Spectral Linear Mixing Model application in passive microwave data to analyze Antarctic surface melting dynamics (1978-2018).

Several studies have utilized passive microwave imagery for monitoring snowmelt in Antarctica. However, due to the low spatial resolution of these images (25 km), the quantification of snowmelt is not precise. To enhance the accuracy of these estimations, this study proposed a subpixel analysis approach based on a Spectral Linear Mixing Model. This approach was applied to images obtained from channels 18/19 GHz and 37 GHz, both horizontally and vertically polarized, acquired from the Scanning Multichannel Microwave Radiometer (SMMR), Special Sensor Microwave Imager (SSM/I), and Special Sensor Microwave Imager/Sounder (SSM/IS) instruments, spanning the period 1978-2018. The spatiotemporal analysis of the estimated snowmelt fraction images indicated that the most persistent and intensive melt was observed on the Antarctic Peninsula, particularly on the Larsen, Wilkins, George VI, and Wordie ice shelves. The melting period in the Antarctic Peninsula began in late October, with a peak in early January, and ended in late March. Other regions with persistent and intensive snowmelt were Mary Bird Land and Wilkes Land, followed by Dronning Maud Land, Amery Ice Shelf, Filchner-Ronne Ice Shelf, and Ross Ice Shelf. These snowmelt data are valuable for modeling the impacts of snowmelt on glacial systems, local coastal environments, and sea-level rise.

Snowmelt retrieval algorithm for the Antarctic Peninsula using SAR imageries.

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.

Anthropogenic influences on coastal environmental changes in the Mekong Delta: a study from Ben Tre Province, Southern Vietnam.

Low-lying coastal environments are highly dynamic and sensitive to natural as well as anthropogenic perturbations. Climate change, sea level rise, storms and tsunamis are the natural phenomena that affect the deltaic coasts in Southeast Asia in general and Vietnam in particular. The effects of these phenomena can be exacerbated by human activities such as mangrove deforestation, aquaculture and infrastructure development. Conversely, the low-lying coastal areas are important in the economic development of Southeast Asian countries. In the Vietnamese Mekong Delta, coastal areas have been affected by a number of factors, such as climate change, sea level rise, aquaculture, pollution and tourism-related activities in recent decades. The present study investigated shoreline changes, expansion of aquaculture ponds, soil salinity changes and salinity intrusion in the river systems along the coastal areas of Ben Tre Province in the Vietnamese Mekong Delta between 1998 and 2020 using satellite imagery and field data. Variations in erosion and accretion were found to be not unique along the coast of Ben Tre. There was a rapid expansion of aquaculture ponds between 1998 and 2015 and a slight decline since then. Soil salinity has been increased between 1998 and 2020; it is seen from recent satellite data that soil is becoming more saline in the inland areas of Ben Tre. Saltwater intrusion into the rivers of Ben Tre is considered associated with El Niño-La Niña conditions. It is suggested that reforestation of abandoned shrimp ponds in Ben Tre by mangrove vegetation can be effective as a bioshield against coastal hazards, such as sea level rise and shoreline erosion.

Statistical modeling of sea ice concentration in the northwest region of the Antarctic Peninsula.

Sea ice is one of the main components of the cryosphere that modifies the exchange of heat and moisture between the ocean and atmosphere, regulating the global climate. In this sense, it is important to identify the concentration of sea ice in different regions of Antarctica in order to measure the impact of environmental changes on the region's ecosystem. The objective of this study was to evaluate the performance of the multiple linear regression and Box-Jenkins methods for predicting the concentration of sea ice along the northwest coast of the Antarctic Peninsula. Sea ice concentration data from May to November for the period 1979-2018 were extracted from passive remote sensors including a scanning multichannel microwave radiometer, special sensor microwave imager, and special sensor microwave imager/sounder. Meteorological variables from the atmospheric reanalysis model ERA5 of the European Center for Medium-Range Weather Forecasts were used as predictor variables, and the leave-one-out cross-validation technique was used to calibrate and validate the models. It was found that both statistical models have similar performance when analyzing residual analysis results, root mean square error of cross-validation, and final accuracy and residual standard deviation, these responses being related to the regionalization of the study area and to the Box-Jenkins presents strong, homogeneous, and stable correlations in the time series modeled for each pixel.