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Modeling spatio-temporal distribution of soil moisture by deep learning-based cellular automata model

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Journal of Arid Land 2016年第5期8卷 734-748页

作者： SONG Xiaodong ZHANG Ganlin liU Feng li decheng ZHAO Yuguo YANG Jinling State Key Laboratory of Soil and Sustainable Agriculture Institute of Soil ScienceChinese Academy of Sciences

Soil moisture content （SMC） is a key hydrological parameter in agriculture,meteorology and climate change,and understanding of spatio-temporal distributions of SMC in farmlands is important to address the precise irrigation ***,the hybrid interaction of static and dynamic environmental parameters makes it particularly difficult to accurately and reliably model the distribution of *** present,deep learning wins numerous contests in machine learning and hence deep belief network （DBN） ,a breakthrough in deep learning is trained to extract the transition functions for the simulation of the cell state *** this study,we used a novel macroscopic cellular automata （MCA） model by combining DBN to predict the SMC over an irrigated corn field （an area of 22 km^2） in the Zhangye oasis,Northwest *** and dynamic environmental variables were prepared with regard to the complex hydrological *** widely used neural network,multi-layer perceptron （MLP） ,was utilized for comparison to *** hybrid models （MLP-MCA and DBN-MCA） were calibrated and validated on SMC data within four months,*** to September 2012,which were automatically observed by a wireless sensor network （WSN） .Compared with MLP-MCA,the DBN-MCA model led to a decrease in root mean squared error （RMSE） by 18%.Thus,the differences of prediction errors increased due to the propagating errors of variables,difficulties of knowing soil properties and recording irrigation amount in *** sequential Gaussian simulation （s Gs） was performed to assess the uncertainty of soil moisture *** with a threshold of SMC for each grid cell,the local uncertainty of simulated results in the post processing suggested that the probability of SMC less than 25% will be difference in different areas at different time *** current results showed that the DBN-MCA model performs better than the MLP-MCA model,and the DBN-MCA model provides a powerful tool for predicti

关键词： soil moisture soil moisture sensor network macroscopic cellular automata (MCA) deep belief network (DBN) multi-layer perceptron (MLP) uncertainty assessment hydropedology

Experimental Investigation on Fracturing Behaviors after liquid Nitrogen Pre-Injection in High-Temperature Sandstone

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Energy Engineering 2023年第11期120卷 2503-2516页

作者： decheng li Yan Zhang Dongdong Ma Haozhe Geng Yu Wu School of Mechanics and Civil Engineering China University of Mining and TechnologyXuzhou221116China State Key Laboratory for Geomechanics andDeepUnderground Engineering China University of Mining and TechnologyXuzhou221116China

The fracturing process of sandstone is inherently complex due to its loose internal structure and deformation *** nitrogen pre-injection has emerged as a promising approach to damage reservoir rocks,effectively reducing fracture pressure and establishing intricate fracture networks,thus offering a potential solution for reservoir *** unravel the fundamental mechanisms governing sandstone fracturing behaviors following liquid nitrogen pre-injection,sandstone fracturing experiments were conducted under varying durations of liquid nitrogen injection,rock temperature,and in-situ stress *** experiments showcased the evolution of injection pressure and fracture characteristics under different testing conditions,complemented by electron microscope analysis to elucidate the factors driving the complex fracture characteristics of *** findings revealed a significant decrease in fracture pressure after liquid nitrogen pre-injection,accompanied by a notable increase in the complexity of the fracture network and the roughness of the fracture ***,prolonging the duration of liquid nitrogen injection and elevating reservoir temperature further contributed to reducing fracture pressure,consequently enhancing fracture complexity and surface ***,the application of confining pressure amplified fracture pressure while intensifying the degree of ***,the investigation highlighted the increased presence of microcracks in sandstone resulting from liquid nitrogen preinjection,facilitating fluid diffusion during fracturing and yielding lower fracture pressures,thereby enhancing the effectiveness of sandstone reservoir *** research results can provide theoretical guidance for geothermal reservoir reconstruction.

关键词： Sandstone high temperature liquid nitrogen pre-injection fracturing behaviors

Mapping Soil Organic Carbon Using Local Terrain Attributes： AComparison of Different Polynomial Models

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Pedosphere 2017年第4期27卷 681-693页

作者： SONG Xiaodong liU Feng ZHANG Ganlin li decheng ZHAO Yuguo YANG Jinling State Key Laboratory of Soil and Sustainable Agriculture Institute of Soil ScienceChinese Academy of SciencesNanjing 210008China

Local terrain attributes,which are derived directly from the digital elevation model,have been widely applied in digital soil *** study aimed to evaluate the mapping accuracy of soil organic carbon(SOC) concentration in 2 zones of the Heihe River in China,by combining prediction methods with local terrain attributes derived from different polynomial *** prediction accuracy was used as a benchmark for those who may be more concerned with how accurately the variability of soil properties is modeled in practice,rather than how morphometric variables and their geomorphologic interpretations are understood and *** this study,2 neighborhood types(square and circular) and 6 representative algorithms(Evans-Young,Horn,Zevenbergen-Thorne,Shary,Shi,and Florinsky algorithms) were *** general,35 combinations of first-and second-order derivatives were produced as candidate predictors for soil mapping using two mapping methods(i.e.,kriging with an external drift and geographically weighted regression).The results showed that appropriate local terrain attribute algorithms could better capture the spatial variation of SOC concentration in a region where soil properties are strongly influenced by the *** the different combinations of firstand second-order derivatives used,there was a best combination with a more accurate *** different prediction methods,the relative improvement in the two zones varied between 0.30% and 9.68%.The SOC maps resulting from the higher-order algorithms(Zevenbergen-Thorne and Florinsky) yielded less interpolation ***,it was concluded that the performance of predictive methods,which incorporated auxiliary variables,could be improved by attempting different terrain analysis algorithms.

关键词： cross-validation digital soil mapping geographically weighted regression kriging with an external drift mapping accuracy

Iron loss of paddy soil in China and its environmental implications

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Science China Earth Sciences 2022年第7期65卷 1277-1291页

作者： liumei CHEN Dongbo ZHAO Guangzhong HAN Fei YANG Zitong GONG Xiaodong SONG decheng li Ganlin ZHANG College of Resources and Environment Zunyi Normal UniversityZunyi 563006China State Key Laboratory of Soil and Sustainable Agriculture Institute of Soil ScienceChinese Academy of SciencesNanjing 210008China College of Geography and Resources Science Neijiang Normal UniversityNeijiang 641112China College of Advanced Agricultural Sciences University of the Chinese Academy of SciencesBeijing 100049China State Key Laboratory of Lake Science and Environment Nanjing Institute of Geography and LimnologyChinese Academy of SciencesNanjing 210008China

Iron(Fe)is an important element for the terrestrial and marine ecosystems through its biogeochemical cycling on the Earth’s *** has a long rice cultivation history,with extensive rice distribution across many types of paddy *** soils are the largest anthropogenic wetlands on earth with critical roles in ecosystem *** periodic artificial submergence and drainage during paddy soil evolution result in significant changes in soil moisture regime and redox conditions from the natural soils,which facilitate the increase of Fe solubility and ***,there is a lack of systematic assessment on the magnitude of the migration and loss amount of Fe from paddy *** order to quantify the Fe loss and assess the dynamic evolution of Fe in the soils after rice cultivation,seven paddy soil chronosequences derived from different landscapes(bog,plain,terrace)and parent materials(acidic,neutral,calcareous)with cultivation history from 0 to 2,000 yr were *** showed that the rates and trajectories of Fe evolution showed distinct patterns among the studied seven paddy soil ***,net losses of Fe from 1 m soil depth occurred at all studied paddy soil chronosequences regardless of the original landscapes and parent *** in the paddy soils derived from the calcareous lacustrine sediments in the bog area showed a slight accumulation during the initial stage(50 yr)of paddy cultivation,with a loss rate of0.026 kg m^(-2)yr^(-1) during the 50-to 500-yr time *** the paddy soils developed on the calcareous marine sediments in the plain area,Fe evolution was dominated by the internal movement in soil profiles through coupled reducing-eluviation reactions in the surface horizons and oxidation-illuviation in the subsurface horizons within 1,000 yr of paddy cultivation,with an averaged net loss rate of 0.029 kg m^(-2)yr^(-1) during the 1,000-to 2,000-yr time period of rice *** contrast,Fe in the paddy s

关键词： Soil-water interface Land-sea interface Paddy soil Soil Fe loss Biogeochemical cycle

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Mapping Soil Organic Carbon Stocks of Northeastern China Using Expert Knowledge and GIS-based Methods

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Chinese Geographical Science 2017年第4期27卷 516-528页

作者： SONG Xiaodong liU Feng JU Bing ZHI Junjun li decheng ZHAO Yuguo ZHANG Ganlin State Key Laboratory of Soil and Sustainable Agriculture Institute of Soil ScienceChinese Academy of SciencesNanjing 210008China

The main aim of this paper was to calculate soil organic carbon stock(SOCS) with consideration of the pedogenetic horizons using expert knowledge and GIS-based methods in northeastern China.A novel prediction process was presented and was referred to as model-then-calculate with respect to the variable thicknesses of soil horizons(MCV).The model-then-calculate with fixed-thickness(MCF),soil profile statistics(SPS),pedological professional knowledge-based(PKB) and vegetation type-based(Veg) methods were carried out for *** respect to the similar pedological information,nine common layers from topsoil to bedrock were grouped in the *** results suggested that the MCV method generated better performance than the other methods *** the comparison of polygon based approaches,the Veg method generated better accuracy than both SPS and PKB,as limited soil data were *** prediction of the pedogenetic horizons within MCV benefitted the regional SOCS estimation and provided information for future soil classification and understanding of soil *** intermediate product,that is,horizon thickness maps were fluctuant enough and reflected many details in *** linear mixed model indicated that mean annual air temperature(MAAT) was the most important predictor for the SOCS *** minimal residual of the linear mixed models was achieved in the vegetation type-based model,whereas the maximal residual was fitted in the soil type-based *** 95% of SOCS could be found in Argosols,Cambosols and *** largest SOCS was found in the croplands with vegetation of Triticum aestivum L.,Sorghum bicolor(L.) Moench,Glycine max(L.) Merr.,Zea mays *** Setaria italica(L.) ***.

关键词： soil organic carbon stock model-then-calculate random forest linear mixed model northeastern China

Mapping Soil Texture Based on Field Soil Moisture Observations at a High Temporal Resolution in an Oasis Agricultural Area

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Pedosphere 2016年第5期26卷 699-708页

作者： YANG Renmin liU Feng ZHANG Ganlin ZHAO Yuguo li decheng YANG Jinling YANG Fei YANG Fan State Key Laboratory of Soil and Sustainable Agriculture Institute of Soil ScienceChinese Academy of SciencesNanjing 210008(China) University of Chinese Academy of Sciences Beijing 100049(China)

Due to the almost homogeneous topography in low relief areas, it is usually difficult to make accurate predictions of soil properties using topographic covariates. In this study, we examined how time series of field soil moisture observations can be used to estimate soil texture in an oasis agricultural area with low relief in the semi-arid region of northwest China. Time series of field-observed soil moisture variations were recorded for 132 h beginning at the end of an irrigation event during which the surface soil was *** correlation between two time-adjacent soil moisture conditions was used to select the factors for fuzzy c-means clustering. In each of the ten generated clusters, soil texture of the soil sample with the maximum fuzzy membership value was taken as the cluster centroid. Finally, a linearly weighted average was used to predict soil texture from the centroids. The results showed that soil moisture increased with the increase of clay and silt contents, but decreased with the increase of sand content. The spatial patterns of soil moisture changed during the entire soil drying phase. We assumed that these changes were mainly caused by spatial heterogeneity of soil texture. A total of 64 independent samples were used to evaluate the prediction accuracy. The root mean square error(RMSE)values of clay, silt and sand were 1.63, 2.81 and 3.71, respectively. The mean relative error(RE) values were 9.57% for clay, 3.77% for silt and 12.83% for sand. It could be concluded that the method used in this study was effective for soil texture mapping in the low-relief oasis agricultural area and could be applicable in other similar irrigation agricultural areas.

关键词： digital soil mapping fuzzy c-means clustering low relief particle-size distribution semi-arid region water content

Regional Soil Mapping Using Multi-Grade Representative Sampling and a Fuzzy Membership-Based Mapping Approach

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Pedosphere 2017年第2期27卷 344-357页

作者： YANG lin A-Xing ZHU ZHAO Yuguo li decheng ZHANG Ganlin ZHANG Shujie Lawrence E. BAND State Key Laboratory of Soil and Sustainable Agriculture Institute of Soil Science Chinese Academy of Sciences Nanjing 210008 (China) State Key Laboratory of Resources and Environment Information System Institute of Geographical Sciences and Resources Research Chinese Academy of Sciences Beijing 100101 (China) Key Laboratory of Virtual Geographic Environment (Nanjing Normal University) Ministry of Education Nanjing 210023 (China) Jiangsu Center for Collaborative Innovation in Geographical Information Resource Development and Application School of Geography Nanjing Normal University Nanjing 210023 (China) State Key Laboratory Cultivation Base of Geographical Environment Evolution (Jiangsu Province) Nanjing 210023 (China) Department of Geography University of Wisconsin-Madison Madison WI 53706 (USA) China Academy of Urban Planning & Design Beijing 100037 (China) Institute for the Environment University of North Carolina Chapel Hill Chapel Hill NC 27599 (USA)

High-resolution and detailed regional soil spatial distribution information is increasingly needed for ecological modeling and land resource management. For areas with no point data, regional soil mapping includes two steps： soil sampling and soil mapping. Because sampling over a large area is costly, efficient sampling strategies are required. A multi-grade representative sampling strategy, which designs a small number of representative samples with different representative grades to depict soil spatial variations at different scales, could be a potentially efficient sampling strategy for regional soil mapping. Additionally, a suitable soil mapping approach is needed to map regional soil variations based on a small number of samples. In this study, the multi-grade representative sampling strategy was applied and a fuzzy membership-weighted soil mapping approach was developed to map soil sand percentage and soil organic carbon （SOC） at 0-20 and 20-40 cm depths in a study area of 5 900 km2 in Anhui Province of China. First, geographical sub-areas were delineated using a parent lithology data layer. Next, fuzzy c-means clustering was applied to two climate and four terrain variables in each stratum. The clustering results （environmental cluster chains） were used to locate representative samples. Evaluations based on an independent validation sample set showed that the addition of samples with lower representativeness generally led to a decrease of root mean square error （RMSE）. The declining rates of RMSE with the addition of samples slowed down for 20-40 cm depth, but fluctuated for 0-20 cm depth. The predicted SOC maps based on the representative samples exhibited higher accuracy, especially for soil depth 20-40 cm, as compared to those based on legacy soil data. Multi-grade representative sampling could be an effective sampling strategy at a regional scale. This sampling strategy, combined with the fuzzy membership-based mapping approach, could be an optional effec

关键词： fuzzy clustering parent lithology representative grade samphng strategy soil spatial variations

An Insight into Machine Learning Algorithms to Map the Occurrence of the Soil Mattic Horizon in the Northeastern Qinghai-Tibetan Plateau

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Pedosphere 2018年第5期28卷 739-750页

作者： ZHI Junjun ZHANG Ganlin YANG Renmin YANG Fei JIN Chengwei liU Feng SONG Xiaodong ZHAO Yuguo li decheng State Key Laboratory of Soil and Sustainable Agriculture Institute of Soil Science Chinese Academy of Sciences Nanjing 210008(China) University of Chinese Academy of Sciences Beijing 100049 (China)

Soil diagnostic horizons, which each have a set of quantified properties, play a key role in soil classification. However, they are difficult to predict, and few attempts have been made to map their spatial occurrence. We evaluated and compared four machine learning algorithms, namely, the classification and regression tree(CART), random forest(RF), boosted regression trees(BRT), and support vector machine(SVM), to map the occurrence of the soil mattic horizon in the northeastern Qinghai-Tibetan Plateau using readily available ancillary data. The mechanisms of resampling and ensemble techniques significantly improved prediction accuracies(measured based on area under the receiver operator characteristic curve score(AUC)) and produced more stable results for the BRT(AUC of 0.921 ± 0.012, mean ± standard deviation) and RF(0.908 ± 0.013) algorithms compared to the CART algorithm(0.784 ± 0.012), which is the most commonly used machine learning method. Although the SVM algorithm yielded a comparable AUC value(0.906 ± 0.006) to the RF and BRT algorithms, it is sensitive to parameter settings, which are extremely ***, we consider it inadequate for occurrence-distribution modeling. Considering the obvious advantages of high prediction accuracy, robustness to parameter settings, the ability to estimate uncertainty in prediction, and easy interpretation of predictor variables, BRT seems to be the most desirable method. These results provide an insight into the use of machine learning algorithms to map the mattic horizon and potentially other soil diagnostic horizons.

关键词： boosted regression trees classification and regression tree digital soil mapping random forest soil diagnostic horizons,support vector machine

Vertical distribution and storage of soil organic and inorganic carbon in a typical inland river basin,Northwest China

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Journal of Arid Land 2018年第2期10卷 183-201页

作者： YANG Fan HUANG Laiming YANG Renmin YANG Fei li decheng ZHAO Yuguo YANG Jinling liU Feng ZHANG Ganlin State Key Laboratory of Soil and Sustainable Agriculture Institute of Soil ScienceChinese Academy of SciencesNanjing 210008China University of Chinese Academy of Sciences Beijing 100049China Key Laboratory of Ecosystem Network Observation and Modeling Institute of Geographic Sciences and Natural Resources ResearchChinese Academy of SciencesBeijing 100101China

Knowledge of soil carbon（C） distribution and its relationship with the environment can improve our understanding of its biogeochemical cycling and help to establish sound regional models of C cycling. However, such knowledge is limited in environments with complex landscape configurations. In this study, we investigated the vertical distribution and storage of soil organic carbon（SOC） and soil inorganic carbon（SIC） in the 10 representative landscapes（alpine meadow, subalpine shrub and meadow, mountain grassland, mountain forest, typical steppe, desert steppe, Hexi Corridor oases cropland, Ruoshui River delta desert, Alxa Gobi desert, and sandy desert） with contrasting bioclimatic regimes in the Heihe River Basin, Northwest China. We also measured the 87 Sr/86 Sr ratio in soil carbonate to understand the sources of SIC because the ratio can be used as a proxy in calculating the contribution of pedogenic inorganic carbon（PIC） to total SIC. Our results showed that SOC contents generally decreased with increasing soil depth in all landscapes, while SIC contents exhibited more complicated variations along soil profiles in relation to pedogenic processes and parent materials at the various landscapes. There were significant differences of C stocks in the top meter among different landscapes, with SOC storage ranging from 0.82 kg C/m^2 in sandy desert to 50.48 kg C/m^2 in mountain forest and SIC storage ranging from 0.19 kg C/m^2 in alpine meadow to 21.91 kg C/m^2 in desert steppe. SIC contributed more than 75% of total C pool when SOC storage was lower than 10 kg C/m^2, and the proportion of PIC to SIC was greater than 70% as calculated from Sr isotopic ratio, suggesting the critical role of PIC in the C budget of this region. The considerable variations of SOC and SIC in different landscapes were attributed to different pedogenic environments resulted from contrasting climatic regimes, parent materials and vegetation types. This study provides an evidence for a general t

关键词： Heihe River Basin arid ecosystem soil landscape pedogenesis pedogenic carbonate SOC CaCO3