Soil organic carbon (SOC) is an essential property for soil function, fertility and sustainability of agricultural systems. It can be measured with visible and near-infrared reflectance (VIS-NIR) spectroscopy efficiently based on empirical equations and spectra data for air/oven-dried samples. However, the spectral signal is interfered with by soil moisture content (MC) under in situ conditions, which will affect the accuracy of measurements and calibration transfer among different areas. This study aimed to (1) quantify the influences of MC on SOC prediction by VIS-NIR spectroscopy; and (2) explore the potentials of orthogonal signal correction (OSC) and generalized least squares weighting (GLSW) methods in the removal of moisture interference. Ninety-eight samples were collected from the Jianghan plain, China, and eight MCs were obtained for each sample by a rewetting process. The VIS-NIR spectra of the rewetted soil samples were measured in the laboratory. Partial least squares regression (PLSR) was used to develop SOC prediction models. Specifically, three validation strategies, namely moisture level validation, transferability validation and mixed-moisture validation, were designed to test the potentials of OSC and GLSW in removing the MC effect. Results showed that all of the PLSR models generated at different moisture levels (e.g., 50–100, 250–300 g·kg⁻¹) were moderately successful in SOC predictions (r²_pre = 0.58–0.85, RPD = 1.55–2.55). These models, however, could not be transferred to soil samples with different moisture levels. OSC and GLSW methods are useful filter transformations improving model transferability. The GLSW-PLSR model (mean of r²_pre = 0.77, root mean square error for prediction (RMSEP) = 3.08 g·kg⁻¹, and residual prediction deviations (RPD) = 2.09) outperforms the OSC-PLSR model (mean of r²_pre = 0.67, RMSEP = 3.67 g·kg⁻¹, and RPD = 1.76) when the moisture-mixed protocol is used. Results demonstrated the use of OSC and GLSW combined with PLSR models for efficient estimation of SOC using VIS-NIR under different soil MC conditions.

Niche and neutral theories emphasize different processes contributing to the maintenance of species diversity. In this study, we calculated the local contribution to beta diversity (LCBD) of every cell, using variation partitioning in combination with spatial distance and environmental variables of the 25-ha Badagongshan plot (BDGS), to determine the contribution of environmentally-related variation versus pure spatial variation. We used topography and soil characteristics as environmental variables, distance-based Moran’s eigenvectors maps (dbMEM) to describe spatial relationships among cells and redundancy analysis (RDA) to apportion the variation in beta diversity into three components: pure environmental, spatially-structured environmental and pure spatial. Results showed LCBD values were negatively related to number of common species and positively related to number of rare species. Environment and space jointly explained ~60% of the variation in species composition; soil variables alone explained 21.6%, slightly more than the topographic variables that explained 15.7%; topography and soil together explained 27%, slightly inferior to spatial variables that explained 34%. The BDGS forest was controlled both by the spatial and environmental variables and the results were consistent across different life forms and life stages.

The transferability of a visible and near-infrared (VNIR) model for soil organic matter (SOM) estimation in riparian landscapes is explored. The results indicate that for the soil samples with air-drying, grinding and 2-mm sieving pretreatment, the model calibrated from the soil sample set with mixed land-use types can be applied in the SOM prediction of cropland soil samples (r²_Pre = 0.66, RMSE = 2.78 g∙kg⁻¹, residual prediction deviation (RPD) = 1.45). The models calibrated from cropland soil samples, however, cannot be transferred to the SOM prediction of soil samples with diverse land-use types and different SOM ranges. Wavelengths in the region of 350–800 nm and around 1900 nm are important for SOM estimation. The correlation analysis reveals that the spectral wavelengths from the soil samples with and without the air-drying, grinding and 2-mm sieving pretreatment are not linearly correlated at each wavelength in the region of 350–1000 nm, which is an important spectral region for SOM estimation in riparian landscapes. This result explains why the models calibrated from samples without pretreatment fail in the SOM estimation. The Kennard–Stone algorithm performed well in the selection of a representative subset for SOM estimation using the spectra of soil samples with pretreatment, but failed in soil samples without the pretreatment. Our study also demonstrates that a widely applicable SOM prediction model for riparian landscapes should be based on a wide range of SOM content.