This document discusses the use of remote sensing and GIS applications in agriculture in India. It provides details on the advantages of remote sensing for agricultural surveys compared to conventional methods. It also describes typical spectral reflectance characteristics of vegetation and bands of the IRS-1D LISS-III satellite. The document outlines India's satellite capabilities for remote sensing and examples of applications like crop inventory, yield prediction, and drought monitoring. It discusses challenges in India including small fields and issues developing methodology for crop surveys and inventory using remote sensing techniques.

1. REMOTE SENSING AND GIS APPLICATIONS
IN AGRICULTURE – Indian Experience
S. K. Saha
Agriculture & Soils Division
Indian Institute of Remote Sensing, NRSC
Dehradun

2. Advantages of Remote Sensing based Agricultural
Resource Survey over Conventional Survey
• The potential for accelerated survey;
• Capability to achieve synoptic view under relatively uniform
illumination conditions;
• Availability of multi-spectral data providing increased information;
• Capability of repetitive coverage to depict seasonal and long term
changes;
• Permitting direct measurement of several important agro-physical
parameters which are used in crop growth assessment and yield
prediction;
• Relatively inexpensive - monitoring from space;
• Remotely sensed data provide a permanent record.

3. TYPICAL SPECTRAL REFLECTANCE CHARACTERISTICS OF VEGETATION
Leaf Cell
pigments structure Water content
80
70
Chlorophyll Water absorption
absorption
R 60
E
F
L 50
C
T 40
A
N 30
C
E 20
(%)
10
0
0.4 0.6 0.8 1.0 1.2 1.4 1.6 1.8 2.0 2.2 2.4 2.6
WAVELENGTH (um)

4. IRS-1D LISS-III
Multi-spectral bands
Band-2 (0.52-0.59um)
Band-3 (0.62-0.69um)
Image interpretability is limited with
any one of the bands
Band-4 (0.77-0.89um)

5. Band-4 (NIR) Band-3 (Red) Band-2 (Green)
IRS-1D LISS-III False Colour Composite (FCC) image : Improved interpretability

6. Spatial Resolution
Parts of Guntur / Krishna
districts in A.P. as seen at three
spatial resolutions by IRS-1D
on 3-Jan-2002
188m IRS-1D WiFS
24m LISS-III
6m IRS-1D PAN

7. PAN DATA (6m) LISS-III (24m)
PAN + LISS-III FUSED (6m) LISS-III (6m)

8. QUICKBIRD DATA (06-12-2003) OF GUNTUR DISTRICT, A.P
Cotton
Pedda Parimi
Chillies

9. IKONOS DATA OF PART OF NALGONDA DT., AP

10. Temporal Resolution
09-Dec-2001 03-Jan-2002 19-Mar-2002
IRS-1D LISS-III imagery of part of Guntur district, A.P

11. INDIAN IMAGING CAPABILITY
KALPANA
IRS-P4
IRS-1C/1D
IRS-P6
IRS-1C/1D STEREO
IRS-P6 CAPABILITY
CLIMATE/
IRS-1C/1D
WEATHER
IRS-P6
OCEAN
APPLICATION
NATIONAL
SURVEYS
Cartosat - 2
NATURAL
CARTOSAT-1 RESOURCE
2.5 M MAPPING
DETAILED
PLANNING

12. Major Areas of Applications of RS & GIS in Crop Inventory
Crop Acreage Estimation
Cropping Pattern / System Analysis
Crop Yield Prediction / Modeling
Agricultural Drought Assessment & Monitoring

13. Issues Related to crop inventory assessment
using RS technique & Satellite Data Requirement
Small holdings and resultant small field sizes; ( High spatial resolution
satellite data: IRS – LISS IV; LISS IV + Cartosat; IKONOS; Quick Bird )
A large diversity of crops sown in an area; (Multispectral data covering
VIS, NIR, SWIR, MIR, MW EMR regions & High Radiometric Resolutions)
Large field to field variability in sowing and harvesting dates, cultural
practices and crop management; (Multitemporal satellite data)
Large areas under rain-fed agriculture with poor crop canopies;
( Use soil back ground corrected spectral indices)
Practice of mixed and intercropping; (High spatial resolution satellite
data)
Extensive cloud cover during Kharif season; and
Extensive smog cover in winter in part of Northern India
(Microwave- Envisat, Radarsat and high temporal optical RS satellite
data - IRS – AWiFS)

14. Use of Remote Sensing In Crop Inventory – Indian Experience
• A project on Crop Acreage and Production Estimation (CAPE) under the
Remote Sensing Applications Mission (RSAM) was formulated in 1986
which is a joint programme of the Department of Agriculture and
Cooperation (DAC) of Union Ministry of Agriculture, and Department of
Space (DOS).
• The major objectives of CAPE project were: (i) to develop methodology
for state level acreage and production estimation of important crops,
such as wheat, rice, sorghum, mustard, groundnut and (ii) to transfer
technology to state level agencies for its operational applications.

15. Small Area Crop Inventory
(Complete Enumeration Approach)

16. CAPE - Large Area Crop Inventory (Sample Segment Approach)

17. FASAL (Forecasting Agricultural Output using Space,
Agro-meteorology & Land Observation)

18. Accuracy Evaluation of National Wheat Production Forecast
(NWPF) Using Remote Sensing
Source: SAC, Ahmedabad

20. Crop Inventory using multi-spatial resolution IRS data
- a case study of Bhopal district (M.P)
IRS : LISS III

21. IRS : LISS II
IRS : LISS I

22. IRS : WiFS

23. (A) (B)
Figure-: (a) LISS III+PAN merged FCC of part of Kiratpur Block (Bijnore district);
(b) Village -wise crop inventory of Kiratpur Block prepared by digital classification of IRS-
LISS III+PAN data and GIS aided analysis.

24. IRS-1C/1D WiFS DATA OF SOUTH ASIANS NATIONS
Kharif-1999 (Sep-Oct) Rabi-2000 (Feb-Mar)
Classified images
RICE
WHEAT
OTHER CROPS
POST KHARIF RICE
FALLOW LANDS

25. METHODOLOGY - MAPPING CROP & OTHER LANDUSE AND
CROPPING PATTERN INVENTORY
DIGITAL DATA OF IRS-ID LISS-III
KHARIF 1997 RABI 1998 KHARIF 2008 RABI 2009
RECTIFIED RECTIFIED RECTIFIED RECTIFIED
IMAGE IMAGE IMAGE IMAGE
GROUND TRUTH
COLLECTION & DEV.BLOCK MAP
TRAINING SITES DIGITAL SUPERVISED
GENERATION CLASSIFICATION
TOPO SHEETS
ACCURACY
ASSESSMENT
CROPPING PATTERN MAP DEV.BLOCKWISE CROPPING PATTERN MAP
& ITS INDICES CROP & ITS INDICES
(1997- 1998) DISTRIBUTION ( 2008 - 2009)
ANALYSIS OF CHANGES IN CROPPING PATTERN & ITS
INDICES

31. RS Derived Parameters for Cropping Systems Analysis
Cropping system indices:
Several indices have been proposed to evaluate and compare the
efficiencies of different cropping systems (Palaniappan 1985).
These indices which can be derived using RS are –
Multiple Cropping Index (MCI)
Area Diversity Index (ADI)
Cultivated Land Utilization Index (CLUI).
Cropping system indices are essential in the evaluation of the
performance of existing agricultural systems in an area, and for carrying
out effective measures to achieve desired systems in the long run.

32. Cropping Pattern Indices & Its Changes
Multiple Cropping Index (MCI):
This index measures the cropping intensity. It is calculated by dividing
the sum of the areas planted with different crops and harvested in a
single year by the total cultivated area, times 100.
Where, n = total number of crops, ai = area occupied of the ith crop planted and harvested within a year, and
A = total cultivated land area available.
MCI Change

33. Area Diversity Index (ADI):
It represents the diversity of crops grown in an area over a crop
year, both in time and space. It measures the multiplicity of crops or
farm products planted in a single year
Where, n = total numbers of enterprises (crops or farm products), ai is the area under each crop that was
derived from district-level crop statistics generated using remote sensing data. If one is interested in
comparing the crop diversity in each season, n is used as the number of crops grown in a season.
ADI Change

34. Cultivated Land Utilization Index (CLUI):
This index is calculated by summing the products of land area
planted to each crop, multiplying by the actual duration of that
crop and dividing by the total cultivated land area times 365 days.
This index measures how efficiently the available land area has
been used over the year.
Where, n = total number of crops, ai = the area occupied by the ith crop, di =days that the ith crop occupied; ai,
and A = total cultivated land area available during the 365 day period.
CLUI Change

35. VALUES OF CROPPING PATTERN INDICES
Threshold value use for rating of different indices
No. Rating MCI ADI CLUI
1 Low <130 <2.0 <0.5
Blocks based of Rating for 2 Medium 130-160 2-5 0.5-0.6
Cropping System 3 High >160 >5.0 >0.6
MCI ADI CLUI Cropping System
No. Development Block
Value Rate Value Rate Value Rate Plan
Intensification &
1 Chakrata Low 1.48 Low 0.45 Low
114.05 Diversification
Intensification with
2 Doiwali Medium 2.14 Medium 0.54 Medium
141.16 short duration crop
Diversification with
3 Vikasnagar Medium 2.16 Medium 0 .55 Medium
143.93 short duration crop
Intensification with
4 Sahaspur Medium 2.25 Medium 0.53 Medium
137.36 short duration crop
Intensification &
5 Kalsi Low 1.48 Low 0.45 Low
112.81 Diversification
6 Raipur Medium 2.14 Medium 0.51 Medium Intensification
135.61

36. Types of Crop Yield Models
Spectral yield models
(These are empirical models which directly relate satellite derived parameters
e.g. Spectral vegetation indices (SVI) to crop yield)
Agromet - spectral yield models
(RS derived SVI is coupled with meteorological indices and or the yield
derived from meteorological models)
Integrated yield model
( GIS is used to integrate spatial data of agro-climate, soil and management
practices in conjunction with SVI to develop yield model)
Linking RS & Crop Growth Simulation Model
( These models predict crop growth & yield as well as soil, plant, water &
nutrients balances as a function of environmental conditions & crop
management practices. RS provide actual sate of crop parameters viz. leaf
area, crop distribution, surface canopy temperture etc., while GIS allow
spatial organization of soil, weather, crop parameters & management data
and display crop model simulation results.)

37. Commonly Used
Spectral
Vegetation Indices
(a)
(b)
Figure: (a) Relationship between
wheat yield & VI (Haryana) ;
(b) Relationship between rice yield &
VI (Orissa)

38. (a) (b)
Figure: (a) Satellite derived NDVI estimated wheat LAI map;
(b) LAI yield model estimated wheat yield map

39. Agromet - spectral Yield Models

40. Integrated Yield Model
Figure: (a) Flow diagram of methodology of crop yield prediction using R.S. & GIS based
integrated yield model; (b) Agro-climatic yield potential index of wheat crop
( Central Madhya Pradesh)

41. GIS and Crop Growth Simulation Model
Figure: Schematic diagram of a crop growth
monitoring system showing the linkages between
inputs, spatial layers in GIS, and relational database
to WTGROWS simulation model (Sehgal et al, 2001).
Figure: Spatial map of wheat yield (t/ha) Haryana (1996-
1997), Grid-wise simulated wheat yields by WTGROWS
simulated model (Sehgal et al, 2001).

42. Agricultural Drought Assessment & Monitoring

43. Vegetation Status GROUND SYSTEM
GROUND SYSTEM SATELLITE SYSTEM
SATELLITE SYSTEM
ARIDITY
ARIDITY CROP
CROP
RAINFALL
RAINFALL INDEX
INDEX CALANDER
CALANDER
CURRENT HISTORICAL
CURRENT HISTORICAL LANDUSE
VI VI LANDUSE
VI VI
GIS
GIS
DECISION SUPPORT SYSTEM
DECISION SUPPORT SYSTEM
DROUGHT ASSESSMENT & MAP
DROUGHT ASSESSMENT & MAP
BI-WEEKLY COMPOSITE BI-WEEKLY COMPOSITE NOAA
NOAA-NDVI IMAGE NDVI IMAGE OF PART OF INDIA
OF INDIA (ANDHRA PRADESH STATE)
NADAMS (NATIONAL AGRICULTURAL DROUGHT ASSESSMENT & MONITORING SYSTEM )
SYSTEM

45. R S and GIS Applications
in
Soil Resource Management

46. INTRODUCTION
Soil resource information plays a critical role in :
to understand the present level of soil
productivity.
to assess degradation status of soils.
for optimum land use planning
the management of agricultural production systems

47. REQUIREMENT OF SOIL MAPS
INFORMATION REQUIRED SCALE
National level 1:1,000,000
State level 1:250,000
District level 1:50,000
Tehsil / Sub-watershed level 1:25,000
Farm level / Micro-watershed 1:4,000 – 1:8000
Soil Conservation planning /Implementation 1:4,000 – 1:8000
Reclamation of salt affected soil 1:4,000 – 1:8000
Command areas & Pre-Irrigation Surveys 1:50,000 – 1:25,000
Optimum land use planning – District level 1:50,000
- Village level 1:4,000 – 1:8000

48. SOIL SCALES , SENSORS AND LEVELS OF SOIL
MAPPING
SNO SOIL SENSORS SOIL USEFUL FOR
SURVEY CLASSIFICATIO
SCALE N
1 1:250,000 LANDSAT-MSS, SUBGROUPS/FAMIL RESOURCE
IRS-LISS-I & II, IES AND THEIR INVENTORY AT
WIFS; AWiFS ASSOCIATION REGIONAL LEVEL
2 1:50,000 IRS-LISS-III SOIL SERIES AND DISTRICT/SUB-
LANDSAT-TM THEIR DISTRICT LEVEL
SPOT ASSOCIATION
3 1:25,000 IRS-IC/ID SOIL SERIES AND BLOCK / TALUK /
(PAN+LISS-III THEIR MANDAL LEVEL
MERGED DATA) ASSOCIATION
IRS-P6: LISS- IV
4 1:8000 OR CARTOSAT; TYPES AND PHASES VILLAGE LEVEL
LARGER IRS-P6: LISS- IV
IKONOS

49. Methodology For Soil /Land Degradation Mapping
RS Satellite data Preliminary Visual Interpretation Ancillary data
2 Seasons data
SOI Topo maps
Scale of Mapping
Climatic data
RS Sensor
Published literature etc
Soil Profile Study Ground truth collection Soil samples collection
Soils -pH, Ece, ESP Soil Sample Analysis Soils
Characterization
Finalization of thematic map
Soil / Land Degradation Map

50. Concept for soil mapping
The soil–landscape model captures the relationships
between the soils in the area and the different landscape
units.
Soil surveyor detects different soil formative
environments through visual interpretation of geological
maps, topographical maps and satellite images. The spatial
extents of the soil formative environments are then used to
delineate soil-landscape units known as physiographic units.
Thus, Physiographic units are based on the relationships
between these environmental conditions and the soil-
mapping units.

51. S
P
A
Sample strip

52. INTERPRETATION LEGEND FOR PHYSIOGRAPHIC ANALYSIS
1) Siwalik Hills (S) , 2) Piedmont (P) , 3) Alluvial Plain (A) , 4) Uplifted terrace (U)
1. Siwalik hill (S)
a) Top of the Siwalik hill (S1)
b) Upper side slope of Siwalik hill (S21)
c) Lower side slope of Siwalik hill(S22)
2. Piedmont(P)
a) Upper Piedmont forest (P11)
b) Upper Piedmont cultivated (P12)
c) Upper Piedmont barren/scrub(P13)
d) Lower Piedmont cultivated (P21)
e) Lower Piedmont barren/scrub(P22)
3. Alluvial plain (A)
a) Alluvial upland (A1)
c) Alluvial lowland (A2)
d) Dissected plain (A3)
e) Flood plain (A4)
4. Uplifted terraces (U)
a) Moderately steep to steep slope Forest (U1)
b) Cultivated (U2)
c) Barren/Scrub(U3)

53. Soil Profile
Soil is arranged in a
series of zones called
– Horizons.
Cross-sectional view of
the horizons in a soil is
called Soil Profile
Profiles
– O Horizon
– A Horizon
– B Horizon
– C Horizon

54. SOIL RESOURCE INVENTORY AND LAND USE PLANNING
IN TILLARI IRRIGATION COMMAND AREA USING RS &
GIS
A CASE STUDY IN GOA STATE

55. IRS 1D LISS III FCC
OF STUDY AREA
March 17, 2000
150 to 30’ to 150 to 55” North Lat
730 45’ to 740 00’ East Long

56. IRS 1D LISS III + IRS 1C PAN IMAGE – PART OF BARDEZ
A
R
A KALANGUT
B
I
A
N
S
E
A Mandovi River

57. 3 D VIEW OF STUDY AREA UNDER TIP (part)
LISS III FCC draped on DEM

58. PHYSIOGRASPHIC LEGEND
Sr No Physiographic Unit Map Symbol
1. Denudational Hills --
A Hill Top (Plateau/Mesa) --
(i) ROC with Scrub DH11
(ii) Agriculture/Plantation DH12
B Hillside Slope --
(i) ROC with Scrub DH21
(ii) Agriculture/Plantation DH22
2. Residual Hills RH
3. Buried Pediments --
a. SHALLOW BP - Gently Sloping/Undulating (3-8% slope) BP1
b. DEEP BP -Nearly Level to very gently sloping (1-3% slope) BP2
4. River Terraces RT
5. Valley Fills VF
6. Coastal Plains --
a. Coastal Plains CP1
b. Mudflats/marshy lands CP2
c. Salt Pans CP3
d. Beach CP4
7. Habitation Hb

59. SIDE SLOPES OF SOIL PROFILE
DENUDATIONAL HILLS

60. DEEP BURIED
PEDIMENT
SOIL PROFILE

61. VALLEY FILLS
SOIL PROFILE IN
VALLEY FILLS

62. PHYSIOGRAPHIC SOIL MAP OF STUDY
AREA (Part of Tillari Command
Area)
Rock Out Crops
L.S. Typic Ustorthents
Rock Out Crops
C.L. Typic Dystrusteps
L.S. Typic Dystrusteps
L.S. Typic Dystrusteps
F.L. Typic Dystrusteps
C.L. Typic Ustifluents
F.L. Typic Haplustepts
C. L Aquic Ustifluents
MudFlats/MarshyLand
s
Salt Pans
Beach
Habitation
----
Scale ----

63. METHODOLOGY FOR LAND EVALUATION USING FAO FRAMEWORK
SATELITE DATA SOI TOPOSHEET
VISUAL INTERPRETATION
PRESENT LAND USE PHYSIOGRAPHIC SOIL MAP
LAND USE REQUIREMENTS
LAND QUALITIES FOR LUTs
AND LIMITATIONS
LAND CAPABILITY MAP
COMPARISION OF LAND OVERLAY LAND IRRIGABILITY MAP
USE WITH LAND
LAND SUITABILITY
CLASSIFICATION
SUGGESTED LAND USE EXPECTED CHANGE

64. LAND CAPABILITY MAP OF PERNEM TALUKA
LEGEND
Suitable for Crops with Mod Lim
Suitable for Crops with Mod Lim
Suitable for Crops with Mod Lim
Suitable for Crops with Severe Lim
Suitable for Forestry/Plantations
Suitable for Forestry – Mod Lim
Suitable for Forestry – Mod Lim
Suitable for Forestry - Severe Lim
Not Suitable for Vegetation

65. LAND IRRIGABILITY MAP OF PERNEM TALUKA
LEGEND

66. PROPOSED LAND USE MAP
FOR PERNEM TALUKA
LEGEND

67. PRESENT LAND USE MAP OF PERNEM TALUKA
LEGEND

68. SCA=SINGLE CROPPED AREA,
DCA= DOUBLE CROPPED AREA,
EXPECTED CHANGES
LSc/WSc= LAND WITH/WITHOUT SCRUB
SC=SCRUB FOREST, WL=WATER LOGGED
AH= AGRO HORTICULTURE
IN LAND USE -
PERNEM TALUKA
LEGEND

69. Potential change in land use / land cover
Suitability Class Area in %
No change 39.78
SCA to DCA 14.66
Sc to DCA with limitation 7.30
LSc / WSc to DCA with limitations 8.26
SCA to AH 2.22
Sc to AH 7.74
LSc / WSc to AH 7.74
Barren fallow to Industrial Use 7.25
Waterlogged (WL to Mangrove/ 1.17
Aqua-culture
Settlements 0.71
River 5.23

70. DEHRADUN DISTRICT (UTTARANCHAL)
PHYSIOGRAPHIC SOIL MAP

72. Evaluation of Soils Information Land irrigabilty assessment
Land capability assessment
Land productivity assessment

73. The FAO framework describe a scheme for land suitability
classification. According to the FAO Framework, ‘Land
suitability is the fitness of given tract of land for a defined use’
(1976).
Four levels of decreasing generalization are defined:
1. Land Suitability Orders: Kind of Suitability, S or N
2. Land suitability classes: Degree if suitability within orders.
Highly suitable (S1), Moderately suitable (S2,S3) or not suitable
(N1, N2)
3. Land suitability subclasses: Kind limitation within classes
4. Land suitability units: Management type within subclasses

74. FAO Based Land Evaluation
Land-use match Land
requirements qualities
suitability
Land-use
planning
policies & plans

75. SUITABILITY MAP
FOR PADDY
LEGEND

76. SUITABILITY MAP
FOR SUGARCANE
LEGEND

77. SUITABILITY MAP
FOR COCONUT
LEGEND

78. SUITABILITY MAP
FOR CASHEW
LEGEND

79. SUGGESTED CROPS
FOR PERNEM TALUKA
LEGEND

80. Land evaluation based on parametric methods
1. Land Productivity Index (Storie Index)
Land Productivity Index (LPI)= A*B*C*X*Y
Where factors are decimal equivalents of percentage ratings.
A = General characteristics of soil profile
B =Texture of the surface soil
C = Slope of the land
X = Miscellaneous factors; reaction of surface soil, fertility, erosion
Y = Average annual rainfall

81. 2. Soil Productivity Index (SPI) (Requier et al)
It is also known as FAO productivity rating. It consider nine
properties or factors. Each factor being rated on a scale of from 0 to
100.
SPI = H*D*P*T*N*O*A
Where factors are percent ratings-
H= Soil Moisture D = Drainage conditions
P= Effective soil depth T =Texture/Structure
N= Base Saturation O = Organic matter
A= Nature/CEC of clay mineral
The resulting index of soil productivity is classified into 5
productivity classes in excellent, good, average, poor and
extremely poor.

82. Physiographic – soil map
Legend
P12 A14
P13 A21
P21 A22
P22 A23
P23 FP
P211 D1
P212 D2
A11 River
A12 Settlement
A13

83. Soil and land productivity Indices of map units
Map unit Land Productivity Soil Productivity Index
Index (LPI) (SPI)
P11 42 33
P12 41-53 44-50
P13 39 31
P211 51 39
P22 56 50
A11 92-95 56-69
A12 93-95 62-66
A13 54 33
A14 44 23
A21 78-89 56-71
A22 86-89 65-70
A23 72 66
FP 54 27

84. Land Productivity Index
N
Legend
Excellent (80 - 100)
Good (60 - 79)
Fairly Good (40 - 59)
Average (20 - 39)
River
Settlement

85. Soil Productivity Index
N
Legend
Excellent ( 65- 100)
Good ( 35 - 64)
Average (20 - 34)
Poor (8 - 19)
River
Settlements

86. TOPOGAPHIC
TOPOGAPHIC ERS-1 SAR
ERS-1 SAR IRS LISSII
MAP
MAP IMAGE
IMAGE IMAGE
DIGITIGATION OF
DIGITIGATION OF SUB-IMAGE
SUB-IMAGE
SUB-IMAGE EXTRACTION
SUB-IMAGE EXTRACTION
SAMPLING LOCATION
SAMPLING LOCATION EXTRACTION
EXTRACTION
(REFERENCE IMGE)
(REFERENCE IMGE)
REFERENCE POINT
REFERENCE POINT REFERENCE POINT
REFERENCE POINT
IDENTIFICATION
IDENTIFICATION IDENTIFICATION
IDENTIFICATION
SIGNATURE
SIGNATURE
EXTRACTION(SAR,NDVI)
EXTRACTION(SAR,NDVI)
MAP TO IMAGE
MAP TO IMAGE REGISTRATION OF
REGISTRATION OF
LINKAGE
LINKAGE IIRS IMAGE
IIRS IMAGE
SIGNATURE
SIGNATURE
ANALYSIS
ANALYSIS
TRANSFER OF
TRANSFER OF NDVI IMAGE
NDVI IMAGE
SAMPLING LOCATIONS
SAMPLING LOCATIONS GENERATION
GENERATION
TO SAR IMAGE
TO SAR IMAGE
SOIL MOISTURE MODEL
SOIL MOISTURE MODEL
DEVELOPMENT/UPDATE
DEVELOPMENT/UPDATE
GROUND TROUTH
GROUND TROUTH
MODEL TEST
MODEL TEST
SOIL MOISTURE MAP
SOIL MOISTURE MAP
METHODLOGY OF SOIL MOISTURE
ESTIMATION USING SATELLITE SAR DATA ACCURACY ASSESSMENT
ACCURACY ASSESSMENT

87. REGIONAL SOIL MOISTURE
MAPPING USING ERS-1
SAR DATA
RADAR BACK SCATTERING & SOIL MOISTURE RELATIONSHIPS - (A) 0-5cm ;(B) 0-10 cm
(A) (B)

88. Integrated modeling approach for predicting soil C dynamics
Remote sensing inputs –
Spatial databases of soil,
Land use, management
Practices etc.
Simulation models handle-
O.M. decomposition,
water balance, heat fluxes
GIS provides –
organization of databases
& spatial analysis; merging
data sets with R.S. data
Fig. : Framework of regional analysis to predict soil C
dynamics ( Paustian et al., 1997)

89. Soil C mapping in Amazon basin using a neural
Network that combines ground data with
AVHRR satellite data ( Levene & Kimes, 1998)
Derivation of soil organic C and iron content using
using AVIRS data following spectral mixture
analysis technique

90. Watershed Prioritization
Approaches of prioritization of watershed
Erosional Soil Loss Estimation Model
Several quantitative erosional soil loss estimation models
viz., Universal Soil Loss Equation (USLE), Physical Process
based model (MMF – Morgan, Morgan and Finney Model);
Sediment Yield Prediction Equation (SYPE) etc. are used for
prioritization of watershed based on weighted average
erosional soil loss estimate watershed-wise.

91. Flow chart showing methodology
Satellite Remote
Sensing Data
Land use/ land cover
Field
work Soil attributes
Terrain attributes
SOI Toposheet & Soil erosion
ancillary data Soil
models
Erosion
Assessment
Erosivity factor
Meteorological data

92. TOPOGRAPHICAL LANDUSE MAP SOIL MAP METEOROLOGICAL
MAP DATA,RAINFALL
Experimental values
& literatures
SOIL DATA-
CONTOUR TEXTURE,OM,
MAP STRUCTURE,
C P
PERMEABILITTY R
FACTOR FACTOR
FACTOR
DEM
K
FACTOR
SLOPE
MAP
LS
SLOPE FACTOR
LENGTH
USLE MODEL
SOIL LOSS MAP
FIG 2:METHODOLOGY FOR ESTIMATION OF SOIL LOSS USING USLE MODEL

93. FALSECOLOR COMPOSITE
UMKHEN WATERSHED (Part of East Khasi Hill District)
Scale :
PAN+LISS-III MERGED ACQUIRED ON : PAN : 07-JAN-2000
. LISS : 07-JAN 2000

95. DRAINAGE MAP OF UMKHEN WATERSHED

96. SLOPE MAP OF UMKHEN WATERSHED
( Part of East Khasi Hill District)
LEGEND

97. LANDUSE/LANDCOVER MAP
UMKHEN WATERSHED (Part of east Khasi Hill District)

99. WATERSHED PRIORITISATION BASED ON USLE MODEL

100. COMPUTATION OF SEDIMENT YIELD INDEX
Sum (Ai*Wi*Di)
SYI= --------------------- * 100
Aw
Where,
Ai = Area of ith unit (EIMU)
Wi = Weightage value of ith mapping unit
Di = Adjusted delivery ratio assigned to ith mapping unit
n = No. of mapping units
Aw = Total area mapping

101. SOIL MAP LAND USE MAP DERIVED SLOPE MAP
EROSION INTENSITY UNITS
Reclassification Reclassification
LITERATURE BASED WEIGHTAGE
VALUE &DELIVERY RATIO
WEIGHTAGE VALUE DELIVERY RATIO
GROSS SEDIMENT YIELD
MICROWATERSHEDS
SEDIMENT YIELD INDEX AND PRIORITY MAP
FIG1:METHODOLOGY FOR ESTIMATION OF SYI

102. PRIORITIZATION OF MICRO WATERSHED
BASED ON SEDIMENT YIELD INDEX
LEGEND

103. Morgan, Morgan & Finney (MMF) Model for Prediction of Soil Loss
Operative functions
Dehradun District (U.A.)
Methodology
Model parameters & soil loss map

104. An example of integrated use of RS &GIS for soil conservation planning

105. R.S. & GIS for Evaluating Impact of IWDP on State of
Natural Resources
Satellite R.S. by virtue of temporal
coverage allows to undertake
change detection study.
R.S. & GIS are very effective
toolsto assess the impact of
IWDP on natural resources
status in a Watershed between
pre & post treatments periods.

106. Agricultural Sustainability Index (ASI)
ASI uses set of biophysical, chemical, economic & social indicators, and
Can be expressed as ( Nambair et al., 2001) –
ASI = Y* S* M* Q* B* I
Where, Y – crop yield; S – soil quality; M – agricultural biodiversity; Q –
I – socio-economic aspects of sustainable agriculture
GIS can be used as a tool for integrating all the above parameters for
computing ASI
Change in ASI pre and post treat periods in Jhakhan Rao sub_watershed

108. Remote Sensing in Land Degradation and
Desertification Assessment

109. Land / Soil Degradation
Soil Degradation is defined as “a process which lowers the current
and/or the potential capability of soil to produce goods or services”
(FAO, 1979).
It refers to decline in the soil's productivity through adverse changes
in nutrient status and soil organic matter, structural attributes, and
concentrations of electrolytes and toxic chemicals (Lal, 1997)
Planning Commission, India in 1987 has defined wasteland as
"Degraded land which can be brought under vegetative cover with
reasonable effort and which is currently under-utilized land which is
deteriorating for lack of appropriate water and soil management or
on account of natural causes.
Wastelands can result from inherent/imposed disabilities such as
by location, environment, chemical and physical properties of soil or
financial or management constraints".

110. Global Soil / Land Degradation

112. Types of Land / Soil Degradation
GLASOD (Global Assessment of Soil Degradation) distinguishes
following types of soil degradation :
1. Water Erosion – loss of top soil
2. Wind Erosion : more or less uniform displacement of soil
3. Chemical degradation:
acidification
salinization, alkalinization
laterization, podzolozation
4. Physical degradation:
bulk density, porosity
permeability, infiltration capacity
structural stability
5. Biological Degradation : loss of O.M. due to mineralization

113. RS Data Analysis and Interpretation
Visual (Manual)
• False colour composites: Tone, texture, association, physiography
size and shape
Digital (Machine)
• Unsupervised: Grouping based on spectral similarity (Clustering)
Non parametric, distance criteria
Neural networks
• Supervised: User driven
Parametric – mean, variance covariance, min & max
Neural networks
Pixel, and segment based approaches
• Image enhancements : PC; SBI; WI; IHS

114. National Waste Land Mapping
Major categories of wasteland (degraded land) identified for
mapping using remote sensing techniques are given below :
1. Gullied and/or ravinous (eroded) lands
2. Land affected by salinity/alkalinity (coastal or inland)
3. Water-logged and marshy land
4. Upland with or without scrub
5. Shifting cultivation area
6. Sandy (desert or coastal)
7. Mining/industrial wasteland
8. Under utilized/degraded notified forest land
9. Degraded pastures/grazing land
10. Degraded land under plantation crop
11. Barren rocky/stony waste/sheet-rock area
12. Steep sloping areas.
13. Snow covered area

116. Soil Erosion
Soil erosion is the most important processes contributing to land degradation
over large areas of terrestrial Earth
Rill & Gully erosion in unvegetated / sparse
vegetated landscape can be identified
directly using aero-space R.S. data
R.S. can effectively provides temporal
& spatial information that can be coupled
with soil erosion models, such as –
• Soil map,
• Soil moisture, Rill Erosion
• Vegetation cover,
• Land use
• Digital elevation ( slope, slope length, aspect)
• Sediment transport
Soil erosion models are categorized as-
• Empirical ( e.g. USLE ; SYPE )
• Semi – empirical ( e.g. MMF; MUSLE)
• Physical process based (e.g. WEPP; EUROSUM)
Gully Erosion (Ravines)

117. Ravinous land along Chambal River

118. Salinization
Soil degradation related to salinization &
alkalinization represents an increasing 1986
environmental hazard to natural &
agro-ecosystems
1997
Change
(1986 –1997)
Monitoring of salt-affected soils using temporal satellite data

119. Mapping soil salinity levels using Microwave Remote Sensing data
Because of the differential
behaviour of the real & imaginary
parts of dielectric constants,
microwaves are efficient in
detecting soil salinity
The imaginary part is highly
sensitive to variations in soil
electrical conductivity
Various modeling approaches
are applied to retrieve dielectric
constants viz.

120. Mapping Salt-affected Land using
IRS –WiFS data ( Uttar Pradesh)
UTTARANCHAL
UTTAR PRADESH

121. Mapping Salt-affected Land using IRS – LISS III + PAN data
( part of western Krishna Delta , Prakasham District, A.P.)

122. Monitoring of Water logging around Baropal, Rajasthan
1975
1985
1990
1995

123. Shifting Cultivated Land
GROUND PHOTOGRAPH SHIFTING
CULTIVATED AREAS ( NORTH-EAST INDIA)
IRS-LISS II FCC SHOWING VARIOUS
CATEGORIES OF SHIFTING CULTIVATED
AREAS( A - RECENT ; B - OLD &
C – ABANDONED )

124. Land Degradation due to Mining (Goa State)

125. Multiple Degraded Lands ( eroded; degraded forest; steep
Sloping & undulating up land)

126. Socio economic and
Socio economic and Felt perceived
Felt perceived A
A Monitoring
Monitoring
Demographic data Analysis
Analysis Development needs C
C
Demographic data Development needs
T
T
II
Service centre
Service centre Identify gaps and
Identify gaps and O
O
Infrastructure
Infrastructure Implementation
Hierarchy analysis
Hierarchy analysis Suitable sites
Suitable sites N
N Implementation
Land use
Land use P
P
L
L
Soils
Soils Service centre Recommendations
Recommendations A
Service centre A Feedback
Feedback
Hierarchy analysis && N
Hydro-geomorphology Hierarchy analysis N
Alternate plans
Alternate plans
Slope
Slope
METHODOLOGY OF IMSD

127. Precision Agriculture / Farming

128. Remote sensing image data from the soil and crops is processed and then added to
the GIS database
NDVI showing variation within field
IKONOS Multispectral image
Field boundaries in LISS III+ PAN