# Use of GIS and Remote sensing in assessing soil erosion

Sanjay Kumar Singh, Ajeet Kumar, A.K Singh

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Sanjay Kumar Singh, Ajeet Kumar, A.K Singh. Use of GIS and Remote sensing in assessing soil erosion. DR. RAJENDRA PRASAD CENTRAL AGRICULTURAL UNIVERSITY PUSA, SAMASTIPUR-848125, BIHAR, INDIA. 2022, pp.1-33. hal-05205718

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Technical Bulletin
On

Use of GIS & Remote sensing in assessing soil erosion

मृदा क्षरण का आकलन करने में जीआईएस और रिमोट सेंसिंग का
उपयोग

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Department of Soil Science
Tirhut College of Agriculture,
Dholi, Muzaffarpur-843 121

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Dr. Rajendra Prasad Central Agricultural University
Pusa (Samastipur)-Bihar

August-2022
Technical Bulletin No. SS-1/2022: Use of GIS & Remote sensing in assessing soil erosion

Technical Bulletin, August, 2022

Tirhut College of Agriculture, Dholi, Muzaffarpur-843 121,
RPCAU, Pusa (Samastipur), Bihar, India

Published by

**Dr. A.K. Singh**

Dean, Tirhut College of Agriculture, Dholi

RPCAU, Pusa (Samastipur), Bihar

Phone: 9415920101

Email: dean.tca@rpcau.ac.in

Year of publication: August, 2022

Copy right reserved

All rights reserved. No part of this
publication shall be reproduced or
transmitted in any form by print,
microfilm or any other means
without written permission of the
Dean, TCA, Dholi, Muzaffarpur, ,
RPCAU, Pusa (Samastipur),
Bihar.

Authors: Conceptualize and written by

SANJAY KUMAR SINGH. Assistant Professor (Soil Science), TCA, Dholi, RPCAU, Pusa, Bihar.

AJEET KUMAR, Assistant Professor-cum-Scientist (Soil Science), SRI, RPCAU, Pusa, Bihar

Critically reviewed and edited by

A. K. SINGH, Dean, Tirhut College of Agriculture, Dholi, RPCAU, Pusa, Bihar

Designed and Print by

MITHUN KUMAR, Sr. Technician, SRI, RPCAU, Pusa, Bihar, India

Citation

Singh, S.K., Kumar, A. and Singh, A.K (2022). Use of GIS & Remote sensing in assessing soil erosion, Technical Bulletin No. SS-1/2022. Tirhut College of Agriculture, Dholi, RPCAU, Pusa (Samastipur)-Bihar, India, pp. 1-33.
Use of GIS and Remote sensing in
assessing soil erosion

मृदा क्षरण का आकलन करने में जीआईएस और रिमोट सेंसिंग का
उपयोग

By

SANJAY KUMAR SINGH

AJEET KUMAR

A.K. SINGH

Department of Soil Science
Tirhut College of Agriculture
Dholi, Muzaffarpur-843 121

DR. RAJENDRA PRASAD CENTRAL AGRICULTURAL UNIVERSITY
PUSA, SAMASTIPUR-848125, BIHAR, INDIA

August-2022
CONTENTS

<table><thead><tr><td>Sl. No.</td><td>PARTICULARS</td><td>PAGE</td></tr></thead><tbody><tr><td>1.</td><td>Introduction</td><td>5-6</td></tr><tr><td>2.</td><td>Mechanism of soil erosion</td><td>6-6</td></tr><tr><td>3.</td><td>Soil erosion modelling</td><td>6-8</td></tr><tr><td>4.</td><td>RUSLE model of soil erosion</td><td>9-16</td></tr><tr><td>5.</td><td>Satellites and sensors used in erosion study</td><td>17-19</td></tr><tr><td>6.</td><td>Annual soil loss (A) (t ha<sup>-1</sup>yr<sup>-1</sup>)</td><td>19-20</td></tr><tr><td>7.</td><td>Land use suitability of the study area</td><td>21-24</td></tr><tr><td>8.</td><td>Remote Sensing</td><td>24-25</td></tr><tr><td>9.</td><td>Application of RS in agriculture</td><td>25-27</td></tr><tr><td>10.</td><td>Conclusion</td><td>27-27</td></tr><tr><td>11.</td><td>References</td><td>28-33</td></tr></tbody></table>
# 1. Introduction

In nature, land is the most crucial natural resource for sustaining livelihoods for all living beings. Almost all the human activities are performed to land resources, either directly or indirectly (Jiang et. al. 2014). Land maintains numerous varieties of ecosystem services and therefore remains perilous for the survival of humanity (Raj et al., 2019). Land considered as a highly dynamic and complex medium consisting insufficient organic matter, erosion, landslides, and pollution are all potential degradation hazards of land resources (Gianinetto et. al. 2019). A report depicted approximately 2 billion hectares of land are already being degraded, accounting to 12 to 15 tonnes of soil per hectare every year (Biggelaar et. al. 2003). It is also reported that soil erosion accounts for 25% of total land degradation in Europe, 18% in Asia, 16% in Africa, and 5% in North America (Oldeman et al. 1992). The soil erosion process is both natural and dynamic triggered by various agents such as water and wind that results into degradation of the top soil. Soil erosion is widely defined as the process of detachment and transportation of soil particles from one place to another and the cumulative result of accelerated changes in agricultural practices and intensification, land degradation and climate change. Anthropogenic activities considered as land use-land cover alteration, overgrazing, deforestation, mining, improper agricultural practices, and construction practices speed up the pace of soil erosion (Kouli et al., 2009, John et al 2021). The continuous and widespread soil erosion requires accurate and timely estimation has become critical for many countries. The scientist have been working since many years on soil erosion research for a long time, and much effort has gone into understanding the process of soil erosion and estimating the rate of soil erosion and soil loss at both the catchment scale and the plot scale (Fu et al., 2005). The earliest assessment of the area affected by the land degradation in India was made by the National Commission on Agriculture at 148 M ha, followed by 175 M ha by the Ministry of Agriculture (Soil and Water Conservation Division). The NBSS&LUP estimates projected an area of 187 M ha as degraded lands in 1994 following GLASOD methodology (Oldeman, 1992), and revised it to 147 Mha in 2004. The efficiency of soil erosion research is determined by the model selection, which is based on the objectives, catchment characteristics, and data availability (Keesstra et al., 2014). The remarkable advancements in the assessment of land degradation in India using remote sensing technology, from mere
interpretation of satellite imagery [Arya et al., 2009: Arya et al., 2011: Bhandari et al., 2012: Gandhi et al., 2015] to the application of complex statistical analysis. Remote sensing has traditionally been utilised in soil erosion research by interpreting aerial photos to locate erosion features and acquire model input data (Stephens et al., 1985). Satellite data became more available to researchers since the advent of Landsat-1 in 1972 and thereby development of soil erosion models.

## 2. Mechanism of soil erosion

Soil erosion refers to a natural geomorphic activity that occurs on the surface of the land and is both endogenic and exogenic. It is more common and widely dispersed than other geomorphic processes. Anthropogenic disturbances and poor land management increases the process of soil erosion (Malleswara Rao, 2005). The process of soil erosion comprises three main stages. These three phases are detachability, transportability, and deposition by various factors of soil erosion (Jain et al. 2001). The soil particles break down during the detachment phase as a result of stresses brought on by tillage, wind shear, raindrop impact, and other factors (Malleswara Rao et al. 2005; Sahu et al. 2017). Among the other agents of soil erosion, water-induced soil erosion is considered to be the most severe one (Thlakma et al. 2018).

## 3. Soil erosion modelling

Soil erosion modelling is a complex dynamic process that exposes subsurface soil, causes siltation in reservoirs or natural streams, and detaches, transports, and accumulates fertile surface soil across a large area (Verma et al. 1995). Some of the soil erosion models employed by researchers are listed in table 1. Modelling soil erosion is necessary for the following three reasons viz. Prediction tool for assessing soil loss for conservation planning, project planning, soil erosion inventories, and the creation of laws; Predicting where and when erosion will occur using physical-based mathematical models and to be utilised as a tool for determining research priorities by helping to understand processes and how they interact (Jasrotia et al. 2002). Mathematical expressions are used in soil erosion models to relate key variables and processes that take place on the surface of the land (Jetten et al. 2003). Soil erosion models have been generally divided into three groups viz. based on the physical
processes that need to be simulated, the algorithms that characterise those processes,
and the model's reliance on data (Merritt et al. 2003).

**3.1. Physical model:**

By offering answers to basic physics equations, physical models are recognised to
be able to explain the process of soil erosion within a watershed (Roshani et al. 2013).
These models are based on a particular class of differential equations known as the
continuity equation, which is sometimes hypothesised as the law of conservation of
matter through space and time (Mitasova et al. 2013). However, physical models are
complex, require huge amounts of data and difficult for modelling larger catchments
(Malleswara Rao et al. 2005). Some of the physical model for assessing soil erosion
includes European Soil Erosion Model (EUROSEM), Water Erosion Prediction
Project model (WEPP), Griffith University Erosion System Template model
(GUEST), Productivity, Erosion and Runoff, Functions to Evaluate Conservation
Techniques model (PERFECT) and Areal Nonpoint Source Watershed Environment
Response Simulation model (ANSWERS).

**3.2. Empirical Models:**

These models have the benefit of being very easy to use and make use of
inductive reasoning, past experiences, and experimental results (Merritt et al. 2003). To
simulate the amount of detached soil, empirical models link the characteristics that may
be assessed scientifically, like basin area and slope gradient. The fact that empirical
models require less data and fewer computations makes them widely applicable (Asadi
et al. 2017). Common examples of empirical models include Universal Soil Loss
Equation (USLE), Modified Universal Soil Loss Equation (MUSLE), Revised
Universal Soil Loss Equation (RUSLE), Sediment Delivery Ratio (SDR), Sediment
Delivery Distributed (SEDD) and Agricultural Nonpoint Pollution Source (AGNPS).

**3.3. Conceptual Models:**

This model outlines the main processes that control the movement of water and
sediment between various reservoirs (Merritt et al. 2003). However, when it comes to
interpreting physical processes including sediment production and soil erosion, the
parameters utilised in conceptual models pose a constrained range of application
(Sujatha and Sridhar, 2018). Common examples of conceptual models include Large
Scale Catchment (LASCAM) and Chemical Runoff and Erosion from the Agricultural Management Systems (CREAMS).

<table><caption>Table 1: Soil erosion models</caption><thead><tr><td></td><td>Models</td><td>References</td></tr></thead><tbody><tr><td>MUSLE</td><td>Modified Universal Soil Loss Equation</td><td>Williams (1975)</td></tr><tr><td>USLE</td><td>Universal Soil Loss Equation</td><td>Wischmeier and Smith (1978)</td></tr><tr><td>CREAMS</td><td>Chemical Runoff and Erosion from Agriculture Management Systems</td><td>Knisel (1980)</td></tr><tr><td>ANSWERS</td><td>Areal Nonpoint Source Watershed Environment Response System</td><td>Beasley and Huggins (1982)</td></tr><tr><td>WEPP</td><td>Water Erosion Prediction Project</td><td>Lane and Nearing (1989)</td></tr><tr><td>DUSLE</td><td>Differentiated Universal Soil Loss Equation</td><td>Flacke et al. (1990)</td></tr><tr><td>KINEROS</td><td>Kinematic Erosion Simulation</td><td>Woolhiser et al. (1990)</td></tr><tr><td>RUSLE</td><td>Revised Universal Soil Loss Equation</td><td>Renard et al. (1991)</td></tr><tr><td>EROSION2D</td><td>Erosion- 2D</td><td>Schmidt (1991)</td></tr><tr><td>OPUS</td><td>Advanced Simulation Model for Nonpoint Source Pollution Transport</td><td>Ferreira and Smith (1992)</td></tr><tr><td>PEPP</td><td>Process-Oriented Erosion Prognosis Program</td><td>Schramm (1994)</td></tr><tr><td>LISEM</td><td>Limburg Soil Erosion Model</td><td>De Roo et al. (1994)</td></tr></tbody></table>
## 4. RUSLE model of soil erosion

In 1954, USLE was developed for field crops as an empirical model where the main purpose was to imitate sheet and rill erosion that associated with overland flow in agricultural areas (Merritt et al. 2003). The RUSLE model is a advanced developed version of USLE used for better assessment of soil depletion assessment and it may be utilized in natural settings (Renard et al. 1997). The scientists worked as that aspects and having extensive knowledge in soil erosion issues developed the model (Angima, 2003). Revised Universal Soil Loss Equation (RUSLE) is preferred over traditional physical models due to its simplicity in terms of usage (Ayinla and Jona, 2018), fewer computations and data involved (Merritt et al., 2003, Efthimiou and Karavitis, 2014, Asadi et al., 2017), wide area coverage, and less expensive and time consuming (Malleswara Rao et al., 2005). RUSLE model computes average annual soil loss (A) by using five important input factors viz. rainfall erosivity (R), soil erodibility (K), slope length and slope steepness (LS), cover management (C), and support practice (P). The equation and the input factors are described in the following.

$$A=R \times K \times LS \times C \times P$$

Where, A is the mean annual soil loss in t ha⁻¹ yr⁻¹, R is the rainfall erosivity (MJ mmha⁻¹ yr⁻¹), K is the soil erodibility factor (t ha⁻¹ ha⁻¹ MJ⁻¹ mm⁻¹), LS is the slope length and slope steepness factor (dimensionless), C is the cover management factor (dimensionless), and P is the support practice factor (dimensionless). The methodology for computing annual soil loss is represented schematically in figure 1.
Methodology

<figure><img src="image_5.png" /><figcaption>Figure 1: Schematic representation for computing annual soil loss</figcaption></figure>

**4.1. Rainfall erosivity (R) factor:**

The R factor indicated the erosive power of a specified rainfall. Wischmeier and Smith (1978) established a correlation between rainfall depth and rainfall erosivity. The volume and rate of runoff most likely to be brought on by rain are represented by the rainfall-runoff erosivity factor (R), which measures the influence of precipitation. It provides a quantitative analysis of how much soil erosion is caused by rainfall (Wischmeier and Smith 1978). Rainfall erosivity within the RUSLE is often calculated using the EI₃₀ measurement (Renard et al. 1997). Here, EI₃₀ refers to the product of total energy E, (MJ ha⁻¹) and maximum intensity I, (mmh⁻¹) of rainfall in 30 minutes. The amount of soil loss is proportional to the product of the total storm's energy and the storm's maximum intensity in a time of 30 minute (Renard et al. 1997). Different researchers have developed equations from where the R factor has been derived and applied in different regions (Table 2).
<table><caption>Table 2: R factor developed for RUSLE soil erosion model</caption><thead><tr><td>Equation</td><td>Area of application</td><td>References</td></tr></thead><tbody><tr><td>R = 0.55MAR – 24.7</td><td>Ethiopia and Egypt</td><td>Hurni, 1985</td></tr><tr><td>R = 79 + 0.363MAR</td><td>Entire India</td><td>Singh et al. 1981</td></tr><tr><td>R = 50 + 0.389 MSR</td><td>Entire India</td><td>Singh and Phadke, 2006</td></tr><tr><td>R = 0.5MAR</td><td>Ivory Coast and Burkina Faso</td><td>Roose, 1975; Morgan, 1986</td></tr><tr><td>R = 117.6 (1.00105<sup>MAR</sup>) for &lt;2000mm</td><td>Kenya</td><td>Kassam et al. 1992</td></tr><tr><td>R = 0.38 + 0.35MAR</td><td>&amp;iland</td><td>Harper, 1987</td></tr></tbody></table>

## 4.2. Soil erodibility (K) factor:

The K factor quantifies how easily soil will erode. The K factor can be simply stated as the soil's innate vulnerability to erosion (Farhan et al. 2013). The K factor depends on various biological and chemical aspects of the soil, including its mineralogy, particle size, permeability, and presence of organic matter (Wischmeier and Smith 1978). Soil detachability shows direct proportion with the size of particles while soil transportability shows indirect proportion with its particle size (Schwab et al. 2002). Some of the different K factor algorithms developed based on suitability and requirement are summarised in Table 3.
<table><caption>Table 3: Different K factor equations developed</caption><thead><tr><th>Equation</th><th>References</th></tr></thead><tbody><tr><td>K = 27.66m<sup>1.14</sup> * (12-a) + 0.0043 (b-2) + 0.0033 (c-3)<br>K= soil erodibility factor (t hr- 1ha- 1MJ mm), m= (silt %+ sand %) x (100 - clay %)<br>a = percent organic matter, b = soil structure code: (1) very structured or particulate, (2) fairly structured, (3) slightly structured, and (4) solid, c = soil profile permeability code: (1) rapid, (2) moderate to rapid, (3) moderate, (4) moderate to slow, (5) slow, and (6) very slow<br>Soil organic matter is derived by the following equation:<br>SOM= 1.72 * OC [99], where, SOM= soil organic matter, OC= soil percentage organic carbon content</td><td>Wischmeir and Smith, 1978</td></tr><tr><td>K = 311.63-4.48 * (SG% + S%) + 613.4 + 6.45 * EC<br>Where<br>SG = coarse sand content (%), S = sand content (%), EC = electrical conductivity</td><td>Merzoul, 1985</td></tr><tr><td>K = -0.03970 + 0.00311A<sub>1</sub> + 0.00043A<sub>2</sub> + 0.00185A<sub>3</sub> + 0.00258A<sub>4</sub> - 0.00823A<sub>5</sub><br>Where,<br>A<sub>1</sub> = % unstable aggregates less than 0.250mm; A<sub>2</sub> = product of % silt (0.002-0.01 mm) and % sand (0.1-2 mm);<br>A<sub>3</sub> = % base saturation of the soil; A<sub>4</sub> = % silt (0.002-0.050 mm); A<sub>5</sub> = % sand (0.1-2mm);<br>Units for K: Mg h MJ<sup>-1</sup>mm<sup>-1</sup></td><td>El-Swaify and Dangler, 1976</td></tr></tbody></table>
4.3. LS factor:

The LS factor considers both the steepness (S), which increases runoff velocity, and the slope length (L), which helps to increase the land surface affected by runoff. Two independent algorithms were used to calculate this factor, one for a slope up to a 20 percent gradient and another for a steeper slope (Arnoldus 1980). The slope length is the horizontal distance between the point of overland flow to the point at which either deposition starts or runoff concentrates in a specific channel, depending on which happens first (Wischmeier and Smith 1978). The raster calculator in ArcMap can be used to calculate the LS factor for RUSLE and create an equation for calculating LS based on flow accumulation and slope steepness (Anamika et al. 2013). The digital elevation model is a crucial input component for the RUSLE model where the change of topographic features on a specific terrain is quantitatively represented (Phinzi and Ngetar, 2019). Some of the LS equation developed by researcher is tabulated below (Table 4).
<table><caption>Table 4: Some LS factor equation developed.</caption><thead><tr><th>Equation</th><th>Reference</th></tr></thead><tbody><tr><td>LS = (Flow Accumulation * (cell size/22.31))<sup>0.4</sup> * (sin(slope)/0.0896)<sup>1.3</sup>,<br>Where,<br>LS = topographic factor<br>Cell size = grid size<br>LS = (Flow accumulation * grid size/22.13)<sup>0.4</sup> * (sin(slope) * 0.1745/0.09)<sup>1.4</sup>,<br>Where,<br>Flow accumulation = raster of flow accumulated to each cell<br>Grid size = cell size<br>Sin (slope) = sin of the slope degree value<br>LS = (QaM/22.13)<sup>y</sup> * (0.065 + 0.045 S<sub>g</sub> + 0.0065 S<sub>g</sub><sup>2</sup>),<br>Where,<br>LS = topographical factor<br>Q<sub>a</sub> = flow accumulation grid<br>S<sub>g</sub> = grid slope in percentage<br>M = grid size (vertical length × horizontal length) y = a constant, which depends on the slope gradient<br>0.5 for S<sub>g</sub> ≥ 4.5%; 0.4 for 3 ≤ S<sub>g</sub> < 4.5%; 0.3 for 1 ≤ S<sub>g</sub> < 3%; 0.2 for S<sub>g</sub> < 1%</td><td>Moore and Burch, 1986<br>Mitasova et al. 1996<br>Wischmeier and Smith, 1978</td></tr></tbody></table>
### 4.4. Cover Management Factor (C):

The influence of vegetation and management on soil erosion rates is reflected in the cover management factor (Renard et al. 1997). It is the ratio of soil loss caused by a particular crop to soil loss caused by uninterrupted bare land. Vegetation type, growth stage, and Percentage cover all have a significant impact on it (Mallick et al. 2014). The amount of soil surface protection provided by a crop affects how quickly soil erodes. The energy of raindrops is dissipated by vegetation cover before they hit the soil surface, protecting it from the impact of the raindrops (Ghosal and Bhattacharya, 2020). Depending on the types of land cover, the C factor can have values between 0 and 1. The normal range for bare soils is 1, for root and tuber crops 1 to 0.01, for grasslands and cover plants 0.01 and for forests 0.001 (Rao 1981). Numerous techniques have been developed by researchers for calculating the C factor based on the normalised difference vegetation index (NDVI) for the RUSLE model assessment of soil loss (Table 5).

<table><caption>Table 5: C factor equation for RUSLE model assessment of soil loss.</caption><thead><tr><th>Equation</th><th>Reference</th></tr></thead><tbody><tr><td>C = 0.1((− NDVI + 1)/2),<br>Where, C = land cover factor</td><td>Durigon et al. 2014</td></tr><tr><td>C = 0.431 − 0.805 × NDVI</td><td>De Jong, 1994</td></tr><tr><td>C = exp(− α(NDVI/(β − NDVI))),<br>Where α and β defines the NDVI curve</td><td>Knijff et al. 2000</td></tr></tbody></table>

### 4.5. Support Practice Factor (P):

The influence of support practices and the average annual rate of erosion are both included in the support practice (P) factor (Renard et al. 2011). The P factor is the ratio of the soil loss on farmland sites with a particular support technique to the comparable loss with upslope and downslope tillage (Merritt et al. 2003). The P factor takes into account any farming technique used to minimise soil erosion caused by water flow. The most popular support practices include using contours, terraces, strip
crop and cross-slope farming (Renard et al. 1997). P factor values typically fall between 0 and 1. A score of 1 represents lands with no support practises (particularly grasslands and barren lands), while readings that are close to 0 represent lands with a specific support practise (Koirala et al. 2019). By changing the flow pattern, gradients, or direction of surface runoff and by lowering runoff amounts and rates, the support practise effectively has an impact on soil erosion (Renard et al. 1997). By taking into account various support practises, including contouring, terracing, and strip cropping, Wischmeier and Smith were able to calculate various P factor values is presented in table 6.

<table><caption>Table 6: P factor for contour ploughing developed by (Wischmeier and Smith 1978)</caption><thead><tr><td>Land slope percentage (%)</td><td>P value</td><td>Maximum length (Feet)</td></tr></thead><tbody><tr><td>1-2</td><td>0.6</td><td>400</td></tr><tr><td>3-5</td><td>0.5</td><td>300</td></tr><tr><td>6-8</td><td>0.5</td><td>200</td></tr><tr><td>9-12</td><td>0.6</td><td>120</td></tr><tr><td>13-16</td><td>0.7</td><td>80</td></tr><tr><td>17-20</td><td>0.8</td><td>60</td></tr><tr><td>21-25</td><td>0.9</td><td>50</td></tr></tbody></table>
5. Satellites and sensors used in erosion study

The satellites has been established to record frequently imaged by a vast number
of earth observation on the earth surface where, many of these satellites that able to
provide important information for assessing erosion. Satellite image sensors are
classified as either sensing the reflection of sunlight in the visible and infrared parts of
the electromagnetic spectrum and thermal infrared radiance (optical systems) or
actively emitting microwave pulses and storing the received signal (imaging radars).
For erosion studies, optical satellite systems have been most commonly employed. In
the electromagnetic spectrum, the optical system encompasses the visible and near-
infrared (VNIR), shortwave infrared (SWIR), and thermal infrared (TIR). Initially,
Landsat satellites equipped with the Multispectral Scanner (MSS) featured four bands
at 80-m resolution, but subsequent versions added the Thematic Mapper (TM) and
Enhanced TM (ETM) sensors, providing greater resolution and more spectral bands.
The SPOT family of satellites began collecting data in 1986, with the HRV-sensor
(High Resolution Visible) having a 10 metre panchromatic mode and a three band 20-
m resolution multispectral mode. The Indian Remote Sensing Satellites (IRS) consists
of 1A, 1B, 1C, and 1D. IRS 1A and 1B contain two sensors called LISS-1 and LISS-2
(Linear Imaging and Self-Scanning Sensor), while IRS 1C and 1D have an identical
5.8-m resolution panchromatic camera (PAN) and a 23.5-m resolution multispectral
sensor called LISS-3. Terra, TIROS, IKONOS, QuickBird and Sentinel also provide
high-resolution image ranging from 0.61m (QuickBird) to 10 m (Sentinel). Sensors
such as Advanced Very High Resolution Radiometer (AVHRR) have five bands with
a resolution of 1.1 km and have been flown on a variety of platforms, such as
Television Infrared Observation System (TIROS-N) and various National Oceanic
and Atmospheric Administration (NOAA) satellites. Sentinel-2 is equipped with an
optical payload that includes visible, near infrared, and shortwave infrared sensors
with 13 spectral bands: four at 10 m, six at 20 m, and three at 60 m spatial resolution.
The satellites and sensor properties employed in erosion assessment are summarised
in Table 7.
<table><caption>Table 7: Overview of optical satellite sensors applied in erosion study</caption><thead><tr><th>Satellite</th><th>Sensor</th><th>Spatial resolution (m)</th><th>Spectral bands</th><th>Spectral domain</th></tr></thead><tbody><tr><td>Landsat-1,2,3</td><td>MSS</td><td>80</td><td>4</td><td>VNIR</td></tr><tr><td>Landsat-4,5</td><td>TM</td><td>30</td><td>6</td><td>VNIR, SWIR</td></tr><tr><td rowspan="3">Landsat-7</td><td rowspan="3">ETM</td><td>120</td><td>1</td><td>TIR</td></tr><tr><td>15</td><td>1</td><td>VNIR</td></tr><tr><td>30</td><td>6</td><td>VNIR, SWIR</td></tr><tr><td rowspan="3">SPOT-1,2,3</td><td rowspan="3">HRV</td><td>60</td><td>1</td><td>TIR</td></tr><tr><td>10</td><td>1</td><td>VNIR</td></tr><tr><td>20</td><td>3</td><td>VNIR</td></tr><tr><td rowspan="2">SPOT-4</td><td rowspan="2">HRVIR</td><td>10</td><td>1</td><td>VIS</td></tr><tr><td>20</td><td>4</td><td>VNIR,SWIR</td></tr><tr><td rowspan="2">IRS-1A,1B</td><td>LISS-1</td><td>72.25</td><td>4</td><td>VNIR</td></tr><tr><td>LISS-2</td><td>36.25</td><td>4</td><td>VNIR</td></tr><tr><td rowspan="3">IRS-1C,1D</td><td>PAN,</td><td>5.8</td><td>1</td><td>VNIR</td></tr><tr><td rowspan="2">LISS-3</td><td>23.5</td><td>3</td><td>VNIR</td></tr><tr><td>70</td><td>1</td><td>SWIR</td></tr><tr><td rowspan="3">Terra</td><td rowspan="3">ASTER</td><td>15</td><td>3</td><td>VNIR</td></tr><tr><td>30</td><td>6</td><td>SWIR</td></tr><tr><td>90</td><td>5</td><td>TIR</td></tr><tr><td rowspan="2">NOAA/ TIROS</td><td rowspan="2">AVHRR</td><td>1.1</td><td>5</td><td>VNIR, SWIR,</td></tr><tr><td></td><td></td><td>TIR</td></tr><tr><td rowspan="2">IKONOS</td><td>Panchromatic</td><td>1.0</td><td>1</td><td>VNIR</td></tr><tr><td>Multispectral</td><td>4.0</td><td>4</td><td>VNIR</td></tr><tr><td rowspan="2">QuickBird</td><td>Panchromatic</td><td>0.61</td><td>1</td><td>VNIR</td></tr><tr><td>Multispectral</td><td>2.44</td><td>4</td><td>VNIR</td></tr><tr><td rowspan="3">Sentinel-2A, 2B</td><td rowspan="3">MSI</td><td>10</td><td>2,3,4,8</td><td>VNIR</td></tr><tr><td>20</td><td>5,6,7,8A,11,12</td><td>Red, SWIR</td></tr><tr><td>60</td><td>1,9,10</td><td>Atmospheric correction</td></tr></tbody></table>

(Anton Vrieling, 2006; MSI overview, 2018)
Soil erosion leaves the impact on the ecosystem is particularly noticeable in emerging nations. The variability occurred in environmental conditions and the paucity of high-quality data make it difficult to assess soil erosion. The technology used to assess the soil loss may be conducted more effectively using the RUSLE model when it is used in a geospatial environment. The use of empirical hydrological models, such as RUSLE, along with remote sensing and GIS tools has expanded the utility of these models for locating areas those are more prone to soil erosion and assessing the best management strategies to stop it. Therefore, close coordination is needed between the remote sensing community and field-based erosion experts.

## Annual soil loss (A) (t ha⁻¹yr⁻¹)

Annual soil loss for study area were estimated in raster calculator tool of ArcGIS 10.7.1 by combining all the Revised Universal Soil Loss Equation (RUSLE) parameters viz. Rainfall Erosivity Factor (R), Soil Erodibility Factor (K), Slope Length and Steepness Factor (LS), Cover and Management Factor (C) and Support Practice Factor (P) of the (Renard *et al*., 1991). The yearly soil erosion rates were categorized according to its severity: 0-5 t ha⁻¹yr⁻¹ - very slight, 5-10 t ha⁻¹yr⁻¹ - slight, 10-20t ha⁻¹yr⁻¹ - moderate, 20-40 t ha⁻¹yr⁻¹ - moderately high, 40-80 t ha⁻¹yr⁻¹ - severe and greater than 8 t ha⁻¹yr⁻¹ - very severe. Study revealed that highest soil loss (23.65 t ha⁻¹yr⁻¹) was occurred in the year 2020 followed by annual soil loss of 15.76 and 11.15 t ha⁻¹yr⁻¹ during the year 2019 and 2021 (Table 4.7). Higher amount of soil loss during the year 2020 may be due to high amount rainfall (Table 3.1), low C-factor (Fig. 4.16) and low P-factor (Fig. 4.22) in the same year. The study observed that lowest annual soil loss of 11.5 t ha⁻¹yr⁻¹ (Table 4.7) occurred despite the highest annual rainfall received during the year 2021 (Table 3.1). This may be due to the lowest C-factor (high vegetation index) as observed during the year 2021 (Fig. 4.17) that might have dominated the effect of R-factor in influencing soil erosion loss. Among the tehsils, the lowest annual soil erosion was observed in Aurai tehsil in all the year (Table 4.7). This could be as a result of the larger amount of conservation and support practices in Aurai tehsil where the agricultural land class was dominant compared to other land cover class leading to low P-factor (Fig. 4.21, 4.22 and 4.23). Paroo and Musahari tehsil recorded higher amount of soil loss as compared to other tehsils of Muzaffarpur district (Table 4.7). This may be due to the stream bank erosion as it serves as the part of drainage basin area of the river Gandak. The study revealed
that in year 2019 and 2020, annual soil losses was influenced mostly by R-factor
while in the year 2021, vegetation showed dominant influence on the annual soil loss
of the study area. The mean annual soil loss in the Muzaffarur district was recorded
15.76, 23.65 and 11.15 t ha⁻¹yr⁻¹ for the year 2019, 2020 and 2021. Annual soil
erosion intensity map of the study for the year 2019, 2020 and 2021 area are depicted
in figure 4.23, 4.24 and 4.25 respectively.

<figure><img src="image_6.png" /><figcaption>Fig. 4.25: Soil erosion intensity map of Muzaffarpur district for the year 2021</figcaption></figure>
## 3 Land use suitability of the study area

Land suitability was determined using Analytical hierarchy process (AHP) and using spatial analyst tool “Weighted Overlay Analysis” in ArcGIS. The criterion factors that affect crop land suitability consisted of slope, rainfall, NDVI, LULC, soil pH and soil organic carbon. All these criterion factors were already generated from the objective 1 of the study in form of vector layer. For standardization of the criterion/factors, the vector layers were converted to thematic layers by using a reclassify tool in ArcGIS software. All the criteria were reclassified into different sub-criterion classes and scoring (priority) value was assigned as per the method identified by Saaty 2008. AHP pairwise comparison table giving the relative importance of the criterion factors by Saaty 2008 was used for assigning score value (Table 3.8). After giving a relative scoring (priority) value to each criterion factor a pairwise comparison matrix was generated using the AHP method (Table 4.22). Subsequently, normalized comparison matrix (Table 4.23) was generated to further identify the weights of each criterion factor, consistency index (CI) and consistency ratio (CR) using AHP method (Saaty 1977). The result of the methodology described above is presented in table 4.22, 4.23 and 4.24 respectively.

<table><caption>**Table 4.22 Pairwise comparison matrix for land suitability analysis of the study area**</caption><thead><tr><th>CRITERIA</th><th>Slope</th><th>Rainfall</th><th>NDVI</th><th>LULC</th><th>Soil pH</th><th>SOC</th></tr></thead><tbody><tr><th>Slope</th><td>1</td><td>4</td><td>6</td><td>3</td><td>2</td><td>5</td></tr><tr><th>Rainfall</th><td>1/4</td><td>1</td><td>5</td><td>2</td><td>2</td><td>3</td></tr><tr><th>NDVI</th><td>1/6</td><td>1/5</td><td>1</td><td>1/3</td><td>1/4</td><td>1/2</td></tr><tr><th>LULC</th><td>1/3</td><td>1/2</td><td>3</td><td>1</td><td>1/2</td><td>3</td></tr><tr><th>Soil pH</th><td>1/2</td><td>1/2</td><td>4</td><td>2</td><td>1</td><td>2</td></tr><tr><th>SOC</th><td>1/5</td><td>1/3</td><td>2</td><td>1/3</td><td>1/2</td><td>1</td></tr></tbody></table>
<table><caption>Table 4.23 Normalised pairwise comparison matrix for land suitability analysis of the study area</caption><thead><tr><th>CRITERIA</th><th>Slope</th><th>Rainfall</th><th>NDVI</th><th>LULC</th><th>Soil pH</th><th>SOC</th></tr></thead><tbody><tr><th>Slope</th><td>0.4082</td><td>0.6122</td><td>0.2857</td><td>0.3462</td><td>0.3200</td><td>0.3448</td></tr><tr><th>Rainfall</th><td>0.1020</td><td>0.1531</td><td>0.2381</td><td>0.2308</td><td>0.3200</td><td>0.2069</td></tr><tr><th>NDVI</th><td>0.0680</td><td>0.0306</td><td>0.0476</td><td>0.0385</td><td>0.0400</td><td>0.0345</td></tr><tr><th>LULC</th><td>0.1361</td><td>0.0765</td><td>0.1429</td><td>0.1154</td><td>0.0800</td><td>0.2069</td></tr><tr><th>Soil pH</th><td>0.2041</td><td>0.0765</td><td>0.1905</td><td>0.2308</td><td>0.1600</td><td>0.1379</td></tr><tr><th>SOC</th><td>0.0816</td><td>0.0510</td><td>0.0952</td><td>0.0385</td><td>0.0800</td><td>0.0690</td></tr></tbody></table>

<table><caption>Table 4.24 Weight of Criterion factors, Influence (%), Score, CI and CR value for analysis of land suitability in study area</caption><thead><tr><th>Main criteria</th><th>Weight</th><th>Influence (%)</th><th>Reclassified criteria</th><th>Score</th></tr></thead><tbody><tr><td rowspan="3">Slope (degree)</td><td rowspan="3">0.0342</td><td rowspan="3">3.42</td><td>0.0-10</td><td>4</td></tr><tr><td>10-40</td><td>2</td></tr><tr><td>>40</td><td>1</td></tr><tr><td rowspan="3">Rainfall (mm)</td><td rowspan="3">0.0643</td><td rowspan="3">6.43</td><td>800-1000</td><td>4</td></tr><tr><td>1000-1200</td><td>3</td></tr><tr><td>>1200</td><td>1</td></tr><tr><td rowspan="3">NDVI</td><td rowspan="3">0.4483</td><td rowspan="3">44.83</td><td>-0.3 to -0.9</td><td>1</td></tr><tr><td>-0.9 to + 0.5</td><td>2</td></tr><tr><td>>+0.5</td><td>5</td></tr><tr><td rowspan="5">LULC</td><td rowspan="5">0.0387</td><td rowspan="5">3.87</td><td>Water bodies</td><td>1</td></tr><tr><td>Forest land</td><td>2</td></tr><tr><td>Agriculture land</td><td>5</td></tr><tr><td>Built up/waste land</td><td>1</td></tr><tr><td>Bare land</td><td>1</td></tr><tr><td rowspan="3">Soil pH</td><td rowspan="3">0.0512</td><td rowspan="3">5.12</td><td>6.1-6.5</td><td>3</td></tr><tr><td>6.5-7.5</td><td>5</td></tr><tr><td>>7.5</td><td>4</td></tr><tr><td rowspan="3">SOC</td><td rowspan="3">0.3633</td><td rowspan="3">36.33</td><td>0.1-0.3</td><td>1</td></tr><tr><td>0.3-0.6</td><td>3</td></tr><tr><td>>0.6</td><td>5</td></tr></tbody></table>

CI = 0.0812

RI (n=6) = 1.24

CR = 0.0654
Highest weighted were found in NDVI factor (0.4483) followed by soil organic carbon (0.3633) and Rainfall factor (Table 4.24). The value of consistency ratio was found to be 0.0654 which is lesser than 0.1 indicating that the scoring/priority value assigned to criterion for finding factors weights were significant. The final land use suitability map generated through weighted overlay analysis is depicted in figure 4.48 and the area under various suitability class are presented in table 4.25. Based on the FAO land suitability categorization, five types of land suitability were recognised viz. highly suitable (S1), moderately suitable (S2), marginally suitable (S3), currently unsuitable (N1), and permanently unsuitable (N2). The study outlined that out of the total area (3122.56 km²), nearly 69.3% area was found under suitable (S) category while 30.7% under non-suitable (N) category. The study also identified largest area (68%) under highly suitability class (S1) followed by 30% area under permanently unsuitable (N2), 0.1% area under moderately suitable (S2), 0.7% area under currently unsuitable (N1) and 1.2% area under marginally suitable (S3) class (Table 4.25). The area under suitable (S) category was found to be concentrated mainly in the eastern region (Fig. 4.48). This reason attributed may be due to higher value of vegetation index (Fig. 4.12, 4.13, 4.14), dominance of agriculture area (Fig. 4.20, 4.21, 4.22) with higher irrigation facilities, moderate amount of annual rainfall (Table 3.1) and dominance of loamy to silty clay loam type of soil texture (Table 4.2). The study also observed area under unsuitable (N) category to be concentrated mostly in central and western region. This might be because the central and western region covers high in terms of build-up area and bare land (Fig. 4.20, 4.21, 4.22) while receiving lower amount of annual rainfall (Table 3.1), lower vegetation index (Fig. 4.12, 4.13, 4.14), poor soil texture (Table 4.2).

<table><caption>Table 4.25 Area under land suitability class in Muzaffarpur district (Bihar)</caption><thead><tr><td>Sl.No.</td><td>Suitability classes</td><td>Area (km²)</td><td>Area (%)</td></tr></thead><tbody><tr><td>1</td><td>S1</td><td>1811.11</td><td>68</td></tr><tr><td>2</td><td>S2</td><td>3.13</td><td>0.1</td></tr><tr><td>3</td><td>S3</td><td>37.47</td><td>1.2</td></tr><tr><td>4</td><td>N1</td><td>21.86</td><td>0.7</td></tr><tr><td>5</td><td>N2</td><td>1249.04</td><td>30</td></tr></tbody></table>
<figure><img src="image_7.png" /><figcaption>Fig. 4.48 Land use suitability map of Muzaffarpur district of Bihar</figcaption></figure>

REMOTE SENSING

Remote sensing is art, science & technology adopted for obtaining reliable information of the objects on the surface of earth without coming into physical contact. In fact we are measuring and recording the electro- magnetic radiations reflected and emitted by the bodies. The remote sensing is a multidisciplinary science which includes a combination of various disciplines such as optics, spectroscopy, photography, computer, electronics and telecommunication, satellite launching etc. Detection and discrimination of objects or surface features.

* Major objective is to Analyse /Understand the Environmental and Natural resources information.

Stages of remote sensing

* Emission of electromagnetic radiation or EMR (sun/self-emission)

* Transmission of energy from the source to the surface of the earth, as well as absorption and scattering.

* Interaction of EMR with the earth's surface reflection and emission.

* Transmission of energy from the surface to the remote sensor.

* Sensor data and output
* Data transmission, processing and analysis

**Remote sensing process**

There are two basic Processes involved in Electromagnetic remote sensing of earth resources:

* 1- Data acquisition

* 2- Data analysis

**Data Acquisition**

* The sensors acquire data on the various earth's surface features that emit and reflect electromagnetic energy, and these data are analyzed to provide information about the resources under investigation

**Data analysis**

* The data analysis process involves examining the data using various viewing and interpretation devices to analyze pictorial data or a computer to analyze numerical sensor data. Reference data about the resources being studied (soil, vegetation...etc) or field check are used to assist in data analysis

* The obtained information about type, extent, location and condition of various resources over which the sensor data were collected is then presented in form of maps, tables and reports.

**APPLICATION OF RS IN AGRICULTURE**

* Identification, area estimation and monitoring

* Crop nutrient deficiency detection

* Soil mapping

* Crop condition assessment

* Agricultural drought assessment

* Crop yield modelling and production forecasting

* Insect damaging

* Hail damage

* Wind damage

* Plant population

* Grazing area

* Weed infestation

* Salt-affected soil
# IDENTIFICATION, AREA ESTIMATION AND MONITORING

* The specific requirement of climate and soil conditions coupled with the specialized management practices make the distribution of plantation crops rather more localized in comparison to other agricultural crops.

* The identification, estimation of growing stock, analysis of distribution and monitoring at regular intervals are major aspects in plantation crops.

## CROP NUTRIENT DEFICIENCY DETECTION:

* The nutrient deficiency in plants affects the colour, moisture content and internal structures of the leaves and as a result their reflecting power changes.

## SOIL MAPPING

* Advancements in space technology opened application possibilities of remote sensing in soil mapping.

* Soil properties have also been inferred from optical and microwave data using physically-based and empirical methods. Soil properties that have been measured using remote or proximal sensing approaches include mineralogy, texture, soil iron, soil moisture, soil organic carbon, soil salinity and carbonate content.

## CROP CONDITION ASSESSMENT

* The physiological changes that occur in plant due to stress may change its spectral reflectance characteristics resulting in the detection of stress amenable to remote sensing techniques.

* Crop monitoring at regular intervals during the crop growth cycle is essential to take appropriate measures and to assess information on probable loss of production.

## AGRICULTURAL DRAUGHT ASSESSMENT

* Draught assessment is yet another area wherein remote sensing data has been used at operational level.

* Total soil salinity, alkalinity and degraded land can be identified.

## REFLECTANCE MODELLING

* Physical reflectance models for crops serve the important purpose of understanding the complex interaction between solar radiation and plant canopies.
* In order to obtain a reliable yield prediction, growth of cropshas to be modelled by means of crop growth models.

* Crop growth models describe the relation between physiological process in plants and environmental factors such as solar radiation, temperature, water and nutrient availability

## CROP YIELD MODELLING AND PRODUCTION FORECASTING

* The information on production of crops before the harvest is very vital to the national food policy planning and economy of the country.

* Reliable crop yield estimate is one of the most important components of crop production forecasting

## CONCLUSION

* Remote sensing technology has developed from balloon photography to aerial photography to multi-spectral satellite imaging. Some of the benefits that are gained from the use of remote sensing -

* Early identification of crop health and stress

* Ability to use this information to do remediation work on the problem

* Improve crop yield

* Crop yield predictions

* Reduce costs

* Reduce environmental impact

* Crop management to maximise returns through theseason

Crop management to maximise returns during harvest time.
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