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of the most serious and continuous environmental problem is soil erosion and
land degradation particularly in third world country where agriculture is the
main economic activity.  More than 50% of
the total area of India is affected by land degradation resulting from soil
erosion (Sehgal and Abrol 1994). It is now established fact that each year more
than 75 billion tons of soil is removed from agricultural land due to erosion
(Pandey et al. 2009). Broadly acceptable to the geomorphologists, erosion is
the progressive removal of soil or rock particles from the parent mass by a
fluid agent (Strahler 1964). The forms of soil erosion are mainly sheet, rill
and gully erosion. But this type of erosion widely varied in different spatial
and temporal scale depending on morpho-climatic and pedo-geomorphic factors
(Ghosh and Maji 2011). The amount of soil erosion is measured on the basis of
two models i.e. physical based and empirical based model.  GIS and remote sensing (RS) provide spatial
input data to the empirical model and predict the potential soil erosion rate.

using different type of models many scholar have worked on soil erosion
throughout the world. The most commonly used empirical model is Universal Soil
Loss Equation (USLE) developed by Wischmeier and Smith in 1965 for measuring
sheet and rill erosion (Ganasri and Ramesh 2016). Revised universal Soil Loss
Equation (RUSLE) uses the same empirical principles as USLE; however it
includes numerous improvements, such as monthly factors, incorporation
of the influence of profile convexity/concavity using segmentation of irregular
slopes, improved empirical equations for the computation of LS factor (Foster
and Wischmeier1974, Renard et al. 1991).  In this study LISS III image has been used for
generating C factor by using Normalized Difference Vegetation Index (NDVI). The
present study is an attempt to focus on the estimation of soil erosion in the
upper Kangsabati watershed by RUSLE model.

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