Remote sensing of soil properties in precision agriculture: A review
The success of precision agriculture (PA) depends strongly upon an efficient and accurate method for in-field soil property determination. This information is critical for farmers to calculate the proper amount of inputs for best crop performance and least environmental effect. Grid sampling, as a t...
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Veröffentlicht in: | Frontiers of Earth Science 2011, Vol.5 (3), p.229-238 |
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description | The success of precision agriculture (PA) depends strongly upon an efficient and accurate method for in-field soil property determination. This information is critical for farmers to calculate the proper amount of inputs for best crop performance and least environmental effect. Grid sampling, as a traditional way to explore in-field soil variation, is no longer considered appropriate since it is labor intensive, time consuming and lacks spatial exhaustiveness. Remote sensing (RS) provides a new tool for PA information gathering and has advantages of low cost, rapidity, and relatively high spatial resolution. Great progress has been made in utilizing RS for in-field soil property determination. In this article, recent publications on the subject of RS of soil properties in PA are reviewed. It was found that a large array of agriculturally-important soil properties (including textures, organic and inorganic carbon content, macro- and micro-nutrients, moisture content, cation exchange capacity, electrical conductivity, pH, and iron) were quantified with RS successfully to the various extents. The applications varied from laboratory-analysis of soil samples with a bench-top spectrometer to field-scale soil mapping with satellite hyper-spectral imagery. The visible and near-infrared regions are most commonly used to infer soil properties, with the ultraviolet, mid-infrared, and thermal-infrared regions have been used occasionally. In terms of data analysis, MLR, PCR, and PLSR are three techniques most widely used. Limitations and possibilities of using RS for agricultural soil property characterization were also identified in this article. |
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It was found that a large array of agriculturally-important soil properties (including textures, organic and inorganic carbon content, macro- and micro-nutrients, moisture content, cation exchange capacity, electrical conductivity, pH, and iron) were quantified with RS successfully to the various extents. The applications varied from laboratory-analysis of soil samples with a bench-top spectrometer to field-scale soil mapping with satellite hyper-spectral imagery. The visible and near-infrared regions are most commonly used to infer soil properties, with the ultraviolet, mid-infrared, and thermal-infrared regions have been used occasionally. In terms of data analysis, MLR, PCR, and PLSR are three techniques most widely used. 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Alex</creatorcontrib><creatorcontrib>SUI, Ruixiu</creatorcontrib><title>Remote sensing of soil properties in precision agriculture: A review</title><title>Frontiers of Earth Science</title><addtitle>Front Earth Sci</addtitle><addtitle>Front. Earth Sci</addtitle><description>The success of precision agriculture (PA) depends strongly upon an efficient and accurate method for in-field soil property determination. This information is critical for farmers to calculate the proper amount of inputs for best crop performance and least environmental effect. Grid sampling, as a traditional way to explore in-field soil variation, is no longer considered appropriate since it is labor intensive, time consuming and lacks spatial exhaustiveness. Remote sensing (RS) provides a new tool for PA information gathering and has advantages of low cost, rapidity, and relatively high spatial resolution. Great progress has been made in utilizing RS for in-field soil property determination. In this article, recent publications on the subject of RS of soil properties in PA are reviewed. It was found that a large array of agriculturally-important soil properties (including textures, organic and inorganic carbon content, macro- and micro-nutrients, moisture content, cation exchange capacity, electrical conductivity, pH, and iron) were quantified with RS successfully to the various extents. The applications varied from laboratory-analysis of soil samples with a bench-top spectrometer to field-scale soil mapping with satellite hyper-spectral imagery. The visible and near-infrared regions are most commonly used to infer soil properties, with the ultraviolet, mid-infrared, and thermal-infrared regions have been used occasionally. In terms of data analysis, MLR, PCR, and PLSR are three techniques most widely used. 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subjects | Agricultural land Agricultural management Agriculture Agronomy Cation exchange Earth and Environmental Science Earth Sciences Environmental effects Inorganic carbon Moisture content near-infrared reflectance spectroscopy precision agriculture (PA) Precision farming Remote sensing remote sensing (RS) Review Article sensor soil Soil analysis Soil mechanics Soil properties soil property Spectrum analysis |
title | Remote sensing of soil properties in precision agriculture: A review |
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