Proximal Soil Sensing for Soil Monitoring

Mustafa, Banaz M.; Al-Quraishi, Ayad M. Fadhil; Gholizadeh, Asa; Saberioon, Mohammadmehdi

doi:10.1007/978-3-030-21344-2_5

Cited by 2 publications

(1 citation statement)

References 80 publications

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“…Further, atmospheric interference and geometric fidelity issues complicate the interpretation of results (Guyot et al 1996). Remotely sensed data can be gathered and analysed using specialized software and specified methods to extract soil characteristics and to support digital soil mapping (Ben-Dor et al, 2008, Slaymaker, 2001Mustafa et al 2019;Almamalachy et al 2019). Finally, remote sensing techniques can facilitate mapping inaccessible areas by reducing the need for extensive time-consuming and costly field surveys.…”

Section: Introductionmentioning

confidence: 99%

Characterization and Modeling Surface Soil Physicochemical Properties Using Landsat Images: A Case Study in the Iraqi Kurdistan Region

Al-Quraishi¹,

Sadiq

Messina

2019

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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<p><strong>Abstract.</strong> Quickly and accurately soil properties characterization is critical for many environmental and agricultural activities. This paper aimed at characterizing and modelling the surface soil physicochemical properties using soil laboratory analyses, Landsat-7 ETM+ images, statistical analyses, remote sensing and GIS techniques. A mosaic of two Landsat-7 ETM+ images and forty geo-referenced surface soil samples (0&ndash;10&thinsp;cm) have been collected in 2012 from forty sites in the Iraqi Kurdistan region were used. Several physicochemical properties for the collected soil samples were estimated using laboratory analyses. In addition, the corresponding top-of-atmosphere(ToA) reflectance values, which extracted from the Landsat ETM+ images have been used. Statistical correlation and regression analyses using the SPSS were carried out to explore the most significant relationships among soil properties and the spectral bands of the ETM+ images. Predicted maps for the surface soil physicochemical characteristics were generated using ERDAS ER-Mapper. The study results revealed some significant relationships between the ETM+ images bands values and the studied soil properties. The strongest relationship was found to be between the air-dry soil moisture content (ADSMC) and the ETM+ images bands. Given a representative collection of field data, the methods presented offer a reliable set of tools for the rapid production of surface soil maps in arid environments.</p>

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Section: Introductionmentioning

confidence: 99%

Characterization and Modeling Surface Soil Physicochemical Properties Using Landsat Images: A Case Study in the Iraqi Kurdistan Region

Al-Quraishi¹,

Sadiq

Messina

2019

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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Evaluating performance of miniaturized spectrometers in predicting soil properties using multivariate statistical analysis

Gowera,

Sorenson,

Bedard-Haughn

et al. 2024

Can. J. Soil. Sci.

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Despite the increasing use of portable, low-cost spectrometers in estimating soil properties, there is lack of documentation regarding the factors contributing to the lower performance of these spectrometers when compared to conventional ones. This study investigates potential factors influencing performance of the Nanoquest, a low-cost spectrometer, in estimating soil organic carbon (SOC) and total nitrogen (TN). To conduct the study, five different models (cubist, partial least squares regression, support vector machines, random forest, and generalised boosted models) were tested for the estimation SOC and TN and a fivefold cross-validation analysis was conducted for model hyperparameter optimization. The Nanoquest achieved a Lin’s concordance correlation coefficient (CCC) value of 0.84 and an R2 value of 0.74 for SOC. For TN, CCC values of 0.86 and an R2 value of 0.78 were obtained. To understand the impact of the spectral range and spectral resolution on SOC and TN estimation, the ASD spectra were digitally resampled to match the Nanoquest spectral range and resolution. This resampling resulted in a slight decrease in model performance for the spectral range and a more pronounced decrease for the spectral resolution.

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Proximal Soil Sensing for Soil Monitoring

Cited by 2 publications

References 80 publications

Characterization and Modeling Surface Soil Physicochemical Properties Using Landsat Images: A Case Study in the Iraqi Kurdistan Region

Characterization and Modeling Surface Soil Physicochemical Properties Using Landsat Images: A Case Study in the Iraqi Kurdistan Region

Evaluating performance of miniaturized spectrometers in predicting soil properties using multivariate statistical analysis

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