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FOREWORD
CONTENTS Satellite Remote Sensing and GIS Applications in Agricultural .... 1 Meteorology and WMO Satellite Activities – M.V.K. Sivakumar and Donald E. Hinsman Principles of Remote Sensing ......... 23 Shefali Aggarwal Earth Resource Satellites ......... 39 – Shefali Aggarwal Meteorological Satellites ......... 67 – C.M. Kishtawal Digital Image Processing ......... 81 – Minakshi Kumar Fundamentals of Geographical Information System ......... 103 – P.L.N. Raju Fundamentals of GPS ......... 121 – P.L.N. Raju Spatial Data Analysis ......... 151 – P.L.N. Raju Retrieval of Agrometeorological Parameters using Satellite ......... 175 Remote Sensing data – S. K. Saha Retrieval of Agrometeorological Parameters from Satellites ......... 195 – C.M. Kishtawal Remote Sensing and GIS Application in Agro-ecological ......... 213 zoning – N.R. Patel Crop Growth Modeling and its Applications in ......... 235 Agricultural Meteorology – V. Radha Krishna Murthy
Page 2 and 3: Satellite Remote Sensing and GIS Ap
Page 6 and 7: Crop Growth and Productivity Monito
Page 8 and 9: 2 Satellite Remote Sensing and GIS
Page 28 and 29: PRINCIPLES OF REMOTE SENSING Shefal
Page 30 and 31: Shefali Aggarwal 25 Satellite Sun R
Page 32 and 33: Shefali Aggarwal 27 After the wars
Page 34 and 35: Shefali Aggarwal 29 Table 2: Princi
Page 36 and 37: Shefali Aggarwal 31 by the earth’
Page 38 and 39: Shefali Aggarwal 33 Reflectance (%)
Page 40 and 41: Shefali Aggarwal 35 Atmospheric Sca
Page 42 and 43: Shefali Aggarwal 37 reduces the amo
Page 44 and 45: EARTH RESOURCE SATELLITES Shefali A
Page 46 and 47: Shefali Aggarwal 41 Figure 1. Geo-s
Page 48 and 49: Shefali Aggarwal 43 REMOTE SENSING
Page 50 and 51: Shefali Aggarwal 45 Many electronic
Page 52 and 53: Shefali Aggarwal 47 emissions. Ther
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Shefali Aggarwal 49 until January 6
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Shefali Aggarwal 51 Table 4. Charac
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Shefali Aggarwal 57 6.6., 10.6, 18
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Shefali Aggarwal 63 Satellite Launc
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Shefali Aggarwal 65 CONCLUSIONS Sin
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68 Meteorological satellites there
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70 Meteorological satellites the ph
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72 Meteorological satellites measur
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74 Meteorological satellites DATA F
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76 Meteorological satellites window
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78 Meteorological satellites and pr
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DIGITAL IMAGE PROCESSING Minakshi K
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Minakshi Kumar 83 COLOR COMPOSITES
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Minakshi Kumar 85 (A) (B) (C) (D) F
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Minakshi Kumar 87 255 Frequency His
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Minakshi Kumar 89 image behaviour w
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Minakshi Kumar 91 Sunlight Figure 5
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Minakshi Kumar 93 observing and sep
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Minakshi Kumar 95 the identity of t
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Minakshi Kumar 97 Parallelepiped Cl
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Minakshi Kumar 99 Band 3 digital nu
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Minakshi Kumar 101 omission (exclus
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FUNDAMENTALS OF GEOGRAPHICAL INFORM
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P.L.N. Raju 105 related to geograph
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P.L.N. Raju 107 A related term that
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P.L.N. Raju 109 Geographic informat
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P.L.N. Raju 111 through education a
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P.L.N. Raju 113 beautiful. Figure 1
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P.L.N. Raju 115 capabilities for st
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P.L.N. Raju 117 (ii) Errors associa
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P.L.N. Raju 119 • Soil resources
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FUNDAMENTALS OF GPS P.L.N. Raju Geo
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P.L.N. Raju 123 term for receiver c
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P.L.N. Raju 125 SEGMENTS OF GPS For
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P.L.N. Raju 127 Under Block-I, NAVS
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P.L.N. Raju 129 GPS receiver normal
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P.L.N. Raju 131 With a bit rate of
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P.L.N. Raju 133 - Monitoring and co
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P.L.N. Raju 135 Antenna Sensitive a
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P.L.N. Raju 137 Power Supply First
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P.L.N. Raju 139 T1 4100 GPS Navigat
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P.L.N. Raju 141 The dual frequency
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P.L.N. Raju 143 and has a codeless
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P.L.N. Raju 145 - Low power consump
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P.L.N. Raju 147 Table 5. Error Sour
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P.L.N. Raju 149 TRIMBLE GPS AS A RO
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SPATIAL DATA ANALYSIS P.L.N. Raju G
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P.L.N. Raju 153 Parcel Size Value L
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P.L.N. Raju 155 Example: Creating a
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P.L.N. Raju 157 Figure 6: The most
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P.L.N. Raju 159 As stated above, th
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P.L.N. Raju 161 (a) (b) Figure 9: (
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P.L.N. Raju 163 The most common bas
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P.L.N. Raju 165 RASTER BASED SPATIA
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P.L.N. Raju 167 Local Functions Loc
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P.L.N. Raju 169 Zonal Functions Zon
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P.L.N. Raju 171 Another useful glob
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P.L.N. Raju 173 specify to find 10
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RETRIEVAL OF AGROMETEOROLOGICAL PAR
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S.K. Saha 177 µs = Cos θs π. L =
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S.K. Saha 179 Saha and Pande (1995a
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S.K. Saha 181 Surface temperature c
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S.K. Saha 183 where, V is the wave
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S.K. Saha 185 Figure 5: (1) FCC ( C
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S.K. Saha 187 ∧ 24 hrs = ∧ inst
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S.K. Saha 189 APAR = fPAR . PAR whe
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S.K. Saha 191 where, ∧ is the eva
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S.K. Saha 193 Brutsaert, W. and Sug
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RETRIEVAL OF AGROMETEOROLOGICAL PAR
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C.M. Kishtawal 197 imagery this inf
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C.M. Kishtawal 199 Because cloud to
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C.M. Kishtawal 201 vertically, it g
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C.M. Kishtawal 203 Soil Moisture Re
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C.M. Kishtawal 205 ground (pyranome
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C.M. Kishtawal 207 Wavelength (µm)
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C.M. Kishtawal 209 Deserts and Vege
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C.M. Kishtawal 211 ACKNOWLEDGEMENTS
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214 Remote Sensing and GIS Applicat
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CROP GROWTH MODELING AND ITS APPLIC
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V. Radha Krishna Murthy 237 systems
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V. Radha Krishna Murthy 239 GENOTYP
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V. Radha Krishna Murthy 241 h. Desc
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V. Radha Krishna Murthy 243 is to p
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V. Radha Krishna Murthy 245 adaptat
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V. Radha Krishna Murthy 247 Table 3
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V. Radha Krishna Murthy 249 conditi
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V. Radha Krishna Murthy 251 Now, cr
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V. Radha Krishna Murthy 253 2. Defi
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V. Radha Krishna Murthy 255 (1995)
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V. Radha Krishna Murthy 257 9. One
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V. Radha Krishna Murthy 259 Hammer,
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V. Radha Krishna Murthy 261 Thornto
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264 Crop Growth and Productivity Mo
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DROUGHTS & FLOODS ASSESSMENT AND MO
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A.T. Jeyaseelan 293 Based on the ca
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A.T. Jeyaseelan 295 (NCRC) of Unite
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A.T. Jeyaseelan 297 during daytime
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A.T. Jeyaseelan 299 is used in vari
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A.T. Jeyaseelan 301 Vegetation Moni
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A.T. Jeyaseelan 303 Joint Research
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A.T. Jeyaseelan 305 NOAA images can
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A.T. Jeyaseelan 307 coastline. In a
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A.T. Jeyaseelan 309 gains from usin
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A.T. Jeyaseelan 311 Gruber, A. 1973
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A.T. Jeyaseelan 313 Scofield, R.A.,
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316 Water and Wind induced Soil Ero
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332 Satellite Based Weather Forecas
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348 Satellite-Base Agro-Advisory Se
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FOREST FIRE AND DEGRADATION ASSESSM
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P.S. Roy 363 of tropical forest and
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P.S. Roy 365 “burnt center” of
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P.S. Roy 367 Comparatively little i
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P.S. Roy 369 which would be a basic
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P.S. Roy 371 evaporation. Lack of a
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P.S. Roy 373 such wild land fires.
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P.S. Roy 375 monitoring and assessi
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P.S. Roy 377 The fire episode of 19
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P.S. Roy 379 • Rainfall data •
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P.S. Roy 381 FR = [10Vi = 1-10 (5H
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P.S. Roy 383 as 130.96 km 2 or 2.96
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P.S. Roy 385 Table 5. Area (km 2 )
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P.S. Roy 387 The Forest Canopy Dens
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P.S. Roy 389 80%. However, some dev
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P.S. Roy 391 different techniques.
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P.S. Roy 393 • Identification of
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P.S. Roy 395 map derived from FCD M
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P.S. Roy 397 Ehrlich D., E.F. Lambi
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P.S. Roy 399 Phillips, J. 1965. Fir
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DESERT LOCUST MONITORING SYSTEM - R
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D. Dutta, et al. 403 unusually warm
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D. Dutta, et al. 405 ii. Structured
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D. Dutta, et al. 407 Spatial Databa
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D. Dutta, et al. 409 Figure 5a: Dat
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D. Dutta, et al. 411 From locust si
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D. Dutta, et al. 413 (A) (B) (C) (D
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D. Dutta, et al. 415 Figure 11: Att
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D. Dutta, et al. 417 favourable (Fi
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D. Dutta, et al. 419 Climate suitab
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D. Dutta, et al. 421 Figure 18: Air
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D. Dutta, et al. 423 the input valu
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426 Workshop Evaluation fundamental
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