Spatial variability of soil organic matter using remote sensing data

Mirzaee, Salman; Ghorbani-Dashtaki, Shoja; Mohammadi, J.; Asadi, Hossein; Asadzadeh, Farrokh

doi:10.1016/j.catena.2016.05.023

Cited by 186 publications

(76 citation statements)

References 25 publications

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“…Furthermore, the analysis result of the remote sensing variables was similar to the report of Mirzaee et al (2016), in which B4 and NDVI had a greater influence on the spatial variability of soil organic matter. This may be due to vegetation and organic matter containing a major carbon source, thus forming a potential strong correlation and driving force.…”

Section: Driving Force Analysis Of Auxiliary Variables For Somsupporting

confidence: 64%

“…The estimated results of the SOM content by ELMOK were much closer to the measured values, which were demonstrated an acceptable range for monitoring site-specific SOM and its quality evaluation in comparison with [8,13,14]. Generally, a higher resolution and higher precision of SOM can capture its changes more acutely in a regional spatial range, while it is difficult to reflect the continuous dynamic changes because only one year of data is used.…”

Section: Sustainable Monitoring Of Digital Mapping For Sommentioning

confidence: 67%

“…More importantly, the run time of the ELM model was faster than ANN, which exhibited an efficient analytic ability between the correlative factors and SOM content. The prediction performance of ELM was comparable to, but better than, the regression and ANN reported by Li et al (2010) [28] and Mirzaee et al (2016) [13]. The residuals were obtained using the difference between the measured SOM data and the results of the MLR, ANN, and ELM prediction data.…”

Section: Prediction Of Som Content By Mlr Ann and Elm Modelsmentioning

confidence: 99%

“…However, it is hard to obtain a high prediction accuracy of SOM content when only considering environmental variables at the local scale, because there is less spatial variation and a weak nonlinear relationship. In recent years, remote sensing (RS) data has also been used to estimate the spatial variability of SOM because of the advantage of its multi-spectrum feature, which has a satisfactory forecast effect [12,13]. Remote sensing variables have a better spatial transitivity, but do not easily explain the driving mechanism behind global change and the carbon sink of SOM in comparison with environmental variables.…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Spatial Prediction of Soil Organic Matter Using a Hybrid Geostatistical Model of an Extreme Learning Machine and Ordinary Kriging

Song

Lian-an

et al. 2017

Sustainability

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An accurate estimation of soil organic matter (SOM) content for spatial non-point prediction is an important driving force for the agricultural carbon cycle and sustainable productivity. This study proposed a hybrid geostatistical method of extreme learning machine-ordinary kriging (ELMOK), to predict the spatial variability of the SOM content. To assess the feasibility of ELMOK, a case study was conducted in a regional scale study area in Shaanxi Province, China. A total of 472 topsoil (0-20 cm) samples were collected. A total of 14 auxiliary variables (predictors) were obtained from remote sensing data and environmental factors. The proposed method was compared with the ability of traditional geostatistical methods such as simple kriging (SK) and ordinary kriging (OK), in addition to hybrid geostatistical methods such as regression-ordinary kriging (ROK) and artificial neural network-ordinary kriging (ANNOK). The results showed that the extreme learning machines (ELM) model used principal components (PCs) as input variables, and performed better than both multiple linear regression (MLR) and artificial neural network (ANN) models. Compared with geostatistical and hybrid geostatistical prediction methods of SOM spatial distribution, the ELMOK model had the highest coefficient of determination (R 2 = 0.671) and ratio of performance to deviation (RPD = 2.05), as well as the lowest root mean square error (RMSE = 1.402 g kg −1 ). In conclusion, the application of remote sensing imagery and environmental factors has a deeper driven significance of a non-linear and multi-dimensional hierarchy relationship for explaining the spatial variability of SOM, tracing local carbon sink and high quality SOM maps. More importantly, it is possibly concluded that the sustainable monitoring of SOM is a significant process through the pixel-based revisit sampling, an analysis of the mapping results of SOM, and methodological integration, which is the primary step in spatial variations and time series. The proposed ELMOK methodology is a promising and effective approach which can play a vital role in predicting the spatial variability of SOM at a regional scale.

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Section: Driving Force Analysis Of Auxiliary Variables For Somsupporting

confidence: 64%

Section: Sustainable Monitoring Of Digital Mapping For Sommentioning

confidence: 67%

Section: Prediction Of Som Content By Mlr Ann and Elm Modelsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Spatial Prediction of Soil Organic Matter Using a Hybrid Geostatistical Model of an Extreme Learning Machine and Ordinary Kriging

Song

Lian-an

et al. 2017

Sustainability

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“…La evaluación, caracterización y determinación de las propiedades del suelo, mediante la utilización de datos procedentes de sensores remotos ha sido extensamente aplicada durante los últimos años (Rawls et al, 2004;Vrieling, 2006;Lagacherie et al, 2012;Poggio et al, 2013;Mirzaee et al, 2016). La capacidad de la espectrometría en condiciones de laboratorio está demostrada para la predicción de importantes propiedades del suelo (Schulten y Schnitzer, 1997;ViscarraRossel et al, 2006;Ben-Dor et al, 2009).…”

Section: Introductionunclassified

Efectos de las variables ambientales en la estimación de materia orgánica del suelo a escala regional en un ambiente semiarido (Región de Murcia, España)

Pérez‐Cutillas¹,

Barberá²,

García³

2017

BAGE

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RESUMENLa estimación de valores edáficos mediante el empleo de técnicas de modelización espacial, basada en procesos y variables ambientales, es utilizada en este trabajo para analizar el efecto que tienen estas variables sobre la materia orgánica del suelo (MOS). La discusión es enormemente interesante por las conclusiones que se pueden extraer de las distintas implicaciones que ejercen los patrones ecológicos en su distribución y variabilidad espacial. En este estudio se ha utilizado una base de datos de 1126 muestras de materia orgánica en capa arable del suelo en la Región de Murcia (SE de España), con el fin de estimar el contenido de la MOS a escala regional (más de 10000 km 2 ) mediante el uso de 21 variables ambientales como predictores. Entre las variables más influyentes destacan las asociadas al relieve (i.e. la pendiente, las curvaturas del terreno), la acumulación de flujo vinculada a las redes de drenaje, la precipitación, la radicación solar y ciertos índices específicos obtenidos mediante técnicas de teledetección, como el NDVI o el Índice Mineralógico de Arcillas CMI.Palabras clave: Propiedades del suelo, materia orgánica del suelo, modelización, SIG, variables ambientales, teledetección.Boletín de la Asociación de Geógrafos Españoles N.º 75 -2017, págs. 175-191 I.S.S.N.: 0212-9426

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A Review on Soil Organic Carbon Estimation via Remote Sensing Approaches

Batool¹

2022

Preprint

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Spatial variability of soil organic matter using remote sensing data

Cited by 186 publications

References 25 publications

Spatial Prediction of Soil Organic Matter Using a Hybrid Geostatistical Model of an Extreme Learning Machine and Ordinary Kriging

Spatial Prediction of Soil Organic Matter Using a Hybrid Geostatistical Model of an Extreme Learning Machine and Ordinary Kriging

Efectos de las variables ambientales en la estimación de materia orgánica del suelo a escala regional en un ambiente semiarido (Región de Murcia, España)

A Review on Soil Organic Carbon Estimation via Remote Sensing Approaches

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