Resumo em Inglês:

Abstract: Many efforts have been made to understand climatic and hydrological variables’ variability, magnitude, and standards. In this sense, spatial data has been a fundamentally important tool in supporting the development of agriculture and environmental management research. We start this review by giving a brief overview of the use of satellites in Brazilian agriculture. Besides that, we present a couple of examples of satellite applications in managing water resources in agriculture. The second part of this review illustrates a detailed scenario concerning the orbital sensors available for water applications in agriculture. Finally, we provide a synthesis of the future of satellites in agriculture in terms of nanosatellites, artificial intelligence, and onboard processing.

Resumo em Inglês:

Abstract: Due to the absence of studies related to the digital processing of synthetic aperture radar (SAR) images in the semi-arid region of Brazil, the aim of this study was to test different filters for reducing speckle noise in SAR images, serving as a reference for choosing the most suitable filter for different studies in this vegetation. The filters: Gamma Map, Lee Sigma, Median, Frost and Refined Lee in different window sizes were tested on the VV, VH and VH/VV polarizations in the Sentinel-1A images, verifying the responses under the influence of the dry and post-rainy period in the Caatinga vegetation. In the state of Sergipe, Brazil, 30 samples of Caatinga fragments obtained from Sentinel-1A images for the dry and post-rainy season were selected. For all images evaluated, the values of the averages of the equivalent number of looks (ENL) were compared by the Tukey test at 5% significance. The Gamma filter showed the highest amount of means (22) with the highest ENL values, followed by Median (5). The generation of ENL results and their comparison, considering all variables used, was essential to serve as a basis for choosing the filtering method in studies that use data from Sentinel-1A in the Caatinga region.

Resumo em Inglês:

Abstract: Vegetation Indices (VIs) provide spatial information on the vegetation state, which has been associated with landslide propensity. To evaluate how VIs information indicate the landslide propensity, the current study analyzed nine different IVs to identify the categories of vegetation states in the hydrographic basin of Pedra Branca before and after landslide event. The different VIs were obtained using Sentinel-2A (2016) and Sentinel-2B (2018) images. All VIs were tested by cross-table analysis regard to the ability to identify the calculated area for landslide scars, and the VIs were also compared to the NDVI reference by error matrix for the analysis of the accuracy in identifying the vegetation state before the landslide occurrence. The areas with landslide scars totalized 86700m² in 2018 image and NDVI matched ~57% of the No Vegetation category. Before the landslide event, almost all VIs indicated a loss of vegetation vigor (with exception of RENDVI and ARVI) in 2016 image. In addition, the indices (exceptionality MSI) also presented high rates of match to the analysis of NDVI in discerning both Intermediate and Vigorous Vegetation states. However, the areas presenting a healthy vegetation state are reduced, which therefore might be indicating the propensity to landslide event before their occurrences.