Abstract Characterization of the spatiotemporal distribution of soil volumetric water content (θ) is fundamental to agriculture, ecology, and earth science. Given the labor intensive and inefficient nature of determining θ, apparent electrical conductivity (EC a ) measured by electromagnetic induction has been used as a proxy. A number of previous studies have employed inversion algorithms to convert EC a data to depth‐specific electrical conductivity (σ) which could then be correlated to soil θ and other soil properties. The purpose of this study was to develop a spatiotemporal inversion algorithm which accounts for the temporal continuity of EC a . The algorithm was applied to a case study where time‐lapse EC a was collected on a 350 m transect on seven different days on an alfalfa farm in the USA. Results showed that the approach was able to map the location of moving wetting front along the transect. Results also showed that the spatiotemporal inversion algorithm was more precise (RMSE = 0.0457 cm 3 /cm 3 ) and less biased (ME = −0.0023 cm 3 /cm 3 ) as compared with the nonspatiotemporal inversion approach (0.0483 cm 3 /cm 3 and ME = −0.0030 cm 3 /cm 3 , respectively). In addition, the spatiotemporal inversion algorithm allows for a reduced set of EC a surveys to be used when non abrupt changes of soil water content occur with time. To apply this spatiotemporal inversion algorithm beyond low induction number condition, full solution of the EM induction phenomena can be studied in the future.
Abstract To generate baseline data for the purpose of monitoring the efficacy of remediation of a degraded landscape, we demonstrate a method for 3‐dimensional mapping of electrical conductivity of saturated soil paste extract (EC e ) across a study field in central Haryana, India. This is achieved by establishing a linear relationship between calculated true electrical conductivity (σ) and laboratory measured EC e at various depths (0–0.3, 0.3–0.6, 0.6–0.9, and 0.9–1.2 m). We estimate σ by inverting DUALEM‐21S apparent electrical conductivity (EC a ) data using a quasi‐3‐dimensional inversion algorithm (EM4Soil‐V302). The best linear relationship (EC e = −11.814 + 0.043 × σ) was achieved using full solution (FS), S1 inversion algorithm, and a damping factor (λ) of 0.6 that had a large coefficient of determination ( R 2 = 0.84). A cross‐validation technique was used to validate the model, and given the high accuracy (RMSE = 8.31 dS m −1 ), small bias (mean error = −0.0628 dS m −1 ), large R 2 = 0.82, and Lin's concordance (0.93), between measured and predicted EC e , we were well able to predict the EC e distribution at all the four depths. However, the predictions made in the topsoil (0–0.3 m) at a few locations were poor due to limited data availability in areas where EC a changed rapidly. In this regard, improvements in prediction can be achieved by collection of EC a in more closely spaced transects, particularly in areas where EC a varies over short spatial scales. Also, equivalent results can be achieved using smaller combinations of EC a data (i.e., DAULEM‐1S, DUALEM‐2S), although with some loss in precision, bias, and concordance.
Abstract Effective management of soil requires the spatial distribution of its various physical, chemical and hydrological properties. This is because properties, for example clay content, determine the ability of soil to hold cations and retain water. However, data acquisition is labour intensive and time‐consuming. To add value to the limited soil data, remote sensing (e.g. airborne gamma‐ray spectrometry) and proximal sensing, such as electromagnetic ( EM ) induction, are being used as ancillary data. Here, we provide examples of developing Digital Soil Maps ( DSM ) of soil physical, chemical and hydrological properties, for seven cotton‐growing areas of southeastern Australia, by coupling soil data with remote and proximal sensed ancillary data. A greater challenge is how to get these DSM to a stakeholder in a way that is useful for practical soil use and management. This study describes how we facilitate access to the DSM s, using a simple‐to‐use web GIS platform, called terra GIS . The platform is underpinned by Google Maps API , which is an open‐source development environment for building spatially enabled Internet applications. In conclusion, we consider that terra GIS and the supporting information, available on the sister web page ( http://www.terragis.bees.unsw.edu.au/ ), allow easy access to explanation of DSM of soil properties, which are relevant to cotton growers, farm managers, consultants, extension staff, researchers, state and federal government agency personnel and policy analysts. Future work should be aimed at developing error budget maps to identify where additional soil and/or ancillary data is required to improve the accuracy of the DSM s.
The paper presents an automatic region detection based method to reconstruct street scenes from driving recorder images. The driving recorder in this paper is a dashboard camera that collects images while the motor vehicle is moving. An enormous number of moving vehicles are included in the collected data because the typical recorders are often mounted in the front of moving vehicles and face the forward direction, which can make matching points on vehicles and guardrails unreliable. Believing that utilizing these image data can reduce street scene reconstruction and updating costs because of their low price, wide use, and extensive shooting coverage, we therefore proposed a new method, which is called the Mask automatic detecting method, to improve the structure results from the motion reconstruction. Note that we define vehicle and guardrail regions as “mask” in this paper since the features on them should be masked out to avoid poor matches. After removing the feature points in our new method, the camera poses and sparse 3D points that are reconstructed with the remaining matches. Our contrast experiments with the typical pipeline of structure from motion (SfM) reconstruction methods, such as Photosynth and VisualSFM, demonstrated that the Mask decreased the root-mean-square error (RMSE) of the pairwise matching results, which led to more accurate recovering results from the camera-relative poses. Removing features from the Mask also increased the accuracy of point clouds by nearly 30%–40% and corrected the problems of the typical methods on repeatedly reconstructing several buildings when there was only one target building.
Abstract Primary (e.g., quartz) and secondary (clay) minerals are key factors determining the physical and chemical characteristics of soil. Understanding spatial distribution of minerals at the field scale would, therefore, be of potential benefit for soil management. However, current analysis requires time‐consuming laboratory procedures and computational quantification analysis (e.g., SIROQUANT). Furthermore, mineral composition (e.g., quartz, kaolinite, illite and expandable clay minerals) must sum to 100. We aimed to add value to laboratory data by developing multiple linear regression (MLR) relationships between mineralogy and ancillary data such as digital numbers (DNs) (i.e., Red [R], Green [G] and Blue [B]) acquired from remotely sensed air‐photographs and soil apparent electrical conductivity (EC a – mS/m) measured from proximal sensing electromagnetic (EM) instruments (i.e., EM38 and EM31). To account for composition, we compare results from the MLR approach with those from additive log‐ratio (ALR) transformation of mineralogy prior to MLR modelling. This approach together with various ancillary data and trend surface parameters (i.e., scaled Easting and Northing) has greater precision and less bias of prediction than the MLR approach using untransformed data. Our approach also enables predictions to sum to 100. We conclude that the most useful ancillary data to predict the abundance of quartz, kaolinite and illite are B DNs and EM31, while expandable clays are best predicted with R DNs, EM38 and scaled Northing. The use of ancillary data to map mineralogical components combined with ALR‐MLR is an effective approach, with resulting maps providing insights into soil and water management issues consistent with farmer experience.
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