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Satellite Remote Sensing and GIS Applications in Agricultural Meteorology Proceedings of the Training Workshop 7-11 July, 2003, Dehra Dun, India Editors M.V.K. Sivakumar P.S. Roy K. Harmsen S.K. Saha Sponsors World Meteorological Organization (WMO) India Meteorological Department (IMD) Centre for Space Science and Technology Education in Asia and the Pacific (CSSTEAP) Indian Institute of Remote Sensing (IIRS) National Remote Sensing Agency (NRSA) and Space Application Centre (SAC) AGM-8 WMO/TD No. 1182 World Meteorological Organisation 7bis, Avenue de la Paix 1211 Geneva 2 Switzerland 2004
Published by World Meteorological Organisation 7bis, Avenue de la Paix 1211 Geneva 2, Switzerland World Meteorological Organisation All rights reserved. No part of this publication may be reproduced, stored in a retrieval system, or transmitted in any form or by any means, electronic, mechanical, photocopying, recording, or otherwise, without the prior written consent of the copyright owner. Typesetting and Printing : M/s Bishen Singh Mahendra Pal Singh 23-A New Connaught Place, P.O. Box 137, Dehra Dun -248001 (Uttaranchal), INDIA Ph.: 91-135-2715748 Fax- 91-135-2715107 E.mail: bsmps@vsnl.com Website: http://www.bishensinghbooks.com
Page 4 and 5: FOREWORD
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
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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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