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The main land use and land cover (LULC) changes that a given area passes over the time can be evaluated by using spatial-temporal analysis of satellites images. Then, it is possible to identify the LULC changes, as well as the main causes of environmental impacts. The objective of this paper was to analyze the LULC changes of the main agricultural lands cultivated in the Alto Paraguai Basin (BAP). This paper focused on the summer crops (soybean and corn) and the analysis of agricultural expansion. The results, considering a16-year comparison, showed an increase of 40.60% in the expansion of agricultural areas. The evaluation of the accuracy showed the efficiency of the methodology of agricultural mapping, presenting a Kappa Index of 0.85 for the 2000/2001 and 0.86 for the 2015/2016 crop seasons

The Pantanal is one of the most important and preserved biomes in Brazil. This region is annually flooded due to episodes of precipitation along the Paraguay River and its tributaries. Understanding the dynamics of flooding is extreme important since it influences the entire Pantanal ecosystem. Remote Sensing data is an alternative to the identification of flooded areas and their changes in different periods. Among the possible sensors capable of mapping these flooded areas Radar sensor is one of the most attractive-mainly due to the low influence of cloud cover and atmospheric conditions, allowing imaging in dry or rainy seasons. For this work, Radar images from Sentinel 1 satellites for the years 2016, 2017, and 2018 were used. All available data from these years for the study area were used to generate images that represent the seasonality in the region for each year. In total, 1141 Sentinel 1 radar images were processed. The processing of such amount of data was possible through Google Earth Engine platform, which is capable of robust processing of a large amount of data, especially Remote Sensing data. At the end, it was possible to generate images that represent the seasonality of each year. It was also possible to compare the years, highlighting the differences between flooded areas indicating the periods of major precipitation.