This document provides an overview of a seminar on remote sensing, GIS and their application for soil fertility mapping. It introduces remote sensing concepts like passive and active sensing, platforms like aircraft and satellites, and multispectral sensors. It discusses how GIS organizes geographic data and examples of data sources. The document presents a case study that mapped soil properties in Mathura District, India using remote sensing and GIS techniques. It describes the digitizing process in ArcGIS and studies on mapping clay minerals and soil contamination with spectroscopy. The document concludes that remote sensing and GIS can efficiently map large areas to aid in crop selection and productivity.

1. WELCOME

2. COURSE SEMINAR
Topic: Remote sensing , GIS and Their application for Soil
Fertility Mapping
Supervisor Speaker : Sukirtee
Dr. Y.V. Singh
I.D. No. – S-15195
DEPARTMENTOFSOILSCIENCEANDAGRIL. CHEMISTRY
INSTITUTEOFAGRICULTURALSCIENCES
BANARASHINDUUNIVERSITY
VARANASI-221005
INDIA
ON

3. Introduction to
Remote Sensing

4. REMOTE SENSING
Collecting information primarily by
sensing radiation that is naturally
emitted or reflected by the Earth’s
surface or from the atmosphere, or by
sensing signals transmitted from a
device and reflected back to it.

5. Remote Sensing Process Components
Energy Source or Illumination (A)
Radiation and the Atmosphere (B)
Interaction with the Target (C)
Recording of Energy by the Sensor (D)
Transmission, Reception, and
Processing (E)
Interpretation and Analysis(F)
Application (G)

7. MAJOR COMPONENTS OF REMOTE SENSING TECHNOLOGY
1. Energy Source:
2. Platforms

8. TYPES OF REMOTE SENSING
Two Types:
1.Passive Remote Sensing: Makes use of
sensors that detect reflected or emitted
electro-magnetic radiation (EMR) from
natural sources.
2.Active Remote Sensing: Makes use of
sensors that detect reflected responses from
objects that are irradiated from artificially-
generated energy sources, such as radar.

9. Passive Remote Sensing
Active Remote Sensing

10. PLATFORMS USED TO ACQUIRE REMOTE
SENSING DATA
• Aircraft
– Low, medium & high altitude
– Higher level of spatial detail
• Satellite
– Polar-orbiting
• 800-900 km altitude, 90-100 minutes/orbit
– Geo-synchronous
• 35,900 km altitude, 24 hrs/orbit
• stationary relative to Earth

11. MULTISPECTRAL REMOTE SENSING
SATELLITES
• NOAA-AVHRR (1100 m)
• GOES (700 m)
• MODIS (250, 500, 1000 m)
• Landsat TM and ETM (30 – 60 m)
• SPOT (10 – 20 m)
• IKONOS (4, 1 m)
• Quickbird (0.6 m)

12. MODIS Land Reflectance and Sea Surface
Temperature

13. (Geographical Information System)

14. GIS DEFINITION….
A GIS is an organized collection of computer
hardware, software, geographic data and
personnel designed to efficiently capture, store,
update, manipulate, analyze and display all forms of
geographically referenced information (ESRI 1990).
A GIS may be thought of as a higher order map (star
and estes 1990)

18. DATA SOURCES
1. Conventional Data sources
A) Primary Data Source
First Hand Observation e.g. Socioeconomic
data, Meteorological data
B) Secondary Data Source
Collected, Compiled & Published
2. Direct Data Sources
3. Existing Data Source

19. 3. Existing Data Sources
a) www.nrsa.gov.in
b) www.landcover.gov.in
c) www.ersu.com
d) www.fgdc.gov.in
e) www.spaceimagine.com

21. Assessment of Soil Resources using Remote Sensing
and GIS Techniques in Mathura District, Uttar
Pradesh

22. FCC covering study area as on May 2, 2000 (RGB -B432, IRS-1C, LISS III)
Yamuna River
Vrindavan

23. Undulating Recent Alluvial Plain (Slope 5-10%)
Active Flood Plain (Slope 0-1%)
Old alluvial Plain (Slope1-3%)
Yamuna River
181
181
181
181
181
183 183
183
183
184
178
177
179
170
170
169
185
02 2 4
Km
Physiographic map of the study
area

24. Soil Reaction
Cation Exchange
Capacity
Base Saturation

25. %
Fig. 4.7 Fig. 4.8
(%)
Fig. 4.7 Fig. 4.8

27. DTPA Fe
DTPA Mn
Fig. 4.12
Fig. 4.13
Fig. 4.14
Fig. 4.15
DTPA Zn
DTPA Cu

28. LEGEND LEGEND
LEGEND
LEGEND

29. LEGEND
LEGEND
LEGEND
LEGEND

31. Fig. 4.41 Fig. 4.42

32. MECHANISM OF MAPPING
Three step process:
 Collection of information
 Quantitative and Qualitative assessment of soil
properties by using regression model
• PLSR (Partial least square regression) for sand,silt,clay
• MARS( multivariate adaptive Regression splin) salinity
• SMLR ( Stepwise multiple linear regression ) heavy
metal
• PCR(principal component regression ) slope information
 Mapping using ArcGIS

33. DIGITIZING WITH ARCGIS
 Add the new feature
classes to the data
frame that holds
your source map
 Turn on the Editor
toolbar
(View>Toolbars
>Editor)

34. DIGITIZING WITH ARCGIS (CONT.)
 Click Editor>Start
Editing
 Select the layer
you’d like to edit
from the Target
drop- down list

35. DIGITIZING WITH ARCGIS (CONT.)
 Select the Create
New Feature task
from the Task drop-
down list
 Click the sketch tool
(pencil)

36. DIGITIZING WITH ARCGIS (CONT.)
 If digitizing point features, a single left- click
with the sketch tool will create a new point.
 If digitizing line or polygon features, left-
clicking will place a vertex. Vertices should
be placed along the length of the map feature.
A line or polygon feature is completed and
added to the feature class by double-clicking on
the last vertex or by right-clicking and choosing
Finish Sketch

37. DIGITIZING WITH ARCGIS (CONT.)
After a feature is
added click the
Attributes button to
access a dialog box
where you can enter
the attribute values for
the newly added
feature

38. DIGITIZING WITH ARCGIS (CONT.)
 Continue digitizing features in the feature
class. To add features to a different
feature class, choose another layer in the
Target drop-down list.
 When all editing is complete, choose
Editor>Stop Editing.
 Note: it is a good idea to periodically save your
work when digitizing a lot of features by
clicking Editor>Save Edits

39. STUDY OF CLAY MINERALS…..
AVIRIS data (van-der-Meer, 2004) According to
these studies absorption band position of
 hematite the strong absorption in the visible
light range.
 In calcite, the major component of limestone, the
carbonate ion (CO3 = ) is responsible for a series of
absorption bands between 1.8 and 2.4 mm
 Kaolinite – Spectral signature of kaolinite is around
weak 1.9 mm band( Hydroxyl ions)
 Montmorillonite – spectral signature is in Strong 1.9
mm band( Bound water molecules)

40. CONT….
According to Silva et al. (2016)
 (325–1075 nm) – spectral signature for silt and
clay fraction.
 (400–980 nm) for clay prediction in Oxisols
achieved relative good results
The combination of spectroscopy reflectance data
and hyper spectral satellite images give
remarkable results for deriving dominant clay mineral.

41. SPECTRAL LIBRARIES
Spectral libraries For example, the ASTER spectral
library version 2.0, which is a collection of
contributions from the Jet Propulsion Laboratory,
Johns Hopkins University and the United States
Geological Survey, is a widely used spectral library
which contains over 2400 spectra of a wide variety of
minerals, rocks, vegetation and manmade materials
covering the wavelength range 0.4–15.4 μm
(Baldridge et.al., 2008).Used to match collected
spectral samples to those in the spectra.

42. Spectral reflectance of different clay minerals. (Source: Clark,
1999).

43. SOIL CONTAMINATION
Diffuse reflectance spectroscopy (DRS) in visible-
near infrared (VNIR) region (400–2500 nm) has been
used to quickly analyse contamination of heavy
metals.
 Pb and Zn – 2010 and 2149 nm respectively.
 Mn and Cu – 2072 and 2139 nm
Mohamed et al. (2016)

44.  The areas affected by concentration of heavy elements. (Source;
Mohamed et al., 2016).

45. LAND SUITABILITY FOR COTTON IN RINGNABODY
WATERSHED IN MAHARASHTRA….
 Study conducted by N. Walke et.al in Ringnabodi
watershed is located in Nagpur district,
Maharashtra.
 Methodology given in the FAO frame work on land
evaluation (FAO,1976,1985)
 Land area is assigned a suitability for a land use
 S1 – Highly suited
 S2- Moderately suited
 S3 – Marginally suited
 N1 – Unsuited for economic reasons
 N2 – Unsuited for physical reasons

46. Soil suitability for cotton (N.walke et.al. 2012)

47. CONCLUSION
 Under the current situations, the conventional
methods of soil analyses takes a long time, in
addition to their expensive costs.
 Different remote sensing data and NIRS can be
maximized the ability to cover and investigate large
surfaces in a single flight.
 The generated GIS-based thematic maps could be
used by extension scientists and farmers to choose
a crop for specific areas to enhance crop
productivity.

48. Thank You