The Thematic Mapper onboard Landsat 4, 5, and Enhanced Thematic Mapper Plus (TM/ETM+) onboard Landsat 7 have frequency bands (green and SWIR) to effectively measure water body extents and their changes via the Modified Normalized Difference Water Index (MNDWI). Here, we developed a technique, called the thematic imagery-altimetry system (TIAS), to infer the vertical water changes from MNDWI horizontal water extent changes by integrating long-term TM/ETM+ imageries with available digital elevation models (DEMs). The result is a technique to quantify water level changes of natural or artificial water bodies over two decades. Several DEMs were used to compute intersects with TM/ETM+ water extent time series to evaluate the robustness of the technique. These DEMs include: the Advanced Spaceborne Thermal Emission and Reflection Radiometer Global Digital Elevation Map version 2 (ASTER-GDEM2, at 1 arcsec resolution), the Shuttle Radar Topography Mission version 2 (SRTM C-band at 1 arcsec), and the Global Multiresolution Terrain Elevation Data (GMTED2010 at 7.5 arcsec). We demonstrated our technique near Hoover Dam (HD) in Lake Mead to quantify its respective decadal water level.

The Qinghai-Tibetan plateau (QTP), also known as the Third Pole and the World Water Tower, is the largest and highest plateau with distinct and competing surface and subsurface processes. It is covered by a large layer of discontinuous and sporadic alpine permafrost which has degraded 10% during the past few decades. The average active layer thickness (ALT) increase rate is approximately 7.5 cm·yr⁻¹ from 1995 to 2007, based on soil temperature measurements from 10 borehole sites along Qinghai-Tibetan Highway, and approximately 6.3 cm·yr⁻¹, 2006–2010, using soil temperature profiles for 27 monitoring sites along Qinghai-Tibetan railway. In this study, we estimated the ALT and its AL thickening rate in the northern QTP near the railway using ALOS PALSAR L-band small baseline subset interferometric synthetic aperture radar (SBAS-InSAR) data observed land subsidence and the corresponding ALT modeling. The InSAR estimated ALT and AL thickening rate were validated with ground-based observations from the borehole site WD4 within our study region, indicating excellent agreement. We concluded that we have generated high spatial resolution (30 m) and spatially-varying ALT and AL thickening rates, 2007–2009, over approximately an area of 150 km² of permafrost-covered region in the northern QTP.

Bottle marine debris (BMD) remains one of the most pressing global issues. This study proposes a detection method for BMD using unmanned aerial vehicles (UAV) and machine learning techniques to enhance the efficiency of marine debris studies. The UAVs were operated at three designed sites and at one testing site at twelve fly heights corresponding to 0.12 to 1.54 cm/pixel resolutions. The You Only Look Once version 2 (YOLO v2) object detection algorithm was trained to identify BMD. We added data augmentation and image processing of background removal to optimize BMD detection. The augmentation helped the mean intersection over the union in the training process reach 0.81. Background removal reduced processing time and noise, resulting in greater precision at the testing site. According to the results at all study sites, we found that approximately 0.5 cm/pixel resolution should be a considerable selection for aerial surveys on BMD. At 0.5 cm/pixel, the mean precision, recall rate, and F1-score are 0.94, 0.97, and 0.95, respectively, at the designed sites, and 0.61, 0.86, and 0.72, respectively, at the testing site. Our work contributes to beach debris surveys and optimizes detection, especially with the augmentation step in training data and background removal procedures.

Water level (WL) and water volume (WV) of surface-water bodies are among the most crucial variables used in water-resources assessment and management. They fluctuate as a result of climatic forcing, and they are considered as indicators of climatic impacts on water resources. Quantifying riverine WL and WV, however, usually requires the availability of timely and continuous in situ data, which could be a challenge for rivers in remote regions, including the Mekong River basin. As one of the most developed rivers in the world, with more than 20 dams built or under construction, Mekong River is in need of a monitoring system that could facilitate basin-scale management of water resources facing future climate change. This study used spaceborne sensors to investigate two dams in the upper Mekong River, Xiaowan and Jinghong Dams within China, to examine river flow dynamics after these dams became operational. We integrated multi-mission satellite radar altimetry (RA, Envisat and Jason-2) and Landsat-5/-7/-8 Thematic Mapper (TM)/Enhanced Thematic Mapper plus (ETM+)/Operational Land Imager (OLI) optical remote sensing (RS) imageries to construct composite WL time series with enhanced spatial resolutions and substantially extended WL data records. An empirical relationship between WL variation and water extent was first established for each dam, and then the combined long-term WL time series from Landsat images are reconstructed for the dams. The R² between altimetry WL and Landsat water area measurements is >0.95. Next, the Tropical Rainfall Measuring Mission (TRMM) data were used to diagnose and determine water variation caused by the precipitation anomaly within the basin. Finally, the impact of hydrologic dynamics caused by the impoundment of the dams is assessed. The discrepancy between satellite-derived WL and available in situ gauge data, in term of root-mean-square error (RMSE) is at 2–5 m level. The estimated WV variations derived from combined RA/RS imageries and digital elevation model (DEM) are consistent with results from in situ data with a difference at about 3%. We concluded that the river level downstream is affected by a combined operation of these two dams after 2009, which has decreased WL by 0.20 m·year⁻¹ in wet seasons and increased WL by 0.35 m·year⁻¹ in dry seasons.

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.

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.