In this study, a coupling model of agricultural and urban non-point source (AUNPS) is established in order to estimate complex non-point source (NPS) pollutant loads in the urban–rural fringe. Furthermore, a spatial–temporal change model of non-point source (CA-AUNPS) is established by the coupling of AUNPS and cellular automata (CA) models. The total nitrogen (TN) and total phosphorus (TP) loads in Tangxun watershed were simulated during 1991–2020, and the results show that: (1) the CA-AUNPS model is an effective tool to simulate the spatial–temporal changes of NPS pollutant loads in urban–rural fringe; (2) in terms of the spatial changes, the TN and TP loads generally showed as rural/urban construction land > farmland > forest/green land, and the high-value areas of NPS pollutant loads expanded from north to south with the increase of construction land; with regard to temporal changes, the TN and TP loads generally displayed an increasing tendency during 1991–2020, and by 2020 the TN and TP loads will increase to 370.06 and 33.89 t yr−1, respectively; (3) the spatial–temporal changes of TN and TP loads in Tangxun watershed correlate strongly with the land-use, slope, and rainfall.

According to the samples of 2924 articles about non point source of SCI and SSCI databases from 1975 to 2010, this study analysed the articles in the growth trend of article outputs, subject categories and journals, international collaborations, geographic distribution and scientific research issues by using bibliometric analysis. The results showed that non point source research steadily increased over the past 35 years and the annual number of articles published in 2010 was 79 times of that in 1975. Non point source was involved into 67 kinds of subjects and appeared in 451 journals. The main study area was concentrated in North America and Europe, following by East Asia. There were 79 countries/territories participated in non point source research, and USA was the largest contributor in non point source research and had a central position in collaboration networks. A keyword analysis indicated that water quality, non point pollutions, and watershed were the hottest issues of non point source research; “GIS, “watershed management”, “modeling”, “simulation”, “monitoring”, and “remote sensing” were the most popular research methods; and “agriculture”, “land use”, “runoff”, and “pollution” were the leading causes of non point pollution.

Accurate classification of land cover data can facilitate the intensive use of urban land and provide scientific and reasonable data support for urban development. Rapid changes in land cover due to economic growth are occurring in the megacities of developing countries more and more. A land cover classification method with a high spatiotemporal resolution and low cost is needed to support sustainable urban development for continuous land monitoring. This study discusses better machine learning algorithms for land cover classification in Ho Chi Minh City. We used band combination 764 and band combination 543 of LANDSAT8-OLI image data to classify the land cover in Ho Chi Minh City by combining three machine learning algorithms: Back-Propagation Neural Network, Support Vector Machine, and Random Forest. We divided the land cover into six types and collected 2221 samples, 60% of which were used for training and 40% for validation. Our results show that using the band combination 764 combined with the Random Forest algorithm is the most appropriate, with an overall classification accuracy of 99.41% and a Kappa coefficient of 0.99. Moreover, it shows a more significant advantage regarding city-level land cover details than other classification products.