Laboratory-based Vis–NIR spectroscopy and partial least square regression with spatially correlated errors for predicting spatial variation of soil organic matter content

Conforti, Massimo; Castrignanò, Annamaria; Robustelli, Gaetano; Scarciglia, Fabio; Stelluti, M.; Buttafuoco, Gabriele

doi:10.1016/j.catena.2014.09.004

Cited by 87 publications

(50 citation statements)

References 40 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Spectroscopy techniques that include the visible (VIS, 400-700 nm), near-infrared (NIR, 700-1100 nm), shortwave infrared (SWIR, 1100-2500 nm), and thermal infra-red (TIR, 8000-12000 nm) spectral ranges are well-known tools for monitoring and studying different aspects of soil surface properties and SQ with various levels of prediction accuracy [10][11][12][13][14]. Spectroscopy is a rapid, non-destructive, reproducible, and cost-effective analytical method that is used in food science, medical science, and all geoscientific disciplines, in general, and in soil science, in particular [11,14,15].…”

Section: Introductionmentioning

confidence: 99%

“…While regression methods are used to model the spectral signature of a target based on specific physical, biological, or chemical soil properties, classification is used to group the spectral signatures of soil into categories [24][25][26][27]. The statistical models have included parametrical methods such as partial least squares-regression (PLS-R), which is perhaps the most commonly used regression method technique (e.g., [12,14,28]). An example of a parametric classification method is the partial least squares-discriminant analysis (PLS-DA), which is a method for the supervised classification of spectral data.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Mapping the Spectral Soil Quality Index (SSQI) Using Airborne Imaging Spectroscopy

et al. 2015

View full text Add to dashboard Cite

Soil quality (SQ) assessment has numerous applications for managing sustainable soil function. Airborne imaging spectroscopy (IS) is an advanced tool for studying natural and artificial materials, in general, and soil properties, in particular. The primary goal of this research was to prove and demonstrate the ability of IS to evaluate soil properties and quality across anthropogenically induced land-use changes. This aim was fulfilled by developing and implementing a spectral soil quality index (SSQI) using IS obtained by a laboratory and field spectrometer (point scale) as well as by airborne hyperspectral imaging (local scale), in two experimental sites located in Israel and Germany. In this regard, 13 soil physical, biological, and chemical properties and their derived soil quality index (SQI) were measured. Several mathematical/statistical procedures, consisting of a series of operations, including a principal component analysis OPEN ACCESSRemote Sens. 2015, 7 15749(PCA), a partial least squares-regression (PLS-R), and a partial least squares-discriminate analysis (PLS-DA), were used. Correlations between the laboratory spectral values and the calculated SQI coefficient of determination (R 2 ) and ratio of performance to deviation (RPD) were R 2 = 0.84; RPD = 2.43 and R 2 = 0.78; RPD = 2.10 in the Israeli and the German study sites, respectively. The PLS-DA model that was used to develop the SSQI showed high classification accuracy in both sites (from laboratory, field, and imaging spectroscopy). The correlations between the SSQI and the SQI were R 2 = 0.71 and R 2 = 0.7, in the Israeli and the German study sites, respectively. It is concluded that soil quality can be effectively monitored using the spectral-spatial information provided by the IS technology. IS-based classification of soils can provide the basis for a spatially explicit and quantitative approach for monitoring SQ and function at a local scale.

show abstract

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Mapping the Spectral Soil Quality Index (SSQI) Using Airborne Imaging Spectroscopy

et al. 2015

View full text Add to dashboard Cite

show abstract

“…Many scholars also realized the importance of spatial characteristics of soil spectra in constructing soil spectral models [16,21,59]. The linear mixed effect model was used by Conforti et al [21]. Meanwhile, the ordinary kriging was used by Ge et al [16,21] to weaken the influence of the spatial autocorrelation of spectral reflectance on the prediction accuracy of soil spectral models.…”

Section: Advantages Of Gwrmentioning

confidence: 99%

“…However, most of these methods do not consider the spatial dependence of soil properties and VNIRS in the modeling processes [11,38]. Many scholars also realized the importance of spatial characteristics of soil spectra in constructing soil spectral models [16,21,59]. The linear mixed effect model was used by Conforti et al [21].…”

Section: Advantages Of Gwrmentioning

confidence: 99%

“…The linear mixed effect model was used by Conforti et al [21]. Meanwhile, the ordinary kriging was used by Ge et al [16,21] to weaken the influence of the spatial autocorrelation of spectral reflectance on the prediction accuracy of soil spectral models. Guo et al [22] also showed that GWR can consider the spatial autocorrelation of soil properties and spectral reflectance to improve its prediction accuracy over that of PLSR.…”

Section: Advantages Of Gwrmentioning

confidence: 99%

See 1 more Smart Citation

Exploring the Role of the Spatial Characteristics of Visible and Near-Infrared Reflectance in Predicting Soil Organic Carbon Density

Guo

Chen

Shi

et al. 2017

IJGI

View full text Add to dashboard Cite

Soil organic carbon stock plays a key role in the global carbon cycle and the precision agriculture. Visible and near-infrared reflectance spectroscopy (VNIRS) can directly reflect the internal physical construction and chemical substances of soil. The partial least squares regression (PLSR) is a classical and highly commonly used model in constructing soil spectral models and predicting soil properties. Nevertheless, using PLSR alone may not consider soil as characterized by strong spatial heterogeneity and dependence. However, considering the spatial characteristics of soil can offer valuable spatial information to guarantee the prediction accuracy of soil spectral models. Thus, this study aims to construct a rapid and accurate soil spectral model in predicting soil organic carbon density (SOCD) with the aid of the spatial autocorrelation of soil spectral reflectance. A total of 231 topsoil samples (0-30 cm) were collected from the Jianghan Plain, Wuhan, China. The spectral reflectance (350-2500 nm) was used as auxiliary variable. A geographically-weighted regression (GWR) model was used to evaluate the potential improvement of SOCD prediction when the spatial information of the spectral features was considered. Results showed that: (1) The principal components extracted from PLSR have a strong relationship with the regression coefficients at the average sampling distance (300 m) based on the Moran's I values. (2) The eigenvectors of the principal components exhibited strong relationships with the absorption spectral features, and the regression coefficients of GWR varied with the geographical locations. (3) GWR displayed a higher accuracy than that of PLSR in predicting the SOCD by VNIRS. This study aimed to help people realize the importance of the spatial characteristics of soil properties and their spectra. This work also introduced guidelines for the application of GWR in predicting soil properties by VNIRS.

show abstract

Modified self‐adaptive model for improving the prediction accuracy of soil organic matter by laser‐induced breakdown spectroscopy

et al. 2020

Soil Science Soc of Amer J

View full text Add to dashboard Cite

Soil organic matter (SOM) significantly influences soil fertility, biology, and the global C cycle. Laser‐induced breakdown spectroscopy (LIBS) is attractive, as it can measure SOM accurately and quickly with minimal sample preparation and effective cost. Our earlier self‐adaptive (SA) model based on individual distances between LIBS soil spectra coupled with partial least squares (PLS) was favorable for SOM prediction. To optimize this model by including soil identification via the use of a hybrid distance between the spectra, two hybrid distance SA models to enhance SOM predictions from LIBS spectra in 250 Chinese topsoils were built and studied. The models used LIBS soil spectra and SOM content measured by the traditional potassium dichromate oxidation method for calibration. The performance of these models for the validation set was compared with the conventional no‐identification PLS model and SA‐PLS models. The modified models that used (a) a hybrid of Euclidean distance (ED) and angle cosine (SAxc–PLS), and (b) a hybrid of ED and the distance of response y (SAxy–PLS) improved SOM predictions , with a R2 of 0.908, a RMSE of 5.65 g kg−1, and a residual prediction deviation (RPD) of 2.91 in the validation set for SAxc–PLS; and a R2 of 0.900, a RMSE of 5.49 g kg−1, and a RPD of 2.99 in the validation set for SAxy–PLS. The improved performance mainly resulted from simultaneous consideration of diversity and correlation of the spectra. Therefore, modified SA models hold great potential for SOM prediction, especially for different soils.

show abstract

Laboratory-based Vis–NIR spectroscopy and partial least square regression with spatially correlated errors for predicting spatial variation of soil organic matter content

Cited by 87 publications

References 40 publications

Mapping the Spectral Soil Quality Index (SSQI) Using Airborne Imaging Spectroscopy

Mapping the Spectral Soil Quality Index (SSQI) Using Airborne Imaging Spectroscopy

Exploring the Role of the Spatial Characteristics of Visible and Near-Infrared Reflectance in Predicting Soil Organic Carbon Density

Modified self‐adaptive model for improving the prediction accuracy of soil organic matter by laser‐induced breakdown spectroscopy

Contact Info

Product

Resources

About