فصلنامه علمی- پژوهشی اطلاعات جغرافیایی « سپهر»

شماره جاری

بر اساس شماره‌های نشریه

بر اساس نویسندگان

بر اساس موضوعات

نمایه نویسندگان

نمایه کلیدواژه ها

درباره نشریه

اهداف و چشم انداز

اعضای هیات تحریریه

اصول اخلاقی انتشار مقاله

بانک ها و نمایه نامه ها

پیوندهای مفید

پرسش‌های متداول

فرایند پذیرش مقالات

اخبار و اعلانات

فرم های ضروری نشریه

شرایط و ضوابط ارسال مقاله

راهنمای تنظیم مقاله

تخمین مقادیر شن در خاک های استان مازندران با استفاده از فن­اوری سنجش مجاورتی

نوع مقاله : مقاله پژوهشی

نویسندگان

1 استادیار حفاظت و فرسایش خاک، دانشگاه علوم کشاورزی و منابع طبیعی ساری

2 استاد فیزیک، حفاظت و فرسایش خاک، دانشگاه تربیت مدرس

3 دانشیار سنجش از دور و علوم اطلاعات زمین، دانشگاه توئنته، هلند

4 دانشیار پژوهشکده حفاظت خاک و آبخیزداری کشور، تهران

چکیده

شن از مهم‌ترین اجزای بافت خاک است که برای عملیات مدل‌سازی زیست‌محیطی و پهنه‌بندی رقومی خاک، باید مورد توجه واقع شود. از آن­جا که این ویژگی، دستخوش تغییرپذیری­‌های مکانی بوده، لذا تشخیص، پهنه‌بندی و پایش آن در مقیاس وسیع و با روش‌های نمونه‌برداری و تحلیل آزمایشگاهی معمول، بسیار هزینه­‌بر و وقت­‌گیر است. با ظهور فناوری مجاورت‌سنجی بازتابی(VNIR-DRS)، روزنه‌­ای در بررسی این ویژگی خاک ایجاد شده است. طی تحقیق حاضر، از طیف‌سنجی بازتابی مجاورتی، برای بررسی اجزای شن در قسمت­‌هایی از استان مازندران استفاده شد. جمعاً 128 نمونه از عمق 20 سانتی‌متری سطح خاک، براساس روش نمونه‌برداری طبقه‌بندی شدۀ تصادفی و نیز با کمک اطلاعات جانبی همچون: زمین‌شناسی، کاربری ­اراضی، نقشۀ راه­‌ها، و خاک­شناسی استان جمع­‌آوری شد. در ابتدا مجموع نمونه‌ها به دو قسمت برای عملیات واسنجی و اعتبارسنجی تقسیم شد. با بهره‌گیری از تحلیل رگرسیون چندمتغیرۀ PLSR و براساس روش اعتبارسنجی متقاطع و عملیات پیش‌­پردازشی چون: میانگین­‌گیری (روش کاهش داده‌های طیفی)، هموارسازی و مشتق اول طیفی براساس الگوریتم ساویتسکی-گولای، مدل نهایی با تعداد 2 و 4 عامل؛ به‌ترتیب: با همبستگی دوطرفۀ پیرسون(RP) حدود 0.83 و0.82 ، ضریب تبیین(R2P) حدود 0.68 و 0.67 ، میانگین مربعات خطای کالیبراسیون(RMSEP) حدود 8.68 و 8.83%، و نیز RPDP تقریبی 1.78 و 1.75، RPIQP تقریبی 2.45 و 2.41 (ست اعتبارسنجی مستقل)، به‌عنوان مطلوب­‌ترین مدل به‌منظور برآورد مقادیر شن منطقۀ مورد مطالعه، شناخته شد که نتایج، نشان‌دهندۀ توانایی مناسب مدل در برآورد مقادیر شن منطقه بوده است. درنهایت، قابلیت فن­اوری مجاورت‌سنجی، در بررسی اجزای شن منطقه به اثبات رسید. همچنین، از این مدل و نیز دامنه‌های طیفی مؤثر به‌دست آمده می‌توان به‌عنوان مبنایی برای بررسی مقادیر شن در مقیاس بسیار وسیع، با عملیات بیش­‌مقیاس‌­سازی توسط داده‌های ابرطیفی هوایی- ماهواره‌­ای، بهره برد. که این امر نشان‌دهندۀ اهمیت طیف‌سنجی مجاورتی در تشخیص طول‌موج‌های مفید و نیز ایجاد مدل، به‌منظور استفادۀ آن در داده‌های ماهواره‌­ای، خواهد بود.

کلیدواژه‌ها

عنوان مقاله [English]

Estimating soil sand content in Mazandaran province using proximal sensing technology

نویسندگان [English]

  • Majid Danesh 1
  • HosseinAli Bahrami 2
  • Roshanak Darvishzadeh 3
  • Ali Akbar Noroozi 4

1 Department of soil science, Sari agricultural sciences and natural resources University (SANRU), Sari, Iran

2 Department of soil science, Tarbiat Modares University (TMU), Tehran, Iran

3 Faculty of geo-information science and earth observation (ITC), Department of natural resources (NRS), University of Twente, Enschede, Netherlands

4 Institute of soil conservation and watershed management research, Tehran, Iran

چکیده [English]

Extended Abstract

Introduction

Soil is considered to be dynamic and complex both spatially and temporally and thus, many physical, chemical and biological properties should be determined before assessing its quality. To reach this purpose, a large sample must be collected for laboratory tests which is both time-consuming and costly and requires lots of attention and precision. Compared to other components of soil, sand is closely related with the quality of soil and crop growth. Therefore, environmental modeling and digital soil mapping projects should pay special attention to this part of soil texture. However, large-scale detection, mapping and monitoring of sand content using common traditional sampling and usual laboratory analytical procedures are both time-consuming and costly due to the vast spatial variability of sand. Compared to laboratory-based and field spectroscopy, spaceborne and airborne remote sensing have a lower level of accuracy due to atmospheric effects, compositional and structural effects, lower spatial and spectral resolution, geometric distortions and spectral mixing. Hence, an appropriate technology is required to overcome these imperfections and study spatially variable factors. Lab Diffuse reflectance Spectroscopy (LDRS) which utilizes fundamental vibration, overtones and a combination of functional groups has been introduced as a promising tool for soil investigation. The present research uses proximal soil sensing technology to study sand content.

 

Materials and Methods

128 samples were collected from a soil depth of 20cm in accordance with stratified randomized sampling method and supplementary data (geology, pedology, land use, etc.). The samples were then divided into two subsets: calibration subset with 96 and validation subset with 32 samples. Afterward, definitive calibration model was developed and reviewed with two & four latent variables in accordance with R, R2, RMSE, RPD and RPIQ indices using multivariate regression analysis-PLSR method, LOOCV cross-validation technique and preprocessing algorithms such as spectral averaging (spectral reduction method), smoothing and 1st derivative (Savitzky-Golay algorithm).

 

Results & Discussion

The estimating model indicated that out of the seven latent variables, the first two and four variables can provide the best estimate of the volume of sand in 96 calibration samples and the 32 validation subset. Since more than 60% of the variance of sand variable and 95% of the variance of spectral variables can be concentrated in these selected factors, the predicting model was calibrated based on the first four LVs and the full LOOCV procedure. The best model was calibrated with these features: Rc=0.76, R2C=0.57, RMSEc= 9.77 and SEc of about 9.82. The correlation coefficients (R) between sand contents and the effective spectral bands were calculated and equaled UV-390nm= 0.46, Vis-510 to 540nm about 0.53, 680 to 690 about 0.55, NIR- 950 to 970 about 0.67 and 1100nm= 0.70, SWIR- 1410 nm=0.76, 1860 to 1900 about 0.76, 2180 to 2220 about 0.77 indicating that the selected spectral bands (spectral ranges) with the maximum R contents were the most effective independent predictors in the present modeling process. Furthermore, the most influential spectral domains in the modeling process were determined as follows: UV-390 nm, Vis-440-540 nm, NIR- 740-990 nm, SWIR- 1430-1890, 1930, 2190-2240, 2330-2440 nm which was in agreement with previous studies. The quality of the calibrated sand predicting model was evaluated with Hotelling, Adjusted leverage and residual variances tests. The model was validated based on 32 independent samples. General characteristics of the validation process for LV=4 were Rp= 0.82, R2p= 0.67, RMSEp= 8.83, SEp= 8.92 and bias= -0.93 and Rp= 0.83, R2p= 0.68, RMSEp= 8.68, SEp= 8.72 and bias= -1.26 for LV=2.

 

Conclusion

Results indicate that the final model was capable of predicting sand contents and thus for two factors (LV=2): RPDc= 1.51, RPIQc= 2.44, RPDp= 1.78 and RPIQp= 2.45 were obtained while for four factors (LV=4): RPDc= 1.54, RPIQc= 2.48, RPDp= 1.75 and RPIQp= 2.41 were reached. A RPIQ of more than 2 shows that the model is capable of estimating soil sand content in Mazandaran province using data collected through diffuse reflectance spectroscopy. Since a new generation of hyperspectral remote sensors with high spectral resolution is now available, results of the present study can be the starting point for more accurate mapping of sand particles in soil texture using RS platforms. However, proximal spectroscopy must be more thoroughly investigated. Determining and detecting the key wavelengths in the modeling process can enhance the upscaling operation and the new airborne/satellite hyperspectral sensors and thus result in more precise mapping of the soil texture. Finally, the VNIR-DRS technology was proved to be potentially capable of estimating soil sand content in Mazandaran province. The present model and key spectral domains identified in the present study can make a basis for future studies investigating the sand content in very large-scale samples using airborne/satellite hyperspectral data. This shows the importance of LDRS and its role in identifying optical wavelengths which will be used in space-borne data (upscaling process).

کلیدواژه‌ها [English]

  • Cross validation
  • Digital mapping
  • Partial least squares regression
  • Proximal soil sensing
  • Sand
مراجع
1- دانش، م.، بهرامی، ح.ع، درویش‌زاده، ر.، نوروزی، ع.1.، 1395. بررسی مقادیر رس با استفاده از طیف‌سنجی ابرطیفی آزمایشگاهی (LDRS). سنجش از دور و جی آی اس ایران: 8 (1): 71-94.
2- Abdu, H., Robinson, D., Seyfried, M., Jones, S.B., 2008. Geophysical imaging of water-shed subsurface patterns and prediction of soil texture and water holdingcapacity. Water Resour. Res., 44.
3- Aïchi, H., Fouad, Y., Walter, C., Viscarra Rossel, R.A., Chabaane, Zohra Lili, Sanaa, Mustapha, 2009. Regional predictions of soil organic carbon content from spectral reflectance measurements. Biosyst. Eng. 104, 442–446.
4-  Andrade, F., Mendonc¸a, E., Silva, I., 2013. Organic acids and diffusive flux of organicand inorganic phosphorus in sandy-loam and clayey latosols. Commun. Soil Sci.Plant Anal. 44, 1211–1223.
5- Annea, N.J.P., Abd-Elrahmana, A.H., Lewisb, D.B., HewittaaSchoo, N.A., 2014. Modeling soil parameters using hyperspectral image reflectance insubtropical coastal wetlands. International Journal of Applied Earth Observation and Geoinformation 33 (2014) 47–56.
6- Barnes, E.M., Baker, M.G., 2000. Multispectral data for mapping soil texture: possibilities and limitations. Applied Engineering in Agriculture 16, 731–741.
7- Bellon-Maurel, V., Fernandez-Ahumada, E., Palagos, B., Roger, J.M., McBratney, A., 2010, Critical review of chemometric indicators commonly used for assessing the quality of the prediction of soil attributes by NIR spectroscopy, Trends Anal.Chem., 29 (9): 1073–1081.
8- Ben-Dor, E. 2002. Quantitative remote sensing of soil properties. Adv. Agron. 75: 173–243.
9- Ben-Dor, E., Heller, D., Chudnovsky, A., 2008a, A novel method of classifying soil profiles in the field using optical means, Soil.Sci.Soc.Am.J., 72: 1113–1123.
10- Bengtsson, S., Bergl¨of, T., Kylin, H., 2007. Near infrared reflectance spectroscopy as a tool to predict pesticide sorption in soil. B. Environ. Contam. Tox. 78: 295–298.
11- Bishop, J.L., Pieters, C.M., Edwards, J.O., 1994, Infrared spectroscopic analyses on the nature of water in montmorillonite. Clay, Clay Miner. 42: 702–716.
12- Bresson, L.M., Le Bissonnais, Y., Andrieux, P., 2006. Soil surface crusting and structure slumping in Europe. In: Boardman, J., Poesen, J. (Eds.), Soil Erosion in Europe. Wiley & Sons Ltd, West Sussex, pp. 489–500.
13- Brown, D.J., Shepherd, K.D., Walsh, M.G., Dewayne Mays, M., Reinsch, T.G., 2006. Global soil characterization with VNIR diffuse reflectance spectroscopy. Geoderma 132, 273–290.
14- Camargo, O.A., Moniz, A.C., Jorge, J.A., Valadares, J.M., 2009, Methods of Chemical, Mineralogical and Physical Analysis of Soils Used in the Pedology Section (Technical Bulletin n.106), Instituto Agronômico (IAC), Campinas.
15- Carroll, D., 1958. Role of clay minerals in the transportation of iron. Geochimica et Cosmochimica Acta 14, 1–16.
16- Casa, R., Castaldi, F., Pascucci, S., Palombo, A., Pignatti, S., 2013. A comparison ofsensor resolution and calibration strategies for soil texture estimation fromhyperspectral remote sensing. Geoderma 197, 17–26.
17- Chabrillat, S., Ben-Dor, E., Rossel, R.A.V., Demattê, J.A.M., 2013. Quantitative soilspectroscopy. Appl. Environ. Soil Sci. 3, 1–3.
18- Chang, C.W., Laird, D.A., Mausbach, M.J., Hurburgh, Jr.C.R., 2001. Near-infrared reflectance spectroscopy—principal components regression analyses of soil properties. Soil Sci. Soc. Am. J. 65: 480– 490.
19- Chang, C.W., Laird, D.A., 2002, Near-infrared reflectance spectroscopy analysis of soil C and N, Soil Science, 167: 110–116.
20- Conforti, M., Buttafuoco, G., Leone, A.P., Aucelli, P.P.C., Robustelli, G., Scarciglia, F., 2013. Studying the relationship between water-induced soil erosion and soil organic matter using Vis–NIR spectroscopy and geomorphological analysis: A case study in southern Italy. Catena 110 (2013) 44–58.
21- Curcio, D., Ciraolo, G., D’Asaro, F., Minacapilli, M., 2013. Prediction of soil texture distributions using VNIR-SWIR reflectance spectroscopy. Procedia Environmental Sciences 19 ( 2013 ) 494 – 503.
22- Darvishzadeh, R., Atzberger, C., Skidmore, A., Schlerf, M., 2011. Mapping grassland leaf area index with airborne hyperspectral imagery: A comparison study of statistical approaches and inversion of radiative transfer models. ISPRS Journal of Photogrammetry and Remote Sensing 66 (2011) 894–906.
23- Demattê, J.A.M., Garcia, G.J., 1999. Alteration of soil properties through a weathering sequence as evaluated by spectral reflectance. Soil Sci. Soc. Am. J. 63 (2), 327–342.
24- Demattê, J.A.M., Terra, F.S., 2014, Spectral pedology: A new perspective on evaluation of soils along pedogenetic alterations, Geoderma 217–218(2014): 190–200.
25- Eisele, A., Lau, I., Hewson, R., Carter, D., Wheaton, B., Ong, C., Cudahy, T.J., Chabrillat, S.,Kaufmann, H., 2012. Applicability of the thermal infrared spectral region for theprediction of soil properties across semi-arid agricultural landscapes. RemoteSens. 4, 3265–3286.
26- Ge, Y., Thomasson, J.A., Morgan, C.L.S., 2014. Mid-infrared attenuated total reflectance spectroscopy for soil carbon and particle size determination. Geoderma 213 (2014) 57–63.
27- Geladi, P., Kowalski, B.R., 1986. Partial least-squares regression: a tutorial. Analytica Chimica Acta 185, 1–17.
28- Goldshleger, N., Ben-Dor, E., Lugassi, R., Eshel, G., 2010. Soil degradation monitoringby remote sensing: examples with three degradation processes. Soil Sci. Soc.Am. J. 74, 1433–1445.
29- Gomez, C., Lagacherie, P., Coulouma, G., 2008. Continuum removal versus PLSR method for clay and calcium carbonate content estimation from laboratory and airborne hyperspectral measurements. Geoderma 148, 141–148.
30- Gomez, C., Le Bissonnais, Y., Annabi, M., Bahri, H., Raclot, D., 2013. Laboratory Vis–NIR spectroscopy as an alternative method for estimating the soil aggregate stability indexes of Mediterranean soils. Geoderma 209–210 (2013) 86–97.
31- Goncalves, J., Carlyle, J., 1994. Modelling the influence of moisture and temperatureon net nitrogen mineralization in a forested sandy soil. Soil Biol. Biochem. 26,1557–1564.
32- Grevea, M.H., Kheira, R.B., Grevea, M.B., Bøcherb, P.K., 2012. Quantifying the ability of environmental parameters to predict soil texture fractions using regression-tree model with GIS and LIDAR data: The case study of Denmark. Ecological Indicators 18 (2012) 1–10.
33- Hahn, C., Gloaguen, R., 2008. Estimation of soil types by non linear analysis of remote sensing data. Nonlinear Processes Geophys. 15 (1), 115–126.
34- Hewson, R.D., Cudahy, T.J., Jones, M., Thomas, M., 2012. Investigations into soilcomposition and texture using infrared spectroscopy. Appl. Environ. Soil Sci.,12.
35- Islam, K., Singh, B., McBratney, A., 2003. Simultaneous estimation of several soil properties by ultra-violet, visible, and near-infrared reflectance spectroscopy. Soil Res. 41 (6), 1101–1114.
36- Jong, S.M.D., Addink, E.A., van Beek, L.P.H., Duijsings, D., 2011. Physical characterization, spectral response and remotely sensed mapping of Mediterranean soil surface crusts. Catena 86 (2011) 24–35.
37- Kagan, T.P., Shachak, M., Zaady, E., Karnieli, A., 2014. A spectral soil quality index (SSQI) for characterizing soil function in areas of changed land use. Geoderma 230–231 (2014) 171–184.
38- Kempen, B., Brus, D.J., Stoorvogel, J.J., Heuvelink, G., de Vries, F., 2012. Efficiencycomparison of conventional and digital soil mapping for updating soil maps.Soil Sci. Soc. Am. J. 76, 2097–2115.
39- Kosmas, C., Tsara, M., Moustakas, N., Kosma, D., Yassoglou, N., 2006. Environmentallysensitive areas and indicators of desertification. In: Kepner, W., Rubio, J., Mouat,D., Pedrazzini, F. (Eds.), Desertification in the Mediterranean Region. A SecurityIssue. Springer, Netherlands, pp. 525–547.
40- Kuang, B., Mahmood, H.S., Quraishi, M.Z., Hoogmoed, W.B., Mouazen, A.M., van Henten, E.J., 2012. Sensing Soil Properties in the Laboratory, In Situ, and On-Line: A Review. Advances in Agronomy, Volume 114 # 2012 Elsevier Inc. ISSN 0065-2113, DOI: 10.1016/B978-0-12-394275-3.00003-1.
41- Lagacherie, P., Baret, F., Feret, J.B., Netto, J.M., Robbez-Masson, J.M., 2008. Estimation of soil clay and calcium carbonate using laboratory, field and airborne hyperspectral measurements. Remote Sensing of Environment 112, 825–835.
42- Li, D., Durand, M., Margulis, S.A., 2012. Potential for hydrologic characterization ofdeep mountain snowpack via passive microwave remote sensing in the KernRiver basin, Sierra Nevada, USA. Remote Sens. Environ. 125, 34–48.
43- Li, M. Z. (ed.) 2006. Spectral Analysis Technology and Application (in Chinese). Science Press, Beijing.
44- Liang, S., 1997. An investigation of remotely sensed soil depth in the optical region. International Journal of Remote Sensing, 18(16): 3395−3408.
45- Lu, P., Wang, L., Niu, Z., Li, L., Zhang, W., 2013, Prediction of soil properties using laboratory VIS–NIR spectroscopy and Hyperion imagery, Journal of Geochemical Exploration, 132(2013): 26–33.
46- Madari, B.E., Reeves III, J.B., Machado, P.L.O.A., Guimar˜aes, C.M., Torres, E., McCarty, G.W., 2006. Mid- and near-infrared spectroscopic assessment of soil compositional parameters and structural indices in two Ferralsols. Geoderma. 136: 245–259.
47- McDowell, M.L., Bruland, G.L., Deenik, J.L., Grunwald, S., Knox, N.M., 2012, Soil total carbon analysis in Hawaiian soils with visible, near-infrared and mid-infrared diffuse reflectance spectroscopy, Geoderma 189–190(2012): 312–320.
48- Miller, D.T.G.A.R.W., 2004. Soils in Our Environment: Upper Saddle River, 10th ed.Prentice Hall PTR, New Jersey.
49- Minasny, B., McBratney, A.B., Tranter, G., Murphy, B.W., 2008. Using soil knowledge for the evaluation of mid-infrared diffuse reflectance spectroscopy for predicting soil physical and mechanical properties. Eur. J. Soil Sci. 59 (5), 960–971.
50- Minasny, B., Hartemink, A.E., 2011, Predicting soil properties in the tropics, Earth-Science Reviews 106(2011): 52–62.
51- Mulder, V.L., de Bruin, S., Schaepman, M.E., Mayr, T.R., 2011. The use of remote sensing in soil and terrain mapping — A review. Geoderma 162 (2011) 1–19.
52- Nanni, M.R., Demattê, J.A.M., 2006. Spectral reflectance methodology in comparison to traditional soil analysis. Soil Sci. Soc. Am. J. 70 (2), 393–407.
53- Pasikatan, M.C., Steele, J.L., Spillman, C.K., Haque, E., 2001. Review: Near infrared reflectance spectroscopy for online particle size analysis of powders and ground materials. J. Near Infrared Spec. 9: 153–164.
54- Ray, N.C.B.a.R.W., 2001. The Nature and Properties of Soils: Upper Saddle River.Prentice Hall, New Jersey.
55- Rawlins, B.G., Kemp, S.J., Milodowski, A.E., 2011. Relationships between particle size distribution and VNIR reflectance spectra are weaker for soils formed from bedrock compared to transported parent materials. Geoderma 166 (2011) 84–91.
56- 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.
57- Sawut, M., Ghulam, A., Tiyip, T., Zhang, Y.J., Ding, J.L., Zhang, F., Maimaitiyiming, M., 2014. Estimating soil sand content using thermal infrared spectra in arid lands. International Journal of Applied Earth Observation and Geoinformation 33 (2014) 203–210.
58- Scheidegger, A., Borkovec, M., Sticher, H., 1993. Coating of silica sand with goethite: preparation and analytical identification. Geoderma 58, 43–65.
59- 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.
60- Selige, T., Bohner, J., Schmidhalter, U., 2006. High resolution topsoil mapping using hyperspectral image and field data in multivariate regression modeling procedures. Geoderma 136, 235–244.
61- Shepherd, K.D., Walsh, M.G., 2002. Development of reflectance spectral libraries forcharacterization of soil properties. Soil Sci. Soc. Am. J. 66, 988–998.
62- Small, C., Steckler, M., Seeber, L., Akhter, S.H., Goodbred Jr., S., Mia, B., Imam, B., 2009, Spectroscopy of sediments in the Ganges–Brahmaputra delta: Spectral effects of moisture, grain size and lithology, Remote Sensing of Environment, 113(2009): 342–361.
63- Stenberg, B., Viscarra Rossel, R.A., Mouazen, A.M., Wetterlind, J., 2010. Visible and near infrared spectroscopy in soil science. In: Sparks, D.L. (Ed.), Advances in Agronomy, vol. 107. Academic Press, Burlington, pp. 163–215.
64- 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.
65- Stevens, A., Nocita, M., Toth, G., Montanarella, L., vanWesemael, B., 2013. Prediction of soil organic carbon at the European scale by visible and near infrared reflectance spectroscopy. PLoSONE 8 (6), e66409. http://dx.doi.org/10.1371/ journal.pone.0066409.
66- Stoner, E.R., Baumgardner, M.F., 1981. Characteristic variations in reflectance of surface soils. Soil Sci. Soc. Am. J. 45 (6), 5.
67- Summers, D., Lewis, M., Ostendorf, B., Chittleborough, D., 2011, Visible near-infrared reflectance spectroscopy as a predictive indicator of soil properties. Ecological Indicators, 11(2011): 123–131.
68- Vašát, R., Kodešová, R., Borůvka, L., Klement, A., Jakšík, O., Gholizadeh, A., 2014, Consideration of peak parameters derived from continuum-removed spectra to predict extractable nutrients in soils with visible and near-infrared diffuse reflectance spectroscopy (VNIR-DRS), Geoderma 232–234(2014): 208–218.
69- Viscarra Rossel RA, Walvoort DJJ, McBratney AB, Janik LJ, Skjemstad JO.. Visible, near-infrared, mid-infrared or combined diffuse reflectance spectroscopy for simultaneous assessment of various soil properties. Geoderma 2006:131: 59–75.
70- Viscarra Rossel, R.A., Cattle, S.R., Ortega, A., Fouad, Y., 2009. In situ measurements of soil colour, mineral composition and clay content by vis-NIR spectroscopy. Geoderma. 150: 253–266.
71- Xian-Li, X., Xian-Zhang, P., Bo, S., 2012. Visible and Near-Infrared Diffuse Reflectance Spectroscopy for Prediction of Soil Properties near a Copper Smelter. Pedosphere 22(3): 351–366, 2012. ISSN 1002-0160/CN 32-1315/P.
72- Zornoza, R., Guerrero, C., Mataix-Solera, J., Scow, K., Arcenegui, V., Mataix-Beneyto,J., 2008. Near infrared spectroscopy for determination of various physical, chem-ical and biochemical properties in Mediterranean soils. Soil Biol. Biochem. 40,1923–1930.
فصلنامه علمی- پژوهشی اطلاعات جغرافیایی « سپهر»
دوره 31، شماره 122 - شماره پیاپی 122
شهریور 1401
صفحه 63-86
فایل ها
هم رسانی
ارجاع به این مقاله
آمار