Significant loss of soil inorganic carbon at the continental scale.

Widespread soil acidification due to atmospheric acid deposition and agricultural fertilization may greatly accelerate soil carbonate dissolution and CO2 release. However, to date, few studies have addressed these processes. Here, we use meta-analysis and nationwide-survey datasets to investigate changes in soil inorganic carbon (SIC) stocks in China. We observe an overall decrease in SIC stocks in topsoil (0-30 cm) (11.33 g C m-2 yr-1) from the 1980s to the 2010s. Total SIC stocks have decreased by ∼8.99 ± 2.24% (1.37 ± 0.37 Pg C). The average SIC losses across China (0.046 Pg C yr-1) and in cropland (0.016 Pg C yr-1) account for ∼17.6%-24.0% of the terrestrial C sink and 57.1% of the soil organic carbon sink in cropland, respectively. Nitrogen deposition and climate change have profound influences on SIC cycling. We estimate that ∼19.12%-19.47% of SIC stocks will be further lost by 2100. The consumption of SIC may offset a large portion of global efforts aimed at ecosystem carbon sequestration, which emphasizes the importance of achieving a better understanding of the indirect coupling mechanisms of nitrogen and carbon cycling and of effective countermeasures to minimize SIC loss.

[Prediction of total nitrogen and alkali hydrolysable nitrogen content in loess using hyperspectral data based on correlation analysis and partial least squares regression].

Wuqi County of Shaanxi Province, where the vegetation recovering measures have been carried out for years, was taken as the study area. A total of 100 loess samples from 24 different profiles were collected. Total nitrogen (TN) and alkali hydrolysable nitrogen (AHN) contents of the soil samples were analyzed, and the soil samples were scanned in the visible/near-infrared (VNIR) region of 350-2500 nm in the laboratory. The calibration models were developed between TN and AHN contents and VNIR values based on correlation analysis (CA) and partial least squares regression (PLS). Independent samples validated the calibration models. The results indicated that the optimum model for predicting TN of loess was established by using first derivative of reflectance. The best model for predicting AHN of loess was established by using normal derivative spectra. The optimum TN model could effectively predict TN in loess from 0 to 40 cm, but the optimum AHN model could only roughly predict AHN at the same depth. This study provided a good method for rapidly predicting TN of loess where vegetation recovering measures have been adopted, but prediction of AHN needs to be further studied.

[Accumulation Characteristics and Evaluation of Heavy Metals in Soil-Crop System Affected by Wastewater Irrigation Around a Chemical Factory in Shenmu County].

Soil heavy metals Cu, Pb, Zn, and Cd, are regarded as "chemical time bombs" because of their propensity for accumulation in the soil and uptake by crops. This ultimately causes human toxicity in both the short and long-term, making farmland ecosystems dangerous to health. In this paper, accumulation and spatial variability of Cu, Zn, Pb and Cd in soil-crop system affected by wastewater irrigation around a chemical factor in northern Shaanxi province were analyzed. Results showed that wastewater irrigation around the chemical factory induced significant accumulation in soils compared with control areas. The average concentrations of available Cu and total Cu were 4.32 mg x kg(-1) and 38.4 mg x kg(-1), which were twice and 1.35 times higher than those of the control area, respectively. Soil Zn and Pb were slightly accumulated. Whereas soil Cd was significantly accumulated and was higher than the critical level of soil environmental quality (II), the available and total Cd concentrations were 0.248 mg x kg(-1) and 1.21 mg x kg(-1), which were 10 and 6.1 times higher than those of the control areas. No significant correlations were found between available and total heavy metals except between available Cd and total Cd. All the heavy metals were mainly accumulated in the top layer (0-10 cm). Spatially, soils and plants high in heavy metal concentration were distributed within the radius of about 100 m from the waste water outlet for Cu, Zn and Cd and about 200 m for Pb, and decreased exponentially with the distance from the factory. Affected by wastewater irrigation, contents of Cu, Pb and Cd in maize were 4.74, 0.129 and 0.036 mg x kg(-1) which were slightly higher than those in the control area. The content of Zn was similar to that in the control area. Affected by the vehicle exhaust, the over standard rate of Pb was 5.7% in maize. All the heavy metals did not show significant correlation between soil and crop, except Cd. The square correlation coefficients were 0.83 and 0.75 between soil available and total Cd with maize. Therefore, the chemical factory contributed to the accumulation of heavy metals in the soil around it, but the contribution to the crop was limited, and thus temporarily caused no threat to human health. The reason for the lower accumulation was the high soil pH and low soil organic matter content. But more attentions should be paid to the higher accumulation of Pb in the maize caused by the vehicle and dust.

[Study of spatial interpolation of soil Cd contents in sewage irrigated area based on soil spectral information assistance].

To acquire the accuracy distribution information of soil heavy metal, improving interpolation precision is very important for agricultural safety production and soil environment protection. In the present study, the spatial variation and Cokriging interpolation of soil Cd was studied in a sewage irrigation area. Fifty two soil samples were collected to measure the contents of soil total Cd (TCd), available Cd (ACd), pH, organic matter (OM), iron oxide (Fe2 O3) and soil reflection spectrum. Through correlation analysis, it was found that TCd and ACd had a significant correlation with soil first-order differential spectrum (-0.585** at 759 nm and -0.551** at 719 nm, respectively), which were much higher than the correlation coefficients between soil Cd contents and other environmental variables (pH, OM and Fe2O3). The spatial patterns of soil Cd were predicted by Cokriging which used soil first-order differential spectrum as covariate. Compared with the Kriging, the root-mean-square error decreased by 8.22% for TCd and 20.09% for ACd, respectively; the correlation coefficients between the predicted values and measured values increased by 27.45% for TCd and by 53.13% for ACd, respectively. Meanwhile, the prediction accuracy improved by Cokriging with soil spectrum as covariate was still higher than by Cokriging with soil environment variables (OM and Fe2O3). Therefore, it was found that Cokriging was a more accurate interpolation method which could provide more precise distribution information of soil heavy metal. At the same time, soil reflection spectrum was shown to be more economic, time-saving and easier to acquire than these usual environment variables, which indicated that soil spectrum information is more suited as a covariate used in Cokriging.

[Diagnosis of phosphorus nutrition in winter wheat based on first derivative spectra and radial basis function neural network].

The hyperspectral leaf reflectance in winter wheat was measured under 4 phosphorus levels at different growth stages, i.e. revival stage, jointing stage, tassel stage and grouting stage. And their first derivative of spectra were calculated and denoised by the threshold denoising method based on wavelet transform. After studying characteristics of the two kinds of spectra resulting from different phosphorus contents levels as well as correlations between leaf phosphorus contents and spectral values, sensitive wavebands and four kinds of absorption areas were extracted. Then the four kinds of absorption areas and their corresponding leaf phosphorus content were normalized and input to RBFNN. Results show that: (1) Sensitive wavebands for monitoring leaf phosphorus contents in original leaf spectra are 426-435 and 669-680 nm. (2) Sensitive wavebands in first derivative of spectra are 481-493 and 685-696 nm. (3) Trained RBFNN can learn and seize the linearity/non-linearity mapping between samples and output targets.

[Extraction of first derivative spectrum features of soil organic matter via wavelet de-noising].

The hyperspectral reflectance of soil was measured by a ASD FieldSpec within 400-1 000 nm. Next, its first derivative of spectra were acquired and de-noised by the threshold de-noising method based on wavelet transform. From the de-noised derivative spectra, absorption areas used as indicatoresas for soil organic matter content were acquired by numerical integration. Results show that: (1) Because of much noise, it is difficult to identify spectra contour and features in the first derivative of soil spectra resulting from different organic content levels. (2) When the scale of wavelet decomposition was 3, the threshold de-noising method based on wavelet transform can keep the balance between smoothing curve and holding spectra features. (3) Absorption area S (538, 586) is extracted from de-noised first derivative of soil spectra, and the coefficient of correlation between it and organic matter content is 0.896 3.

[Research on identification of species of fruit trees by spectral analysis].

Using the spectral reflectance data (R2) of canopies, the present paper identifies seven species of fruit trees bearing fruit in the fruit mature period. Firstly, it compares the fruit tree species identification capability of six kinds of satellite sensors and four kinds of vegetation index through re-sampling the spectral data with six kinds of pre-defined filter function and the related data processing of calculating vegetation indexes. Then, it structures a BP neural network model for identifying seven species of fruit trees on the basis of choosing the best transformation of R(lambda) and optimizing the model parameters. The main conclusions are: (1) the order of the identification capability of the six kinds of satellite sensors from strong to weak is: MODIS, ASTER, ETM+, HRG, QUICKBIRD and IKONOS; (2) among the four kinds of vegetation indexes, the identification capability of RVI is the most powerful, the next is NDVI, while the identification capability of SAVI or DVI is relatively weak; (3) The identification capability of RVI and NDVI calculated with the reflectance of near-infrared and red channels of ETM+ or MODIS sensor is relatively powerful; (4) Among R(lambda) and its 22 kinds of transformation data, d1 [log(1/R(lambda))](derivative gap is set 9 nm) is the best transformation for structuring BP neural network model; (5) The paper structures a 3-layer BP neural network model for identifying seven species of fruit trees using the best transformation of R(lambda) which is d1 [log(1/R(lambda))](derivative gap is set 9 nm).

[Dynamic changes of land desertification landscape pattern in agriculture and pasturage interlaced zone of northern Shaanxi].

By using the 1986, 1993 and 2003 Landsat TM images and with the help of GIS, the dynamic changes of land desertification landscape pattern in agriculture and pasturage interlaced zone of northern Shaanxi in 1986-2003 were analyzed. The results showed that in the past 17 years, the desertification area in the zone decreased by 206,655.2 hm2, with the patches in landscape structure reduced and fragmentation abated. Fortunately, the desertification degree decreased obviously, and moderate and light desertification took the leading position. From 1986 to 2003, the spatial centroid of desertification landscape patches expanded southwestward and northeastward, giving serious threat to the ecological safety of the southeast and northeast loess gully and hilly areas.