Uso do sensoriamento remoto para avaliar o processo de salinização no perímetro irrigado de Morada Nova - CE

• Ano de defesa: 2014
• Autor(a) principal: Moreira, Luís Clênio Jário
• Tipo de documento: Tese
• Tipo de acesso: Acesso aberto
• Idioma: por
• Palavras-chave em Português: Solo salino; Espectroscopia de laboratório; Sensoriamento remoto hiperespectral; Estresse salino no arroz; Região semiárida
• Link de acesso: http://www.repositorio.ufc.br/handle/riufc/10574

The characterization, delineation and assessment of areas affected by salt/sodium is extremely important for the Irrigation Perimeter of Morada Nova – Ceará and can contribute in decision-making processes on local farming. Remote sensing (RS) is an attractive alternative to traditional methods of soil salinization studies due to its low cost, spatial coverage and temporal frequency of image acquisition. It may provide a fast and non-destructive mapping of the salinized areas. This study aimed to use RS data in the development of methodological strategies to identify areas with salinity problems allowing a preliminary assessment of salt effects on soil and vegetation. Initially, we used laboratory spectroscopy to characterize and quantify variations in reflectance and spectral absorption bands as function of the changes in electrical conductivity (EC) of the soils. Neossolos samples (n = 180) were salinized in laboratory with increasing concentrations of NaCl, MgCl2 and CaCl2. Half of them were previously treated with gypsum. Reflectance spectra were measured at nadir viewing in a controlled laboratory environment using the FieldSpec spectrometer. Variations in reflectance and absorption bands attributes were evaluated by using principal component analysis (PCA) and the continuum removal (CR) technique, respectively, for soils treated with gypsum (TG) and non-treated with gypsum (NTG). Using soil samples of NTG (n = 62) and a set of independent samples (n = 32) collected from various sites within the irrigated perimeter, predictive models were developed using linear regressions of individual bands, the normalized salinity index (NDSI) and partial least squares regression (PLSR). Another part of this work was focused on the use of multispectral images (TM/Landsat-5 and OLI/Landsat-8) and hyperspectral (Hyperion/EO-1). Using the 0.53 threshold over the soil fraction image from the spectral mixture model applied to the OLI data (September, 2013) and information on the temporal behavior (1984-2011) of the Normalized Difference Vegetation Index (NDVI) obtained from the TM sensor, exposed soils were evaluated for their differentiation in the saline and non-saline classes. For the discrimination of these classes and salinity levels, PCA was applied to OLI and Hyperion data. By using data from these two sensors, the ability of multispectral and hyperspectral vegetation indices to identify and evaluate salt stress in rice canopies was investigated. Linear regressions were used to describe the relationship between the indices and soil EC. Results from laboratory reflectance spectroscopy showed that NTG samples presented a decrease in reflectance and brightness after salinization with CaCl2 and MgCl2, and an increase of them after salinization with NaCl. Gypsum increased the soil reflectance and was crucial to the appearance of the absorption band at 1750 nm in the TG samples. The most important spectral features were observed in salinized spectra at 1450, 1950 and 1750 nm. The predictive model developed with NDSI (R2 = 0.836) from bands positioned close to 1900 nm showed the best results when individual bands were considered in the analysis (R2 = 0.50). However, PLSR (R2 = 0.883) using all the spectral bands showed the best model suggesting that the greatest number of bands produced the largest predictive power for the models. Using information from the OLI, statistically significant correlations of the Salinity Index (SI) (r = 0.84) and first principal component (PC1) (r = 0.83) with the soil EC were obtained. A significant correlation (r = 0.77) was also observed with the PC1 of Hyperion data. Under field conditions, the spectral profiles and PCA indicated that areas with higher EC had also greater brightness relative to the non-salinized areas, which enabled the use of data from the two sensors to discriminate the exposed salinized soils from the non-salinized ones. For rice, canopy reflectance in the near infrared (NIR) and shortwave infrared (SWIR) was reduced with increasing soil EC. In the red spectral region of chlorophyll absorption, the salt stress caused a slightly reflectance increase. This explained the good results presented by NDVI (R2 = 0.68) and Enhanced Vegetation Index (EVI) (R2 = 0.70) obtained from the OLI sensor to characterize the spectral response of rice under different soil ECs. The most promising hyperspectral indices were the Salinity and Water Stress Index (SWSI1) (R2 = 0.70) and Saline Stress Index for Rice (IESA) (R2 = 0.59), which are combinations of regions related to chlorophyll regions with absorption of water that vary with water stress. Overall, this study showed that the RS has a good potential to detect and characterize salinization areas. The use of images is very promising, but information obtained from laboratory spectroscopy provides the necessary understanding of the particularities of spectral characteristics of the saline soils.