National calibration of soil organic carbon concentration using diffuse infrared reflectance spectroscopy

Clairotte, Michaël; Grinand, Clovis; Kouakoua, Ernest; Thébault, Aurélie; Saby, Nicolas P.A.; Bernoux, Martial; Barthès, Bernard

doi:10.1016/j.geoderma.2016.04.021

Cited by 101 publications

(90 citation statements)

References 47 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The SOC estimation was gained from calibration models obtained by subsetting the LUCAS soil database into classes based on 12 soil variables, and the comparison between measured and estimated SOC values showed an overall RMSE of 4.3 g·kg −1 and a RPD of 2.5 for the calibration datasets (Table 3). Nocita et al [58] and Clairotte et al [59] showed similar results in terms of RPD subsetting large spectral library by means of a local PLSR models; however, the validation datasets are not completely independent for both studies, because the samples used for the validation were selected from the same library as that used for the calibration, i.e., the LUCAS dataset for [58] and the French national spectral library for [59].…”

Section: Discussionmentioning

confidence: 79%

Soil Organic Carbon Estimation in Croplands by Hyperspectral Remote APEX Data Using the LUCAS Topsoil Database

et al. 2018

View full text Add to dashboard Cite

Abstract:The most commonly used approach to estimate soil variables from remote-sensing data entails time-consuming and expensive data collection including chemical and physical laboratory analysis. Large spectral libraries could be exploited to decrease the effort of soil variable estimation and obtain more widely applicable models. We investigated the feasibility of a new approach, referred to as bottom-up, to provide soil organic carbon (SOC) maps of bare cropland fields over a large area without recourse to chemical analyses, employing both the pan-European topsoil database from the Land Use/Cover Area frame statistical Survey (LUCAS) and Airborne Prism Experiment (APEX) hyperspectral airborne data. This approach was tested in two areas having different soil characteristics: the loam belt in Belgium, and the Gutland-Oesling region in Luxembourg. Partial least square regression (PLSR) models were used in each study area to estimate SOC content, using both bottom-up and traditional approaches. The PLSR model's accuracy was tested on an independent validation dataset. Both approaches provide SOC maps having a satisfactory level of accuracy (RMSE = 1.5-4.9 g·kg −1 ; ratio of performance to deviation (RPD) = 1.4-1.7) and the inter-comparison did not show differences in terms of RMSE and RPD either in the loam belt or in Luxembourg. Thus, the bottom-up approach based on APEX data provided high-resolution SOC maps over two large areas showing the within-and between-field SOC variability.

show abstract

Section: Discussionmentioning

confidence: 79%

Soil Organic Carbon Estimation in Croplands by Hyperspectral Remote APEX Data Using the LUCAS Topsoil Database

et al. 2018

View full text Add to dashboard Cite

show abstract

“…In this case, the reduced dataset usually outperformed the national dataset, but to obtain reliable results they ‘spiked’ both datasets with local samples. Classification of large spectral libraries, and thus the use of local models, has always appeared to improve the accuracy of estimation compared with global models (e.g Araújo et al ., ; Clairotte et al ., ).…”

Section: Discussionmentioning

confidence: 99%

“…() and Clairotte et al . () used a national spectral library to develop local PLSR models. The validation datasets, however, were selected from the same library as the one used for the calibration.…”

Section: Discussionmentioning

confidence: 99%

“…The large variation in spectral response to soil properties and characteristics means that a rigorous process is necessary to subdivide large spectral libraries and to avoid the failure of global models to predict local variation in SOC content (Stevens et al ., ). The subdivision of large spectral libraries to improve the estimation of soil variables can be carried out with two types of information: (i) auxiliary variables (Wijewardane et al ., ), parent material or land cover (Stevens et al ., ) to derive the classes and (ii) vis–NIR spectral data to decrease the number of samples in the calibration models by a local approach, whereby sets of samples spectrally similar to each sample of the validation dataset can be selected (Clairotte et al ., ; Viscarra Rossel et al ., ), or by cluster analysis to group spectrally similar samples together (Araújo et al ., , Shi et al ., ). Two examples where the local approach with spectral data was enhanced by including an auxiliary variable are use of the LUCAS spectral data with sand content and geographical coordinates (Nocita et al, ), and Shi et al .…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Estimation of soil organic carbon in arable soil in Belgium and Luxembourg with the LUCAS topsoil database

Castaldi

Chabrillat

Chartin

et al. 2018

European J Soil Science

View full text Add to dashboard Cite

Summary Quantification of the soil organic carbon (SOC) content over large areas is mandatory to obtain accurate soil characterization and classification, which can improve site‐specific management at local or regional scales. In this context, soil spectroscopy is a well‐consolidated and widespread method to estimate soil variables, and in particular SOC content, at a low cost for routine analysis. The increasing number of large soil spectral libraries collected worldwide reflects the importance of spectroscopy in soil science. These large libraries contain soil samples derived from a large number of pedological regions and thus from different parent materials and soil types. In the light of the huge variation in the spectral responses to SOC content and composition, a rigorous process is necessary to subdivide large spectral libraries to avoid calibration with global models that fail to predict local variation in SOC content. Here, we propose to classify the European LUCAS topsoil database with a cluster analysis based on a large number of soil properties. The soil samples collected from arable land in the LUCAS database were chosen to apply a standardized multivariate calibration approach, valid for large areas, to calibrate local models without the need for further field and laboratory work. Cluster analysis detected seven soil classes and the samples belonging to each class were used to calibrate specific partial least squares regression (PLSR) models to estimate SOC content in three spectral libraries collected in Belgium and Luxembourg. Soil organic carbon was predicted with good accuracy, both within each library (root mean square error (RMSE), 1.2–5.1 g kg−1; ratio of performance to prediction (RPD), 1.41–2.24) and for the samples of the three libraries together (RMSE, 3.7 g kg−1; RPD, 2.54). The proposed approach could enable SOC to be estimated for arable soils in Europe with only the spectra of soil samples and without the need for laboratory analyses. Highlights We investigated the potential of the LUCAS database to estimate SOC in spectral libraries. We proposed a routine approach to estimate SOC with less laboratory work. The classification of the LUCAS topsoil dataset improved estimation accuracy of SOC SOC content can be predicted from soil spectra with models calibrated on the LUCAS database.

show abstract

“…It also effects considerably on the interaction between soil and plants. Soil Organic Carbon tends to be found in the topsoil 1 . Organic C ranges from 0.5% to 3.0% in topsoil for most upland soils.…”

Section: Introductionmentioning

confidence: 99%

Monitoring Organic Carbon Content in Soil using Hyperspectral Signature

2017

JECET

View full text Add to dashboard Cite

Abstract:The world is highly dependent on the food production. Thus, food production mostly depends on the soil quality. So, knowledge about soil quality along with its components should be known. Organic carbon is an important component in soil as it plays an essential role in important functions. Soil Organic Carbon (SOC) has impact over different physical, compound and organic procedures. The Visible and Near Infrared (Vis-NIR) reflectance spectroscopy with spectral resolution of 350-2500 nm procedure has been considered as a strategy with fast and minimal effort to determine SOC content. Partial Least Square Regression (PLSR) quantitatively derives information which is an important method from reflectance spectral data. PLSR is the mostly used statistical method for determining SOC contents obtained from the spectral data. The results shows that the model based on Partial Least Square Regression PLSR can only make approximate predictions for Absorbance (Log 1/R), RMSE (Root Mean Square Error) = 0.0581-0.0417; R 2 (Coefficient of Determination) = 0.9078-0.8959 and for First Derivative, RMSE (Root Mean Square Error) = 0.0044-0.0033; R 2 (Coefficient of Determination) = 0.9994-0.9993. This research indicates the importance of a practical strategy for the improvement of SOC prediction with Vis-NIR spectroscopy.

show abstract

National calibration of soil organic carbon concentration using diffuse infrared reflectance spectroscopy

Cited by 101 publications

References 47 publications

Soil Organic Carbon Estimation in Croplands by Hyperspectral Remote APEX Data Using the LUCAS Topsoil Database

Soil Organic Carbon Estimation in Croplands by Hyperspectral Remote APEX Data Using the LUCAS Topsoil Database

Estimation of soil organic carbon in arable soil in Belgium and Luxembourg with the LUCAS topsoil database

Monitoring Organic Carbon Content in Soil using Hyperspectral Signature

Contact Info

Product

Resources

About