattern vision is assumed for agricultural cultivation to maximize the water-saving for the selected pilot study area.

Assessment of an Automated Calibration of the SEBAL Algorithm to Estimate Dry-Season Surface-Energy Partitioning in a Forest–Savanna Transition in Brazil

Evapotranspiration ( E T ) provides a strong connection between surface energy and hydrological cycles. Advancements in remote sensing techniques have increased our understanding of energy and terrestrial water balances as well as the interaction between surface and atmosphere over large areas. In this study, we computed surface energy fluxes using the Surface Energy Balance Algorithm for Land (SEBAL) algorithm and a simplified adaptation of the CIMEC (Calibration using Inverse Modeling at Extreme Conditions) process for automated endmember selection. Our main purpose was to assess and compare the accuracy of the automated calibration of the SEBAL algorithm using two different sources of meteorological input data (ground measurements from an eddy covariance flux tower and reanalysis data from Modern-Era Reanalysis for Research and Applications version 2 (MERRA-2)) to estimate the dry season partitioning of surface energy and water fluxes in a transitional area between tropical rainforest and savanna. The area is located in Brazil and is subject to deforestation and cropland expansion. The SEBAL estimates were validated using eddy covariance measurements (2004 to 2006) from the Large-Scale Biosphere-Atmosphere Experiment in the Amazon (LBA) at the Bananal Javaés (JAV) site. Results indicated a high accuracy for daily ET, using both ground measurements and MERRA-2 reanalysis, suggesting a low sensitivity to meteorological inputs. For daily ET estimates, we found a root mean square error (RMSE) of 0.35 mm day−1 for both observed and reanalysis meteorology using accurate quantiles for endmembers selection, yielding an error lower than 9% (RMSE compared to the average daily ET). Overall, the ET rates in forest areas were 4.2 mm day−1, while in grassland/pasture and agricultural areas we found average rates between 2.0 and 3.2 mm day−1, with significant changes in energy partitioning according to land cover. Thus, results are promising for the use of reanalysis data to estimate regional scale patterns of sensible heat (H) and latent heat (LE) fluxes, especially in areas subject to deforestation.

Spatial variability of coffee plant water consumption based on the SEBAL algorithm

ABSTRACT: Awareness of evapotranspiration (ET) and crop coefficient (Kc) is necessary for irrigation management in coffee crops. ET and Kc spatial variabilities are disregarded in traditional methods. Methods based on radiometric measurements have potential to obtain these spatialized variables. The Kc curve and spatial variability of actual evapotranspiration (ETa) were determined using images from Landsat 8 satellite. We used images of young and adult coffee plantations from OLI (Operational Land Imager) and TIRS (Thermal Infrared Sensor) sensors over a two-year period. Evapotranspiration was estimated using the Surface Energy Balance Algorithm for Land (SEBAL). Moreover, the reference evapotranspiration (ETo) was estimated through the Penman-Monteith method. We obtained the values for the evapotranspiration fraction (ETf), analogous to Kc, according to ET and ETo values. The study was conducted in Buritis, Minas Gerais State, Brazil, in areas cropped with Coffea arabica irrigated by central pivots. A comparative analysis was made using different statistical indices. Average ETa was 2.17 mm d−1 for young coffee plantations, , and the Kc mean value was 0.6. For adult coffee plantations, average ETa was 3.95 mm d−1, , and the K mean value was 0.85. The ET and K data obtained based on the SEBAL algorithm displayed similar values to studies that used traditional methods. This model has huge potential to estimate ET of different stages of coffee plantation for the region studied.

Estimating evapotranspiration of pistachio crop based on SEBAL algorithm using Landsat 8 satellite imagery

Stationary actual evapotranspiration (ET) estimation methods deal with some issues such as maintenance problems, the point data recording for a particular location, the complex hydrological process calculation, and requiring the expert workers. To solve this, ET estimation methods like Surface Energy Balance Algorithm for Land (SEBAL) method can be used based on the satellite images. However, the efficiency of the SEBAL model varies in ET estimation for different products and environmental conditions. On the other hand, pistachio is an important crop of Iran which little investigation were accomplished to estimate its ET using the presented models. Therefore, the purpose of this study is to investigate the efficiency of SEBAL algorithm in estimating ET of pistachio crop. In this regard, actual ET of a 100 hectares pistachio farm, located in Safaeieh region in the Semnan province, Iran, was selected as the study area. Implementations were performed using 29 Landsat 8 Operational Land Imager (OLI) images during the 2013–2017 period. The results were verified and compared with the results of evapotranspiration in standard condition estimated by the Intelligent Meteorological instrument (iMetos-Pessl) as the ground truth. The results of this study indicate the determination coefficient and Root Mean Square Error (RMSE) of the estimated actual ET for pistachio plant as 0.8 and 2.5 mm, respectively. Moreover, the results indicate the high spatial variability of ET in the pistachio growth period and the need to use a satellite data analysis method, like the SEBAL method, to prepare the ET map. Overall, the results show the good efficiency of the SEBAL model for estimating the actual ET of the pistachio product.

An evaluation of energy balance parameters, and the relations between topographical and biophysical characteristics using the mountainous surface energy balance algorithm for land (SEBAL)

ABSTRACTEnergy balance parameters are very important in various applications such as energy global cycle, environment, climatic change monitoring, weather forecast, agriculture. The present study evaluates the influence of topographical conditions on the amount of downward shortwave radiation (SWD) of the surface, as well as the relation between surface Net radiation with biophysical characteristics and region’s land use’s. For this purpose, Landsat images, Digital Elevation Model (DEM), and a set of sample points have been used. To calculate the downward shortwave radiation and Net radiation fluxes, it has employed the mountainous SEBAL algorithm. Greenness, Brightness, Wetness, Normalization Difference Built-up Index (NDBI), Normalization Difference Vegetation Index (NDVI) and Albedo parameters considered as surface biophysical characteristics. Brightness, NDBI and Albedo information shows the percentage of impervious‏ surfaces while Greenness and NDVI information is related to vegetation percentage and Wetness information concerns water-related rates as well as the wetness of the soil, plant, and built-up lands. Statistical analysis has been used to study the relationship between different parameters. Results show that the slope is the most influential topographical parameters on the amount of SWD of the surface. There is a reverse relation between the variants of SWD of the surface, and the slope of the surface, in second-degree polynomial form with a Pearson’s correlation coefficient (r) of 0.82. North aspect receive the minimum SWD average with a 681 W m–2 while flat lands receive the maximum SWD average with a 783 W m–2. Areas with the highest elevation, lowest slope, and flatlands, receive the highest SWD. Built-up lands have the lowest Net radiation flux average with a 595 W m–2 while water has the highest Net radiation flux average with a 761 W m–2. Net radiation flux is directly related with NDVI, Greenness and Wetness parameters, while it is reversely related to NDBI, Albebo and Brightness with a high r.

Evapotranspiration Estimation Using Remote Sensing Technology Based on SEBAL Algorithm

The aim of this study is to obtain the actual evapotranspiration (ETa) values using Surface Energy Balance Algorithm for Land (SEBAL) algorithm, during the year 2008 in Nekooabad agricultural and drainage network and Ben-Saman region located in Gavkhouni basin in Iran. For this purpose, the time series of Moderate Resolution Imaging Spectroradiometer images from Terra satellite are prepared and ETa values of the regions are estimated for this year. In order to evaluate the SEBAL algorithm performance, the algorithm was executed on Nekooabad agricultural network located in Najafabad region with enough land information and three different methods were used to verify the results. (1) Epan-method: The correlation coefficient between obtained ETa by SEBAL algorithm and the reference ET values (ET0) from pan evaporation method is about 86.72 %. (2) Penman–Monteith method: This method is used for result comparison (the correlation coefficient is about 90.47 %). (3) Water balance method: Considering the water withdrawal of the wells, precipitation and inflow and outflow to the region to the water balance equation, the ETa is estimated as the unknown variable. The results are compared with the result of SEBAL algorithm that shows an error of 7.21 %. SEBAL measured the ETa of Ben-Saman region about 228.16 mm, while there is not enough information of land change in recent years about this region. The results indicate that more cooperation is needed for implementing the special plans for water resources allocation to prevent irregular withdrawals.

Assessment and validation of evapotranspiration using SEBAL algorithm and Lysimeter data of IARI agricultural farm, India

Evapotranspiration (ET) is a vital process in land surface atmosphere research. In this study, Surface Energy Balance Algorithm for Land (SEBAL) for the assessment of ET (for 23 December 2010, 8 January 2011, 24 January 2011, 9 February 2011, 25 February 2011, 29 March 2011 and 14 April 2011) from LANDSAT7-ETM+ and validation with Lysimeter data set is illustrated. It is based on the evaporative fraction concept, and it has been applied to LANDSAT7-ETM + (30 m resolution) data acquired over the Indian Agricultural Research Institute’s agricultural farm land. The ET from SEBAL was compared with Lysimeter ET using four statistical tests (root-mean-square error (RMSE), relative root-mean-square error (R-RMSE), mean absolute error (MAE), and normalized root-mean square error (NRMSE)), and each test showed a good correlation between the predicted and observed ET values. Results from this study revealed that the RMSE of crop-growing period was 0.51 mm d−1 for ETSEBAL, i.e. ETSEBAL having good accuracy with respect to observed ETLysimeter. Results were also validated using R-RMSE test, which also proved that ETSEBAL data are having good accuracy with respect to observed ETLysimeter as R-RMSE of crop-growing period is 0.19 mm d−1. MAE (0.19), NRMSE (0.21) and r2 (0.91) tests indicated that model prediction is significant, and model can be effectively used for the estimation of ET from SEBAL as input of remote sensing data sets. Finally, the SEBAL has been useful for remote agricultural land where ground-based data (Lysimeter data) are not available for daily ET (ET24 h) estimation. The temporal study of the ET24 h values analysed has revealed that the highest ET24 h values are owing to the higher development (high greenness) of crops, whereas the lower values are related to the lower development (low greenness) or null crop.

Estimativa da produtividade da cana-de-açúcar utilizando o Sebal e imagens Landsat

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.

Estimation of actual evapotranspiration by using MODIS images (a case study: Tajan catchment)

Evapotranspiration (ET) is one of the most effective variables to determine water balance in a region. Accurate estimation of ET flux is vital for agricultural scheduling and water resource management. One of the ways for estimation of ET is applying energy budget methods in which parameters are determined by using remote-sensing technique. The surface energy balance algorithm for land (SEBAL) is one of the most important algorithms for estimation of ET using satellite images. This model estimates ET via estimation of all energy components in the surface such as net radiation flux, soil heat flux and sensible heat flux. This paper aims to investigate accuracy of actual ET estimation using SEBAL algorithm in comparison with calculated values by Food and Agriculture Organization (FAO) Penman–Monteith in Tajan catchment and area of Sari Dasht Naz Agricultural Corporation, Iran. In this investigation, seven images of moderate resolution imaging spectroradiometer (MODIS) were used from February to July 2011. The obtained results show that there is not a significant difference between SEBAL and FAO Penman–Monteith methods for estimation of hourly and daily ET (P-value > 0.05). In addition, hourly and daily ET can be estimated with acceptable accuracy, RMSE = 0.091 mm and RMSE = 1.49 mm, respectively.

Estimativa dos componentes do balanço de energia e da evapotranspiração para áreas de cultivo de soja no sul do Brasil utilizando imagens do sensor TM Landsat 5

O presente estudo teve como objetivo avaliar a eficiência do algoritmo SEBAL na estimativa dos componentes de balanço de energia e da evapotranspiração para áreas de cultivo de soja irrigada no município de Cruz Alta (RS), assim como analisar a variação temporal e espacial da evapotranspiração a partir de imagens do sensor TM Landsat 5. O algoritmo Surface Energy Balance Algorithm for Land (SEBAL) foi aplicado a seis imagens distribuídas ao longo do ciclo fenológico da cultura da soja, no município de Cruz Alta, RS, nas safras 2009 e 2010. As estimativas obtidas foram comparadas com os dados de superfície, obtidos por meio da estação micrometeorológica localizada na área de estudo. As estimativas mais precisas foram as do saldo de radiação, enquanto que a estimativa que apresentou maior erro foi a do fluxo de calor no solo. O SEBAL mostrou potencial para mapear a variabilidade espacial e temporal da evapotranspiração, evidenciando sensibilidade aos diferentes tipos de ocupação do solo e aos diferentes estádios de desenvolvimento da cultura.

Up-scaling of SEBAL derived evapotranspiration maps from Landsat (30 m) to MODIS (250 m) scale

Summary Remotely sensed imagery of the Earth’s surface via satellite sensors provides information to estimate the spatial distribution of evapotranspiration (ET). The spatial resolution of ET predictions depends on the sensor type and varies from the 30–60 m Landsat scale to the 250–1000 m MODIS scale. Therefore, for an accurate characterization of the regional distribution of ET, scaling transfer between images of different resolutions is important. Scaling transfer includes both up-scaling (aggregation) and down-scaling (disaggregation). In this paper, we address the up-scaling problem. The Surface Energy Balance Algorithm for Land (SEBAL) was used to derive ET maps from Landsat 7 Enhanced Thematic Mapper Plus (ETM+) and Moderate Resolution Imaging Spectroradiometer (MODIS) images. Landsat 7 bands have spatial resolutions of 30–60 m, while MODIS bands have resolutions of 250, 500 and 1000 m. Evaluations were conducted for both “output” and “input” up-scaling procedures, with aggregation accomplished by both simple averaging and nearest neighboring resampling techniques. Output up-scaling consisted of first applying SEBAL and then aggregating the output variable (daily ET). Input up-scaling consisted of aggregating 30 m Landsat pixels of the input variable (radiance) to obtain pixels at 60, 120, 250, 500 and 1000 m before SEBAL was applied. The objectives of this study were first to test the consistency of SEBAL algorithm for Landsat and MODIS satellite images and second to investigate the effect of the four different up-scaling processes on the spatial distribution of ET. We conclude that good agreement exists between SEBAL estimated ET maps directly derived from Landsat 7 and MODIS images. Among the four up-scaling methods the output simple averaging method produced aggregated data and aggregated differences with the most statistically and spatially predictable behavior. The input nearest neighbor method was the least predictable but was still acceptable. Overall, the daily ET maps over the Middle Rio Grande Basin aggregated from Landsat images were in good agreement with ET maps directly derived from MODIS images.

Estimativa da evapotranspiração real diária utilizando-se imagens digitais TM - Landsat 5

A quantificação da evapotranspiração é de grande importância na modelagem hidrológica e meteorológica, sendo indispensável no manejo hídrico sustentável de cultivos irrigados. Neste sentido, o presente trabalho tem por objetivo estimar a evapotranspiração real diária - ETr, em escalas local e regional, através de imagens TM - Landsat 5 e dados complementares coletados em estação meteorológica, através da aplicação do SEBAL - Surface Energy Balance Algorithm for Land. A área estudada fica localizada no Cariri cearense, sendo composta por áreas irrigadas, vegetação nativa e parte da floresta da Chapada do Araripe. Foram utilizadas quatro imagens, obtidas em 29 de setembro, 15 e 31 de outubro e 16 de novembro de 2005. A ETr diária foi estimada a partir da densidade de fluxo de calor latente (LE), obtida como resíduo da equação do balanço de energia. O SEBAL é constituído de várias etapas, que incluem calibração radiométrica, cômputo da reflectância e albedo, índices de vegetação e emissividade, baseados nas bandas reflectivas de sensores orbitais, e neste caso o TM - Landsat 5, além da temperatura da superfície (banda termal). Os resultados obtidos foram validados com medições da ETr obtidas em dois campos irrigados com mamona (Ricinus communis L.) e algodão (Gossypium hirsutum). A ETr diária média da área estudada apresentou valores de 3,8, 3,7, 4,6 e 3,4 mm.dia-1, para os dias 272, 288, 304 e 320, respectivamente. Quando comparados com medidas efetuadas nos campos irrigados de algodão e mamona os valores da ETr estimados pelo SEBAL apresentaram diferenças inferiores a 1,0 mm dia-1, evidenciando a sua importância e acurácia.

Intercomparison of Evapotranspiration Over the Savannah Volta Basin in West Africa Using Remote Sensing Data.

This paper compares evapotranspiration estimates from two complementary satellite sensors - NASA's Moderate Resolution Imaging Spectroradiometer (MODIS) and ESA's ENVISAT Advanced Along-Track Scanning Radiometer (AATSR) over the savannah area of the Volta basin in West Africa. This was achieved through solving for evapotranspiration on the basis of the regional energy balance equation, which was computationally-driven by the Surface Energy Balance Algorithm for Land algorithm (SEBAL). The results showed that both sensors are potentially good sources of evapotranspiration estimates over large heterogeneous landscapes. The MODIS sensor measured daily evapotranspiration reasonably well with a strong spatial correlation (R²=0.71) with Landsat ETM+ but underperformed with deviations up to ~2.0 mm day-1, when compared with local eddy correlation observations and the Penman-Monteith method mainly because of scale mismatch. The AATSR sensor produced much poorer correlations (R²=0.13) with Landsat ETM+ and conventional ET methods also because of differences in atmospheric correction and sensor calibration over land.