Historical and Operational Monitoring of Surface Sediments in the Lower Mekong Basin Using Landsat and Google Earth Engine Cloud Computing

Abstract

Reservoir construction and land use change are altering sediment transport within river systems at a global scale. Changes in sediment transport can impact river morphology, aquatic ecosystems, and ultimately the growth and retreat of delta environments. The Lower Mekong Basin is crucial to five neighboring countries for transportation, energy production, sustainable water supply, and food production. In response, countries have coordinated to develop programs for regional scale water quality monitoring that including surface sediment concentrations (SSSC); however, these programs are based on a limited number of point measurements and due to resource limitations, cannot provide comprehensive insights into sediment transport across all strategic locations within the Lower Mekong Basin. To augment in situ SSSC data from the current monitoring program, we developed an empirical model to estimate SSSC across the Lower Mekong Basin from Landsat observations. Model validation revealed that remotely sensed SSSC estimates captured the spatial and temporal dynamics in a range of aquatic environments (main stem of Mekong river, tributary systems, Mekong Floodplain, and reservoirs) while, on average, slightly underestimating SSSC by about 2 mg•L −1 across all settings. The operational SSSC model was developed and implemented using Google Earth Engine and Google App Engine was used to host an online application that allows users, without any knowledge of remote sensing, to access SSSC data across the region. Expanded access to

Temporal and Spatial Variations of Secchi Depth and Diffuse Attenuation Coefficient from Sentinel-2 MSI over a Large Reservoir

“…The 6SV is an advanced radiative transfer code capable of accounting for radiation polarization in a mixed molecular-aerosol atmosphere being a vector version of the 6S [33]. The 6S atmospheric correction has already been successfully used in lakes and reservoirs [21,32,[34][35][36][37].…”

Land Cover Mapping in Data Scarce Environments: Challenges and Opportunities

“…Fortunately, in many cases the cost and technical expertise needed to apply these technologies and practices to address gaps is generally decreasing. The availability of high-quality remotely sensed and GIS data has steadily increased with processing packages and large public data repositories such as that hosted by Google Earth Engine (GEE) now freely available to academic, NGO, and public sectors (Gorelick et al, 2017;Markert et al, 2018;Poortinga et al, 2018). Many new tools, sites, and organizations have emerged to increase the power and ease of web-mapping and web-based map and GIS services, while reducing the cost and other barriers to entry.…”

Determination of Vegetation Thresholds for Assessing Land Use and Land Use Changes in Cambodia using the Google Earth Engine Cloud-Computing Platform

“…Phenology-based threshold methods have increasingly gained public and scientific attention in geospatial science. The Phenology-based classification has shown a promising level of accuracy for mapping vegetation, crops, and land cover using moderate to high spatial resolution satellite data [24,27,[54][55][56] with aid of GEE cloud computing technology [23,53,[57][58][59][60][61]. There has been some research effort to study surface phenology of vegetation, rubber plantation, and cropland mapping in tropical regions [62][63][64].…”