Water is an imperative part of the Earth and an essential resource in human life and production. Under the effects of climate change and human activities, the spatial and temporal distribution of water bodies has been changing, and the shortage of water resources is becoming increasingly serious worldwide. Therefore, the monitoring of water bodies is indispensable. Remote sensing has the advantages of real time, wide coverage, and rich information and has become a brand-new technical means to quickly obtain water information. This study summarizes the current common methods of water extraction based on optical and radar images, including the threshold method, support vector machine, decision tree, object-oriented extraction, and deep learning, as well as the advantages and disadvantages of each method. These methods were applied to the Huai River Basin in China and Nam Co on the Qinghai-Tibet Plateau. The extraction results show that all the aforementioned approaches can obtain reliable results. Among them, the threshold segmentation method based on normalized difference water index is more robust than others. In the water extraction process, there are still many problems that restrict the accuracy of the results. In the future, researchers will continue to search for more automatic, extensive, and high-precision water extraction methods.

The OLI (Operational Land Imager) sensor on Landsat-8 has the potential to meet the requirements of remote sensing of water color. However, the optical properties of inland waters are more complex than those of oceanic waters, and inland atmospheric correction presents additional challenges. We examined the performance of atmospheric correction (AC) methods for remote sensing over three highly turbid or hypereutrophic inland waters in China: Lake Hongze, Lake Chaohu, and Lake Taihu. Four water-AC algorithms (SWIR (Short Wave Infrared), EXP (Exponential Extrapolation), DSF (Dark Spectrum Fitting), and MUMM (Management Unit Mathematics Models)) and three land-AC algorithms (FLAASH (Fast Line-of-sight Atmospheric Analysis of Spectral Hypercubes), 6SV (a version of Second Simulation of the Satellite Signal in the Solar Spectrum), and QUAC (Quick Atmospheric Correction)) were assessed using Landsat-8 OLI data and concurrent in situ data. The results showed that the EXP (and DSF) together with 6SV algorithms provided the best estimates of the remote sensing reflectance (R_rs) and band ratios in water-AC algorithms and land-AC algorithms, respectively. AC algorithms showed a discriminating accuracy for different water types (turbid waters, in-water algae waters, and floating bloom waters). For turbid waters, EXP gave the best R_rs in visible bands. For the in-water algae and floating bloom waters, however, all water-algorithms failed due to an inappropriate aerosol model and non-zero reflectance at 1609 nm. The results of the study show the improvements that can be achieved considering SWIR bands and using band ratios, and the need for further development of AC algorithms for complex aquatic and atmospheric conditions, typical of inland waters.

Global rapid expansion of the coastal aquaculture industry has made great contributions to enhance food security, but has also caused a series of ecological and environmental issues. Sustainable management of coastal areas requires the explicit and efficient mapping of the spatial distribution of aquaculture ponds. In this study, a Google Earth Engine (GEE) application was developed for mapping coastal aquaculture ponds at a national scale with a novel classification scheme using Sentinel-1 time series data. Relevant indices used in the classification mainly include the water index, texture, and geometric metrics derived from radar backscatter, which were then used to segment and classify aquaculture ponds. Using this approach, we classified aquaculture ponds for the full extent of the coastal area in Vietnam with an overall accuracy of 90.16% (based on independent sample evaluation). The approach, enabling wall-to-wall mapping and area estimation, is essential to the efficient monitoring and management of aquaculture ponds. The classification results showed that aquaculture ponds are widely distributed in Vietnam’s coastal area and are concentrated in the Mekong River Delta and Red River delta (85.14% of the total area), which are facing the increasing collective risk of climate change (e.g., sea level rise and salinity intrusion). Further investigation of the classification results also provides significant insights into the stability and deliverability of the approach. The water index derived from annual median radar backscatter intensity was determined to be efficient at mapping water bodies, likely due to its strong response to water bodies regardless of weather. The geometric metrics considering the spatial variation of radar backscatter patterns were effective at distinguishing aquaculture ponds from other water bodies. The primary use of GEE in this approach makes it replicable and transferable by other users. Our approach lays a solid foundation for intelligent monitoring and management of coastal ecosystems.

The concentration and composition of suspended particulate matter provide important information for evaluating water quality and understanding the variability in the underwater light field in lakes. In this study, inherent optical property (IOP)-centered algorithms were developed to estimate the concentrations of chlorophyll-a (Chla, [mg/m3]) and suspended particulate matter (SPM, [g/m3]) and the Chla/SPM ratio (an indicator of the suspended particulate composition) of 118 lakes in the middle and lower reaches of the Yangtze and Huai Rivers (MLYHR) of China using Sentinel-3A/OLCI (Ocean and Land Colour Instrument) data collected from August 2016 to July 2018. The mean Chla concentration and Chla/SPM ratio were high in summer and low in winter, while the mean SPM peaked in winter and decreased in summer. The 94 lakes in the Yangtze River basin had a higher mean Chla concentration (30.94 ± 14.84) and Chla/SPM ratio (0.97 × 10-3 ± 0.60 × 10-3), but a lower mean SPM (44.87 ± 12.61) than the 24 lakes in the Huai River basin (Chla: 27.35 ± 12.18, Chla/SPM: 0.79 × 10-3 ± 0.48 × 10-3, SPM: 47.31 ± 13.40). Regarding the mean values of each lake, Chla and Chla/SPM ratio correlated well with temperature, whereas the wind speed and precipitation had little effect on the variations of suspended particulate matter. Moreover, shipping transportation and sand dredging activities affected the spatial distribution of Chla, SPM, and Chla/SPM in several large lakes (e.g., Lake Poyang and Lake Dongting). Chla/SPM related well with other proxies that express the suspended particulate composition, and had a significant correlation with the Chla-specific absorption coefficient of phytoplankton at 443 nm (aph⁎(443)). The remotely sensed concentration and composition of suspended particulate matter can provide a comprehensive reference for water quality monitoring and expand our knowledge of the trophic status of the lakes.