The availability of accurate meteorological data is important for the modeling of atmospheric flows, enabling the understanding of climate change in a given environment over time. Therefore, this study aimed to evaluate the quality of the meteorological data of 2 m temperature (T) and mean sea level pressure (MSLP), obtained through the atmospheric reanalysis models ERA5 and ERA-Interim (ERA-i) in the northern region of the Antarctic Peninsula, covering the period from 1979 to 2018. To carry out the statistical process, local observations from nine weather stations installed in the study region were used, with data available from the Scientific Committee on Antarctic Research. Statistical analysis evaluated Pearson's linear correlation coefficient (R), standard deviation (sigma), normalized bias, normalized mean absolute error, and normalized root mean square error. Regarding the difference in (T) over bar between weather stations and reanalysis models, we found a better result for ERA5 (Delta(T) over bar = -0.34 degrees C) compared to ERA-i (Delta(T) over bar = 0.37 degrees C); however, for the difference in (MSLP) over bar the ERA-i (Delta(MSLP) over bar = -0.04 hPa) presented a better response in relation to ERA5 (Delta(MSLP) over bar = 0.24 hPa). When verifying the local meteorological observations trend, an increase of T in 0.31 degrees C decade(-1) (Esperanza station) and a reduction of MSLP between -0.56 (Bellingshausen station) and -0.80 hPa decade(-1) (Jubany station) were recorded.

The classification of Synthetic Aperture Radar (SAR) images by knowledge-based algorithms with elevation and backscatter thresholds were used in several studies to detect the Wet Snow Radar Zone (WSZ) in the Antarctic Peninsula. To identify it more accurately based on its seasonal variations, this study proposed the additional use of a threshold in synthetic images, created by rationing summer and winter sigma linear images. In our algorithm we used the following thresholds to detect the WSZ in Envisat ASAR imageries, using the Radarsat Antarctic Map Digital Elevation Model as ancillary data: i) -25 dB < s0 < -14 dB; ii) slinear summer / slinear winter < 0.4; iii) elevation H < 1,200 m for northern tip and H < 800 m for southern tip of the Antarctic Peninsula. The classified images were post-processed by a focal majority 5 x 5 filter and superimposed by an image of rock outcrops derived from the Antarctic Digital Database. The ratio image threshold allowed discriminating the WSZ from the Dry Snow Radar Zone and radar shadows, as well as transitional areas between this glacier zone and the Frozen Percolation Radar Zone, which would be classified incorrectly if we used only elevation and backscatter thresholds.

O presente trabalho objetivou avaliar a utilização de imagens ópticas para diagnosticar a atividade fotossintética através do uso de sensor multiespectral embarcado em um Remotely Piloted Aircraft System (RPAS) aplicando os índices vegetativos Normalized Difference Vegetation Index (NDVI) e Normalized Difference Red Edge (NDRE) para as culturas da soja e do milho. O estudo foi realizado no Campus da Universidade de Cruz Alta, onde foram realizados voos utilizando uma RPAS modelo Phantom 4 Standart, equipado com um sensor RGB próprio e adaptada para embarcar concomitantemente uma câmera multiespectral MicaSense Parrot Sequoia. As imagens foram obtidas automaticamente pelo plano de voo com o programa computacional Dronedeploy, e posteriormente georreferenciadas, ortorretificadas e derivados os mapas dos índices de vegetação pelo programa computacional Agisoft Metashape. Constatamos que o índice de vegetação NDRE expressou melhor neste trabalho, podendo em determinadas regiões com dossel denso identificar áreas com algum estresse foliar não detectadas pelo NDVI. Assim, encontramos que o NDRE em estádios fenológicos mais avançados expressa melhor a biomassa fotossinteticamente ativa em relação ao NDVI.