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Erlong Lake is considered one of the largest lakes in midwest Jilin, China, and one of the drinking water resources in neighboring cities. The present study aims to explore the usage of Landsat TM5, ETM7, and OLI8 images to assess water quality (V-phenol, dissolved oxygen (DO), NH4-N, NO3-N) in Erlong Lake, Jilin province, northeast China. Thirteen multispectral images were used in this study for May, July, August, and September in 2000, 2001, 2002, and October 2020. Radiometric and atmospheric corrections were applied to all images. All in situ water quality parameters were strongly correlated to each other, except DO. The in situ measurements (V-phenol, dissolved oxygen, NH4-N, NO3-N) were statistically correlated with various spectral band combinations (blue, green, red, and NIR) derived from Landsat imagery. Regression analysis reported that there are strong relationships between the estimated and retrieved water quality from the Landsat images. Moreover, in calibrations, the highest value of the coefficient of determination (R2) was ≥0.85 with (RMSE) = 0.038; the lowest value of R2 was >0.30 with RMSE= 0.752. All generated models were validated in different statistical indices; R2 was up to 0.95 for most cases, with RMSE ranging from 1.390 to 0.050. Finally, the empirical algorithms were successfully assessed (V-phenol, dissolved oxygen, NH4-N, NO3-N) in Erlong Lake, using Landsat images with very good accuracy. Both in situ and model retrieved results showed the same trends with non-significant differences. September of 2000, 2001, and 2002 and October of 2020 were selected to assess the spatial distributions of V-phenol, DO, NH4-N, and NO3-N in the lake. V-phenol, NH4-N, and NO3-N were reported low in shallow water but high in deep water, while DO was high in shallow water but low in deep water of the lake. Domestic sewage, agricultural, and urban industrial pollution are the most common sources of pollution in the Erlong Lake. [ABSTRACT FROM AUTHOR]

[Display omitted] • New evaluation index system and perspectives for ECC and its influencing factors in river basin. • ECC showed different geospatial distribution and variation patterns. • The variations of ECC in the DRB are closely associated with the nature and topography factors. Ecological carrying capacity (ECC) is a key indicator for assessing the sustainable development capacity of a region. A comprehensive assessment of regional ECC and the influencing factors has scientific and practical significance for promoting functional and sustainable regional development. The Driver-Pressure-State-Impact-Response-Management (DPSIRM) framwork and subjective-objective combination method were used to develop a framework to establish an ECC evaluation index system. In this study, six internal and six external factors from the perspectives of nature, topography, society, and landscape patterns were selected from the Dongliao River Basin (DRB) in the Jilin province, China. Geographically weighted regression, redundancy analysis, and multiple linear regression models were used to reveal the coupling relationships among the internal and external factors. The results showed that the ECC in the study area was spatially heterogeneous with higher ECC in the southeast and northwest areas of the DRB and lower ECC in the central of DRB had a low carrying capacity. ECC showed a gradual upward trend from 2005 to 2018, which was attributed to the implementation of the ecological environment protection policy in DRB. Primary internal factors that influenced the ECC were precipitation, temperature, altitude, and slope. Precipitation had a positive effect on ECC by influencing Shannon's diversity and landscape shape, while temperature, altitude, and slope had a negative effect on ECC by affecting aggregation and patch density. The methods used herein and the presented results are instructive to sustainable development and ecological restoration in the DRB and other similar areas. [ABSTRACT FROM AUTHOR]