Near-Infrared-Spectroscopy for Determination of Carbon and Nitrogen in Indian Soils

Dinakaran, J.; Bidalia, Ankita; Kumar, Amit; Hanief, M.; Meena, Archana; Rao, K. S.

doi:10.1080/00103624.2016.1194990

Cited by 14 publications

(10 citation statements)

References 38 publications

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“…Prediction results in Table 2 indicate the ability of VNIR-SWIR spectra and the models to predict SOC and N on a large scale was satisfactory in forest soils profile (merged individual layers). This may ascribe to strong correlation of SOC and N with soil reflectance as often reported in the literature (Peltre et al, 2011;Dinakaran et al, 2016). The results also show that for both SOC and N, statistics outputs were considerably higher, when all layers (profile) were used for model development.…”

Section: Discussionsupporting

confidence: 80%

“…SOC has several significant functions in the environment (Murphy, 2014) such as maintaining soil structure and physical stability. Accordingly, it is crucial to a number of natural processes linked to soil health and fertility (Dinakaran et al, 2016). Soil nitrogen (N) is also one of the important indicators of the ecological environment and part of the organic matter components and mineralization process in soil (Li et al, 2014;Shang et al, 2014).…”

Section: Introductionmentioning

confidence: 99%

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Quantification and depth distribution analysis of carbon to nitrogen ratio in forest soils using reflectance spectroscopy

Gholizadeh¹,

Saberioon²,

Pouladi³

et al. 2022

Preprint

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Forest soils have large contents of carbon (C) and nitrogen (N), which have significant spatial variability laterally across landscapes and vertically with depth due to decomposition, erosion and leaching. Therefore, the ratio of C to N contents (C:N), a crucial indicator of soil quality and health, is also different depending on soil horizon. These attributes can cost-effectively and rapidly be estimated using visible–near infrared–shortwave infrared (VNIR–SWIR) spectroscopy. Nevertheless, the effect of different soil layers, particularly over large scales of highly heterogeneous forest soils, on the performance of the technique has rarely been attempted. This study evaluated the potential of VNIR–SWIR spectroscopy in quantification and variability analysis of C:N in soils from different organic and mineral layers of forested sites of the Czech Republic. At each site, we collected samples from the litter (L), fragmented (F) and Humus (H) organic layers, and from the A1 (depth of 2–10 cm) and A2 (depth of 10–40 cm) mineral layers providing a total of 2505 samples. Support vector machine regression (SVMR) was used to train the prediction models of the selected attributes at each individual soil layer and the merged layer (profile). We further produced the spatial distribution maps of C:N as the target attribute at each soil layer. Results showed that the prediction accuracy based on the profile spectral data was adequate for all attributes. Moreover, F was the most accurately predicted layer, regardless of the soil attribute. C:N models and maps in the organic layers performed well although in mineral layers, models were poor and maps were reliable only in areas with low and moderate C:N. On the other hand, the study indicated that VNIR–SWIR spectroscopy could efficiently predict and map organic layers of the Czech Republic’s forested sites but in mineral layers, it didn’t differentiate classes of C:N higher than 50.

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Section: Discussionsupporting

confidence: 80%

Section: Introductionmentioning

confidence: 99%

Quantification and depth distribution analysis of carbon to nitrogen ratio in forest soils using reflectance spectroscopy

Gholizadeh¹,

Saberioon²,

Pouladi³

et al. 2022

Preprint

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“…These results corroborate previous findings indicating that NIRS is a good alternative for conducting soil carbon analyses, with the portability and low-cost advantages for field research in developing countries (Shepherd and Walsh, 2007;Milori et al, 2011;Ferraresi et al, 2012;Beltrame et al, 2016;Dinakaran et al, 2016;Kusumo and Sukartono, 2018). Bushong et al (2015) assessed the accuracy for quantify SOC using NIRS, concluding that NIRS can be used as a quick and accurate method for measuring soil carbon.…”

Section: Carbon Quantification Using Nirssupporting

confidence: 87%

“…Although near infrared is not useful for chemical structure analysis, the detection of much simpler overtones that heavily overlap to give a characteristic broad absorption band, facilitates the use of NIRS for soil analyses Shepherd and Walsh, 2007). Applications of this technique to estimate soil carbon contents indicate that NIRS is a reproducible and low-cost method for quantitative analysis with high potential for field application (Madari et al, 2006;Milori et al, 2011;Shepherd and Walsh, 2007;Dinakaran et al, 2016), including the use of portable visible-NIRS equipment (Shepherd and Walsh, 2007;Li et al, 2015). In addition, NIRS has the potential to be a suitable method for soil organic carbon (Li et al, 2015) mapping for monitoring carbon sequestration (Martin et al, 2002;Kusumo et al, 2018).…”

Section: Introductionmentioning

confidence: 99%

Quantifying soil carbon stocks and humification through spectroscopic methods: A scoping assessment in EMBU-Kenya

Segnini

Posadas²,

Silva

et al. 2019

Journal of Environmental Management

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A B S T R A C TA soil carbon assessment was performed comparing agricultural cropping systems with natural vegetation along a sampling transect spanning different agro-ecologies on the eastern foot slopes of Mount Kenya in Embu county, 125 km from Nairobi, Kenya. The aim was to determine differences in soil carbon stocks and carbon recalcitrance and relate these to soil textural class, altitude, climatic parameters and land use. Soils from main agricultural systems as tea, coffee and maize-based intercropping, as well as from natural vegetation cover were sampled in triplicates, in five layers from 0 to 30 cm in depth and processed for total carbon analysis. The whole soil samples were also analysed using Laser-Induced Fluorescence Spectroscopy (LIFS) to assess carbon humification. Prototype portable equipment intended for future in situ analysis was used in the lab to ascertain the structure of the most recalcitrant and stable carbon present in different agro-ecosystems. In addition, Near Infrared Spectroscopy (NIRS) was tested for the quantitative analysis of soil carbon, showing that it is a reproducible and low-cost method that provided satisfactory results under the processing conditions of the samples. Results showed wide variation in the level and quality of carbon stored in the soils, depending on soil texture, land use, elevation, climate, agricultural practices and land use history. Considering the heterogeneous nature of sampled soils and the performance of NIRS and LIFS, these results can be used as a basis for the development of fully portable systems able to provide rapid, clean and potentially cost-effective relevant information for soil management.

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“…B8 band, which is the near-infrared band (NIR), ranks third in importance for dry land. Studies have shown that the near-infrared band has a good predictive effect on the carbon and nitrogen content in the soil [72]. In paddy field, B11 is the most important (Figure 6c).…”

Section: Variable Importancementioning

confidence: 96%

Prediction of Soil Organic Carbon under Different Land Use Types Using Sentinel-1/-2 Data in a Small Watershed

Wang

Zhang

et al. 2021

Remote Sensing

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Soil organic carbon (SOC) is a key property for evaluating soil quality. SOC is thus an important parameter of agricultural soils and needs to be regularly monitored. The aim of this study is to explore the potential of synthetic aperture radar (SAR) satellite imagery (Sentinel-1), optical satellite imagery (Sentinel-2), and digital elevation model (DEM) data to estimate the SOC content under different land use types. The extreme gradient boosting (XGboost) algorithm was used to predict the SOC content and evaluate the importance of feature variables under different land use types. For this purpose, 290 topsoil samples were collected and 49 features were derived from remote sensing images and DEM. Feature selection was carried out to prevent data redundancy. Coefficient of determination (R2), mean absolute error (MAE), mean squared error (MSE), percent root mean squared error (%RMSE), ratio of performance to interquartile range (RPIQ), and corrected akaike information criterion (AICc) were employed for evaluating model performance. The results showed that Sentinel-1 and Sentinel-2 data were both important for the prediction of SOC and the prediction accuracy of the model differed with land use types. Among them, the prediction accuracy of this model is the best for orchard (R2 = 0.86 and MSE = 0.004%), good for dry land (R2 = 0.74 and MSE = 0.008%) and paddy field (R2 = 0.66 and MSE = 0.009%). The prediction model of SOC content is effective and can provide support for the application of remote sensing data to soil property monitoring.

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Near-Infrared-Spectroscopy for Determination of Carbon and Nitrogen in Indian Soils

Cited by 14 publications

References 38 publications

Quantification and depth distribution analysis of carbon to nitrogen ratio in forest soils using reflectance spectroscopy

Quantification and depth distribution analysis of carbon to nitrogen ratio in forest soils using reflectance spectroscopy

Quantifying soil carbon stocks and humification through spectroscopic methods: A scoping assessment in EMBU-Kenya

Prediction of Soil Organic Carbon under Different Land Use Types Using Sentinel-1/-2 Data in a Small Watershed

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