The aim of this study was to identify areas susceptible to drought and under desertification in the Brazilian Northeast, more specifically in the states of Alagoas, Pernambuco, and Sergipe. The study was based on the 2000-2014 time period, relative frequency, Vegetation Condition Index (VCI) dataset derived from MOD13Q1 products (vegetation indices derived from the Moderate Resolution Imaging Spectroradiometer sensor). The areas located within the desertification nucleus of Cabrobó, in the southwestern of Pernambuco and surrounding the Águas Belas municipality were considered susceptible to drought and desertification. Some municipalities in the countryside of Alagoas showed a very low relative frequency of VCI data (45-50%), and occurrence of areas between 55-60%. Tthe Sergipe state presented a relative frequency VCI data around 50% only in the Poço Verde municipality. Among the areas with low rates of VCI and the most frequently identified in this research, therefore, with greater frequency of droughts, the Southwestern and the Southern Pernambuco hinterlands were not considered as pilot areas by MMA (Brazilian Environment Ministry) in its desertification studies and were included as susceptible for desertification in this study. The results suggest the use of this methodology to subsidize formulations of public policies with application in land use and land cover studies or sustainable development and environmental risk analysis.

The culture of açaí (Euterpe oleraceae M.), originating from floodplain areas, was planted on dry land in many properties in Pará, Brazil, making necessary the use of irrigation. To irrigate adequately with less waste, it is necessary that studies aim at increasing efficiency in the use of water in this sector, and one of the ways to do so is to estimate evapotranspiration (ET). The objective of this study was to estimate the actual daily evapotranspiration using the Surface Energy Balance Algorithm for Land (SEBAL) in eastern Amazonia. Six images from the Landsat 8 satellite were used, and the estimates of evapotranspiration with the SEBAL algorithm showed good agreement with the results obtained by the Bowen ratio method in the area of açaí planting, including the mean absolute error (MAE), mean relative error (MRE), root of mean square error (RMSE), and the concordance index (d index) equal to 0.45 mm day⁻¹, 4.23%, 0.52 mm day⁻¹, and 0.80, respectively. SEBAL showed the ability to distinguish the soil cover, demonstrating the sensitivity of the model, which provided the mapping of the components analyzed. The use of the algorithm helps in decision making regarding irrigation management and reducing costs and water losses.

A simplified atmospheric transmittance model based on the Beer-Lambert law was utilized to analyze surface solar radiation (SSR) variability based on different sources of cloud cover datasets (CMIP6, ERA5, NCEP, ISCCP, and EUMETSAT). This study evaluated the performance of various modeled SSR datasets against observed data from the Brazilian Daily Weather Gridded Data (BR-DWGD) over the period from 1983 to 2009. Contour plots of annual average SSR from the five modeled datasets were compared with BR-DWGD observations, revealing spatial agreements and discrepancies. The highest SSR values were consistently observed in the Brazilian semi-arid Northeast, while the Amazon region exhibited the lowest values. In the analysis of annual averages, the International Satellite Cloud Climatology Project (ISCCP) demonstrated the closest agreement with BR-DWGD, while the National Center for Environmental Prediction (NCEP) showed the most significant deviations. Root mean square error (RMSE) analysis highlighted seasonal variability in model performance, with the Coupled Model Intercomparison Project Phase 6 (CMIP6) and the European Organisation for the Exploitation of Meteorological Satellites (EUMETSAT) performing best during equinoxes, and ISCCP showing the lowest annual RMSE (16.9 Wm⁻²). Hierarchical clustering further grouped EUMETSAT and CMIP6 as the most similar and accurate datasets, while NCEP remained the least consistent. Global horizontal irradiance maps corroborated SSR patterns, with higher values in the Northeast and lower values in the Amazon and Southern regions. These findings underscored the importance of dataset selection for accurate SSR modeling in Brazil, with ISCCP, EUMETSAT, and CMIP6 emerging as the most reliable options.

A temperature do ar media diária – TMD (°C) é utilizada em diversas atividades envolvendo pesquisa e planejamento agrícola. O objetivo desta pesquisa foi avaliar a precisão e desempenho de diferentes expressões usadas no cômputo da temperatura do ar média diária (TMD), e a proposição de uma novo modelo para o Brasil. Foram consideradas vinte e uma estações distribuídas em diferentes regiões do país e seis modelos usados no cálculo da TMD: SERRA, AUSTRIA, KAEMTZ, SINOTICO, UFCG e MAXMIN. O desempenho de cada modelo foi analisado por meio do Erro Absoluto Médio (EAM), Raiz do Erro Quadrático Médio (REQM), Índice de Concordância de Wilmott (ICW) e Coeficiente de Correlação de Pearson (CCP). Os resultados indicam que o melhor desempenho/precisão foi obtido pelo modelo UFCG, segundo todas as métricas usadas, tendo apresentado o melhor desempenho em 59, 58, 59 e 43, segundo o EAM, REQM, WIA e CCP, dentre as 84 situações analisadas (21 EMAS x 4 estações do ano), respectivamente. Por outro lado, a pior precisão foi claramente verificada com o modelo MAXMIN, seguido do AUSTRIA e KAEMTZ. Conclui-se que para as condições brasileiras os métodos UFCG, SINOTICO e SERRA se apresentaram como os de melhor desempenho e precisão. No entanto, para a revisão de séries de dados pretéritos da TMD recomenda-se o UFCG pela maior precisão apresentada. Deve-se evitar, sempre que possível, a determinação da TMD com base nas temperaturas do ar máxima e mínima diárias, ainda muito em uso no Brasil e Estados Unidos, pela sua alta imprecisão.