Determining soil organic carbon concentration in agricultural fields using a handheld spectroradiometer: Implication for soil fertility measurement

Tola, ElKamil; Al‐Gaadi, Khalid A.; Madugundu, Rangaswamy; Kayad, Ahmed; Alameen, Ahmed A.; Edrees, Haroon F.; Edrris, Mohamed K.

doi:10.25165/j.ijabe.20181106.4061

Cited by 4 publications

(3 citation statements)

References 16 publications

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“…However, soils collected at 30 cm showed higher reflectance, higher gypsum and less SOC ( Table 1). Lower SOC content lead to higher reflectance, which is frequent in drylands [35,62], especially when calcium carbonate and gypsum are abundant in soils [27]. Differences in soil reflectance of the plot of study can be observed by brightness index calculated from Sentinel 2 images ( Figure 1d).…”

Section: Prediction Of Physical-chemical Propertiesmentioning

confidence: 96%

Estimating Soil Organic Carbon in Agricultural Gypsiferous Soils by Diffuse Reflectance Spectroscopy

Marques

Álvarez

Carral

et al. 2020

Water

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Contents of soil organic carbon (SOC), gypsum, CaCO3, and quartz, among others, were analyzed and related to reflectance features in visible and near-infrared (VIS/NIR) range, using partial least square regression (PLSR) in ParLes software. Soil samples come from a sloping olive grove managed by frequent tillage in a gypsiferous area of Central Spain. Samples were collected in three different layers, at 0–10, 10–20 and 20–30 cm depth (IPCC guidelines for Greenhouse Gas Inventories Programme in 2006). Analyses were performed by C Loss-On-Ignition, X-ray diffraction and water content by the Richards plates method. Significant differences for SOC, gypsum, and CaCO3 were found between layers; similarly, soil reflectance for 30 cm depth layers was higher. The resulting PLSR models (60 samples for calibration and 30 independent samples for validation) yielded good predictions for SOC (R2 = 0.74), moderate prediction ability for gypsum and were not accurate for the rest of rest of soil components. Importantly, SOC content was related to water available capacity. Soils with high reflectance features held c.a. 40% less water than soils with less reflectance. Therefore, higher reflectance can be related to degradation in gypsiferous soil. The starting point of soil degradation and further evolution could be established and mapped through remote sensing techniques for policy decision making.

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Section: Prediction Of Physical-chemical Propertiesmentioning

confidence: 96%

Estimating Soil Organic Carbon in Agricultural Gypsiferous Soils by Diffuse Reflectance Spectroscopy

Marques

Álvarez

Carral

et al. 2020

Water

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“…The Walkley-Black (colorimetric) method using a spectrophotometer and following FAO (2019) and Tola et al (2018) procedures was used. Soil organic carbon content was calculated according to the Equation 1 (Haile et al, 2021); SOC= %.OC ˟ 𝜌ƅ ˟ d………………... (Equation 1) Where; % OC is the soil organic carbon concentration, 𝜌ƅ is soil bulk density (gcm -3 ), and d is the soil depth of the sample (cm).…”

Section: The Description Of the Area Of The Studymentioning

confidence: 99%

Land use and land cover change influence on soil organic carbon content for a pastoral area: use of geographical information system

Shadrack M. NJAGI,

Julius Lejju,

John Bosco Nkurunungi

2022

EAJSTI

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Data on land use and cover change and soil organic carbon (SOC) in rangelands are essential. This is because rangelands ecosystems are fragile, and poor land-use practices can significantly threaten their sustainability by depleting SOC and increasing atmospheric carbon dioxide. This study investigated soil organic carbon variations as influenced by land use/land cover changes in the unprotected area of the Sanga agropastoral ecosystem, southwestern Uganda. Landsat images provided data for land use and cover for 1987 and 2020. Soil organic carbon contents were investigated in farmland (FL), grassland (GL), woodland (WL), and bare land (BL) as control at 0-15 cm and 15-30 cm depths. Soil samples were analyzed for organic carbon and bulk density using the colorimetric and core ring methods, respectively. Total soil organic carbon content was significantly high in grassland (31.55 Mg C ha-1), p=0.005, and woodland (27.89 Mg C ha-1), p=0.028 compared to bare land (16.17 Mg C ha-1). Additionally, total soil organic carbon concentration in grassland (2.10%) was higher than SOC concentration in farmland (1.39%) p=0.001 and bare land (1.00%), p<0.001, respectively. Similarly, woodlands soil organic carbon concentration (1.98%) was higher than soil organic concentrations in bare lands, p=0.003 and farmlands, p=0.028. Bulk density was significantly different at lower horizons, with farmland having a higher bulk density than other land use types, p=0.013. Roots and litter inputs in woodlands and grasslands contributed to higher organic carbon than farmland and eroded/bare lands. Cultivation also increased the soil bulk density. This study concludes that land use and the land cover change affected soil organic carbon sequestration and bulk density. Therefore, farmers need to increase farm management practices to avoid an increase in bulk density. Further study to compare the effects of grazing intensity on SOC and modeling future SOC content in the study area is recommended.

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“…At larger scale, Schillaci et al (2016) and Darwish and Fadel (2017) prepared SOC maps for the Mediterranean region/Arab region. At the regional scale, Tola et al (2018Tola et al ( , 2019 assessed the SOC concentration in the parts of Al-Kharj region of Saudi Arabia, with special emphasis on the long-term impact of tillage on the soil organic carbon. Another study by Mallick et al (2020) was developed SOC prediction models for Asir Province, Saudi Arabia.…”

mentioning

confidence: 99%

Estimation of soil organic carbon in agricultural fields: A remote sensing approach

Madugundu¹,

Al‐Gaadi²,

Tola³

et al. 2022

JEB

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Aim: In view of the importance of Soil Organic Carbon (SOC) in agricultural management, a study was conducted to develop a digital SOC map using remotely sensed spectral indices. The present study was conducted on the Tawdeehiya Farms, located in the central region of Saudi Arabia between Al-Kharj and Haradh cities. Methodology: Landsat-8 (L8) and Sentinel-2 (S2A) satellite images were used for the characterization of SOC stocks in the topsoil layer (0-10 cm) of the experimental fields. Soil samples were randomly collected from six (50 ha each) agricultural fields and analyzed in the laboratory for SOC (SOCA) following Walkley and Black method. While, vegetation indices (VI), such as the Normalized Difference Vegetation Index (NDVI), NDVIRedEdge, Enhanced Vegetation Index (EVI), Bare Soil Index (BSI), and Reduced Simple Ratio (RSR) were computed and subsequently used for the development of SOC prediction models. Results: Univariate linear regression technique was employed for the recognition of a suitable band/VI for SOC (SOCP) mapping. The SWIR-1 band of both L8 (R2 = 0.86) and S2A (R2 = 0.77) data was promising for predicting SOC with 16% (S2A) and 18% (L8) of BIAS. Interpretation: The NDVI and BSI (for L8 data) and BSI and RSR (for S2A data) were found most suitable VI in the prediction of SOC. The R2 values of linear regression models were 0.68 (BSI) and 0.78 (RSR), indicating that nearly 68% and 78% of the SOC could be predicted through L8 and S2A datasets, respectively.

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Determining soil organic carbon concentration in agricultural fields using a handheld spectroradiometer: Implication for soil fertility measurement

Cited by 4 publications

References 16 publications

Estimating Soil Organic Carbon in Agricultural Gypsiferous Soils by Diffuse Reflectance Spectroscopy

Estimating Soil Organic Carbon in Agricultural Gypsiferous Soils by Diffuse Reflectance Spectroscopy

Land use and land cover change influence on soil organic carbon content for a pastoral area: use of geographical information system

Estimation of soil organic carbon in agricultural fields: A remote sensing approach

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