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Modeling and Monitoring Soil Erosion by Water Using Remote Sensing Satellite Data and GIS

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Anthropogeomorphology

Part of the book series: Geography of the Physical Environment ((GEOPHY))

Abstract

Among the various land degradation processes that are operating globally and adversely affecting agricultural production as well as environmental quality, soil erosion, more precisely, erosion by water, stands out prominently. Understanding the various processes involved and factors affecting erosion is a prime prerequisite with respect to soil erosion studies. The various physical, chemical, and biological weathering processes provide raw material on the Earth’s surface to be carried away by water and other erosive agents. Factors such as climate, especially rainfall, topography, vegetative cover, and land use practices, as well as various management practices including tillage operations, are crucial in determining the rates of erosion under different climatic regions as well as geographic locations. Use of appropriate remote sensing data obtained from different satellites as well as sensors, when coupled with various geospatial analyses, aid us in understanding and studying erosion processes at diverse spatial scales. The use of remote sensing data including terrain information, coupled with various field-based observations, enables us to study the erosion processes and their impact on the environment by employing different empirical, conceptual, as well as physical process-based erosion models, thus improving understanding on both spatial and temporal scales. In addition, remote sensing aids in soil erosion monitoring as well as planning and implementation of diverse conservation and management strategies and assessing their impacts.

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References

  • Abbott, M. B., Bathurst, J.C., Cunge, J.A., O’Connell, P.E., Rasmussen, J. (1986). An introduction to the European hydrological system—systeme hydrologique Europeen, SHEQ: 1. History and philosophy of a physically-based, distributed modeling system. Journal of Hydrology, 87, 45–59.

    Google Scholar 

  • Alexandridis, T. K., Sotiropoulou, A. M., Bilas, G., Karapetsas, N., & Silleos, N. G. (2015). The effects of seasonality in estimating the C-factor of soil erosion studies. Land Degradation & Development, 26(6), 596–603.

    Article  Google Scholar 

  • Ali, K. A. (2010). Remote sensing. Laser Branch Department of Applied Sciences, University of Technology. Available at: http://www.uotechnology.edu.iq/appsciences/Laser/Lacture_-laser/thrid_class/Remote_Sensing/3-Remote_Sensing.pdf

    Google Scholar 

  • Arnold, J. G., Williams, J. R., Srinivasan, R., & King, K. W. (1996). SWAT: Soil and water assessment tool (p. 190). User’s Manual USDA Agriculture Research Service Grassland, Soil and Water Research Laboratory.

    Google Scholar 

  • Aronoff, S. (1989). Geographic information systems: A management perspective. WDL Publication.

    Google Scholar 

  • Beck, M. B. (1987). Water quality modelling: a review of uncertainty. Water Resources Research, 23(8), 1393–1442.

    Google Scholar 

  • Bennett, J.P. (1974). Concepts of mathematical modeling of sediment yield. Water Resources Research, 10(3), 485–492.

    Google Scholar 

  • Beven, K. (1989). Changing ideas in hydrology—the case of physically based models. Journal of Hydrology, 105, 157–172.

    Google Scholar 

  • Bingner, R. L., & Theurer, F. D. (2003). AnnAGNPS technical processes documentation, version 3.3, USDA-ARS.

    Google Scholar 

  • Blanco, H., & Lal, R. (2010). Soil erosion and food security. Principles of soil conservation and management (pp. 493–512). Springer.

    Google Scholar 

  • Borrelli, P., Meusburger, K., Ballabio, C., Panagos, P., & Alewell, C. (2018). Objectoriented soil erosion modelling: A possible paradigm shift from potential to actual risk assessments in agricultural environments. Land Degradation & Development, 29(1270), e1281. https://doi.org/10.1002/ldr.2898

    Article  Google Scholar 

  • d’Oleire-Oltmanns, S., Marzolff, I., Peter, K. D., & Ries, J. B. (2012). Unmanned aerial vehicle (UAV) for monitoring soil erosion in Morocco. Remote Sensing, 4, 3390–3416.

    Article  Google Scholar 

  • Desmet, P. J. J., & Govers, G. (1996). A GIS procedure for automatically calculating the USLE LS factor on topographically complex landscape units. J. Soil Water Conservation, 51, 427–433.

    Google Scholar 

  • Desmet, P., & Govers, G. (1997). Comment on ‘Modelling topographic potential for erosion and deposition using GIS’. Int. J. Geogr. Inf. Sci., 11, 603–610.

    Google Scholar 

  • Dhakal, A. S., Amada, T., Aniya, M., Sharma R. R. (2002). Detection of areas associated with flood and erosion caused by a heavy rainfall using multi temporal Landsat TM data. Photogrammetric Engineering and Remote Sensing, 68(3), 233–239.

    Google Scholar 

  • Durigon, V. L., Carvalho, D. F., Antunes, M. A. H., Oliveira, P. T. S., & Fernandes, M. M. (2014). NDVI time series for monitoring RUSLE cover management factor in a tropical watershed. International Journal of Remote Sensing, 35, 441–453. https://doi.org/10.1080/01431161.2013.871081

    Article  Google Scholar 

  • FAO (2011). The state of the world’s land and water resources for food and agriculture (SOLAW): Managing systems at risk. FAO, Rome and Earth scan, London. http://www.fao.org/docrep/015/i1688e/i1688e00.pdf.

  • Fadul, H. M., Salih, A. A., Imad-eldin, A. A., & Inanaga, S. (1999). Use of remote sensing to map gully erosion along the Atbara River, Sudan. International Journal of Applied Earth Observation, 1(3–4), 175–180.

    Article  Google Scholar 

  • Flanagan, D. C., & Nearing, M. A. (Eds.). (1995). USDA-Water Erosion Prediction Project: Hillslope profile and watershed model documentation (NSERL Report No. 10). USDA-ARS National Soil Erosion Research Laboratory.

    Google Scholar 

  • Fu, S., Cao, L., Liu, B., Wu, Z., & Savabi, M. R. (2014). Effects of DEM grid size on predicting soil loss from small watersheds in China. Environment and Earth Science, 73, 2141–2151.

    Article  Google Scholar 

  • Fulajtar, E. (2001). Identification of severely eroded soils from remote sensing data tested in Risnovce, Slovakia. In D. E. Stott, R. H. Mohtar, & G. C. Steinhardt (Eds.), Sustaining the Global Farm (pp. 1075–1081). Purdue University.

    Google Scholar 

  • Joseph, G., & Jeganathan, C. (2018). Fundamentals of remote sensing. Universities Press (India) Private Limited. ISBN 978-93-86235-46-6.

    Google Scholar 

  • Gessler, P. E., Moore, I. D., McKenzie, N. J., & Ryan, P. J. (1995). Soil-Landscape modelling and spatial prediction of soil attributes. Geographical Information Systems, 9(4), 421–432.

    Google Scholar 

  • Gupta, S., & Kumar, S. (2017). Simulating climate change impact on soil erosion using RUSLE model − A case study in a watershed of mid-Himalayan landscape. Journal of Earth System Science, 126, 43.

    Article  Google Scholar 

  • Xu, H., Hu, X., Guan, H., Zhang, B., Wang, M., Chen, S., & Chen, M. (2019). A remote sensing based method to detect soil Erosion in forests. Remote Sensing, 2019(11), 513. https://doi.org/10.3390/rs11050513

    Article  Google Scholar 

  • Haan, C. T., Barfield, B. J., & Hayes, J. C. (1994). Design hydrology and sedimentology for small catchments. Academic Press 588 pp.

    Google Scholar 

  • Ilienko, T., Tarariko, O., Syrotenko, O., & Kuchma T. (2019). Merging remote and in-situ land degradation indicators in soil erosion control system. In: Proceedings of the global Symposium on Soil Erosion. Rome, 190–195.

    Google Scholar 

  • Jain, M. K., & Kothyari U. C. (2000). Estimation of soil erosion and sediment yield using GIS. Hydrological Science Journal, 45(5), 771–786.

    Google Scholar 

  • Jiu, J., Wu, H., & Li, S. (2019). The implication of land-use/land-cover change for the declining soil Erosion risk in the three Gorges reservoir region. International Journal of Environmental Research and Public Health, 16, 1856. https://doi.org/10.3390/ijerph16101856

    Article  Google Scholar 

  • Justin, G. K., & Kumar, S. (2017). Modelling soil erosion risk in a mountainous watershed of Mid-Himalaya by integrating RUSLE model with GIS. Eurasian Journal of Soil Science, 6(2), 92–105.

    Google Scholar 

  • Karami, A., Khoorani, A., Noohegar, A., Shamsi, S. R. F., & Moosavi, V. (2015). Gully erosion mapping using object-based and pixel-based image classification methods. Environmental and Engineering Geoscience, 27(2), 101–110.

    Article  Google Scholar 

  • Kinnell, P. I. A. (1999). Discussion on The European soil erosion model (EUROSEM): a dynamic approach for predicting sediment transport from fields and small catchments. Earth Surface Processes and Landforms, 24, 563–565.

    Google Scholar 

  • King, C., Baghdadi, N., Lecomte, V., & Cerdan, O. (2005). The application of remote sensing data to monitoring and modelling of soil erosion. Catena, 62, 79–93.

    Article  Google Scholar 

  • Kumar, S., & Gupta, S. (2016). Geospatial approach in mapping soil erodebility using CartoDEM – A case study in hilly watershed of lower Himalayan range. Journal of Earth System Science, 125, 1–10.

    Article  Google Scholar 

  • Kumar, S., & Kushwaha, S. P. S. (2013). Modelling soil Erosion risk based on RUSLE-3D using GIS in a Shivalik sub-watershed. Journal of Earth System Science, 122(2), 389–398.

    Article  Google Scholar 

  • Lillesand, T. M., Kiefer, R. W., & Chipman, J. W. (2004). Remote sensing and image interpretation (5th ed.). Wiley.

    Google Scholar 

  • Lillesand, T. M., & Kiefer, R. W. (1994). Remote sensing and image interpretation. Wiley.

    Google Scholar 

  • Lobser, S. E., & Cohen, W. B. (2007). MODIS tasselled cap: Land cover characteristics expressed through transformed MODIS data. International Journal of Remote Sensing, 28, 5079–5101.

    Article  Google Scholar 

  • Manchanda, M. L., Kudrat, M., & Tiwari, A. K. (2002). Soil survey and mapping using remote sensing. Tropical Ecology, 43(1), 61–74.

    Google Scholar 

  • McKenzie, N. J., Jacquier, D. W., Ashton, L. J., & Cresswell, H. P. (2000). Estimation of soil properties using the Atlas of Australian soils. CISRO Land and Water, Technical report 11/100.

    Google Scholar 

  • Merritt, W. S., Latcher, R. A., Jakeman, A. J. (2003). A review of erosion and sediment transport models. Environmental Modelling & Software, 18, 761–799.

    Google Scholar 

  • Morgan, R. P. C. (2001). A simple approach to soil loss prediction: a revised Morgan–Morgan–Finney model, Catena, 44(4), 305–322.

    Google Scholar 

  • Moore, I. D., & Wilson, J. P. (1992). Length-slope factors for the revised universal soil loss equation: Simplified method of estimation. Journal of Soil and Water Conservation, 47, 423–428.

    Google Scholar 

  • Moritani, S., Yamamoto, T., Andry, H., Inoue, M., & Kaneuchi, T. (2010). Using digital photogrammetry to monitor soil erosion under conditions of simulated rainfall and wind. Australian Journal of Soil Research, 48(1), 36–42. https://doi.org/10.1071/SR09058

    Article  Google Scholar 

  • Mukherjee, S., et al. (2013). Evaluation of vertical accuracy of open source Digital Elevation Model (DEM). International Journal of Applied Earth Observation and Geoinformation, 21, 205–217.

    Article  Google Scholar 

  • NAAS. (2010). Degraded and wastelands of India – Status of spatial distribution. National Academy of Agricultural Sciences.

    Google Scholar 

  • Nearing, M. A., Yin, S., Borrelli, P., & Polyakov, V. O. (2017). Rainfall erosivity: An historical review. Catena, 157, 357–362.

    Article  Google Scholar 

  • Oliveira, J. A., Dominguez, J. M. L., Nearing, M. A., & Oliveira, P. T. S. (2015). A GIS based procedure for automatically calculating soil loss from the universal soil loss equation: GISus-m. Applied Engineering in Agriculture, 31, 907e917. https://doi.org/10.13031/aea.31.11093

    Article  Google Scholar 

  • Panagos, P., Karydas, C. J., Gitas, I. Z., & Montanarella, L. (2012). Monthly soil erosion monitoring based on remotely sensed biophysical parameters: A case study in Strymonas river basin towards a functional pan-European service. International Journal of Digital Earth, 5(6), 461–487. https://doi.org/10.1080/17538947.2011.587897

    Article  Google Scholar 

  • Panagos, P. V., Liedekerke, M., Jones, A., Montanarella, L. (2012). European Soil Data Centre (ESDAC): response to European policy support and public data requirements. Land Use Policy, 29(2), 329–338.

    Google Scholar 

  • Price, K. P. (1993). Detection of soil erosion within pinyon-juniper woodlands using Thematic Mapper (TM) data. Remote Sensing of Environment, 45(3), 233–248.

    Article  Google Scholar 

  • Panagos, P., Borrelli, P., Meusburger, K., Yu, B., Klik, A., Lim, K. J., Yang, J. E., Ni, J., Miao, C., Chattopadhyay, N., Sadeghi, S. H., Hazbavi, Z., Zabihi, M., Larionov, G. A., Krasnov, S. F., Gorobets, A. V., Levi, Y., Erpul, Y. G., Birkel, C., Hoyos, N., Naipal, V., Oliveira, P. T. S., Bonilla, C. A., Meddi, M., Nel, W., Dashti, H., Boni, M., Diodato, N., Van Oost, K., Nearing, M. A., & Ballabio, C. (2017). Global rainfall erosivity assessment based on high-temporal resolution rainfall records. Scientific Reports, 7, 4175. https://doi.org/10.1038/s41598-017-04282-8

    Article  Google Scholar 

  • Pimentel, D. (2006). Soil erosion: a food and environmental threat. Environment, Development and Sustainability, 8, 119–137.

    Google Scholar 

  • Poesen, J. (1993). Gully typology and gully control measures in the European loess belt. In S. Wicherek (Ed.), Farmland erosion in temperate plains environment and hills (pp. 221–239). Elsevier.

    Google Scholar 

  • Prabhakara, K., Hively, W. D., & McCarty, G. W. (2015). Evaluating the relationship between biomass, percent groundcover and remote sensing indices across six winter cover crop fields in Maryland, United States. International Journal of Applied Earth Observation and Geoinformation, 39, 88–102.

    Google Scholar 

  • Prince S.D., Becker-Reshef I., Rishmawi K. (2009). Detection and mapping of long-term land degradation using local net production scaling: Application to Zimbabwe. Remote Sensing of Environment, 113, 1046–1057.

    Google Scholar 

  • Renard, K. G., Foster, G. R., Weesies, G. A., McCool, D. K., & Yoder, D. C. (1997). Predicting soil erosion by water: a guide to conservation planning with the Revised Universal Soil Loss Equation (RUSLE) (Vol. 703). Washington, DC: US Government Printing Office.

    Google Scholar 

  • Renard, K. G., Foster, G. R., Yoder, D. C., & McCool, D. K. (1994). RUSLE revisited: status, questions, answers, and the future. Journal of Soil and Water Conservation, 213–220.

    Google Scholar 

  • Renschler, C. S., Flanagan, D. C. Engel, B. A., & Frankenberger, J. R. (2002). GeoWEPP: The geospatial interface to the Water Erosion Prediction Project. ASAE Paper No. 022171. St. Joseph, Mich.: ASAE.

    Google Scholar 

  • Rouse, J., Jr., Haas, R. H., Schell, J. A., & Deering, D. (1974). Monitoring vegetation systems in the Great Plains with ERTS, NASA SP-351 (Third ERTS-1 Symposium) (Vol. 1, pp. 309–317). WNASA.

    Google Scholar 

  • Sadeghi, S. H. R., Gholami, L., Khaledi Darvishan, A., & Saeidi, P. (2007). Conformity of MUSLE estimates and erosion plot data for storm-wise sediment yield estimation. Terrestrial, Atmospheric and Oceanic Sciences, 18(1), 117–128.

    Google Scholar 

  • Sayao, V. M., Dematte, J. A. M., Bedin, L. G., Nanni, M. R., & Rizzo, R. (2018). Satellite land surface temperature and reflectance related with soil attributes. Geoderma, 325, 125–140.

    Article  Google Scholar 

  • Sabins, F. F., Jr. (1978). Remote sensing: Principles and interpretation (p. 1). W.H. Freeman and Co.

    Google Scholar 

  • SCS (Soil Conservation Service) (1975). Urban hydrology for small watersheds. Technical release no. 55, Soil Conservation Service, United States Dept. of Agric., Washington DC, USA.

    Google Scholar 

  • Seitz, S., Scholten, T., & Schmidt, K. (2020). Soil erosion monitoring at small scales: Using close range photogrammetry and laser scanning to evaluate initial sediment delivery, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020–16685, https://doi.org/10.5194/egusphere-egu2020-16685.

  • Sepuru, T. K., & Dube, T. (2018). Understanding the spatial distribution of eroded areas in the former rural homelands of South Africa: Comparative evidence from two new non-commercial multispectral sensors. International Journal of Applied Earth Observation and Geoinformation, 69, 119–132.

    Google Scholar 

  • Shan, L., Yang, X., & Zhu, Q. (2019). Effects of DEM resolutions on LS and hillslope erosion estimation in a burnt landscape. Soil Research, 57, 797.

    Article  Google Scholar 

  • Sharda, V. N., & Ojasvi, P. R. (2016). A revised soil erosion budget for India: Role of reservoir sedimentation and land-use protection measures. Earth Surface Processes and Landforms, 41, 2007–2023.

    Article  Google Scholar 

  • Singh, A. K. & Kumar, S. (2019). Modelling soil erosion and predicting sediment yield for sub- watershed prioritization using geospatial technique in North West Himalayan region. Indian Journal of Soil Conservation (Accepted).

    Google Scholar 

  • Sooryamol R., Kumar, S., Mary, R. F, & Annu, D. (2020). Calibrating SWAT model in simulating climate change impact on sediment loss – Case study in a watershed of lesser-Himalayan landscape. Earth Systems and Environment (Communicated).

    Google Scholar 

  • Sorooshian, S. (1991). Parameter estimation, model identification, and model validation: Conceptual-type models. Recent Adv. Model. Hydrol. Syst., 443–467.

    Google Scholar 

  • Thenkabail, P. S. (2015). Land Resources Monitoring, Modeling, and Mapping with Remote Sensing, Taylor and Francis Inc. CRC Press. ISBN: SBN 9781482217957 - CAT# K22130.

    Google Scholar 

  • UNCCD secretariat (2013). Role of parliamentarians in the implementation process of the UN Convention to Combat Desertification. A guide to parliamentary action.

    Google Scholar 

  • USDA-NRCS. (2004). Chapter 10: Estimation of direct runoff from storm rainfall. In Part 630: Hydrology: NRCS National Engineering Handbook. USDA National Resources Conservation Service. Available at: http://www.wsi.nrcs.usda.gov/products/W2Q/H&H/tech_refs/eng_Hbk/chap.html. Accessed 16 June 2008

    Google Scholar 

  • Vrieling, A. (2006). Satellite remote sensing for water erosion assessment: a review. Catena, 65, 2–18.

    Google Scholar 

  • Vrieling, A., Hoedjes, J. C., & van der Velde, M. (2014). Towards large-scale monitoring of soil erosion in Africa: Accounting for the dynamics of rainfall erosivity. Global and Planetary Change, 115, 33–43.

    Article  Google Scholar 

  • Wheater, H. S., Jakeman, A. J., & Beven, K. J. (1993). Progress and directions in rainfall-runoff modelling. In: Jakeman, A.J., Beck, M.B., McAleer, M.J. (Eds.), Modelling Change in Environmental Systems. John Wiley and Sons, Chichester, 101–132.

    Google Scholar 

  • Williams, J. R. (1975). Sediment routings for for agricultural watersheds. Wat. Resour Bull., 11, 965–975.

    Google Scholar 

  • Williams, J. R., & Berndt, H. D. (1977). Sediment yields prediction based on watershed hydrology. Transactions of ASAE, 20(6), 1100–1104.

    Article  Google Scholar 

  • Williams, J. R. (2008). Agricultural policy/environmental eXtender model: Theoretical documentation version 0604 (Draft). BREC Report # 2008–17. Texas AgriLIFE Research, Texas A&M University, Blackland Research and Extension Center, Temple, TX.

    Google Scholar 

  • Wischmeier, W. H., & Smith, D. D. (1978). Predicting Rainfall Erosion Losses-A Guide to Conservation Planning. In: Agriculture Handbook 537. US Government Print Office, Washington, DC.

    Google Scholar 

  • Xue, J., & Su, B. (2017). Significant remote sensing vegetation indices: A review of developments and applications. Journal of Sensors, 2017, 1–17.

    Article  Google Scholar 

  • Yang, X. H. (2014). Deriving RUSLE cover factor from time-series fractional vegetation cover for hillslope erosion modelling in New South Wales. Soil Research, 52, 253–261.

    Article  Google Scholar 

  • Young, R. A., Onstad, C. A., Bossch, D. D., & Anderson, W. P. (1989). AGNP S: A non-point source pollution model for evaluating agricultural watersheds. Journal of Soil and Water Conservation, 44(2), 168–173.

    Google Scholar 

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Authors and Affiliations

  1. Agriculture & Soils Department, Indian Institute of Remote Sensing, Dehradun, India

    Suresh Kumar & Justin George Kalambukattu

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  1. Suresh Kumar
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  2. Justin George Kalambukattu
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Correspondence to Suresh Kumar .

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Editors and Affiliations

  1. Department of Geography, Visiting Faculty, Seacom Skills University, West Bengal, West Bengal, India

    Gouri Sankar Bhunia

  2. Department of Geography, Bhatter College, Dantan, Paschim Medinipore, India

    Uday Chatterjee

  3. Geography & Resource Management, Mizoram University, Aizawl, India

    K.C. Lalmalsawmzauva

  4. PG Department of Geography, Raja N. L. Khan Women’s College (Autonomous), Midnapore, West Bengal, India

    Pravat Kumar Shit

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Kumar, S., Kalambukattu, J.G. (2022). Modeling and Monitoring Soil Erosion by Water Using Remote Sensing Satellite Data and GIS. In: Bhunia, G.S., Chatterjee, U., Lalmalsawmzauva, K., Shit, P.K. (eds) Anthropogeomorphology. Geography of the Physical Environment. Springer, Cham. https://doi.org/10.1007/978-3-030-77572-8_14

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