Multitemporal satellite imagery analysis for soil organic carbon assessment in an agricultural farm in southeastern Brazil

AbstractThe National Map of Saline &#8211; Alkaline Soils of Greece was recently developed within the initiative of the European Soil Partnership (ESP) of FAO. The technique combines between other MODIS satellite imagery, spatial interpolation methods and ground surveying to derive at 1 km spatial resolution maps of soil&#8217;s salinity (SS) and soil organic carbon (SOC).The present study investigates for the first time the development of higher resolution maps of these soil properties adopting the aforementioned methodology. Furthermore, this study attempted to estimate the Carbon sequestration (SOC) using Remote Sensing and geostatistic methods of spatial analysis, a concern that is eminent today due to its effect on climate change mitigation.As a case study the island of Mytilene in Greece is used, for which detailed information on soil properties as well as climatic, geomorphological, geological and soil data was available from previous studies. An MCDA (Multiple Criteria Decision Analysis) method was applied in a GIS environment using Landsat satellite imagery for the composition of a Saline - Alkaline map. Between the key soil parameters estimated spatially included the Electrical Conductivity (EC), Exchangeable Sodium Percentage (ESP) and pH. Geospatial data analysis methods were implemented to visualize all the derived parameters related for the study area and to analyze the final products in the spatial domain.Finding suggests that climate change and soil directly affect one another. The impact of environmental and climate change in addition to unsustainable agricultural practices seems to be linked to salinity increase, soil erosion and loss of organic matter.&#160; In addition, when land degradation as well as erosion and loss of vegetation occur, SOC emissions increase. Under these conditions, soil cannot absorb enough amounts of CO2, especially when soil salinization and sodicity exists; inputs are further limited due to declines in vegetation health. The role of geoinformation technologies in support of sustainable agricultural production under the pressure of both climate change and anthropogenic activities is also discussed within the present study framework. &#160;KEYWORDS: geoinformation, soil, pH, salinity, soil organic carbon, geostatistics, earth observation, GIS, Greece

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Soil organic carbon dynamics following afforestation of degraded pastures with eucalyptus in southeastern Brazil

Forest Ecology and Management ◽

10.1016/j.foreco.2006.08.331 ◽

2006 ◽

Vol 235 (1-3) ◽

pp. 219-231 ◽

Cited By ~ 90

Author(s):

Augusto M.N. Lima ◽

Ivo R. Silva ◽

Júlio C.L. Neves ◽

Roberto F. Novais ◽

Nairam F. Barros ◽

...

Keyword(s):

Organic Carbon ◽

Soil Organic Carbon ◽

Carbon Dynamics ◽

Southeastern Brazil ◽

Soil Organic Carbon Dynamics ◽

Degraded Pastures

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The role of positional errors while interpolating soil organic carbon contents using satellite imagery

Precision Agriculture ◽

10.1007/s11119-018-9575-4 ◽

2018 ◽

Vol 19 (6) ◽

pp. 1085-1099 ◽

Cited By ~ 3

Author(s):

V. P. Samsonova ◽

J. L. Meshalkina ◽

Y. N. Blagoveschensky ◽

A. M. Yaroslavtsev ◽

J. J. Stoorvogel

Keyword(s):

Organic Carbon ◽

Soil Organic Carbon ◽

Satellite Imagery

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

Remote Sensing ◽

10.3390/rs13071229 ◽

2021 ◽

Vol 13 (7) ◽

pp. 1229

Author(s):

Huan Wang ◽

Xin Zhang ◽

Wei Wu ◽

Hongbin Liu

Keyword(s):

Remote Sensing ◽

Land Use ◽

Organic Carbon ◽

Soil Organic Carbon ◽

Satellite Imagery ◽

Prediction Accuracy ◽

Mean Squared Error ◽

Squared Error ◽

Land Use Types ◽

Sentinel 2

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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