برآورد مقدار کربن آلی خاک با استفاده از داد‌های طیفی در گستره VIS-NIR-SWIR-TIR

نوع مقاله : مقالات پژوهشی

نویسندگان

• حمید رضا متین فر ¹
• محبوبه جلالی ²
• زهرا دیبایی ³

¹ دانشگاه لرستان. گروه آموزشی مهندسی کشاورزی خاکشناسی

² گروه علوم و مهندسی خاک، دانشگاه لرستان

³ گروه علوم و مهندسی خاک - دانشگاه لرستان

10.22067/jsw.2021.71065.1062

چکیده

شناخت توزیع مکانی کربن آلی خاک یکی از ابزارهای کاربردی در پیشبرد مدیریت پایدار اراضی و محیط زیست می‌باشد. داده­کاوی و مدل‌سازی مکانی همراه با تکنیک‌های یادگیری ماشینی به منظور بررسی میزان کربن آلی خاک مبتنی بر داده‌های سنجش از دور به صورت گسترده مورد توجه قرار گرفته است. هدف از این مطالعه،استفاده از تصاویر با دامنه طیفی مرئی تا مادون قرمز حرارتی و داده‌های زمینی برای مدل‌سازی میزان کربن آلی خاک می­باشد. با استفاده از الگوی نمونه‌برداری تصادفی156نمونه از خاک سطحی (30-0 سانتی­متر) جمع‌آوری شد. داده‌ها به دو دسته 80 درصد برای آموزش و 20 درصد جهت اعتبارسنجی دسته‌بندی شدند و از سه الگوریتم یادگیری ماشین شامل جنگل تصادفی، کوبیست و رگرسیون حداقل مربعات جزئی برای براورد و تهیه نقشه کربن آلی خاک استفاده شد. متغیرهای کمکی جهت پیش‌بینی کربن آلی خاک شامل باندها و شاخص‌های منتج از سنجنده‌ی OLI و TIRS لندست 8 می­باشد. به منظور کاهش حجم داده‌ها و انتخاب ویژگی‌هایی با بیشترین تأثیر بر براورد کربن آلی خاک، از روش آنالیز مؤلفه‌های اصلی استفاده شد. آنالیز مؤلفه‌های اصلی داده‌­های سنجش از دور منجر به گزینش 4 متغیر کمکی TSAVI، RVI، Band10 و Band11 به­عنوان مؤثرترین عوامل کمکی محیطی انتخاب گردیدند. همچنین مقایسه رویکردهای مختلف تخمین نشان داد که مدل جنگل تصادفی به ترتیب با مقادیر ضریب تبیین، خطای جذر میانگین مربعات و میانگین مربعات خطا 74/0، 17/0 و 02/0 بهترین کارایی را نسبت به سایر رویکردهای مورد استفاده در برآورد کربن آلی خاک سطحی در منطقه مطالعاتی ارائه نمود. به طور کلی نتایج این مطالعه بر قابلیت دادهای سنجش از دور و مدل یادگیری جنگل تصادفی در تخمین مکانی کربن آلی خاک به طور همزمان دلالت دارد. لذا می‌تواند به عنوان روشی جایگزین برای روش‌های مرسوم آزمایشگاهی در تعیین برخی ویژگی‌های خاک از جمله کربن آلی خاک مورد توجه قرار گیرد.

کلیدواژه‌ها

• توزیع مکانی
• طیف مرئی مادون قرمز
• کربن آلی خاک
• مدل‌سازی
• سنجنده لندست 8

20.1001.1.20084757.1400.35.4.8.9

موضوعات

• علوم خاک

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عنوان مقاله [English]

Estimation of the Amount of Soil Organic Carbon Using Spectral Data in the VIS-NIR-SWIR-TIR Spectral Range

نویسندگان [English]

• Hamid Reza Matinfar ¹
• M. Jalali ²
• Z. Dibaei ³

¹ Soil Sci. Faculty of Agriculture, Lorestan University

² Soil Science Dept. Lorestan University

³ Soil Science Dept. Lorestan University

چکیده [English]

Introduction: Understanding the spatial distribution of soil organic carbon (SOC) is one of the practical tools in determining sustainable land management strategies. Over the past two decades, the use of data mining approaches in spatial modeling of soil organic carbon using machine learning techniques to investigate the amount of carbon to soil using remote sensing data has been widely considered. Accordingly, the aim of this study was to investigate the feasibility of estimating soil organic matter using satellite imagery and to assess the ability of spectral and terrestrial data to model the amount of soil organic matter.
Materials and Methods: The study area is located in Lorestan province, and Sarab Changai area. This area has hot and dry summers and cold and wet winters and the wet season starts in November and ends in May. A total of 156 samples of surface soil (0-30 cm) were collected using random sampling pattern. Data were categorized into two categories: 80% (117 points) for training and 20% (29 points) for validation. Three machine learning algorithms including Random Forest (RF), Cubist, and Partial least squares regression (PLSR) were used to prepare the organic soil carbon map. In the present study, auxiliary variables for predicting SOC included bands related to Lands 8 OLI measurement images, and in order to reduce the volume of data, the principle component analysis method (PCA) was used to select the features that have the greatest impact on quality.
Results and Discussion: The results of descriptive statistics showed that soil organic carbon from 0.02 to 2.34% with an average of 0.56 and a coefficient of variation of 69.64% according to the Wilding standard was located in a high variability class (0.35). According to the average amount of soil organic carbon, it can be said that the amount of soil organic carbon in the region is low. At the same time, the high value of organic carbon change coefficient confirms its high spatial variability in the study area. These drastic changes can be attributed to land use change, land management, and other environmental elements in the study area. In other words, the low level of soil organic carbon can be attributed to the collection of plant debris and their non-return to the soil. Another factor in reducing the amount of organic carbon is land use change, which mainly has a negative impact on soil quality and yield. In general, land use, tillage operations, intensity and frequency of cultivation, plowing, fertilizing, type of crop, are effective in reducing and increasing the amount of soil organic carbon. Based on the analysis of effective auxiliary variables in predicting soil organic carbon, based on the principle component analysis for remote sensing data, it led to the selection of 4 auxiliary variables TSAVI, RVI, Band10, and Band11 as the most effective environmental factors. Comparison of different estimation approaches showed that the random forest model with the values of coefficient of determination (R²), root mean square error (RMSE) and mean square error (MSE) of 0.74, 0.17, and 0.02, respectively, was the best performance ratio another study used to estimate the organic carbon content of surface soil in the study area.
Conclusion: In this study, considering the importance of soil organic carbon, the efficiency of three different digital mapping models to prepare soil organic carbon map in Khorramabad plain soils was evaluated. The results showed that auxiliary variables such as TSAVI, RVI, Band 10, and Band11 are the most important variables in estimating soil organic carbon in this area. The wide range of soil organic carbon changes can be affected by land use and farmers' managerial behaviors. Also, the results indicated that different models had different accuracy in estimating soil organic carbon and the random forest model was superior to the other models. On the other hand, it can be said that the use of remote sensing and satellite imagery can overcome the limitations of traditional methods and be used as a suitable alternative to study carbon to soil changes with the possibility of displaying results at different time and space scales. Due to the determination of soil organic carbon content and their spatial distribution throughout the region, the present results can be a scientific basis as well as a suitable database and data for the implementation of any field operations, management of agricultural inputs, and any study in sustainable agriculture with soil properties in this area. In general, the results of this study indicated the ability of remote sensing techniques and random forest learning model in simultaneous estimation of soil organic carbon location. Therefore, this method can be used as an alternative to conventional laboratory methods in determining some soil characteristics, including organic carbon.

کلیدواژه‌ها [English]

• Modeling
• Remote Sensing
• Soil Organic Carbon
• Spatial Distribution

مراجع

• Baret F., and Guyot G. 1991. Potentials and limits of vegetation indices for LA APAR assesment. Remote Sensing of Environment 35: 161-173.
• Camdevyren H., Demyr N., Kanik A., and Keskyn S. 2005. Use of principal component scores in multiple linear regression models for prediction of Chlorophyll-a in reservoirs. Ecological Modelling 181: 581-589.
• Chang C.-W., Laird D.A., Mausbach M.J., and Hurburgh C.R. 2001. Near-infrared reflectance spectroscopy – principal components regression analyses of soil properties. Soil Science Society of America Journal 65(2): 480–490.
• Chang C.W., Laird D.A., and Hurburgh Jr C.R. 2005. Influence of soil moisture on near-infrared reflectance spectroscopic measurement of soil properties. Soil Science 170(4): 244-255.
• Chen D.Z., Zhang J.X., and Chen J.M. 2010. Adsorption of methyl tert-butyl ether using granular activated carbon: Equilibrium and kinetic analysis. International Journal of Environmental Science & Technology 7(2): 235-242.
• Chen F., Kissel D.E., West L.T., and Adkins W. 2000. Field-scale mapping of surface soil organic carbon using remotely sensed imagery. Soil Science Society of America Journal 64(2): 746-753.
• Dalal R.C., and Henry R.J. 1986. Simultaneous determination of moisture, organic carbon, and total nitrogen by near infrared reflectance spectrophotometry 1. Soil Science Society of America Journal 50(1): 120-123.
• Dashti A., Soodi M., and Amani N. 2015. Evaluation of Cr (VI) induced Neurotoxicity and Oxidative Stress in PC12 Cells. Modares Journal of Medical Sciences: Pathobiology 18(1): 55-65.
• Datta A., Setia R., Barman A., Guo Y., and Basak N. 2019. Carbon Dynamics in Salt-affected Soils. Springer Nature Singapore Pte Ltd. https://doi.org/10.1007/978-981-13-5832-6_12 .
• De Santana F B., de Souza A M., and Ronei Poppi J. 2018. Visible and near infrared spectroscopy coupled to random forest to quantify some soil quality parameters. Spectrochimica Acta Part A: Molecular and Biomolecular Spectroscopy 191: 454–462.
• Eswaran H., Van Den Berg E., and Reich P. 1993. Organic carbon in soils of the world. Soil Science Society of America Journal 57(1): 192-194.
• Falahatkar S., Hosseini S.M., Ayoubi S., and Salmanmahiny A. 2016. Predicting soil organic carbon density using auxiliary environmental variables in northern Iran. Archives of Agronomy and Soil Science 62(3): 375-393.
• FAO I. 2015. Status of the World’s Soil Resources (SWSR)–technical summary. Food and Agriculture Organization of the United Nations and Intergovernmental Technical Panel on Soils, Rome, Italy.
• Gelsleichter Y.A., dos Anjos L.H.C., Costa E.M., Valente G., Debiasi P., Homem Antunes M.A., and Robson Altiellys Tosta Marcondes. 2019. Machine Learning Algorithms for Soil Properties Prediction with Treated Vis–NIR Spectrums from the Itatiaia National Park.Geoderma. doi:10.20944/preprints 201911.0053.v1.
• Habibzade A.M., Nikjou R., and Peyrovan H.R. 2013. Survey amount of runoff and sediment in Marn East Azerbaijan. Rangeland Scientific Research Journal 17(43): 71-91. (In Persian)
• He T., Wang J., Lin Z., and Cheng Y. 2009. Spectral features of soil organic matter. Geo-spatial Information Science 12(1): 33-40.
• He Y., Huang M., García A., Hernández A., and Song H. 2007. Prediction of soil macronutrients content using near-infrared spectroscopy. Computers and Electronics in Agriculture 58(2): 144-153.
• Heng T., Heuvelink G.B., Kempen B., Leenaars J.G., Walsh M.G., Shepherd K.D., and Tondoh J.E. 2015. Mapping soil properties of Africa at 250 m resolution: Random forests significantly improve current predictions. PloS one. 10:6.
• Huete A., Huete A.R. 1988. Soil-adjusted vegetation index (SAVI). Remote Sensing of Environment. Remote Sensing of Environment 25: 295-309.
• Ingram L.J., Stahl P.D., Schuman G.E., Buyer J.S., Vance G.F., Ganjegunte G.K., and Derner J.D. 2008. Grazing impacts on soil carbon and microbial communities in a mixed-grass ecosystem. Soil Science Society of America Journal 72(4): 939-948.
• Jamshidi M., Delavar M.A., Taghizadehe-Mehrjerdi R., and Brungard C. 2019. Evaluating Digital Soil Mapping Approaches for 3D Mapping of Soil Organic Carbon. Soil Research Journal (Soil and Water Sciences) 33(2): 228-240. (In Persian)
• Kumar N., Velmurugan A., Hamm N.A., and Dadhwal V.K. 2018. Geospatial mapping of soil organic carbon using regression kriging and remote sensing. Journal of the Indian Society of Remote Sensing 46(5): 705-716.
• Lal R. 2006. Enhancing crop yields in the developing countries through restoration of the soil organic carbon pool in agricultural lands. 2006. Land Degradation and Development 17(2): 197–209.
• Ludwig B., Khanna P.K., Bauhus J., and Hopmans P. 2002. Near infrared spectroscopy of forest soils to determine chemical and biological properties related to soil sustainability. Forest Ecology Managment 171(1-2): 121-132.
• Mahmoudzadeh H., Matinfar H.R., Taghizadeh-Mehrjardi R., and Kerry R. 2020. Spatial prediction of soil organic carbon using machine learning techniques in western Iran. 2020. Geoderma Regional 21) e00260.
• Major D.J., Baret F., and Guyot G. 1990. A ratio vegetation index adjusted for soil brightness International Journal of Remote Sensing 11: 727-740.
• Matinfar H.R., Sarvi Moganloo V., and Dibaei Z. 2017. Evaluation of the ability of red and infrared wavelengths near the OLI sensor in estimating the leaf area index of different stages of grain growth of corn (Moghan plain). In: Geomatic Tehran .(In Persian)
• Matinfar H.R., and Sadikhani R. 2015. Measurement of soil moisture using remote sensing Measurement of soil moisture using remote sensing. In: The first national conference on sustainable management of soil resources and the environment. Kerman Shahid Bahonar University. 2015 (In Persian)
• Matinfar H.R., Mahmodzadeh H., and Fariabi A. 2018. Estimation Soil Organic Matter (SOM) Content Using Visible and Near Infrared Spectral data, PLSR and PCR Statistical Models. Iranian Remote Sensing & GIS. 10(2): 15-32. (In Persian)
• McCarty G.W., Reeves J.B., Reeves V.B., Follett R.F., and Kimble J.M. 2002. Mid-infrared and nearinfrared diffuse reflectance spectroscopy for soil carbon measurements. Soil Science Society of America Journal 66: 640–646.
• Pouladi N., Møller A.B., Tabatabai S., and Greve M.H. 2019. Mapping soil organic matter contents at field level with Cubist, Random Forest and kriging. Geoderma 342: 85-92.
• Qi Y., Darilek J.L., Huang B., Zhao Y., Sun W., and Gu Z. 2009. Evaluating soil quality indices in an agricultural region of Jiangsu Province, China. Geoderma 149(3-4): 325-334.
• Rondeaux G., Steven M., and Baret F. 1996. Optimisation of soil-adjusted vegetation indices. Remote Sensing of Environment 55: 95−107.
• Robertson G.P., Gross K.L., and Hamilton S.K. L. Doug Farming for ecosystem services: an ecological approach to production agriculture. Bioscience 64: 404–415.
• Rossel R.V., McBratney P.A.B., Minasny D.B., Stenberg B., and Rossel R.A.V. 2010. Diffuse Reflectance Spectroscopy for High-Resolution Soil Sensing Proximal Soil Sensing. Soil Science. Germany, 434p.
• Smith J.A. 2004. Reflecting on the development of interpretative phenomenological analysis and its contribution to qualitative research in psychology. Qualitative Research in Psychology 1(1): 39-54.
• Song Y.Q., Yang L.A., Li B., Hu Y.M., Wang A.L., Zhou W., and Liu Y.L. 2017. Spatial prediction of soil organic matter using a hybrid geostatistical model of an extreme learning machine and ordinary kriging. Sustainability 9(5): 754.
• Sabaghzadeh S., Zare M., and Mokhtari M. 2017. Estimating of above ground biomass of Haloxylon using sattelite based vegetation indices (Case Study: Marak, Birjand). Iranian Journal of Range and Desert Research 23(4): 844-855. (In Persian)
• S. Geology Survey. 2014. Geology.com/news/2010/free-lansatimages-from-USGS-2. http://glovis.usgs.gov.
• Walkley A., and Black I.A. 1934. An examination of the Degtjareff method for determining soil organic matter, and a proposed modification of the chromic acid titration method. Soil Science 37(1): 29-38.
• Wiegand C.L., Richardson A.J., Escobar D.E., and Gerbermann A.H. 1991. Vegetation indices in crop assessments. Remote Sensing of Environment 35(2–3): 105-119.
• Wilding L.P. 1985. Spatial variability: its documentation, accommodation and implicationto soil surveys. P 166-194, In: D.R. Nielsen and J. Bouma (eds.), Soil Spatial Variability, Pudoc, Wageningen, the Netherlands.
• Walker, S.M., and Desanker, P.V. 2004. The impact of land use on soil carbon in Miombo Woodlands of Malawi. Forest Ecology and Management 203: 345–360.
• Zeraatpisheh M., Ayoubi S., Jafari A., Tajik S., and Finke P. 2019. Digital mapping of soil properties using multiple machine learning in a semi-arid region, central Iran. Geoderma 338: 445-452.
• Zhou J., Li Enming Wei., Haixia Li., Chuanqi Qiao Qiuqiu., and Danial J.A. 2019. Random Forests and Cubist Algorithms for Predicting Shear Strengths of Rockfill Materials. Appl. Sci. 9: 1621.