Modeling Soil Organic Carbon Variations Using Remote Sensing Indices in Ardabil Balikhli Chay Watershed | ||
| تحقیقات آب و خاک ایران | ||
| Volume 51, Issue 9, November 2020, Pages 2417-2429 PDF (1.19 M) | ||
| Document Type: Research Paper | ||
| DOI: 10.22059/ijswr.2020.299509.668542 | ||
| Authors | ||
| Solmaz Fathololoumi* 1; Alireza Vaezi1; Seyed Kazem Alavipanah2; Ardavan Ghorbani3 | ||
| 1Department of Soil Science, Faculty of Agriculture, University of Zanjan, Zanjan, Iran | ||
| 2Department of Remote Sensing & GIS, Faculty of Geography, University of Tehran,, Tehran, Iran | ||
| 3Department of Natural Resources, Faculty of Agriculture and Natural Resources, University of Mohaghegh Ardebili, Ardabil, Iran | ||
| Abstract | ||
| Modeling and providing accurate information on the spatial distribution of soil properties is a key factor in many environmental and agricultural applications. Therefore, the purpose of the present study was to model and prepare a digital map of soil organic carbon using remote sensing indices in the Balikhli Chay watershed. At first, topographic and spectral characteristics affecting soil organic carbon content were extracted from digital elevation model and Landsat 8 satellite image. Then the performance of soil organic carbon modeling for different states was evaluated and compared based on random forest models. The states including 1) terrain covariates, 2) spectral indices, and 3) combination of terrain and spectral covariates, were evaluated and compared together. To this end, the correlation coefficient (R2) between the estimated and measured soil organic carbon and root mean square error (RMSE) were calculated for the different states. The results showed that the amount of organic carbon in the study area varied from 0.32 to 6.98 and the mean value was 3.04%. Carbon changes in the study area mostly dependent on changes in spectral indices. Elevation and Emissivity were respectively the most important terrain and spectral covariates in soil organic carbon modeling. The R2 values in the three models were 0.61, 0.62 and 0.75 and the RMSE values were 0.88, 0.67 and 0.57, respectively, which indicates the better performance of the third model. The use of a combination of terrestrial and spectral variables significantly increases the accuracy of soil organic carbon modeling. | ||
| Keywords | ||
| Digital soil map; Environmental covariates; Random forest model; Remote sensing; Soil organic carbon | ||
| References | ||
|
Akpa, S. I., Odeh, I. O., Bishop, T. F., & Hartemink, A. E. (2014). Digital mapping of soil particle-size fractions for Nigeria. Soil Science Society of America Journal, 78(6), 1953-1966 Barsi, J. A., Schott, J. R., Hook, S. J., Raqueno, N. G., Markham, B. L., & Radocinski, R. G. (2014). Landsat-8 thermal infrared sensor (TIRS) vicarious radiometric calibration. Remote Sensing, 6(11), 11607-11626 Behrens, T., Schmidt, K., Ramirez-Lopez, L., Gallant, J., Zhu, A.-X., & Scholten, T. (2014). Hyper-scale digital soil mapping and soil formation analysis. Geoderma, 213, 578-588 Bogunovic, I., Trevisani, S., Pereira, P., & Vukadinovic, V. (2018). Mapping soil organic matter in the Baranja region (Croatia): Geological and anthropic forcing parameters. Science of the Total Environment, 643, 335-345 Böhner, J., Koethe, R., Conrad, O., Gross, J., Ringeler, A., & Selige, T. (2001). Soil regionalisation by means of terrain analysis and process parameterisation. Soil classification(7), 213 Böhner, J., & Selige, T. (2006). Spatial prediction of soil attributes using terrain analysis and climate regionalisation. Breiman, L. (2001). Random forests. Machine learning, 45(1), 5-32 Carré, F., McBratney, A. B., Mayr, T., & Montanarella, L. (2007). Digital soil assessments: Beyond DSM. Geoderma, 142(1-2), 69-79 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 Sensing of Environment, 179, 54-65 Chen, H., Fan, L., Wu, W., & Liu, H.-B. (2017). Comparison of spatial interpolation methods for soil moisture and its application for monitoring drought. Environmental monitoring and assessment, 189(10), 525 Costa, E. M., Samuel-Rosa, A., & Anjos, L. H. C. d. (2018). Digital elevation model quality on digital soil mapping prediction accuracy. Ciência e Agrotecnologia, 42(6), 608-622 Croft, H., Kuhn, N., & 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 Davidson, E. A., & Janssens, I. A. (2006). Temperature sensitivity of soil carbon decomposition and feedbacks to climate change. Nature, 440(7081), 165 Davy, M., & Koen, T. (2014). Variations in soil organic carbon for two soil types and six land uses in the Murray Catchment, New South Wales, Australia. Soil Research, 51(8), 631-644 Desmet, P., & Govers, G.(1996). A GIS procedure for automatically calculating the USLE LS factor on topographically complex landscape units. Journal of Soil and Water Conservation, 51(5), 427-433 Dharumarajan, S., Hegde, R., & Singh, S. (2017). Spatial prediction of major soil properties using Random Forest techniques-A case study in semi-arid tropics of South India. Geoderma Regional, 10, 154-162 Fathololoumi, S., Vaezi, A. R., Alavipanah, S. K., Ghorbani, A., & Biswas, A. (2020). Comparison of spectral and spatial-based approaches for mapping the local variation of soil moisture in a semi-arid mountainous area. Science of the Total Environment, 138319 Firozjaei, M. K., Kiavarz, M., Alavipanah, S. K., Lakes, T., & Qureshi, S. (2018). Monitoring and forecasting heat island intensity through multi-temporal image analysis and cellular automata-Markov chain modelling: A case of Babol city, Iran. Ecological indicators, 91, 155-170 Firozjaei, M. K., Kiavarz, M., Nematollahi, O., Karimpour Reihan, M., & Alavipanah, S. K. (2019). An evaluation of energy balance parameters, and the relations between topographical and biophysical characteristics using the mountainous surface energy balance algorithm for land (sebal). International Journal of Remote Sensing, 1-31 Forkuor, G., Hounkpatin, O. K., Welp, G., & Thiel, M. (2017). High resolution mapping of soil properties using remote sensing variables in south-western Burkina Faso: a comparison of machine learning and multiple linear regression models. PloS one, 12(1), e0170478. Freeman, T. G. (1991). Calculating catchment area with divergent flow based on a regular grid. Computers & geosciences, 17(3), 413-422. Frey, H., & Paul, F. (2012). On the suitability of the SRTM DEM and ASTER GDEM for the compilation of topographic parameters in glacier inventories. International Journal of Applied Earth Observation and Geoinformation, 18, 480-490. Gallant, J. C., & Dowling, T. I. (2003). A multiresolution index of valley bottom flatness for mapping depositional areas. Water Resources Research, 3(12)9. Gao, Z., Tong, B., Horton, R., Mamtimin, A., Li, Y., & Wang, L. (2017). Determination of desert soil apparent thermal diffusivity using a conduction‐convection algorithm. Journal of Geophysical Research: Atmospheres, 122(18), 9569-9578 Gholizadeh, A., Žižala, D., Saberioon, M., & Borůvka, L. (2018). Soil organic carbon and texture retrieving and mapping using proximal, airborne and Sentinel-2 spectral imaging. Remote Sensing of Environment, 218, 89-103. Goodman, J., Owens, P., & Libohova, Z. (2012). Predicting soil organic carbon using mixed conceptual and geostatistical models. Digital soil assessments and beyond, 155-159. Grimm, R., Behrens, T., Märker, M., & Elsenbeer, H. (2008). Soil organic carbon concentrations and stocks on Barro Colorado Island—Digital soil mapping using Random Forests analysis. Geoderma, 146(1-2), 102-113. Guo, L. B., & Gifford, R. (2002). Soil carbon stocks and land use change: a meta analysis. Global change biology, 8(4), 345-360 İmamoğlu, M. Z., & Sertel, E. (2016). Analysis of different interpolation methods for soil moisture mapping using field measurements and remotely sensed data. International Journal of Environment and Geoinformatics, 3(3), 11-25 Jarvis, A., Reuter, H. I., Nelson, A., & Guevara, E. (2008). Hole-filled SRTM for the globe Version 4. available from the CGIAR-CSI SRTM 90m Database (http://srtm. csi. cgiar. org). Kopacková, V., Jelének, J., Koucká, L., Fárová, K., & Pikl, M. (2018). Modelling soil Organic Carbon and mineral composition using reflectance and emissivity data. Paper presented at the EGU General Assembly Conference Abstracts Köthe, R., & Lehmeier, F. (1996). SARA—System zur Automatischen Relief-Analyse, Benutzerhandbuch. Department of Geography, University of Göttingen, Göttingen Liang, S. (2001). Narrowband to broadband conversions of land surface albedo I: Algorithms. Remote Sensing of Environment, 76(2), 213-238 Lindsay, J. B. (2018). WhiteboxTools User Manual. In: DOI Lloyd, C. (2005). Assessing the effect of integrating elevation data into the estimation of monthly precipitation in Great Britain. Journal of Hydrology, 308(1-4), 128-150 Ma, Y., Minasny, B., Malone, B., & McBratney, A. (2019). Pedology and digital soil mapping (DSM). European Journal of Soil Science Ma, Y., Minasny, B., & Wu, C. (2017). Mapping key soil properties to support agricultural production in Eastern China. Geoderma Regional, 10, 144-153 Mahmoodi, M. A., Mirzaie, M., & Bavaghar, M. P. (2018). Assessment of soil organic matter status using regression kriging technique and Landsat images. (In Farsi) Mahmoudabadi, E., Karimi, A., Haghnia, G. H., & Sepehr, A. (2017). Digital soil mapping using remote sensing indices, terrain attributes, and vegetation features in the rangelands of northeastern Iran. Environmental monitoring and assessment, 189(10), 500 McBratney, A. B., Santos, M. M., & Minasny, B. (2003). On digital soil mapping. Geoderma, 117(1-2), 3-52. Mishra, N., Haque, M. O., Leigh, L., Aaron, D., Helder, D., & Markham, B. (2014). Radiometric cross calibration of Landsat 8 operational land imager (OLI) and Landsat 7 enhanced thematic mapper plus (ETM+). Remote Sensing, 6(12), 12619-12638. Müller, B., Bernhardt, M., Jackisch, C., & Schulz, K. (2016). Estimating spatially distributed soil texture using time series of thermal remote sensing-a case study in central Europe. Hydrology and Earth System Sciences(9), 3765-3775. Osunbitan, J., Oyedele, D., & Adekalu, K. (2005). Tillage effects on bulk density, hydraulic conductivity and strength of a loamy sand soil in southwestern Nigeria. Soil and Tillage Research, 82(1), 57-64. Popescu, R., Deodatis, G., & Nobahar, A. (2005). Effects of random heterogeneity of soil properties on bearing capacity. Probabilistic Engineering Mechanics, 20(4), 324-341. Ramcharan, A., Hengl, T., Nauman, T., Brungard, C., Waltman, S., Wills, S., & Thompson, J. (2018). Soil property and class maps of the conterminous United States at 100-meter spatial resolution. Soil Science Society of America Journal, 82(1), 186-201 Tabachnick, B. G., & Fidell, L. S. (2007). Experimental designs using ANOVA: Thomson/Brooks/Cole Belmont, CA. Ullah, S., Schlerf, M., Skidmore, A. K., & Hecker, C. (2012). Identifying plant species using mid-wave infrared (2.5–6 μm) and thermal infrared (814 μm) emissivity spectra. Remote Sensing of Environment, 118, 95-102 Vidana Gamage, D. N., Biswas, A., Strachan, I. B., & Adamchuk, V. I. (2018). Soil water measurement using actively heated fiber optics at field scale. Sensors, 18(4), 1116 Walkley, A., & 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 Weng, Q., Firozjaei, M. K., Sedighi, A., Kiavarz, M., & Alavipanah, S. K. (2019). Statistical analysis of surface urban heat island intensity variations: A case study of Babol city, Iran. GIScience & remote sensing, 56(4), 576-604 Wiesmeier, M., Barthold, F., Blank, B., & Kögel-Knabner, I. (2011). Digital mapping of soil organic matter stocks using Random Forest modeling in a semi-arid steppe ecosystem. Plant and soil, 340(1-2), 7-24 Zevenbergen, L. W., & Thorne, C. R. (1987). Quantitative analysis of land surface topography. Earth Surface Processes and Landforms, 12(1), 47-56 Zhao, W., Duan, S.-B., Li, A., & Yin, G. (2019). A practical method for reducing terrain effect on land surface temperature using random forest regression. Remote Sensing of Environment, 221, 635-649. | ||
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