Digital soil mapping of soil organic carbon stocks in Western Ghats, South India

Dharumarajan, S.; Kalaiselvi, B.; Suputhra, Amar; Lalitha, M.; Vasundhara, R.; Kumar, K. S. Anil; Nair, K. M.; Hegde, Rajendra; Singh, Surendra; Lagacherie, Philippe

doi:10.1016/j.geodrs.2021.e00387

Cited by 42 publications

(29 citation statements)

References 47 publications

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“…The average SOC stock in 0-60 cm for all forest soils in this study (111.0 ± 6.5 Mg ha −1 ) was well in line with estimated stocks based on digital soil mapping using remote sensing data for the Western Ghats ecoregion (107.1 ± 47.0 Mg ha −1 in 0-60 cm), but with lower uncertainty (Dharumarajan et al, 2021). In fact, the coarser resolution of the S.-L. Bellè et al cited study missed the SOC stock heterogeneity (ranging here from 73.2 to 169.4 Mg ha −1 ) due to local variations in vegetation, geology and soil properties.…”

Section: Soil Organic Carbon Stocks Under Different Vegetation and La...supporting

confidence: 85%

“…In fact, the coarser resolution of the S.-L. Bellè et al cited study missed the SOC stock heterogeneity (ranging here from 73.2 to 169.4 Mg ha −1 ) due to local variations in vegetation, geology and soil properties. This was depicted in the present study by high replication in small, contrasting watersheds; however, these small-scale watersheds cannot be considered as representative for the whole Western Ghats ecoregion as compared to Dharumarajan et al (2021). If we would extrapolate our SOC stocks for sites (F4 and F6 respectively) where we have measured SOC contents up to 1 m (by approximating the BD for 60-100 cm from the average BD of 45-60 cm), the total SOC stock would account for 174.2 and 159.8 Mg ha −1 respectively (data not shown), which would fall in the range of SOC stocks up to 1 m depth predicted for deciduous forests (125-240 Mg ha −1 ) across India (Nayak et al, 2020).…”

Section: Soil Organic Carbon Stocks Under Different Vegetation and La...mentioning

confidence: 62%

“…For the Indian subcontinent, SOC stock estimations are ranging from 9.6 Pg C (0-30 cm) to 29.9 Pg C (0-150 cm) (Bhattacharyya et al, 2009;Gupta and Rao, 1994). Indian forest soils store around 4.1 Pg C (0-50 cm) and 6.8 Pg C (0-100 cm) (Chhabra et al, 2003), and SOC stocks estimations for the Western Ghats region in southwestern India are around 140 Mg ha −1 in the first metre (Dharumarajan et al, 2021). However, data coverage is scarce and soils from the Indian subcontinent account for only 199 profiles in the WoSIS (Batjes et al, 2020), evoking a need for more accurate field-data on SOC quantity and quality.…”

Section: Introductionmentioning

confidence: 99%

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Soil organic carbon stocks and quality in small-scale tropical, sub-humid and semi-arid watersheds under shrubland and dry deciduous forest in southwestern India

et al. 2022

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Soil organic carbon is regulated by a dynamic interaction of vegetation inputs, organic matter degradation and stabilization processes in soils, and its redistribution in the landscape. Tropical ecosystems are highly important in terms of carbon stored in vegetation and soil, but many processes of the soil carbon cycle in the tropics are yet to be fully understood. Here, we studied soil organic carbon stocks and quality in small-scale tropical, sub-humid and semi-arid watersheds along a climate gradient in southwestern India with varying vegetation and geology to identify major drivers of soil organic carbon dynamics in three prevalent soil types (Lixisol, Vertisol and Ferralsol) under shrubland and dry deciduous forest. We used a combination of organic carbon analysis (total organic carbon content, 13C, C:N), midinfrared spectroscopy and soil property information (bulk density, texture, oxides, pH, cation-exchange capacity). Soil organic carbon stocks in these watersheds showed a substantial range from 58.2 to 169.4 Mg C ha− 1 in the first 60 cm, and the differences depended on local-to watershed-scale variations in vegetation type and history, geology, soil physio-chemical (clay, oxides) and biological (bioturbation) properties. Considerable parts of the organic carbon stored in these soils was found below 30 cm (up to 40 %), stressing the importance of tropical subsoils. From our analysis of the soil organic carbon quality and literature data on paleoclimate and vegetation, we could identify land-use changes in these watersheds, from tropical moist evergreen forests, forest-savannah transitions and plantations to secondary regrowth forest over time. Our study provides new data and insights into the local-scale drivers of soil organic carbon quantity and quality of tropical, sub-humid and semi-arid watersheds under shrubland and dry deciduous forest with varying geology and soil types.

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Section: Soil Organic Carbon Stocks Under Different Vegetation and La...supporting

confidence: 85%

Section: Soil Organic Carbon Stocks Under Different Vegetation and La...mentioning

confidence: 62%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Soil organic carbon stocks and quality in small-scale tropical, sub-humid and semi-arid watersheds under shrubland and dry deciduous forest in southwestern India

et al. 2022

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“…In this study, the FA, Slope and aspect likewise provide valuable information for SOC prediction, each with a certain degree of contribution. These DEM-derived variables were used in the studies of Dharumarajan, S. and Kabindra, A. as important topographic factors for predicting the spatial distribution of SOC contents [ 76 , 77 ]. In the present study, Sentinel-1A was used as a variable to obtain better SOC prediction performance than a single optical image model when combined with optical images.…”

Section: Discussionmentioning

confidence: 99%

Estimation of Soil Organic Carbon Content in the Ebinur Lake Wetland, Xinjiang, China, Based on Multisource Remote Sensing Data and Ensemble Learning Algorithms

Xie

Ding

et al. 2022

Sensors

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Soil organic carbon (SOC), as the largest carbon pool on the land surface, plays an important role in soil quality, ecological security and the global carbon cycle. Multisource remote sensing data-driven modeling strategies are not well understood for accurately mapping soil organic carbon. Here, we hypothesized that the Sentinel-2 Multispectral Sensor Instrument (MSI) data-driven modeling strategy produced superior outcomes compared to modeling based on Landsat 8 Operational Land Imager (OLI) data due to the finer spatial and spectral resolutions of the Sentinel-2A MSI data. To test this hypothesis, the Ebinur Lake wetland in Xinjiang was selected as the study area. In this study, SOC estimation was carried out using Sentinel-2A and Landsat 8 data, combining climatic variables, topographic factors, index variables and Sentinel-1A data to construct a common variable model for Sentinel-2A data and Landsat 8 data, and a full variable model for Sentinel-2A data, respectively. We utilized ensemble learning algorithms to assess the prediction performance of modeling strategies, including random forest (RF), gradient boosted decision tree (GBDT) and extreme gradient boosting (XGBoost) algorithms. The results show that: (1) The Sentinel-2A model outperformed the Landsat 8 model in the prediction of SOC contents, and the Sentinel-2A full variable model under the XGBoost algorithm achieved the best results R2 = 0.804, RMSE = 1.771, RPIQ = 2.687). (2) The full variable model of Sentinel-2A with the addition of the red-edge band and red-edge index improved R2 by 6% and 3.2% over the common variable Landsat 8 and Sentinel-2A models, respectively. (3) In the SOC mapping of the Ebinur Lake wetland, the areas with higher SOC content were mainly concentrated in the oasis, while the mountainous and lakeside areas had lower SOC contents. Our results provide a program to monitor the sustainability of terrestrial ecosystems through a satellite perspective.

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“…The quantile regression forest (QRF) approach was used to analyze the model's uncertainty in a spatially explicit manner [42][43][44]. The model uncertainty was estimated as a function of the specified prediction variables as represented by the full conditional distribution of the response variable (SOC in our case) using the QRF technique in the 'quantregForest' package on the R software platform [45,46].…”

Section: Measure Of Prediction Uncertaintymentioning

confidence: 99%

Use of Multi-Seasonal Satellite Images to Predict SOC from Cultivated Lands in a Montane Ecosystem

Lamichhane

Adhikari

Kumar³

2021

Remote Sensing

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Although algorithms are well developed to predict soil organic carbon (SOC), selecting appropriate covariates to improve prediction accuracy is an ongoing challenge. Terrain attributes and remote sensing data are the most common covariates for SOC prediction. This study tested the predictive performance of nine different combinations of topographic variables and multi-season remotely sensed data to improve the prediction of SOC in the cultivated lands of a middle mountain catchment of Nepal. The random forest method was used to predict SOC contents, and the quantile regression forest for quantifying the prediction uncertainty. Prediction of SOC contents was improved when remote sensing data of multiple seasons were used together with the terrain variables. Remote sensing data of multiple seasons capture the dynamic conditions of surface soils more effectively than using an image of a single season. It is concluded that the use of remote sensing images of multiple seasons instead of a snapshot of a single period may be more effective for improving the prediction of SOC in a digital soil mapping framework. However, an image with the right timing of cropping season can provide comparable results if a parsimonious model is preferred.

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Digital soil mapping of soil organic carbon stocks in Western Ghats, South India

Cited by 42 publications

References 47 publications

Soil organic carbon stocks and quality in small-scale tropical, sub-humid and semi-arid watersheds under shrubland and dry deciduous forest in southwestern India

Soil organic carbon stocks and quality in small-scale tropical, sub-humid and semi-arid watersheds under shrubland and dry deciduous forest in southwestern India

Estimation of Soil Organic Carbon Content in the Ebinur Lake Wetland, Xinjiang, China, Based on Multisource Remote Sensing Data and Ensemble Learning Algorithms

Use of Multi-Seasonal Satellite Images to Predict SOC from Cultivated Lands in a Montane Ecosystem

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