Reducing the influence of soil moisture on the estimation of clay from hyperspectral data: A case study using simulated PRISMA data

Remote Sensing

Volumn 7, Issue 11, 2015, Pages 15561-15582

  Castaldi, Fabio ^a   Palombo, Angelo ^b   Pascucci, Simone ^b   Pignatti, Stefano ^b   Santini, Federico ^b   Casa, Raffaele ^a  

^a UNIVERSITY OF TUSCIA   (Italy)

^b CNR   (Italy)

Author keywords

Clay indices; PLSR; Proximal sensing; Remote sensing; Satellite; Spectral features

Indexed keywords

FORECASTING; LEAST SQUARES APPROXIMATIONS; MOISTURE; MOISTURE DETERMINATION; REMOTE SENSING; SATELLITES; SOIL MOISTURE; SOIL SURVEYS; SOILS; SPECTROSCOPY;

HYPERSPECTRAL IMAGERS; HYPERSPECTRAL REMOTE SENSING DATA; PARTIAL LEAST SQUARES REGRESSIONS (PLSR); PLSR; PROXIMAL SENSING; SPECTRAL ABSORPTION FEATURES; SPECTRAL CHARACTERISTICS; SPECTRAL FEATURE;

DATA REDUCTION;

EID: 84950150743     PISSN: None     EISSN: 20724292     Source Type: Journal    
DOI: 10.3390/rs71115561     Document Type: Article

References (42)

1. Viscarra Rossel, R.A.; Walvoort, D.J.J.; McBratney, A.B.; Janik, L.J.; Skjemstad, J.O. Visible, near infrared, mid infrared or combined diffuse reflectance spectroscopy for simultaneous assessment of various soil properties. Geoderma 2006, 131, 59-75.
2. Ben-Dor, E.; Chabrillat, S.; Dematte, J.A.M.; Taylor, G.R.; Hill, J.; Whiting, M.L.; Sommer, S. Using imaging spectroscopy to study soil properties. Remote Sens. Environ. 2009, 113, S38-S55.
3. Richter, K.; Hank, T.; Atzberger, C.; Locherer, M.; Mauser, W. Regularization strategies for agricultural monitoring: The EnMAP vegetation analyzer (AVA). In Proceedings of the IEEE International Geoscience and Remote Sensing Symposium, Munich, Germany, 22-27 July 2012; pp. 6613-6616.
4. Tanii, J.; Iwasaki, A.; Kawashima, T.; Inada, H. Results of evaluation model of Hyperspectral Imager Suite (HISUI). In Proceedings of the IEEE International Geoscience and Remote Sensing Symposium, Munich, Germany, 22-27 July 2012; pp. 131-134.
5. Pignatti, S.; Acito, N.; Amato, U.; Casa, R.; de Bonis, R.; Diani, M.; Laneve, G.; Matteoli, S.; Palombo, A.; Pascucci, S.; et al. Development of algorithms and products for supporting the Italian hyperspectral PRISMA mission: The SAP4PRISMA project. In Proceedings of the IEEE International Geoscience and Remote Sensing Symposium, Munich, Germany, 22-27 July 2012; pp. 127-130.
6. Houborg, R.; Anderson, M.; Gao, F.; Schull, M.; Cammalleri, C. Monitoring water and carbon fluxes at fine spatial scales using HyspIRI-like measurements. In Proceedings of the IEEE International Geoscience and Remote Sensing Symposium, Munich, Germany, 22-27 July 2012; pp. 7302-7305.
7. Lobell, D.B.; Asner, G.P. Moisture effects on soil reflectance. Soil Sci. Soc. Am. J. 2002, 66, 722-727.
8. Morgan, C.L.S.; Waiser, T.H.; Brown, D.J.; Hallmark, C.T. Simulated in situ characterization of soil organic and inorganic carbon with visible near-infrared diffuse reflectance spectroscopy. Geoderma 2009, 151, 249-256.
9. Nocita, M.; Stevens, A.; Noon, C.; van Wesemael, B. Prediction of soil organic carbon for different levels of soil moisture using Vis-NIR spectroscopy. Geoderma 2013, 199, 37-42.
10. Wu, C.-Y.; Jacobson, A.R.; Laba, M.; Baveye, P.C. Alleviating Moisture Content Effects on the Visible Near-Infrared Diffuse-Reflectance Sensing of Soils. Soil Sci. 2009, 174, 456-465.
11. Waiser, T.H.; Morgan, C.L.S.; Brown, D.J.; Hallmark, C.T. In Situ Characterization of Soil Clay Content with Visible Near-Infrared Diffuse Reflectance Spectroscopy. Soil Sci. Soc. Am. J. 2007, 71, 389-396.
12. Ge, Y.; Morgan, C.L.S.; Ackerson, J.P. VisNIR spectra of dried ground soils predict properties of soils scanned moist and intact. Geoderma 2014, 221-222, 61-69.
13. Ackerson, J.P.; Demattê, J.A.M.; Morgan, C.L.S. Predicting clay content on field-moist intact tropical soils using a dried, ground VisNIR library with external parameter orthogonalization. Geoderma 2015, 259-260, 196-204.
14. Rienzi, E.A.; Mijatovic, B.; Mueller, T.G.; Matocha, C.J.; Sikora, F.J.; Castrignanò, A. Prediction of soil organic carbon under varying moisture levels using reflectance spectroscopy. Soil Sci. Soc. Am. J. 2014, 78, 958-967.
15. Minasny, B.; McBratney, A.B.; Bellon-Maurel, V.; Roger, J.-M.; Gobrecht, A.; Ferrand, L.; Joalland, S. Removing the effect of soil moisture from NIR diffuse reflectance spectra for the prediction of soil organic carbon. Geoderma 2011, 167-168, 118-124.
16. Kuang, B.; Mouazen, A.M. Effect of spiking strategy and ratio on calibration of on-line visible and near infrared soil sensor for measurement in European farms. Soil Tillage Res. 2013, 128, 125-136.
17. Ji, W.; Viscarra Rossel, R.A.; Shi, Z. Accounting for the effects of water and the environment on proximally sensed vis-NIR soil spectra and their calibrations. Eur. J. Soil Sci. 2015, 66, 555-565.
18. Haubrock, S.-N.; Chabrillat, S.; Lemmnitz, C.; Kaufmann, H. Surface soil moisture quantification models from reflectance data under field conditions. Int. J. Remote Sens. 2008, 29, 3-29.
19. Gomez, C.; Lagacherie, P.; Coulouma, G. Continuum removal versus PLSR method for clay and calcium carbonate content estimation from laboratory and airborne hyperspectral measurements. Geoderma 2008, 148, 141-148.
20. Casa, R.; Castaldi, F.; Pascucci, S.; Basso, B.; Pignatti, S. Geophysical and hyperspectral data fusion techniques for in-field estimation of soil properties. Vadose Zone J. 2013, doi:10.2136/vzj2012.0201.
21. Pascucci, S.; Casa, R.; Belviso, C.; Palombo, A.; Pignatti, S.; Castaldi, F. Estimation of soil organic carbon from airborne hyperspectral thermal infrared data: A case study. Eur. J. Soil Sci. 2014, 65, 865-875.
22. Li, D.; Chen, X.; Peng, Z.; Chen, S.; Chen, W.; Han, L.; Li, Y. Prediction of soil organic matter content in a litchi orchard of South China using spectral indices. Soil Tillage Res. 2012, 123, 78-86.
23. Casa, R.; Castaldi, F.; Pascucci, S.; Palombo, A.; Pignatti, S. A comparison of sensor resolution and calibration strategies for soil texture estimation from hyperspectral remote sensing. Geoderma 2013, 197-198, 17-26.
24. FAO-ISRIC-ISSS. World Reference Base for Soil Resources; World Soil Resources Report 84. Food and Agriculture Organisation: Rome, Italy, 1998.
25. Soil Survey Staff. Keys to Soil Taxonomym, 12th ed.; USDA-Natural Resources Conservation Service: Washington, DC, USA, 2014.
26. Beuselinck, L.; Govers, G.; Poesen, J.; Degraer, G.; Froyen, L. Grain-size analysis by laser diffractometry: Comparison with the sieve-pipette method. Catena 1998, 32, 193-208.
27. Liu, W.; Baret, F.; Gu, X.; Zhang, B.; Tong, Q.; Zheng, L. Evaluation of methods for soil surface moisture estimation from reflectance data. Int. J. Remote Sens. 2003, 24, 2069-2083.
28. Bellon-Maurel, V.; Fernandez-Ahumada, E.; Palagos, B.; Roger, J.-M.; McBratney, A. Critical review of chemometric indicators commonly used for assessing the quality of the prediction of soil attributes by NIR spectroscopy. TrAC Trends Anal. Chem. 2010, 29, 1073-1081.
29. Saxton, K.E.; Rawls, W.J. Soil water characteristic estimates by texture and organic matter for hydrologic solutions. Soil Sci. Soc. Am. J. 2006, 70, 1569-1578.
30. Clark, R.N.; Roush, T.L. Reflectance spectroscopy: Quantitative analysis techniques for remote sensing applications. J. Geophys. Res. 1984, 89, 6329-6340.
31. Wold, S.; Sjöström, M.; Eriksson, L. PLS-regression: A basic tool of chemometrics. Chemom. Intell. Lab. Syst. 2001, 58, 109-130.
32. R Development Core Team. R: A Language and Environment for Statistical Computing; R Foundation for Statistical Computing: Vienna, Austria, 2011.
33. Berk, A.; Bernstein, L.S.; Anderson, G.P.; Acharya, P.K.; Robertson, D.C.; Chetwynd, J.H.; Adler-Golden, S.M. MODTRAN Cloud and Multiple Scattering Upgrades with Application to AVIRIS. Remote Sens. Environ. 1998, 65, 367-375.
34. Clark, R.N. Chapter 1: Spectroscopy of rocks and minerals, and principles of spectroscopy. In Manual of Remote Sensing, Remote Sensing for the Earth Sciences; Rencz, A.N., Ed.; John Wiley and Sons: New York, NY, USA, 1999; Volume 3, pp. 3-58.
35. Tóth, G.; Jones, A.; Montanarella, L. The LUCAS topsoil database and derived information on the regional variability of cropland topsoil properties in the European Union. Environ. Monit. Assess. 2013, 185, 7409-7425.
36. Summers, D.; Lewis, M.; Ostendorf, B.; Chittleborough, D. Visible near-infrared reflectance spectroscopy as a predictive indicator of soil properties. Ecol. Indic. 2011, 11, 123-131.
37. Bilgili, A.V.; Akbas, F.; van Es, H.M. Combined use of hyperspectral VNIR reflectance spectroscopy and kriging to predict soil variables spatially. Precis. Agric. 2010, 12, 395-420.
38. Lagacherie, P.; Baret, F.; Feret, J.-B.; Madeira Netto, J.; Robbez-Masson, J.M. Estimation of soil clay and calcium carbonate using laboratory, field and airborne hyperspectral measurements. Remote Sens. Environ. 2008, 112, 825-835.
39. Viscarra Rossel, R.A.; Cattle, S.R.; Ortega, A.; Fouad, Y. In situ measurements of soil colour, mineral composition and clay content by vis-NIR spectroscopy. Geoderma 2009, 150, 253-266.
40. Ackerson, C.; Morgan, L.S.; Ge, Y.; McBratney, A.; Minasny, B. A tale of two projections: Removing the effects of water content and intactness from in situ VisNIR spectra using external parameter orthagonalization and direct standardization. In Proceedings of the Pedometrics 2015, Cordoba, Spain, 14-17 September 2015; p. 70.
41. Folkman, M.A.; Pearlman, J.; Liao, L.B.; Jarecke, P.J. EO-1/Hyperion hyperspectral imager design, development, characterization, and calibration. Proc. SPIE 2001, doi:10.1117/12.417022.
42. Castaldi, F.; Casa, R.; Castrignanò, A.; Pascucci, S.; Palombo, A.; Pignatti, S. Estimation of soil properties at the field scale from satellite data: A comparison between spatial and non-spatial techniques. Eur. J. Soil Sci. 2014, 65, 842-851.