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The Pacific island countries are particularly vulnerable to the effects of global warming including more frequent and intense natural disasters. Seawater inundation, one of the most serious disasters, could damage human property and life. Regional sea level rise, highest astronomic tide, vertical land motions, and extreme sea level could result in episodic, recurrent, or permanent coastal inundation. Therefore, assessing potential flooding areas is a critical task for coastal management plans. In this study, a simulation of the static flooding situation in the southwest coast of Taiwan (Tainan city) at the end of this century was conducted by using a combination of the Taiwan Digital Elevation Model (DEM), regional sea level changes reconstructed by tide gauge and altimetry data, vertical land deformation derived from leveling and GPS data, and ocean tide models. In addition, the extreme sea level situation, which typically results from high water on a spring tide and a storm surge, was also evaluated by the joint probability method using tide gauge records. To analyze the possible static flood risk and avoid overestimation of inundation areas, a region-based image segmentation method was employed in the estimated future topographic data to generate the flood risk map. In addition, an extreme sea level situation, which typically results from high water on a spring tide and a storm surge, was also evaluated by the joint probability method using tide gauge records. Results showed that the range of inundation depth around the Tainan area is 0–8 m with a mean value of 4 m. In addition, most of the inundation areas are agricultural land use (60% of total inundation area of Tainan), and two important international wetlands, 88.5% of Zengwun Estuary Wetlands and 99.5% of Sihcao Wetlands (the important Black-faced Spoonbills Refuge) will disappear under the combined situation. The risk assessment of flooding areas is potentially useful for coastal ocean and land management to develop appropriate adaptation policies for preventing disasters resulting from global climate change. [ABSTRACT FROM AUTHOR]

Multi-mission satellite altimetry (e.g., ERS, Envisat, TOPEX/Poseidon, Jason) data have enabled a synoptic-scale view of ocean variations in past decades. Since 2016, the Sentinel-3 mission has provided better spatial and temporal sampling compared to its predecessors. The Sentinel-3 Ku/C Radar Altimeter (SRAL) is one of the synthetic aperture radar altimeters (SAR Altimeter) which is more precise for coastal and lake observations. The article studies the performance of the Sentinel-3 Level-2 sea level altimetry products in the coastal areas of the Baltic Sea and on two lakes of Estonia. The Sentinel-3 data were compared with (i) collocated Global Navigation Satellite System (GNSS) ship measurements, (ii) the Estonian geoid model (EST-GEOID2017) together with sea-level anomaly corrections from the tide gauges, and (iii) collocated buoy measurements. The comparisons were carried out along seven Sentinel-3A/B tracks across the Baltic Sea and Estonian lakes in 2019. In addition, the Copernicus Marine Environment Monitoring Service (CMEMS) Level-3 sea-level products and the Nucleus for European Modelling of the Ocean (NEMO) reanalysis outcomes were compared with measurements from Estonia's 21 tide gauges and the buoy deployed offshore. Our results showed that the uncertainty of the Sentinel-3 Level-2 altimetry product was below decimetre level for the seacoast and the selected lakes of Estonia. Results from CMEMS Level-3 altimetry products showed a correlation of 0.83 (RMSE 0.18 m) and 0.91 (RMSE 0.27 m) when compared against the tide gauge measurements and the NEMO model, respectively. The overall performance of the altimetry products was very good, except in the immediate vicinity of the coastline and for the lakes, where the accuracy was nearly three times lower than for the open sea, but still acceptably good. [ABSTRACT FROM AUTHOR]

The 2019 International Symposium on Remote Sensing (ISRS-2019) took place in Taipei, Taiwan from 17 to 19 April 2019. ISRS is one of the distinguished conferences on the photogrammetry, remote sensing and spatial information sciences, especially in East Asia. More than 220 papers were presented in 37 technical sessions organized at the conference. This Special Issue publishes a limited number of featured peer-reviewed papers extended from their original contributions at ISRS-2019. The selected papers highlight a variety of topics pertaining to innovative concepts, algorithms and applications with geospatial sensors, systems, and data, in conjunction with emerging technologies such as artificial intelligence, machine leaning and advanced spatial analysis algorithms. The topics of the selected papers include the following: the on-orbit radiometric calibration of satellite optical sensors, environmental characteristics assessment with remote sensing, machine learning-based photogrammetry and image analysis, and the integration of remote sensing and spatial analysis. The selected contributions also demonstrate and discuss various sophisticated applications in utilizing remote sensing, geospatial data, and technologies to address different environmental and societal issues. Readers should find the Special Issue enlightening and insightful for understanding state-of-the-art remote sensing and spatial information science research, development and applications. [ABSTRACT FROM AUTHOR]

The Indus River, which flows through China, India, and Pakistan, is mainly fed by melting snow and glaciers that are spread across the Hindukush–Karakoram–Himalaya Mountains. The downstream population of the Indus Plain heavily relies on this water resource for drinking, irrigation, and hydropower generation. Therefore, its river runoff variability must be properly monitored. Gilgit Basin, the northwestern part of the Upper Indus Basin, is selected for studying cryosphere dynamics and its implications on river runoff. In this study, 8-day snow products (MOD10A2) of moderate resolution imaging spectroradiometer, from 2001 to 2015 are selected to access the snow-covered area (SCA) in the catchment. A non-parametric Mann–Kendall test and Sen's slope are calculated to assess whether a significant trend exists in the SCA time series data. Then, data from ground observatories for 1995–2013 are analyzed to demonstrate annual and seasonal signals in air temperature and precipitation. Results indicate that the annual and seasonal mean of SCA show a non-significant decreasing trend, but the autumn season shows a statistically significant decreasing SCA with a slope of −198.36 km2/year. The annual mean temperature and precipitation show an increasing trend with highest values of slope 0.05 °C/year and 14.98 mm/year, respectively. Furthermore, Pearson correlation coefficients are calculated for the hydro-meteorological data to demonstrate any possible relationship. The SCA is affirmed to have a highly negative correlation with mean temperature and runoff. Meanwhile, SCA has a very weak relation with precipitation data. The Pearson correlation coefficient between SCA and runoff is −0.82, which confirms that the Gilgit River runoff largely depends on the melting of snow cover rather than direct precipitation. The study indicates that the SCA slightly decreased for the study period, which depicts a possible impact of global warming on this mountainous region. [ABSTRACT FROM AUTHOR]

The Indus River, which flows through China, India, and Pakistan, is mainly fed by melting snow and glaciers that are spread across the Hindukush⁻Karakoram⁻Himalaya Mountains. The downstream population of the Indus Plain heavily relies on this water resource for drinking, irrigation, and hydropower generation. Therefore, its river runoff variability must be properly monitored. Gilgit Basin, the northwestern part of the Upper Indus Basin, is selected for studying cryosphere dynamics and its implications on river runoff. In this study, 8-day snow products (MOD10A2) of moderate resolution imaging spectroradiometer, from 2001 to 2015 are selected to access the snow-covered area (SCA) in the catchment. A non-parametric Mann⁻Kendall test and Sen's slope are calculated to assess whether a significant trend exists in the SCA time series data. Then, data from ground observatories for 1995⁻2013 are analyzed to demonstrate annual and seasonal signals in air temperature and precipitation. Results indicate that the annual and seasonal mean of SCA show a non-significant decreasing trend, but the autumn season shows a statistically significant decreasing SCA with a slope of -198.36 km²/year. The annual mean temperature and precipitation show an increasing trend with highest values of slope 0.05 °C/year and 14.98 mm/year, respectively. Furthermore, Pearson correlation coefficients are calculated for the hydro-meteorological data to demonstrate any possible relationship. The SCA is affirmed to have a highly negative correlation with mean temperature and runoff. Meanwhile, SCA has a very weak relation with precipitation data. The Pearson correlation coefficient between SCA and runoff is -0.82, which confirms that the Gilgit River runoff largely depends on the melting of snow cover rather than direct precipitation. The study indicates that the SCA slightly decreased for the study period, which depicts a possible impact of global warming on this mountainous region.

Fine particulate matter (PM 2.5 ) has a small particle size, which allows it to directly enter the respiratory mucosa and reach the alveoli and even the blood. Many countries are already aware of the adverse effects of PM 2.5 , and determination of the sources of PM 2.5 is a critical step in reducing its concentration to protect public health. This study monitored PM 2.5 in the summer (during the southwest monsoon season) of 2017. Three online monitoring systems were used to continuously collect hourly concentrations of key chemical components of PM 2.5 , including anions, cations, carbon, heavy metals, and precursor gases, for 24 h per day. The sum of the concentrations of each compound obtained from the online monitoring systems is similar to the actual PM 2.5 concentration (98.75%). This result suggests that the on-line monitoring system of this study covers relatively complete chemical compounds. Positive matrix factorization (PMF) was adopted to explore and examine the proportion of each source that contributed to the total PM 2.5 concentration. According to the source contribution analysis, 55% of PM 2.5 can be attributed to local pollutant sources, and the remaining 45% can be attributed to pollutants emitted outside Taipei City. During the high-PM 2.5 -concentration (episode) period, the pollutant conversion rates were higher than usual due to the occurrence of vigorous photochemical reactions. Moreover, once pollutants are emitted by external stationary pollutant sources, they move with pollution air masses and undergo photochemical reactions, resulting in increases in the secondary pollutant concentrations of PM 2.5 . The vertical monitoring data indicate that there is a significant increase in PM 2.5 concentration at high altitudes. High-altitude PM 2.5 will descend to the ground and thereby affect the ground-level PM 2.5 concentration., Competing Interests: The authors declare no conflict of interest.

Spherical harmonics (SH) and mascon solutions are the two most common types of solutions for Gravity Recovery and Climate Experiment (GRACE) mass flux observations. However, SH signals are degraded by measurement and leakage errors. Mascon solutions (the Jet Propulsion Laboratory (JPL) release, herein) exhibit weakened signals at submascon resolutions. Both solutions require a scale factor examined by the CLM4.0 model to obtain the actual water storage signal. The Slepian localization method can avoid the SH leakage errors when applied to the basin scale. In this study, we estimate SH errors and scale factors for African hydrological regimes. Then, terrestrial water storage (TWS) in Africa is determined based on Slepian localization and compared with JPL-mascon and SH solutions. The three TWS estimates show good agreement for the TWS of large-sized and humid regimes but present discrepancies for the TWS of medium and small-sized regimes. Slepian localization is an effective method for deriving the TWS of arid zones. The TWS behavior in African regimes and its spatiotemporal variations are then examined. The negative TWS trends in the lower Nile and Sahara at -1.08 and -6.92 Gt/year, respectively, are higher than those previously reported.