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Point and Imaging Spectroscopy in Geospatial Analysis of Soils

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Geospatial Technologies for Crops and Soils

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Abstract

The regular monitoring of soil physical, chemical, and biological properties is very essential, due to its role in soil ecosystem functions. A cost-effective alternative for soil monitoring corresponds to spectral sensing techniques. Soil spectral sensing techniques can support decision-making in agricultural systems at both time and spatial scales, maximizing food production while preserving an adequate soil condition. Due to the large number of ground, airborne, and orbital spectral sensors operating today, this technology has been increasingly assimilated by soil scientists. However, it is important to have an adequate comprehension about the technique principles and limitations. This chapter provides a wide perspective about the soil spectral sensing in the visible (vis: 350–700 nm), near-infrared (NIR: 700–1000 nm), and shortwave infrared (SWIR: 1000–2500 nm), considering reflectance data at different acquisition levels. Here, it is discussed how soil constituents interact with EMR and the resulting soil spectral behaviors. We describe the predictive potential of vis-NIR-SWIR data for quantitative assessment of soil and which soil attributes have been reliably estimated and the most commonly used vis-NIR-SWIR equipment, as well as their advantages and limitations. Finally, we discuss the current application in soil science and future perspectives.

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Abbreviations

ACORN:

Atmospheric CORrection Now

ANN:

Artificial Neural Network

ATCOR:

ATmospheric CORrection

ATREM:

ATmospheric REMoval algorithm

AVIRIS:

Airborne Visible/Infrared Imaging Spectrometer

CIE:

Commission internationale de l’éclairage

CHRIS:

Compact High-Resolution Imaging Spectrometer

DS:

Direct Standardization

EMR:

Electromagnetic Radiation

EnMAP:

Environmental Mapping and Analysis Program

EPO:

External Parameter Organization

FAO:

Food and Agriculture Organization

FLAASH:

Fast Line-of-sight Atmospheric Analysis of Spectral Hypercubes

FOV:

Field of View

FS:

Field Spectroscopy

GPS:

Global Positioning System

HATCH:

High-accuracy ATmosphere Correction for Hyperspectral data

HISUI:

Hyperspectral Imager Suite

HyspIRI:

Hyperspectral InfraRed Imager

IS:

Imaging Spectroscopy

ISDAS:

Imaging Spectrometer Data Analysis System

LS:

Laboratory Spectroscopy

MESMA:

Multiple Endmember Spectral Mixture Analysis

ML:

Machine Learning

MSC:

Multiplicative Scatter Correction

NIR:

Near-infrared

OSC:

Orthogonal Signal Correction

PA:

Precision Agriculture

PLSR:

Partial Least Square Regression

PRISMA:

Hyperspectral Precursor of the Application Mission

PS:

Proximal Sensing

PSp:

Point Spectroscopy

QUAC:

Quick Atmospheric Correction

RF:

Random Forest

RMS:

Root Mean Square

RMSE:

Root Mean Square Error

RPD:

Ratio of Performence to Deviation

RPIQ:

Ratio of Performence to Interquartile

RS:

Remote Sensing

SHALOM:

Spaceborne Hyperspectral Applicative Land and Ocean Mission

SM:

Soil moisture

SNR:

Signal-to-Noise Ratio

SNV:

Standard Normal Variate

SOC:

Soil Organic Carbon

SOM:

Soil Organic Matter

SS:

Soil Spectroscopy

SSLs:

Soil Spectral Libraries

SSS:

Soil Spectral Sensing

SVM:

Support Vector Machine

SWIR:

Shortwave Infrared

VNIR:

Visible and Near-Infrared

References

  • Ackerson JP, Demattê JA, Morgan CL (2015) Predicting clay content on field-moist intact tropical soils using a dried, ground VisNIR library with external parameter orthogonalization. Geoderma 259:196–204

    Article  Google Scholar 

  • ACORN (2002) ACORN 4.0, User’s guide, analytical imaging and geophysics. LLC, Boulder

    Google Scholar 

  • Adams JB, Filice AL (1967) Spectral reflectance 0.4 to 2.0 microns of silicate rock powders. J Geophys Res 72(22):5705–15 Res. https://doi.org/10.1029/jz072i022p0570

    Article  Google Scholar 

  • Adamchuk VI, Hummel JW, Morgan MT, Upadhyaya SK (2004) On-the-go soil sensors for precision agriculture. Comput Electron Agric 44(1):71–91

    Article  Google Scholar 

  • Adler-Golden S, Berk A, Bernstein LS, Richtsmeier S, Acharya PK, Matthew MW, Anderson GP, Allred CL, Jeong LS, Chetwynd JH (1998) FLAASH, a MODTRAN4 atmospheric correction package for hyperspectral data retrievals and simulations. In: Summaries of the seventh JPL Airborne Earth Science Workshop 1998 Dec 12 (vol 1, pp 9–14). JPL Pub

    Google Scholar 

  • Agassi M, Shainberg I, Morin J (1981) Effect of electrolyte concentration and soil sodicity on infiltration rate and crust formation 1. Soil Sci Soc Am J 45(5):848–851. https://doi.org/10.2136/sssaj1981.03615995004500050004x

    Article  Google Scholar 

  • Aitkenhead MJ, Gaskin GJ, Lafouge N, Hawes C (2017) Phylis: a low-cost portable visible range spectrometer for soil and plants. Sensors 17(1):99. https://doi.org/10.3390/s17010099

    Article  Google Scholar 

  • Ammer U, Koch B, Schneider T, Wittmeier H (1991) High resolution spectral measurements of vegetation and soil in field and laboratory. Proceedings of the 5th international Colloquium, physical measurements and signatories in remote sensing, Courchevel, France. I: pp 213–218

    Google Scholar 

  • Anderson GP, Wang J, Hoke ML, Kneizys FX, Chetwynd JH, Rothman LS, Kimball LM, McClatchey RA, Shettle EP, Clough ST, Gallery WO, Abreu LW, Selby JEA (1994) History of one family of atmospheric radiative transfer codes, Proc. SPIE 2309, passive infrared remote sensing of clouds and the atmosphere II. https://doi.org/10.1117/12.196674

    Book  Google Scholar 

  • Anne NJ, Abd-Elrahman AH, Lewis DB, Hewitt NA (2014) Modeling soil parameters using hyperspectral image reflectance in subtropical coastal wetlands. Int J Appl Earth Obs Geoinf 33:47–56. https://doi.org/10.1016/j.jag.2014.04.007

    Article  Google Scholar 

  • Bach H, Mauser W (1994) Modelling and model verification of the spectral reflectance of soils under varying moisture conditions. In: International Geoscience and Remote Sensing Symposium (IGARSS). https://doi.org/10.1109/igarss.1994.399735

  • Bajwa SG, Tian LF (2005) Soil fertility characterization in agricultural fields using hyperspectral remote sensing. Trans ASAE 48(6):2399–2406. https://doi.org/10.13031/2013.20079

    Article  Google Scholar 

  • Bartholomeus H, Epema G, Schaepman M (2007) Determining iron content in Mediterranean soils in partly vegetated areas, using spectral reflectance and imaging spectroscopy. Int J Appl Earth Obs Geoinf 9(2):194–203

    Google Scholar 

  • Bartholomeus H, Kooistra L, Stevens A, van Leeuwen M, van Wesemael B, Ben-Dor E, Tychon B (2011) Soil organic carbon mapping of partially vegetated agricultural fields with imaging spectroscopy. Int J Appl Earth Obs Geoinf 13(1):81–88

    Google Scholar 

  • Baumgardner MF, Silva LF, Biehl LL, Stoner ER (1986) Reflectance properties of soils. In: Advances in agronomy, vol 38. Academic, New York, pp 1–44

    Google Scholar 

  • Bellon-Maurel V, Fernandez-Ahumada E, Palagos B, Roger JM, McBratney A (2010) Critical review of chemometric indicators commonly used for assessing the quality of the prediction of soil attributes by NIR spectroscopy. TrAC Trends Anal Chem 29(9):1073–1081

    Article  Google Scholar 

  • 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 Biol Biochem https://doi.org/10.1016/j.soilbio.2011.02.019

  • Ben-Dor E (2002) Quantitative remote sensing of soil properties. Adv Agron 75:173–244

    Article  Google Scholar 

  • Ben-Dor E, Banin A (1995) Near-infrared analysis as a rapid method to simultaneously evaluate several soil properties. Soil Sci Soc Am J 59(2):364–372

    Article  Google Scholar 

  • Ben-Dor E, Irons JR, Epema GF (1999) Soil reflectance. In: Rencz AN (ed) Remote sensing for the earth sciences: manual of remote sensing, vol 3. Wiley, New York, pp 111–188

    Google Scholar 

  • Ben-Dor E, Patkin K, Banin A, Karnieli A (2002) Mapping of several soil properties using DAIS-7915 hyperspectral scanner data-a case study over clayey soils in Israel. Int J Remote Sens 23(6):1043–1062

    Article  Google Scholar 

  • Ben-Dor E, Levin N, Singer A, Karnieli A, Braun O, Kidron GJ (2006) Quantitative mapping of the soil rubification process on sand dunes using an airborne hyperspectral sensor. Geoderma 131(1–2):1–21

    Article  Google Scholar 

  • Ben-Dor E, Heller D, Chudnovsky A (2008) A novel method of classifying soil profiles in the field using optical means. Soil Sci Soc Am J 72(4):1113–1123

    Article  Google Scholar 

  • Ben-Dor E, Chabrillat S, Demattê JA, Taylor GR, Hill J, Whiting ML, Sommer S (2009) Using imaging spectroscopy to study soil properties. Remote Sens Environ 113:38–55

    Article  Google Scholar 

  • Bernstein LS, Adler-Golden SM, Sundberg RL, Levine RY, Perkins TC, Berk A, Ratkowski AJ, Felde G, Hoke ML (2005) A new method for atmospheric correction and aerosol optical property retrieval for VIS-SWIR multi-and hyperspectral imaging sensors: QUAC (QUick atmospheric correction). Spectral Sciences Inc Burlington MA; 2005 Jan.

    Google Scholar 

  • Ben-Dor E, Sabine C, Demattê J (2018) Characterization of soil properties using reflectance spectroscopy. In: Thenkabail P, Lyon S, John G, Huete A (eds) Fundamentals, sensor systems, spectral libraries, and data mining for vegetation. https://doi.org/10.1201/9781315164151-8

    Chapter  Google Scholar 

  • Bouma J, Montanarella L (2016) Facing policy challenges with inter- and transdisciplinary soil research focused on the UN sustainable development goals. Soil 2(2):135–145

    Article  Google Scholar 

  • Bowers SA, Hanks RJ (1971) Reflection of radiant energy from soils. (Doctoral dissertation, Kansas State University)

    Google Scholar 

  • Bracken A, Coburn C, Staenz K, Rochdi N, Segl K, Chabrillat S, Schmid T (2019) Detecting soil erosion in semi-arid Mediterranean environments using simulated EnMAP data. Geoderma 340:164–174

    Article  Google Scholar 

  • Bricklemyer RS, Brown DJ (2010) On-the-go VisNIR: potential and limitations for mapping soil clay and organic carbon. Comput Electron Agric 70(1):209–216

    Article  Google Scholar 

  • Briottet X, Marion R, Carrere V, Jacquemoud S, Chevrel S, Prastault P, D’oria M, Gilouppe P, Hosford S, Lubac B, Bourguignon A (2011) HYPXIM: a new hyperspectral sensor combining science/defence applications. In: 3rd workshop on hyperspectral image and signal processing: evolution in remote sensing (WHISPERS). IEEE, Lisbon, pp 1–4. https://doi.org/10.1109/WHISPERS.2011.6080957

    Chapter  Google Scholar 

  • Brodský L, Klement A, Penížek V, Kodešová R, Borůvka L (2011a) Building soil spectral library of the Czech soils for quantitative digital soil mapping. Soil Water Res 26(4):165–172

    Article  Google Scholar 

  • Brodský L, Klement A, Penížek V, Kodešová R, Boruvka L (2011b) Building soil spectral library of the czech soils for quantitative digital soil mapping. Soil Water Res 6(4):165–172

    Article  Google Scholar 

  • Brown DJ (2007) Using a global VNIR soil-spectral library for local soil characterization and landscape modeling in a 2nd-order Uganda watershed. Geoderma 140(4):444–453

    Article  Google Scholar 

  • Brown DJ, Shepherd KD, Walsh MG, Dewayne Mays M, Reinsch TG (2006) Global soil characterization with VNIR diffuse reflectance spectroscopy. Geoderma 132(3–4):273–290

    Article  Google Scholar 

  • Casa R, Castaldi F, Pascucci S, Basso B, Pignatti S (2013) Geophysical and hyperspectral data fusion techniques for in-field estimation of soil properties. Vadose Zone J 12(4):vzj2012.0201

    Article  Google Scholar 

  • Cambou A, Cardinael R, Kouakoua E, Villeneuve M, Durand C, Barthès BG (2016) Prediction of soil organic carbon stock using visible and near infrared reflectance spectroscopy (VNIRS) in the field. Geoderma 261:151–159

    Article  Google Scholar 

  • Cambule AH, Rossiter DG, Stoorvogel JJ, Smaling EMA (2012) Building a near infrared spectral library for soil organic carbon estimation in the Limpopo National Park, Mozambique. Geoderma 183–184:41–48

    Article  Google Scholar 

  • Castaldi F, Casa R, Castrignanò A, Pascucci S, Palombo A, Pignatti S (2014) Estimation of soil properties at the field scale from satellite data: a comparison between spatial and non-spatial techniques. Eur J Soil Sci 65(6):842–851

    Article  Google Scholar 

  • Castaldi F, Palombo A, Santini F, Pascucci S, Pignatti S, Casa R (2016) Evaluation of the potential of the current and forthcoming multispectral and hyperspectral imagers to estimate soil texture and organic carbon. Remote Sens Environ 179:54–65

    Article  Google Scholar 

  • Castaldi F, Chabrillat S, Van Wesemael B (2019) Sampling strategies for soil property mapping using multispectral sentinel-2 and hyperspectral EnMAP satellite data. Remote Sens 11(3):309

    Article  Google Scholar 

  • Cécillon L, Barthès BG, Gomez C, Ertlen D, Génot V, Hedde M, Stevens A, Brun JJ (2009) Assessment and monitoring of soil quality using near-infrared reflectance spectroscopy (NIRS). Eur J Soil Sci 60(5):770–784

    Article  Google Scholar 

  • Chabrillat S, Ben-Dor E, Rossel RA, Demattê JA (2013) Quantitative soil spectroscopy. Appl Environ Soil Sci 2013:616578

    Article  Google Scholar 

  • Chabrillat S, Ben-Dor E, Cierniewski J, Gomez C, Schmid T, van Wesemael B (2019) Imaging spectroscopy for soil mapping and monitoring. Surv Geophys 40(3):361–399

    Article  Google Scholar 

  • Chakraborty S, Li B, Weindorf DC, Morgan CL (2019) External parameter orthogonalisation of eastern European VisNIR-DRS soil spectra. Geoderma 337:65–75

    Article  Google Scholar 

  • Chang C, Laird D, Mausbach MJ (2001) Near-infrared reflectance spectroscopy – principal components regression analyses of soil properties. Soil Sci Soc Am J 65:480–490

    Article  Google Scholar 

  • Choe E, van der Meer F, van Ruitenbeek F, van der Werff H, de Smeth B, Kim K-W (2008) Mapping of heavy metal pollution in stream sediments using combined geochemistry, field spectroscopy, and hyperspectral remote sensing: a case study of the Rodalquilar mining area, SE Spain. Remote Sens Environ 112(7):3222–3233

    Article  Google Scholar 

  • Christy CD (2008) Real-time measurement of soil attributes using on-the-go near infrared reflectance spectroscopy. Comput Electron Agric 61(1):10–19

    Article  Google Scholar 

  • Cierniewski J, Gulinski M (2010) Furrow microrelief influence on the directional hyperspectral reflectance of soil at various illumination and observation conditions. IEEE Trans Geosci Remote Sens 48(11):4143–4148

    Google Scholar 

  • Clark RN (1999) Spectroscopy of rocks and minerals, and principles of spectroscopy. Manual Remote Sens 3:3–58

    Google Scholar 

  • Clark RN, Roush TL (1984) Reflectance spectroscopy: quantitative analysis techniques for remote sensing applications. J Geophys Res Solid Earth 89(B7):6329–6340

    Article  Google Scholar 

  • Clark RN, Swayze GA, Livo KE, Kokaly RF, King TVV, Dalton JB, Vance JS, Rockwell BW, Hoefen T, McDougal RR (2002) Surface reflectance calibration of terrestrial imaging spectroscopy data: a tutorial using AVIRIS. In: Proceedings of the 10th airborne earth science workshop. JPL publication, Pasadena, CA. http://speclab.cr.usgs.gov/PAPERS.calibration.tu

    Google Scholar 

  • Conel JE, Green RO, Vane G, Bruegge CJ, Alley RE (1987) AIS-2 radiometry and comparison of methods for the recovery of ground reflectance. In: Vane G (ed) Proceedings of the 3rd airborne imaging spectrometer data analysis workshop vol 87(30), JPL Publication, Pasadena, CA, pp 18–47

    Google Scholar 

  • Corbane C, Raclot D, Jacob F, Albergel J, Andrieux P (2008) Remote sensing of soil surface characteristics from a multiscale classification approach. Catena 75(3):308–318

    Article  Google 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

    Article  Google Scholar 

  • Dalal RC, Henry RJ (1986) Simultaneous determination of moisture, organic carbon, and total nitrogen by near infrared reflectance spectrophotometry. Soile Sci Soc Am J 50(1):120–123

    Google Scholar 

  • De Alba S (2003) Simulating long-term soil redistribution generated by different patterns of mouldboard ploughing in landscapes of complex topography. Soil Till Res 71(1):71–86

    Article  Google Scholar 

  • Demattê JA, da Silva Terra F (2014) Spectral pedology: a new perspective on evaluation of soils along pedogenetic alterations. Geoderma 217:190–200

    Article  Google Scholar 

  • Demattê JA, Sousa AA, Alves MC, Nanni MR, Fiorio PR, Campos RC (2006) Determining soil water status and other soil characteristics by spectral proximal sensing. Geoderma 135:179–195

    Article  Google Scholar 

  • Demattê JA, Morgan CL, Chabrillat S, Rizzo R, Franceschini MH, Vasques GM, Wetterlind J, Thenkabail PS (2015) Spectral sensing from ground to space in soil science: state of the art, applications, potential and perspectives. In: Land resources monitoring, modeling, and mapping with remote sensing. CRC Press, pp 661–732

    Google Scholar 

  • Demattê JA, Fongaro CT, Rizzo R, Safanelli JL (2018) Geospatial soil sensing system (GEOS3): a powerful data mining procedure to retrieve soil spectral reflectance from satellite images. Remote Sens Environ 212:161–175

    Article  Google Scholar 

  • Demattê JA, Dotto AC, Paiva AF, Sato MV, Dalmolin RS, Maria do Socorro B, da Silva EB, Nanni MR, ten Caten A, Noronha NC, Lacerda MP (2019) The Brazilian soil spectral library (BSSL): a general view, application and challenges. Geoderma 354:113793

    Article  Google Scholar 

  • DeTar WR, Chesson JH, Penner JV, Ojala JC (2008) Detection of soil properties with airborne hyperspectral measurements of bare fields. Trans ASABE 51(2):463–470

    Article  Google Scholar 

  • Dor EB, Ong C, Lau IC (2015) Reflectance measurements of soils in the laboratory: standards and protocols. Geoderma 245:112–124

    Google Scholar 

  • Doran JW (2002) Soil health and global sustainability: translating science into practice. Agric Ecosyst Environ 88(2):119–127

    Article  Google Scholar 

  • Doran JW, Zeiss MR (2000) Soil health and sustainability: managing the biotic component of soil quality. Appl Soil Ecol 15(1):3–11. https://doi.org/10.1016/S0929-1393(00)00067-6

    Article  Google Scholar 

  • Escribano P, Schmid T, Chabrilla, S, Rodríguez-Caballero E, García M (2017) Optical remote sensing for soil mapping and monitoring. Soil mapping and process modeling for sustainable land use management, 87–125. https://doi.org/10.1016/b978-0-12-805200-6.00004-9

  • Eswaran H, Lal R, Reich PF (2001) Land degradation: an overview. In: Bridges EM, Hannam ID, Oldeman LR, de Vries FWTP, Scherr SJ, Sompatpanit S (eds) Responses to land degradation. Proceedings 2nd. International conference on land degradation and desertification, Khon Kaen, Thailand. New Delhi, Oxford Press

    Google Scholar 

  • FAO (2019) Global soil partnership. http://www.fao.org/global-soil-partnership/en/

    Google Scholar 

  • Finn MP, Lewis M, Bosch DD, Giraldo M, Yamamoto K, Sullivan DG, Kincaid R, Luna R, Allam GK, Kvien C, Williams MS (2011) Remote sensing of soil moisture using airborne hyperspectral data. GISci Remote Sen 48(4):522–540

    Article  Google Scholar 

  • Franceschini MHD, Demattê JAM, da Silva Terra F, Vicente LE, Bartholomeus H, de Souza Filho CR (2015) Prediction of soil properties using imaging spectroscopy: considering fractional vegetation cover to improve accuracy. Int J Appl Earth Obs Geoinf 38:358–370

    Google Scholar 

  • Franceschini MH, Demattê JA, Kooistra L, Bartholomeus H, Rizzo R, Fongaro CT, Molin JP (2018) Effects of external factors on soil reflectance measured on-the-go and assessment of potential spectral correction through orthogonalisation and standardisation procedures. Soil Till Res 177:19–36

    Article  Google Scholar 

  • Francis RE, Reeves RG (1977) Manual of remote sensing. J Range Manag

    Google Scholar 

  • Franzen DW, Peck TR (1995) Field soil sampling density for variable rate fertilization. J Prod Agric 8(4):568–574

    Article  Google Scholar 

  • Fussel J, Rundquist D, Harrington JA (1986) On defining remote sensing. Photogramm Eng Remote Sens 52(9):1507–1511

    Google Scholar 

  • Gao BC, Heidebrecht KB, Goetz AF (1993) Derivation of scaled surface reflectances from AVIRIS data. Remote Sens Environ 44(2–3):165–178

    Article  Google Scholar 

  • Ge Y, Thomasson JA, Sui R (2011) Remote sensing of soil properties in precision agriculture: a review. Front Earth Sci 5(3):229–238

    Google Scholar 

  • Genot V, Colinet G, Bock L, Vanvyve D, Reusen Y, Dardenne P (2011) Near infrared reflectance spectroscopy for estimating soil characteristics valuable in the diagnosis of soil fertility. J Near Infrared Spectrosc 19(2):117–138

    Article  Google Scholar 

  • Gerighausen H, Menz G, Kaufmann H (2012) Spatially explicit estimation of clay and organic carbon content in agricultural soils using multi-annual imaging spectroscopy data. Appl Environ Soil Sci 2012:1–23

    Article  Google Scholar 

  • Gholizadeh A, Borůvka L, Saberioon M, Vašát R (2013) Visible, near-infrared, and mid-infrared spectroscopy applications for soil assessment with emphasis on soil organic matter content and quality: state-of-the-art and key issues. Appl Spectrosc 67(12):1349–1362

    Article  Google Scholar 

  • Gholizadeh A, Saberioon M, Ben-Dor E, Borůvka L (2018) Monitoring of selected soil contaminants using proximal and remote sensing techniques: background, state-of-the-art and future perspectives. Crit Rev Environ Sci Technol 48(3):243–278

    Article  Google Scholar 

  • Glanz JT (1995) Saving our soil: solutions for sustaining earth’s vital resource. Johnson Books, Boulder, CO

    Google Scholar 

  • Goetz AFH (2009) Three decades of hyperspectral remote sensing of the Earth: a personal view. Remote Sens Environ 113:S5–S16

    Article  Google Scholar 

  • Goetz AFH, Vane G, Solomon JE, Rock BN (1985) Imaging spectrometry for earth remote sensing. Science 228(4704):1147–1153

    Article  Google Scholar 

  • Gogé F, Joffre R, Jolivet C, Ross I, Ranjard L (2012) Optimization criteria in sample selection step of local regression for quantitative analysis of large soil NIRS database. Chemom Intell Lab Syst 110(1):168–176

    Article  Google Scholar 

  • Gogé F, Gomez C, Jolivet C, Joffre R (2014) Which strategy is best to predict soil properties of a local site from a national Vis-NIR database? Geoderma 213:1–9

    Article  Google Scholar 

  • Goidts E, Van Wesemael B, Crucifix M (2009) Magnitude and sources of uncertainties in soil organic carbon (SOC) stock assessments at various scales. Eur J Soil Sci 60(5):723–739

    Article  Google Scholar 

  • Goldshleger N, Ben-Dor E, Chudnovsky A, Agassi M (2009) Soil reflectance as a generic tool for assessing infiltration rate induced by structural crust for heterogeneous soils. Eur J Soil Sci 60(6):1038–1051

    Article  Google Scholar 

  • Gomez C, Raphael A, Rossel V, McBratney AB (2008) Soil organic carbon prediction by hyperspectral remote sensing and field vis-NIR spectroscopy: an Australian case study. Geoderma 146(3-4):403–411

    Article  Google Scholar 

  • 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

    Article  Google Scholar 

  • Guanter L, Kaufmann H, Segl K, Foerster S, Rogass C, Chabrillat S, Kuester T, Hollstein A, Rossner G, Chlebek C, Straif C (2015) The EnMAP spaceborne imaging spectroscopy mission for earth observation. Remote Sens 7(7):8830–8857

    Article  Google Scholar 

  • Guerrero C, Zornoza R, Gómez I, Mataix-Beneyto J (2010) Spiking of NIR regional models using samples from target sites: effect of model size on prediction accuracy. Geoderma 158(1–2):66–77

    Article  Google Scholar 

  • Guerrero C, Wetterlind J, Stenberg B, Mouazen AM, Gabarrón-Galeote MA, Ruiz-Sinoga JD, Zornoza R, Rossel RA (2016) Do we really need large spectral libraries for local scale SOC assessment with NIR spectroscopy? Soil Tillage Res 155:501–509

    Article  Google Scholar 

  • Guo L, Zhang H, Shi T, Chen Y, Jiang Q, Linderman M (2019) Prediction of soil organic carbon stock by laboratory spectral data and airborne hyperspectral images. Geoderma 337:32–41

    Article  Google Scholar 

  • Haubrock S-N (2008) Surface soil moisture quantification and validation based on hyperspectral data and field measurements. J Appl Remote Sens 2(1):023552

    Article  Google Scholar 

  • Haubrock S, Chabrillat S, Kaufmann H (2004) Application of hyperspectral imaging and laser scanning for the monitoring and assessment of soil erosion in a recultivation mining area. In: Erasmi Cyffka B, Kappas M (eds) Remote Sens GIS Environ Stud Appl Geogr Goltze

    Google Scholar 

  • Haubrock SN, Chabrillat S, Lemmnitz C, Kaufmann H (2008) Surface soil moisture quantification models from reflectance data under field conditions. Int J Remote Sen 29(1):3–29

    Article  Google Scholar 

  • Hill J, Schütt B (2000) Mapping complex patterns of erosion and stability in dry Mediterranean ecosystems. Remote Sens Environ 74(3):557–569

    Article  Google Scholar 

  • Hbirkou C, Pätzold 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

    Article  Google Scholar 

  • Hollas JM (2005) Modern spectroscopy, 4th edn. Wiley, Chichester

    Google Scholar 

  • Hunt GR (1977) Spectral signatures of particulate minerals in the visible and near infrared. Geophysics 42(3):501–513

    Article  Google Scholar 

  • Hunt GR, Salisbury JW (1971a) Visible and near-infrared spectra of mineral and rocks: I. silicate minerals. Moderns Geology 1:283–300

    Google Scholar 

  • Hunt GR, Salisbury JW (1971b) Visible and near-infrared spectra of mineral and rocks: II. carbonates. Moderns Geology 2:23–30

    Google Scholar 

  • Igne B, Reeves JB, McCarty G, Hively WD, Lund E, Hurburgh CR (2010) Evaluation of spectral pretreatments, partial least squares, least squares support vector machines and locally weighted regression for quantitative spectroscopic analysis of soils. J Near Infrared Spectrosc 18(3):167–176

    Article  Google Scholar 

  • IUSS Working Group WRB (2015) World reference base for soil resources 2014, update 2015. International soil classification system for naming soils and creating legends for soil maps. World soil resources reports no. 106. FAO, Rome

    Google Scholar 

  • Iznaga AC, Orozco MR, Alcantara EA, Pairol MC, Sicilia YE, De Baerdemaeke J, Saeys W (2014) Vis/NIR spectroscopic measurement of selected soil fertility parameters of Cuban agricultural Cambisols. Biosyst Eng 125:105–121

    Article  Google Scholar 

  • Janik L, Skjemstad J (1995) Characterization and analysis of soils using mid-infrared partial least-squares.2. Correlations with some laboratory data. Aust J Soil Res 33:637

    Article  Google Scholar 

  • Janik LJ, Merry RH, Skjemstad JO (1998) Can mid infrared diffuse reflectance analysis replace soil extractions? Aust J Exp Agric 38(7):681–696

    Article  Google Scholar 

  • Jarmer T, Hill J, Mader S (2007) The use of hyperspectral remote sensing data for the assessment of chemical properties of dryland soils in SE-Spain. In: Reusen I, Cools J (eds) Proceedings of the 5th EARSeL workshop imaging spectroscopy: innovation in environmental research, 23–25 April 2007. Bruges, Belgium. On CD-ROM

    Google Scholar 

  • Jensen JR (2005) Introductory digital image processing: a remote sensing perspective (4th edn), Keith CC (ed), Prentice Hall Ser Geogr Inf, Sci Saddle River

    Google Scholar 

  • Jensen JR, Jensen RR (2013) Introductory geographic information systems. Pearson, Boston, 400 p

    Google Scholar 

  • Ji W, Viscarra Rossel RA, Shi Z (2015) Accounting for the effects of water and the environment on proximally sensed Vis–NIR soil spectra and their calibrations. Eur J Soil Sci 66(3):555–565

    Article  Google Scholar 

  • Johannsen CJ, Daughtry CST (2009) Chapter 17: Surface reference data collection. In: Warner TA, Nellis MD, Foody GM (eds) The handbook of remote sensing. Sage Publications, Los Angeles, pp 244–256

    Chapter  Google Scholar 

  • Joshi N, Baumann M, Ehammer A, Fensholt R, Grogan K, Hostert P, Jepsen MR, Kuemmerle T, Meyfroidt P, Mitchard ET, Reiche J (2016) A review of the application of optical and radar remote sensing data fusion to land use mapping and monitoring. Remote Sens 8(1):70

    Article  Google Scholar 

  • Karnieli A, Tsoar H (1995) Spectral reflectance of biogenic crust developed on desert dune sand along the Israel-Egypt border. Remote Sens 16(2):369–374

    Article  Google Scholar 

  • Karlen DL, Mausbach MJ, Doran JW, Cline RG, Harris RF, Schuman GE (1997) Soil quality: a concept, definition, and framework for evaluation (A guest editorial). Soil Sci Soc Am J 61(1):4–10

    Article  Google Scholar 

  • Knadel M, Deng F, Thomsen A, Greve MH (2012) Development of a Danish national Vis-NIR soil spectral library for soil organic carbon determination. Digit Soil Assess Beyond

    Google Scholar 

  • Kodaira M, Shibusawa S (2013) Using a mobile real-time soil visible-near infrared sensor for high resolution soil property mapping. Geoderma 199:64–79

    Article  Google Scholar 

  • Kriebel KT (1978) Average variability of the radiation reflected by vegetated surfaces due to differing irradiations. Remote Sens Environ 7(1):81–83

    Article  Google Scholar 

  • Krishnan P, Alexander JD, Butler BJ, Hummel JW (1980) Reflectance technique for predicting soil organic matter 1. Soil Sci Soc Am J 44(6):1282–1285

    Article  Google Scholar 

  • Kruse FA, Raines GI, Watson K (1985) Analytical techniques for extracting geologic information from multichannel airborne spectroradiometer and airborne imaging spectrometer data. In: Proceedings of the 4th thematic conference on remote sensing for exploration geology. 1–4 April, 1985, 309–324, California

    Google Scholar 

  • Kweon G, Lund E, Maxton C (2013) Soil organic matter and cation-exchange capacity sensing with on-the-go electrical conductivity and optical sensors. Geoderma 199:80–89

    Article  Google Scholar 

  • La WJ, Sudduth KA, Kim HJ, Chung SO (2016) Fusion of spectral and electrochemical sensor data for estimating soil macronutrients. Trans ASABE 59(4):787–794

    Article  Google Scholar 

  • Lagacherie P, Baret F, Feret J-B, Netto JM, Robbez-Masson JM (2008) Estimation of soil clay and calcium carbonate using laboratory, field and airborne hyperspectral measurements. Remote Sens Environ 112(3):825–835

    Article  Google Scholar 

  • Lal R (2014) Societal value of soil carbon. J Soil Water Conserv 69(6):186A–192A

    Article  Google Scholar 

  • Lee CM, Cable ML, Hook SJ, Green RO, Ustin SL, Mandl DJ, Middleton EM (2015) An introduction to the NASA hyperspectral InfraRed imager (HyspIRI) mission and preparatory activities. Remote Sens Environ 167:6–19

    Article  Google Scholar 

  • Lekner J, Dorf MC (1988) Why some things are darker when wet. Appl Opt 27(7):1278–1280

    Article  Google Scholar 

  • Li S, Ji W, Chen S, Peng J, Zhou Y, Shi Z (2015) Potential of VIS-NIR-SWIR spectroscopy from the Chinese soil spectral library for assessment of nitrogen fertilization rates in the paddy-rice region, China. Remote Sens 7(6):7029–7043

    Article  Google Scholar 

  • Lobell DB, Asner GP (2002) Moisture effects on soil reflectance. Soil Sci Soc Am J 66(3):722–727

    Article  Google Scholar 

  • Lobsey CR, Viscarra Rossel RA (2016) Sensing of soil bulk density for more accurate carbon accounting. Eur J Soil Sci 67(4):504–513

    Article  Google Scholar 

  • Lopez RD, Frohn RC (2017) Remote sensing for landscape ecology: New metric indicators. CRC Press, Boca Raton. 2017 Aug 9

    Book  Google Scholar 

  • Lu P, Wang L, Zheng N, Li L, Zhang W (2013) Prediction of soil properties using laboratory VIS–NIR spectroscopy and hyperion imagery. J Geochem Explor 132:26–33

    Article  Google Scholar 

  • Makisara KM, Meinander M, Rantasuo M, Okkonen J, Aikio M, Sipola K, Pylkko P, Braam B (1995) Airborne imaging spectrometer for applications (ASIA). In: Proceedings international geosciences and remote sensing symposium, Digest, pp 479–481

    Google Scholar 

  • Maleki MR, Mouazen AM, Ramon H, De Baerdemaeker J (2007) Optimisation of soil VIS–NIR sensor-based variable rate application system of soil phosphorus. Soil Till Res 94(1):239–250

    Article  Google Scholar 

  • Mendes WDS, Medeiros Neto LG, Demattê JAM, Gallo BC, Rizzo R, Safanelli JL, Fongaro CT (2019) Is it possible to map subsurface soil attributes by satellite spectral transfer models? Geoderma 343:269–279

    Article  Google Scholar 

  • Matthias AD, Fimbres A, Sano EE, Post DF, Accioly L, Batchily AK, Ferreira LG (2000) Surface roughness effects on soil albedo. Soil Sci Soc Am J 64(3):1035–1041

    Article  Google Scholar 

  • McCarty GW, Reeves JB, Reeves VB, Follett RF, Kimble JM (2002) Mid-infrared and near-infrared diffuse reflectance spectroscopy for soil carbon measurement. Soil Sci Soc Am J 66(2):640–646

    Article  Google Scholar 

  • McDowell ML, Bruland GL, Deenik JL, Grunwald S (2012) Effects of subsetting by carbon content, soil order, and spectral classification on prediction of soil total carbon with diffuse reflectance spectroscopy. Appl Environ Soil Sci 2012

    Google Scholar 

  • Miltz J, Don A (2012) Optimizing sample preparation and near infrared spectra measurements of soil samples to calibrate organic carbon and total nitrogen content. J Near Infrared Spectrosc 20(6):695–706

    Article  Google Scholar 

  • Minasny B, McBratney AB, Bellon-Maurel V, Roger JM, Gobrecht A, Ferrand L, Joalland S (2011) Removing the effect of soil moisture from NIR diffuse reflectance spectra for the prediction of soil organic carbon. Geoderma 167:118–124

    Article  Google Scholar 

  • Minu S, Shetty A, Gopal B (2016) Review of preprocessing techniques used in soil property prediction from hyperspectral data. Cogent Geosci 2(1):1–7

    Article  Google Scholar 

  • Morgan CLS, Waiser TH, Brown DJ, Tom Hallmark C (2009) Simulated in situ characterization of soil organic and inorganic carbon with visible near-infrared diffuse reflectance spectroscopy. Geoderma 151(3-4):249–256

    Article  Google Scholar 

  • 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 Res 93(1):13–27

    Article  Google Scholar 

  • Mulder VL, De Bruin S, Schaepman ME, Mayr TR (2011) The use of remote sensing in soil and terrain mapping—a review. Geoderma 162(1–2):1–9

    Article  Google Scholar 

  • Murphy RJ, Wadge G (1994) The effects of vegetation on the ability to map soils using imaging spectrometer data. Remote Sens 15(1):63–86

    Article  Google Scholar 

  • Natale VG et al (2013) SHALOM—Space-borne hyperspectral applicative land and ocean mission, 2013 5th Workshop on hyperspectral image and signal processing: evolution in remote sensing (WHISPERS). Gainesville, FL, pp 1–4. https://doi.org/10.1109/WHISPERS.2013.8080667

    Book  Google Scholar 

  • Nocita M, Stevens A, van Wesemael B, Aitkenhead M, Bachmann M, Barthès B, Dor EB, Brown DJ, Clairotte M, Csorba A, Dardenne P (2015) Soil spectroscopy: an alternative to wet chemistry for soil monitoring. In: Advances in agronomy, vol 132. Academic Press, pp 139–159

    Google Scholar 

  • Notarnicola C, Angiulli M, Posa F (2006) Use of radar and optical remotely sensed data for soil moisture retrieval over vegetated areas. IEEE Trans Geosci Remote Sens 44(4):925–935

    Article  Google Scholar 

  • O'neill AL (1994) Reflectance spectra of microphytic soil crusts in semi-arid Australia. Remote Sens 15(3):675–681

    Article  Google Scholar 

  • Padarian J, Minasny B, McBratney AB (2019a) Machine learning and soil sciences: a review aided by machine learning tools. SOIL discussions 2019 Sept 3:1–29

    Google Scholar 

  • Padarian J, Minasny B, McBratney AB (2019b) Using deep learning to predict soil properties from regional spectral data. Geoderma Reg 16:e00198

    Article  Google Scholar 

  • Palmer KF, Williams D (1974) Optical properties of water in the near infrared. J Opt Soc Am 64(8):1107–1110

    Article  Google Scholar 

  • Peón J, Recondo C, Fernández S, Calleja JF, De Miguel E, Carretero L (2017) Prediction of topsoil organic carbon using airborne and satellite hyperspectral imagery. Remote Sens 9(12):1211

    Article  Google Scholar 

  • Petropoulos GP, Ireland G, Barrett B (2015) Surface soil moisture retrievals from remote sensing: current status, products & future trends. Phys Chem Earth A/B/C 83:36–56

    Article  Google Scholar 

  • Pinker RT, Karnieli A (2007) Characteristic spectral reflectance of a semi-arid environment. Int J Remote Sens 16(7):1341–1363

    Article  Google Scholar 

  • Pignatti S, Palombo A, Pascucci S, Romano F, Santini F, Simoniello T, Umberto A, Vincenzo C, Acito N, Diani M, Matteoli S (2013) The PRISMA hyperspectral mission: Science activities and opportunities for agriculture and land monitoring. In: 2013 IEEE International Geoscience and Remote Sensing Symposium-IGARSS 2013 Jul 21 (pp 4558–4561). IEEE

    Google Scholar 

  • Potter KN, Horton R, Cruse RM (1987) Soil surface roughness effects on radiation reflectance and soil heat flux. Soil Sci Soc Am J 51(4):855–860

    Article  Google Scholar 

  • Price M (1986) The analysis of vegetation change by remote sensing. Prog phys geogr earth environt 10(4):473–491

    Article  Google Scholar 

  • Priori S, Fantappiè M, Bianconi N, Ferrigno G, Pellegrini S, Costantini EA (2016) Field-scale mapping of soil carbon stock with limited sampling by coupling gamma-ray and Vis-NIR spectroscopy. Soil Sci Soc Am J 80(4):954–964

    Article  Google Scholar 

  • Qi H, Paz-Kagan T, Karnieli A, Jin X, Li S (2018) Evaluating calibration methods for predicting soil available nutrients using hyperspectral VNIR data. Soil Tillage Res 175:267–275

    Article  Google Scholar 

  • Qu Z, Goetz AFH, Heidbrecht KB (2001) High accuracy atmosphere correction for hyperspectral data (HATCH). In: Proceedings of the ninth JPL airborne earth science workshop, 00-18. JPL Publication, Pasadena, CA, pp 373–381

    Google Scholar 

  • Ramirez-Lopez L, Behrens T, Schmidt K, Rossel RV, Demattê JA, Scholten T (2013a) Distance and similarity-search metrics for use with soil Vis–NIR spectra. Geoderma 199:43–53

    Article  Google Scholar 

  • Ramirez-Lopez L, Behrens T, Schmidt K, Stevens A, Demattê JAM, Scholten T (2013b) The spectrum-based learner: a new local approach for modeling soil Vis-NIR spectra of complex datasets. Geoderma 195–196:268–279

    Article  Google Scholar 

  • Ramirez-Lopez L, Wadoux AC, Franceschini MH, Terra FS, Marques KP, Sayão VM, Demattê JA (2019) Robust soil mapping at the farm scale with Vis–NIR spectroscopy. Eur J Soil Sci 70(2):378–393

    Article  Google Scholar 

  • Rast M, Painter TH (2019) Earth observation imaging spectroscopy for terrestrial systems: an overview of its history, techniques, and applications of its missions. Surv Geophys 40(3):303–331

    Article  Google Scholar 

  • Reda R, Saffaj T, Ilham B, Saidi O, Issam K, Brahim L (2019) A comparative study between a new method and other machine learning algorithms for soil organic carbon and total nitrogen prediction using near infrared spectroscopy. Chemom Intell Lab Syst 195:103873

    Article  Google Scholar 

  • Richter R, Schläpfer D (2002) Geo-atmospheric processing of airborne imaging spectrometry data. Part 2: atmospheric/topographic correction. Int J Remote Sens 23(13):2631–2649

    Article  Google Scholar 

  • Ricker N (1953) The form and laws of propagation of seismic wavelets. Geophysics 18(1):10–40

    Article  Google Scholar 

  • Rizzo R, Demattê JA, Lepsch IF, Gallo BC, Fongaro CT (2016) Digital soil mapping at local scale using a multi-depth Vis–NIR spectral library and terrain attributes. Geoderma 274:18–27

    Article  Google Scholar 

  • Roberts DA, Y Yamaguchi, R Lyon (1986) Comparison of various techniques for calibration of AIS data Proceedings of the 2nd airborne imaging spectrometer data analysis workshop 86:35, JPL Publication, Pasadena, CA, pp 21–30

    Google Scholar 

  • Roger JM, Chauchard F, Bellon-Maurel V (2003) EPO–PLS external parameter orthogonalisation of PLS application to temperature-independent measurement of sugar content of intact fruits. Chemom Intell Lab Syst 66(2):191–204

    Article  Google Scholar 

  • Rogers RW, Lange RT (1972) Soil surface lichens in arid and subarid South-Eastern Australia. I. Introduction and floristics. Aust J Botany 20(2):197–213

    Article  Google Scholar 

  • Rossel VA (2011) Fine-resolution multiscale mapping of clay minerals in Australian soils measured with near infrared spectra. J Geophys Res Earth 116:F4

    Google Scholar 

  • Rossel RV, Behrens T (2010) Using data mining to model and interpret soil diffuse reflectance spectra. Geoderma 158(1–2):46–54

    Article  Google Scholar 

  • Rossel VA, Hicks WS (2015) Soil organic carbon and its fractions estimated by visible–near infrared transfer functions. Eur J Soil Sci 66(3):438–450

    Article  Google Scholar 

  • Rossel RAV, Webster R (2012) Predicting soil properties from the Australian soil visible-near infrared spectroscopic database. Eur J Soil Sci 63(6):848–860

    Article  Google Scholar 

  • Rossel RV, Walvoort DJ, 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(1–2):59–75

    Article  Google Scholar 

  • Rossel RAV, Jeon YS, Odeh IOA, McBratney AB (2008) Using a legacy soil sample to develop a mid-IR spectral library. Aust J Soil Res 46(1):1–16

    Article  Google Scholar 

  • Rossel VR, Rizzo R, Demattê JA, Behrens T (2010) Spatial modeling of a soil fertility index using visible–near-infrared spectra and terrain attributes. Soil Sci Soc Am J 74(4):1293–1300

    Article  Google Scholar 

  • Rossel RV, Adamchuk VI, Sudduth KA, McKenzie NJ, Lobsey C (2011) Proximal soil sensing: an effective approach for soil measurements in space and time. In: Advances in agronomy, vol 113. Academic Press, pp 243–291

    Google Scholar 

  • Rossel RV, Behrens T, Ben-Dor E, Brown DJ, Demattê JA, Shepherd KD, Shi Z, Stenberg B, Stevens A, Adamchuk V, Aïchi H (2016) A global spectral library to characterize the world’s soil. Earth Sci Rev 155:198–230

    Article  Google Scholar 

  • Rossel VA, Lobsey CR, Sharman C, Flick P, McLachlan G (2017) Novel proximal sensing for monitoring soil organic C stocks and condition. Environ Sci Technol 51(10):5630–5641

    Article  Google Scholar 

  • Roudier P, Hedley CB, Ross CW (2015) Prediction of volumetric soil organic carbon from field-moist intact soil cores. Eur J Soil Sci 66(4):651–660

    Article  Google Scholar 

  • Sarathjith MC, Das BS, Wani SP, Sahrawat KL (2014) Dependency measures for assessing the covariation of spectrally active and inactive soil properties in diffuse reflectance spectroscopy. Soil Sci Soc Am J 78(5):1522–1530

    Article  Google Scholar 

  • Savitzky A, Golay MJ (1964) Smoothing and differentiation of data by simplified least squares procedures. Anal Chem 36(8):1627–1639

    Article  Google Scholar 

  • Schirrmann M, Gebbers R, Kramer E, Seidel J (2011) Soil pH mapping with an on-the-go sensor. Sensors 11(1):573–598

    Article  Google Scholar 

  • Schmid T, Rodriguez-Rastrero M, Escribano P, Palacios-Orueta A, Ben-Dor E, Plaza A, Milewski R, Huesca M, Bracken A, Cicuendez V, Pelayo M, Chabrillat S (2016) Characterization of soil erosion indicators using hyperspectral data from a Mediterranean rainfed cultivated region. IEEE J Sel Top Appl Earth Obs Remote Sens https://doi.org/10.1109/JSTARS.2015.2462125

  • Schumann U, Fahey DW, Wendisch M, Brenguier JL (2013) Introduction to airborne measurements of the earth atmosphere and surface, in: airborne measurements for environmental research: methods and instruments. https://doi.org/10.1002/9783527653218.ch1

    Book  Google Scholar 

  • Selige T, Böhner J, Schmidhalter U (2006) High resolution topsoil mapping using hyperspectral image and field data in multivariate regression modeling procedures. Geoderma 136(1–2):235–244

    Article  Google Scholar 

  • Shepherd KD, Walsh MG (2002) Development of reflectance spectral libraries for characterization of soil properties. Soil Sci Soc Am J 66(3):988–998

    Article  Google Scholar 

  • Sherman DM, Waite TD (1985) Electronic spectra of Fe3+ oxides and oxide hydroxides in the near IR to near UV. Am Mineral 70(11–12):1262–1269

    Google Scholar 

  • Shi Z, Ji W, Viscarra Rossel RA, Chen S, Zhou Y (2015) Prediction of soil organic matter using a spatially constrained local partial least squares regression and the Chinese Vis-NIR spectral library. Eur J Soil Sci 66(4):679–687

    Article  Google Scholar 

  • Shoshany M, Goldshleger N, Chudnovsky A (2013) Monitoring of agricultural soil degradation by remote-sensing methods: a review. Int J Remote Sens 34(17):6152–6181

    Article  Google Scholar 

  • Soriano-Disla JM, Janik LJ, Viscarra Rossel RA, MacDonald LM, McLaughlin MJ (2014) The performance of visible, near-, and mid-infrared reflectance spectroscopy for prediction of soil physical, chemical, and biological properties. Appl Spectrosc Rev 49(2):139–186

    Article  Google Scholar 

  • Sparks DL (2002) Environmental soil chemistry, 2nd edn. Academic Press, San Diego, 352p

    Google Scholar 

  • Staenz K, Szeredi T, Schwarz J (1998) ISDAS–A System for processing/analysing hyperspectral data: technical note. Can J Remote Sens 24:99–113. https://doi.org/10.1080/07038992.10855

    Article  Google Scholar 

  • Stamatiadis S, Evangelou L, Blanta A, Tsadilas C, Tsitouras A, Chroni C, Christophides C, Tsantila E, Samaras V, Dalezios N, Dimogiannis D (2013) Satellite visible–near infrared reflectance correlates to soil nitrogen and carbon content in three fields of the Thessaly plain (Greece). Commun Soil Sci Plant Anal 44(1–4):28–37

    Article  Google Scholar 

  • Steinberg A, Chabrillat S, Stevens A, Segl K, Foerster S (2016) Prediction of common surface soil properties based on Vis-NIR airborne and simulated EnMAP imaging spectroscopy data: prediction accuracy and influence of spatial resolution. Remote Sens 8(7):613

    Article  Google Scholar 

  • Stenberg B, Rossel RA, Mouazen AM, Wetterlind J (2010) Visible and near infrared spectroscopy in soil science. In: Advances in agronomy, vol 107. Academic, Burlington, pp 163–215

    Google Scholar 

  • 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(1–2):32–45

    Article  Google Scholar 

  • Stevens A, Nocita M, Tóth G, Montanarella L, van Wesemael B (2013) Prediction of soil organic carbon at the European scale by visible and near infrared reflectance spectroscopy. PLoS One 8(6)

    Google Scholar 

  • Stoorvogel JJ, Kooistra L, Bouma J (2015) Managing soil variability at different spatial scales as a basis for precision agriculture. In: Lal R, Stewart BA, (eds). Soil-specific farming: precision agriculture. 1:37–72

    Google Scholar 

  • Stuart MB, McGonigle AJ, Willmott JR (2019) Hyperspectral imaging in environmental monitoring: a review of recent developments and technological advances in compact field deployable systems. Sensors 19(14):3071

    Article  Google Scholar 

  • Tan K, Wang H, Chen L, Qian D, Peijun D, Pan C (2020) Estimation of the spatial distribution of heavy metal in agricultural soils using airborne hyperspectral imaging and random forest. J Hazard Mater 382:120987

    Article  Google Scholar 

  • Tekin Y, Kuang B, Mouazen AM (2013) Potential of on-line visible and near infrared spectroscopy for measurement of pH for deriving variable rate lime recommendations. Sensors 13(8):10177–10190

    Article  Google Scholar 

  • Terra FS, Demattê JAM, Viscarra Rossel RA (2015) Spectral libraries for quantitative analyses of tropical Brazilian soils: comparing Vis-NIR and mid-IR reflectance data. Geoderma 255:81–93

    Article  Google Scholar 

  • Terra FS, Demattê JA, Rossel RA (2018) Proximal spectral sensing in pedological assessments: Vis–NIR spectra for soil classification based on weathering and pedogenesis. Geoderma 318:123–136

    Article  Google Scholar 

  • Terra FS, Rizzo R, Ben Dor E, Demattê JAM (2021) Chapter 41 – Soil sensing by visible and IR radiation. In: Ciurczak EW, Igne B, Workman J, Burns DA (eds) Handbook of near-infrared analysis, vol 1, 4th edn. CRC Press Taylor & Francis Group, Boca Raton, pp 479–519

    Google Scholar 

  • Tian J, Philpot WD (2015) Relationship between surface soil water content, evaporation rate, and water absorption band depths in SWIR reflectance spectra. Remote Sens Environ 169:280–289

    Article  Google Scholar 

  • Tóth G, Montanarella L, Rusco E (2008) Threats to soil quality in Europe. Institute Environment Sustainability, Ispra

    Google Scholar 

  • Townsend TE (1987) Discrimination of iron alteration minerals in visible and near-infrared reflectance data. J Geophys Res Solid Earth 92(B2):1441–1454

    Article  Google Scholar 

  • Vågen T-G, Shepherd KD, Walsh MG (2006) Sensing landscape level change in soil fertility following deforestation and conversion in the highlands of Madagascar using Vis-NIR spectroscopy. Geoderma 133(3–4):281–294

    Article  Google Scholar 

  • Vasques GM, Grunwald S, Harris WG (2010) Spectroscopic models of soil organic carbon in Florida, USA. J Environ Qual 39(3):923–934

    Article  Google Scholar 

  • Vasques GM, Demattê JA, Viscarra Rossel RA, Ramírez López L, Terra FD, Rizzo R, De Souza Filho CR (2015) Integrating geospatial and multi-depth laboratory spectral data for mapping soil classes in a geologically complex area in southeastern Brazil. Eur J Soil Sci 66(4):767–779

    Article  Google Scholar 

  • Vohland M, Ludwig M, Thiele-Bruhn S, Ludwig B (2017) Quantification of soil properties with hyperspectral data: selecting spectral variables with different methods to improve accuracies and analyze prediction mechanisms. Remote Sens 9(11):1103

    Article  Google Scholar 

  • Vrieling A (2006) Satellite remote sensing for water erosion assessment: a review. Catena 65(1):2–18

    Article  Google Scholar 

  • Waiser TH, Morgan CLS, Brown DJ, Hallmark CT (2007) In situ characterization of soil clay content with visible near-infrared diffuse reflectance spectroscopy. Soil Sci Soc Am J 71(2):389–396

    Article  Google Scholar 

  • Wang J, He T, Lv C, Chen Y, Wu J (2010) Mapping soil organic matter based on land degradation spectral response units using Hyperion images. Int J Appl Earth Obs Geoinf 12:S171–S180

    Google Scholar 

  • Wetterlind J, Stenberg B (2010) Near-infrared spectroscopy for within-field soil characterization: small local calibrations compared with national libraries spiked with local samples. Eur J Soil Sci 61(6):823–843

    Article  Google Scholar 

  • Whiting ML, Li L, Ustin SL (2004) Predicting water content using gaussian model on soil spectra. Remote Sens Environ 89(4):535–552

    Article  Google Scholar 

  • Xu S, Zhao Y, Wang M, Shi X (2018) Comparison of multivariate methods for estimating selected soil properties from intact soil cores of paddy fields by Vis–NIR spectroscopy. Geoderma 310:29–43

    Article  Google Scholar 

  • Yang LY, Gao XH, Zhang W, Shi FF, He LH, Jia W (2016) Estimating heavy metal concentrations in topsoil from vegetation reflectance spectra of Hyperion images: a case study of Yushu county, Qinghai, China. Chinese J Appl Ecol. https://doi.org/10.13287/j.1001-9332.201606.030

  • Yang M, Xu D, Chen S, Li H, Shi Z (2019) Evaluation of machine learning approaches to predict soil organic matter and pH using Vis-NIR spectra. Sensors 19(2):263

    Article  Google Scholar 

  • Zhang T, Lin L, Zheng B (2013) Estimation of agricultural soil properties with imaging and laboratory spectroscopy. J Appl Remote Sens 7(1):073587

    Article  Google Scholar 

  • Žížala D, Zádorová T, Kapička J (2017) Assessment of soil degradation by erosion based on analysis of soil properties using aerial hyperspectral images and ancillary data, Czech Republic. Remote Sens 9(1):28

    Article  Google Scholar 

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Authors and Affiliations

  1. Department of Soil Science, “Luiz de Queiroz” College of Agriculture, University of São Paulo, Piracicaba, SP, Brazil

    Rodnei Rizzo, Wanderson de Souza Mendes, Nélida Elizabet Quiñonez Silvero, André C. Dotto, Natasha V. dos Santos, Benito R. Bonfatti, Raul R. Poppiel & José A. M. Demattê

  2. Institute of Agricultural Sciences, Federal University of Jequitinhonha and Mucuri Valleys, Unaí, MG, Brazil

    Fabricio da Silva Terra

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  1. Rodnei Rizzo
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  2. Wanderson de Souza Mendes
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  4. Fabricio da Silva Terra
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  8. Raul R. Poppiel
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  9. José A. M. Demattê
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Editors and Affiliations

  1. Soil and Land Resources Assessment Division, National Remote Sensing Centre Department of Space, ISRO, Hyderabad, Telangana, India

    Tarik Mitran

  2. Department of Agronomy, Institute of Agricultural Science, Banaras Hindu University, Varanasi, Uttar Pradesh, India

    Ram Swaroop Meena

  3. Agroecosystem and Modeling Division Agricultural Sciences and Applications Group, National Remote Sensing Centre Department of Space, ISRO, Hyderabad, Telangana, India

    Abhishek Chakraborty

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Rizzo, R. et al. (2021). Point and Imaging Spectroscopy in Geospatial Analysis of Soils. In: Mitran, T., Meena, R.S., Chakraborty, A. (eds) Geospatial Technologies for Crops and Soils. Springer, Singapore. https://doi.org/10.1007/978-981-15-6864-0_8

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