Revista de Teledetección

Analysis of synergies between Urban Heat Island and Heat Waves using Sentinel-3 images over the city of Granada

2022-06-20T09:59:57+02:00

Understanding the synergies between the Urban Heat Island (ICU) phenomenon and one of the extreme climatic events such as heat waves has become one of the great challenges of society that seeks to improve the quality of life. In this research, the Terrestrial Surface Temperature (TST) and the Urban Surface Heat Island (ICUS) have been determined using Sentinel-3 images of the city of Granada (Spain) during the months of July and August of the years 2019 and 2020. The purpose is to determine the possible synergies between both phenomena in an area classified as highly vulnerable to the effects of climate change. Using the Data Panel statistical analysis method, multivariate relationships were obtained during the heat wave periods. The results obtained, in line with previous research, indicate that TST and ICUS are intensified under heat wave conditions (Daytime: TST=2.2 °C and ICUS=0.2 °C; Nighttime: TST=4.4 °C and ICUS= 0.3 °C) and there are relationships between ICUS and wind direction and solar radiation that intensify in periods of heat wave.

Cover classifications in wetlands using Sentinel-1 data (Band C): a case study in the Parana river delta, Argentina

2022-06-01T11:18:06+02:00

With the launch of the Sentinel-1 mission, for the first time, multitemporal and dual-polarization C-band SAR data with a short revisit time is freely available. How can we use this data to generate accurate vegetation cover maps on a local scale? Our main objective was to assess the use of multitemporal C-Band Sentinel-1 data to generate wetland vegetation maps. We considered a portion of the Lower Delta of the Paraná River wetland (Argentina). Seventy-four images were acquired and 90 datasets were created with them, each one addressing a combination of seasons (spring, autumn, winter, summer, complete set), polarization (VV, HV, both), and texture measures (included or not). For each dataset, a Random Forest classifier was trained. Then, the kappa index values (κ) obtained by the 90 classifications made were compared. Considering the datasets formed by the intensity values, for the winter dates the achieved kappa index values (κ) were higher than 0.8, while all summer datasets achieved κ up to 0.76. Including feature textures based on the GLCM showed improvements in the classifications: for the summer datasets, the κ improvements were between 9% and 22% and for winter datasets improvements were up to 15%. Our results suggest that for the analyzed context, winter is the most informative season. Moreover, for dates associated with high biomass, the textures provide complementary information.

Improved rainfall and temperature satellite dataset in areas with scarce weather stations data: case study in Ancash, Peru

2022-07-06T11:39:07+02:00

Rainfall and temperature variables play an important role in understanding meteorology at global and regional scales. However, the availability of meteorological information in areas of complex topography is difficult, as the density of weather stations is often very low. In this study, we focused on improving existing satellite products for these areas, using Tropical Rainfall Measuring Mission (TRMM) and Global Precipitation Measurement (GPM) data for rainfall and Modern Era Retrospective Analysis for Research and Applications Version 2 (MERRA-2) data for air temperature. Our objective was to propose a model that improves the accuracy and correlation of satellite data with observed data on a monthly scale during 2012-2017. The improvement of rainfall satellite data was performed using 4 regions: region 1 Santa (R1Sn), region 2 Marañón (R2Mr), region 3 Pativilca (R3Pt) and region 4 Pacific (R4Pc). For temperature, a model based on the use of the slope obtained between temperature and altitude data was used. In addition, the reliability of the TRMM, GPM and MERRA-2 data was analyzed based on the ratio of the mean square error, PBIAS, Nash-Sutcliffe efficiency (NSE) and correlation coefficient. The final products obtained from the model for temperature are reliable with R2 ranging from 0.72 to 0.95 for the months of February and August respectively, while the improved rainfall products obtained are shown to be acceptable (NSE≥0.6) for the regions R1Sn, R2Mr and R3Pt. However, in R4Pc it is unacceptable (NSE<0.4), reflecting that the additive model is not suitable in regions with low rainfall values.

High Resolution Land Cover Mapping and Crop Classification in the Loukkos Watershed (Northern Morocco): An Approach Using SAR Sentinel-1 Time Series

2022-07-04T10:29:31+02:00

Remote sensing has become more and more a reliable tool for mapping land cover and monitoring cropland. Much of the work done in this field uses optical remote sensing data. In Morocco, active remote sensing data remain under-exploited despite their importance in monitoring spatial and temporal dynamics of land cover and crops even during cloudy weather. This study aims to explore the potential of C-band Sentinel-1 data in the production of a high-resolution land cover mapping and crop classification within the irrigated Loukkos watershed agricultural landscape in northern Morocco. The work was achieved by using 33 dual-polarized images in vertical-vertical (VV) and vertical-horizontal (VH) polarizations. The images were acquired in ascending orbits between April 16 and October 25, 2020, with the purpose to track the backscattering behavior of the main crops and other land cover classes in the study area. The results showed that the backscatter increased with the phenological development of the monitored crops (rice, watermelon, peanuts, and winter crops), strongly for the VH and VV bands, and slightly for the VH/VV ratio. The other classes (water, built-up, forest, fruit trees, permanent vegetation, greenhouses, and bare lands) did not show significant variation during this period. Based on the backscattering analysis and the field data, a supervised classification was carried out, using the Random Forest Classifier (RF) algorithm. Results showed that radiometric characteristics and 6 days’ time resolution covered by Sentinel-1 constellation gave a high classification accuracy by dual-polarization with Radar Ratio (VH/VV) or Radar Vegetation Index and textural features (between 74.07% and 75.19%). Accordingly, this study proves that the Sentinel-1 data provide useful information and a high potential for multi-temporal analyses of crop monitoring, and reliable land cover mapping which could be a practical source of information for various purposes in order to undertake food security issues.

Water area and volume calculation of two reservoirs in Central Cuba using Remote Sensing Methods. A new perspective

2022-06-24T12:18:48+02:00

The availability, quality and management of water constitute essential activities of national, regional and local governments and authorities. Historic annual rain (between 1961 and 2020) in Chambas River Basin (Central Cuba) was evaluated. Two remote sensing methods (Normalized Difference Water Index and RADAR images) were used to calculate the variation of water area and volumes of two reservoirs (Chambas II and Cañada Blanca) of Ciego de Ávila Province at end of wet and dry seasons from 2014-2021. The results showed that mean annual rain was 1330.9 ± 287.4 mm and it did not showed any significant tendency at evaluated period. For both reservoirs, mean water areas measured with two methods were 19 % and 8 % smaller than the mean water area reported by authorities for the same period. The static water storage capacity (water volume) of both reservoirs varied (as area) between seasons with the greatest volume in both reservoirs recorded in October of 2017 (30.5 million of m3 in Chambas II and 45.1 million of m3 in Cañada Blanca reservoir). Large deviations of water area and volumes occurred during the dry season (lower values) and the wet season of 2017 (influenced by rain associated to of Hurricane Irma) and wet season of 2020 (influenced by rain associated to tropical storm Laura). Calculated area – volume models with significant statistical correlation are another useful tool that could be used to improve water management in terms of accuracy and to increase reliable results in cases where gauge measurements are scarce or not available.