Exploring the driving forces and digital mapping of soil organic carbon using remote sensing and soil texture

Hamzehpour, Nikou; Shafizadeh‐Moghadam, Hossein; Valavi, Roozbeh

doi:10.1016/j.catena.2019.104141

Cited by 68 publications

(30 citation statements)

References 69 publications

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“…Nowadays, significant progress has been made towards the development and use of optical sensing tools to determine the N content of crops (Hatfield, Gitelson, Schepers, & Walthall, 2008;Wang et al, 2012). The N status of crops at field scale can be detected by spectral indices (Bodo & Schmidhalter, 2008;Zubillaga & Urricariet, 2005), and efforts have been directed to the validation of diagnostic tools that can relate spectral indices to soil fertility (Gholizadeh, Zizala, Mohammadmehdi, & Boruvka, 2018;Hamzehpour, Shafizadeh-Moghadam, & Valavi, 2018;Li et al, 2001). In previous work (Zubillaga, Mosso, & Redel, 2012), consistent relationships were found between N mineralization potential and vegetation indices derived from remote sensing.…”

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confidence: 99%

Assessment of apparent N mineralization at field scale

Zubillaga

Redel

Cabrera

2020

Soil Use and Management

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Quantification of nitrogen mineralization at the field scale is necessary to improve current N fertilizer recommendations. The objective of this study was to identify a suitable set of explanatory variables and an appropriate modelling framework to estimate the average daily amount of N mineralized (DANM) during a corn (Zea mays L.) growing season. In six field experiments in Flat Inland Pampas, Argentina, DANM was evaluated in three different homogeneous zones (HZ), which were defined based on terrain elevation. Mixed-effect generalized linear models were developed that included growing season as random effect as well as HZ, sand, clay, silt, soil organic carbon, and normalized difference vegetation index (NDVI) as fixed effects. Results showed that DANM ranged from 0.19 to 1.25 kg N ha −1 day −1 with a mean of 0.64 kg N ha −1 day −1 , which is equivalent to 110 kg N ha −1 for the corn growing season. The best model included HZ, sand, and early NDVI measurements, and predicted DANM with an RMSE of 0.19 kg N ha −1 day −1 . Thus, the model should be improved and tested in the Inland Flat Pampas before it could be used to assess N fertilizer application rates.

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mentioning

confidence: 99%

Assessment of apparent N mineralization at field scale

Zubillaga

Redel

Cabrera

2020

Soil Use and Management

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“…These studies report confidence interval, obtained by bootstrapping the original set of observations (e.g. Chen et al, 2019;Padarian et al, 2019;Hamzehpour et al, 2019). A few studies use the kriging variance computed on the residuals of a trend obtained by predicting with a ML algorithm (e.g.…”

Section: Validation and Uncertainty Quantificationmentioning

confidence: 99%

Machine learning for digital soil mapping: applications, challenges and suggested solutions

Wadoux¹,

Minasny²,

McBratney³

2020

Preprint

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The uptake of machine learning (ML) algorithms in digital soil mapping (DSM) is transforming the way soil scientists produce their maps. Machine learning is currently applied to mapping soil properties or classes much in the same way as other unrelated fields of science. Mapping of soil, however, has unique aspects which require adaptations of the ML algorithms. These features are for example, but not limited to, the inclusion of pedological knowledge into the ML algorithm, the accounting of spatial structure present in the soil data, or the desire to increase our scientific understanding of the distribution and genesis of soil from a calibrated ML model. Tackling these challenges is critical for machine learning to gain credibility and scientific consistency in soil science. In this article, we review the current applications of machine learning in digital soil mapping and suggest improvements. We found a growing interest of the use of ML in DSM. Most studies focus on obtaining accurate maps and disregard the characteristics of soil data, such as spatial autocorrelation. Only a few studies account for existing soil knowledge or quantify the uncertainty of the predicted maps. We then discuss the challenges related to the application of ML for soil mapping and offer solutions from existing studies in the natural sciences. The challenges are organized as follows: sampling, resampling, accounting for the spatial information, multivariate mapping, uncertainty analysis, validation, integration of pedological knowledge and, interpretation of the models. We conclude that for future developments, machine learning should incorporate three core elements: plausibility, interpretability, and explainability, which will trigger soil scientists to move beyond model prediction and towards explanation of soil processes.

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“…The S function is used to calculate the similarity between the point x and the-i neighbor. The value of the S function is shown in (9).…”

Section: Research Proceduresmentioning

confidence: 99%

“…Whereas in research [3] used imaging spectroscopy and produced characterization of spatial variability of soil organic carbon and nitrogen at soil aggregate scale. The other research related to the classification and mapping of soil organic carbon has also been carried out using satellite images and airborne image [4][5][6][7][8][9]. Although this technique is quite effective, but not all satellite imagery data is easily obtained especially for certain areas with a narrow area coverage.…”

Section: Introductionmentioning

confidence: 99%

Color moment and gray level co-occurrence matrix in classification of soil organic matter for patchouli plantation

Dewi¹,

Grahadhuita²,

Muflikhah³

2020

IJEECS

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<span>Patchouli is one of the essential plants that have the most potential and widely cultivated in Indonesia. Patchouli is greedily absorbing soil nutrients and organic matter. Therefore, the selection of soil with high organic matter will maximize the patchouli’s productivity. This paper aims to facilitate soil’s organic matter identification by classifying soil image based on the combination of color and texture features. The color feature extraction was done using the Color Moments method and the texture feature was done using Gray Level Co-occurrence Matrix (GLCM) method. The selection of features was performed to obtain the best combination of color and texture features. The selected features then was used as input of classification by using Modified K-Nearest Neighbor (MKNN). The samples of soil that used as data were taken from several districts in Blitar, East Java province. The testing result of this research showed the highest accuracy of 93,33% by using 180 training data, and also particular color and texture feature combination.</span>

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Exploring the driving forces and digital mapping of soil organic carbon using remote sensing and soil texture

Cited by 68 publications

References 69 publications

Assessment of apparent N mineralization at field scale

Assessment of apparent N mineralization at field scale

Machine learning for digital soil mapping: applications, challenges and suggested solutions

Color moment and gray level co-occurrence matrix in classification of soil organic matter for patchouli plantation

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