Spatial variability of surface energy and water fluxes at local scales is strongly controlled by soil and micrometeorological conditions. Thus, the accurate estimation of these fluxes from space at high spatial resolution has the potential to improve prediction of the impact of land-use changes on the local environment. In this study, Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) and Large-Scale Biosphere-Atmosphere Experiment in Amazonia (LBA) data were used to examine the partitioning of surface energy and water fluxes over different landcover types in one wet year (2004) and one drought year (2005) in eastern Rondonia state, Brazil. The spatial variation of albedo, net radiation (Rn), soil (G) and sensible (H) heat fluxes, evapotranspiration (ET), and evaporative fraction (EF) were primarily related to the lower presence of forest (primary [PF] or secondary [SF]) in the western side of the Ji-Parana River in comparison with the eastern side, located within the Jaru Biological Reserve protected area. Water limitation in this part of Amazonia tends to affect anthropic (pasture [PA] and agriculture [AG]) ecosystems more than the natural land covers (PF and SF). We found statistically significant differences on the surface fluxes prior to and~1 year after the deforestation. Rn over forested areas is~10% greater in comparison with PA and AG. Deforestation and consequent transition to PA or AG increased the total energy (~200-400%) used to heat the soil subsurface and raise air temperatures. These differences in energy partitioning contributed to approximately three times higher ET over forested areas in comparison with nonforested areas. The conversion of PF to AG is likely to have a higher impact in the local climate in this part of Amazonia when compared with the change to PA and SF, respectively. These results illustrate the importance of conserving secondary forest areas in Amazonia.
In the Amazon region, the estimation of radiation fluxes through remote sensing techniques is hindered by the lack of ground measurements required as input in the models, as well as the difficulty to obtain cloud-free images. Here, we assess an approach to estimate net radiation (Rn) and its components under all-sky conditions for the Amazon region through the Surface Energy Balance Algorithm for Land (SEBAL) model utilizing only remote sensing and reanalysis data. The study period comprised six years, between January 2001–December 2006, and images from MODIS sensor aboard the Terra satellite and GLDAS reanalysis products were utilized. The estimates were evaluated with flux tower measurements within the Large-Scale Biosphere-Atmosphere Experiment in Amazonia (LBA) project. Comparison between estimates obtained by the proposed method and observations from LBA towers showed errors between 12.5% and 16.4% and 11.3% and 15.9% for instantaneous and daily Rn, respectively. Our approach was adequate to minimize the problem related to strong cloudiness over the region and allowed to map consistently the spatial distribution of net radiation components in Amazonia. We conclude that the integration of reanalysis products and satellite data, eliminating the need for surface measurements as input model, was a useful proposition for the spatialization of the radiation fluxes in the Amazon region, which may serve as input information needed by algorithms that aim to determine evapotranspiration, the most important component of the Amazon hydrological balance.
Resumo -O objetivo deste trabalho foi avaliar a eficiência da aplicação do modelo SEBAL em estimar os fluxos de energia em superfície e a evapotranspiração diária, numa extensa área de cultivo de arroz irrigado, no município de Paraíso do Sul, RS, tendo como parâmetros dados do sensor ASTER. As variáveis estudadas constituem importantes parâmetros do tempo e do clima em estudos agrometeorológicos e de racionalização no uso da água. As metodologias convencionais de estimativa desses parâmetros são pontuais e geralmente apresentam incertezas, que aumentam quando o interesse é o comportamento espacial desses parâmetros. Aplicou-se o algoritmo "Surface Energy Balance Algorithm for Land" (SEBAL), em uma imagem do sensor "Advanced Spaceborne Thermal Emission and Reflection Radiometer" (ASTER). As estimativas obtidas foram comparadas com medições em campo, realizadas por uma estação micrometeorológica localizada no interior da área de estudo. As estimativas mais precisas foram as de fluxo de calor sensível e de evapotranspiração diária, e a estimativa que apresentou maior erro foi a do fluxo de calor no solo. A metodologia empregada foi capaz de reproduzir os fluxos de energia em superfície de maneira satisfatória para estudos agrometeorológicos e de rendimento de culturas.Termos para indexação: arroz, calor latente, calor sensível, sensoriamento remoto, uso da água, resolução espacial. Evaluation of heat fluxes and evapotranspiration using SEBAL model with data from ASTER sensorAbstract -The objective of this study was to evaluate the efficiency of SEBAL model in estimating soil surface energy fluxes and daily evapotranspiration for a large area of irrigated rice farms, near the municipality of Paraíso do Sul, RS, Brazil, using data from ASTER sensor. The evaluated variables are important weather and climatic parameters for agrometeorological studies and rationalization of water use. The conventional methodologies for estimating these parameters generally present uncertainties, which increase when concern is in the spatial behavior of such parameters. The Surface Energy Balance Algorithm for Land (SEBAL) was applied in an Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) scene and the estimates were compared to micrometeorological data retrieved from a station located in the studied area. The most accurate modeled parameter estimatives were sensitive heat and evapotranspiration, and the one which presented the highest error was soil heat flux. The adopted methodology was able to reproduce surface energy fluxes for agrometeorological and crop yield studies.Index terms: rice, latent heat, sensitive heat, remote sensing, water use, spatial resolution. IntroduçãoA evapotranspiração é um dos mais importantes fatores mediadores do clima e do tempo, tanto em escala global quanto local, consistindo da ligação entre energia, clima e hidrologia. Estima-se que, por meio da evapotranspiração global, aproximadamente 62% do volume precipitado sobre os continentes retorne à atmosfera (Dingman, 2002), fato que lh...
Soybean rust (SBR) is a disease of significant impact to Brazilian soybean production. Twenty-four locations in a major growing region in southern Brazil, where long-term (30 years) weather information was available, were selected to estimate the risk of SBR epidemics and identify potential predictors derived from El Niño 3.4 region. A rainfall-based model was used to predict SBR severity in an "epidemic development window" (the months of February and March for the studied region) in the time series. Twenty-eight daily simulations for each year-location (n = 720) were performed considering each day after 31 January as a hypothetical detection date (HDD) to estimate a severity index (SBRindex). The mean SBRindex in a single year was defined as the 'growing season severity index' (GSSI) for that year. A probabilistic risk assessment related GSSI and sea surface temperatures (SST) at the El Niño 3.4. region (here categorized as warm, cold or neutral phase) in October-November-December (OND) of the same growing season. Overall, the median GSSI across location-years was 34.5%. The risk of GSSI exceeding 60% was generally low and ranged from 0 to 20 percentage points, with the higher values found in the northern regions of the state when compared to the central-western. During a warm OND-SST phase, the probability of GSSI exceeding its overall mean (locations pooled) increased significantly by around 25 percentage points compared to neutral and cold SST phases, especially over the central western region. This study demonstrates the potential to use El Niño/Southern Oscillation information to anticipate the risk of SBR epidemics up to 1 month in advance at a regional scale.
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