Estimation of surface water quality parameters based on hyper-spectral and 3D-EEM fluorescence technologies in the Ebinur Lake Watershed, China

Extracting soil salinization information with a fractional-order filtering algorithm and grid-search support vector machine (GS-SVM) model

Estimation of soil salt content (SSC) in the Ebinur Lake Wetland National Nature Reserve (ELWNNR), Northwest China, based on a Bootstrap-BP neural network model and optimal spectral indices

Recognizing the Relationship between Spatial Patterns in Water Quality and Land-Use/Cover Types: A Case Study of the Jinghe Oasis in Xinjiang, China

Estimating Soil Salt Content Using Fractional Derivatives and Optional Spectral Indices in the Ebinur Lake Oasis, Northwestern China

Effects of land use/cover on surface water pollution based on remote sensing and 3D-EEM fluorescence data in the Jinghe Oasis

New methods for improving the remote sensing estimation of soil organic matter content (SOMC) in the Ebinur Lake Wetland National Nature Reserve (ELWNNR) in northwest China

Multi-scale analysis of the relationship between landscape patterns and a water quality index (WQI) based on a stepwise linear regression (SLR) and geographically weighted regression (GWR) in the Ebinur Lake oasis

Spectral response characteristics and identification of typical plant species in Ebinur lake wetland national nature reserve (ELWNNR) under a water and salinity gradient

Recognition of spatial framework for water quality and its relation with land use/cover types from a new perspective: A case study of Jinghe Oasis in Xinjiang, China

Quality evaluation for surface water is an important issue in water resource management and protection. To understand the relation between the spatial framework of water quality in Jinghe Oasis and the change in land use/cover type, we first divide 47 water sampling sites measured in May and October 2015 into 6 cluster layers using the self-organizing map (SOM) method based on non-hierarchical <i>k</i>-means classification, and then determine the distribution characteristics of water quality from the time sequence. Water quality indices include chemical oxygen demand (COD), biological oxygen demand (BOD), suspended solids (SS), total phosphorus (TP), total nitrogen (TN), ammonia nitrogen (NH₃−N), chromaticity (SD), and turbidity (NUT). On the basis of the results, we collect data regarding changes in farmland land, forest-grass land, water body, salinized land, and other land types during the wet and dry seasons and combine these data with the classification results of the GF-1 remote sensing satellite obtained in May and October 2015. We then discuss the influences of land use/cover type on water quality at different layers and seasons. The results indicate that Clusters 1 to 3 provide monitoring samples for the wet season (May 2015), whereas Clusters 4 to 6 provide monitoring samples for the dry season (October 2015). In general, the COD, SS, NUT, TN, and NH₃−N contents are high in Clusters 1 and 2. The SD values for Clusters 1, 4, and 6 are high. Moreover, high BOD and TP values are mainly concentrated in Clusters 4 and 6. Through the discussion on the relation between different layers of water quality and land use/cover type change, we determine that the influences of farmland land, forest-grassland, and salinized land are significant on the water quality parameters in Jinghe Oasis. In Clusters 1, 2, and 6, the size of the water area also influences the change in water quality parameters to a certain extent. In addition, the influences of various land use/cover types on the water quality parameters in the research zone during different seasons exhibit the following order: farmland land → forest-grass land → salinized land → water body → others. Moreover, influence is less during the wet season than during the dry season. In conclusion, developing research on the relation between the spatial framework of water quality in Jinghe Oasis and land use/cover type change will be significant for the time sequence distribution of water quality in arid regions from both theoretical and practical perspectives.