Soil nutrients are essential factors that reflect farmland quality. Nitrogen, phosphorus, and potassium are essential elements for plants, while silicon is considered a “quasi-essential” element. This study investigated the spatial distribution of plant nutrients in soil in a hilly region of the Pearl River Delta in China. A total of 201 soil samples were collected from farmland topsoil (0–20 cm) for the analysis of total nitrogen (TN), available phosphorus (AP), available potassium (AK), and available silicon (ASi). The coefficients of variation ranged from 47.88% to 76.91%. The NSRs of TN, AP, AK, and ASi were 0.15, 0. 07, 0.12, and 0.13, respectively. The NSRs varied from 0.02 to 0.20. All variables exhibited weak spatial dependence (R² < 0.5), except for TN (R² = 0.701). After comparing the prediction accuracy of the different methods, we used the inverse distance weighting method to analyze the spatial distribution of plant nutrients in soil. The uniform spatial distribution of AK, TN overall showed a trend of increasing from northeast to southwest, and the overall spatial distribution of AP and ASi showed that the northeast was higher than the southwest. This study provides support for the delimitation of basic farmland protection areas, the formulation of land use spatial planning, and the formulation of accurate farmland protection policies.

Soil fertility affects crop yield and quality. A quick, accurate evaluation of soil fertility is crucial for agricultural production. Few satellite image-based evaluation studies have quantified soil fertility during the crop growth period. Therefore, this study proposes a new approach to the quantitative evaluation of soil fertility. Firstly, the optimal crop spectral variables were selected using the integration of an extreme gradient boosting (XGBoost) algorithm with variance inflation factor (VIF). Then, based on the optimal crop spectral variables where the red-edge indices were introduced for the first time, the estimation models were developed using the backpropagation neural network (BPNN) algorithm to assess soil fertility. The model was finally adopted to map the soil fertility using Sentinel-2 imagery. This study was performed in the Conghua District of Guangzhou, Guangdong Province, China. The results of our research are as follows: (1) five crop spectral variables (inverted red-edge chlorophyll index (IRECI), chlorophyll vegetation index (CVI), normalized green-red difference index (NGRDI), red-edge position (REP), and triangular greenness index (TGI)) were the optimal variables. (2) The BPNN model established with optimal variables provided reliable estimates of soil fertility, with the determination coefficient (R²) of 0.66 and a root mean square error (RMSE) of 0.17. A nonlinear relation was found between soil fertility and the optimal crop spectral variables. (3) The BPNN model provides the potential for soil fertility mapping using Sentinel-2 images, with an R² of 0.62 and an RMSE of 0.09 for the measured and estimated results. This study suggests that the proposed method is suitable for the estimation of soil fertility in paddy fields.

Trace elements in farmland soil are important indicators of soil quality and farmland health, and also maintain the nutrient balance and promote the healthy growth of plants. In this study, taking Conghua District of Guangzhou city as the study area, the effects of topography, soil, land use, and other factors on trace elements in soil were investigated, and the spatial variability of boron (B), manganese (Mn), molybdenum (Mo), copper (Cu), and zinc (Zn) in farmland soil in a typical red soil region were mapped using a geographically weighted regression (GWR) method. The pH and land economic index (LEI) were important factors affecting the changes in trace element concentrations in the five soils, and the Cu and Zn concentrations were clearly affected by human factors. In the study area, 86.99% of B measurements were classified as low and very low levels, 50.61% and 49.20% of Mo measurements were also low and very low, 71.79% of Mn measurements were classified as moderate, while 91.02% of Cu and 52.95% of Zn measurements were classified as high. After a cross validation, the GWR Kriging (GWRK) model results of each element were relatively stable, and the order of the fitting coefficient (R²) was Cu > Zn > B> Mn > Mo. This study clarifies the spatial distribution and influencing factors of soil microelements in the studied region. This information can be used to improve the nutrient imbalance, further guide agricultural production, strengthen the management of farmland, and improve the healthy productivity of cultivated land.

Cropland is a comprehensive system influenced by the natural environment and human activities. This article collects the data of cropland use, soil, and other geographic, social and economic factors in the study area and then uses the methods of system analysis, induction and deduction to propose a new research perspective for establishing a cognitive framework and analyzing cropland resources and their functions. The framework is used to assess the rapidly urbanizing region of Guangzhou and investigate the production, ecological, and living functions provided by cropland resources. Synergistic relationships between functions are analyzed using the hot and cold spot methods. The results indicate that the production function of cropland resources in Guangzhou is good, the ecological function is favorable, and the living function is relatively low. A synergistic relationship between the three functions is observed in 91% of areas of Guangzhou, whereas a balanced relationship occurs in some areas of the southern part of Zengcheng, the northwestern and northeastern parts of Conghua, and the western part of Nansha. This research provides guidance for managing cropland resources and ensuring their sustainable utilization.

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.