Urban sustainable development has attracted widespread attention worldwide as it is closely linked with human survival. However, the growth of urban areas is frequently disproportionate in relation to population growth in developing countries; this discrepancy cannot be monitored solely using statistics. In this study, we integrated earth observation (EO) and statistical data monitoring the Sustainable Development Goals (SDG) 11.3.1: “The ratio of land consumption rate to the population growth rate (LCRPGR)”. Using the EO data (including China’s Land-Use/Cover Datasets (CLUDs) and the Defense Meteorological Satellite Program/Operational Linescan System (DMSP/OLS) nighttime light data) and census, we extracted the percentage of built-up area, disaggregated the population using the geographically weighted regression (GWR) model, and depicted the spatial heterogeneity and dynamic tendency of urban expansion and population growth by a 1 km × 1 km grid at city and national levels in mainland China from 1990 to 2010. Then, the built-up area and population density datasets were compared with other products and statistics using the relative error and standard deviation in our research area. Major findings are as follows: (1) more than 95% of cities experienced growth in urban built-up areas, especially in the megacities with populations of 5–10 million; (2) the number of grids with a declined proportion of the population ranged from 47% in 1990–2000 to 54% in 2000–2010; (3) China’s LCRPGR value increased from 1.69 in 1990–2000 to 1.78 in 2000–2010, and the land consumption rate was 1.8 times higher than the population growth rate from 1990 to 2010; and (4) the number of cities experiencing uncoordinated development (i.e., where urban expansion is not synchronized with population growth) increased from 93 (27%) in 1990–2000 to 186 (54%) in 2000–2010. Using EO has the potential for monitoring the official SDGs on large and fine scales; the processes provide an example of the localization of SDG 11.3.1 in China.

Understanding the spatial distribution of populations at a finer spatial scale has important value for many applications, such as disaster risk rescue operations, business decision-making, and regional planning. In this study, a random forest (RF)-based population density mapping method was proposed in order to generate high-precision population density data with a 100 m × 100 m grid in mainland China in 2015 (hereafter referred to as ‘Popi’). Besides the commonly used elevation, slope, Normalized Vegetation Index (NDVI), land use/land cover, roads, and National Polar Orbiting Partnership/Visible Infrared Imaging Radiometer Suite (NPP/VIIRS), 16,101,762 records of points of interest (POIs) and 2867 county-level censuses were used in order to develop the model. Furthermore, 28,505 township-level censuses (74% of the total number of townships) were collected in order to evaluate the accuracy of the Popi product. The results showed that the utilization of multi-source data (especially the combination of POIs and NPP/VIIRS data) can effectively improve the accuracy of population mapping at a finer scale. The feature importances of the POIs and NPP/VIIRS are 0.49 and 0.14, respectively, which are higher values than those obtained for other natural factors. Compared with the Worldpop population dataset, the Popi data exhibited a higher accuracy. The number of accurately-estimated townships was 19,300 (67.7%) in the Popi product and 16,237 (56.9%) in the Worldpop product. The Root Mean Squared Error (RMSE) and Mean Absolute Error (MAE) were 14,839 and 7218, respectively, for Popi, and 18,014 and 8572, respectively, for Worldpop. The research method in this paper could provide a reference for the spatialization of other socioeconomic data (such as GDP).

Accurate monitoring of water bodies is essential for the management and regulation of water resources. Traditional methods for measuring water quality are always time-consuming and expensive; furthermore, it can be very difficult capture the full spatiotemporal variations across regions. Many studies have shown the possibility of remote-sensing-based water monitoring work in many areas, especially for water quality monitoring. However, the use of optical remotely sensed imagery depends on several factors, including weather, quality of images and the size of water bodies. Hence, in this study, the feasibility of optical remote sensing for water quality monitoring in the Republic of Ireland was investigated. To assess the value of remote sensing for water quality monitoring, it is critical to know how well water bodies and the existing in situ monitoring stations are mapped. In this study, two satellite platforms (Sentinel-2 MSI and Landsat-8 OLI) and four indices for separating water and land pixel (Normalized Difference Vegetation Index—NDVI; Normalized Difference Water Index—NDWI; Modified Normalized Difference Water Index—MNDWI; and Automated Water Extraction Index—AWEI) have been used to create water masks for two scenarios. In the first scenario (Scenario 1), we included all pixels classified as water, while for the second scenario (Scenario 2) accounts for potential land contamination and only used water pixels that were completed surround by other water pixels. The water masks for the different scenarios and combinations of platforms and indices were then compared with the existing water quality monitoring station and to the shapefile of the river network, lakes and coastal and transitional water bodies. We found that both platforms had potential for water quality monitoring in the Republic of Ireland, with Sentinel-2 outperforming Landsat due to its finer spatial resolution. Overall, Sentinel-2 was able to map ~25% of the existing monitoring station, while Landsat-8 could only map ~21%. These percentages were heavily impacted by the large number of river monitoring stations that were difficult to map with either satellite due to their location on smaller rivers. Our results showed the importance of testing several indices. No index performed the best across the different platforms. AWEI_nsh (Automated Water Extraction Index—no shadow) and Sentinel-2 outperformed all other combinations and was able to map over 80% of the area of all non-river water bodies across the Republic of Ireland. While MNDWI was the best index for Landsat-8, it was the worst performer for Sentinel-2. This study showed that optical remote sensing has potential for water monitoring in the Republic of Ireland, especially for larger rivers, lakes and transitional and coastal water bodies.

Chlorophyll is widely used to assess the level of eutrophication, which is recognized as one of the major causes of deterioration in water quality, especially across Irish inland waters. The use of optical remote sensing for chlorophyll monitoring has already been shown to be able to complement existing in-situ chlorophyll. However, as cloud cover impacts all optical sensors this limits the usefulness in many locations to develop long term records. A potential solution is to combine information from multiple satellites using a machine learning approach. In this study, we develop long-term time series of chlorophyll concentrations for lakes in Ireland using four different remote sensing platforms: Landsat-8, MODIS, Sentinel-2, and Sentinel-3. Several machine learning approaches have been tested, including K-nearest neighbourhood, random forest (RF), XGBoost (Extreme Gradient Boosting), artificial neutral network (ANN), and support vector regression (SVR).Initial result indicate that a machine learning model utilising all four platforms and in-situ observation is effective in developing long-term chlorophyll concentrations. While the methods are tested and validated for Irish lakes, the methodology has the potential to be applied in a global context.

Water plays a vital role in Earth’s ecosystems, hence, the management and monitoring of water resources are significantly important. Ireland has more than 12200 lakes, 3,192 river water bodies including rivers, streams, and tributaries that exceed in total 70,000 km. However, few of them are currently monitored (2% of lake and 1.8% of river). Earth observation (EO) has shown promise in understanding and monitoring water resources. However, the sizes of Ireland’s water bodies have remained a challenge for EO monitoring.In this research, three different platforms (Landsat 8-OLI, Sentinel-2A and PlanetScope Ortho Scene 3B product) are used to calculate their individual spatial coverage of water bodies across Ireland and quantity their usefulness for water quality monitoring. To explore if water extraction methods impact the results, four different water extract methods (NDWI, NDVI, MNDWI, and AWE have been used to create water masks for Ireland. These water masks created for each platform were then compared with existing map of river network, lakes, and water monitoring stations. The results indicated that AWE’s water mask is the best performing extraction method compared to the existing maps, while the high-resolution platforms (Planetscope and Sentinel-2) clearly outperforms Landsat, Landsat is still able to detect at least 51.90% and 1.63% of rivers. This shows that even the coarser resolution Landsat imagery is useful in monitoring water quality across Ireland.

The water quality in Republic of Ireland is regulated under the water framework directive (WFD), which requires all EU countries achieve good ecological and chemical status before 2027. However, the Irish Environmental Protection Agency (EPA) reports in 2021 that just half of the rivers, lakes, estuaries, and coastal waters achieved satisfactory or higher status.Water quality in Ireland is monitored by traditional methods, which cannot provide timely spatiotemporal information. While remote sensing (RS)-based water quality monitoring work have been carried out in many EU countries in accordance with WFD directive, the use of RS for water quality estimation in Ireland has not been fully explored.To explore the feasibility of RS for Irish waters, Sentinel-2 surface reflectance has been used to assess several water quality parameters (chlorophyll-a, transparency, turbidity, suspended solids (SS), total nitrogen, total phosphorus, biological oxygen demand (BOD), dissolved oxygen, and chemical oxygen demand (COD)) from March 2017 to July 2022. These were compared with the Sentinel-2 surface reflectance data resulting in a total of 6509 corresponding data points.Initially, empirical algorithms were used to derive water quality concentrations in rivers, lakes, estuaries, and coastal waters separately. Initial results indicate that the combination of green and blue bands was correlated to coastal waters’ chlorophyll-a (R2 = 0.27). For chlorophyll-a in transitional waters, the combination of red and red edge was highly correlated. However, no single band or combination were suitable for deriving chlorophyll-a in lakes. For SS, red and near infrared band are useful in detecting changes in coastal and transitional waters. Whereas, for lakes and rivers, blue and shortwave infrared band were best to derive SS. In addition to empirical algorithms, multiple machine learning methods have been used to derive water quality parameters from Sentinel-2 reflectance, with the aim of exploring if machine learning approaches can improve estimates compared with the empirical approaches.