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Cécile Gomez
LISAH, Univ. Montpellier, IRD, INRAE, Institut Agro, Montpellier, France

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Journal article
Published: 26 March 2021 in Geoderma Regional
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Mapping soil properties is becoming more and more challenging due to the increase in anthropogenic modification of the landscape, calling for new methods to identify these changes. A striking example of anthropogenic modifications of soil properties is the widespread practice in South India of applying large quantities of silt from dry river dams (or “tanks”) to agricultural fields. Whereas several studies have demonstrated the interest of tank silt for soil fertility, no assessment of the actual extent of this age-old traditional practice exists. Over South-Indian pedological context, this practice is characterized by an application of black-colored tank silt to red-colored soils such as Ferralsols. The objective of this work was to evaluate the usefulness of Sentinel-2 images for mapping tank silt applications, hypothesizing that observed changes in soil surface color can be a proxy for tank silt application. We used data collected in a cultivated watershed in South India including 217 soil surface samples characterized in terms of Munsell color. We used two Sentinel-2 images acquired on February and April 2017. The surface soil color over each Sentinel-2 image was classified into two soil types (“Black” and “Red” soils). A change of soil color from “Red” in February 2017 to “Black” in April 2017 was attributed to tank silt application. Soil color changes were analyzed accounting for possible surface soil moisture changes. The proposed methodology was based on a well-balanced Calibration data created from the initial imbalanced Calibration dataset thanks to the Synthetic Minority Over-sampling Technique (SMOTE) methodology, coupled to the Cost-Sensitive Classification And Regression Trees (Cost-Sensitive CART) algorithm. To estimate the uncertainties of i) the two-class classification at each date and ii) the change of soil color from “Red” to “Black”, a bootstrap procedure was used providing fifty two-class classifications for each Sentinel-2 image. The results showed that 1) the CART method allowed to classify the “Red” and “Black” soil with correct overall accuracy from both Sentinel-2 images, 2) the tank silt application was identified over 202 fields and 3) the soil color changes were not related to a surface soil moisture change between both dates. With the actual availability of the Sentinel-2 and the past availability of the LANDSAT satellite imageries, this study may open a way toward a simple and accurate method for delivering tank silt application mapping and so to study and possibly quantify retroactively this farmer practice.

ACS Style

C. Gomez; S. Dharumarajan; P. Lagacherie; J. Riotte; S. Ferrant; M. Sekhar; L. Ruiz. Mapping of tank silt application using Sentinel-2 images over the Berambadi catchment (India). Geoderma Regional 2021, 25, e00389 .

AMA Style

C. Gomez, S. Dharumarajan, P. Lagacherie, J. Riotte, S. Ferrant, M. Sekhar, L. Ruiz. Mapping of tank silt application using Sentinel-2 images over the Berambadi catchment (India). Geoderma Regional. 2021; 25 ():e00389.

Chicago/Turabian Style

C. Gomez; S. Dharumarajan; P. Lagacherie; J. Riotte; S. Ferrant; M. Sekhar; L. Ruiz. 2021. "Mapping of tank silt application using Sentinel-2 images over the Berambadi catchment (India)." Geoderma Regional 25, no. : e00389.

Preprint content
Published: 04 March 2021
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Mid-Infrared reflectance spectroscopy (MIRS, 4000 – 400 cm-1) is being considered to provide accurate estimations of soil inorganic carbon (SIC) contents. Usually, the prediction performances by MIRS are analyzed using figures of merit based on entire test datasets characterized by large SIC ranges, without paying attention to performances at sub-range scales. This work aims to 1) evaluate the performances of MIR regression models for SIC prediction, for a large range of SIC test data (0-100 g/kg) and for several regular sub-ranges of SIC values (0-5, 5-10, 10-15 g/kg, etc.) and 2) adapt the prediction model depending on sub-ranges of test samples, using the absorbance peak at 2510 cm-1 for separating SIC-poor and SIC-rich test samples. This study used a Tunisian MIRS topsoil dataset including 96 soil samples, mostly rich in SIC, to calibrate and validate SIC prediction models; and a French MIRS topsoil dataset including 2178 soil samples, mostly poor in SIC, to test them. Two following regression models were used: a partial least squares regression (PLSR) using the entire spectra and a simple linear regression (SLR) using the height of the carbonate absorbance peak at 2150 cm-1.

First, our results showed that PLSR provided 1) better performances than SLR on the Validation Tunisian dataset (R2test of 0.99 vs. 0.86, respectively), but 2) lower performances than SLR on the Test French dataset (R2test of 0.70 vs. 0.91, respectively). Secondly, our results showed that on the Test French dataset, predicted SIC values were more accurate for SIC-poor samples (< 15 g/kg) with SLR (RMSEtest from 1.5 to 7.1 g/kg, depending on the sub-range) than with PLSR prediction model (RMSEtest from 7.3 to 14.8 g/kg, depending on the sub-range). Conversely, predicted SIC values were more accurate for carbonated samples (> 15 g/kg) with PLSR (RMSEtest from 4.4 to 10.1 g/kg, depending on the sub-range) than with SLR prediction model (RMSEtest from 6.8 to 14 g/kg, depending on the sub-range). Finally, our results showed that the absorbance peak at 2150 cm-1 could be used before prediction to separate SIC-poor and SIC-rich test samples (452 and 1726 samples, respectevely). The SLR and PLSR regression methods applied to these SIC-poor and SIC-rich test samples, respectively, provided better prediction performances (test of 0.95 and RMSEtest of 3.7 g/kg).

Finally, this study demonstrated that the use of the spectral absorbance peak at 2150 cm-1 provided useful information on Test samples and helped the selection of the optimal prediction model depending on SIC level, when using calibration and test sample sets with very different SIC distributions.

ACS Style

Cécile Gomez; Tiphaine Chevallier; Patricia Moulin; Bernard G. Barthès. Using absorbance peak of carbonate to select suitable regression model before predicting soil inorganic carbon concentration by mid-infrared reflectance spectroscopy. 2021, 1 .

AMA Style

Cécile Gomez, Tiphaine Chevallier, Patricia Moulin, Bernard G. Barthès. Using absorbance peak of carbonate to select suitable regression model before predicting soil inorganic carbon concentration by mid-infrared reflectance spectroscopy. . 2021; ():1.

Chicago/Turabian Style

Cécile Gomez; Tiphaine Chevallier; Patricia Moulin; Bernard G. Barthès. 2021. "Using absorbance peak of carbonate to select suitable regression model before predicting soil inorganic carbon concentration by mid-infrared reflectance spectroscopy." , no. : 1.

Preprint content
Published: 04 March 2021
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Mapping soil properties is becoming more and more challenging due to the increase in anthropogenic modification of the landscape, calling for new methods to identify these changes. A striking example of anthropogenic modifications of soil properties is the widespread practice in South India of applying large quantities of silt from dry river dams (or “tanks”) to agricultural fields. Whereas several studies have demonstrated the interest of tank silt for soil fertility, no assessment of the actual extent of this age-old traditional practice exists. Over pedological contexts characterized by Vertisol, Ferralsols and Chromic Luvisols in sub-humid and semi-arid Tropical climate, this practice is characterized by an application of black-colored tank silt providing from Vertisol, to red-colored soils such as Ferralsols. The objective of this work was to evaluate the usefulness of Sentinel-2 images for mapping tank silt applications, hypothesizing that observed changes in soil surface color can be a proxy for tank silt application.

We used data collected in a cultivated watershed (Berambadi, Karnataka state, South India) including 217 soil surface samples characterized in terms of Munsell color. We used two Sentinel-2 images acquired on February 2017 and April 2017. The surface soil color over each Sentinel-2 image was classified into two-class (“Black” and “Red” soils). A change of soil color from “Red” in February 2017 to “Black” in April 2017 was attributed to tank silt application. Soil color changes were analyzed accounting for possible surface soil moisture changes. The proposed methodology was based on a well-balanced Calibration data created from the initial imbalanced Calibration dataset thanks to the Synthetic Minority Over-sampling Technique (SMOTE) methodology, coupled to the Cost-Sensitive Classification And Regression Trees (Cost-Sensitive CART) algorithm. To estimate the uncertainties of i) the two-class classification at each date and ii) the change of soil color from “Red” to “Black”, a bootstrap procedure was used providing fifty two-class classifications for each Sentinel-2 image.

The results showed that 1) the CART method allowed to classify the “Red” and “Black” soil with overall accuracy around 0.81 and 0.76 from the Sentinel-2 image acquired on February and April 2017, respectively, 2) a tank silt application was identified over 97 fields with high confidence and over 107 fields with medium confidence, based on the bootstrap results and 3) the identified soil color changes are not related to a surface soil moisture change between both dates. With the actual availability of the Sentinel-2 and the past availability of the LANDSAT satellite imageries, this study may open a way toward a simple and accurate method for delivering tank silt application mapping and so to study and possibly quantify retroactively this farmer practice.

ACS Style

Cécile Gomez; Dharumarajan Subramanian; Philippe Lagacherie; Jean Riotte; Sylvain Ferrant; Muddu Sekhar; Laurent Ruiz. Mapping of tank silt application using Sentinel-2 images over the Berambadi catchment (India). 2021, 1 .

AMA Style

Cécile Gomez, Dharumarajan Subramanian, Philippe Lagacherie, Jean Riotte, Sylvain Ferrant, Muddu Sekhar, Laurent Ruiz. Mapping of tank silt application using Sentinel-2 images over the Berambadi catchment (India). . 2021; ():1.

Chicago/Turabian Style

Cécile Gomez; Dharumarajan Subramanian; Philippe Lagacherie; Jean Riotte; Sylvain Ferrant; Muddu Sekhar; Laurent Ruiz. 2021. "Mapping of tank silt application using Sentinel-2 images over the Berambadi catchment (India)." , no. : 1.

Discussion
Published: 19 August 2020 in Sustainability
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The Paris Climate Agreements and Sustainable Development Goals, signed by 197 countries, present agendas and address key issues for implementing multi-scale responses for sustainable development under climate change—an effort that must involve local, regional, national, and supra-national stakeholders. In that regard, Continental Carbon Sequestration (CoCS) and conservation of carbon sinks are recognized increasingly as having potentially important roles in mitigating climate change and adapting to it. Making that potential a reality will require indicators of success for various stakeholders from multidisciplinary backgrounds, plus promotion of long-term implementation of strategic action towards civil society (e.g., law and policy makers, economists, and farmers). To help meet those challenges, this discussion paper summarizes the state of the art and uncertainties regarding CoCS, taking an interdisciplinary, holistic approach toward understanding these complex issues. The first part of the paper discusses the carbon cycle’s bio-geophysical processes, while the second introduces the plurality of geographical scales to be addressed when dealing with landscape management for CoCS. The third part addresses systemic viability, vulnerability, and resilience in CoCS practices, before concluding with the need to develop inter-disciplinarity in sustainable science, participative research, and the societal implications of sustainable CoCS actions.

ACS Style

Tiphaine Chevallier; Maud Loireau; Romain Courault; Lydie Chapuis-Lardy; Thierry Desjardins; Cécile Gomez; Alexandre Grondin; Frédéric Guérin; Didier Orange; Raphaël Pélissier; Georges Serpantié; Marie-Hélène Durand; Pierre Derioz; Gildas Laruelle Goulven; Marie-Hélène Schwoob; Nicolas Viovy; Olivier Barrière; Eric Blanchart; Vincent Blanfort; Michel Brossard; Julien Demenois; Mireille Fargette; Thierry Heulin; Gil Mahe; Raphaël Manlay; Pascal Podwojewski; Cornélia Rumpel; Benjamin Sultan; Jean-Luc Chotte. Paris Climate Agreement: Promoting Interdisciplinary Science and Stakeholders’ Approaches for Multi-Scale Implementation of Continental Carbon Sequestration. Sustainability 2020, 12, 6715 .

AMA Style

Tiphaine Chevallier, Maud Loireau, Romain Courault, Lydie Chapuis-Lardy, Thierry Desjardins, Cécile Gomez, Alexandre Grondin, Frédéric Guérin, Didier Orange, Raphaël Pélissier, Georges Serpantié, Marie-Hélène Durand, Pierre Derioz, Gildas Laruelle Goulven, Marie-Hélène Schwoob, Nicolas Viovy, Olivier Barrière, Eric Blanchart, Vincent Blanfort, Michel Brossard, Julien Demenois, Mireille Fargette, Thierry Heulin, Gil Mahe, Raphaël Manlay, Pascal Podwojewski, Cornélia Rumpel, Benjamin Sultan, Jean-Luc Chotte. Paris Climate Agreement: Promoting Interdisciplinary Science and Stakeholders’ Approaches for Multi-Scale Implementation of Continental Carbon Sequestration. Sustainability. 2020; 12 (17):6715.

Chicago/Turabian Style

Tiphaine Chevallier; Maud Loireau; Romain Courault; Lydie Chapuis-Lardy; Thierry Desjardins; Cécile Gomez; Alexandre Grondin; Frédéric Guérin; Didier Orange; Raphaël Pélissier; Georges Serpantié; Marie-Hélène Durand; Pierre Derioz; Gildas Laruelle Goulven; Marie-Hélène Schwoob; Nicolas Viovy; Olivier Barrière; Eric Blanchart; Vincent Blanfort; Michel Brossard; Julien Demenois; Mireille Fargette; Thierry Heulin; Gil Mahe; Raphaël Manlay; Pascal Podwojewski; Cornélia Rumpel; Benjamin Sultan; Jean-Luc Chotte. 2020. "Paris Climate Agreement: Promoting Interdisciplinary Science and Stakeholders’ Approaches for Multi-Scale Implementation of Continental Carbon Sequestration." Sustainability 12, no. 17: 6715.

Journal article
Published: 05 June 2020 in Geoderma
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Mid-infrared reflectance spectroscopy (MIRS, 4000–400 cm−1) is being considered to provide accurate estimations of soil properties, including soil organic carbon (SOC) and soil inorganic carbon (SIC) contents. This approach has mainly been demonstrated by using datasets originating from the same area A, with similar geopedological conditions, to build, validate and test prediction models. The objective of this study was to analyse how MIRS performs when applied to predict SOC and SIC contents, from a calibration database collected over a region A, to predict over a region B, where A and B have no common area and different soil and climate conditions. This study used a French MIRS soil dataset including 2178 topsoil samples to calibrate SIC and SOC prediction models with partial least squares regression (PLSR), and a Tunisian MIRS topsoil dataset including 96 soil samples to test them. Our results showed that when using the French MIRS soil database, i) the SOC and SIC of French validation samples were successfully predicted using global models (R2val = 0.88 and 0.98, respectively), ii) the SIC of Tunisian samples was also predicted successfully both using a global model and using a selection of spectral neighbours from the French calibration database (R2test of 0.96 for both), iii) the SOC of Tunisian samples was predicted moderately well by global model (R2test of 0.64) and a transformation by natural logarithm of the calibration SOC values significantly improved the SOC prediction of Tunisian samples (R2test of 0.97), and iv) a transformation by natural logarithm of SOC values provided more benefit than a selection of spectral neighbours from the French calibration database for predicting Tunisian SOC values. Therefore, in the future, MIRS might replace conventional physico-chemical analysis techniques, or at least be considered as an alternative technique, especially when optimally exhaustive calibration databases will become available.

ACS Style

Cécile Gomez; Tiphaine Chevallier; Patricia Moulin; Imane Bouferra; Kaouther Hmaidi; Dominique Arrouays; Claudy Jolivet; Bernard G. Barthès. Prediction of soil organic and inorganic carbon concentrations in Tunisian samples by mid-infrared reflectance spectroscopy using a French national library. Geoderma 2020, 375, 114469 .

AMA Style

Cécile Gomez, Tiphaine Chevallier, Patricia Moulin, Imane Bouferra, Kaouther Hmaidi, Dominique Arrouays, Claudy Jolivet, Bernard G. Barthès. Prediction of soil organic and inorganic carbon concentrations in Tunisian samples by mid-infrared reflectance spectroscopy using a French national library. Geoderma. 2020; 375 ():114469.

Chicago/Turabian Style

Cécile Gomez; Tiphaine Chevallier; Patricia Moulin; Imane Bouferra; Kaouther Hmaidi; Dominique Arrouays; Claudy Jolivet; Bernard G. Barthès. 2020. "Prediction of soil organic and inorganic carbon concentrations in Tunisian samples by mid-infrared reflectance spectroscopy using a French national library." Geoderma 375, no. : 114469.

Preprint content
Published: 23 March 2020
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Mid-Infrared Reflectance Spectroscopy (MIRS, 4000–400 cm-1) is being considered to provide accurate estimations of soil properties, including soil organic carbon (SOC) and soil inorganic carbon (SIC) contents. This has mainly been demonstrated when datasets used to build, validate and test the prediction model originate from the same area A, with similar geopedological conditions. The objective of this study was to analyze how MIRS performed when used to predict SOC and SIC contents, from a calibration database collected over a region A, to predict over a region B, where A and B have no common area and different soil and climate conditions. This study used a French MIRS soil dataset including 2178 soil samples to calibrate SIC and SOC prediction models with partial least squares regression (PLSR), and a Tunisian MIRS soil dataset including 96 soil samples to test them. Our results showed that using the French MIRS soil database i) SOC and SIC of French samples were successfully predicted, ii) SIC of Tunisian samples was also predicted successfully, iii) local calibration significantly improved SOC prediction of Tunisian samples and iv) prediction models seemed more robust for SIC than for SOC. So in future, MIRS might replace, or at least be considered as, a conventional physico-chemical analysis technique, especially when as exhaustive as possible calibration database will become available.

ACS Style

Tiphaine Chevallier; Cécile Gomez; Patricia Moulin; Imane Bouferra; Kaouther Hmaidi; Dominique Arrouays; Claudy Jolivet; Bernard Barthès. Prediction of soil organic and inorganic carbon concentrations in Tunisian samples by mid-infrared reflectance spectroscopy using a French national library. 2020, 1 .

AMA Style

Tiphaine Chevallier, Cécile Gomez, Patricia Moulin, Imane Bouferra, Kaouther Hmaidi, Dominique Arrouays, Claudy Jolivet, Bernard Barthès. Prediction of soil organic and inorganic carbon concentrations in Tunisian samples by mid-infrared reflectance spectroscopy using a French national library. . 2020; ():1.

Chicago/Turabian Style

Tiphaine Chevallier; Cécile Gomez; Patricia Moulin; Imane Bouferra; Kaouther Hmaidi; Dominique Arrouays; Claudy Jolivet; Bernard Barthès. 2020. "Prediction of soil organic and inorganic carbon concentrations in Tunisian samples by mid-infrared reflectance spectroscopy using a French national library." , no. : 1.

Journal article
Published: 14 September 2019 in Remote Sensing
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The spatial assessment of soil organic carbon (SOC) is a major environmental challenge, notably for evaluating soil carbon stocks. Recent works have shown the capability of Sentinel-2 optical data to predict SOC content over temperate agroecosystems characterized by annual crops, using a single acquisition date. Considering a Sentinel-2 time series, this work intends to analyze the impact of acquisition date, and related weather and soil surface conditions on the prediction performance of topsoil SOC content (plough layer). A Sentinel-2 time-series was gathered, comprised of the dates corresponding to both the maximum of bare soil coverage and minimum of cloud coverage. Cross-validated partial least squares regression (PLSR) models were constructed between soil reflectance image spectra, and SOC content analyzed from 329 top soil samples collected over the study area. Cross-validation R2 ranged from 0.005 to 0.58, root mean square error from 5.86 to 3.02 g·kg−1 and residual prediction deviation values from 1.0 to 1.5 (without unit), according to date. The main factors influencing these differences were soil roughness, in conjunction with soil moisture, and the cloud and cloud shadow cover of the entire tile. The best performing dates were spring dates characterized by both lowest soil surface roughness and moisture content. Normalized difference vegetation index (NDVI) values below 0.35 did not influence prediction performance. This consolidates the previous results obtained during single date acquisitions and offers wider perspectives for the further use of Sentinel-2 into multidate mosaics for digital soil mapping.

ACS Style

Emmanuelle Vaudour; Cécile Gomez; Thomas Loiseau; Nicolas Baghdadi; Benjamin Loubet; Dominique Arrouays; Leïla Ali; Philippe Lagacherie. The Impact of Acquisition Date on the Prediction Performance of Topsoil Organic Carbon from Sentinel-2 for Croplands. Remote Sensing 2019, 11, 2143 .

AMA Style

Emmanuelle Vaudour, Cécile Gomez, Thomas Loiseau, Nicolas Baghdadi, Benjamin Loubet, Dominique Arrouays, Leïla Ali, Philippe Lagacherie. The Impact of Acquisition Date on the Prediction Performance of Topsoil Organic Carbon from Sentinel-2 for Croplands. Remote Sensing. 2019; 11 (18):2143.

Chicago/Turabian Style

Emmanuelle Vaudour; Cécile Gomez; Thomas Loiseau; Nicolas Baghdadi; Benjamin Loubet; Dominique Arrouays; Leïla Ali; Philippe Lagacherie. 2019. "The Impact of Acquisition Date on the Prediction Performance of Topsoil Organic Carbon from Sentinel-2 for Croplands." Remote Sensing 11, no. 18: 2143.

Article
Published: 20 March 2019 in Surveys in Geophysics
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There is a renewed awareness of the finite nature of the world’s soil resources, growing concern about soil security and significant uncertainties about the carrying capacity of the planet. Regular assessments of soil conditions from local through to global scales are requested, and there is a clear demand for accurate, up-to-date and spatially referenced soil information by the modelling scientific community, farmers and land users, and policy- and decision-makers. Soil and imaging spectroscopy, based on visible–near-infrared and shortwave infrared (400–2500 nm) spectral reflectance, has been shown to be a proven method for the quantitative prediction of key soil surface properties. With the upcoming launch of the next generation of hyperspectral satellite sensors in the next years, a high potential to meet the demand for global soil mapping and monitoring is appearing. In this paper, we briefly review the basic concepts of soil spectroscopy with a special attention to the effects of soil roughness on reflectance and then provide a review of state of the art, achievements and perspectives in soil mapping and monitoring based on imaging spectroscopy from air- and spaceborne sensors. Selected application cases are presented for the modelling of soil organic carbon, mineralogical composition, topsoil water content and characterization of soil crust, soil erosion and soil degradation stages based on airborne and simulated spaceborne imaging spectroscopy data. Further, current challenges, gaps and new directions toward enhanced soil properties modelling are presented. Overall, this paper highlights the potential and limitations of multiscale imaging spectroscopy nowadays for soil mapping and monitoring, and capabilities and requirements of upcoming spaceborne sensors as support for a more informed and sustainable use of our world’s soil resources.

ACS Style

S. Chabrillat; E. Ben-Dor; J. Cierniewski; Cecile Gomez; Thomas Schmid; B. Van Wesemael. Imaging Spectroscopy for Soil Mapping and Monitoring. Surveys in Geophysics 2019, 40, 361 -399.

AMA Style

S. Chabrillat, E. Ben-Dor, J. Cierniewski, Cecile Gomez, Thomas Schmid, B. Van Wesemael. Imaging Spectroscopy for Soil Mapping and Monitoring. Surveys in Geophysics. 2019; 40 (3):361-399.

Chicago/Turabian Style

S. Chabrillat; E. Ben-Dor; J. Cierniewski; Cecile Gomez; Thomas Schmid; B. Van Wesemael. 2019. "Imaging Spectroscopy for Soil Mapping and Monitoring." Surveys in Geophysics 40, no. 3: 361-399.

Journal article
Published: 08 March 2019 in Remote Sensing
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The Sentinel-2 mission of the European Space Agency (ESA) Copernicus program provides multispectral remote sensing data at decametric spatial resolution and high temporal resolution. The objective of this work is to evaluate the ability of Sentinel-2 time-series data to enable classification of an inherent biophysical property, in terms of accuracy and uncertainty estimation. The tested inherent biophysical property was the soil texture. Soil texture classification was performed on each individual Sentinel-2 image with a linear support vector machine. Two sources of uncertainty were studied: uncertainties due to the Sentinel-2 acquisition date and uncertainties due to the soil sample selection in the training dataset. The first uncertainty analysis was achieved by analyzing the diversity of classification results obtained from the time series of soil texture classifications, considering that the temporal resolution is akin to a repetition of spectral measurements. The second uncertainty analysis was achieved from each individual Sentinel-2 image, based on a bootstrapping procedure corresponding to 100 independent classifications obtained with different training data. The Simpson index was used to compute this diversity in the classification results. This work was carried out in an Indian cultivated region (84 km2, part of Berambadi catchment, in the Karnataka state). It used a time-series of six Sentinel-2 images acquired from February to April 2017 and 130 soil surface samples, collected over the study area and characterized in terms of texture. The classification analysis showed the following: (i) each single-date image analysis resulted in moderate performances for soil texture classification, and (ii) high confusion was obtained between neighboring textural classes, and low confusion was obtained between remote textural classes. The uncertainty analysis showed that (i) the classification of remote textural classes (clay and sandy loam) was more certain than classifications of intermediate classes (sandy clay and sandy clay loam), (ii) a final soil textural map can be produced depending on the allowed uncertainty, and iii) a higher level of allowed uncertainty leads to increased bare soil coverage. These results illustrate the potential of Sentinel-2 for providing input for modeling environmental processes and crop management.

ACS Style

Cécile Gomez; Subramanian Dharumarajan; Jean-Baptiste Féret; Philippe Lagacherie; Laurent Ruiz; Muddu Sekhar. Use of Sentinel-2 Time-Series Images for Classification and Uncertainty Analysis of Inherent Biophysical Property: Case of Soil Texture Mapping. Remote Sensing 2019, 11, 565 .

AMA Style

Cécile Gomez, Subramanian Dharumarajan, Jean-Baptiste Féret, Philippe Lagacherie, Laurent Ruiz, Muddu Sekhar. Use of Sentinel-2 Time-Series Images for Classification and Uncertainty Analysis of Inherent Biophysical Property: Case of Soil Texture Mapping. Remote Sensing. 2019; 11 (5):565.

Chicago/Turabian Style

Cécile Gomez; Subramanian Dharumarajan; Jean-Baptiste Féret; Philippe Lagacherie; Laurent Ruiz; Muddu Sekhar. 2019. "Use of Sentinel-2 Time-Series Images for Classification and Uncertainty Analysis of Inherent Biophysical Property: Case of Soil Texture Mapping." Remote Sensing 11, no. 5: 565.

Journal article
Published: 16 January 2019 in Remote Sensing of Environment
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To be fully operational for facilitating decisions made at any spatial level, models and indicators of soil ecosystem functions require the use of precise spatially referenced soil information as inputs. This study aimed at exploring the capacity for Sentinel-2A (S2A) multispectral satellite images to predict several topsoil properties in two contrasted pedoclimatic environments: a temperate region marked by intensive annual crop cultivation patterns and soils derived from loess or colluvium and/or marine limestone or chalk (Versailles Plain, 221 km2); and a Mediterranean region marked by vineyard cultivation and soils derived from lacustrine limestone, calcareous sandstones, colluvium, or alluvial deposits (Peyne catchment, 48 km2). Prediction models of soil properties based on partial least squares regressions (PLSR) were built from S2A spectra of 72 and 143 sampling locations across the Versailles Plain and Peyne catchment, respectively. Eight soil surface properties were investigated in both regions: pH, cation exchange capacity (CEC), texture fractions (Clay, Silt, Sand), Iron, Calcium Carbonate (CaCO3) and Soil Organic Carbon (SOC) content. Predictive abilities were studied according to the root mean square error of cross-validation (RMSECV) tests, cross-validated coefficient of determination (R2cv) and ratio of performance to deviation (RPD). Intermediate prediction performance outcomes (R2cv and RPD greater than or equal to 0.5 and 1.4, respectively) were obtained for 4 topsoil properties found across the Versailles Plain (SOC, pH, CaCO3 and CEC), and near-intermediate performance outcomes (0.5 > R2cv > 0.39, 1.4 > RPD > 1.3) were yielded for 3 topsoil properties (Clay, Iron, and CEC) found across the Peyne catchment and for 1 property (Clay) found across the Versailles Plain. The study results show what can be expected from Sentinel-2 images in terms of predictive capacities at the regional scale. The spatial structure of the estimated soil properties for bare soils pixels is highlighted, promising further improvements made to spatial prediction models for these properties based on the use of Digital Soil Mapping (DSM) techniques.

ACS Style

E. Vaudour; Cecile Gomez; Y. Fouad; P. Lagacherie. Sentinel-2 image capacities to predict common topsoil properties of temperate and Mediterranean agroecosystems. Remote Sensing of Environment 2019, 223, 21 -33.

AMA Style

E. Vaudour, Cecile Gomez, Y. Fouad, P. Lagacherie. Sentinel-2 image capacities to predict common topsoil properties of temperate and Mediterranean agroecosystems. Remote Sensing of Environment. 2019; 223 ():21-33.

Chicago/Turabian Style

E. Vaudour; Cecile Gomez; Y. Fouad; P. Lagacherie. 2019. "Sentinel-2 image capacities to predict common topsoil properties of temperate and Mediterranean agroecosystems." Remote Sensing of Environment 223, no. : 21-33.

Journal article
Published: 01 November 2018 in Geoderma
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Visible, near-infrared and short-wave infrared (VNIR/SWIR, 400–2500 nm) laboratory soil spectrometry is now considered to provide accurate estimations of primary soil properties (clay, calcium carbonate, iron, soil organic carbon, etc.). The performances of primary soil property prediction models are evaluated in regard to figures of merit calculated over calibration and validation databases but not in regard to the spatial extent of predicted soil samples. The objective of this study was to analyze regional model performances for soil property prediction at regional and within-field extents within contrasted representative geopedological situations. This study used a database of 240 soil samples collected over eight vineyard fields located in the Languedoc Region (southern France) (between 20 and 36 soil samples per field) for which VNIR/SWIR laboratory spectra were acquired and two soil physico-chemical properties (clay and calcium carbonate) were measured. Soil property prediction models were built using the classical partial least square regression (PLSR) method, which links the VNIR/SWIR laboratory spectra and the physico-chemical soil property. Our results showed that both clay and calcium carbonate prediction models are accurate at the regional extent, whereas prediction model performances at the within-field extent depend on the model robustness. Therefore, primary soil properties predicted by VNIR/SWIR laboratory spectra must be used with care at different extents.

ACS Style

C. Gomez; G. Coulouma. Importance of the spatial extent for using soil properties estimated by laboratory VNIR/SWIR spectroscopy: Examples of the clay and calcium carbonate content. Geoderma 2018, 330, 244 -253.

AMA Style

C. Gomez, G. Coulouma. Importance of the spatial extent for using soil properties estimated by laboratory VNIR/SWIR spectroscopy: Examples of the clay and calcium carbonate content. Geoderma. 2018; 330 ():244-253.

Chicago/Turabian Style

C. Gomez; G. Coulouma. 2018. "Importance of the spatial extent for using soil properties estimated by laboratory VNIR/SWIR spectroscopy: Examples of the clay and calcium carbonate content." Geoderma 330, no. : 244-253.

Journal article
Published: 01 November 2018 in Vadose Zone Journal
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To account for the diversity of agricultural and ecosystem situations in hilly Mediterranean areas, the agro-hydrological observatory OMERE (Observatoire Méditerranéen de l’Environnement Rural et de l’Eau) monitors two farmed catchments—one in northern Tunisia and the other in southern France. Mediterranean regions are typified by a highly variable climate, with an alternation of long droughts and intense storms, and by a strong heterogeneity of soil properties, due to a combination of climate, relief, parent materials, sparse vegetation, intense land use, man-made infrastructure (ditches, terraces, etc.), and agricultural activities. In this context, OMERE aims to document the impacts of agricultural and land management on mass fluxes in Mediterranean farmed headwater catchments. The observation strategy is motivated by monitoring water, sediment, and contaminant fluxes and hydrologic and climatic variables at different spatial scales from cultivated plots and landscape elements to the catchment scale. These measurements have been performed at a fine time resolution over a long-term scale and by surveying land use, agricultural practices, and soil surface characteristics. The long-term observation strategy intends to support integrative multidisciplinary research for elucidating the conditions that improve soil and water management and delivery of ecosystem services in a Mediterranean rainfed cultivated context. The observatory has led to scientific insights regarding three scientific objectives: (i) to better understand the fluxes of water, erosion, and contaminants, especially pesticides, and of their natural and anthropogenic drivers on short- and long-term scales; (ii) to analyze the aggregate effects of farming and land management on mass fluxes across scales, from plot to catchment or landscape scales; and (iii) to derive new scenarios for sustainable agricultural management and improved delivery of ecosystem services. Copyright © 2018. . Copyright © by the Soil Science Society of America, Inc.

ACS Style

J. Molénat; D. Raclot; R. Zitouna; P. Andrieux; G. Coulouma; D. Feurer; O. Grunberger; J.M. Lamachère; J.S. Bailly; J.L. Belotti; K. Ben Azzez; N. Ben Mechlia; M. Ben Younès Louati; A. Biarnès; Y. Blanca; D. Carrière; H. Chaabane; C. Dagès; A. Debabria; A. Dubreuil; J.C. Fabre; D. Fages; C. Floure; F. Garnier; C. Geniez; C. Gomez; R. Hamdi; O. Huttel; F. Jacob; Z. Jenhaoui; P. Lagacherie; Y. Le Bissonnais; R. Louati; X. Louchart; I. Mekki; R. Moussa; S. Negro; Y. Pépin; L. Prévot; A. Samouelian; J.L. Seidel; G. Trotoux; S. Troiano; Fabrice Vinatier; P. Zante; J. Zrelli; J. Albergel; M. Voltz. OMERE: A Long-Term Observatory of Soil and Water Resources, in Interaction with Agricultural and Land Management in Mediterranean Hilly Catchments. Vadose Zone Journal 2018, 17, 180086 .

AMA Style

J. Molénat, D. Raclot, R. Zitouna, P. Andrieux, G. Coulouma, D. Feurer, O. Grunberger, J.M. Lamachère, J.S. Bailly, J.L. Belotti, K. Ben Azzez, N. Ben Mechlia, M. Ben Younès Louati, A. Biarnès, Y. Blanca, D. Carrière, H. Chaabane, C. Dagès, A. Debabria, A. Dubreuil, J.C. Fabre, D. Fages, C. Floure, F. Garnier, C. Geniez, C. Gomez, R. Hamdi, O. Huttel, F. Jacob, Z. Jenhaoui, P. Lagacherie, Y. Le Bissonnais, R. Louati, X. Louchart, I. Mekki, R. Moussa, S. Negro, Y. Pépin, L. Prévot, A. Samouelian, J.L. Seidel, G. Trotoux, S. Troiano, Fabrice Vinatier, P. Zante, J. Zrelli, J. Albergel, M. Voltz. OMERE: A Long-Term Observatory of Soil and Water Resources, in Interaction with Agricultural and Land Management in Mediterranean Hilly Catchments. Vadose Zone Journal. 2018; 17 (1):180086.

Chicago/Turabian Style

J. Molénat; D. Raclot; R. Zitouna; P. Andrieux; G. Coulouma; D. Feurer; O. Grunberger; J.M. Lamachère; J.S. Bailly; J.L. Belotti; K. Ben Azzez; N. Ben Mechlia; M. Ben Younès Louati; A. Biarnès; Y. Blanca; D. Carrière; H. Chaabane; C. Dagès; A. Debabria; A. Dubreuil; J.C. Fabre; D. Fages; C. Floure; F. Garnier; C. Geniez; C. Gomez; R. Hamdi; O. Huttel; F. Jacob; Z. Jenhaoui; P. Lagacherie; Y. Le Bissonnais; R. Louati; X. Louchart; I. Mekki; R. Moussa; S. Negro; Y. Pépin; L. Prévot; A. Samouelian; J.L. Seidel; G. Trotoux; S. Troiano; Fabrice Vinatier; P. Zante; J. Zrelli; J. Albergel; M. Voltz. 2018. "OMERE: A Long-Term Observatory of Soil and Water Resources, in Interaction with Agricultural and Land Management in Mediterranean Hilly Catchments." Vadose Zone Journal 17, no. 1: 180086.

Journal article
Published: 16 October 2018 in International Journal of Remote Sensing
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ACS Style

Anis Gasmi; Cecile Gomez; Philippe Lagacherie; Hédi Zouari. Surface soil clay content mapping at large scales using multispectral (VNIR–SWIR) ASTER data. International Journal of Remote Sensing 2018, 40, 1506 -1533.

AMA Style

Anis Gasmi, Cecile Gomez, Philippe Lagacherie, Hédi Zouari. Surface soil clay content mapping at large scales using multispectral (VNIR–SWIR) ASTER data. International Journal of Remote Sensing. 2018; 40 (4):1506-1533.

Chicago/Turabian Style

Anis Gasmi; Cecile Gomez; Philippe Lagacherie; Hédi Zouari. 2018. "Surface soil clay content mapping at large scales using multispectral (VNIR–SWIR) ASTER data." International Journal of Remote Sensing 40, no. 4: 1506-1533.

Journal article
Published: 28 September 2018 in Geoderma
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Digital Soil Map uncertainty is usually evaluated from a set of independent soil observations that are used to determine various uncertainty indicators. However, the number and locations of the sites that constitute these evaluations may impact the value of these indicators. In this paper, a numerical experiment on uncertainty indicators was performed using the pseudo values of topsoil clay content obtained from an airborne hyperspectral image in the Cap Bon region (Tunisia). These pseudo values form a soil pattern with a large extent (46% of 300 km2), high resolution (5 m) and good accuracy (R2val = 0.75) while being free of visible artefacts and pedologically plausible. Therefore, the dataset was considered a fair representation of reality while providing a quasi-unlimited choice of sites. The numerical experiment considered three Quantile Regression Forests as examples of DSM models by using inputs from relief soil covariates and geographical locations that were calibrated from 200, 2000 and 100,000 individuals respectively (low, medium and high quality models). Their uncertainty indicators were first evaluated by calculating four uncertainty indicators (ME, MSE, SSMSE and PICP) from a large independent validation set of 100,000 sites. These uncertainty indicators were then computed from independent evaluation sets of different sizes (from 50 to 500 sites) and from different locations (500 evaluation sets of each size). The independent evaluation sets were selected following a stratified random sampling using compact geographical strata. The numerical experiment showed that the values of the uncertainty indicators were highly variable across numbers and locations of sites. The largest variations were observed for evaluation sets with fewer than 100 sites, but non-negligible variations remained for larger evaluation datasets. This result suggested that evaluations from independent sets convey a non-negligible error on the uncertainty indicators, which increases as the number of sites decrease. Evaluations of DSM models from independent evaluation sets should be interpreted with care and uncertainty on validation results should be systematically estimated. For that, numerical experiments for benchmarking DSM models on known soil patterns across the world would be a valuable complement to the analytical expressions for the uncertainty indicators and the many DSM applications for which these analytical expressions are not valid. This would serve also to improve the sampling techniques for the calibration and evaluation datasets to reduce the error when estimating the uncertainty of a DSM product.

ACS Style

Philippe Lagacherie; Dominique Arrouays; Hocine Bourennane; Cécile Gomez; Manuel Martin; Nicolas P.A. Saby. How far can the uncertainty on a Digital Soil Map be known?: A numerical experiment using pseudo values of clay content obtained from Vis-SWIR hyperspectral imagery. Geoderma 2018, 337, 1320 -1328.

AMA Style

Philippe Lagacherie, Dominique Arrouays, Hocine Bourennane, Cécile Gomez, Manuel Martin, Nicolas P.A. Saby. How far can the uncertainty on a Digital Soil Map be known?: A numerical experiment using pseudo values of clay content obtained from Vis-SWIR hyperspectral imagery. Geoderma. 2018; 337 ():1320-1328.

Chicago/Turabian Style

Philippe Lagacherie; Dominique Arrouays; Hocine Bourennane; Cécile Gomez; Manuel Martin; Nicolas P.A. Saby. 2018. "How far can the uncertainty on a Digital Soil Map be known?: A numerical experiment using pseudo values of clay content obtained from Vis-SWIR hyperspectral imagery." Geoderma 337, no. : 1320-1328.

Original articles
Published: 22 June 2018 in International Journal of Remote Sensing
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Visible near-infrared and shortwave infrared data acquired by spaceborne sensors contain atmospheric noise, along with target reflectance that may affect its end applications, e.g. geological, vegetation, soil surface studies, etc. Several atmospheric correction algorithms have been already developed to remove unwanted atmospheric components of a spectral signature of Earth targets obtained from airborne/spaceborne hyperspectral image. In spite of this, choosing of an appropriate atmospheric correction algorithm is an ongoing research. In this study, two hybrid atmospheric correction (HAC) algorithms incorporating a modified empirical line (ELm) method were proposed. The first HAC model (named HAC_1) combines (i) a radiative transfer (RT) model based on the concepts of RT equations, which uses real-time in situ atmospheric and climatic data, and (ii) an ELm technique. The second one (named HAC_2) combines (i) the well-known ATmospheric CORrection (ATCOR) model and (ii) an ELm technique. Both HAC algorithms and their component single atmospheric correction algorithms (ATCOR, RT, and ELm) were applied to radiance data acquired by Hyperion satellite sensor over study sites in Australia. The performances of both HAC algorithms were analysed in two ways. First, the Hyperion reflectances obtained by five atmospheric correction algorithms were analysed and compared using spectral metrics. Second, the performance of each atmospheric correction algorithm was analysed for prediction of soil organic carbon (SOC) using Hyperion reflectances obtained from atmospheric correction algorithms. The prediction model of SOC was built using partial least square regression model. The results show that (i) both the hybrid models produce a good spectrum with lower Spectral Angle Mapper and Spectral Information Divergence values and (ii) both hybrid algorithms provided better SOC prediction accuracy, in terms of coefficient of determination (R2), residual prediction deviation (RPD), and ratio of performance to interquartile (RPIQ), with R2 ≥ 0.75, RPD ≥ 2, and RPIQ ≥ 2.58 than single algorithms. HAC algorithms, developed using ELm technique, may be recommended for atmospheric correction of Hyperion radiance data, when archived Hyperion reflectance data have to be used for SOC prediction mapping.

ACS Style

Sukumaran Minu; Amba Shetty; Cécile Gomez. Hybrid atmospheric correction algorithms and evaluation on VNIR/SWIR Hyperion satellite data for soil organic carbon prediction. International Journal of Remote Sensing 2018, 39, 8246 -8270.

AMA Style

Sukumaran Minu, Amba Shetty, Cécile Gomez. Hybrid atmospheric correction algorithms and evaluation on VNIR/SWIR Hyperion satellite data for soil organic carbon prediction. International Journal of Remote Sensing. 2018; 39 (22):8246-8270.

Chicago/Turabian Style

Sukumaran Minu; Amba Shetty; Cécile Gomez. 2018. "Hybrid atmospheric correction algorithms and evaluation on VNIR/SWIR Hyperion satellite data for soil organic carbon prediction." International Journal of Remote Sensing 39, no. 22: 8246-8270.

Chapter
Published: 25 April 2018 in Pedometrics
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Since the early ages of soil surveys, air photographs have been intensively used by soil surveyors for depicting the soil variations across landscapes. The variations of soil surfaces, specifically color and ratio of vegetation cover, that were revealed by this early remote sensing product were a great help for interpolating the scarce soil observations and for delineating the soil class boundaries. This was further transposed in digital soil mapping (McBratney et al. 2003), thanks to the large availability of remote sensing images provided by the emerging spatial data infrastructures. Up to now, digital soil mappers have mainly used remote sensing images as spatial data inputs for representing the landscape variables that are related with soil, such as vegetation and parent material (the soil covariates). Boettinger et al. (2008) reviewed the main indicators that could be retrieved for estimating these soil covariates, using multispectral data acquired in the visible near-infrared and short-wave infrared (VIS, 400–700 nm; NIR, 700–1100 nm; SWIR, 1100–2500 nm) spectral domain. After a spatial overlay with the sparse sets of observed and measured sites collected in a given area, the indicators derived from remote sensing have been used as independent variables in regression-like models or as external drift in geostatistic models (McBratney et al. 2003, Chap. 12 of this book).

ACS Style

Philippe Lagacherie; Cécile Gomez. Vis-NIR-SWIR Remote Sensing Products as New Soil Data for Digital Soil Mapping. Pedometrics 2018, 415 -437.

AMA Style

Philippe Lagacherie, Cécile Gomez. Vis-NIR-SWIR Remote Sensing Products as New Soil Data for Digital Soil Mapping. Pedometrics. 2018; ():415-437.

Chicago/Turabian Style

Philippe Lagacherie; Cécile Gomez. 2018. "Vis-NIR-SWIR Remote Sensing Products as New Soil Data for Digital Soil Mapping." Pedometrics , no. : 415-437.

Journal article
Published: 01 January 2018 in Remote Sensing of Environment
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ACS Style

C. Gomez; K. Adeline; S. Bacha; B. Driessen; N. Gorretta; P. Lagacherie; J.M. Roger; X. Briottet. Sensitivity of clay content prediction to spectral configuration of VNIR/SWIR imaging data, from multispectral to hyperspectral scenarios. Remote Sensing of Environment 2018, 204, 18 -30.

AMA Style

C. Gomez, K. Adeline, S. Bacha, B. Driessen, N. Gorretta, P. Lagacherie, J.M. Roger, X. Briottet. Sensitivity of clay content prediction to spectral configuration of VNIR/SWIR imaging data, from multispectral to hyperspectral scenarios. Remote Sensing of Environment. 2018; 204 ():18-30.

Chicago/Turabian Style

C. Gomez; K. Adeline; S. Bacha; B. Driessen; N. Gorretta; P. Lagacherie; J.M. Roger; X. Briottet. 2018. "Sensitivity of clay content prediction to spectral configuration of VNIR/SWIR imaging data, from multispectral to hyperspectral scenarios." Remote Sensing of Environment 204, no. : 18-30.

Journal article
Published: 01 August 2017 in Geoderma
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The limited availability of soil information has been recognized as a main limiting factor in Digital Soil mapping (DSM) studies. It is therefore important to optimize the joint use of the three sources of soil data that can be used as inputs of DSM models, namely spatial sets of measured sites, soil maps and soil sensing products. In this paper, we propose to combine these three inputs, through a cok-riging with a categorical external drift (CKCED). This new interpolationtechnique was applied for mapping seven soil properties over a 24.6 km2 area located in the vineyard plain of Languedoc (Southern France), using an hyperspectral imagery product as example of a soil sensing data. Cross-validation results of CKCED were compared with those of five spatial and non-spatial techniques using one of these inputs or a combination of two of them. The results obtained in the La Peyne Catchment showed i) the utility of soil map and hyperspectral imagery products as auxiliary data for improving soil property predictions ii) the greater added-value of the latter against the former in most situations and iii) the feasibility and the interest of CKCED in a limited number of soil properties and data configurations. Testing CKCED in case study with soil maps of better quality and soil sensing techniques covering more area and depths should be necessary to better evaluate the benefits of this new technique

ACS Style

Emily Walker; Pascal Monestiez; Cecile Gomez; Philippe Lagacherie. Combining measured sites, soilscapes map and soil sensing for mapping soil properties of a region. Geoderma 2017, 300, 64 -73.

AMA Style

Emily Walker, Pascal Monestiez, Cecile Gomez, Philippe Lagacherie. Combining measured sites, soilscapes map and soil sensing for mapping soil properties of a region. Geoderma. 2017; 300 ():64-73.

Chicago/Turabian Style

Emily Walker; Pascal Monestiez; Cecile Gomez; Philippe Lagacherie. 2017. "Combining measured sites, soilscapes map and soil sensing for mapping soil properties of a region." Geoderma 300, no. : 64-73.

Articles
Published: 26 July 2017 in International Journal of Remote Sensing
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In this study, the role of atmospheric correction algorithm in the prediction of soil organic carbon (SOC) from spaceborne hyperspectral sensor (Hyperion) visible near-infrared (vis-NIR, 400–2500 nm) data was analysed in fields located in two different geographical settings, viz. Karnataka in India and Narrabri in Australia. Atmospheric correction algorithms, (1) ATmospheric CORection (ATCOR), (2) Fast Line-of-sight Atmospheric Analysis of Spectral Hypercubes (FLAASH), (3) 6S, and (4) QUick Atmospheric Correction (QUAC), were employed for retrieving spectral reflectance from radiance image. The results showed that ATCOR corrected spectra coupled with partial least square regression prediction model, produced the best SOC prediction performances, irrespective of the study area. Comparing the results across study areas, Karnataka region gave lower prediction accuracy than Narrabri region. This may be explained due to difference in spatial arrangement of field conditions. A spectral similarity comparison of atmospherically corrected Hyperion spectra of soil samples with field-measured vis-NIR spectra was performed. Among the atmospheric correction algorithms, ATCOR corrected spectra found to capture the pattern in soil reflectance curve near 2200 nm. ATCOR’s finer spectral sampling distance in shortwave infrared wavelength region compared to other models may be the main reason for its better performance. This work would open up a great scope for accurate SOC mapping when future hyperspectral missions are realized.

ACS Style

S. Minu; Amba Shetty; Budiman Minasny; Cecile Gomez. The role of atmospheric correction algorithms in the prediction of soil organic carbon from Hyperion data. International Journal of Remote Sensing 2017, 38, 6435 -6456.

AMA Style

S. Minu, Amba Shetty, Budiman Minasny, Cecile Gomez. The role of atmospheric correction algorithms in the prediction of soil organic carbon from Hyperion data. International Journal of Remote Sensing. 2017; 38 (23):6435-6456.

Chicago/Turabian Style

S. Minu; Amba Shetty; Budiman Minasny; Cecile Gomez. 2017. "The role of atmospheric correction algorithms in the prediction of soil organic carbon from Hyperion data." International Journal of Remote Sensing 38, no. 23: 6435-6456.

Journal article
Published: 01 June 2017 in CATENA
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ACS Style

Mohamed Annabi; Damien Raclot; Haithem Bahri; Jean Stephane Bailly; Cecile Gomez; Yves Le Bissonnais. Spatial variability of soil aggregate stability at the scale of an agricultural region in Tunisia. CATENA 2017, 153, 157 -167.

AMA Style

Mohamed Annabi, Damien Raclot, Haithem Bahri, Jean Stephane Bailly, Cecile Gomez, Yves Le Bissonnais. Spatial variability of soil aggregate stability at the scale of an agricultural region in Tunisia. CATENA. 2017; 153 ():157-167.

Chicago/Turabian Style

Mohamed Annabi; Damien Raclot; Haithem Bahri; Jean Stephane Bailly; Cecile Gomez; Yves Le Bissonnais. 2017. "Spatial variability of soil aggregate stability at the scale of an agricultural region in Tunisia." CATENA 153, no. : 157-167.