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

Rapid and accurate acquisition of soil organic matter(SOM)information in cultivated land is important for sustainable agricultural development and carbon balance management.This study proposed a novel approach to predict SOM with high accuracy using multiyear synthetic remote sensing variables on a monthly scale.We 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 images.Then 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 variables.Results 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 November.Secondly,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 January.When 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 accuracy.The overall best performance was observed when all monthly synthetic variables were incorporated into the models.Thirdly,among the three models compared,the RF model was consistently more accurate than the SVM and GBRT models,achieving an R^(2)value of 0.56.Except for band 12 in December,the importance of the remaining bands did not exhibit significant differences.This research offers a new attempt to map SOM with high accuracy and fine spatial resolution based on monthly synthetic Sentinel-2 images.

Soil pore identification with the adaptive fuzzy C-means method based on computed tomography images

The complex geometry and topology of soil is widely recognised as the key driver in many ecological processes. X-ray computed tomography (CT) provides insight into the internal structure of soil pores automatically and accurately. Until recently, there have not been methods to identify soil pore structures. This has restricted the development of soil science, particularly regarding pore geometry and spatial distribution. Through the adoption of the fuzzy clustering theory and the establishment of pore identification rules, a novel pore identification method is described to extract pore structures from CT soil images. The robustness of the adaptive fuzzy C-means method (AFCM), the adaptive threshold method, and Image-Pro Plus tools were compared on soil specimens under different conditions, such as frozen, saturated, and dry situations. The results demonstrate that the AFCM method is suitable for identifying pore clusters, especially tiny pores, under various soil conditions. The method would provide an optional technique for the study of soil micromorphology.

Soil Salinity and Soil Water Content Estimation Using Digital Images in Coastal Field:A Case Study in Yancheng City of Jiangsu Province,China

Soil is the essential part for agricultural and environmental sciences,and soil salinity and soil water content are both the important influence factors for sustainable development of agriculture and ecological environment.Digital camera,as one of the most popular and convenient proximal sensing instruments,has its irreplaceable position for soil properties assessment.In this study,we collected 52 soil samples and photographs at the same time along the coast in Yancheng City of Jiangsu Province.We carefully analyzed the relationship between soil properties and image brightness,and found that soil salt content had higher correlation with average image brightness value than soil water content.From the brightness levels,the high correlation coefficients between soil salt content and brightness levels concentrated on the high brightness values,and the high correlation coefficients between soil water content and brightness levels focused on the low brightness values.Different significance levels(P)determined different brightness levels related to soil properties,hence P value setting can be an optional way to select brightness levels as the input variables for modeling soil properties.Given these information,random forest algorithm was applied to develop soil salt content and soil water content inversion models using randomly 70%of the dataset,and the rest data for testing models.The results showed that soil salt content model had high accuracy(R_(v)^(2)=0.79,RMSE_(v)=12 g/kg,and RPD_(v)=2.18),and soil water content inversion model was barely satisfied(R_(v)^(2)=0.47,RMSE_(v)=3.04%,and RPD_(v)=1.38).This study proposes a method of modeling soil properties with a digital camera.Combining unmanned aerial vehicle(UAV),it has potential popularization and application value for precise agriculture and land management.

The Digital Mapping of Ukrainian Soils on the Base of High Resolution Space Images

The first Ukrainian using experience of multispectral space scanning for digital soil mapping is described in this paper. Methodical approaches for detailed soil observation of Ukrainian forest regions are elaborated based on modem mapping principles. For the first time in Ukraine, digital soil maps based on GIS （geographic information system） were obtained for individual farms. In GIS based on space images and digital relief models, the medium-scale and large-scale soil maps were created by geo-statistical methods. According to elaborated methods, modem digital soil mapping should provide all combined works： remote sensing and traditional soil observations. The modem digital soil mapping should be based just on quantitative principles： on remote sensing data, geomorphologic field parameters, and chemical analyses. The methodological approaches, which were used for the first time in Ukraine during digital soil mapping by remote sensing methods, are described in this paper.

Physical modelling of pipe piles under oblique pullout loads using transparent soil and particle image velocimetry

A small-scale physical modelling method was developed to investigate the pile bearing capacity and the soil displacement around the pile using transparent soil and particle image velocimetry(PIV) technique. Transparent sand was made of baked quartz and a pore fluid with a matching refractive index. The physical modelling system consists of a loading system, a laser light, a CCD camera, an optical platform and a computer for image analyzing. A distinctive laser speckle was generated by the interaction between the laser light and transparent soil. Two laser speckle images before and after deformation were used to calculate the soil displacement field using PIV. Two pipe piles with different diameters under oblique pullout loads at angles of 0°, 30°, 45°, 60° and 90° were used in tests. The load-displacement response, oblique pullout ultimate resistances and soil displacement fields were then studied. The test results show that the developed physical modelling method and transparent soil are suitable for pile-soil interaction problems. The soil displacements around the pipe piles will improve the understanding on the capacity of pipe piles under oblique pullout loads.

Stereo particle image velocimetry measurement of 3D soil deformation around laterally loaded pile in sand

A developed stereo particle image velocimetry(stereo-PIV) system was proposed to measure three-dimensional(3D) soil deformation around a laterally loaded pile in sand.The stereo-PIV technique extended 2D measurement to 3D based on a binocular vision model,where two cameras with a well geometrical setting were utilized to image the same object simultaneously.This system utilized two open software packages and some simple programs in MATLAB,which can easily be adjusted to meet user needs at a low cost.The failure planes form an angle with the horizontal line,which are measured at 27°-29°,approximately three-fourths of the frictional angle of soil.The edge of the strain wedge formed in front of the pile is an arc,which is slightly different from the straight line reported in the literature.The active and passive influence zones are about twice and six times of the diameter of the pile,respectively.The test demonstrates the good performance and feasibility of this stereo-PIV system for more advanced geotechnical testing.

Historical Development in Soil Micromorphological Imaging

The book ＂micropedolog＂ by Kubieana and a large number of publications has induced many people to practice soil micromorphology. Quantification of the soil fabric and its components was a major challenge. The use of the image analyses in soil science was a breakthrough. Attempts to make soil thin sections go back to the beginning of the 2oth century. Microscopic techniques and recently high resolution electron microscope and use of computer assisted imaging techniques enabled the in vitro study of soils in three dimensional levels. It is now possible to store and process massive amounts of data. Micro- morphological concepts and techniques are applied in paleopedological, ecological, and archaeological studies. The aim of this work was to examine soil micromorphological imaging in historical perspective.

Measurement on soil deformation caused by expanded-base pile in transparent soil using particle image velocimetry (PIV)

A new small-scale geotechnical physical model in 1-g and unconfined condition, combining the transparent soil, close-range photogrammetry and particle image velocimetry(PIV), was employed, which provides a non-intrusively internal deformation measurement approach to monitor the internal deformation of soil caused by expanded-base pile jacking with casing. The transparent soil was made of fused quartz and its refractive index matched blended oil, adding reflective particles(glass beads). Closerange photogrammetry was employed to record the images of the process of casing jacking and extraction in transparent soil, allowing the use of Matlab-based Geo-PIV to figure out the displacement field converted from image space to object space. Analysis of test results indicates that the maximum displacement caused by casing jacking for expandedconical-base pile is decreased by 29% compared with that for expanded-flat-base pile. The main movement happens at the early stage of casing extraction. The maximum displacement caused by casing extraction for the conical base is about 43% of that for the flatbase, while the affected zone caused by casing extraction for the conical base accounts for about 1/3 of that for the flat base. The contraction for horizontal displacements tends to decrease with the depth increasing. By contrast, the contraction under pile base decreases with the increasing of displacement. The displacements generated by jacking a conventional pile having a diameter equal to the casing diameter of the expanded-base pile were comparable to the net displacement taking place due to expanded-base pile installation for the conical base pile.

Evaluating the Potentials of PLSR and SVR Models for Soil Properties Prediction Using Field Imaging,Laboratory VNIR Spectroscopy and Their Combination

Pedo-spectroscopy has the potential to provide valuable information about soil physical,chemical,and biological properties.Nowadays,wemay predict soil properties usingVNIRfield imaging spectra(IS)such as Prisma satellite data or laboratory spectra(LS).The primary goal of this study is to investigate machine learning models namely Partial Least Squares Regression(PLSR)and Support Vector Regression(SVR)for the prediction of several soil properties,including clay,sand,silt,organic matter,nitrate NO3-,and calcium carbonate CaCO_(3),using five VNIR spectra dataset combinations(%IS,%LS)as follows:C1(0%IS,100%LS),C2(20%IS,80%LS),C3(50%IS,50%LS),C4(80%IS,20%LS)and C5(100%IS,0%LS).Soil samples were collected at bare soils and at the upper(0–30 cm)layer.The data set has been split into a training dataset 80%of the collected data(n=248)and a validation dataset 20%of the collected data(n=61).The proposed PLSR and SVR models were trained then tested for each dataset combination.According to our results,SVR outperforms PLSR for both:C1(0%IS,100%LS)and C5(100%IS,0%LS).For Soil Organic Matter(SOM)prediction,it achieves(R^(2)=0.79%,RMSE=1.42%)and(R^(2)=0.76%,RMSE=1.3%),respectively.The data fusion has improved the soil property prediction.The highest improvement was obtained for the SOM property(R^(2)=0.80%,RMSE=1.39)when using the SVR model and applying the second Combination C2(20% of IS and 80%LS).

Photovoltaic Cell Panels Soiling Inspection Using Principal Component Thermal Image Processing

Intended for good productivity and perfect operation of the solar power grid a failure-free system is required.Therefore,thermal image processing with the thermal camera is the latest non-invasive(without manual contact)type fault identification technique which may give good precision in all aspects.The soiling issue,which is major productivity affecting factor may import from several reasons such as dust on the wind,bird mucks,etc.The efficient power production sufferers due to accumulated soil deposits reaching from 1%–7%in the county,such as India,to more than 25%in middle-east countries country,such as Dubai,Kuwait,etc.This research offers a solar panel soiling detection system built on thermal imaging which powers the inspection method and mitigates the requirement for physical panel inspection in a large solar production place.Hence,in this method,solar panels can be verified by working without disturbing production operation and it will save time and price of recognition.India ranks 3rd worldwide in the usage use age of Photovoltaic(PV)panels now and it is supported about 8.6%of the Nation’s electricity need in the year 2020.In the meantime,the installed PV production areas in India are aged 4–5 years old.Hence the need for inspection and maintenance of installed PV is growing fast day by day.As a result,this research focuses on finding the soiling hotspot exactly of the working solar panels with the help of Principal Components Thermal Analysis(PCTA)on MATLAB Environment.

运用Image J软件分析土壤结构特征

以土壤团聚体、土壤裂隙以及土壤优先流特征数码图像为研究对象,介绍了ImageJ软件在土壤结构特征分析中的应用。研究结果表明:①马尾松林地表层土壤团聚体圆度大于母质层土壤颗粒的圆度;②运用图像测量方法能够快速地测定土壤裂隙几何特征以及土壤收缩曲线;③土壤优先流示踪图像分析结果显示红壤性水稻土犁底层具有显著的防渗功能,而同一土壤剖面内土壤连通性孔隙存在较大的空间分异。最后,本文还对以上研究结果和图像分析方法进行了探讨。

利用遥感图像处理软件(ERDAS IMAGINE)对土壤孔隙率进行测定分析

比较了传统的土壤切片的数字图像处理软件与ERDAS IMAGINE 8.5软件的不同,从处理流程、结果精度等几个方面指出了新软件的优越性。利用土壤切片的数字图像,以孔隙面积为基础数据,定量评价小尺度上的土壤孔隙率的变异。结果表明,土壤孔隙率在毫米尺度上存在较为明显的差异,在水头压力下,孔隙率随着深度增加而变大。

Impact of land use change on soil resources in the peri-urban area of Suzhou city

The Yangtze delta area is among the fastest developing areas in China. Here there are mega-cities like Shanghai, Nanjing and the attached urban areas of different sizes including those along the lower reaches of the Yangtze River from Shanghai up to Nanjing as well as their satellite cities and towns, forming one of the most densely distributed urban areas in China. This is a case study done in Suzhou city at the center of the Yangtze delta to reflect the impact of urban sprawl on soil resources using satellite images and digital soil databases. The extent of the developed land in the studied area and the impact of development on soil resources at 1:100,000 scale are estimated and the soil types impacted most by urbanization development are determined through overlaying the soil map on the satellite images (Landsat-7) of the studied area at different times (1984, 1995, 2000 and 2003). The methodology for this study consists of analyzing data resulting from using a geographic information system (GIS) to combine urban land use maps of different times derived from satellite images with data on soil characteristics contained in the established soil databases by which some results come into being to present the fast expanding trend of urbanization in the Yangtze delta area, the urban spread and the soils occupied by the urbanization process, and also the quality of the occupied soils.

Micromorphological Analysis of Soil Structure under No Tillage Management in the Black Soil Zone of Northeast China

The structure of the ＂black soil＂ in Northeast China has been greatly deteriorated by long-term intensive conventional mouldboard plow tillage （CT） practices. In this study, micro- morphological observation and image analysis of soil thin sections were conducted to evaluate the impacts of 21 years （1986-2007） of no tillage （NT） on soil structure as compared to CT in an experiment near Gongzhuling City, Jilin Province. Soil organic matter （SOM）, wet aggregate stability and saturated hydraulic conductivity （Ks） were also analyzed. Total SOM was not significantly affected by tillage systems, but fresher SOM was observed in the surface layer under NT. The aggregates under NT showed different hierarchies in the form of crumbs, and the mean weight diameter （MWD） of NT was significant higher than that of CT in the surface layer. Platy and blocky aggregates were frequently observed in the lower layers under CT practice. The compound pore structure with intertwined intra- and inter- aggregates pores under NT was well developed in a layer from 0-5 cm to 20-25 era. While under CT system, more inter-aggregate pores and fewer intra- aggregate pores were observed, and planes and channels were frequently found in the 20-25 cm layer, where maeroporosity decreased significantly and a plow pan was evident. The Ks values of NT weresignificantly lower at o-5 cm but significantly higher at 20-95 cm compared with CT, which showed the same trend with macroporosity. These results confirmed that long-term CT practice fragmented the tillage layer soil and compacted the lower layer soil and formed a plow pan. While long-term NT practice in the black soil region favored soil aggregation and a stable porous soil structure was formed, which are important to the water infiltration and prevent soil erosion.

Effects of seasonal water-level fluctuation on soil pore structure in the Three Gorges Reservoir,China

Inundation of the Three Gorges Reservoir has created a 30-m water-level fluctuation zone with seasonal hydrological alternations of submergence and exposure, which may greatly affect soil properties and bank stability. The aim of this study was to investigate the response of soil pore structure to seasonal water-level fluctuation in the reservoir, and particularly, the hydrological change of wetting and drying cycles. Soil pore structure was visualized with industrial X-ray computed tomography and digital image analysis techniques. The results showed that soil total porosity(? 100 ?m), total pore number, total throat number, and mean throat surface area increased significantly under wetting and drying cycles. Soil porosity, pore number and throat numberwithin each size class increased in the course of wetting and drying cycles. The coordination number, degree of anisotropy and fractal dimension were indicating an increase. In contrast, the mean shape factor, pore-throat ratio, and Euler-Poincaré number decreased due to wetting and drying cycles. These illustrated that the wetting and drying cycles made soil pore structure become more porous, continuous, heterogeneous and complex. It can thus be deduced that the water-level fluctuation would modify soil porosity, pore size distribution, and pore morphology in the Three Gorges Reservoir, which may have profound implications for soil processes, soil functions, and bank stability.

Monitoring Soil Salt Content Using HJ-1A Hyperspectral Data: A Case Study of Coastal Areas in Rudong County, Eastern China

Hyperspectral data are an important source for monitoring soil salt content on a large scale. However, in previous studies, barriers such as interference due to the presence of vegetation restricted the precision of mapping soil salt content. This study tested a new method for predicting soil salt content with improved precision by using Chinese hyperspectral data, Huan Jing-Hyper Spectral Imager(HJ-HSI), in the coastal area of Rudong County, Eastern China. The vegetation-covered area and coastal bare flat area were distinguished by using the normalized differential vegetation index at the band length of 705 nm(NDVI705). The soil salt content of each area was predicted by various algorithms. A Normal Soil Salt Content Response Index(NSSRI) was constructed from continuum-removed reflectance(CR-reflectance) at wavelengths of 908.95 nm and 687.41 nm to predict the soil salt content in the coastal bare flat area(NDVI705 < 0.2). The soil adjusted salinity index(SAVI) was applied to predict the soil salt content in the vegetation-covered area(NDVI705 ≥ 0.2). The results demonstrate that 1) the new method significantly improves the accuracy of soil salt content mapping(R2 = 0.6396, RMSE = 0.3591), and 2) HJ-HSI data can be used to map soil salt content precisely and are suitable for monitoring soil salt content on a large scale.

Status and prospects of frozen soil studies using CT technology

This paper introduces the characteristics of Computed Tomography （CT） technology and reviews its history, current situation, representative achievements, and use of using CT technology on frozen soil study, including auxiliary equipment specially de- signed for frozen soil studies. CT numbers are used to analyze frozen soil internal structure change, defining and exploring dam- age evolution, and use of CT images on observing soil mesostructure. Finally, this paper presents existing problems confronted by using CT in frozen soil studies, possible solutions and challenges, among which, we introduce high quality CT image processing for frozen soils, and relations between CT number change and each component change on frozen soil samples within the region of interest. It is shown that present CT technology is one of the most ideal and effective technology to study frozen soil mesostructure using non-desmactive testing. CT technology will play a key role in the study and development in the field of frozen soil by means of auxiliary equipment and the digital imaging processing.

Land Parcel Land Use History as a Key to Site Selectionfor Documenting Soil Contamination Risk: a Case Study from Australian Suburbia

In that orcharding in early to mid twentieth century southeastern Australia involved use of certain heavy metal and As compounds in regular pest control spray procedures, some interest attaches to the possibility that these landparcels are underlain by soils with above background Cu, Pb and As levels. Interpretation of Land cover changes allowed land parcels previously occupied by orchards to be identified in the 1950s through time series air photos. A comparison of soil analysis results referring to soil samples from control sites, and from land parcels formerly occupied by orchardists, shows that contamination (above background) levels of cations in the pesticides can be found in the top 6 cm of former orchard soils. It is clear that digital spatial data handling and culturally informed air photo interpretation has a place in soil contamination studies, land use planning (with particular reference to re development) and in administration of public health.

Combination of effective color information and machine learning for rapid prediction of soil water content

Soil water content(SWC)is one of the critical indicators in various fields such as geotechnical engineering and agriculture.To avoid the time-consuming,destructive,and laborious drawbacks of conventional SWC measurements,the image-based SWC prediction is considered based on recent advances in quantitative soil color analysis.In this study,a promising method based on the Gaussian-fitting gray histogram is proposed for extracting characteristic parameters by analyzing soil images,aiming to alleviate the interference of complex surface conditions with color information extraction.In addition,an identity matrix consisting of 32 characteristic parameters from eight color spaces is constituted to describe the multi-dimensional information of the soil images.Meanwhile,a subset of 10 parameters is identified through three variable analytical methods.Then,four machine learning models for SWC prediction based on partial least squares regression(PLSR),random forest(RF),support vector machines regression(SVMR),and Gaussian process regression(GPR),are established using 32 and 10 characteristic parameters,and their performance is compared.The results show that the characteristic parameters obtained by Gaussian-fitting can effectively reduce the interference from soil surface conditions.The RGB,CIEXYZ,and CIELCH color spaces and lightness parameters,as the inputs,are more suitable for the SWC prediction models.Furthermore,it is found that 10 parameters could also serve as optimal and generalizable predictors without considerably reducing prediction accuracy,and the GPR model has the best prediction performance(R^(2)≥0.95,RMSE≤2.01%,RPD≥4.95,and RPIQ≥6.37).The proposed image-based SWC predictive models combined with effective color information and machine learning can achieve a transient and highly precise SWC prediction,providing valuable insights for mapping soil moisture fields.