Temporal mosaicking approaches of Sentinel-2 images for extending topsoil organic carbon content mapping in croplands

Vaudour, Emmanuelle; Gomez, Cécile; Lagacherie, Philippe; Loiseau, Thomas; Baghdadi, Nicolas; Salazar, Diego Fernando Urbina; Loubet, Benjamin; Arrouays, Dominique

doi:10.1016/j.jag.2020.102277

Cited by 47 publications

(87 citation statements)

References 38 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The influence of land use type on SOC prediction and the importance of optical and radar data under different land use types were analyzed. The overall prediction result of the model is better than some existing researches based on satellite remote sensing data [16,21,[23][24][25]33], and is similar to the result of Taghizadeh-Mehrjardi et al [17]. Among the three land use types, the prediction results of orchard (R 2 = 0.86 and MSE = 0.004%) are better than dry land and paddy field.…”

Section: Discussionsupporting

confidence: 74%

“…Therefore, it is necessary to explore the contribution of remote sensing data to SOC prediction. There have been some studies on SOC prediction based on remote sensing data that achieved good prediction results, especially optical data (e.g., Sentinel-2 [15][16][17][18][19][20][21], Landsat [22][23][24][25][26][27][28], and MODIS satellite data [29][30][31]); their bands cover from visible to short-wave infrared, providing more information. However, the application of optical data is susceptible to weather conditions, especially in the Sichuan Basin where clouds occur most frequently [32], so the available optical data are very limited.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

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

View full text Add to dashboard Cite

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.

show abstract

Section: Discussionsupporting

confidence: 74%

Section: Introductionmentioning

confidence: 99%

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

View full text Add to dashboard Cite

show abstract

“…The MG2 mask was applied to the imagery, a spatial subset of which was extracted using the French land parcel register of 2018 [35]. Then, based on previous observation in the Versailles plain [26], only bare fields were retained, i.e., having a Normalized Difference Vegetation Index value lower than 0.35.…”

Section: Satellite Image Reflectance Spectramentioning

confidence: 99%

“…SOC content is a measurement of the organic compounds contained in the soil in a wide range of chemical forms: carbohydrates, polysacharrides, protein and protein-derived compounds, lipids, phenols, humic and fulvic acids, having spectral trends in the visible, NIR and SWIR regions that can be explained by combination and vibration modes of organic functional groups [17,20]. While SOC content can be predicted from bare soil surface, notably from Sentinel-2 multispectral satellite images [21][22][23][24][25][26][27], the performance of such prediction is degraded by the presence of crop residues [22,23], a form of organic matter left in the field to decompose. Crop residues are composed of insoluble carbon compound such as cellulose, hemicellulose and lignin.…”

Section: Introductionmentioning

confidence: 99%

“…Some indices have been used to detect crop residues using the broader wavelength bands of the Landsat Thematic Mapper, such as "Normalized Difference Index 5" (NDI5) and "Normalized Difference Index 7" (NDI7) [28], which utilize normalized differences between the SWIR and the near infrared (NIR) spectral regions; Normalized Difference Senescent Vegetation Index (NDSVI) [29] and Normalized Difference Residue Index (NDRI) [30], both utilizing normalized differences between the SWIR and the red spectral regions; and, finally, the "Normalized Difference Tillage index" (NDTI) [31], which utilizes normalized differences between the SWIR bands. Renamed the "Normalized Burn ratio 2" (NBR2), the NDTI was adapted to Landsat8 bands [32], then used for Sentinel-2 [23,26] in order to characterize the presence of crop residues on surface and assess its influence on soil organic carbon.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Potential of Sentinel-2 Satellite Images for Monitoring Green Waste Compost and Manure Amendments in Temperate Cropland

et al. 2021

Self Cite

View full text Add to dashboard Cite

Increasing attention has been placed on the agroecological impact of applying exogenous organic matter (EOM) amendments, such as green waste compost (GWC) and livestock manure, to agricultural landscapes. However, monitoring the frequency and locality of this practice poses a major challenge, as these events are typically unreported. The purpose of this study is to evaluate the utility of Sentinel-2 imagery for the detection of EOM amendments. Specifically, we investigated the spectral shift resulting from GWC and manure application at two spatial scales, satellite and proximal. At the satellite scale, multispectral Sentinel-2 image pairs were analyzed before and after EOM application to six cultivated fields in the Versailles Plain, France. At the proximal scale, multi-temporal spectral field measurements were taken of experimental plots consisting of 14 total treatments: EOM variety, amendment quantity (15, 30 and 60 t.ha−1) and tillage. The Sentinel-2 images showed significant spectral differences before and after EOM application. Exogenous Organic Matter Indices (EOMI) were developed and analyzed for separative performance. The best performing index was EOMI2, using the B4 and B12 Sentinel-2 spectral bands. At the proximal scale, simulated Sentinel-2 reflectance spectra, which were created using field measurements, successfully monitored all EOM treatments for three days, except for the buried green waste compost at a rate of 15 t.ha−1.

show abstract

Estimating Soil Organic Carbon from Multispectral Images Using Physics-Informed Neural Networks

Sargeant,

Wei Teng,

Murshed

et al. 2024

Lecture Notes in Computer Science

View full text Add to dashboard Cite

Temporal mosaicking approaches of Sentinel-2 images for extending topsoil organic carbon content mapping in croplands

Cited by 47 publications

References 38 publications

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

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

Potential of Sentinel-2 Satellite Images for Monitoring Green Waste Compost and Manure Amendments in Temperate Cropland

Estimating Soil Organic Carbon from Multispectral Images Using Physics-Informed Neural Networks

Contact Info

Product

Resources

About