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soil organic matter Fractions under Different Vegetation Types in Permafrost Regions along the Qinghai-Tibet Highway, North of Kunlun Mountains, China

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Journal of Mountain Science 2015年第4期12卷 1010-1024页

作者： SHANG Wen ZHAO Lin WU Xiao-dong LI Yu-qiang YUE Guang-yang ZHAO Yong-hua QIAO Yong-ping Cryosphere Research Station on Qinghai-Tibet Plateau State Key laboratory of Cryospheric Sciences Cold and Arid Regions Environmental and Engineering Research Institute Chinese Academy of Sciences University of Chinese Academy of Sciences Naiman Desertification Research Station Cold and Arid Regions Environmental and Engineering Research InstituteChinese Academy of Sciences

As a key attribute of soil quality, soil organic matter(SOM) and its different fractions play an important role in regulating soil nutrient cycling and soil *** study evaluated the soil carbon(C) and nitrogen(N) concentrations in different SOM fractions(light– and heavy fractions,microbial biomass) under different vegetation types and analyzed their influencing factors in continuous permafrost regions along the Qinghai-Tibet Highway in the North of Kunlun Mountains, *** samples were collected in pits under four vegetation types — Alpine swamp meadow(ASM), Alpine meadow(AM), Alpine steppe(AS) and Alpine desert(AD) — at the depth of 0-50 *** vegetation coverage was the highest at ASM and AM, followed byAS and *** results indicated that the concentrations of light fraction carbon(LFC) and nitrogen(LFN), and microbial biomass carbon(MBC)and nitrogen(MBN) decreased as follows: ASM > AM >AS > AD, with the relatively stronger decrease of LFC,whereas the heavy fraction carbon(HFC) and nitrogen(HFN) concentrations were lower in AS soils than in the AD *** relatively higher proportions of LFC/SOC and MBC/SOC in the 0-10 cm depth under the ASM soils are mainly resulted from its higher substrate input and soil moisture *** analysis demonstrated that aboveground biomass, soil moisture content, soil organic carbon(SOC) and total nitrogen(TN) positively correlated to LFC, LFN, HFC, HFN, MBC and MBN, while p H negatively correlated to LFC, LFN, HFC, HFN, MBC and *** was no relationship between active layer thickness and SOM fractions, except for the *** suggested that vegetation cover, soil moisture content, and SOC and TN concentrations were significantly correlated with the amount and availability of SOM fractions, while permafrost had less impact on SOM fractions in permafrost regions of the central Qinghai–Tibet Plateau.

关键词： soil organic matter Light fraction Heavy fraction Microbial biomass Vegetation types Tibetan permafrost soil

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Evaluation of Landsat 8 image pansharpening in estimating soil organic matter using multiple linear regression and artificial neural networks

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Geo-Spatial Information Science 2022年第3期25卷 353-364页

作者： Abdelkrim Bouasria Khalid Ibno Namr Abdelmejid Rahimi El Mostafa Ettachfini Badr Rerhou Faculty of Sciences Chouaib Doukkali UniversityEl JadidaMorocco Agricultural Development Department Doukkala Regional Agricultural Development OfficeEl JadidaMorocco soil Science Department Hassan II Agronomic and Veterinary InstituteRabatMorocco

In agricultural systems,the regular monitoring of soil organic matter(SOM)dynamics is *** task is costly and time-consuming when using the conventional method,especially in a very fragmented area and with intensive agricultural activity,such as the area of Sidi *** study area is located in the Doukkala irrigated perimeter in *** data can provide an alternative and fill this gap at a low *** to predict SOM from a satellite image,whether linear or nonlinear,have shown considerable *** study aims to compare SOM prediction using Multiple Linear Regression(MLR)and Artificial Neural Networks(ANN).A total of 368 points were collected at a depth of 0-30 cm and analyzed in the *** image at 15 m resolution(MSPAN)was produced from a 30 m resolution(MS)Landsat-8 image using image pansharpening processing and panchromatic band(15 m).The results obtained show that the MLR models predicted the SOM with(training/validation)R^(2)values of 0.62/0.63 and 0.64/0.65 and RMSE values of 0.23/0.22 and 0.22/0.21 for the MS and MSPAN images,*** contrast,the ANN models predicted SOM with R2 values of 0.65/0.66 and 0.69/0.71 and RMSE values of 0.22/0.10 and 0.21/0.18 for the MS and MSPAN images,*** pansharpening improved the prediction accuracy by 2.60%and 4.30%and reduced the estimation error by 0.80%and 1.30%for the MLR and ANN models,respectively.

关键词： Digital soil mapping soil organic matter remote sensing multiple linear regression artificial neural networks irrigated area Doukkala Morocco

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Effects of free iron oxyhydrates and soil organic matter on copper sorption-desorption behavior by size fractions of aggregates from two paddy soils

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Journal of Environmental Sciences 2009年第5期21卷 618-624页

作者： WANG Fang, PAN Genxing, LI Lianqing Institute of Resource, Ecosystem and Environment of Agriculture, Nanjing Agricultural University, Nanjing 210095, China. [a]Institute of Resource Ecosystem and Environment of Agriculture Nanjing Agricultural University Nanjing 210095 China

Effects of free iron oxyhydrates （Fed） and soil organic matter （SOM） on copper （Cu^2＋） sorption-desorption behavior by size fractions of aggregates from two typical paddy soils （Ferric-Accumulic Stagnic Anthrosol （soil H） and Gleyic Stagnic Anthrosol （soil W）） were investigated with and without treatments of dithionite-citrate-bicarbonate and of H2O2. The size fractions of aggregates were obtained from the undisturbed bulk topsoil using a low energy ultrasonic dispersion procedure. Experiments of equilibrium sorption and subsequent desorption were conducted at soil water ratio of 1：20, 25℃. For soil H, Cu^2＋ sorption capacity of the DCB-treated size fractions was decreased by 5.9% for fine sand fraction, by 40.4% for coarse sand fraction, in comparison to 2.9% for the bnlk sample. However, Cu^2＋ sorption capacities of the H2O2-treated fractions were decreased by over 80% for the coarse sand fraction and by 15% for the clay-sized fraction in comparison to 88% for bulk soil. For soil W, Cu^2＋ sorption capacity of the DCB-treated size fraction was decreased by 30% for the coarse sand fraction and by over 75% for silt sand fraction in comparison to 44.5% for the bulk sample. Cu^2＋ sorption capacities of the H2O2-treated fractions were decreased by only 2.0% for the coarse sand fraction and by 15% for the fine sand fraction in comparison to by 3.4% for bulk soil. However, Cu^2＋ desorption rates were increased much in H2O2-treated samples by over 80% except the clay-sized fraction （only 9.5%） for soil H. While removal of SOM with H2O2 tendend to increase the desorption rate, DCB- and H2O2-treatments caused decrease in Cu^2＋ retention capacity of size fractions, Particularly, there hardly remained Cu^2＋ retention capacity by size fractions from soil H after H2O2 treatment except for clay-sized fraction. These findings supported again the dominance of the coarse sand fraction in sorption of metals and the preference of absorbed metals bound to SOM in differently stabilized

关键词： paddy soils Cn^2+ sorption-desorption soil organic matter free iron oxyhydrates size fraction of aggregates

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Impacts of soil organic matter, pH and exogenous copper on sorption behavior of norfloxacin in three soils

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Journal of Environmental Sciences 2009年第5期21卷 632-640页

作者： ZHANG Jie LI Zhaojun GE Gaofei SUN Wanchun LIANG Yongchao WU Laosheng College of Natural Resources and Environmental Sciences Nanjing Agricultural University Nanjing 210095 China Ministry of Agriculture Key Laboratory of Crop Nutrition and Fertilization Institute of Agricultural Resources and Regional Planning Chinese Academy of Agricultural Sciences Beijing 100081 China Key Laboratory of Oasis Eco-Agriculture and College of Agriculture Shihezi University Shihezi 832003 China Department of Environmental Sciences University of California Riverside Riverside CA 92521 USA

Norfloxacin sorption and the factors （soil organic matter （SOM）, pH, and exogenous copper （Cu） influencing the sorption were investigated in a black soil （soil B）, a fluvo-aquic soil （soil F）, and a red soil （soil R）. With increasing norfloxacin concentrations, sorption amount of norfloxacin increased in both the bulk soils and their SOM-removed soils, but the sorption capacity of SOM-removed soils was higher than that of their corresponding bulk soils, indicating that the process of norfloxacin sorption in soil was influenced by the soil properties including SOM. The sorption data in all bulk soils and SOM-removed soils were fitted to Freundlich and Langmuir models. The correlation coefficients suggested that the experimental data fitted better to Freundlich equation than to Langmuir equation. Furthermore, the data from soil F and SOM-removed F could not be described by Langmuir equation. The norfloxacin sorption amount decreased in soil B and soil F, whereas it increased in soil R as solution pH increased. The maximum KD and Koc were achieved in soil R when the equilibrium solution pH was 6. The norfloxacin sorption was also influenced by the exogenous Cu^2＋, which depended on the soil types and Cu^2＋ concentrations. With increasing Cu^2＋ concentrations in solution, generally, sorption amount, KD and Koc for norfloxacin in soils increased and were up to a peak at 100 mg/L Cu^2＋, and then the sorption amount decreased regardless of norfloxacin levels.

关键词： copper norfloxacin pH soil organic matter sorption

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Mapping soil organic matter in cultivated land based on multi-year composite images on monthly time scales

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Journal of Integrative Agriculture 2024年第4期23卷 1393-1408页

作者： Jie Song Dongsheng Yu Siwei Wang Yanhe Zhao Xin Wang Lixia Ma Jiangang Li State Key Laboratory of soil and Sustainable Agriculture Institute of Soil ScienceChinese Academy of SciencesNanjing 210008China Agricultural and Rural Bureau of Luanping County Luanping 068250China Chinese Academy of Sciences University Beijing 100049China

Rapid and accurate acquisition of soil organic matter(SOM)information in cultivated land is important for sustainable agricultural development and carbon balance *** study proposed a novel approach to predict SOM with high accuracy using multiyear synthetic remote sensing variables on a monthly *** obtained 12 monthly synthetic Sentinel-2 images covering the study area from 2016 to 2021 through the Google Earth Engine(GEE)platform,and reflectance bands and vegetation indices were extracted from these composite *** the random forest(RF),support vector machine(SVM)and gradient boosting regression tree(GBRT)models were tested to investigate the difference in SOM prediction accuracy under different combinations of monthly synthetic *** showed that firstly,all monthly synthetic spectral bands of Sentinel-2 showed a significant correlation with SOM(P<0.05)for the months of January,March,April,October,and ***,in terms of single-monthly composite variables,the prediction accuracy was relatively poor,with the highest R^(2)value of 0.36 being observed in *** monthly synthetic environmental variables were grouped in accordance with the four quarters of the year,the first quarter and the fourth quarter showed good performance,and any combination of three quarters was similar in estimation *** overall best performance was observed when all monthly synthetic variables were incorporated into the ***,among the three models compared,the RF model was consistently more accurate than the SVM and GBRT models,achieving an R^(2)value of *** for band 12 in December,the importance of the remaining bands did not exhibit significant *** research offers a new attempt to map SOM with high accuracy and fine spatial resolution based on monthly synthetic Sentinel-2 images.

关键词： soil organic matter Sentinel-2 monthly synthetic images machine learning model spatial prediction

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A hybrid model for predicting spatial distribution of soil organic matter in a bamboo forest based on general regression neural network and interative algorithm

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Journal of Forestry Research 2020年第5期31卷 1673-1680页

作者： Eryong Liu Jian Liu Kunyong Yu Yunjia Wang Ping He Fujian Provincial Key Laboratory of soil Environmental Health and Regulation College of Resources and EnvironmentFujian Agriculture and Forestry UniversityFuzhou 350002People’s Republic of China College of Forestry Fujian Agriculture and Forestry UniversityFuzhou 350002People’s Republic of China School of Environment and Spatial Informatics China University of Mining and TechnologyXuzhou 221006JiangsuPeople’s Republic of China

A general regression neural network model,combined with an interative algorithm(GRNNI)using sparsely distributed samples and auxiliary environmental variables was proposed to predict both spatial distribution and variability of soil organic matter(SOM)in a bamboo *** auxiliary environmental variables were:elevation,slope,mean annual temperature,mean annual precipitation,and normalized difference vegetation *** prediction accuracy of this model was assessed via three accuracy indices,mean error(ME),mean absolute error(MAE),and root mean squared error(RMSE)for validation in sampling *** the prediction accuracy and reliability of this model were compared to those of regression kriging(RK)and ordinary kriging(OK).The results show that the prediction accuracy of the GRNNI model was higher than that of both RK and *** three accuracy indices(ME,MAE,and RMSE)of the GRNNI model were lower than those of RK and *** improvements of RMSE of the GRNNI model compared with RK and OK were 13.6%and 17.5%,*** addition,a more realistic spatial pattern of SOM was produced by the model because the GRNNI model was more suitable than multiple linear regression to capture the nonlinear relationship between SOM and the auxiliary environmental ***,the GRNNI model can improve both prediction accuracy and reliability for determining spatial distribution and variability of SOM.

关键词： General regression neural network Interative algorithm Ordinary kriging Regression kriging Spatial prediction soil organic matter

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Deadwood affects the soil organic matter fractions and enzyme activity of soils in altitude gradient of temperate forests

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Forest Ecosystems 2023年第3期10卷 316-327页

作者： Ewa Błońska Wojciech Prazuch Jarosław Lasota Department of Ecology and Silviculture Faculty of ForestryUniversity of Agriculture in Krakow29 Listopada 46 Str.31-425KrakowPoland

The main objective of our study has been to determine the role of deadwood in the shaping of the amount of soil organic matter fractions in mountain forest *** this purpose,a climosequence approach comprising north(N)and south(S)exposure along the altitudinal gradient(600,800,1000 and 1200 m a.s.l.)was set *** comparing the properties of decomposing deadwood and those of the soils located directly beneath the decaying wood we drew conclusions about the role of deadwood in the shaping of soil organic matter fractions and soil carbon storage in different climate *** basic properties,enzymatic activity and fractions of soil organic matter(SOM)were determined in deadwood and affected directly by the components released from decaying *** decomposed deadwood impacts soil organic matter stabilization more strongly than the less decayed deadwood and the light fraction of SOM is more sensitive to deadwood effects than the heavy fraction regardless of the location in the altitude *** in SOM mineral-associated fraction C content is more pronounced in soils under the influence of deadwood located in lower locations of warmer *** released from decaying wood stimulate the enzymatic activity of soils that are within the range of deadwood influence.

关键词： Enzyme activity Forest soils Heavy fraction Light fraction soil organic matter

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Influence of Climate and Socio-Economic Factors on the Spatio-Temporal Variability of soil organic matter: A Case Study of Central Heilongjiang Province, China

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Journal of Integrative Agriculture 2014年第7期13卷 1486-1500页

作者： SHI Shu-qin CAO Qi-wen YAO Yan-min TANG Hua-jun YANG Peng WU Wen-bin XU Heng-zhou LIU Jia LI Zheng-guo School of Management Tianjin Polytechnic University Key Laboratory of Agri-Informatics Ministry of Agriculture/Institute of Agricultural Resources & Regional Planning Chinese Academy of Agricultural Sciences College of Management Tianjin University

For the scientific management of farmland, it is significant to understand the spatio-temporal variability of soil organic matter and to study the influences of related factors. Using geostatistical theory, GIS spatial analysis, trend analysis and a Geographically Weighted Regression （GWR） model, this study analyzed the response of soil organic matter to climate and socio-economic factors in central Heilongjiang Province during the past 25 years. Second soil survey data of China for 1979-1985, 2005 field sampling data, climate observations and socio-economic data for 1980-2005 were analyzed. First, soil organic matter in 2005 was spatially interpolated using the Co-Kriging method along with auxiliary data sets of soil type and pH. The spatio-temporal variability was then studied by comparison with the 1980s second soil census data. Next, the temporal trends in climate and socio-economic factors over the past 25 years were investigated. Finally, we examined the variation of the response of soil organic matter to climate and socio-economic factors using the GWR model spatially and temporally. The model showed that 53.82% area of the organic matter content remained constant and 29.39% has decreased during the past 25 years. The impact of precipitation on organic matter content is mainly negative, with increasing absolute values of the regression coefficient. The absolute value of regression coefficient of annual average temperature has decreased, and more areas are now under its negative effects. In addition, the areas of positive regression coefficient of annual sunshine hours have northward shifted, with the increasing absolute value of positive coefficient and decreasing absolute value of negative coefficient. The areas of positive regression coefficient of mechanized farming as a socio-economic factor have westward shifted, with the increasing absolute value of negative coefficient and decreasing absolute value of positive coefficient. The area of regions with the

关键词： soil organic matter climate socio-economic factors GWR Heilongjiang Province

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Improving model performance in mapping cropland soil organic matter using time-series remote sensing data

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Journal of Integrative Agriculture 2024年第8期23卷 2820-2841页

作者： Xianglin Zhang Jie Xue Songchao Chen Zhiqing Zhuo Zheng Wang Xueyao Chen Yi Xiao Zhou Shi Institute of Applied Remote Sensing and Information Technology College of Environmental and Resource SciencesZhejiang UniversityHangzhou 310058China Department of Land Management Zhejiang UniversityHangzhou 310058China ZJU-Hangzhou Global Scientific and Technological Innovation Center Zhejiang UniversityHangzhou 311215China Institute of Digital Agriculture Zhejiang Academy of Agricultural SciencesHangzhou 310021China Key Laboratory of Spectroscopy Sensing Ministry of Agriculture and Rural AffairsHangzhou 310058China

Faced with increasing global soil degradation,spatially explicit data on cropland soil organic matter(SOM)provides crucial data for soil carbon pool accounting,cropland quality assessment and the formulation of effective management *** a spatial information prediction technique,digital soil mapping(DSM)has been widely used to spatially map soil information at different ***,the accuracy of digital SOM maps for cropland is typically lower than for other land cover types due to the inherent difficulty in precisely quantifying human *** overcome this limitation,this study systematically assessed a framework of“information extractionfeature selection-model averaging”for improving model performance in mapping cropland SOM using 462 cropland soil samples collected in Guangzhou,China in *** results showed that using the framework of dynamic information extraction,feature selection and model averaging could efficiently improve the accuracy of the final predictions(R^(2):0.48 to 0.53)without having obviously negative impacts on *** the dynamic information of the environment was an efficient way to generate covariates that are linearly and nonlinearly related to SOM,which improved the R^(2)of random forest from 0.44 to 0.48 and the R^(2)of extreme gradient boosting from 0.37to *** recursive feature selection(FRFS)is recommended when there are relatively few environmental covariates(500).The Granger-Ramanathan model averaging approach could improve the prediction accuracy and average *** the structures of initial prediction models are similar,increasing in the number of averaging models did not have significantly positive effects on the final *** the advantages of these selected strategies over information extraction,feature selection and model averaging have a great potential for high-accuracy soil mapping

关键词： cropland soil organic matter digital soil mapping machine learning feature selection model averaging

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The application of proximal visible and near-infrared spectroscopy to estimate soil organic matter on the Triffa Plain of Morocco

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International soil and Water Conservation Research 2020年第2期8卷 195-204页

作者： Ayoub Lazaar Abdul Mounem Mouazen Kamal EL Hammouti Michael Fullen Biswajeet Pradhan Muhammad Sohail Memon Karim Andich Abdelilah Monir Laboratory of Applied Geosciences Department of GeologyFaculty of ScienceMohammed First UniversityOujdaMorocco Division of Mechatronics Biostatistics and Sensors(MeBioS)Faculty of Bioscience EngineeringKasteelpark Arenberg 30B-3001 HeverleeBelgium Faculty of Science and Engineering University of WolverhamptonWolverhamptonWV11LYUnited Kingdom Center for Advanced Modelling and Geospatial Information Systems(CAMGIS) Faculty of Engineering and Information TechnologyUniversity of Technology SydneySydney2007NSWAustralia Faculty of Agricultural Engineering Sindh Agriculture UniversityTandojam70060Pakistan Laboratory of Applied Geomatics and soil Science National Institute of Agronomic Research(INRA)OujdaMorocco Department of Mathematics EDP and Scientific Computing TeamMoulay Ismail UniversityMéknesMorocco

soil organic matter (SOM) is a fundamental soil constituent. The estimation of this parameter in the laboratory using the classical method is complex time-consuming and requires the use of chemical re-agents. The objectives of this study were to assess the accuracy of two laboratory measurement setups of the VIS-NIR spectroscopy in estimating SOM content and determine the important spectral bands in the SOM estimation model. A total of 115 soil samples were collected from the non-root zone (0-20 cm) of soil in the study area of the Triffa Plain and then analysed for SOM in the laboratory by the Walkley-Black method. The reflectance spectra of soil samples were measured by two protocols, Contact Probe (CP) and Pistol Grip (PG)) of the ASD spectroradiometer (350-2500 nm) in the laboratory. Partial least squares regression (PLSR) was used to develop the prediction models. The results of coefficient of determination (R2) and the root mean square error (RMSE) showed that the pistol grip offers reasonable accuracy with an R2=0.93 and RMSE=0.13 compared to the contact probe protocol with an R2=0.85 and RMSE = 0.19. The near-Infrared range were more accurate than those in the visible range for pre-dicting SOM using the both setups (CP and PG). The significant wavelengths contributing to the pre-diction of SOM for (PG) setup were at:424, 597, 1432, 1484, 1830,1920, 2200, 2357 and 2430 nm, while were at 433, 587, 1380, 1431, 1929, 2200 and 2345 nm for (CP) setup.

关键词： soil organic matter VIS-NIR spectroscopy Reflectance spectra SOM estimation SOM analysis

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