Skip to main content

Advertisement

SpringerLink
Log in

Imaging Spectroscopy for Soil Mapping and Monitoring

  • Published:
Surveys in Geophysics Aims and scope Submit manuscript

Abstract

There is a renewed awareness of the finite nature of the world’s soil resources, growing concern about soil security and significant uncertainties about the carrying capacity of the planet. Regular assessments of soil conditions from local through to global scales are requested, and there is a clear demand for accurate, up-to-date and spatially referenced soil information by the modelling scientific community, farmers and land users, and policy- and decision-makers. Soil and imaging spectroscopy, based on visible–near-infrared and shortwave infrared (400–2500 nm) spectral reflectance, has been shown to be a proven method for the quantitative prediction of key soil surface properties. With the upcoming launch of the next generation of hyperspectral satellite sensors in the next years, a high potential to meet the demand for global soil mapping and monitoring is appearing. In this paper, we briefly review the basic concepts of soil spectroscopy with a special attention to the effects of soil roughness on reflectance and then provide a review of state of the art, achievements and perspectives in soil mapping and monitoring based on imaging spectroscopy from air- and spaceborne sensors. Selected application cases are presented for the modelling of soil organic carbon, mineralogical composition, topsoil water content and characterization of soil crust, soil erosion and soil degradation stages based on airborne and simulated spaceborne imaging spectroscopy data. Further, current challenges, gaps and new directions toward enhanced soil properties modelling are presented. Overall, this paper highlights the potential and limitations of multiscale imaging spectroscopy nowadays for soil mapping and monitoring, and capabilities and requirements of upcoming spaceborne sensors as support for a more informed and sustainable use of our world’s soil resources.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4

Modified from Haubrock et al. (2008a, b)

Fig. 5
Fig. 6

Modified from Schmid et al. (2016)

Fig. 7

Extracted from Gomez et al. (2015b)

Fig. 8

Similar content being viewed by others

References

  • Adeline K, Gomez C, Gorretta N, Roger JM (2017) Predictive ability of soil properties to spectral degradation from laboratory Vis–NIR spectroscopy data. Geoderma 288:143–153

    Google Scholar 

  • Agassi M, Shainberg I, Morin J (1981) Effect of electrolyte concentration and soil sodicity on infiltration rate and crust formation. Soil Sci Soc Am J 45(5):848–851

    Google Scholar 

  • Agassi M, Morin J, Shainberg I (1985) Effect of raindrop impact energy and water salinity on infiltration rates of sodic soils. Soil Sci Soc Am J 49:186–190

    Google Scholar 

  • Aldana-Jague E, Heckrath G, Macdonald A, Van Wesemael B, Van Oost K (2016) UAV-based soil carbon mapping using VIS–NIR (480–1000 nm) multi-spectral imaging: potential and limitations. Geoderma 275(1):55–66

    Google Scholar 

  • Alonso M, Rodríguez-Caballero E, Chamizo S, Escribano P, Cantón Y (2014) Evaluación de los diferentes índices para cartografiar biocostras a partir de información espectral. Rev Esp Teledetec 42:63–82

    Google Scholar 

  • Arrouays D, Lagacherie P, Hartemink AE (2017) Digital soil mapping across the globe. Geoderma Reg 9:1–4

    Google Scholar 

  • Bablet A, Jacquemoud S, Viallefond F, Fabre S, Briottet X (2017) Modeling bare soil reflectance in the solar domain as a function of water content and surface roughness. In: Abstract 10th EARSeL SIG Imaging Spectroscopy Workshop, 19–21 April 2017, Zurich, Switzerland

  • Bablet A, Vu PVH, Jacquemoud S, Viallefont-Robinet F, Fabre S, Briottet X, Sadeghid M, Whiting ML, Baret F, Tian J (2018) MARMIT: a multilayer radiative transfer model of soil reflectance to estimate surface soil moisture content in the solar domain (400–2500 nm). Remote Sens Environ 217:1–17

    Google Scholar 

  • Bach H, Mauser W (1994) Modelling and model verification of the spectral reflectance of soils under varying moisture conditions. In: Proceedings IGARSS 94. ‘Surface and atmospheric remote sensing: technologies, data analysis and interpretation’, Pasadena, California, 8–12 August 1994, IEEE international. IEEE, Piscataway, NJ, pp 2354–2356

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

    Google Scholar 

  • Bartholomeus H, Kooistraa 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 13:81–88

    Google Scholar 

  • Baumgardner MF, Silva LF, Biehl LL, Stoner ER (1985) Reflectance properties of soils. Adv Agron 38:1–44

    Google Scholar 

  • Ben-Dor E, Banin A (1995a) Near infrared analysis (NIRA) as a method to simultaneously evaluate spectral featureless constituents in soils. Soil Sci 159(4):259–270

    Google Scholar 

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

    Google Scholar 

  • 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) during a controlled decomposition process. Remote Sens Environ 61:1–15

    Google Scholar 

  • Ben-Dor E, Irons JR, Epema GF (1999) Remote sensing of the earth sciences: manual of remote sensing. Soil Reflectance 3:111

    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 23:1043–1062

    Google Scholar 

  • Ben-Dor E, Goldlshleger N, Benyamini Y, Agassi M, Blumberg DG (2003) The spectral reflectance properties of soil structural crusts in the 1.2 to 2.5 µm spectral region. Soil Sci Soc Am J 67(1):289–299

    Google Scholar 

  • Ben-Dor E, Goldshalager N, Braun O, Kindel B, Goetz AFH, Bonfil D, Agassi M, Margalit N, Binayminy Y, Karnieli A (2004) Monitoring of infiltration rate in semiarid soils using airborne hyperspectral technology. Int J Remote Sens 25:1–18

    Google Scholar 

  • Ben-Dor E, Levin TN, 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 131:1–21

    Google Scholar 

  • Ben-Dor E, Taylor GR, Hill J, Demattê JAM, Whiting ML, Chabrillat S, Sommer S (2008) Imaging spectrometry for soil applications. Adv Agron J 97:321–392

    Google Scholar 

  • Ben-Dor E, Chabrillat S, Demattê JAM, Taylor GR, Hill J, Whiting ML, Sommer S (2009) Using imaging spectroscopy to study soil properties. Remote Sens Environ 113:S38–S55

    Google Scholar 

  • Ben-Dor E, Kafri A, Varacalli G (2014) SHALOM: an Italian–Israeli hyperspectral orbital mission—update. In: International geoscience and remote sensing symposium, Quebec, Canada, 13–18 July 2014

  • Ben-Dor E, Granot A, Notesco G (2017) A simple apparatus to measure soil spectral information in the field under stable conditions. Geoderma 306:73–80

    Google Scholar 

  • Ben-Dor E, Chabrillat S, Demattê JAM (2018) Characterization of soil properties using reflectance spectroscopy. In: Thenkabail PS, Lyon JG, Huete A (eds) Hyperspectral remote sensing of vegetation, 2nd edn. Four volume set—volume I: fundamentals, sensor systems, spectral libraries, and data mining for vegetation. CRC Press, Boca Raton, pp 187–247. ISBN 978-1-138-05854-5

    Google Scholar 

  • Benyamini Y, Unger PW (1984) Crust development under simulated rainfall on four soils. In: Agronomy abstracts. ASA, Madison, WI, pp 243–244

  • Bishop JL, Pieters CM, Edwards JO (1994) Infrared spectroscopic analyses on the nature of water in montmorillonite. Clays Clay Miner 42:702–716

    Google Scholar 

  • Blasch G, Spengler D, Hohmann C, Neumann C, Itzerott I, Kaufmann H (2015) Multitemporal soil pattern analysis with multispectral remote sensing data at the field-scale. Comput Electron Agric 113:1–13

    Google Scholar 

  • Bogrekci I, Lee WS (2006) Effects of soil moisture content on absorbance spectra of sandy soils in sensing phosphorus concentrations using UV–VIS–NIR spectroscopy. Trans ASABE 49:1175–1180. https://doi.org/10.13031/2013.21717

    Google Scholar 

  • Boiffin J (1986) Stages and time-depedency of soil crusting in situ. In: Callebaut F, Gabriels D, De Boodt M (eds) Assessment of soil surface sealing and crusting. Flanders Research Center for Soil Erosion and Soil Conservation, Ghent, pp 91–98

    Google Scholar 

  • Bowers S, Hanks RJ (1965) Reflectance of radiant energy from soils. Soil Sci 100:130–138

    Google Scholar 

  • Brennan B, Bandeen WR (1970) Anisotropic reflectance characteristics of natural Earth surfaces. Appl Opt 9:405–412

    Google Scholar 

  • Briottet X et al (2017) European Hyperspectral Explorer: HYPEX-2—a new space mission for vegetation biodiversity, bare continental surfaces, coastal zones and urban area ecosystems. In: Abstract 10th EARSeL SIG imaging spectroscopy workshop, 19–21 April 2017, Zurich, Switzerland

  • Brodsky L, Klement A, Penizek V, Kodesova R, Boruvka L (2011) Building soil spectral library of the Czech soils for quantitative digital soil mapping. Soil Water Res 6:165–172

    Google Scholar 

  • Brook A, Ben-Dor E (2014) Practical example of the supervised vicarious calibration (SVC) method: VALCALHYP airborne hyper spectra campaigns under EUFAR. In: EaRSeL eProceedings, no 2, pp 38–48

  • Brook A, Ben-Dor E (2015) Supervised vicarious calibration (SVC) of multi-source hyperspectral remote-sensing data. Remote Sens 7:6196–6223

    Google Scholar 

  • Bryant R, Thoma D, Moran S, Holifield C, Goodrich D, Keefer T, Paige G, Williams D, Skirvin S (2003) Evaluation of hyperspectral, infrared temperature and radar measurements for monitoring surface soil moisture. In: Proceedings of the first interagency conference on research in the watersheds, Benson (USA), pp 528–533

  • Carmon N, Ben-Dor E (2017) An advanced analytical approach for spectral-based modelling of soil properties. Int J Emerg Technol Adv Eng 7:90–97

    Google Scholar 

  • Castaldi F, Palombo A, Pascucci S, Pignatti S, Santini F, Casa R (2015) Reducing the influence of soil moisture on the estimation of clay from hyperspectral data: a case study using simulated PRISMA data. Remote Sens 7:15561–15582. https://doi.org/10.3390/rs71115561

    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

    Google Scholar 

  • Castaldi F, Chabrillat S, Jones A, Vreys K, Bomans B, van Wesemael B (2018a) Soil organic carbon estimation in croplands by hyperspectral remote APEX Data using the LUCAS topsoil database. Remote Sens 10:153. https://doi.org/10.3390/rs10020153

    Google Scholar 

  • Castaldi F, Chabrillat S, Chartin C, Genot V, Jones AR, van Wesemael B (2018b) Using LUCAS topsoil database to estimate soil organic carbon content in croplands sampled in Belgium and Luxembourg. Eur J Soil Sci 69(4):592–603

    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 (in press)

  • Chabrillat S (2006) Land degradation indicators: spectral indices. Ann Arid Zone Spec Issue Land Special Assess 45(3–4):331–354

    Google Scholar 

  • Chabrillat S, Goetz AFH, Krosley L, Olsen HW (2002) Use of hyperspectral images in the identification and mapping of expansive clay soils and the role of spatial resolution. Remote Sens Environ 82:431–445

    Google Scholar 

  • Chabrillat S, Eisele A, Guillaso S, Rogaß C, Ben-Dor E, Kaufmann H (2011) HYSOMA: an easy-to-use software interface for soil mapping applications of hyperspectral imagery. In: Proceedings 7th EARSeL SIG imaging spectroscopy workshop, Edinburgh, Scotland

  • Chabrillat S, Whiting ML, Guillaso S, Eisele A, Haubrock SN, Kaufmann H (2012) Quantitative mapping of surface soil moisture with hyperspectral imagery using the HYSOMA interface. In: Abstract 2012 IEEE international geoscience and remote sensing symposium, IGARSS 2012, Muenchen, Germany

  • Chabrillat S, Foerster S, Steinberg A, Segl K (2014) Prediction of common surface soil properties using airborne and simulated EnMAP hyperspectral images: impact of soil algorithm and sensor characteristic. In: Proceedings 2014 IEEE international geoscience and remote sensing symposium, IGARSS 2014, Québec city, QC, Canada, pp 2914–2917

  • Chabrillat S, Hanegraaf M, Sommer R, van den Bosch R, Montanarella L, van Wesemael B, Schwilch G, Harthoorn J, Skalsky R, Braslow J, Mills J, Verzandvoort S, Obersteiner M (2015) Soil and land information: how to support decision making? Rapporteurs report, Global Soil Week 2015, Germany, 20–23 April 2015

  • Chabrillat S, Guillaso S, Rabe A, Foerster S, Guanter L (2016) From HYSOMA to ENSOMAP—a new open source tool for quantitative soil properties mapping based on hyperspectral imagery from airborne to spaceborne applications. General Assembly European Geosciences Union, Vienna, Austria, 2016, geophysical research abstracts, vol 18, EGU2016-14697

  • Chamizo S, Stevens A, Cantón Y, Miralles I, Domingo F, Van Wesemael B (2012) Discriminating soil crust type, development stage and degree of disturbance in semiarid environments from their spectral characteristics. Eur J Soil Sci 63:42–53

    Google Scholar 

  • Chang DH, Islam S (2000) Estimation of soil physical properties using remote sensing and artificial neural network. Remote Sens Environ 74(3):534–544

    Google Scholar 

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

    Google Scholar 

  • Ciani A, Goss KU, Schwarzenbach RP (2005) Light penetration in soil and particulate minerals. Eur J Soil Sci 56(5):561–574

    Google Scholar 

  • Cierniewski J (1987) A model for soil surface roughness influence on the spectral response of bare soils in the visible and near-infrared range. Remote Sens Environ 23:97–115

    Google Scholar 

  • Cierniewski J (2012) Satellite observation of bare soils for their average diurnal albedo approximation. In: Ist international conference on sensor networks, 24–26 February, 2012, Rome, Italy, pp 1–6

  • Cierniewski J, Courault D (1993) Bidirectional reflectance of bare soil surface in the visible and near-infrared range. Remote Sens Rev 7:321–339

    Google Scholar 

  • Cierniewski J, Guliński 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 

  • Cierniewski J, Gdala T, Karnieli A (2004) A hemispherical–directional reflectance model as a tool for understanding image distinctions between cultivated and uncultivated bare surfaces. Remote Sens Environ 90:505–552

    Google Scholar 

  • Cierniewski J, Karnieli A, Hermann I, Królewicz S, Kuśnierek K (2010) Soil surface illumination at micro-relief scale and soil BRDF data collected by a hyperspectral camera. Int J Remote Sens 31:2151–2157

    Google Scholar 

  • Cierniewski J, Karnieli A, Kuśnierek K, Herrmann I (2013) Approximating the average daily surface albedo with respect to soil roughness and latitude. Int J Remote Sens 34(9–10):3416–3424

    Google Scholar 

  • Cierniewski J, Kaźmierowski C, Królewicz S (2015) Evaluation of the effects of surface roughness on the relationship between soil BRF data and broadband albedo. IEEE J Sel Top Appl Earth Obs Remote Sens 8(4):1528–1533

    Google Scholar 

  • Cipra JE, Baumgardner MF, Stoner ER, MacDonald RB (1971) Measuring radiance characteristics of soil with a field spectroradiometer. Soil Sci Soc Am J 35:1014–1017

    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 75:308–318

    Google Scholar 

  • Coulson KL, Reynolds DW (1971) The spectral reflectance of natural surfaces. J Appl Meteorol 10:1285–1295

    Google Scholar 

  • Croft H, Anderson K, Kuhn NJ (2012) Reflectance anisotropy for measuring soil surface roughness of multiple soil types. CATENA 93:87–96

    Google Scholar 

  • Croft H, Anderson K, Brazier ER, Kuhn NJ (2013) Modeling fine-scale soil surface structure using geostatistics. Water Resour Res 49:1858–1870

    Google Scholar 

  • Croft H, Anderson K, Kuhn NJ (2014) Evaluating the influence of surface soil moisture and soil surface roughness on optical directional reflectance factors. Eur J Soil Sci 65:605–612. https://doi.org/10.1111/ejss.12142

    Google Scholar 

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

    Google Scholar 

  • De Jong SM (1992) The analysis of spectroscopical data to map soil and types and soil crust of Mediterranean eroded soil. Soil Technol 5:199–211

    Google Scholar 

  • Deering DW, Eck TF, Otterman J (1990) Bidirectional reflectance of selected desert surfaces and their three-parameter soil characterization. Agric For Meteorol 52:71–90

    Google Scholar 

  • Dehaan R, Taylor GR (2003) Image-derived spectral endmembers as indicators of salinization. Int J Remote Sens 24:775–794

    Google Scholar 

  • Demattê JA, Campos RC, Alves MC, Fiorio PR, Nanni MR (2004) Visible–NIR reflectance: a new approach on soil evaluation. Geoderma 121(1):95–112

    Google Scholar 

  • Demattê JAM, Morgan CLS, Chabrillat S, Rizzo R, Franceschini MHD, FdaS Terra, Vasques GM, Wetterlind J (2015) Spectral sensing from ground to space in soil science: state of the art, applications, potential and perspectives. In: Thenkabail PS (ed) Remote sensing handbook—three volume set: land resources monitoring, modeling, and mapping with remote sensing. CRC Press, Boca Raton, pp 661–732. ISBN-10: 1482217953

  • Demattê JAM, 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

    Google Scholar 

  • Demetriades-Shah TH, Steven MD, Clark JA (1990) High resolution derivative spectra in remote sensing. Remote Sens Environ 33:55–64

    Google Scholar 

  • Denis A, Stevens A, van Wesemael B, Udelhoven T, Tychon B (2014) Soil organic carbon assessment by field and airborne spectrometry in bare croplands: accounting for soil surface roughness. Geoderma 226–227:94–102. https://doi.org/10.1016/j.geoderma.2014.02.015

    Google Scholar 

  • Diek S, Schaepman ME, de Jong R (2016) Creating multi-temporal composites of airborne imaging spectroscopy data in support of digital soil mapping. Remote Sens 8:906–934. https://doi.org/10.3390/rs8110906

    Google Scholar 

  • Diek S, Schaepmann M, de Jong R (2017a) Correcting airborne imaging spectroscopy data for soil moisture and soil surface roughness effects in support of digital soil mapping. In: Abstract 10th EARSeL SIG imaging spectroscopy workshop, 19–21 April 2017, Zurich, Switzerland

  • Diek S, Fornallaz F, Schaepman ME, de Jong R (2017b) Barest pixel composite for agricultural area using time series of Landsat data. Remote Sens 9(12):1245. https://doi.org/10.3390/rs9121245

    Google Scholar 

  • Diek S, Chabrillat S, Nocita M, Schaepman ME, de Jong R (2019) Minimizing soil moisture variations in multi-temporal airborne imaging spectrometer data for digital soil mapping. Geoderma 337:607–621

    Google Scholar 

  • Eisele A, Lau IC, Hewson R, Carter D, Wheaton B, Ong C, Cudahy TJ, Chabrillat S, Kaufmann H (2012) Applicability of the thermal infrared spectral region for the prediction of soil properties across semi-arid agricultural landscapes. Remote Sens 4(11):3265–3286. https://doi.org/10.3390/rs4113265

    Google Scholar 

  • Eisele A, Chabrillat S, Hecker C, Hewson R, Lau IC, Rogass C, Segl K, Cudahy TJ, Udelhoven T, Hostert P, Kaufmann H (2015) Advantages using the longwave infrared (LWIR) to detect and quantify semi-arid soil properties. Remote Sens Environ 163:296–311. https://doi.org/10.1016/j.rse.2015.04.001

    Google Scholar 

  • Escribano P, Schmid T, Chabrillat S, Rodríguez-Caballero E, García M (2017) Optical remote sensing for soil mapping and monitoring. In: Pereira P, Brevik E, Muñoz-Rojas M, Miller B (eds) Soil mapping and process modelling for sustainable land use management. Elsevier, Amsterdam, pp 87–125. ISBN 978-0-12-805200-6

    Google Scholar 

  • Eshel G, Levey G (2004) Spectral reflectance properties of crusted soils under solar illumination. Soil Sci Soc Am J 66:1982–1991

    Google Scholar 

  • Eswaran H, Lal R, Reich PF (2001) Land degradation: an overview. In: Bridges EM, Penning de Vries FWT, Oldeman LR, Sombatpanit S, Scherr SJ (eds) Response to land degradation. Science Publishers, Inc., Enfield, NH, pp 20–35

    Google Scholar 

  • European Commission (EC) (2006) Soil protection: the long story behind the strategy. Office for Official Publications of the European Communities, Luxembourg

    Google Scholar 

  • Fabre S, Briottet X, Lesaignoux A (2015) Estimation of soil moisture content from the spectral reflectance of bare soils in the 0.4–2.5 µm domain. Sensors 15:3262–3281

    Google Scholar 

  • FAO and ITPS (2015) Status of the world’s soil resources (SWSR): main report. Food and Agriculture Organization of the United Nations, Rome

    Google Scholar 

  • FAO/IIASA/ISRIC/ISS-CAS/JRC (2012) Harmonized World Soil Database (version 1.2). FAO, Rome

    Google Scholar 

  • Folkman M, Pearlman J, Liao L, Jarecke P (2001) EO1/Hyperion hyperspectral imager design, development, characterization and prediction. In: Smith WL, Yasuoka Y (eds) Hyperspectral remote sensing of the land and atmosphere. SPIE proceeding, vol 4151, pp 40–51

  • Ge Y, Morgan CLS, Ackerson JP (2014) VisNIR spectra of dried ground soils predict properties of soils scanned moist and intact. Geoderma 221–222:61–69. https://doi.org/10.1016/j.geoderma.2014.01.011

    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. https://doi.org/10.1155/2012/868090

    Google Scholar 

  • Gholizadeh A, Borůvka L, Saberioon MM, Kozák J, Vašát R, Němeček K (2015) Comparing different data preprocessing methods for monitoring soil heavy metals based on soil spectral features. Soil Water Res 10(4):218–227

    Google Scholar 

  • Gilley JE, Kottwitz ER (1995) Random roughness assessment by the pin and chain method. Appl Eng Agric 12(1):39–43

    Google Scholar 

  • Gilliot JM, Vaudour E, Michelin J (2017) Soil surface roughness measurement: a new fully automatic photogrammetric approach applied to agricultural bare fields. Comput Electron Agric 134:63–78

    Google Scholar 

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

    Google Scholar 

  • Goldshleger N, Ben-Dor E, Benyamini Y, Blumberg D, Agassi M (2001) The spectral reflectance of soil’s structural crust in the SWIR region 1.2–2.5 µm. Terra-Nova 13(1):12–17

    Google Scholar 

  • Goldshleger N, Ben-Dor E, Benyamini Y, Blumberg DG, Agassi M (2002) Soil crusting and infiltration process as monitored by soil reflectance spectroscopy in the SWIR Region. Int J Remote Sens 23(19):3909–3920

    Google Scholar 

  • Goldshleger NE, Ben-Dor E, Benyamini Y, Agassi M (2004) Soil reflectance as a tool for assessing physical crust arrangement of four typical soils in Israel. Soil Sci 169:677–687

    Google Scholar 

  • Goldshlager N, Ben-Dor E, Chudnovsky A, Agassi M (2010) Soil reflectance as a generic tool for assessing infiltration rate induced by structural crust for heterogeneous soils. Eur J Soil Sci 60:1038–1951

    Google Scholar 

  • Gomez C, ViscarraRossel RA, McBratney AB (2008a) Soil organic carbon prediction by hyperspectral remote sensing and field VIS–NIR spectroscopy: an Australian case study. Geoderma 146:403–411

    Google Scholar 

  • Gomez C, Lagacherie P, Coulouma G (2008b) Continuum removal versus PLSR method for clay and calcium carbonate content estimation from laboratory and airborne hyperspectral measurements. Geoderma 148:141–148

    Google Scholar 

  • Gomez C, Lagacherie P, Coulouma G (2012a) Regional predictions of eight common soil properties and their spatial structures from hyperspectral Vis–NIR data. Geoderma 189–190:176–185

    Google Scholar 

  • Gomez C, Lagacherie P, Bacha S (2012b) Using Vis–NIR hyperspectral data to map topsoil properties over bare soils in the Cap Bon region, Tunisia. In: Digital soil assessments and beyond—proceedings of the fifth global workshop on digital soil mapping, pp 387–392

  • Gomez C, Drost APA, Roger JM (2015a) Analysis of the uncertainties affecting predictions of clay contents from VNIR/SWIR hyperspectral data. Remote Sens Environ 156:58–70

    Google Scholar 

  • Gomez C, Oltra Carrio R, Lagacherie P, Bacha S, Briottet X (2015b) Sensitivity of soil property prediction obtained from hyperspectral Vis–NIR imagery to atmospheric effects and degradation in image spatial resolutions. Remote Sens Environ 164:1–15

    Google Scholar 

  • Gomez C, Gholizadeh A, Borůvka L, Lagacherie P (2016) Using legacy data for predicting soil surface clay content from VNIR/SWIR hyperspectral airborne images. Geoderma 276:84–92

    Google Scholar 

  • Green R (2018) Global VSWIR imaging spectroscopy and the 2017 Decadal Survey. In: Proceedings of IGARSS 2018, IEEE international geoscience and remote sensing symposium, Valencia, Spain, 22–27 July 2018

  • Grunwald S, Thompson JA, Boettinger JL (2011) Digital soil mapping and modeling at continental scales: finding solutions for global issues. Soil Sci Soc Am J 75:1201

    Google Scholar 

  • Guanter L, Kaufmann H, Segl K, Förster S, Rogass C, Chabrillat S, Küster T, Hollstein A et al (2015) The EnMAP spaceborne imaging spectroscopy mission for Earth observation. Remote Sens 7(7):8830–8857

    Google Scholar 

  • Hapke BW (1981) Bidirectional reflectance spectroscopy I. Theory. J Geophys Res 86:3039–3054

    Google Scholar 

  • Hartemink AE (2008) Soils are back on the global agenda. Soil Use Manag 24(4):327–330

    Google Scholar 

  • Hartemink AE, McBratney AB (2008) A soil science renaissance. Geoderma 148:123–129

    Google Scholar 

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

    Google Scholar 

  • Haubrock S-N, Chabrillat S, Kuhnert M, Hostert P, Kaufmann H (2008b) Surface soil moisture quantification and validation based on hyperspectral data and field measurements. J Appl Remote Sens 2:023552

    Google Scholar 

  • Heng BCP, Chandler JH, Armstrong A (2010) Applying close range digital photogrammetry in soil erosion studies. Photogramm Rec 25(131):240–265

    Google Scholar 

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

    Google Scholar 

  • Hill J, Udelhoven T, Schütt B, Yair A (1999) Differentiating biological soil crusts in a sandy arid eco-system based on multi- and hyperspectral remote sensing data. In: Schaepmann M, Schläpfer D, Itten K (Eds) 1st EARSEL workshop on imaging spectroscopy. Proceedings of the EARSEL workshop, Zürich, 6–8 October 1998. EARSEL Secretariat, Paris, pp 427–436

  • Hook SJ, Hulley G, Cawse-Nicholson K (2017) HyTES, ECOSTRESS and HyspIRI—imaging spectroscopy and broad band imaging in the thermal infrared. In: Abstract 10th EARSeL SIG imaging spectroscopy workshop, 19–21 April 2017, Zurich, Switzerland

  • Hummel JW, Sudduth KA, Hollinger SE (2001) Soil moisture and organic matter prediction of surface and subsurface soils using an NIR soil sensor. Comput Electron Agric 32:149–165

    Google Scholar 

  • Idso SB, Jackson RD, Reginato RJ, Kimball BA, Nakayama FS (1975) The dependence of bare soil albedo on soil water content. J Appl Meteorol 14:109–113

    Google Scholar 

  • Jackson RD, Moran S, Slater PN, Biggar SF (1987) Field calibration of reflectance panels. Remote Sens Environ 22:145–158

    Google Scholar 

  • Janik LJ, Merry RH, Skjemstad JO (1998) Can mid infrared diffuse reflectance analysis replace soil extractions? Anim Prod Sci 38(7):681–696

    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 66:555–565. https://doi.org/10.1111/ejss.12239

    Google Scholar 

  • JRC, Jones A, Panagos P et al (2012) The state of soil in Europe: a contribution of the JRC to the European environments agency’s environment state and outlook report—SOER 2010, JRC68418

  • Karnieli A, Kidron G, Ghassler C, Ben-Dor E (1999) Spectral characteristics of cyanobacteria soil crust in the visible near infrared and short wave infrared (400–2,500 nm) in semiarid environment. Int J Remote Sens 69:67–77

    Google Scholar 

  • Knadel M, Deng F, Thomsen A, Greve HM (2012) Development of a Danish national Vis–NIR soil spectral library for soil organic carbon determination. In: Digital soil assessments and beyond: proceedings of the 5th global workshop on digital soil mapping 2012, Sydney, Australia, vol 43. CRC Press, Boca Raton

  • Kopačková V, Ben-Dor E, Carmon N, Notesco G (2017) Modelling diverse soil attributes with visible to longwave infrared spectroscopy using PLSR employed by an automatic modelling engine. Remote Sens 9(2):134

    Google Scholar 

  • Kriebel KT (1976) On the variability of the reflected radiation field due to differing distributions of the irradiation. Remote Sens Environ 4:257–264

    Google Scholar 

  • Kuester T, Spengler D, Barczi J-F, Segl K, Hostert P, Kaufmann H (2014) Simulation of multitemporal and hyperspectral vegetation canopy bidirectional reflectance using detailed virtual 3-D canopy models. IEEE Trans Geosci Remote Sens 52(4):2096–2108

    Google Scholar 

  • Kuester T, Foerster S, Chabrillat S, Spengler D, Guanter L (2017) Assessing the influence of variable fractional vegetation cover on soil spectral features using simulated canopy reflectance modelling. In: Abstract 10th EARSeL SIG Imaging spectroscopy workshop, 19–21 April 2017, Zurich, Switzerland

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

    Google Scholar 

  • Lal R (2015) Restoring soil quality to mitigate soil degradation. Sustainability 7(5):5875–5895

    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

    Google Scholar 

  • Lefèvre-Fonollosa MJ, Bajouk T, Briottet X, Carrère V, Delacourt, C, Feret JB, Gastellu-Etchegorry JP, Gomez C, Jacquemoud S, Le Dantec N, Marion R, Petit T, Weber C (2016) Preparing the future: the HYPXIM mission. In: 36th EARSeL symposium 2016, Bonn, Germany, 20–24 June 2016

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

    Google Scholar 

  • Li Y, Liu Y, Wu S, Wang C, Xu A, Pan X (2017) Hyperspectral estimation of wheat biomass after alleviating of soil effects on spectra by non-negative matrix factorization. Eur J Agron 84:58–66

    Google Scholar 

  • Liang S, Townshend JR (1996) A modified Hapke model for soil bidirectional reflectance. Remote Sens Environ 55(1):1–10

    Google Scholar 

  • Liu W, Baret F, Gu XF, Zhang B, Tong Q, Zheng L (2003) Evaluation of methods for soil surface moisture estimation from reflectance data. Int J Rem Sens 24(10):2069–2083

    Google Scholar 

  • Liu Y, Pan X, Wang C, Li Y, Shi R (2015) Predicting soil salinity with VisNIR spectra after removing the effects of soil moisture using external parameter orthogonalization. PLoS One. https://doi.org/10.1371/journal.pone.0140688

    Google Scholar 

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

    Google Scholar 

  • Loizzo R, Guarini R, Longo F, Scopa T, Formaro R, Facchinetti C, Varavalli G (2018), PRISMA: the Italian hyperspectral mission. In: Proceedings of IGARSS 2018, IEEE international geoscience and remote sensing symposium, Valencia, Spain, 22–27 July 2018

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

    Google Scholar 

  • Malley DF, Martin P, Ben-Dor E (2004) Application in analysis of soils. Chapter 26. In: Craig R, Windham R, Workman J (eds) Near infrared spectroscopy in agriculture, vol 44. A three Societies Monograph. ASA, SSSA, CSSA, Madison, pp 729–784

    Google Scholar 

  • Marzahn P, Rieke-Zapp D, Ludwig L (2012) Assessment of soil surface roughness statistics for microwave remote sensing applications using a simple photogrammetric acquisition system. ISPRS J Photogramm Remote Sens 72:80–89

    Google Scholar 

  • Matsunaga T, Iwasaki A, Tsuchida S, Iwao K, Tanii, J, Kashimura O, Nakamura R, Yamamoto H, Kato S, Obata K, Mouri K, Tachikawa, T (2018) HISUI status toward FY2019 launch. In: Proceedings of IGARSS 2018, IEEE international geoscience and remote sensing symposium, Valencia, Spain, 22–27 July 2018

  • Matthias AD, Fimbres A, Sano EE, Post DF, Accily L, Batchily AK, Ferreira LG (2000) Surface roughness effects on soil albedo. SSSA J 63(3):1035–1041

    Google Scholar 

  • McIntyre DS (1958) Permeability measurements of soil crusts formed by raindrop impact. Soil Sci 85(4):185–189

    Google Scholar 

  • Meine C, Knight RL (eds) (1999) The essential Aldo Leopold: quotations and commentaries. University of Wisconsin Press, Madison

    Google Scholar 

  • Milton EJ, Webb JP (1987) Ground radiometry and airborne multispectral survey of bare soils. Int J Remote Sens 18:3–14

    Google Scholar 

  • Minasny B, McBratney AB (2006) A conditioned Latin hypercube method for sampling in the presence of ancillary information. Comput Geosci 32(9):1378–1388

    Google Scholar 

  • Minasny B, McBratney AB, Bellon-Maurel V, Roger J-M, Gobrecht A, Ferrand L, Joalland S, Minasny B, Bellon-Maurel V, Gobrecht A, Roger J-M, Ferrand L, Joalland S (2011) Removing the effect of soil moisture from NIR diffuse reflectance spectra for the prediction of soil organic carbon. Geoderma 167–168:118–124. https://doi.org/10.1016/j.geoderma.2011.09.008

    Google Scholar 

  • Moreno RG, Requejo AS, Alonso AMT, Barrington S, Alvarez MCD (2008) Shadow analysis: a method for measuring soil surface roughness. Geoderma 146:201–208

    Google Scholar 

  • Muller E, Decamps H (2000) Modeling soil moisture-reflectance. Remote Sens Environ 76:173–180

    Google Scholar 

  • Nanni MR, Demattê JAM (2006) Spectral reflectance methodology in comparison to traditional soil analysis. Soil Sci Soc Am J 70:393–407

    Google Scholar 

  • Nocita M, Stevens A, Toth G, Panagos P, van Wesemael B, Montanarella L (2014) Prediction of soil organic carbon content by diffuse reflectance spectroscopy using a local partial least square regression approach. Soil Biol Biochem 68:337–347

    Google Scholar 

  • Oldeman L, Hakkeling R, Sombroek W (1990) World map of the status of soil degradation, an explanatory note. International Soil Reference and Information Center, Wageningen

    Google Scholar 

  • Ouerghemmi W, Gomez C, Naceur S, Lagacherie P (2011) Applying blind source separation on hyperspectral data for clay content estimation over partially vegetated surfaces. Geoderma 163(3–4):227–237

    Google Scholar 

  • Ouerghemmi W, Gomez C, Nacer S, Lagacherie P (2016) Semi-blind source separation for estimation of clay content over semi-vegetated areas, from VNIR/SWIR hyperspectral airborne data. Remote Sens Environ 181:251–263

    Google Scholar 

  • Palmer JM (1982) Field standards of reflectance. Photogramm Eng Remote Sens 48:1623–1625

    Google Scholar 

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

    Google Scholar 

  • Panagos P, Borrelli P, Poesen J, Ballabio C, Lugato E, Meusburger K, Montanarella L, Alewell C (2015) The new assessment of soil loss by water erosion in Europe. Environ Sci Policy 54:438–447

    Google Scholar 

  • Piech KR, Walker JE (1974) Interpretation of soils. Photogramm Eng 40:87–94

    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 51:855–860

    Google Scholar 

  • Previtali F (2014) Pedoenvironments of the Mediterranean countries: resources and threats. In: Kapur S, Ersahin S (eds) Soil Security for ecosystem management, chap 4. Springer, New York, NY, pp 61–82

    Google Scholar 

  • Rast M, Nieke J, Ananasso C, Bach H, Ben Dor E, Chabrillat S, Colombo R, Feret JB, Giardino C, Green RO, Guanter L, Marsh S, Ong C, Rum G, Schaepman M, Schlerf M, Skidmore AK, Strobl P, Gascon F, Adams J, Isola C, Del Bello U (2019) The copernicus hyperspectral imaging mission for the environment (CHIME). In: Abstract 2019 ESA living planet symposium, Milan, Italy, 13–17 May 2019

  • Rieke-Zapp DH, Nearing MA (2005) Digital close range photogrammetry for measurement of soil erosion. Photogramm Rec 20(109):69–87

    Google Scholar 

  • Rodger A, Cudahy T (2009) Vegetation corrected continuum depths at 2.20 µm: an approach for hyperspectral sensors. Remote Sens Environ 113:2243–2257

    Google Scholar 

  • Rodríguez-Caballero E, Escribano P, Cantón Y (2014) Advanced image processing methods as a tool to map and quantify different types of biological soil crust. ISPRS J Photogramm Remote Sens 90:59–67

    Google Scholar 

  • Rogge D, Bauer A, Zeidler J, Mueller A, Esch T, Heiden U (2017) Building an exposed soil composite processor (SCMaP) for mapping spatial and temporal characteristics of soils with Landsat imagery (1984–2014). Remote Sens Environ 205:1–17

    Google Scholar 

  • Romero DJ, Ben-Dor E, Demattê JAM, Souza AB, Vicente LE, Tavares TR, Martello M, Strabeli TF, da Silva Barros PP, Fiorio PR, Gallo BC, Sato MV, Eitelweind MT (2018) Internal soil standard method for the Brazilian soil spectral library: performance and proximate analysis. Geoderma 312:95–103

    Google Scholar 

  • Rosa JD, Cooper M, Darboux F, Medeiros JC (2012) Soil roughness evolution in different tillage systems under simulated rainfall using a semivariogram-based index. Soil Tillage Res 124:226–232

    Google Scholar 

  • Rossel RV, 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(1):59–75

    Google Scholar 

  • Schmid T, Koch M, Gumuzzio J (2008) Application of hyperspectral imagery to map soil salinity. In: Metternicht G, Zinck A (eds) Remote sensing of soil salinization: impact and land management. CRC Press, Boca Raton, pp 113–139 Chapter 7

    Google Scholar 

  • Schmid T, Rodríguez-Rastrero M, Escribano P, Palacios-Orueta A, Ben-Dor E, Plaza A, Milewski R, Huesca M, Bracken A, Cicuéndez 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 9(2):845–860

    Google Scholar 

  • Schwanghart W, Jarmer T (2011) Linking spatial patterns of soil organic carbon to topography: a case study from south-eastern Spain. Geomorphology 126:252–263

    Google Scholar 

  • Schwartz G, Eshel G, Ben-Dor E (2011) Reflectance spectroscopy as a tool for monitoring contaminated soils. In: Pascucci S (ed) Soil contamination. InTech, Manhattan, pp 67–90

    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 136:235–244

    Google Scholar 

  • Sellers PJ et al (1995) Remote sensing of the land-surface for studies of global change: models-algorithms-experiments. Remote Sens Environ 51:3–26

    Google Scholar 

  • Shoshany M (1993) Roughness-reflectance relationships of bare desert terrain: an empirical study. Remote Sens Environ 45:15–27

    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

    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

    Google Scholar 

  • Stenberg B, Viscarra Rossel RA, Mouazen AM, Wetterlind J (2015) Visible and near infrared spectroscopy in soil science. In: Sparks DL (ed) Advances in agronomy, vol 107. Academic Press, Burlington, pp 163–215. https://doi.org/10.1016/s0065-2113(10)07005-7

    Google Scholar 

  • 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

    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 158:32–45

    Google Scholar 

  • Stevens A, Miralles I, Van Wesemael B (2012) Soil organic carbon predictions by airborne imaging spectroscopy: comparing cross-validation and validation. Soil Sci Soc Am J 76:2174–2183

    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):1–13

    Google Scholar 

  • Stevens F, Bogaert P, van Wesemael B (2015) Detecting and quantifying field-related spatial variation of soil organic carbon using mixed-effect models and airborne imagery. Geoderma 259–260:93–103

    Google Scholar 

  • Stolte J, Tesfai M, Øygarden L, Kværnø S, Keizer J, Verheijen F, Panagos P, Ballabio C, Hessel R (2016) Soil threats in Europe; EUR 27607

  • Stoner ER, Baumgardner MF (1981) Characteristic variations in reflectance of surface soils. Soil Sci Soc Am J 45:1161–1165

    Google Scholar 

  • Taconet OV, Ciarletti V (2007) Estimating soil roughness indices on a ridge-and-furrow surface using stereo photogrammetry. Soil Tillage Res 93:64–76

    Google Scholar 

  • Thomsen LM, Baartman JEM, Barneveld RJ, Starkloff T, Stolte J (2015) Soil surface roughness: comparing old and new measuring methods and application in a soil erosion model. Soil 1:399–410

    Google Scholar 

  • Toth G, Jones A, Montanarella L (2013) The LUCAS topsoil database and derived information on the regional variability of cropland topsoil properties in the European Union. Environ Monit Assess 185:7409–7425

    Google Scholar 

  • Toy TJ, Foster GR, Renard KG (2002) Soil erosion: processes, prediction, measurement, and control. Wiley, New York

    Google Scholar 

  • Tsakiridis NL, Tziolas N, Dimitrakos A, Galanis G, Ntonou E, Tsirika A, Terzopoulou E, Kalopesa E, Zalidis GC (2017) Predicting soil properties for sustainable agriculture using Vis–NIR spectroscopy: a case study in northern Greece. In: Proceedings of SPIE 10444, fifth international conference on remote sensing and geoinformation of the environment (RSCy2017). https://doi.org/10.1117/12.2277905

  • Ulaby FT, Moore RK, Fung AK (1982) Microwave remote sensing active and passive. Addison-Wesley, Reading

    Google Scholar 

  • UNEP (2012) One planet, how many people? A review of Earth’s carrying capacity, A discussion paper for the year of RIO+20. UNEP Global Environmental Alert Service

  • Ussiri DAN, Lal R (2018) The role of soil management and restoration in advancing sustainable development goals. In: Lal R, Horn R, Kosaki T (eds) Soil and sutainable development goals, vol 5. Catena, Stuttgart, pp 61–81

    Google Scholar 

  • Van der Linden S, Rabe A, Held M, Jakimow B, Leitão PJ, Okujeni A, Schwieder M, Suess S, Hostert P (2015) The EnMAP-Box—a toolbox and application programming interface for EnMAP data processing. Remote Sens 7:11249–11266

    Google Scholar 

  • Vermang J, Norton LD, Baetens JM, Huang C, Cornelis WM, Gabriels D (2013) Quantification of soil surface roughness evolution under simulated rainfall. Trans Am Soc Agric Biol Eng 56(2):505–514

    Google Scholar 

  • Viscarra Rossel R, Behrens T, Ben-Dor E, Brown D, Demattê J, Shepherd K, Shi Z, Stenberg B, Stevens A, Adamchuk V (2016) A global spectral library to characterize the world’s soil. Earth Sci Rev 155:198–230

    Google Scholar 

  • Vrieling A, De Jong SM, Sterk G, Rodrigues SC (2008) Timing of erosion and satellite data: a multi-resolution approach to soil erosion risk mapping. Int J Appl Earth Obs Geoinf 10(3):267–281

    Google Scholar 

  • Wang Z, Coburn CA, Ren X, Teillet PM (2012) Effect of soil surface roughness and scene components on soil surface bidirectional reflectance factor. Can J Soil Sci 92:297–313

    Google Scholar 

  • Weber B, Hill J (2016) Remote sensing of biological soil crusts at different scales. In: Weber B et al (eds) Biological soil crusts: an organizing principle in drylands. Springer, Cham, pp 215–234

    Google Scholar 

  • Weber B, Olehowski C, Knerr T, Hill J, Deutschewitz K, Wessels DCJ et al (2008) A new approach for mapping of biological soil crusts in semidesert areas with hyperspectral imagery. Remote Sens Environ 112:2187–2201

    Google Scholar 

  • Weksler S, Notespo G, Ben-Dor E (2017) an automated procedure for reducing atmospheric features and emphasizing surface emissivity in hyperspectral longwave infrared (LWIR) images. Int J Remote Sens. https://doi.org/10.1080/01431161.2017.1325535

    Google Scholar 

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

    Google Scholar 

  • Whiting ML, Li L, Ustin SL (2004b) Correcting mineral abundance estimates for soil moisture. In: Green RO (ed) 13th annual JPL airborne Earth science workshop, Pasadena, California, March 30–April 3, 2004. JPL Publication 05-3-1

  • Whiting ML, Palacios-Orueta A, Li L, Ustin SL (2005) Light absorption model for water content to improve soil mineral estimates in hyperspectral imagery. In: Pecora 16, global priorities in land remote sensing, Sioux Falls, South Dakota, 23–27 October 2005. American Society of Photogrammetry and Remote Sensing

  • Wu CY, Jacobson AR, Laba M, Baveye PC (2009) Accounting for surface roughness effects in the near-infrared reflectance sensing of soils. Geoderma 152:171–180. https://doi.org/10.1016/j.geoderma.2009.06.002

    Google Scholar 

  • Zhao X, Xue J-F, Zhang X-Q, Kong F-L, Chen F, Lal R et al (2015) Stratification and storage of soil organic carbon and nitrogen as affected by tillage practices in the North China Plain. PLoS ONE 10(6):e0128873

    Google Scholar 

Download references

Acknowledgements

This paper is an outcome of a Workshop on requirements, capabilities and directions in spaceborne imaging spectroscopy held at the International Space Science Institute (ISSI) in Bern, Switzerland, in November 2016. The support of ISSI is gratefully acknowledged. The EnMAP science preparation program and EnMAP coordination team are gratefully acknowledged without which the ISSI Workshop would not have taken place.

Author information

Authors and Affiliations

  1. Section Remote Sensing, Helmholtz Center Potsdam GFZ German Research Center for Geosciences, Telegrafenberg, 14473, Potsdam, Germany

    S. Chabrillat

  2. Department of Geography and Human Environment, Tel Aviv University, 69978, Tel Aviv, Israel

    E. Ben-Dor

  3. Department of Soil and Remote Sensing Soil Science, Adam Mickiewicz University, Poznan, Poland

    J. Cierniewski

  4. UMR LISAH (INRA-IRD-SupAgro), IRD, 34060, Montpellier, France

    C. Gomez

  5. Department of Environment, Centro de Investigaciones Energéticas, Medioambientes y Tecnológicas (CIEMAT), 28040, Madrid, Spain

    T. Schmid

  6. Georges Lemaître Centre for Earth and Climate Research, Earth and Life Institute, Université Catholique de Louvain, 1348, Louvain-la-Neuve, Belgium

    B. van Wesemael

Authors
  1. S. Chabrillat
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. E. Ben-Dor
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. J. Cierniewski
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. C. Gomez
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. T. Schmid
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. B. van Wesemael
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to S. Chabrillat.

Ethics declarations

Conflict of interest

The authors declare that they have no conflict of interest.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Chabrillat, S., Ben-Dor, E., Cierniewski, J. et al. Imaging Spectroscopy for Soil Mapping and Monitoring. Surv Geophys 40, 361–399 (2019). https://doi.org/10.1007/s10712-019-09524-0

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10712-019-09524-0

Keywords

Search

Navigation