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Sensing Technologies and Applications in Digital Soil Mapping

A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Sensing and Imaging".

Deadline for manuscript submissions: closed (20 April 2023) | Viewed by 2443

Special Issue Editor

Special Issue Information

Dear Colleagues,

Digital soil mapping (DSM) in soil science is the creation and the population of a geographically referenced soil database generated at a given resolution by using field- and laboratory-observed data coupled with environmental data through quantitative relationships. The development of digital soil mapping is closely related to the availability of spatially exhaustive and relatively low-cost data in the form of spatial imagery and digital elevation models, also derived from remote sensing, proximal sensing and areal sensing, that can effectively represent various soil forming factors and process. This Special Issue aims to use digital mapping techniques to characterize soils in space and time based on data collected by sensors; design, develop, and validate sensors for the fast, inexpensive, and accurate characterization of soil properties; optimize sampling designs to capture high spatiotemporal variations and in changing soil environments; and to develop modeling and decision-support systems and quantify underlying soil processes to develop better and more sustainable management plans.

Dr. Asim Biswas
Guest Editor

Manuscript Submission Information

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Keywords

  • digital soil mapping
  • proximal soil sensing
  • remote sensing
  • spatial modeling
  • climate change
  • soil physics
  • sustainable management
  • inverse modelling
  • machine learning
  • artificial intelligence
  • data mining

Published Papers (1 paper)

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Research

13 pages, 4004 KiB  
Article
Development of a Soil Organic Matter Content Prediction Model Based on Supervised Learning Using Vis-NIR/SWIR Spectroscopy
Sensors 2022, 22(14), 5129; https://doi.org/10.3390/s22145129 - 08 Jul 2022
Cited by 6 | Viewed by 2001
Abstract
In the current scenario of anthropogenic climate change, carbon credit security is becoming increasingly important worldwide. Topsoil is the terrestrial ecosystem component with the largest carbon sequestration capacity. Since soil organic matter (SOM), which is mostly composed of organic carbon, and can be [...] Read more.
In the current scenario of anthropogenic climate change, carbon credit security is becoming increasingly important worldwide. Topsoil is the terrestrial ecosystem component with the largest carbon sequestration capacity. Since soil organic matter (SOM), which is mostly composed of organic carbon, and can be affected by rainfall, cultivation, and pollutant inflow, predicting SOM content through regular monitoring is necessary to secure a stable carbon sink. In addition, topsoil in the Republic of Korea is vulnerable to erosion due to climate, topography, and natural and anthropogenic causes, which is also a serious issue worldwide. To mitigate topsoil erosion, establish an efficient topsoil management system, and maximize topsoil utilization, it is necessary to construct a database or gather data for the construction of a database of topsoil environmental factors and topsoil composition. Spectroscopic techniques have been used in recent studies to rapidly measure topsoil composition. In this study, we investigated the spectral characteristics of the topsoil from four major rivers in the Republic of Korea and developed a machine learning-based SOM content prediction model using spectroscopic techniques. A total of 138 topsoil samples were collected from the waterfront area and drinking water protection zone of each river. The reflection spectrum was measured under the condition of an exposure time of 136 ms using a spectroradiometer (Fieldspec4, ASD Inc., Alpharetta, GA, USA). The reflection spectrum was measured three times in wavelengths ranging from 350 to 2500 nm. To predict the SOM content, partial least squares regression and support vector regression were used. The performance of each model was evaluated through the coefficient of determination (R2) and root mean square error. The result of the SOM content prediction model for the total topsoil was R2 = 0.706. Our findings identified the important wavelength of SOM in topsoil using spectroscopic technology and confirmed the predictability of the SOM content. These results could be used for the construction of a national topsoil database. Full article
(This article belongs to the Special Issue Sensing Technologies and Applications in Digital Soil Mapping)
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