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RESUMO:Em face da importância em conhecer a evapotranspiração (ET) para uso racional da água na irrigação no contexto atual de escassez desse recurso, algoritmos de estimativa da ET a nível regional foram desenvolvidos utilizando-se de ferramentas de sensoriamento remoto. Este estudo objetivou aplicar o algoritmo SEBAL (Surface Energy Balance Algorithms for Land) em três imagens do satélite Landsat 5, do segundo semestre de 2006. As imagens correspondem a áreas irrigadas, floresta nativa densa e a Caatinga do Estado do Ceará (Baixo Acaraú, Chapada do Apodi e Chapada do Araripe). Este algoritmo calcula a evapotranspiração horária a partir do fluxo de calor latente, estimado como resíduo do balanço de energia na superfície. Os valores de ET obtidos nas três regiões foram superiores a 0,60 mm h -1 nas áreas irrigadas ou de vegetação nativa densa. As áreas de vegetação nativa menos densa apresentaram taxa da ET horária de 0,35 a 0,60 mm h -1 , e valores quase nulos em áreas degradadas. A análise das médias de evapotranspiração horária pelo teste de Tukey a 5% de probabilidade permitiu evidenciar uma variabilidade significativa local, bem como regional no Estado do Ceará. PALAVRAS-CHAVE:sensoriamento remoto, balanço de radiação, Landsat. LOCAL AND REGIONAL VARIABILITY OF EVAPOTRANSPIRATION ESTIMATED BY SEBAL ALGORITHMABSTRACT: In the context of water resources scarcity, the rational use of water for irrigation is necessary, implying precise estimations of the actual evapotranspiration (ET). With the recent progresses of remote-sensed technologies, regional algorithms estimating evapotranspiration from satellite observations were developed. This work aimed at applying the SEBAL algorithm (Surface Energy Balance Algorithms for Land) at three Landsat-5 images during the second semester of 2006. These images cover irrigated areas, dense native forest areas and caatinga areas in three regions of the state of Ceará (Baixo Acaraú, Chapada do Apodi and Chapada do Araripe). The SEBAL algorithm calculates the hourly evapotranspiration from the latent heat flux, estimated from the surface energy balance. The hourly evapotranspiration values obtained were greater than 0.60 mm h -1 in irrigated or dense native vegetation areas, from 0.35 to 0.60 mm h -1 in sparse vegetation areas and almost null in degradated areas. The analysis of hourly evapotranspiration means by Tukey test at 5% probability level showed not only a significant variability locally but also at a regional scale in the state of Ceará.KEYWORDS: remote sensing, radiation balance, Landsat. INTRODUÇÃOA crescente demanda hídrica e, por outro lado, a deterioração dos recursos naturais e sua escassez em algumas regiões tornam o gerenciamento integrado dos recursos hídricos cada vez mais imprescindível (SCHMIDT et al., 2004). Sabe-se que o setor agrícola é o maior consumidor de água, alcançando cerca de 69% de toda a água derivada de rios, lagos e aquíferos subterrâneos. Os

Soil salinization due to irrigation affects agricultural productivity in the semi-arid region of Brazil. In this study, the performance of four computational models to estimate electrical conductivity (EC) (soil salinization) was evaluated using laboratory reflectance spectroscopy. To investigate the influence of bandwidth and band positioning on the EC estimates, we simulated the spectral resolution of two hyperspectral sensors (airborne ProSpecTIR-VS and orbital Hyperspectral Infrared Imager (HyspIRI)) and three multispectral instruments (RapidEye/REIS, High Resolution Geometric (HRG)/SPOT-5, and Operational Land Imager (OLI)/Landsat-8)). Principal component analysis (PCA) and the first-order derivative analysis were applied to the data to generate metrics associated with soil brightness and spectral features, respectively. The three sets of data (reflectance, PCA, and derivative) were tested as input variable for Extreme Learning Machine (ELM), Ordinary Least Square regression (OLS), Partial Least Squares Regression (PLSR), and Multilayer Perceptron (MLP). Finally, the laboratory models were inverted to a ProSpecTIR-VS image (400-2500 nm) acquired with 1-m spatial resolution in the northeast of Brazil. The objective was to estimate EC over exposed soils detected using the Normalized Difference Vegetation Index (NDVI). The results showed that the predictive ability of the linear models and ELM was better than that of the MLP, as indicated by higher values of the coefficient of determination (R 2 ) and ratio of the performance to deviation (RPD), and lower values of the root mean square error (RMSE). Metrics associated with soil brightness (reflectance and PCA scores) were more efficient in detecting changes in the EC produced by soil salinization than metrics related to spectral features (derivative). When applied to the image, the PLSR model with reflectance had an RMSE of 1.22 dS·m −1 and an RPD of 2.21, and was more suitable for detecting salinization (10-20 dS·m −1 ) in exposed soils (NDVI < 0.30) than the other models. For all computational models, lower values of RMSE and higher values of RPD were observed for the narrowband-simulated sensors compared to the broadband-simulated instruments. The soil EC estimates improved from the RapidEye to the HRG and OLI spectral resolutions, showing the importance of shortwave intervals (SWIR-1 and SWIR-2) in detecting soil salinization when the reflectance of selected bands is used in data modelling.

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Irrigation-induced salinization is an important land degradation process that affects crop yield in the Brazilian semi-arid region, and gypsum has been used as a corrective measure for saline soils. Fluvent soil samples (180) were treated with increasing levels of salinization of NaCl, MgCl 2 and CaCl 2 . The salinity was gauged using electrical conductivity (EC). Gypsum was added to one split of these samples before they were treated by the saline solutions. Laboratory reflectance spectra were measured at nadir under a controlled environment using a FieldSpec spectrometer, a 250-W halogen lamp and a Spectralon panel. Variations in spectral reflectance and brightness were evaluated using principal component analysis, as well as the continuum-removed absorption depths of major features at 1450, 1950, 1750 and 2200 nm for both the gypsum-treated (TG) and non-treated (NTG) air-dried soil samples as a function of EC. Pearson's correlation coefficients of reflectance and the band depth with EC were also obtained to establish the relationships with salinity. Results showed that NTG samples presented a decrease in reflectance and brightness with increasing CaCl 2 and MgCl 2 salinization. The reverse was observed for NaCl. Gypsum increased the spectral reflectance of the soil. The best negative correlations between reflectance and EC were observed in the 1500-2400 nm range for CaCl 2 and MgCl 2 , probably because these wavelengths are most affected by water absorption, as Ca and Mg are much more hygroscopic than Na. These decreased after chemical treatment with gypsum. The most prominent features were observed at 1450, OPEN ACCESSRemote Sens. 2014, 6 2648 1950 and 1750 nm in salinized-soil spectra. The 2200-nm clay mineral absorption band depth was inversely correlated with salt concentration. From these features, only the 1750 and 2200 nm ones are within atmospheric absorption windows and can be more easily measured using hyperspectral sensors.