Balanço de energia com base no modelo S-SEBI sobre gramíneas em Barrax, Espanha e no bioma Pampa do sul do Brasil

No Brasil, existem seis biomas, sendo eles Amazônia, Mata Atlântica, Cerrado, Caatinga, Pantanal e Pampa. Cada bioma possui características únicas e importantes para a manutenção dos seus processos ecossistêmicos. Neste sentido, no bioma Pampa há uma dinâmica socioambiental que influencia a vegetação, o manejo agrícola e o modo de vida da população local. Este bioma é único no mundo porque traz na vegetação rasteira sua fonte de biomassa e energia como em nenhum outro ecossistema, seus campos nativos são os responsáveis pela conservação e preservação dos recursos hídricos, da fauna silvestre e da biodiversidade. A supressão da vegetação nativa deste bioma para a monocultura de grãos compromete a manutenção da biodiversidade e gera impactos nos recursos naturais, alterando as suas condições ambientais, a disponibilidade de água e a temperatura de superfície. Além disso, as mudanças climáticas têm modificado os componentes do Balanço de Energia (BE). Em relação ao balanço energético este bioma tem, no estado do Rio Grande do Sul, a mesma importância climática que as florestas em regiões tropicais, já que cobre 63% do Estado e possui influência nas dinâmicas atmosféricas. Sendo assim, o objetivo deste trabalho é avaliar as particularidades ambientais do BE e do cálculo de evapotranspiração (ET) no bioma Pampa. A ET é a responsável pelas interações da biosfera- atmosfera-hidrosfera. Estas interações se dão por utilizar energia eletromagnética para a formação de vapor d’água a partir da transpiração vegetal e da evaporação da água. O uso do Sensoriamento Remoto tem sido eficaz nas estimativas de fluxo de calor sensível e fluxo de calor latente por diferentes métodos, porém a aplicação de forma operacional, a heterogeneidade da superfície e a influência da temperatura de superfície (Ts) são desafios deste trabalho. O modelo S-SEBI para recuperação de dados de ET foi avaliado no bioma Pampa e em Barrax, um sítio de validação localizado no mediterrâneo espanhol. O modelo demonstrou ser eficaz em vegetação campestre, além de ser menos dependente da Ts em relação a outros modelos reportados na literatura. Os resultados deste trabalho visam contribuir para a geração de melhor qualidade de dados de ET em futuras análises de mudanças de uso do solo, mudanças climáticas e gestão dos recursos hídricos para todo o bioma Pampa.In Brazil, there are six biomes, namely the Amazon, Atlantic Forest, Cerrado, Caatinga, Pantanal, and Pampa. Each biome has unique and important characteristics for the maintenance of the ecosystemic processes of each environment. In this sense, in the Pampa biome there is a socio-environmental dynamic that influences the vegetation, agricultural management, and the way of life of the local population. This biome is unique in the world because it brings in its undergrowth vegetation its source of biomass and energy like no other ecosystem; its native grasslands are responsible for the conservation and preservation of water resources, wildlife, and biodiversity. The suppression of the native vegetation of this biome for the monoculture of grains compromises the maintenance of biodiversity and generates impacts on natural resources, altering the environmental conditions of the ecosystem, water availability, and surface temperature. In addition, climate change has modified the components of the Energy Balance (EB). In relation to the energy balance, in the state of Rio Grande do Sul, this biome has the same climatic importance as the forests in tropical regions, since it covers 63% of the state and influences the atmospheric dynamics. Therefore, the objective of this work is to evaluate the environmental particularities of BE and the calculation of evapotranspiration (ET) in the Pampa biome. ET is responsible for biosphere-atmosphere-hydrosphere interactions. These interactions occur by using electromagnetic energy for the formation of water vapor from plant transpiration and water evaporation. The use of Remote Sensing has been effective in estimating sensible heat flux and latent heat flux by different methods, but the application in an operational way, the heterogeneity of the surface and the influence of the surface temperature (Ts) are challenges of this work. The S-SEBI model for ET data retrieval was evaluated in the Pampa biome and in Barrax, a validation site located in the Spanish Mediterranean. The model proved to be effective in grassland vegetation, and is less dependent on Ts compared to other models reported in the literature. The results of this work aim to contribute to the generation of better quality ET data in future analyses of land use change, climate change, and water resource management for the entire Pampa biome

Assessing uncertainties in estimating surface energy fluxes from remote sensing over natural grasslands in Brazil

Evapotranspiration (ET) is one of the main fluxes in the global water cycle. As the Brazilian Pampa biome carries a rich biodiversity, accurate information on the ET dynamics is essential to support its proper monitoring and establish conservation strategies. In this context, we assessed an operational methodology based on the Simplified Surface Energy Balance Index (S-SEBI) model to estimate energy fluxes over the natural grasslands of the Pampa between 2014 and 2019. The S-SEBI is an ET model that requires a minimum of meteorological inputs and has demonstrated reasonable accuracy worldwide. Therefore, we investigated the model performance considering radiation data from both ERA5 reanalysis and Eddy Covariance measurements from a flux tower. Furthermore, comparisons from satellite-based estimates with in situ measurements were performed with and without energy balance closure (EBC). Results indicated that the meteorological inputs have low sensitivity on daily ET estimates from the S-SEBI model. In contrast, the instantaneous energy balance components are more affected. The strong seasonality impacts the evaporative fraction, which is more evident in late summer and autumn and may compromise the performance of the model in the biome. The effects in the daily ET are lower when in situ data without EBC are considered as ground truth. However, they are less correlated with the remote sensing-based estimates. These insights are useful to monitor water and energy fluxes from local to regional scale and provide the opportunity to capture ET trends over the natural grasslands of the Pampa

Mapping Latent Heat Flux in the Western Forest Covered Regions of Algeria Using Remote Sensing Data and a Spatialized Model

The present paper reports on an investigation to monitor the drought status in Algerian forest covered areas with satellite Earth observations because ground data are scarce and hard to collect. The main goal of this study is to map surface energy fluxes with remote sensing data, based on a simplified algorithm to solve the energy balance equation on each data pixel. Cultivated areas, forest cover and a large water surface were included in the investigated surfaces. The input parameters involve remotely sensed data in the visible, near infrared and thermal infrared. The surface energy fluxes are estimated by expressing the partitioning of energy available at the surface between the sensible heat flux (H) and the latent heat flux (LE) through the evaporative fraction (Λ) according to the S-SEBI (Simplified Surface Energy Balance Index) concept. The method is applicable under the assumptions of constant atmospheric conditions and sufficient wet and dry pixels over a Landsat 7 image. The results are analyzed and discussed considering instantaneous latent heat flux at the data acquisition time. The results confirm the relationships between albedo (r0), the surface temperature (T0) and the evaporative fraction. The method provides estimates of air temperature and LE close to reference measurements. The estimate of latent heat flux and other variables are comparable to those of previous studies. Their comparison with other methods shows reasonable agreement. This approach has demonstrated its simplicity and the fact that remote sensing data alone is sufficient; it could be very promising in areas where data are scarce and difficult to collect