A small scale red soil resources information system (RSRIS) with applied mathematical models wasdeveloped and applied in red soil resources (RSR) classification and evaluation, taking Zhejiang Province,a typical distr...A small scale red soil resources information system (RSRIS) with applied mathematical models wasdeveloped and applied in red soil resources (RSR) classification and evaluation, taking Zhejiang Province,a typical distribution area of red soil, as the study area. Computer-aided overlay was conducted to classifyRSR types. The evaluation was carried out by using three methods, i.e., index summation, square root ofindex multiplication and fuzzy comprehensive assessment, with almost identical results. The result of indexsummation could represent the basic qualitative condition of RSR, that of square root of index multiplicationreflected the real condition of RSR qualitative rank, while fuzzy comprehensive assessment could satisfactorilyhandle the relationship between the evaluation factors and the qualitative rank of RSR, and therefore it is afeasible method for RSR evaluation.展开更多
The development of the classification of ferrallitic soils in China is reviewed and the classification ofFerralisols and Ferrisols in Chinese Soil Taxonomy is introduced in order to discuss the correlation betweenthe ...The development of the classification of ferrallitic soils in China is reviewed and the classification ofFerralisols and Ferrisols in Chinese Soil Taxonomy is introduced in order to discuss the correlation betweenthe ferrallitic soil classification in the Chinese Soil Taxonomy and those of the other soil classification systems.In the former soil classification systems of China, the ferrallitic soils were classified into the soil groups ofLatosols, Latosolic red soils, Red soils, Yellow soils and Dry red soils, according to the combination of soilforming conditions, soil-forming processes, soil features and soil properties. In the Chinese Soil Taxonomy,most of ferrallitic soils are classified into the soil orders of Ferralisols and Ferrisols based on the diagnostichorizons and/or diagnostic characteristics with quantitatively defined properties. Ferralisols are the soilsthat have ferralic horizon, and they are merely subdivided into one suborder and two soil groups. Ferrisolsare the soils that have LAC-ferric horizon but do not have ferralic horizon, and they are subdivided intothree suborders and eleven soil groups. Ferralisols may correspond to part of Latosols and Latosolic red soils.Ferrisols may either correspond to part of Red soils, Yellow soils and Dry red soils, or correspond to part ofLatosols and Latosolic red soils.展开更多
Soil visible-near infrared diffuse reflectance spectroscopy(vis-NIR DRS)has become an important area of research in the fields of remote and proximal soil sensing.The technique is considered to be particularly useful ...Soil visible-near infrared diffuse reflectance spectroscopy(vis-NIR DRS)has become an important area of research in the fields of remote and proximal soil sensing.The technique is considered to be particularly useful for acquiring data for soil digital mapping,precision agriculture and soil survey.In this study,1581 soil samples were collected from 14 provinces in China,including Tibet,Xinjiang,Heilongjiang,and Hainan.The samples represent 16 soil groups of the Genetic Soil Classification of China.After air-drying and sieving,the diffuse reflectance spectra of the samples were measured under laboratory conditions in the range between 350 and 2500 nm using a portable vis-NIR spectrometer.All the soil spectra were smoothed using the Savitzky-Golay method with first derivatives before performing multivariate data analyses.The spectra were compressed using principal components analysis and the fuzzy k-means method was used to calculate the optimal soil spectral classification.The scores of the principal component analyses were classified into five clusters that describe the mineral and organic composition of the soils.The results on the classification of the spectra are comparable to the results of other similar research.Spectroscopic predictions of soil organic matter concentrations used a combination of the soil spectral classification with multivariate calibration using partial least squares regression(PLSR).This combination significantly improved the predictions of soil organic matter(R2=0.899;RPD=3.158)compared with using PLSR alone(R2=0.697;RPD=1.817).展开更多
文摘A small scale red soil resources information system (RSRIS) with applied mathematical models wasdeveloped and applied in red soil resources (RSR) classification and evaluation, taking Zhejiang Province,a typical distribution area of red soil, as the study area. Computer-aided overlay was conducted to classifyRSR types. The evaluation was carried out by using three methods, i.e., index summation, square root ofindex multiplication and fuzzy comprehensive assessment, with almost identical results. The result of indexsummation could represent the basic qualitative condition of RSR, that of square root of index multiplicationreflected the real condition of RSR qualitative rank, while fuzzy comprehensive assessment could satisfactorilyhandle the relationship between the evaluation factors and the qualitative rank of RSR, and therefore it is afeasible method for RSR evaluation.
文摘The development of the classification of ferrallitic soils in China is reviewed and the classification ofFerralisols and Ferrisols in Chinese Soil Taxonomy is introduced in order to discuss the correlation betweenthe ferrallitic soil classification in the Chinese Soil Taxonomy and those of the other soil classification systems.In the former soil classification systems of China, the ferrallitic soils were classified into the soil groups ofLatosols, Latosolic red soils, Red soils, Yellow soils and Dry red soils, according to the combination of soilforming conditions, soil-forming processes, soil features and soil properties. In the Chinese Soil Taxonomy,most of ferrallitic soils are classified into the soil orders of Ferralisols and Ferrisols based on the diagnostichorizons and/or diagnostic characteristics with quantitatively defined properties. Ferralisols are the soilsthat have ferralic horizon, and they are merely subdivided into one suborder and two soil groups. Ferrisolsare the soils that have LAC-ferric horizon but do not have ferralic horizon, and they are subdivided intothree suborders and eleven soil groups. Ferralisols may correspond to part of Latosols and Latosolic red soils.Ferrisols may either correspond to part of Red soils, Yellow soils and Dry red soils, or correspond to part ofLatosols and Latosolic red soils.
基金This project was funded in part by the National High Technology Research and Development Program (Grant No. 2013AA102301)the program for New Century Talents in University (Grant No. NCET-10-0694), and the National Natural Science Foundation of China (Grant No. 41271234)
文摘Soil visible-near infrared diffuse reflectance spectroscopy(vis-NIR DRS)has become an important area of research in the fields of remote and proximal soil sensing.The technique is considered to be particularly useful for acquiring data for soil digital mapping,precision agriculture and soil survey.In this study,1581 soil samples were collected from 14 provinces in China,including Tibet,Xinjiang,Heilongjiang,and Hainan.The samples represent 16 soil groups of the Genetic Soil Classification of China.After air-drying and sieving,the diffuse reflectance spectra of the samples were measured under laboratory conditions in the range between 350 and 2500 nm using a portable vis-NIR spectrometer.All the soil spectra were smoothed using the Savitzky-Golay method with first derivatives before performing multivariate data analyses.The spectra were compressed using principal components analysis and the fuzzy k-means method was used to calculate the optimal soil spectral classification.The scores of the principal component analyses were classified into five clusters that describe the mineral and organic composition of the soils.The results on the classification of the spectra are comparable to the results of other similar research.Spectroscopic predictions of soil organic matter concentrations used a combination of the soil spectral classification with multivariate calibration using partial least squares regression(PLSR).This combination significantly improved the predictions of soil organic matter(R2=0.899;RPD=3.158)compared with using PLSR alone(R2=0.697;RPD=1.817).
