Mapping soil organic carbon stocks in Tunisian topsoils

Bahri, Haithem; Raclot, Damien; Barbouchi, Meriem; Lagacherie, Philippe; Annabi, Mohamed

doi:10.1016/j.geodrs.2022.e00561

Cited by 12 publications

(9 citation statements)

References 64 publications

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“…On the other hand, in Tunisia, Bahri et al (2022) reported that SOC stocks range from 0.29 to 18.12 kg/m 2 considering a depth of 30 cm. By normalizing our average and maximum SOC stocks [0.595 and 1 (kg/m 2 )] from 3.5 cm to 30 cm, we obtain 5.1 kg/m 2 and 8.5 kg/m 2 , respectively, which are aligned with the values reported in the study of Bahri et al (2022) [8].…”

Section: Discussionsupporting

confidence: 85%

“…The differences in SOC stocks between our study and those conducted by Wang et al [33] and Bahri et al [8] can be attributed to variations in soil type, land-management practice, and environmental conditions. The Sele River area, where our study was conducted, has different soil characteristics, including clay content, pH, and mineral composition, which may result in different inherent SOC content.…”

Section: Discussioncontrasting

confidence: 80%

“…Although DSM methodologies [8][9][10][11] have been employed to map SOC stocks on large scales, there is still a gap at the hyper-local level of high spatial resolution which needs to be addressed. This is particularly important for improving the accuracies in the ground-truth stages with field observations [12].…”

Section: Introductionmentioning

confidence: 99%

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Mapping Soil Organic Carbon Stock Using Hyperspectral Remote Sensing: A Case Study in the Sele River Plain in Southern Italy

Francos,

Nasta,

Allocca

et al. 2024

Remote Sensing

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Mapping soil organic carbon (SOC) stock can serve as a resilience indicator for climate change. As part of the carbon dioxide (CO2) sink, soil has recently become an integral part of the global carbon agenda to mitigate climate change. We used hyperspectral remote sensing to model the SOC stock in the Sele River plain located in the Campania region in southern Italy. To this end, a soil spectral library (SSL) for the Campania region was combined with an aerial hyperspectral image acquired with the AVIRIS–NG sensor mounted on a Twin Otter aircraft at an altitude of 1433 m. The products of this study were four raster layers with a high spatial resolution (1 m), representing the SOC stocks and three other related soil attributes: SOC content, clay content, and bulk density (BD). We found that the clay minerals’ spectral absorption at 2200 nm has a significant impact on predicting the examined soil attributes. The predictions were performed by using AVIRIS–NG sensor data over a selected plot and generating a quantitative map which was validated with in situ observations showing high accuracies in the ground-truth stage (OC stocks [RPIQ = 2.19, R2 = 0.72, RMSE = 0.07]; OC content [RPIQ = 2.27, R2 = 0.80, RMSE = 1.78]; clay content [RPIQ = 1.6 R2 = 0.89, RMSE = 25.42]; bulk density [RPIQ = 1.97, R2 = 0.84, RMSE = 0.08]). The results demonstrated the potential of combining SSLs with remote sensing data of high spectral/spatial resolution to estimate soil attributes, including SOC stocks.

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Section: Discussionsupporting

confidence: 85%

Section: Discussioncontrasting

confidence: 80%

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Mapping Soil Organic Carbon Stock Using Hyperspectral Remote Sensing: A Case Study in the Sele River Plain in Southern Italy

Francos,

Nasta,

Allocca

et al. 2024

Remote Sensing

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“…Nevertheless, in order to reduce the uncertainty and increase the effectiveness of environmental monitoring, a lot of approaches to RS data are proposed as an alternative to processing climate data, some of them using Landsat products [30] and their integration with the Google Earth Engine platform [31], or mixed approaches with GIS-based mapping [32][33][34], and some of them using hydrological datasets for evaluating physicochemical parameters of groundwater in inflow [35]. Other examples include evaluating the content of soil organic carbon stocks in topsoils using bioclimatic data [36] or monitoring agricultural and irrigated lands and their behaviour during drought periods [37].…”

Section: Introduction 1backgroundmentioning

confidence: 99%

Monitoring Seasonal Fluctuations in Saline Lakes of Tunisia Using Earth Observation Data Processed by GRASS GIS

Lemenkova

2023

Land

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This study documents the changes in the Land Use/Land Cover (LULC) in the region of saline lakes in north Tunisia, Sahara Desert. Remote sensing data are a valuable data source in monitoring LULC in lacustrine landscapes, because variations in the extent of lakes are visible from space and can be detected on the images. In this study, changes in LULC of the salt pans of Tunisia were evaluated using a series of 12 Landsat 8-9 Operational Land Imager (OLI) and Thermal Infrared (TIRS) images. The images were processed with the Geographic Resources Analysis Support System (GRASS) Geographic Information System (GIS) software. The study area included four salt lakes of north Tunisia in the two regions of the Gulf of Hammamet and Gulf of Gabès: (1) Sebkhet de Sidi el Hani (Sousse Governorate), (2) Sebkha de Moknine (Mahdia Governorate), (3) Sebkhet El Rharra and (4) Sebkhet en Noual (Sfax). A quantitative estimate of the areal extent analysed in this study is 182 km × 185 km for each Landsat scene in two study areas: Gulf of Hammamet and Gulf of Gabès. The images were analysed for the period 2017–2023 on months February, April and July for each year. Spatio-temporal changes in LULC and their climate–environmental driving forces were analysed. The results were interpreted and the highest changes were detected by accuracy assessment, computing the class separability matrices, evaluating the means and standard deviation for each band and plotting the reject probability maps. Multi-temporal changes in LULC classes are reported for each image. The results demonstrated that changes in salt lakes were determined for winter/spring/summer months as detected changes in water/land/salt/sand/vegetation areas. The accuracy of the classified images was evaluated using pixel rejection probability values, which were filtered out using the ‘r.mapcalc’ module of GRASS GIS. The confidence levels were computed and visualised with a series of maps along with the error matrix and measured convergence level of classified pixels. This paper contributes to the environmental monitoring of Tunisian landscapes and analysis of climate effects on LULC in landscapes of north Africa.

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“…Spatially explicit estimates of SOC stocks in a country's terrestrial and BCE are needed for conservation and restoration practices, informing national inventories, and climate mitigation policies such as Nationally Determined Contributions under the United Nations Framework Convention on Climate Change 36 . Spatially explicit estimates of SOC stock currently exist for terrestrial habitats with global maps 37 , as well as country-specific estimates from Australia 38 , the USA 39 , France 40 , China 41 , Tunisia 42 , and other countries. There is a continental map of Australian BCE stocks 43 , and also a global map of SOC stocks under mangroves 44 .…”

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confidence: 99%

Multi-scale mapping of Australia’s terrestrial and blue carbon stocks and their continental and bioregional drivers

Walden

Serrano

Zhang

et al. 2023

Commun Earth Environ

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The soil in terrestrial and coastal blue carbon ecosystems is an important carbon sink. National carbon inventories require accurate assessments of soil carbon in these ecosystems to aid conservation, preservation, and nature-based climate change mitigation strategies. Here we harmonise measurements from Australia’s terrestrial and blue carbon ecosystems and apply multi-scale machine learning to derive spatially explicit estimates of soil carbon stocks and the environmental drivers of variation. We find that climate and vegetation are the primary drivers of variation at the continental scale, while ecosystem type, terrain, clay content, mineralogy and nutrients drive subregional variations. We estimate that in the top 0–30 cm soil layer, terrestrial ecosystems hold 27.6 Gt (19.6–39.0 Gt), and blue carbon ecosystems 0.35 Gt (0.20–0.62 Gt). Tall open eucalypt and mangrove forests have the largest soil carbon content by area, while eucalypt woodlands and hummock grasslands have the largest total carbon stock due to the vast areas they occupy. Our findings suggest these are essential ecosystems for conservation, preservation, emissions avoidance, and climate change mitigation because of the additional co-benefits they provide.

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Mapping soil organic carbon stocks in Tunisian topsoils

Cited by 12 publications

References 64 publications

Mapping Soil Organic Carbon Stock Using Hyperspectral Remote Sensing: A Case Study in the Sele River Plain in Southern Italy

Mapping Soil Organic Carbon Stock Using Hyperspectral Remote Sensing: A Case Study in the Sele River Plain in Southern Italy

Monitoring Seasonal Fluctuations in Saline Lakes of Tunisia Using Earth Observation Data Processed by GRASS GIS

Multi-scale mapping of Australia’s terrestrial and blue carbon stocks and their continental and bioregional drivers

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