Rapid and large-scale estimation of soil salt content (SSC) and organic matter (SOM) using multi-source remote sensing is of great significance for the real-time monitoring of arable land quality. In this study, we simultaneously predicted SSC and SOM on arable land in the Yellow River Delta (YRD), based on ground measurement data, unmanned aerial vehicle (UAV) hyperspectral imagery, and Landsat-8 multispectral imagery. The reflectance averaging method was used to resample UAV hyperspectra to simulate the Landsat-8 OLI data (referred to as fitted multispectra). Correlation analyses and the multiple regression method were used to construct SSC and SOM hyperspectral/fitted multispectral estimation models. Then, the best SSC and SOM fitted multispectral estimation models based on UAV images were applied to a reflectance-corrected Landsat-8 image, and SSC and SOM distributions were obtained for the YRD. The estimation results revealed that moderately salinized arable land accounted for the largest proportion of area in the YRD (48.44%), with the SOM of most arable land (60.31%) at medium or lower levels. A significant negative spatial correlation was detected between SSC and SOM in most regions. This study integrates the advantages of UAV hyperspectral and satellite multispectral data, thereby realizing rapid and accurate estimation of SSC and SOM for a large-scale area, which is of great significance for the targeted improvement of arable land in the YRD.

With global urbanization and industrialization, environmental pollution and food safety problems caused by soil heavy metal pollution occur frequently. To realize the safe utilization of cultivated land resources in high-risk areas of heavy metal pollution, we present an approach to safe utilization classification and management in this study. A typical agricultural area around industrial and mining enterprises located in the economic belt of the Yangtze River Delta was chosen as the research area with cultivated soil as the research object. A total of 1,139 geochemical survey sampling sites and soil survey data were used for this research. Initially, the potential ecological risk index was used to assess the potential ecological risk of heavy metals in cultivated soil, and key soil physical and chemical indicators were chosen to assess soil resilience. Next, the safe utilization classification of cultivated land was carried out by combining the potential ecological risk of soil heavy metals with soil resilience. Then, the specific classification management strategy was developed according to ecological risk factor types and crop types. The results showed that the production and operation activities of industrial and mining enterprises in the study area contributed significantly to soil Cd and Hg pollution, and the potential ecological risk of heavy metals in the surrounding soil was high. The soil resilience of cultivated land in the study area was generally not high. The clay content and cation exchange capacity (CEC) were the main strong restrictive indicators, while CaO, soil organic carbon (SOC), and pH were the main medium restrictive indicators. Natural conditions such as topography, hydrogeology, and soil-forming parent material differed by region, resulting in different restrictive factors. There were 16 safe utilization types in the study area. Different management strategies were proposed based on various soil potential ecological risks, soil resilience, ecological risk factor types, and crop types. This study can offer fresh perspectives on the safe utilization classification and management of land resources in high-risk areas of soil pollution and serve as a reference for sustainable intensification.

The Yellow River Delta region is one of the estuarine deltas with the fastest land building speed, and it is an important region for the study of landscape pattern change due to its diverse variety of landscape types. By analyzing the dynamic degree, landscape type transfer matrix, and landscape indices of landscape types in the Yellow River Delta region in 2005, 2012, and 2018, this paper found that the area of construction land, salt fields, and breeding ponds in the Yellow River Delta region has increased to a large extent, with an increase in the aggregation degree and the utilization rate of this landscape type, and the landscape has developed toward the direction of aggregation and unification. The increase in construction land area mainly comes from the transfer of cropland area, part of which is occupied in order to adapt to urban expansion, and the salt fields and breeding ponds mainly come from the transfer of waters and mudflats, which can be seen as the main utilization direction of the water landscape. Moreover, unused land has increased with the degree of dispersion and fragmentation of development and utilization, so the exploitation and utilization of unused land still needs to be optimized. Through the analysis of the dynamic change in landscape pattern, we can explore the direction and extent of the evolution of landscape types, which has certain guiding significance for the sustainable use of land resources and the sustainable development of economy in the Yellow River Delta region.

An efficient, convenient, and accurate method for monitoring the distribution characteristics of soil salinity is required to effectively control the damage of saline soil to the land environment and maintain a virtuous cycle of the ecological environment. There are still problems with single-monitoring data that cannot meet the requirements of different regional scales and accuracy, including inconsistent band reflectance between multi-source sensor data. This article proposes a monitoring method based on the multi-source data fusion of unmanned aerial vehicle (UAV) multispectral remote sensing, Sentinel-2A satellite remote sensing, and ground-measured salinity data. The research area and two experimental fields were located in the Yellow River Delta (YRD). The results show that the back-propagation neural network model (BPNN) in the comprehensive estimation model is the best prediction model for soil salinity (modeling accuracy R² reaches 0.769, verification accuracy R² reaches 0.774). There is a strong correlation between the satellite and UAV imagery, while the Sentinel-2A imagery after reflectivity correction has a superior estimation effect. In addition, the results of dynamic analysis show that the area of non-saline soil and mild-saline soil decreased, while the area of moderately and heavily saline soils and solonchak increased. Additionally, the average area share of different classes of saline soils distributed over the land use types varied in order, from unused land > grassland > forest land > arable land, where the area share of severe-saline soil distributed on unused land changed the most (89.142%). In this study, the results of estimation are close to the true values, which supports the feasibility of the multi-source data fusion method of UAV remote sensing satellite ground measurements. It not only achieves the estimation of soil salinity and monitoring of change patterns at different scales, but also achieve high accuracy of soil salinity prediction in ascending scale regions. It provides a theoretical scientific basis for the remediation of soil salinization, land use, and environmental protection policies in coastal areas.