À propos de cet article

Citez

Avola G., Gennaro, S.F. di Cantini C., Riggi E., Muratore F., Tornambè C., Matese A., 2019. Remotely sensed vegetation indices to discriminate field-grown olive cultivars. Remote Sensing 11. DOI 10.3390/rs11101242. AvolaG. GennaroS.F. di CantiniC. RiggiE. MuratoreF. TornambèC. MateseA. 2019 Remotely sensed vegetation indices to discriminate field-grown olive cultivars Remote Sensing 11 10.3390/rs11101242 Open DOISearch in Google Scholar

Bannari A., Morin D., Bonn F., Huete A.R., 1995. A review of vegetation indices. Remote Sensing Reviews 13: 95–120. DOI 10.1080/02757259509532298. BannariA. MorinD. BonnF. HueteA.R. 1995 A review of vegetation indices Remote Sensing Reviews 13 95 120 10.1080/02757259509532298 Open DOISearch in Google Scholar

Candiago S., Remondino F., Giglio M. de Dubbini M., Gattelli M., 2015. Evaluating multispectral images and vegetation indices for precision farming applications from UAV images. Remote Sensing 7: 4026–4047, MDPI AG. DOI 10.3390/rs70404026. CandiagoS. RemondinoF. GiglioM. de DubbiniM. GattelliM. 2015 Evaluating multispectral images and vegetation indices for precision farming applications from UAV images Remote Sensing 7 4026 4047 MDPI AG 10.3390/rs70404026 Open DOISearch in Google Scholar

Chong I.G., Jun C.H., 2005. Performance of some variable selection methods when multicollinearity is present. Chemometrics and Intelligent Laboratory Systems 78: 103–112. DOI 10.1016/j.chemolab.2004.12.011. ChongI.G. JunC.H. 2005 Performance of some variable selection methods when multicollinearity is present Chemometrics and Intelligent Laboratory Systems 78 103 112 10.1016/j.chemolab.2004.12.011 Open DOISearch in Google Scholar

Crippen R.E., 1990. Calculating the vegetation index faster. Remote Sensing of Environment 34: 71–73. CrippenR.E. 1990 Calculating the vegetation index faster Remote Sensing of Environment 34 71 73 10.1016/0034-4257(90)90085-Z Search in Google Scholar

Croft H., Kuhn N.J., 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. CroftH. KuhnN.J. AndersonK. 2012 On the use of remote sensing techniques for monitoring spatio-temporal soil organic carbon dynamics in agricultural systems Catena 94 64 74 10.1016/j.catena.2012.01.001 Search in Google Scholar

Demattê J.A.M., Fiorio P.R., 2009. Orbital and laboratory spectral data to optimize soil analysis. Scientia Agricola 66(2): 250–257. DemattêJ.A.M. FiorioP.R. 2009 Orbital and laboratory spectral data to optimize soil analysis Scientia Agricola 66 2 250 257 10.1590/S0103-90162009000200015 Search in Google Scholar

Dematte J.A.M., Huete A.R., Ferreira Jr. L.G., Nanni M.R., Alves M.C., Fiorio P.R., 2009. Methodology for bare soil detection and discrimination by landsat TM image. The Open Remote Sensing Journal 2(1): 24–35. DOI 10.2174/1875413900902010024. DematteJ.A.M. HueteA.R. FerreiraL.G.Jr. NanniM.R. AlvesM.C. FiorioP.R. 2009 Methodology for bare soil detection and discrimination by landsat TM image The Open Remote Sensing Journal 2 1 24 35 10.2174/1875413900902010024 Open DOISearch in Google Scholar

De Paul Obade V., Lal R., 2013. Assessing land cover and soil quality by remote sensing and geographical information systems (GIS). Catena 104: 77–92. De Paul ObadeV. LalR. 2013 Assessing land cover and soil quality by remote sensing and geographical information systems (GIS) Catena 104 77 92 10.1016/j.catena.2012.10.014 Search in Google Scholar

FAO [Food and Agriculture Organisation], 2021. Standard operating procedure for soil calcium carbonate equivalent - Titrimetric method. Online: www.fao.org/publications/card/en/c/CA8621EN/ (accessed July 17, 2022). FAO [Food and Agriculture Organisation] 2021 Standard operating procedure for soil calcium carbonate equivalent - Titrimetric method Online: www.fao.org/publications/card/en/c/CA8621EN/ (accessed July 17, 2022). Search in Google Scholar

Gasmi A., Gomez C., Chehbouni A., Dhiba D., Elfil H., 2022. Satellite multi-sensor data fusion for soil clay mapping based on the spectral index and spectral bands approaches. Remote Sensing 14(5). DOI 10.3390/rs14051103. GasmiA. GomezC. ChehbouniA. DhibaD. ElfilH. 2022 Satellite multi-sensor data fusion for soil clay mapping based on the spectral index and spectral bands approaches Remote Sensing 14 5 10.3390/rs14051103 Open DOISearch in Google Scholar

Gasmi A., Gomez C., Lagacherie P., Zouari H., 2019. Surface soil clay content mapping at large scales using multispectral (VNIR–SWIR) ASTER data. International Journal of Remote Sensing 40(4): 1506–1533. DOI 10.1080/01431161.2018.1528018. GasmiA. GomezC. LagacherieP. ZouariH. 2019 Surface soil clay content mapping at large scales using multispectral (VNIR–SWIR) ASTER data International Journal of Remote Sensing 40 4 1506 1533 10.1080/01431161.2018.1528018 Open DOISearch in Google Scholar

Gholizadeh A., Boruvka L., Saberioon M.M., Kozák J., Vašát R., Nemecek K., 2015. Comparing different data preprocessing methods for monitoring soil heavy metals based on soil spectral features. Soil and Water Research 10: 218–227, Czech Academy of Agricultural Sciences. DOI 10.17221/113/2015-SWR. GholizadehA. BoruvkaL. SaberioonM.M. KozákJ. VašátR. NemecekK. 2015 Comparing different data preprocessing methods for monitoring soil heavy metals based on soil spectral features Soil and Water Research 10 218 227 Czech Academy of Agricultural Sciences 10.17221/113/2015-SWR Open DOISearch in Google Scholar

Gunathilaka M.D.K.L., 2021. Modelling the behavior of DVI and IPVI vegetation indices using multi-temporal remotely sensed data. International Journal of Environment, Engineering & Education 3(1): 9–16. GunathilakaM.D.K.L. 2021 Modelling the behavior of DVI and IPVI vegetation indices using multi-temporal remotely sensed data International Journal of Environment, Engineering & Education 3 1 9 16 10.55151/ijeedu.v3i1.42 Search in Google Scholar

International Standard (ISO) 11260, 1994. Soil quality – Determination of effective cation exchange capacity and base saturation level using barium chloride solution. International Standard (ISO) 11260 1994 Soil quality – Determination of effective cation exchange capacity and base saturation level using barium chloride solution Search in Google Scholar

International Standard (ISO) 10693, 2002. Soil quality – Determination of carbonate content – Volumetric method. International Standard (ISO) 10693 2002 Soil quality – Determination of carbonate content – Volumetric method Search in 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. 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 Search in Google Scholar

Lan Y., Thomson S.J., Huang Y., Hoffmann W.C., Zhang H., 2010, October. Current status and future directions of precision aerial application for site-specific crop management in the USA. Computers and Electronics in Agriculture 74: 34–38. DOI 10.1016/j.compag.2010.07.001. LanY. ThomsonS.J. HuangY. HoffmannW.C. ZhangH. 2010 October Current status and future directions of precision aerial application for site-specific crop management in the USA Computers and Electronics in Agriculture 74 34 38 10.1016/j.compag.2010.07.001 Open DOISearch in Google Scholar

Liu W., Chen S., Qin X., Baumann F., Scholten T., Zhou Z., Sun W., et al., 2012. Storage, patterns, and control of soil organic carbon and nitrogen in the northeastern margin of the Qinghai-Tibetan Plateau. Environmental Research Letters 7 035401. DOI 10.1088/1748-9326/7/3/035401. LiuW. ChenS. QinX. BaumannF. ScholtenT. ZhouZ. SunW. 2012 Storage, patterns, and control of soil organic carbon and nitrogen in the northeastern margin of the Qinghai-Tibetan Plateau Environmental Research Letters 7 035401. 10.1088/1748-9326/7/3/035401 Open DOISearch in Google Scholar

Mammadov E., Denk M., Riedel F., Lewinska K., Kaźmierowski C., Glaesser C., 2020. Visible and near-infrared reflectance spectroscopy for assessment of soil properties in the Caucasus Mountains, Azerbaijan. Communications in Soil Science and Plant Analysis 51: 2111–2136. DOI 10.1080/00103624.2020.1820027. MammadovE. DenkM. RiedelF. LewinskaK. KaźmierowskiC. GlaesserC. 2020 Visible and near-infrared reflectance spectroscopy for assessment of soil properties in the Caucasus Mountains, Azerbaijan Communications in Soil Science and Plant Analysis 51 2111 2136 10.1080/00103624.2020.1820027 Open DOISearch in Google Scholar

Martínez M.L.J., 2017. Relación entre el estado nutricional de los cultivos, las mediciones espectrales y las imágenes Sentinel 2. Agronomia Colombiana 35: 205–215. DOI 10.15446/agron.colomb.v35n2.62857. MartínezM.L.J. 2017 Relación entre el estado nutricional de los cultivos, las mediciones espectrales y las imágenes Sentinel 2 Agronomia Colombiana 35 205 215 10.15446/agron.colomb.v35n2.62857 Open DOISearch in Google Scholar

Matese A., Gennaro S.F. di Berton A., 2017. Assessment of a canopy height model (CHM) in a vineyard using UAV-based multispectral imaging. International Journal of Remote Sensing 38: 2150–2160. DOI 10.1080/01431161.2016.1226002. MateseA. GennaroS.F. di BertonA. 2017 Assessment of a canopy height model (CHM) in a vineyard using UAV-based multispectral imaging International Journal of Remote Sensing 38 2150 2160 10.1080/01431161.2016.1226002 Open DOISearch in Google Scholar

Mehlich A., 1984. Mehlich 3 Soil Test Extractant: A Modification of Mehlich 2 Extractant. Communications in Soil Science and Plant Analysis 15: 1409–1416. DOI 10.1080/00103628409367568. MehlichA. 1984 Mehlich 3 Soil Test Extractant: A Modification of Mehlich 2 Extractant Communications in Soil Science and Plant Analysis 15 1409 1416 10.1080/00103628409367568 Open DOISearch in Google Scholar

Milton E.J., 1987. Review article: Principles of field spectroscopy. International Journal of Remote Sensing 8: 1807–1827. DOI 10.1080/01431168708954818. MiltonE.J. 1987 Review article: Principles of field spectroscopy International Journal of Remote Sensing 8 1807 1827 10.1080/01431168708954818 Open DOISearch in Google Scholar

Minasny B., McBratney A.B., 2008. Regression rules as a tool for predicting soil properties from infrared reflectance spectroscopy. Chemometrics and Intelligent Laboratory Systems 94: 72–79. DOI 10.1016/j.chemolab.2008.06.003. MinasnyB. McBratneyA.B. 2008 Regression rules as a tool for predicting soil properties from infrared reflectance spectroscopy Chemometrics and Intelligent Laboratory Systems 94 72 79 10.1016/j.chemolab.2008.06.003 Open DOISearch in Google Scholar

Motohka T., Nasahara K.N., Oguma H., Tsuchida S., 2010. Applicability of Green-Red Vegetation Index for remote sensing of vegetation phenology. Remote Sensing 2: 2369–2387. DOI 10.3390/rs2102369. MotohkaT. NasaharaK.N. OgumaH. TsuchidaS. 2010 Applicability of Green-Red Vegetation Index for remote sensing of vegetation phenology Remote Sensing 2 2369 2387 10.3390/rs2102369 Open DOISearch in Google Scholar

Nanni M.R., Dematte J.A.M., 2006. Spectral Reflectance Methodology in Comparison to Traditional Soil Analysis. Soil Science Society of America Journal 70: 393–407. DOI 10.2136/sssaj2003.0285. NanniM.R. DematteJ.A.M. 2006 Spectral Reflectance Methodology in Comparison to Traditional Soil Analysis Soil Science Society of America Journal 70 393 407 10.2136/sssaj2003.0285 Open DOISearch in Google Scholar

Nelson D.W., Sommers L.E., 1996. Total carbon, organic carbon, and organic matter. In: Sparks D.L., et al. (eds.), Methods of soil analysis. Part 3. Chemical methods. SSSA Book Series No. 5, SSSA and ASA, Madison, WI: 961–1010. NelsonD.W. SommersL.E. 1996 Total carbon, organic carbon, and organic matter In: SparksD.L. (eds.), Methods of soil analysis. Part 3. Chemical methods SSSA Book Series No. 5, SSSA and ASA Madison, WI 961 1010 10.2136/sssabookser5.3.c34 Search in Google Scholar

Ng W., Minasny B., Jeon S.H., McBratney A., 2022. Mid-infrared spectroscopy for accurate measurement of an extensive set of soil properties for assessing soil functions. Soil Security 6: 100043. DOI 10.1016/j.soisec.2022.100043. NgW. MinasnyB. JeonS.H. McBratneyA. 2022 Mid-infrared spectroscopy for accurate measurement of an extensive set of soil properties for assessing soil functions Soil Security 6 100043. 10.1016/j.soisec.2022.100043 Open DOISearch in Google Scholar

Nguyen T.T., Janik L.J., Raupach B.M., 1991. Diffuse Reflectance Infrared Fourier Transform (DRIFT) spectroscopy in soil studies. Australian Journal of Soil Research 29: 49–67. NguyenT.T. JanikL.J. RaupachB.M. 1991 Diffuse Reflectance Infrared Fourier Transform (DRIFT) spectroscopy in soil studies Australian Journal of Soil Research 29 49 67 10.1071/SR9910049 Search in Google Scholar

Peng Y., Xiong X., Adhikari K., Knadel M., Grunwald S., Greve M.H., 2015. Modeling soil organic carbon at regional scale by combining multi-spectral images with laboratory spectra. PLoS ONE 10. DOI 10.1371/journal.pone.0142295. PengY. XiongX. AdhikariK. KnadelM. GrunwaldS. GreveM.H. 2015 Modeling soil organic carbon at regional scale by combining multi-spectral images with laboratory spectra PLoS ONE 10 10.1371/journal.pone.0142295 464083926555071 Open DOISearch in Google Scholar

Peng, Y., Zhao L., Hu Y., Wang G., Wang L., Liu Z., 2019. Prediction of soil nutrient contents using visible and near-infrared reflectance spectroscopy. ISPRS International Journal of Geo-Information 8. DOI 10.3390/ijgi8100437. PengY. ZhaoL. HuY. WangG. WangL. LiuZ. 2019 Prediction of soil nutrient contents using visible and near-infrared reflectance spectroscopy ISPRS International Journal of Geo-Information 8 10.3390/ijgi8100437 Open DOISearch in Google Scholar

PN-ISO-10390, 1997. Soil quality-determination of pH. Polish Committee for Standardization, Warsaw. PN-ISO-10390 1997 Soil quality-determination of pH Polish Committee for Standardization Warsaw Search in Google Scholar

Quinlan J.R., 1992. Learning with continuous classes. Proceedings of the 5th Australian joint Conference on Artificial Intelligence, 16–18 November 1992, Hobart: 343–348. QuinlanJ.R. 1992 Learning with continuous classes Proceedings of the 5th Australian joint Conference on Artificial Intelligence 16–18 November 1992 Hobart 343 348 Search in Google Scholar

Rinnan Å., Berg F. van den Engelsen S.B., 2009, November. Review of the most common pre-processing techniques for near-infrared spectra. Trends in Analytical Chemistry 28(10): 1201–1222. DOI 10.1016/j.trac.2009.07.007. RinnanÅ. BergF. van den EngelsenS.B. 2009 November Review of the most common pre-processing techniques for near-infrared spectra Trends in Analytical Chemistry 28 10 1201 1222 10.1016/j.trac.2009.07.007 Open DOISearch in Google Scholar

Saberioon M., Amin M., Gholizadeh A., 2012. Estimation of nitrogen of rice in different growth stages using Tetracam agriculture digital camera. The Philippine Agricultural Scientist 96(1): 116–121. SaberioonM. AminM. GholizadehA. 2012 Estimation of nitrogen of rice in different growth stages using Tetracam agriculture digital camera The Philippine Agricultural Scientist 96 1 116 121 Search in Google Scholar

Saeys W., Mouazen A.M., Ramon H., 2005. Potential for onsite and online analysis of pig manure using visible and near infrared reflectance spectroscopy. Biosystems Engineering 91: 393–402. DOI 10.1016/j.biosystemseng.2005.05.001. SaeysW. MouazenA.M. RamonH. 2005 Potential for onsite and online analysis of pig manure using visible and near infrared reflectance spectroscopy Biosystems Engineering 91 393 402 10.1016/j.biosystemseng.2005.05.001 Open DOISearch in Google Scholar

Swain K.C., Thomson S.J., Jayasuriya H.P.W., 2010. Adoption of an unmanned helicopter for low-altitude remote sensing to estimate yield and total biomass of a rice crop. Transactions of the ASABE 53: 21–27. SwainK.C. ThomsonS.J. JayasuriyaH.P.W. 2010 Adoption of an unmanned helicopter for low-altitude remote sensing to estimate yield and total biomass of a rice crop Transactions of the ASABE 53 21 27 10.13031/2013.29493 Search in Google Scholar

Vega F.A., Ramírez F.C., Saiz M.P., Rosúa F.O., 2015. Multi-temporal imaging using an unmanned aerial vehicle for monitoring a sunflower crop. Biosystems Engineering 132: 19–27. DOI 10.1016/j.biosystemseng.2015.01.008. VegaF.A. RamírezF.C. SaizM.P. RosúaF.O. 2015 Multi-temporal imaging using an unmanned aerial vehicle for monitoring a sunflower crop Biosystems Engineering 132 19 27 10.1016/j.biosystemseng.2015.01.008 Open DOISearch in Google Scholar

Vestergaard R.J., Vasava H.B., Aspinall D., Chen S., Gillespie A., Adamchuk V., Biswas A., 2021. Evaluation of optimized preprocessing and modeling algorithms for prediction of soil properties using vis-nir spectroscopy. Sensors 21, 6745: 2–18. DOI 10.3390/s21206745. VestergaardR.J. VasavaH.B. AspinallD. ChenS. GillespieA. AdamchukV. BiswasA. 2021 Evaluation of optimized preprocessing and modeling algorithms for prediction of soil properties using vis-nir spectroscopy Sensors 21 6745 2 18 10.3390/s21206745 853919734695958 Open DOISearch in Google Scholar

Viscarra Rossel R.A., Walvoort D.J.J., McBratney A.B., Janik L.J., Skjemstad J.O., 2006. Visible, near infrared, mid infrared or combined diffuse reflectance spectroscopy for simultaneous assessment of various soil properties. Geoderma 131: 59–75. DOI 10.1016/j.geoderma.2005.03.007. Viscarra RosselR.A. WalvoortD.J.J. McBratneyA.B. JanikL.J. SkjemstadJ.O. 2006 Visible, near infrared, mid infrared or combined diffuse reflectance spectroscopy for simultaneous assessment of various soil properties Geoderma 131 59 75 10.1016/j.geoderma.2005.03.007 Open DOISearch in Google Scholar

Wenjun J., Zhou S., Jingyi H., Shuo L., 2014. In situ measurement of some soil properties in paddy soil using visible and near-infrared spectroscopy. PLoS ONE 9(8), e105708. DOI 10.1371/journal.pone.0105708. WenjunJ. ZhouS. JingyiH. ShuoL. 2014 In situ measurement of some soil properties in paddy soil using visible and near-infrared spectroscopy PLoS ONE 9 8 e105708 10.1371/journal.pone.0105708 414327925153132 Open DOISearch in Google Scholar

Wetterlind J., Stenberg B., Söderström M., 2008. The use of near infrared (NIR) spectroscopy to improve soil mapping at the farm scale. Precision Agriculture 9: 57–69. DOI 10.1007/s11119-007-9051-z. WetterlindJ. StenbergB. SöderströmM. 2008 The use of near infrared (NIR) spectroscopy to improve soil mapping at the farm scale Precision Agriculture 9 57 69 10.1007/s11119-007-9051-z Open DOISearch in Google Scholar

Wojewódzki Inspektorat Ochrony Środowiska w Poznaniu 2013. Raport o stanie środowiska w Wielkopolsce w roku 2012. Biblioteka Monitoringu Środowiska, Poznań. Wojewódzki Inspektorat Ochrony Środowiska w Poznaniu 2013 Raport o stanie środowiska w Wielkopolsce w roku 2012 Biblioteka Monitoringu Środowiska Poznań Search in Google Scholar

Wold S., Sjostrom M., Eriksson L., Sweden S., 2001. PLS-regression: A basic tool of chemometrics. Chemometrics and Intelligent Laboratory Systems 58(2): 109–130. DOI 10.1016/S0169-7439(01)00155-1. WoldS. SjostromM. ErikssonL. SwedenS. 2001 PLS-regression: A basic tool of chemometrics Chemometrics and Intelligent Laboratory Systems 58 2 109 130 10.1016/S0169-7439(01)00155-1 Open DOISearch in Google Scholar

Xu S., Wang M., Shi X., Yu Q., Zhang Z., 2021. Integrating hyperspectral imaging with machine learning techniques for the high-resolution mapping of soil nitrogen fractions in soil profiles. Science of the Total Environment 754, 142135. DOI 10.1016/j.scitotenv.2020.142135. XuS. WangM. ShiX. YuQ. ZhangZ. 2021 Integrating hyperspectral imaging with machine learning techniques for the high-resolution mapping of soil nitrogen fractions in soil profiles Science of the Total Environment 754 142135. 10.1016/j.scitotenv.2020.142135 32920400 Open DOISearch in Google Scholar

eISSN:
2081-6383
Langue:
Anglais
Périodicité:
4 fois par an
Sujets de la revue:
Geosciences, Geography