Antônio Heriberto de Castro Teixeira

Remote sensing parameters were used along with weather data to assess the large-scale sugarcane water balance in Northeast Brazil, using a dataset from 2007 to 2024. Rainfall (P) was spatially quantified from interpolated pluviometer data and actual evapotranspiration (ET) was estimated using the SAFER algorithm. Considering the sugarcane crop areas for each year, the mean annual P varied from 8555 million cubic meters in 2009 to 3172 million cubic meters in 2021. Regarding ET, it varied from 5869 million cubic meters in 2007 to 3375 million cubic meters in 2017. The assessments showed time lags of around two months between rainfall water supplies and root zone moisture levels and highlighted the use of evaporative fraction (E_f). Anomalies in P and ET were detected through their standardized indices by comparing the results for the years 2021 to 2024 with the long-term values from 2007 to each of these years, showing that the rainfall water supplies and water fluxes may vary along sugarcane phases and years. It can be concluded that large-scale water balance assessments can potentially serve as the basis for public policies aimed at sustainable crop management and rational expansion in areas with environmental suitability.

O MonitoraOeste é um aplicativo desenvolvido para monitorar e emitir alertas sobre o avanço da ferrugem da soja e da mancha de ramulária do algodão em lavouras do Oeste da Bahia. Os alertas emitidos apontam os locais de ocorrência, trazendo informações atualizadas em tempo real e revelam se as condições climáticas são favoráveis para o desenvolvimento de pragas e doenças, com informações sobre a presença e a dispersão de esporos. No app mobile também estão disponibilizados índices agrometeorológicos, como evapotranspiração, biomassa, NDVI e eficiência no uso da água. Com o MonitoraOeste, os produtores agrícolas e os tomadores de decisão tem acesso a informações complexas, de forma simplificada. A tecnologia auxilia na segurança fitossanitária das lavouras de soja e algodão da região, possibilitando a redução do custo de produção e o impacto ambiental das culturas, considerando as condições climáticas e permitindo um melhor monitoramento das lavouras durante seu desenvolvimento.

Caatinga biome, located in the Brazilian semi-arid region, is the most populous semi-arid region in the world, causing intensification in land degradation and loss of biodiversity over time. The main objective of this paper is to determine and analyze the changes in land cover and use, over time, on the biophysical parameters in the Caatinga biome in the semi-arid region of Brazil using remote sensing. Landsat-8 images were used, along with the Surface Energy Balance Algorithm for Land (SEBAL) in the Google Earth Engine platform, from 2013 to 2019, through spatiotemporal modeling of vegetation indices, i.e., leaf area index (LAI) and vegetation cover (V_C). Moreover, land surface temperature (LST) and actual evapotranspiration (ET_a) in Petrolina, the semi-arid region of Brazil, was used. The principal component analysis was used to select descriptive variables and multiple regression analysis to predict ET_a. The results indicated significant effects of land use and land cover changes on energy balances over time. In 2013, 70.2% of the study area was composed of Caatinga, while the lowest percentages were identified in 2015 (67.8%) and 2017 (68.7%). Rainfall records in 2013 ranged from 270 to 480 mm, with values higher than 410 mm in 46.5% of the study area, concentrated in the northern part of the municipality. On the other hand, in 2017 the lowest annual rainfall values (from 200 to 340 mm) occurred. Low vegetation cover rate was observed by LAI and V_C values, with a range of 0 to 25% vegetation cover in 52.3% of the area, which exposes the effects of the dry season on vegetation. The highest LST was mainly found in urban areas and/or exposed soil. In 2013, 40.5% of the region’s area had LST between 48.0 and 52.0 °C, raising ET_a rates (~4.7 mm day⁻¹). Our model has shown good outcomes in terms of accuracy and concordance (coefficient of determination = 0.98, root mean square error = 0.498, and Lin’s concordance correlation coefficient = 0.907). The significant increase in agricultural areas has resulted in the progressive reduction of the Caatinga biome. Therefore, mitigation and sustainable planning is vital to decrease the impacts of anthropic actions.