Exploring the potential of airborne hyperspectral image for estimating topsoil organic carbon: Effects of fractional-order derivative and optimal band combination algorithm

Abstract Estimating soil organic carbon (SOC) in topsoil can help improve soil quality and food production. This study aimed to explore the potential of airborne hyperspectral image to estimate the SOC of bare topsoil at an agricultural site located in the southeast part of Iowa State, United States. To magnify the subtle spectral signals concerning SOC, and accelerate calibration and improve predictive ability, we developed a framework to combine two advanced spectral algorithms, namely, fractional-order derivative (FOD) and optimal band combination algorithm for SOC predicting. Our case was based on 49 soil samples and a scattered airborne hyperspectral image. Random forest (RF) was utilized to establish SOC estimation models by incorporating the optimal spectral indices processed by different FOD transformations on the basis of the optimal band combination algorithm. Results indicated that when the fractional order increased, overlapping peaks and baseline drifts were gradually removed. However, the magnitude of spectral strength decreased concurrently. More detailed and abundant spectral variability was captured by FOD as compared with those by original reflectance and first and second derivatives. The estimation accuracies developed from the optimal band combination algorithm (cross-validation R2, 0.36–0.66) were generally better than those from full-spectrum data (cross-validation R2, 0.32–0.54). The RF model based on the combination of 0.75-order reflectance and optimal band combination algorithm obtained the highest estimation accuracy for SOC with cross-validation R2 of 0.66. This research provides guidance for future studies in selecting the most appropriate FOD transformation to preprocess spectral data and in using the optimal band combination algorithm to determine the spectral index. Airborne hyperspectral image-based modeling can be further used to map agricultural topsoil SOC to support local-scale agricultural planning.

[1]  Budiman Minasny,et al.  On digital soil mapping , 2003 .

[2]  H. Xie,et al.  Modeling alpine grassland forage phosphorus based on hyperspectral remote sensing and a multi-factor machine learning algorithm in the east of Tibetan Plateau, China , 2019, ISPRS Journal of Photogrammetry and Remote Sensing.

[3]  Hang Cheng,et al.  Transferability of Vis-NIR models for Soil Organic Carbon Estimation between Two Study Areas by using Spiking , 2018, Soil Science Society of America Journal.

[4]  Anne-Katrin Mahlein,et al.  Airborne hyperspectral imaging of spatial soil organic carbon heterogeneity at the field-scale , 2012 .

[5]  Henning Buddenbaum,et al.  Estimation of soil salinity using three quantitative methods based on visible and near-infrared reflectance spectroscopy: a case study from Egypt , 2015, Arabian Journal of Geosciences.

[6]  J. Demattê,et al.  Stratification of a local VIS-NIR-SWIR spectral library by homogeneity criteria yields more accurate soil organic carbon predictions , 2019, Geoderma.

[7]  Mohammadmehdi Saberioon,et al.  Soil organic carbon and texture retrieving and mapping using proximal, airborne and Sentinel-2 spectral imaging , 2018, Remote Sensing of Environment.

[8]  Andy Liaw,et al.  Classification and Regression by randomForest , 2007 .

[9]  Jianli Ding,et al.  Estimation of soil salt content (SSC) in the Ebinur Lake Wetland National Nature Reserve (ELWNNR), Northwest China, based on a Bootstrap-BP neural network model and optimal spectral indices. , 2018, The Science of the total environment.

[10]  A. Savitzky,et al.  Smoothing and Differentiation of Data by Simplified Least Squares Procedures. , 1964 .

[11]  Mohammadmehdi Saberioon,et al.  Comparing different data preprocessing methods for monitoring soil heavy metals based on soil spectral features. , 2016 .

[12]  J. Hill,et al.  Using Imaging Spectroscopy to study soil properties , 2009 .

[13]  Zongming Wang,et al.  Remote estimation of soil organic matter content in the Sanjiang Plain, Northest China: The optimal band algorithm versus the GRA-ANN model , 2016 .

[14]  Haitao Zhang,et al.  Exploring the Sensitivity of Sampling Density in Digital Mapping of Soil Organic Carbon and Its Application in Soil Sampling , 2018, Remote. Sens..

[15]  Bin Li,et al.  Rapid assessment of regional soil arsenic pollution risk via diffuse reflectance spectroscopy , 2017 .

[16]  José A. M. Demattê,et al.  Prediction of soil properties using imaging spectroscopy: Considering fractional vegetation cover to improve accuracy , 2015, Int. J. Appl. Earth Obs. Geoinformation.

[17]  Keith Paustian,et al.  Measuring and monitoring soil organic carbon stocks in agricultural lands for climate mitigation , 2011 .

[18]  R. Lal.  Soil carbon sequestration to mitigate climate change , 2004 .

[19]  R. Casa,et al.  Evaluation of the potential of the current and forthcoming multispectral and hyperspectral imagers to estimate soil texture and organic carbon , 2016 .

[20]  R. V. Rossel,et al.  Soil organic carbon prediction by hyperspectral remote sensing and field vis-NIR spectroscopy: An Australian case study , 2008 .

[21]  Jay Gao,et al.  Hyperspectral sensing of heavy metals in soil and vegetation: Feasibility and challenges , 2018 .

[22]  Ke Yang,et al.  Assessing soil organic matter of reclaimed soil from a large surface coal mine using a field spectroradiometer in laboratory , 2017 .

[23]  G. Coulouma,et al.  Importance of the spatial extent for using soil properties estimated by laboratory VNIR/SWIR spectroscopy: Examples of the clay and calcium carbonate content , 2018, Geoderma.

[24]  Abdul Mounem Mouazen,et al.  Predictive performance of mobile vis-near infrared spectroscopy for key soil properties at different geographical scales by using spiking and data mining techniques , 2017 .

[25]  Andreas Hueni,et al.  Evaluating the capability of the Sentinel 2 data for soil organic carbon prediction in croplands , 2019, ISPRS Journal of Photogrammetry and Remote Sensing.

[26]  Eyal Ben-Dor,et al.  Monitoring of selected soil contaminants using proximal and remote sensing techniques: Background, state-of-the-art and future perspectives , 2018 .

[27]  Budiman Minasny,et al.  Transfer learning to localise a continental soil vis-NIR calibration model , 2019, Geoderma.

[28]  Yoshio Inoue,et al.  Diagnostic mapping of canopy nitrogen content in rice based on hyperspectral measurements , 2012 .

[29]  Yaolin Liu,et al.  Comparisons of spatial and non-spatial models for predicting soil carbon content based on visible and near-infrared spectral technology , 2017 .

[30]  Dominique Arrouays,et al.  Fine resolution map of top- and subsoil carbon sequestration potential in France. , 2018, The Science of the total environment.

[31]  R. V. Rossel,et al.  Using data mining to model and interpret soil diffuse reflectance spectra. , 2010 .

[32]  A. Mouazen,et al.  Calibration of visible and near infrared spectroscopy for soil analysis at the field scale on three European farms , 2011 .

[33]  Huanjun Liu,et al.  Comparison of different satellite bands and vegetation indices for estimation of soil organic matter based on simulated spectral configuration , 2017 .

[34]  Elpídio Inácio Fernandes Filho,et al.  Modelling and mapping soil organic carbon stocks in Brazil , 2019, Geoderma.

[35]  D. W. Nelson,et al.  A Rapid and Accurate Procedure for Estimation of Organic Carbon in Soils , 1974 .

[36]  Hang Cheng,et al.  Application of fractional-order derivative in the quantitative estimation of soil organic matter content through visible and near-infrared spectroscopy , 2019, Geoderma.

[37]  Ting Wu,et al.  Improvement of NIR model by fractional order Savitzky–Golay derivation (FOSGD) coupled with wavelength selection , 2015 .

[38]  Li Hu,et al.  Possibility of optimized indices for the assessment of heavy metal contents in soil around an open pit coal mine area , 2018, Int. J. Appl. Earth Obs. Geoinformation.

[39]  Philippe Lagacherie,et al.  Sensitivity of clay content prediction to spectral configuration of VNIR/SWIR imaging data, from multispectral to hyperspectral scenarios , 2018 .

[40]  L. Hoffmann,et al.  Measuring soil organic carbon in croplands at regional scale using airborne imaging spectroscopy , 2010 .

[41]  Lammert Kooistra,et al.  Soil Organic Carbon mapping of partially vegetated agricultural fields with imaging spectroscopy , 2011, Int. J. Appl. Earth Obs. Geoinformation.

[42]  Sabine Grunwald,et al.  Digital mapping of soil carbon fractions with machine learning , 2019, Geoderma.

[43]  Fei Zhang,et al.  New methods for improving the remote sensing estimation of soil organic matter content (SOMC) in the Ebinur Lake Wetland National Nature Reserve (ELWNNR) in northwest China , 2018, Remote Sensing of Environment.

[44]  A. Karnieli,et al.  Mapping of several soil properties using DAIS-7915 hyperspectral scanner data - a case study over clayey soils in Israel , 2002 .

[45]  R. V. Rossel,et al.  Visible–Near Infrared Spectra as a Proxy for Topsoil Texture and Glacial Boundaries , 2013 .

[46]  Caiyun Zhang,et al.  Mapping salt marsh soil properties using imaging spectroscopy , 2019, ISPRS Journal of Photogrammetry and Remote Sensing.

[47]  Yaqing Ding,et al.  Haar Wavelet Based Implementation Method of the Non–integer Order Differentiation and its Application to Signal Enhancement , 2015 .

[48]  A. McBratney,et al.  Pedometrics timeline , 2019, Geoderma.

[49]  Bo Li,et al.  Adaptive fractional differential approach and its application to medical image enhancement , 2015, Comput. Electr. Eng..

[50]  P. Lagacherie,et al.  Regional predictions of eight common soil properties and their spatial structures from hyperspectral Vis–NIR data , 2012 .

[51]  Shahla Hosseini Bai,et al.  Prediction of soil macro- and micro-elements in sieved and ground air-dried soils using laboratory-based hyperspectral imaging technique , 2019, Geoderma.

[52]  Zhihong Xu,et al.  Using laboratory-based hyperspectral imaging method to determine carbon functional group distributions in decomposing forest litterfall , 2018, CATENA.

[53]  R. Lal,et al.  Soil Carbon Sequestration Impacts on Global Climate Change and Food Security , 2004, Science.

[54]  Bin Li,et al.  Predicting soil arsenic pools by visible near infrared diffuse reflectance spectroscopy , 2017 .

[55]  Nathalie Gorretta,et al.  Clay content mapping from airborne hyperspectral Vis-NIR data by transferring a laboratory regression model , 2017 .

[56]  Philippe Lagacherie,et al.  Continuum removal versus PLSR method for clay and calcium carbonate content estimation from laboratory and airborne hyperspectral measurements , 2008 .

[57]  A. McBratney,et al.  The dimensions of soil security , 2014 .

[58]  Leo Breiman,et al.  Random Forests , 2001, Machine Learning.

[59]  Viacheslav I. Adamchuk,et al.  A global spectral library to characterize the world’s soil , 2016 .

[60]  Qingquan Li,et al.  Proximal and remote sensing techniques for mapping of soil contamination with heavy metals , 2018 .

[61]  Dong Zhang,et al.  Desert soil clay content estimation using reflectance spectroscopy preprocessed by fractional derivative , 2017, PloS one.

[62]  Tiezhu Shi,et al.  Prediction of soil organic carbon stock by laboratory spectral data and airborne hyperspectral images , 2019, Geoderma.

[63]  M. Vohland,et al.  In situ and laboratory soil spectroscopy with portable visible-to-near-infrared and mid-infrared instruments for the assessment of organic carbon in soils , 2019 .

[64]  Kacem Chehdi,et al.  Regional prediction of soil organic carbon content over temperate croplands using visible near-infrared airborne hyperspectral imagery and synchronous field spectra , 2016, Int. J. Appl. Earth Obs. Geoinformation.

[65]  M. Schaepman,et al.  Spectral reflectance based indices for soil organic carbon quantification , 2008 .

[66]  Keping Ma,et al.  Carbon pools in China’s terrestrial ecosystems: New estimates based on an intensive field survey , 2018, Proceedings of the National Academy of Sciences.

[67]  Zhou Shi,et al.  Prediction of soil organic matter using a spatially constrained local partial least squares regression and the Chinese vis–NIR spectral library , 2015 .

[68]  Henning Buddenbaum,et al.  Estimating the soil clay content and organic matter by means of different calibration methods of vis-NIR diffuse reflectance spectroscopy , 2016 .

[69]  R. Clark,et al.  Reflectance spectroscopy: Quantitative analysis techniques for remote sensing applications , 1984 .

[70]  Yiming Xu,et al.  Incorporation of satellite remote sensing pan-sharpened imagery into digital soil prediction and mapping models to characterize soil property variability in small agricultural fields , 2017 .

[71]  J. Schmitt.  Fractional Derivative Analysis of Diffuse Reflectance Spectra , 1998 .

[72]  Guofeng Wu,et al.  Estimating Soil Organic Carbon Content with Visible–Near-Infrared (Vis-NIR) Spectroscopy , 2014, Applied spectroscopy.

[73]  Sabine Grunwald,et al.  A systematic study on the application of scatter-corrective and spectral-derivative preprocessing for multivariate prediction of soil organic carbon by Vis-NIR spectra , 2018 .