Evaluation of the spectral characteristics of five hyperspectral and multispectral sensors for soil organic carbon estimation in burned areas
Juanjo Peón A D , Susana Fernández B , Carmen Recondo A and Javier F. Calleja C
+ Author Affiliations
- Author Affiliations
A Area of Cartographic, Geodesic and Photogrammetric Engineering, Department of Mining Exploitation and Prospecting, University of Oviedo, Gonzalo Gutiérrez Quirós s/n, 33600 Mieres, Asturias, Spain.
B Department of Geology, University of Oviedo, Jesús Arias de Velasco s/n, 33005 Oviedo, Asturias, Spain.
C Department of Physics, Polytechnic School of Mieres, University of Oviedo, Gonzalo Gutiérrez Quirós s/n, 33600 Mieres, Asturias, Spain.
D Corresponding author. Email: juanjopeon@gmail.com
International Journal of Wildland Fire 26(3) 230-239 https://doi.org/10.1071/WF16122
Submitted: 3 July 2016 Accepted: 31 January 2017 Published: 28 February 2017
Abstract
Frequent wildfires in the north-west region of Spain affect soil organic matter. Soil properties can be estimated both spatially and temporally using remote sensing. A wide range of satellite and airborne hyperspectral and multispectral sensors are currently available. The spectral resolution varies substantially among sensors, making it difficult to identify the most suitable sensors and spectral regions for a specific application. This study aims to identify the sensors and wavelengths with the greatest potential for topsoil organic C mapping. Total (TOC) and oxidisable organic carbon (OC) content were measured in 89 soil samples collected in a mountain region of north-western Spain. Reflectance spectra of the samples in the spectral region 400–2450 nm were resampled to the bands of five sensors: Compact Airborne Spectrographic Imager (CASI), Airborne Hyperspectral Scanner (AHS), Hyperion, Landsat 5 Thematic Mapper (TM) and Moderate Resolution Imaging Spectroradiometer (MODIS). Calibration models obtained using partial least-squares regression proved to be effective for hyperspectral sensors and also for the multispectral sensor MODIS (R2 = 0.75–0.89), which suggests that hyperspectral capability is not required to accurately predict topsoil organic C. Models based on Landsat performed well, but with an error ~30–45% greater than that obtained for the hyperspectral sensors and MODIS.
Additional keywords: AHS, CASI, Hyperion, Landsat, MODIS, PLSR, VIS-NIR-SWIR spectroscopy.
References
Akaike H (1969) Fitting autoregressive models for prediction. Annals of the Institute of Statistical Mathematics 21, 243–247.| Fitting autoregressive models for prediction.Crossref | GoogleScholarGoogle Scholar |
Álvarez MA, Marquínez J (Eds) (2007) ‘Impacto de los incendios forestales en Asturias. Análisis de los últimos 30 años.’ (Principado de Asturias, INDUROT, KRK Ediciones: Oviedo, Spain)
Bellon-Maurel V, McBratney A (2011) Near-infrared (NIR) and mid-infrared (MIR) spectroscopic techniques for assessing the amount of carbon stock in soils – Critical review and research perspectives. Soil Biology & Biochemistry 43, 1398–1410.
| Near-infrared (NIR) and mid-infrared (MIR) spectroscopic techniques for assessing the amount of carbon stock in soils – Critical review and research perspectives.Crossref | GoogleScholarGoogle Scholar | 1:CAS:528:DC%2BC3MXmtFCrsro%3D&md5=1f4926264d6c355517e00f9a02c433e4CAS |
Ben-Dor E, Inbar Y, Chen Y (1997) The reflectance spectra of organic matter in the visible near-infrared and short wave infrared region (400–2500 nm) during a controlled decomposition process. Remote Sensing of Environment 61, 1–15.
| The reflectance spectra of organic matter in the visible near-infrared and short wave infrared region (400–2500 nm) during a controlled decomposition process.Crossref | GoogleScholarGoogle Scholar |
Burras L, Cheng HH, Kimble JM, Kissel DE, Lal R, Luxmoore RJ, Mausbach MJ, Rice CW, Uehara G, Wilding LD (2001) ‘Carbon sequestration: position of the Soil Science Society of America’. (SSSA: Madison, WI)
Canfield HE, Wilson CJ, Lane LJ, Crowell KJ, Thomas WA (2005) Modeling scour and deposition in ephemeral channels after wildfire. Catena 61, 273–291.
| Modeling scour and deposition in ephemeral channels after wildfire.Crossref | GoogleScholarGoogle Scholar |
Certini G (2005) Effects of fire on properties of forest soils: a review. Oecologia 143, 1–10.
| Effects of fire on properties of forest soils: a review.Crossref | GoogleScholarGoogle Scholar |
Chang CW, Laird DA (2002) Near-infrared reflectance spectroscopic analysis of soil C and N. Soil Science 167, 110–116.
| Near-infrared reflectance spectroscopic analysis of soil C and N.Crossref | GoogleScholarGoogle Scholar | 1:CAS:528:DC%2BD38XhvFKnsrY%3D&md5=6250d9be110a467a0dd2777cf45d1f0fCAS |
Chang CW, Laird DA, Mausbach MJ, Hurburgh CR (2001) Near-infrared reflectance spectroscopy – principal components regression analyses of soil properties. Soil Science Society of America Journal 65, 480–490.
| Near-infrared reflectance spectroscopy – principal components regression analyses of soil properties.Crossref | GoogleScholarGoogle Scholar | 1:CAS:528:DC%2BD38Xpt1Om&md5=04d9043f952dada528e8725588337899CAS |
Chong IG, Jun CH (2005) Performance of some variable selection methods when multicollinearity is present. Chemometrics and Intelligent Laboratory Systems 78, 103–112.
| Performance of some variable selection methods when multicollinearity is present.Crossref | GoogleScholarGoogle Scholar | 1:CAS:528:DC%2BD2MXlslWisbs%3D&md5=d619c96d551804c63faba470ceccacbdCAS |
Cozzolino D, Morón A (2006) Potential of near-infrared reflectance spectroscopy and chemometrics to predict soil organic carbon fractions. Soil & Tillage Research 85, 78–85.
| Potential of near-infrared reflectance spectroscopy and chemometrics to predict soil organic carbon fractions.Crossref | GoogleScholarGoogle Scholar |
Croft H, Kuhn NJ, Anderson K (2012) On the use of remote sensing techniques for monitoring spatio-temporal soil organic carbon dynamics in agricultural systems. Catena 94, 64–74.
| On the use of remote sensing techniques for monitoring spatio-temporal soil organic carbon dynamics in agricultural systems.Crossref | GoogleScholarGoogle Scholar | 1:CAS:528:DC%2BC38XkvVKltL8%3D&md5=ff31550b6c0c069e86afebcbeb02ebf4CAS |
Datt B, McVicar TR, Van Niel TG, Jupp DLB, Pearlman JS (2003) Preprocessing EO-1 Hyperion hyperspectral data to support the application of agricultural indexes. IEEE Transactions on Geoscience and Remote Sensing 41, 1246–1259.
| Preprocessing EO-1 Hyperion hyperspectral data to support the application of agricultural indexes.Crossref | GoogleScholarGoogle Scholar |
DeTar WR, Chesson JH, Penner JV, Ojala JC (2008) Detection of soil properties with airborne hyperspectral measurements of bare fields. Transactions of the ASABE 51, 463–470.
| Detection of soil properties with airborne hyperspectral measurements of bare fields.Crossref | GoogleScholarGoogle Scholar | 1:CAS:528:DC%2BD1cXntFCqt7c%3D&md5=6abee1b0d789dffd3134515b121cc722CAS |
FAO (2014) World reference base for soil resources 2014. World Soil Resources Report 106. (Food and Agriculture Organization: Rome)
Fernández S, Marquínez J, Duarte RM (2005) A susceptibility model for post wildfire soil erosion in a temperate oceanic mountain area of Spain. Catena 61, 256–272.
| A susceptibility model for post wildfire soil erosion in a temperate oceanic mountain area of Spain.Crossref | GoogleScholarGoogle Scholar |
Fernández S, Peón J, Recondo C, Calleja JF, Guerrero C (2016) Spatial modelling of organic carbon in burned mountain soils using hyperspectral images, field datasets, and NIR spectroscopy (Cantabrian Range; NW Spain). Land Degradation & Development 27, 1479–1488.
| Spatial modelling of organic carbon in burned mountain soils using hyperspectral images, field datasets, and NIR spectroscopy (Cantabrian Range; NW Spain).Crossref | GoogleScholarGoogle Scholar |
Gomez C, Viscarra Rossel RA, McBratney AB (2008) Soil organic carbon prediction by hyperspectral remote sensing and field vis-NIR spectroscopy: an Australian case study. Geoderma 146, 403–411.
| Soil organic carbon prediction by hyperspectral remote sensing and field vis-NIR spectroscopy: an Australian case study.Crossref | GoogleScholarGoogle Scholar | 1:CAS:528:DC%2BD1cXhtVCgur7K&md5=77d083d452396b4428f00188c051c38dCAS |
Gomez C, Lagacherie P, Coulouma G (2012) Regional predictions of eight common soil properties and their spatial structures from hyperspectral Vis-NIR data. Geoderma 189–190, 176–185.
| Regional predictions of eight common soil properties and their spatial structures from hyperspectral Vis-NIR data.Crossref | GoogleScholarGoogle Scholar |
Gomez C, Oltra-Carrió R, Bacha S, Lagacherie P, Briottet X (2015) Evaluating the sensitivity of clay content prediction to atmospheric effects and degradation of image spatial resolution using Hyperspectral VNIR/SWIR imagery. Remote Sensing of Environment 164, 1–15.
| Evaluating the sensitivity of clay content prediction to atmospheric effects and degradation of image spatial resolution using Hyperspectral VNIR/SWIR imagery.Crossref | GoogleScholarGoogle Scholar |
Griffiths PR, Dahm DJ (2007) Continuum and discontinuum theories of diffuse reflection. In ‘Handbook of near-infrared analysis’, 3rd edn. (Eds DA Burns, EW Ciurczak.) pp. 21–64. (CRC Press: Boca Raton, FL)
Haaland DM, Thomas EV (1988) Partial least-squares methods for spectral analyses. 1. Relation to other quantitative calibration methods and the extraction of qualitative information. Analytical Chemistry 60, 1193–1202.
| Partial least-squares methods for spectral analyses. 1. Relation to other quantitative calibration methods and the extraction of qualitative information.Crossref | GoogleScholarGoogle Scholar | 1:CAS:528:DyaL1cXitFGgtLw%3D&md5=ea5829444e2d7063f1126faf019d1a28CAS |
Hbirkou C, Paetzold S, Mahlein A-K, Welp G (2012) Airborne hyperspectral imaging of spatial soil organic carbon heterogeneity at the field-scale. Geoderma 175–176, 21–28.
| Airborne hyperspectral imaging of spatial soil organic carbon heterogeneity at the field-scale.Crossref | GoogleScholarGoogle Scholar |
Heiri O, Lotter AF, Lemcke G (2001) Loss on ignition as a method for estimating organic and carbonate content in sediments: reproducibility and comparability of results. Journal of Paleolimnology 25, 101–110.
| Loss on ignition as a method for estimating organic and carbonate content in sediments: reproducibility and comparability of results.Crossref | GoogleScholarGoogle Scholar |
Huang X, Senthilkumar S, Kravchenko A, Thelen K, Qi J (2007) Total carbon mapping in glacial till soils using near-infrared spectroscopy, Landsat imagery and topographical information. Geoderma 141, 34–42.
| Total carbon mapping in glacial till soils using near-infrared spectroscopy, Landsat imagery and topographical information.Crossref | GoogleScholarGoogle Scholar | 1:CAS:528:DC%2BD2sXosVSju7s%3D&md5=aee8f924966fce62fdcf269b894a7c19CAS |
Irons JR, Weismiller RA, Petersen GW (1989) Soil reflectance. In ‘Theory and applications of optical remote sensing.’ (Ed. G Asrar.) pp. 66–106. (John Wiley and Sons: New York, NY)
Jaber SM, Lant CL, Al-Qinna MI (2011) Estimating spatial variations in soil organic carbon using satellite hyperspectral data and map algebra. International Journal of Remote Sensing 32, 5077–5103.
| Estimating spatial variations in soil organic carbon using satellite hyperspectral data and map algebra.Crossref | GoogleScholarGoogle Scholar |
Jarmer T, Hill J, Lavée H, Sarah P (2010) Mapping topsoil organic carbon in non-agricultural semi-arid and arid ecosystems of Israel. Photogrammetric Engineering and Remote Sensing 76, 85–94.
| Mapping topsoil organic carbon in non-agricultural semi-arid and arid ecosystems of Israel.Crossref | GoogleScholarGoogle Scholar |
Kooistra L, Wanders J, Epema GF, Leuven RSEW, Wehrens R, Buydens LMC (2003) The potential of field spectroscopy for the assessment of sediment properties in river floodplains. Analytica Chimica Acta 484, 189–200.
| The potential of field spectroscopy for the assessment of sediment properties in river floodplains.Crossref | GoogleScholarGoogle Scholar | 1:CAS:528:DC%2BD3sXjs1Wktbc%3D&md5=87cae1e1719935270db3971783fac29aCAS |
Krishnan P, Alexander JD, Butler BJ, Hummel JW (1980) Reflectance technique for predicting soil organic matter. Soil Science Society of America Journal 44, 1282–1285.
| Reflectance technique for predicting soil organic matter.Crossref | GoogleScholarGoogle Scholar |
Lee KS, Lee DH, Sudduth KA, Chung SO, Kitchen NR, Drummond ST (2009) Wavelength identification and diffuse reflectance estimation for surface and profile soil properties. Transactions of the ASABE 52, 683–695.
| Wavelength identification and diffuse reflectance estimation for surface and profile soil properties.Crossref | GoogleScholarGoogle Scholar | 1:CAS:528:DC%2BD1MXpvF2nsL0%3D&md5=5484d3c597ed3369889eb61e0160f3a7CAS |
Li B, Morris J, Martin EB (2002) Model selection for partial least squares regression. Chemometrics and Intelligent Laboratory Systems 64, 79–89.
| Model selection for partial least squares regression.Crossref | GoogleScholarGoogle Scholar | 1:CAS:528:DC%2BD38XntVamtrw%3D&md5=48866c4caee8076b530f7b43e1933901CAS |
Lu P, Wang L, Niu Z, Li L, Zhang W (2013) Prediction of soil properties using laboratory VIS-NIR spectroscopy and Hyperion imagery. Journal of Geochemical Exploration 132, 26–33.
| Prediction of soil properties using laboratory VIS-NIR spectroscopy and Hyperion imagery.Crossref | GoogleScholarGoogle Scholar | 1:CAS:528:DC%2BC3sXmvV2ks7s%3D&md5=79d4fe694a9af7b938d23d015bc68d4fCAS |
Marquínez J, Wozniak E, Fernández S, Martínez R (2008) Analysis of the evolution of soil erosion classes using multitemporal Landsat imagery. International Journal of Wildland Fire 17, 549–558.
| Analysis of the evolution of soil erosion classes using multitemporal Landsat imagery.Crossref | GoogleScholarGoogle Scholar |
Martin PD, Malley DF, Manning G, Fuller L (2002) Determination of soil organic carbon and nitrogen at the field level using near-infrared spectroscopy. Canadian Journal of Soil Science 82, 413–422.
| Determination of soil organic carbon and nitrogen at the field level using near-infrared spectroscopy.Crossref | GoogleScholarGoogle Scholar | 1:CAS:528:DC%2BD3sXhtF2ksLk%3D&md5=645168880d5336316b83255fdc0df34eCAS |
Mcmorrow JM, Cutler MEJ, Evans MG, Al-Roichdi A (2004) Hyperspectral indices for characterizing upland peat composition. International Journal of Remote Sensing 25, 313–325.
| Hyperspectral indices for characterizing upland peat composition.Crossref | GoogleScholarGoogle Scholar |
Menéndez Duarte R, Wozniak E, Recondo C, Cabo C, Marquínez J, Fernández S (2008) Estimation of surface roughness and stone cover in burnt soils using SAR images. Catena 74, 264–272.
| Estimation of surface roughness and stone cover in burnt soils using SAR images.Crossref | GoogleScholarGoogle Scholar |
Morgan CLS, Waiser TH, Brown DJ, Hallmark CT (2009) Simulated in situ characterization of soil organic and inorganic carbon with visible near-infrared diffuse reflectance spectroscopy. Geoderma 151, 249–256.
| Simulated in situ characterization of soil organic and inorganic carbon with visible near-infrared diffuse reflectance spectroscopy.Crossref | GoogleScholarGoogle Scholar | 1:CAS:528:DC%2BD1MXnsFCntrk%3D&md5=dd4c46a52392214194711facb623ab15CAS |
Mouazen AM, Maleki MR, De Baerdemaeker J, Ramon H (2007) On-line measurement of some selected soil properties using a VIS-NIR sensor. Soil & Tillage Research 93, 13–27.
| On-line measurement of some selected soil properties using a VIS-NIR sensor.Crossref | GoogleScholarGoogle Scholar |
Mouazen AM, Kuang B, De Baerdemaeker J, Ramon H (2010) Comparison among principal component, partial least squares and back propagation neural network analyses for accuracy of measurement of selected soil properties with visible and near infrared spectroscopy. Geoderma 158, 23–31.
| Comparison among principal component, partial least squares and back propagation neural network analyses for accuracy of measurement of selected soil properties with visible and near infrared spectroscopy.Crossref | GoogleScholarGoogle Scholar | 1:CAS:528:DC%2BC3cXnvFWhsrw%3D&md5=4ea4c479c6dc15b2adcaa72b251e7956CAS |
Nanni MR, Demattê JAM (2006) Spectral reflectance methodology in comparison to traditional soil analysis. Soil Science Society of America Journal 70, 393–407.
| Spectral reflectance methodology in comparison to traditional soil analysis.Crossref | GoogleScholarGoogle Scholar | 1:CAS:528:DC%2BD28Xis1Crtbo%3D&md5=024b53c3fa8a02b67c4303e503e3148dCAS |
Patzold S, Mertens FM, Bornemann L, Koleczek B, Franke J, Feilhauer H, Welp G (2008) Soil heterogeneity at the field scale: a challenge for precision crop protection. Precision Agriculture 9, 367–390.
| Soil heterogeneity at the field scale: a challenge for precision crop protection.Crossref | GoogleScholarGoogle Scholar |
Pearlman JS, Barry PS, Segal CC, Shepanski J, Beiso D, Carman SL (2003) Hyperion, a space-based imaging spectrometer. IEEE Transactions on Geoscience and Remote Sensing 41, 1160–1173.
| Hyperion, a space-based imaging spectrometer.Crossref | GoogleScholarGoogle Scholar |
Peng Y, Xiong X, Adhikari K, Knadel M, Grunwald S, Greve MH (2015) Modeling soil organic carbon at regional scale by combining multi-spectral images with laboratory spectra. PLoS One 10, e0142295
| Modeling soil organic carbon at regional scale by combining multi-spectral images with laboratory spectra.Crossref | GoogleScholarGoogle Scholar |
Rumpel C, Chaplot V, Planchon O, Bernadou J, Valentin C, Mariotti A (2006) Preferential erosion of black carbon on steep slopes with slash and burn agriculture. Catena 65, 30–40.
| Preferential erosion of black carbon on steep slopes with slash and burn agriculture.Crossref | GoogleScholarGoogle Scholar |
Salomonson VV, Barnes WL, Maymon PW, Montgomery HE, Ostrow H (1989) MODIS: advanced facility instrument for studies of the Earth as a system. Geoscience and Remote Sensing. IEEE Transactions on Geoscience and Remote Sensing 27, 145–153.
| MODIS: advanced facility instrument for studies of the Earth as a system. Geoscience and Remote Sensing.Crossref | GoogleScholarGoogle Scholar |
Santín C, Doerr SH (2016) Fire effects on soils: the human dimension. Philosophical Transactions of the Royal Society of London. Series B, Biological Sciences 371, 20150171
| Fire effects on soils: the human dimension.Crossref | GoogleScholarGoogle Scholar |
Santín C, Knicker H, Fernández S, Menéndez-Duarte R, Álvarez MA (2008) Wildfires influence on soil organic matter in an Atlantic mountainous region (NW of Spain). Catena 74, 286–295.
| Wildfires influence on soil organic matter in an Atlantic mountainous region (NW of Spain).Crossref | GoogleScholarGoogle Scholar |
Selige T, Boehner J, Schmidhalter U (2006) High resolution topsoil mapping using hyperspectral image and field data in multivariate regression modeling procedures. Geoderma 136, 235–244.
| High resolution topsoil mapping using hyperspectral image and field data in multivariate regression modeling procedures.Crossref | GoogleScholarGoogle Scholar | 1:CAS:528:DC%2BD28XhtlahtbvF&md5=55d02da0ac5c6140a01f28f0f3356b0bCAS |
Shakesby RA, Doerr SH (2006) Wildfire as a hydrological and geomorphological agent. Earth-Science Reviews 74, 269–307.
| Wildfire as a hydrological and geomorphological agent.Crossref | GoogleScholarGoogle Scholar |
Stevens A, van Wesemael B, Vandenschrick G, Toure S, Tychon B (2006) Detection of carbon stock change in agricultural soils using spectroscopic techniques. Soil Science Society of America Journal 70, 844–850.
| Detection of carbon stock change in agricultural soils using spectroscopic techniques.Crossref | GoogleScholarGoogle Scholar | 1:CAS:528:DC%2BD28XksVylu7k%3D&md5=eacb3b1b192636e06bfc93d61bb6c5eeCAS |
Stevens A, van Wesemael B, Bartholomeus H, Rosillon D, Tychon B, Ben-Dor E (2008) Laboratory, field and airborne spectroscopy for monitoring organic carbon content in agricultural soils. Geoderma 144, 395–404.
| Laboratory, field and airborne spectroscopy for monitoring organic carbon content in agricultural soils.Crossref | GoogleScholarGoogle Scholar | 1:CAS:528:DC%2BD1cXitl2jt7w%3D&md5=861bdc95d0427ff26bcb3ff381cfa3f3CAS |
Stevens A, Udelhoven T, Denis A, Tychon B, Lioy R, Hoffmann L, van Wesemael B (2010) Measuring soil organic carbon in croplands at regional scale using airborne imaging spectroscopy. Geoderma 158, 32–45.
| Measuring soil organic carbon in croplands at regional scale using airborne imaging spectroscopy.Crossref | GoogleScholarGoogle Scholar | 1:CAS:528:DC%2BC3cXnvFWhsro%3D&md5=ed49af2f8277afc1595f81bcd34394aaCAS |
Stevens A, Miralles I, van Wesemael B (2012) Soil organic carbon predictions by airborne imaging spectroscopy: comparing cross-validation and validation. Soil Science Society of America Journal 76, 2174–2183.
| Soil organic carbon predictions by airborne imaging spectroscopy: comparing cross-validation and validation.Crossref | GoogleScholarGoogle Scholar | 1:CAS:528:DC%2BC38XhvVelsb%2FO&md5=e4219ea92011a7c1d369fe3f17cd856aCAS |
Uno Y, Prasher SO, Patel RM, Strachan IB, Pattey E, Karimi Y (2005) Development of field-scale soil organic matter content estimation models in eastern Canada using airborne hyperspectral imagery. Canadian Biosystems Engineering / Le Genie des biosystems au Canada 47, 9–14.
Vågen T-G, Winowiecki LA, Tondoh JE, Desta LT, Gumbricht T (2016) Mapping of soil properties and land degradation risk in Africa using MODIS reflectance. Geoderma 263, 216–225.
| Mapping of soil properties and land degradation risk in Africa using MODIS reflectance.Crossref | GoogleScholarGoogle Scholar |
Vasques GM, Grunwald S, Sickman JO (2009) Modeling of soil organic carbon fractions using visible-near-infrared spectroscopy. Soil Science Society of America Journal 73, 176–184.
| Modeling of soil organic carbon fractions using visible-near-infrared spectroscopy.Crossref | GoogleScholarGoogle Scholar | 1:CAS:528:DC%2BD1MXitVarsL0%3D&md5=afebfdafb23e924ff0086de4c05f0087CAS |
Viscarra Rossel RA, Behrens T (2010) Using data mining to model and interpret soil diffuse reflectance spectra. Geoderma 158, 46–54.
| Using data mining to model and interpret soil diffuse reflectance spectra.Crossref | GoogleScholarGoogle Scholar |
Viscarra Rossel RA, Walvoort DJJ, McBratney AB, Janik LJ, Skjemstad JO (2006) Visible, near infrared, mid infrared or combined diffuse reflectance spectroscopy for simultaneous assessment of various soil properties. Geoderma 131, 59–75.
| Visible, near infrared, mid infrared or combined diffuse reflectance spectroscopy for simultaneous assessment of various soil properties.Crossref | GoogleScholarGoogle Scholar | 1:CAS:528:DC%2BD28XhtFyhsLg%3D&md5=b427c8dd1eb652f46615d93c91d4a89bCAS |
Viscarra Rossel RA, Jeon YS, Odeh IOA, McBratney AB (2008) Using a legacy soil sample to develop a mid-IR spectral library. Soil Research 46, 1–16.
| Using a legacy soil sample to develop a mid-IR spectral library.Crossref | GoogleScholarGoogle Scholar |
Walkley A, Black IA (1934) An examination of the Degtjareff method for determining soil organic matter, and a proposed modification of the chromic acid titration method. Soil Science 37, 29–38.
| An examination of the Degtjareff method for determining soil organic matter, and a proposed modification of the chromic acid titration method.Crossref | GoogleScholarGoogle Scholar | 1:CAS:528:DyaA2cXitlGmug%3D%3D&md5=33510250f627ecc9d38149d789a3202eCAS |
Wold S, Sjöström M, Eriksson L (2001) PLS-regression: a basic tool of chemometrics. Chemometrics and Intelligent Laboratory Systems 58, 109–130.
| PLS-regression: a basic tool of chemometrics.Crossref | GoogleScholarGoogle Scholar | 1:CAS:528:DC%2BD3MXotF2mtLw%3D&md5=e772bc9e2d93d2f368508704a8cff0d5CAS |
Wondzell SM, King JG (2003) Postfire erosional processes in the Pacific Northwest and Rocky Mountain regions. Forest Ecology and Management 178, 75–87.
| Postfire erosional processes in the Pacific Northwest and Rocky Mountain regions.Crossref | GoogleScholarGoogle Scholar |
Zhang T, Li L, Zheng B (2013) Estimation of agricultural soil properties with imaging and laboratory spectroscopy. Journal of Applied Remote Sensing 7, 073587
| Estimation of agricultural soil properties with imaging and laboratory spectroscopy.Crossref | GoogleScholarGoogle Scholar |
