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Currently, the inversion of remote sensing satellite images of water environment indicators mostly stays in the indicators with active optical characteristics, while there is less research on the inversion of most water quality indicators with non-optical activity properties, weak scattering and absorption of optical radiation, the size of their concentration has little effect on the spectral characteristics of the water body, such as TOC(Total Organic Carbon). In this paper, based on Pingzhai Reservoir, a dammed river in the karst mountainous area, the inversion model of TOC concentration was established based on BP neural network (BPNN) and sentinel-2 satellite remote sensing images. The results showed that the single bands with high correlation with the measured TOC concentration data were two vegetation red-edge bands B6 (740 nm) and B7 (783 nm) and one NIR band B8 (842 nm), and finally b7, b6 + b7, b7 + b8, b7 ×b8 were selected as the input layers of BPNN for modeling through the combination of the bands, and their Pearson's coefficients were -0.667, -0.656, -0.655, - 0.675. The inverse model established could reach a minimum RMSE of 0.235 mg/L and a maximum R2 of 0.889, which was superior to that of the conventional empirical model. Demonstrate the feasibility of a TOC inversion method based on Sentinel-2 data and BPNN to monitor TOC concentrations in Pingzhai Reservoir. The study successfully established a BP neural network inversion model of TOC concentration in Pingzhai Reservoir with low error, meanwhile, we analyzed the correlation between common water quality indicators and TOC in the reservoir, in which TOC showed significant positive correlation with WT and significant negative correlation with TN and EC, with Pearson's coefficients of 0.655, -0.666, and -0.393, respectively. The article provides scientific theoretical foundation and technical support for water quality protection of water sources. [ABSTRACT FROM AUTHOR]

As the largest contiguous karst area in China, the southwestern karst area is a typical ecologically fragile area affecting local vegetation dynamics. Ecosystem water use efficiency (WUE) is an important factor reflecting the ability of vegetation to produce organic matter with a limited water supply. Therefore, determining the WUE variation trends in this ecologically fragile region is important. In this paper, we used MODIS remote sensing datasets, meteorological data, and land cover data to analyze the spatiotemporal changes in vegetation water use efficiency in the southwestern karst region from 2001 to 2017. We also further quantitatively analyzed the effects of climate change and human activities on the spatial and temporal patterns of vegetation WUE in the study area. The main conclusions were as follows. (1) From 2001 to 2017, in terms of temporal characteristics, the interannual variation in WUE fluctuated greatly, ranging from 1.33 to 1.51 g C kg−1 H2O, with a multiyear average of 1.43 g C kg−1 H2O and an average rate of change of − 0.0046 g C kg−1 H2O year−1. In terms of spatial characteristics, areas with a higher WUE were concentrated in central Sichuan and northeastern Yunnan. (2) The annual average WUE of each vegetation type decreased in the following order: evergreen coniferous forest > evergreen broad-leaved forest > mixed forest > deciduous broad-leaved forest > cultivated land > deciduous coniferous forest > grassland > cultivated land and natural vegetation > shrub forest. (3) The vegetation WUE of 70.66% in this area was positively correlated with temperature. Additionally, 79.68% of the vegetation WUE was negatively correlated with precipitation. The relative contribution rates of climate change and human activities to the change trend in WUE were 15% and 85%, respectively. Compared with WUE results in other studies, the WUE of different karst landform areas obtained in this study was quite different, indicating that the geological and landform features of the karst area are complex. Our study provides scientific support for local vegetation restoration and protection policies and promotes the understanding of the principle of the carbon–water cycle in karst areas. [ABSTRACT FROM AUTHOR]

[Objective] The present paper aimed to reveal the landscape pattern evolution and driving factors of karst rocky desertification area, and provide a reference for optimizing the land landscape pattern, rocky desertification control and regional sustainable development in the demonstration area. [Method] Taking the typical Karst Plateau Bijie Salaxi demonstration area as the research object, the remote sensing images of three key time periods were selected, including ETM in 2005, 2010 and OLI in 2015. 3 S technology and landscape index analysis method were employed to study landscape pattern and the driving factors. [Result] The main landscape of the study area was cultivated land, followed by shrub forest. The changes of landscape transfer were mainly manifested in the transformation from cultivated land to forest, grassland and construction land, which accounted for 73. 31 % and 24. 75 % of the cultivated land transfer respectively in 2010; By 2015, they accounted for 79. 80 % and 19. 99 % respectively. At the landscape type level, the fragmentation degree of cultivated land, forestland and shrub land was increasing, and the fragmentation degree of grassland and construction land increased first and then decreased. Except for the unused land, the landscape patch shape tended to be complicated. In the overall level of the landscape, the landscape of demonstration area had developed in the direction of fragmentation, complexity, discretization and homogenization in the last 10 years. [Conclusion] The implementation of rocky desertification control project, urbanization construction and land abandonment were the main factors leading to the change of landscape pattern. [ABSTRACT FROM AUTHOR]