Diffuse reflectance spectroscopy (DRS), including visible and near-infrared (VNIR) and mid-infrared (MIR) radiation, is a rapid, accurate and cost-effective technique for estimating soil organic carbon (SOC). We examined 24 soil cores (0–100 cm) from the Sygera Mountains on the Qinghai–Tibet Plateau, considering field-moist intact VNIR, air-dried ground VNIR and air-dried ground MIR spectra at 5-cm intervals. Preprocessed spectra were used to predict the SOC in the soil cores using partial least squares regression (PLSR) and a support vector machine (SVM). The SVM models performed better with three predictors, with the ratio of performance to inter-quartile distance (RPIQ) and R² values typically exceeding 1.74 and 0.73, respectively. The SVM using the DRS technique indicated accurate predictive results of SOC in each core. The RPIQ values of the shrub meadow, forest and total dataset prediction using air-dried ground VNIR were 1.97, 2.68 and 1.99, respectively; the values using field-moist intact VNIR were 1.95, 2.07 and 1.76 and those using air-dried ground MIR were 1.78, 1.96 and 1.74, respectively. We conclude that the DRS technique is an efficient and rapid method for SOC prediction and has the potential for dynamic monitoring of SOC stock density on the Qinghai–Tibet Plateau.

Potentially toxic elements (PTEs) pollution in the agricultural soil of China, especially in developed regions such as the Yangtze River Delta (YRD) in eastern China, has received increasing attention. However, there are few studies on the long-term assessment of soil pollution by PTEs over large regions. Therefore, in this study, a meta-analysis was conducted to evaluate the current state and temporal trend of PTEs pollution in the agricultural land of the Yangtze River Delta. Based on a review of 118 studies published between 1993 and 2020, the average concentrations of Cd, Hg, As, Pb, Cr, Cu, Zn, and Ni were found to be 0.25 mg kg⁻¹, 0.14 mg kg⁻¹, 8.14 mg kg⁻¹, 32.32 mg kg⁻¹, 68.84 mg kg⁻¹, 32.58 mg kg⁻¹, 92.35 mg kg⁻¹, and 29.30 mg kg⁻¹, respectively. Among these elements, only Cd and Hg showed significant accumulation compared with their background values. The eastern Yangtze River Delta showed a relatively high ecological risk due to intensive industrial activities. The contents of Cd, Pb, and Zn in soil showed an increasing trend from 1993 to 2000 and then showed a decreasing trend. The results obtained from this study will provide guidance for the prevention and control of soil pollution in the Yangtze River Delta.

Visible and near-infrared (Vis–NIR) spectroscopy can provide a rapid and inexpensive estimation for soil organic carbon (SOC). However, with respect to field in situ spectroscopy, external environmental factors likely degrade the model accuracy. Among these factors, moisture has the greatest effect on soil spectra. The external parameter orthogonalization (EPO) algorithm in combination with the Chinese soil spectroscopic database (Dataset A, 1566 samples) was investigated to eliminate the interference of the external parameters for SOC estimation. Two different methods of EPO development, namely, laboratory-rewetting archive soil samples and field-collecting actual moist samples, were compared to balance model performance and analytical cost. Memory-based learning (MBL), a local modeling technique, was introduced to compare with partial least square (PLS), a global modeling method. A total of 250 soil samples from Central China were collected. Of these samples, 120 dry ground samples (Dataset B) were rewetted to different moisture levels to develop EPO P1 matrix. Seventy samples (Dataset C) containing field-moist intact and laboratory dry ground soils were used to establish EPO P2 matrix. The remaining 60 samples (Dataset D) also containing field-moist intact and laboratory dry ground soils were employed to validate the spectral models developed based on Dataset A. Results showed that EPO could correct the effect of external factors on soil spectra. For PLS, the validation statistics were as follows: no correction, validation R² = 0.02; P1 correction, validation R² = 0.56; and P2 correction, validation R² = 0.57. For MBL, the validation results were as follows: no correction, validation R² = 0.06; P1 correction, validation R² = 0.65; and P2 correction, validation R² = 0.69. The P2 consistently yielded better results than P1 did but simultaneously increased the sampling time and economic cost. The use of the P1 matrix and the MBL algorithm was recommended because it could reduce the cost of establishing in situ models for SOC.