SEBAL Algorithm Research Articles

Estimating wheat evapotranspiration through remote sensing utilizing GeeSEBAL and comparing with lysimetric data

Accurate estimation of ET is vital for water resource management. In recent decades, researchers have focused on utilizing satellite imagery for this purpose. The use of RS data has enabled the development of new models that provide detailed spatial assessments. GeeSEBAL, an automated ET estimation tool, employs the SEBAL algorithm via GEE. The current version of GeeSEBAL utilizes Landsat images and ERA5 global reanalysis data to produce time series estimates. Landsat 8 images were processed into a 16-day time series spanning 2013–2022, specifically during the wheat growing season. To validate the GeeSEBAL model for 2013–2014, results were compared against lysimeter data. Subsequently, ET was calculated for the years 2015–2022. The evaluation of GeeSEBAL against lysimetric data, by metrics such as R2, RMSE, MAE, NSE, and NRMSE, yielded values of 0.94, 0.98, 0.07, 0.86, and 0.62, respectively. Those findings underscore the importance of GeeSEBAL for estimating wheat ET in regions with limited data availability.

Improving Evapotranspiration Estimation in SWAT-Based Hydrologic Simulation through Data Assimilation in the SEBAL Algorithm

Improving Evapotranspiration Estimation in SWAT-Based Hydrologic Simulation through Data Assimilation in the SEBAL Algorithm

Evaluation of SEBAL and METRIC algorithms to estimate evaporation over fresh and saline water bodies (case study: Urmia Lake, Dez reservoir, and Karkhe reservoir)

Evaluation of SEBAL and METRIC algorithms to estimate evaporation over fresh and saline water bodies (case study: Urmia Lake, Dez reservoir, and Karkhe reservoir)

Improving the remote sensing process of evapotranspiration in the SEBAL algorithm using meta-heuristic models

Abstract This paper investigated how the meta-heuristic models can be used to facilitate the estimation of evapotranspiration (ET) images. Focusing on estimating daily ET directly from received images of the electromagnetic bands of Landsat 8 satellite utilizing metaheuristic models, authors used daily ET images estimated by the SEBAL algorithm to calibrate and verify these models. The results of this research showed that the ANN model with DC and RMSE of 0.98 and 0.09025 mm/day, respectively, is more accurate compared to the ACO (with DC = 0.65 and RMSE = 1.45 mm/day) and PSO (with DC = 0.23 and RMSE = 1.60 mm/day) models in the verification stage in estimating daily ET images. The ACO model compared to the PSO model is more accurate in estimating ET images with DC of 0.65 and 0.23 in the verification step, respectively. While removing half of the training data, the accuracy of the PSO model surpasses the ACO model with DC of 0.85 and 0.80, respectively. Also, the ANN model is more accurate than the other two models in estimating ET, both when considering all the data and half of the training data (with DC = 0.98 and RMSE = 0.09 mm/day).

Assessing the role of evapotranspiration in reducing surface temperatures in Harare using the SEBAL algorithm

Within urban areas, buildings, roads, and paved surfaces, absorb and re-emit the sun's heat more than natural landscapes. Urban areas, where these structures are highly concentrated and vegetation is limited, become ‘heat islands’ contributing to a range of environmental, economic, and human health problems. Despite this, limited research, has, to date, been carried out to assess the influence of evapotranspiration (ET) on urban surface temperatures for Harare, Zimbabwe. In this study, the uSEBAL algorithm was used to estimate evapotranspiration (ET) and land surface temperature (LST) using Landsat images in Hazare, Zimbabwe. The influence of ET on LST was also examined during the hot and post rain seasons. Results showed significant negative relationships (p < 0.05) between ET and LST. High ET rates were recorded over water bodies and green spaces as compared to built-up areas. High ET rates resulted in correspondingly low LST. This is the first time such results are reported for Harare. The conclusion drawn from these results is that as ET increases, temperature decreases, thus there was a negative relationship between ET and LST during the hot season. Additionally, low ET coincided with high LST in low vegetation and densely built-up areas. From these findings we can deduce ways of mitigating effects of thermal discomfort in urban areas i.e., inclusion of green spaces and water bodies within the cities. It is recommended that more green spaces be created within residential zones and encourage construction of green buildings and cooling technologies.

How Do Different Land Uses/Covers Contribute to Land Surface Temperature and Albedo?

Land surface temperature (LST) and land surface albedo (LSA) are the two key regional and global climate-controlling parameters; assessing their behavior would likely result in a better understanding of the appropriate adaptation strategies to mitigate the consequences of climate change. This study was conducted to explore the spatiotemporal variability in LST and LSA across different land use/cover (LULC) classes in northwest Iran. To do so, we first applied an object-oriented algorithm to the 10 m resolution Sentinel-2 images of summer 2019 to generate a LULC map of a 3284 km2 region in northwest Iran. Then, we computed the LST and LSA of each LULC class using the SEBAL algorithm, which was applied to the Landsat-8 images from the summer of 2019 and winter of 2020. The results showed that during the summer season, the maximum and minimum LSA values were associated with barren land (0.33) and water bodies (0.11), respectively; during the winter season, the maximum LSA value was observed for farmland and snow cover, and the minimum value was observed in forest areas (0.21). The maximum and minimum LST values in summer were acquired from rangeland (37 °C) and water bodies (24 °C), respectively; the maximum and minimum values of winter values were detected in forests (4.14 °C) and snow cover (−21.36 °C), respectively. Our results revealed that barren land and residential areas, having the maximum LSA in summer, were able to reduce the heating effects to some extent. Forest areas, due to their low LSA and high LST, particularly in winter, had a greater effect on regional warming compared with other LULC classes. Our study suggests that forests might not always mitigate the effects of global warming as much as we expect.

FOR A GREATER EFFICIENCY OF THE BRAZILIAN PASTURES:

Climate change, induced by human activities, is already a reality in the most diverse environments on Earth. Modelling forage capacity for herds has become an important management strategy, aiming to reduce environmental impacts and increase efficiency in meat production. This work aimed to estimate the Gross Primary Productivity (GPP) in pasture areas in savanna environments (locally known as Cerrado) of the state of Goiás, Brazil, with specific parameterization data for Brachiaria species. The experiment was carried out in pasture areas in the Rio Vermelho hydrographic basin (BHRV), in the western portion of Goiás, using Landsat 8 OLI / TIRS sensor satellite images, in which the variation in the GPP was recorded in 22 images in the period from October 2014 to May 2018. This parameter was estimated by coupling the SEBAL algorithms to estimate evapotranspiration, combined with the CASA model, which, together with surface data, calculates the GPP. Furthermore, for this same area, an adaptation of the GPP product methodology obtained by MOD17A2H was also carried out for Landsat 8 images to understand better the variation in GPP in medium spatial resolution images (30 m). Among the results, the SEBAL / CASA method proved to be more efficient among the methods applied in this research, following the climatic seasonality of the region and its influences on the pasture areas, with a variation of 0.10 to 5 g C m-2. Therefore, the estimate of the GPP aiming at a reading of the pasture and local climatic data presented better results with the calibration of the models with specific data.

Comparison of SEBAL, METRIC, and ALARM algorithms for estimating actual evapotranspiration of wheat crop

Comparison of SEBAL, METRIC, and ALARM algorithms for estimating actual evapotranspiration of wheat crop

Urbanization-induced impacts on heat-energy fluxes in tropical South America from 1984 to 2020: The Metropolitan Area of Rio de Janeiro/Brazil

Our study aims to better understand the energy balance in tropical and wet climate urban areas by developing a 37-year (1984–2020) energy balance climatology based on SEBAL algorithm and Landsat data for Metropolitan Area of Rio de Janeiro (MARJ). Particularly, our results show that MARJ lost 13% of its vegetation cover and its urban area almost doubled since the 1980s decade. We show a positive (negative) urban-vegetation albedo (net radiation) difference in MARJ contributing to reduce the UHI intensity, but not capable to compensate for the warming effects related to the partitioning of latent and sensible heat fluxes. Our results revealed that the UHI intensity along the year in MARJ is maximum (minimum) during summer (winter) when the urban-vegetation temperature, sensible and latent heat fluxes absolute differences reach maximum values around 9.9°C (3.8°C), 150 Wm-2 (71 Wm-2), and 330 Wm-2 (145 Wm-2). We observe a great reduction of latent heat flux in urban areas in agreement with the pronounced effect of urbanization on wet climate regions. Thus, alternative/original strategies, not only based on increasing green areas, may be needed to reduce the urban warming in MARJ.

A comparative study in quantification of maize evapotranspiration for Iranian maize farm using SEBAL and METRIC-1 EEFLux algorithms

A comparative study in quantification of maize evapotranspiration for Iranian maize farm using SEBAL and METRIC-1 EEFLux algorithms

Assessing the Role of Evapotranspiration in Reducing Surface Temperatures in Harare Using the Sebal Algorithm

Assessing the Role of Evapotranspiration in Reducing Surface Temperatures in Harare Using the Sebal Algorithm

The Surface Energy Balance Algorithm for Land (SEBAL) framework in Graphics Processing Units (GPU) using Cuda and OpenCL

Evapotranspiration (ET) is the second largest component of the terrestrial hydrological cycle on a global scale. The estimation of ET at regional scale using satellite imagery and algorithms allow the conversion of instantaneous measures in daily totals which represents a major contribution to the study of energy exchange between the biosphere and atmosphere. One of the most important remote sensing algorithms used to estimate the spatial partitioning of energy is the SEBAL - Surface Energy Balance Algorithm for Land. The time processing of this algorithm can be very high depending on the size of the image. Thus, the objective of this study was to propose a framework of the use of graphic processing units (GPU) to run the SEBAL to increase the performance and reduce the time required to run it. For implementation of SEBAL’s framework was used local data collected in a micrometeorological tower installed in an area located in the Northern Pantanal (Brazil) and Landsat 5 – TM image. We used the Model Maker function of the ERDAS 9.2 Software to run SEBAL algorithm, and its implementation in Java, CUDA and OpenCL languages. A daily ET estimated by SEBAL in JAVA, CUDA and OpenCL languages were similar to the value performed by ERDAS software. The daily ET computed by the ERDAS software was 10% lower and the SEBAL computed in JAVA language, CUDA and OpenCL was 9% lower than the ET obtained by the Bowen ratio method. The results showed that CUDA was better due to the Speed Up performance up to 35,000 times. Keywords: Wetland, evapotranspiration estimation, hyper seasonal savanna .

Influence of native and exotic tree plantations on biophysical indicators in the Brazilian Savanna

ABSTRACT The monitoring of biophysical indicators can show the conservation or recovery status of a landscape. This study aimed to analyze the influence of tree plantations on the dynamics of biophysical indicators (albedo, NDVI, surface temperature and evapotranspiration), in an experimental area of the Cerrado biome (Brazilian Savanna), by applying remote sensing techniques and the SEBAL algorithm. The indicators dynamics were given as a function of changes in the land use, while assessing the response of the environment to the planting of tree species. SEBAL data on areas that underwent changes in land use and cover during this period were analyzed. In the surroundings of the experimental area, albedo and surface temperature decreased in agricultural and exposed soil areas converted to tree plantations, while the NDVI and evapotranspiration increased. The opposite happened in the conversion of native areas destined to agriculture and livestock. In the experimental area, it was confirmed that the plantations contributed not only to the decrease in the surface and albedo temperature, but also to the increase in the NDVI and evapotranspiration. This confirms the positive influence of tree planting in rural properties of the Cerrado as a support to environmental regularization and more sustainable agricultural systems. Moreover, it highlights the potential of the technique applied to assist in monitoring Cerrado ecosystems in areas larger than those commonly monitored in the field.

Recognition of different yield potentials among rain-fed wheat fields before harvest using remote sensing

An algorithm was proposed to classify wheat fields into high-productive and low-productive classes using satellite images according to a threshold value for rain-fed wheat ETa in the anthesis stage. Since the classification process was based on the ETa values in the rain-fed wheat pixels, an algorithm was proposed to map wheat fields using the combination of MODIS and Landsat-8 images. The wheat area mapping method included two major processes in which the first one used a step by step elimination process of non-wheat pixels which did not follow the standard wheat vegetation index time series; the second one used supervised classification methods for detecting the rain-fed wheat pixels. The assessment of the wheat area map was performed statistically using surveyed wheat plots. After detecting the wheat parcels, the threshold value was determined using a frequency analysis on actual evapotranspiration values in the parcels. The rain-fed wheat ETa values estimated from the SEBAL algorithm and were compared to the results from the Eagleman-Affholder method and MOD16A2 products. To assess if the wheat fields in productivity classes have been categorized correctly, yield values in the two classes were compared with each other. The rain-fed wheat yield was estimated using the light use efficiency model and compared to provincial census data for accuracy assessment. Results showed that the overall accuracy, Kappa coefficient, and F1 score values for the 250-m resolution map from the MODIS images were 82, 0.61, and 0.71, respectively. In the same order, these statistics for the 30-m resolution map from the Landsat-8 images were 92, 0.62, and 0.77, respectively. Both the SEBAL and the Eagleman-Affholder methods closely estimated the average wheat ETa value in the anthesis stage equal to 2.4 mm/day. The mean of the rain-fed wheat yield value from the LUE model in 2015 was 10% lower than the census data. Relying on the low amount of the absolute error between the LUE model and the census data, the mean of the yield values in both high-productive and low-productive classes were compared with together. Results showed that the mean amount of yield in high-productive class was 33% (271 kg/ha) more than that of in low-productive class.

Spatial-temporal dynamics of Caatinga vegetation cover by remote sensing in the Brazilian semiarid region

The Brazilian semiarid region is marked by water scarcity, which causes the loss of leaves from native vegetation to reduce transpiration. With the reduction of the Caatinga leaf area, the soil becomes more exposed, which makes it a great ally for environmental degradation. This study aimed to monitor and analyze the spatial-temporal dynamics of the Caatinga vegetation by orbital remote sensing in the semiarid region of Pernambuco, Brazil, in the rainy and dry season. The study was developed from Landsat-8 satellite images between the years 2013-2018. From the SEBAL algorithm, thematic maps of the biophysical parameters were determined: albedo and surface temperature, normalized difference vegetation index (NDVI), soil-adjusted vegetation index (SAVI), and leaf area index (LAI). The results show that in the dry season, there is a greater aptitude for environmental degradation to occur.

A comparison of actual evapotranspiration estimates based on Remote Sensing approaches with a classical climate data driven method

The knowledge of actual evapotranspiration at farm level is a prerequisite for irrigation planning, farm management, to increase production and reduce water consumption. To accomplish this, comprehensive and accurate assessment methods should be applied. In order to evaluate accurately evapotranspiration processes we compared lysimeter evapotranspiration data with MODIS (Aqua and Terra satellites) and LANDSAT (SEBAL algorithm) satellite images as well as with the FAO Penman-Montith method. The findings indicate the low error rate, high correlation (1) and appropriateness of SEBAL in estimating actual evapotranspiration. The error values MAD, MSE and RMSE between lysimeter and the SEBAL algorithm were 0.59, 0.36 and 0.60 respectively. The second best performance was established for the FAO Penman-Montith method. The obtained error values MAD, MSE and RMSE between the lysimeter and FAO-Penman-Montith method are 0.91, 1.29 and 1.13, respectively.

Changes in the water resources, soil use and spatial dynamics of Caatinga vegetation cover over semiarid region of the Brazilian Northeast

The Brazilian semiarid suffers from the reflex of high rainfall variability, with rainfall concentrated over time, with little spatial distribution. In this sense, presenting a highly dynamic vegetation cover, which requires constant monitoring of the changing conditions of different land uses and the utilization of water resources. The objective was to monitor and evaluate the spatial dynamics of vegetation cover of Caatinga and water availability through the vegetation index and the estimation of biophysical parameters of the energy balance, using remote sensing techniques with satellite images for a semiarid region of the Northeast Brazil. The study was developed from the digital processing of three images from the Landsat-8 satellite, with the implementation of the SEBAL algorithm. The analysis of the orbital data allowed the development of thematic maps of land cover and use, of the MNDWI vegetation index and the parameters of the water balance: evaporative fraction and relative humidity of the soil, these evaluated for their spatial variability. The main results showed a reduction of the areas covered by water bodies close to 50% (2156.18 ha), between the years 2013 to 2015. The semiarid region, in this period, also presented an annual growth of the agriculture and livestock sector in at least 3374.07 ha, followed by the reduction of the native forest of Caatinga. The conditions of changes in soil use have altered over time the spatial dynamics of the Caatinga vegetation, evidenced by changes in the response patterns obtained in the biophysical parameters of the energy balance of the Earth's surface.

Análise do balanço de radiação e energia em áreas de veredas no Norte de Minas Gerais, bioma cerrado

A vereda, típica formação no bioma Cerrado, é reconhecida por sua elevada capacidade de recarga hídrica, em função de uma topografia plana, solos hidromórficos e proximidade de matas de galeria/cursos hidrográficos. Estudos nesses ambientes ainda são escassos, especialmente aqueles voltados para o entendimento da dinâmica do balanço de radiação e energia, relacionado com processos ecológicos e climáticos. O objetivo do trabalho é analisar o comportamento do balanço de energia em ambientes de veredas no Norte de Minas Gerais, em área amostral específica, com técnicas avançadas de sensoriamento remoto, aplicando-se o algoritmo SEBAL (Surface Energy Balance Algorithms for Land). No contexto do balanço de energia, os seguintes valores médios foram obtidos para o inverno e verão, respectivamente: Rn 24 h de 98,71 Wm-2 e 230,95 Wm-2; G de 53,43 Wm-2 e 80,84 Wm-2; H de 26,93 Wm-2 e 88,52 Wm-2; LE de 77,78 Wm-2 e 142,34 Wm-2; e taxa de evapotranspiração de 2,53 mm.d-1 e 5,02 mm.d-1. As técnicas de sensoriamento remoto utilizadas neste estudo foram de grande utilidade para as análises em áreas de vereda, destacando com eficiência o comportamento do balanço de energia e evapotranspiração nesse importante ambiente no Cerrado.

Detection of changes of the soil use and vegetation in the low San Francisco

The quantification and spatialization of environmental degradation is an essential element in the planning of agricultural activities and in the management of the water and natural resources in the semiarid. Thus, the detection of changing land use conditions is necessary for understand with more accurately the dynamics of the different types of soil coverage. Remote sensing techniques make it possible to evaluate this type of environmental monitoring in a practical and efficient manner, and low operating cost in a short time. The objective of this study was to monitor and evaluate the environmental changes caused about the Caatinga vegetation coverage by remote sensing using satellite images in the municipality of Petrolina, semiarid region of Pernambuco state. The study was developed using two Landsat-8 satellite images, processed using SEBAL algorithm steps, in the development of thematic maps of the surface biophysical parameters. The maps expressed the spatial distribution of the albedo parameters and surface temperature, and of the NDVI and SAVI vegetation indices, which served for highlight the dynamics of environmental changes in the Caatinga natural environment of semiarid region. The results showed increased of the albedo and surface temperature when there was a decrease in vegetation indices. This behavior was mainly favored by the region's dry season, which coincides with the satellite's days of passage. The biophysical parameters are effective in the spatial monitoring of semiarid regions, highlighting the spatial variability of the soil uses, identifying possibly degraded areas. Remote sensing environmental monitoring is a viable alternative for mitigate environmental changes caused by anthropogenic actions and drought events.

Estimativa da evapotranspiração em área urbana através de imagens digitais TM - Landsat 5

A evapotranspiração (ET) exerce grande relevância no arrefecimento da temperatura do ar e contribui com o equilíbrio climático em área urbana. Entretanto, o conhecimento das taxas de ET voltados para esta finalidade apresentam-se limitados no campo da pesquisa. Diante disto, esse trabalho objetivou estimar a evapotranspiração na cidade do Recife - PE, visando analisar as diferenças intraurbanas nos diversos tipos de coberturas. Foram utilizadas seis imagens digitais do sensor TM - Landsat 5 referente à órbita 214 e pontos 65, 66. A densidade dos fluxos de calor sensível, e calor latente foram calculados com base no algoritmo SEBAL (Surface Energy Balance Algorithm for Land). Os resultados encontrados na ET estimada apresentaram-se coerentes quando comparados com os valores obtidos pelo método da FAO-Penman-Monteith, com erros absolutos entre 0,18 e 1,23 mm d-1, mínimo e máximo respectivamente e média de 0,86 mm d-1. As maiores taxas evapotranspirativas foram encontradas nas unidades de conservação e corpos hídricos entre 5,0 e 6,0 mm d-1, aproximadamente, enquanto nas superfícies com intenso adensamento construtivo valores próximos de 1,0 mm d-1. A metodologia utilizada representou de forma consistente os diversos tipos de coberturas, podendo promover um avanço significativo no monitoramento do clima urbano.