High-spatial-resolution images play an important role in land cover classification, and object-based image analysis (OBIA) presents a good method of processing high-spatial-resolution images. Segmentation, as the most important premise of OBIA, significantly affects the image classification and target recognition results. However, scale selection for image segmentation is difficult and complicated for OBIA. The main challenge in image segmentation is the selection of the optimal segmentation parameters and an algorithm that can effectively extract the image information. This paper presents an approach that can effectively select an optimal segmentation scale based on land object average areas. First, 20 different segmentation scales were used for image segmentation. Next, the classification and regression tree model (CART) was used for image classification based on 20 different segmentation results, where four types of features were calculated and used, including image spectral bands value, texture value, vegetation indices, and spatial feature indices, respectively. WorldView-3 images were used as the experimental data to verify the validity of the proposed method for the selection of the optimal segmentation scale parameter. In order to decide the effect of the segmentation scale on the object area level, the average areas of different land objects were estimated based on the classification results. Experiments based on the multi-scale segmentation scale testify to the validity of the land object’s average area-based method for the selection of optimal segmentation scale parameters. The study results indicated that segmentation scales are strongly correlated with an object’s average area, and thus, the optimal segmentation scale of every land object can be obtained. In this regard, we conclude that the area-based segmentation scale selection method is suitable to determine optimal segmentation parameters for different land objects. We hope the segmentation scale selection method used in this study can be further extended and used for different image segmentation algorithms.

Research on the consistency of suspended particulate matter (SPM) concentration retrieved from multisource satellite sensors can serve as long-time monitoring of water quality. To explore the influence of the atmospheric correction (AC) algorithm and the retrieval model on the consistency of the SPM concentration values, Landsat 8 Operational Land Imager (OLI) and Sentinel 2 MultiSpectral Imager (MSI) images acquired on the same day are used to compare the remote sensing reflectance (Rrs) SPM retrieval values in two high-turbidity lakes. An SPM retrieval model for Shengjin Lake is established based on field measurements and applied to OLI and MSI images: two SPM concentration products are highly consistent (R² = 0.93, Root Mean Squared Error (RMSE) = 20.67 mg/L, Mean Absolute Percentage Error (MAPE) = 6.59%), and the desired results are also obtained in Chaohu Lake. Among the four AC algorithms (Management Unit of the North Seas Mathematical Models (MUMM), Atmospheric Correction for OLI’lite’(ACOLITE), Second Simulation of Satellite Signal in the Solar Spectrum (6S), Landsat 8 Surface Reflectance Code & Sen2cor (LaSRC & Sen2cor)), the two Rrs products, as well as the final SPM concentration products retrieved from OLI and MSI images, have the best consistency when using the MUMM algorithm in SeaWIFS Data Analyst System (SeaDAS) software. The consistency of SPM concentration values retrieved from OLI and MSI images using the same model or same form of models is significantly better than that retrieved by applying the optimal models with different forms.

Remote sensing is useful for quantifying water-quality parameters for managing inland water systems. However, the single water-quality retrieval model usually has poor applicability in large regions. To solve the issue of low retrieval accuracy of water-quality parameters in inland water, the study area herein is geographically divided into rural water and urban water according to the proportion of land-use types in the riparian zones. Furthermore, the machine-learning regression algorithms are used to construct the retrieval models suitable for the total nitrogen (TN) and total phosphorus (TP) concentrations based on the measured water-quality data and the simultaneous Sentinel-2 Multispectral Imager (MSI) images. Additionally, the optical retrieval models are applied to the MSI images acquired on different dates to analyze the variations of TN and TP concentrations in the water around Chaohu Lake of China. The results show that the three accuracy indices of determination coefficient (R²), mean square error (MSE), and mean absolute percentage error (MAPE) of the TN concentration retrieval models for rural water and urban water were 0.67, 0.37 mg/L, and 36.81%, and 0.78, 0.34 mg/L, and 8.34%, respectively, while those of the TP concentration retrieval model for rural water and urban water reached 0.46, 0.0034 mg/L, and 38.60%, and 0.58, 0.018 mg/L, and 37.57%, respectively. The accuracy of the TN and TP concentration retrieval model constructed using geographical division is significantly better than that which does not use geographical division. According to the retrieval results from MSI images, the TN and TP concentrations in urban water are higher than those in rural water. TN and TP concentrations in urban water are stable throughout the year and peak in December, while those of rural water are highest in March and lowest in November. The method proposed in this study can provide a new idea for improving the retrieval accuracy of water-quality parameters in different water bodies in a large-scale region, and the relevant conclusion can provide a theoretical basis for water pollution control and prevention strategies in agricultural basins.

Nitrate (NO₃⁻) pollution is a serious global problem, and the quantitative analysis of its sources contributions is essential for devising effective water-related environmental-protection policies. The Shengjin Lake basin, located in the middle to lower reaches of the Yangtze River in China was selected as the research area in our study. We first grouped 29 surface water samples and 33 groundwater samples using cluster analysis, and then analyzed potential nitrate sources for each dataset of δ¹⁵N–NO₃⁻ and δ¹⁸O–NO₃⁻ isotope values by applying a Bayesian isotope-mixing model. Our results show that the nitrogen pollution in the surface-ground water in the study area seriously exceeded to class V of the Environmental Quality Standard of Surface Water of China. The NO₃⁻ in surface water from the mid-upper reaches of the drainage basin mainly originates from soil nitrogen (SN) and chemical fertilizer (CF), with contribution rates of 48% and 32%, respectively, and the NO₃⁻ in downstream areas mainly originates from CF and manure and sewage (MS), with contribution rates of 48% and 33%, respectively. For the groundwater samples, NO₃⁻ mainly originates from MS, CF, and SN in the mid-upper reaches of the drainage basin and the northside of Dadukou near the Yangtze River, with contribution rates of 34%, 31%, and 29%, respectively, whereas NO₃⁻ in the lower reaches and the middle part of Dadukou mainly originates from MS, with a contribution rate of 83%. The nitrogen conversion of surface water in lakes and in the mid-upper reaches is mainly affected by water mixing, while the groundwater and surface water in the lower plains are mainly affected by denitrification. The method proposed in this study can expand the ideas for tracking nitrate pollution in areas with complex terrain, and the relevant conclusions can provide a theoretical basis for surface and groundwater pollution control in the hilly basin of Yangtze River.