Validação da Imputação Múltipla via Predictive Mean Matching para Preenchimento de Falhas nos Dados Pluviométricos da Bacia do Médio São Francisco

Um dos principais problemas atualmente para analisar longas séries de dados no Brasil é a falta de um banco de dados diários consistentes de estações meteorológicas. Diante disso, o objetivo deste trabalho é avaliar a acurácia do método de imputação múltipla Predictive Mean Matching (PMM) no preenchimento de dados faltantes de séries diárias de precipitação para a Bacia Hidrográfica do Médio São Francisco (BMSF). Para isso foram adquiridos dados diários de chuva cedidos pelo Instituto Nacional de Meteorologia (INMET) das estações de Bom Jesus da Lapa (BA), Carinhanha (BA), João Pinheiro (MG), Remanso (BA) e Unaí (MG), para o período de 2001 a 2017. Em seguida, dois cenários foram criados com 5% e 15% de falhas para avaliar a precisão do método PMM no preenchimento de dados faltantes. As séries originais, sem falhas, e as preenchidas pelo método adotado apresentaram uma correlação alta (r 0,80), que consiste numa boa relação entre elas. O coeficiente de determinação (R2) foi de 0,7 (0,6) para 5% (15%) de falhas. Além disso, o Erro Médio Absoluto e o Erro Quadrático Médio foram baixos para todas as estações. Também foi aplicado o teste de Wilcoxon, o qual verificou a boa acurácia na aplicação do método PMM para preencher os dados faltantes de chuva

Balanço de radiação através do satélite Landsat-8 na bacia do Rio Pajeú

O presente estudo tem como objetivo realizar a estimativa do saldo de radiação à superfície-Rn através do algoritmo SEBAL e imagens do satélite Landsat-8 para a Bacia do Rio Pajeú. Os dados de Rn estimados pelo SEBAL foram comparados com medições obtidas em duas estações automáticas localizadas nos municípios de Floresta e Serra Talhada. Foi utilizada uma imagem dos sensores OLI (Operational Land Image) e TIRS (Thermal Infrared Sensor) abordo do satélite Landsat-8, orbita 216 e ponto 66, para o dia 20 de novembro de 2016. A partir das imagens se obteve a radiância e reflectividade espectral, seguido do albedo de superfície, índices de vegetação, emissividade, temperatura superficial, radiação de onda curta incidente – Rs, radiação de onda longa incidente e emitida - Rol,atm e Rol,emi, respectivamente, e Rn. Nos resultados encontrados observa-se que os menores valores de albedo e temperatura foram observados em corpos d’água e vegetação, e maiores valores em áreas urbanas. Estas componentes estão ligadas diretamente com as componentes do saldo de radiação, onde se observou menores valores de Rol,atm e Rol,emi que estão diretamente ligadas a maior ou menor Rn. A validação dos dados do algoritmo SEBAL a partir das estações automáticas foi observado um erro relativo entre 9 e 11% para a imagem Landsat-8 para o dia 20/09/2016, verificando a acurácia das imagens para a estimativa do saldo de radiação à superfície – Rn, para a Bacia do Rio Pajeú.The present study aims to estimate the Balance of Radiation Surface-Rn through the SEBAL algorithm and images of the Landsat-8 satellite for the Pajeú River Basin. The Radiation Balance data, estimated by SEBAL, were compared with measurements obtained in two automatic stations, located in the municipalities of Floresta and Serra Talhada. An image of OLI (Operational Land Image) and TIRS (Thermal Infrared Sensor) sensors on Landsat-8 satellite, orbit 216 and point 66, was used for November 20, 2016. From the images obtained if the spectral radiance and reflectance, followed by surface albedo, vegetation index, emissivity, surface temperature, short-wave radiation incident - Rs, incidente and emitted long wave radiation - Role,atm and Rol,emi , Respectively, and Radiation Balance. The results showed that the lowest values of albedo and temperature were observed in bodies of water and vegetation, already the largest in urban areas. These items are linked directly to the components of the radiation balance, which observed lower values of incident and emitted long wave radiation that are directly linked to the higher or lower Radiation Balance. The validation of the SEBAL algorithm data from the automatic stations showed a relative error between 9% and 11% for the Landsat-8 image, thus verifying the accuracy of the images for the estimate of the surface radiation balance in the Pajeu River

Analysis of the Vegetation Density of the Rio Pajeú Watershed Using TM - LANDSAT 5 Data

The use of remote sensing products has become a frequent practice in research studying vegetation cover. Vegetation indices based on satellite imagery have been improving in terms of the accuracy in obtaining information about the terrestrial surface, and these techniques have made a solid contribution to the efficiency and reliability of analyses of processes involved in vegetation change. One of the most frequently used vegetation indices is the normalized difference vegetation index (NDVI). The simplicity and high sensitivity to the magnitude of the density of vegetation cover has made it possible to monitor vegetation at local to global scales. The objective of the present work is to analyze the density of vegetation cover of the Rio Pajeú watershed, situated in the mesoregion of the interior region of the State of Pernambuco and the section of the São Francisco River within the State of Pernambuco using TM - Landsat 5 images and the SEBAL algorithm. The results show significant variation in the magnitude of the NDVI classes between 1995 and 2009, and also demonstrate that the methodology used in this study is reliable for NDVI analyses

Space-temporal evaluation of changes in temperature and soil use and cover in the metropolitan region of baixada santista

The Metropolitan Region of Baixada Santista (MRBS) harbors one of the main port areas of Brazil: the Port of Santos. Due to the accelerated urban development in this region, the monitoring of biophysical parameters is fundamental. Therefore, this paper aims to i) estimate the soil surface temperature (Ts) and identify the Urban Heat Islands (UHI) formation; and ii) compare the Ts and the normalized difference vegetation index (NDVI) for MRBS from 1986 to 2016 using Landsat 5 and 8 images. Remote sensing tools are essential to meet the objectives of this work for providing both the spatial and temporal evaluation of a region. The spatial analysis was based on the NDVI to evaluate the vegetation density and size from five previously established classes (i.e., water bodies, urban grid, exposed soil and road corridors, shrub, and dense vegetation). The NDVI mapping showed a significant reduction in the cover area referred to the dense vegetation class (91.7%), while the urban grid category increased by 29.4%, resulting from the urban expansion and green cover reduction over the region during this period. Surface temperature thematic maps showed high-temperature values related to increased urbanization and decreased rainfall. Moreover, an 8°C rise in surface temperature over the last 30 years was registered due to the regional development, which has replaced natural soils by anthropic materials and reduced dense vegetation. This phenomenon has resulted in the formation and intensification of UHI, especially after the 2000s

Assessing the performance of the south american land data assimilation system version 2 (SALDAS-2) energy balance across diverse biomes

Understanding the exchange of energy between the surface and the atmosphere is important in view of the climate scenario. However, it becomes a challenging task due to a sparse network of observations. This study aims to improve the energy balance estimates for the Amazon, Cerrado, and Pampa biomes located in South America using the radiation and precipitation forcing obtained from the Clouds and the Earth’s Radiant Energy System (CERES) and the precipitation CPTEC/MERGE datasets. We employed three surface models—Noah-MP, Community Land Model (CLSM), and Integrated Biosphere Simulator (IBIS)—and conducted modeling experiments, termed South America Land Data Assimilation System (SALDAS-2). The results showed that SALDAS-2 radiation estimates had the smallest errors. Moreover, SALDAS-2 precipitation estimates were better than the Global Land Data Assimilation System (GLDAS) in the Cerrado (MBE = −0.16) and Pampa (MBE = −0.19). Noah-MP presented improvements compared with CLSM and IBIS in 100% of towers located in the Amazon. CLSM tends to overestimate the latent heat flux and underestimate the sensible heat flux in the Amazon. Noah-MP and Ensemble outperformed GLDAS in terms latent and sensible heat fluxes. The potential of SALDAS-2 should be emphasized to provide more accurate estimates of surface energy balance

Assessing the Performance of the South American Land Data Assimilation System Version 2 (SALDAS-2) Energy Balance across Diverse Biomes

Understanding the exchange of energy between the surface and the atmosphere is important in view of the climate scenario. However, it becomes a challenging task due to a sparse network of observations. This study aims to improve the energy balance estimates for the Amazon, Cerrado, and Pampa biomes located in South America using the radiation and precipitation forcing obtained from the Clouds and the Earth’s Radiant Energy System (CERES) and the precipitation CPTEC/MERGE datasets. We employed three surface models—Noah-MP, Community Land Model (CLSM), and Integrated Biosphere Simulator (IBIS)—and conducted modeling experiments, termed South America Land Data Assimilation System (SALDAS-2). The results showed that SALDAS-2 radiation estimates had the smallest errors. Moreover, SALDAS-2 precipitation estimates were better than the Global Land Data Assimilation System (GLDAS) in the Cerrado (MBE = −0.16) and Pampa (MBE = −0.19). Noah-MP presented improvements compared with CLSM and IBIS in 100% of towers located in the Amazon. CLSM tends to overestimate the latent heat flux and underestimate the sensible heat flux in the Amazon. Noah-MP and Ensemble outperformed GLDAS in terms latent and sensible heat fluxes. The potential of SALDAS-2 should be emphasized to provide more accurate estimates of surface energy balance