Mercury is one of the five most toxic heavy metals to the human body. In order to select a high-precision method for predicting the mercury content in soil using hyperspectral techniques, 75 soil samples were collected in Guangdong Province to obtain the soil mercury content by chemical analysis and hyperspectral data based on an indoor hyperspectral experiment. A multiple linear regression (MLR), a back-propagation neural network (BPNN), and a genetic algorithm optimization of the BPNN (GA-BPNN) were used to establish a relationship between the hyperspectral data and the soil mercury content and to predict the soil mercury content. In addition, the feasibility and modeling effects of the three modeling methods were compared and discussed. The results show that the GA-BPNN provided the best soil mercury prediction model. The modeling $R^2$ is 0.842, the root mean square error (RMSE) is 0.052, and the mean absolute error (MAE) is 0.037; the testing $R^2$ is 0.923, the RMSE is 0.042, and the MAE is 0.033. Thus, the GA-BPNN method is the optimum method to predict soil mercury content and the results provide a scientific basis and technical support for the hyperspectral inversion of the soil mercury content.

The rapid development of industrialization and urbanization has posed serious challenges for coastal farmland ecosystems. Source apportionment of soil heavy metals is an effective way for the detection of non-point source pollution in farmland to help support the high-quality development of coastal agriculture. To this end, 113 surface soil samples were collected in the coastal delta of China, and the contents of As, Cd, Cr, Cu, Ni, Pb, and Zn were determined. A variety of models were integrated to apportion the source of soil heavy metals, including positive matrix factorization (PMF), geographical detector (GD), eXtreme gradient boosting (XGBoost), and structural equation modeling (SEM). The result of PMF models revealed that there was collinearity between various heavy metals, and the same heavy metal may have a mixed source. The XGBoost model analysis indicated that there were significant non-linear relationships between soil heavy metals and source factors. A synergy between air quality and human activity factors was the key source of heavy metal that entered the study area, based on the results of the GD. Furthermore, the input path effect of heavy metals in the soil of the study area was quantified by SEM. The balance of evidence from the above models showed that air quality (SO₂ and NO₂) and factories in the study area had the greatest impacts on Cd, Cr, and Zn. Natural sources were dominant for Pb, while As, Cu, and Ni were contributed by soil parent material and factories. The above results led to the conclusion that there was a cycle path in the study area that continuously promoted the migration and accumulation of heavy metals in farmland soil; that is, the heavy metals discharged during oil exploitation and smelting entered the atmosphere and then accumulated in the farmland soil through precipitation, atmospheric deposition, and other paths. In this study, it is shown that a variety of models can be used to more comprehensively assess the sources of soil heavy metals. This approach can provide effective support for the rapid prevention and decision-making management of soil heavy metal pollution in coastal areas.

High nature value farmland (HNVf) plays an important role in improving biodiversity and landscape heterogeneity, and it is effective in curbing soil non-point source pollution and carbon loss in sustainable eco-agricultural systems. To this end, we developed high-resolution (2 m × 2 m) indicators for the identification of potential HNVf based on GF1B remote sensing imaging, including the land cover (LC), normalized difference vegetation index (NDVI), Shannon diversity (SH), and Simpsons index (SI). The statistical results for LC with high resolution (2 m × 2 m) showed that there was 41.05% of intensive farmland in the study area, and the pixel proportion of the HNVf map (above G3) was 44.30%. These HNVf patches were concentrated in the transition zone around the edge of the intensive farmland and around rivers, with characteristics of HNVf type 2 being significantly reflected. Among the real-life areas from Map World, elements (i.e., linear forests, rivers, and semi-natural vegetation etc.) of HNVf accounted for more than 70% of these regions, while a field survey based on potential HNVf patches also exhibited significant HNVf characteristics in comparison with intensive farmlands. In addition, from 2002 to 2020, the total migration distance of the gravity center of intensive farmland in the study area was 7.65 km. Moreover, four landscape indices (patch COH index, landscape division index, SH, and SI) slowly increased, indicating that the species richness and biodiversity were improved. It was also found that a series of ecological protection policies provide effective guarantees for an improvement in species diversity and the development of HNVf in the study area. In particular, the average contents of As, Cr, Cu, Ni, and Zn in the HNVf were 20.99 mg kg⁻¹, 121.11 mg kg⁻¹, 21.97 mg kg⁻¹, 29.34 mg kg⁻¹, and 41.68 mg kg⁻¹, respectively, which were lower in comparison with the intensive farmland soil. This is the first HNVf exploration for landscape and soil pollution assessment in a coastal delta in China, and could provide powerful guidance for the ecological protection of farmland soil and the high-quality development of sustainable agriculture.

To increase the spatial resolution of Soil Moisture Active Passive (SMAP), this study modifies the downscaling factor model based on the Temperature Vegetation Drought Index (TVDI) using data from the Project for On-Board Autonomy (PROBA-V). In the modified model, TVDI parameters were derived from the temperature-vegetation space and the Enhanced Vegetation Index (EVI). This study was conducted in the north China region using SMAP, PROBA-V, and Moderate Resolution Imaging Spectroradiometer satellite images. The 9-km spatial resolution SMAP data was downscaled to 0.3-km spatial resolution soil moisture using a modified downscaling method. Downscaling accuracies from the original and modified downscaling factor models were compared based on field observations. The results show that both methods generated similar spatial distributions in which soil moisture estimates increased as vegetation coverage increased from built-up areas to forest. However, based on the root mean square error between observations and estimations, the modified model demonstrated an increased estimation accuracy of 4.2% for soil moisture compared to the original method. This study also implies that downscaled soil moisture shows promise as a data source for subsequent watershed scale studies.