Since its official establishment in 2010, Google Earth Engine (GEE) has developed rapidly and has played a significant role in the global remote sensing community. A bibliometric analysis was conducted on 1995 peer-reviewed articles related to GEE, indexed in the Scopus database up to December 2022 to investigate its trends and main applications. Our main findings are as follows: (1) The number of GEE-related articles has increased rapidly, with nearly 85% of them published in the last three years; (2) The top three domains where GEE has been extensively applied are earth and planetary sciences, environmental science, and agricultural and biological sciences. The majority of GEE-related articles were authored by scholars from China and the US, accounting for 58% of the total, with US scholars having the largest impact on the community, contributing to over 50% of the total citations; (3) Remote Sensing published the highest number of articles (26.82%), whereas Remote Sensing of Environment received the highest number of citations (30.40%); (4) The applications of GEE covered a broad range of topics, with a focus on land applications, water resource applications, climate change, and crop mapping; (5) Landsat imagery were the most popular and widely used dataset; and (6) Random forest, decision trees, support vector machines were the most commonly used machine learning algorithms in GEE. Although having a few limitations, this type of analysis should be conducted regularly to observe the development of this field on a regular basis, as the number of publications related to GEE is expected to continue to increase strongly in the coming years.

Coastal wetlands in the Mekong Delta (MD), Vietnam, provide various vital ecosystem services for the region. These wetlands have experienced critical changes due to the increase in regional anthropogenic activities, global climate change, and the associated sea level rise (SLR). However, documented information and research on the dynamics and drivers of these important wetland areas remain limited for the region. The present study aims to determine the long-term dynamics of wetlands in the south-west coast of the MD using remote sensing approaches, and analyse the potential factors driving these dynamics. Wetland maps from the years 1995, 2002, 2013, and 2020 at a 15 m spatial resolution were derived from Landsat images with the aid of a hybrid classification approach. The accuracy of the wetland maps was relatively high, with overall accuracies ranging from 86–93%. The findings showed that the critical changes over the period 1995/2020 included the expansion of marine water into coastal lands, showing 129% shoreline erosion; a remarkable increase of 345% in aquaculture ponds; and a reduction of forested wetlands and rice fields/other crops by 32% and 73%, respectively. Although mangrove forests slightly increased for the period 2013/2020, the overall trend was also a reduction of 5%. Our findings show that the substantial increase in aquaculture ponds is at the expense of mangroves, forested wetlands, and rice fields/other crops, while shoreline erosion significantly affected coastal lands, especially mangrove forests. The interaction of a set of environmental and socioeconomic factors were responsible for the dynamics. In particular, SLR was identified as one of the main underlying drivers; however, the rapid changes were directly driven by policies on land-use for economic development in the region. The trends of wetland changes and SLR implicate their significant effects on environment, natural resources, food security, and likelihood of communities in the region sustaining for the long-term. These findings can assist in developing and planning appropriate management strategies and policies for wetland protection and conservation, and for sustainable development in the region.

Deltas are among the most productive and diverse global ecosystems. However, these regions are highly vulnerable to natural disasters and climate change. Nature-based solutions (Nbs) have been increasingly adopted in many deltas to improve their resilience. Among decision support tools, assessment of ecosystem services (ES) through spatially explicit modelling plays an important role in advocating for Nbs. This study explores the use of the Land Utilisation and Capability Indicator (LUCI) model, a high-resolution model originally developed in temperate hill country regions, to map changes in multiple ecosystem services (ES), along with their synergies and trade-offs, between 2010 and 2018 in the Vietnamese Mekong Delta (VMD). In so doing, this study contributes to the current knowledge in at least two aspects: high-resolution ES modelling in the VMD, and the combination of ES biophysical and economic values within the VMD to support Nbs implementation. To date, this is the highest resolution (5 by 5 m) ES modelling study ever conducted in the VMD, with ~1500 million elements generated per ES. In the process of trialling implementations of LUCI within the VMD’s unique environmental conditions and data contexts, we identify and suggest potential model enhancements to make the LUCI model more applicable to the VMD as well as other tropical deltaic regions. LUCI generated informative results in much of the VMD for the selected ES (flood mitigation, agriculture/aquaculture productivity, and climate regulation), but challenges arose around its application to a new agro-hydrological regime. To address these challenges, parameterising LUCI and reconceptualising some of the model’s mechanisms to specifically account for the productivity and flood mitigation capability of water-tolerant crops as well as flooding processes of deltaic regions will improve future ES modelling in tropical deltaic areas. The ES maps showed the spatial heterogeneity of ES across the VMD. Next, to at least somewhat account for the economic drivers which need to be considered alongside biophysical valuations for practical implementations of ES maps for nature-based solutions (Nbs) in the upstream VMD, economic values were assigned to different parcels using a benefit transfer approach. The spatially explicit ES economic value maps can inform the design of financing incentives for Nbs. The results and related work can be used to support the establishment of Nbs that ultimately contribute to the security of local farmers’ livelihoods and the sustainability of the VMD.

The transition from a command to a market economy resulted in widespread cropland abandonment across the former Soviet Union during the 1990s. Spatial patterns and determinants of abandonment are comparatively well understood for European Russia, but have not yet been assessed for the vast grain belt of Western Siberia, situated in the Eurasian forest steppe. This is unfortunate, as land-use change in Western Siberia is of global significance: Fertile black earth soils and vast mires store large amounts of organic carbon, and both undisturbed and traditional cultural landscapes harbor threatened biodiversity. We compared Landsat images from ca. 1990 (before the break-up of the Soviet Union) and ca. 2015 (current situation) with a supervised classification to estimate the extent and spatial distribution of abandoned cropland. We used logistic regression models to reveal important determinants of cropland abandonment. Ca. 135,000 ha classified as cropland around 1990 were classified as grassland around 2015. This suggests that ca. 20% of all cropland remain abandoned ca. 25 years after the end of the Soviet Union. Abandonment occurred mostly at poorly drained sites. The likelihood of cropland abandonment increased with decreasing soil quality, and increasing distance to medium-sized settlements, roads and railroads. We conclude that soil suitability, access to transport infrastructure and availability of workforce are key determinants of cropland abandonment in Western Siberia.

In this study, a bibliometric analysis is conducted to evaluate bibliographic content of all scientific publications in the field of remote sensing published by scholars with Vietnamese affiliations, indexed in the Scopus database, for the 2000–2019 period. Results indicated that the number of scientific publications in this research field increased gradually by years and then rapidly from 2015 with at least 50 articles per year. Most of the studies were concentrated in a few research institutions, and 65% of the publications were from top-10 Vietnamese research institutions. Vietnamese scholars had the most international collaboration with the USA, followed by Germany and Japan. The Mekong Delta is the most frequently studied area. Climate change, land-use change, groundwater, and landslides were the most popular research topics; machine learning and artificial neural networks were the most popular techniques studied in recent years.