TA 
Crops 
> Was 
1 the 
SAR 
wing 
Idies, 
rissa 
mple 
)6-97 
vland 
ERS 
ze in 
ature 
ields. 
data 
rated. 
were 
, and 
istics 
e. S, 
series 
used 
r and 
on 
nulti- 
Is 
been 
early 
ill be 
The 
otton, 
crop 
istem 
  
(CGMS) with components of RS, weather 
and field information on crop management. 
- . The crop inventory will be multi-sensor with a sample of 
segments being classified with LISS-IV (5.8m ) data. 
- A part of the required weather input data could be 
generated from RS data itself, i.e., solar radiation. 
- Detailed measurement and analysis for retrieval of LAI at 
field scale has been initiated (Rastogi et al., 2000) which 
uses a simple physical model (Price, 1993). 
Recent advances in satellite sensor spectral, spatial and 
radiometric capabilities, modeling results and analysis 
approaches have strengthened the operational scenario for RS- 
based crop production forecasting. RS data can now be 
operationally used for monitoring crop-growing environment 
such as ground insolation, rainfall and soil moisture. The 
improved multi-spectral sensors allow computation of new VI 
that have lower noise from atmosphere, viewing conditions and 
soil background. The retrieval of crop parameters is done 
operationally and 8-day global LAI product is now available 
from MODIS sensor onboard TERRA spacecraft. 
Proposed sensors onboard ISRO’s new satellites to be launched 
in next two years, such as AWiFS (60m, 4 visible-NIR channels 
and 5 day repeat cycle) and LISS-IV (5.8m pixel, 3 visible NIR 
channels) on RESOURCESAT, VHRR and CCD on INSAT 3D 
will substantially improve the capability to make crop 
production forecasts at regional as well as local scales. 
REFERENCES 
Chakraborty M., Panigrahy S., and Parihar J.S., 1995. A 
methodology to assess the pixel-wise crop classification 
accuracy - a study using Landsat Thematic Mapper data and 
color infrared aerial photographs. In 'Proc. Nat. Symp. on 
Rem. Sens. of Env. with special emphasis on Green 
Revolution', Nov. 22-24, 1995, Ludhiana, pp 40-45. 
Chakraborty M, Panigrahy S, Sharma SA, 1997. Discrimination 
of rice crop grown under different cultural practices using 
temporal ERS-1 synthetic aperture radar data. ISPRS J. 
. Photogramm & Remote Sens., 52, pp 183-191. 
Dadhwal VK, 1986. Remote Sensing Studies for Wheat 
inventory assessment. Proc. 5th Asian Agri. Symp. 
Kumomoto, Japan, Nov. 19-20, 1986, pp 1-16 
Dadhwal VK, 1999. Remote Sensing and GIS for agricultural 
crop acreage and yield estimation. Internat. Arch. 
Photogramm. and Remote Sensing, XXXII, 7-W9, pp 58- 
67. i 
Dadhwal VK, and Parihar JS, 1985. Estimation of 1983-84 
wheat acreage of Karnal district (Haryana) using Landsat 
MSS digital data. Technical Note: IRS-UP/SAC/CPF/TN/ 
09/ 85. Space Applications Centre, Ahmedabad. 
Dadhwal VK, Parihar, JS, 1988, Comparison of IRS LISS-I and 
LISS-II digital data with Landsat MSS and TM for land 
cover classification in Orissa. In "Remote Sensing 
Applications Using IRS-1A data’, p77-86, (Scientific Note, 
IRS-UP/SAC/AD/SN/02/88 ). 
Dadhwal VK, Parihar, JS, 1990. Comparative study of digital 
MOS-1 MESSR, Landsat MSS and IRS LISS-I data for 
land cover classification in Sambalpur district, India. Proc 
MOS-1 Data Evaluation, (K. Maeda, S. Ogawa, eds.), HE- 
90015, pp 270-276, NASDA, Japan. 
IAPRS & SIS, Vol.34, Part 7, “Resource and Environmental Monitoring”, Hyderabad, India, 2002 
Dadhwal, VK, and Ray, SS, 2000. Crop Assessment using 
remote sensing — Part II: Crop condition and yield 
assessment. Ind. J. Agric. Economics, 55,(2, Suppl.), 55-67. 
Dadhwal, VK, and Sridhar, VN, 1997. A non-linear regression 
form for VI-crop yield relation incorporating acquisition 
date normalization. Internat. J. Remote Sens., 18, 1403- 
1408. 
Dadhwal VK, Parihar JS, Medhavy TT, Ruhal DS, Jarwal SD, 
Khera AP, 1996. Comparative performance of thematic 
mapper middle-infrared bands in crop discrimination. Int. J. 
Remote Sensing, 17, pp 1727-1734. 
Dadhwal VK, Parihar, JS, Ruhal, DS, Jarwal, SD, Medhavy, 
TT., Khera, AP, Singh, J, 1989. Effect of acquisition date 
and TM spectral bands on wheat, mustard and gram classi- 
fication accuracies. J. Ind. Soc. Remote Sens., 17, pp 19-24. 
Dadhwal V.K., Ruhal, D.S., Medhavy, T.T., Jarwal, S.D., 
Khera, A.P., Singh, J., Sharma, T., Parihar, J.S., 1990. 
Wheat acreage estimation for Haryana using satellite 
digital data. Status Report on Crop Acreage and 
Production Estimation (RSAM/SAC/CAPE/ SR/25/90), 
Space Applications Centre, Ahmedabad, pp 51-75. 
Dadhwal V.K., Ruhal D.S., Medhavy T.T., Jarwal S.D., Khera 
A.P., Singh J., Sharma T. and Parihar J.S., 1991. Wheat 
acreage estimation for Haryana using satellite digital data. 
J. Indian Soc. Remote Sensing, 19, pp 1-15. 
Dadhwal VK, Singh RP, Dutta S, Parihar JS, 2002. Remote 
Sensing based Crop Inventory. Trop. Ecol., 43, pp 107-122. 
Dubey R.P. Ajwani N., Navalgund R.R., 1991. Relation of 
wheat yield with parameters derived from a spectral growth 
profile., J. Indian Soc. Remote Sens., 19, pp 45-58. 
Dubey R.P., Ajwani N., Kalubarme M.H., Sridhar V.N., 
Navalgund R.R., Dubey R.P. Sidhu S.S., Thoran O.P., 
Cheema S.S. and Narang R.S., 1994. Preharvest wheat 
yield and production estimation for the Punjab, India. Inr. J. 
Remote Sensing, 15, pp 2137-2144. 
Dutta S, Sharma SA, Khera AP, Ajai, Yadav M, Hooda RS, 
Mothikumar KE, Manchanda ML, 1994. Accuracy assess- 
ment in cotton acreage estimation using Indian remote 
sensing satellite data. ISPRS J. of Photogramm. Remote 
Sensing, 49, pp 21-26. 
Dutta S, Patel NK, Moharana S, Parihar JS, 2001. Forecasting 
district and state-level rice yield and production using 
modified crop water requirement index model — A three 
year experience. Scientific Note. RSAM/SAC/FASAL- 
TD/SN/ 12/2001. 15p, Space Applications Centre, 
Ahmedabad. 
MacDonald, RB, Hall FG, 1980. Global crop forecasting. 
Science, 208, pp 670-679. 
Mahey RK, Singh R, Sidhu SS, Narang RS, Dadhwal VK, 
Parihar JS, Sharma AK, 1993. Pre-harvest state level wheat 
acreage estimation using IRS-1A LISS-I data in Punjab 
(India). Internat. J. Remote Sensing, 14, pp 1099-1106. 
Medhavy TT, Sharma T., Dubey, RP, Hooda RS, Mothikumar 
KE, Yadav M, Manchanda ML, Ruhal DS, Khera AP, 
Jarwal SD, 1993, Crop classification accuracy as influenced 
by training strategy, data transformations and spatial 
resolution of data. J. Ind. Soc. Remote Sens., 21, pp 21-28. 
Navalgund, R.R., Parihar, J.S., Ajai and Nageshwar Rao P.P., 
1991. Crop inventory using remotely sensed data. Current 
Science, 61, pp 162 — 171. 
Oza M.P., Bhagia N., Dutta, S., Patel J.[H., Dadhwal V.K., 
1996. National wheat acreage estimation for 1995-96 using 
multi-date IRS-1C WiFS data. J. Ind. Soc. Remote Sens., 
24, pp 243-254 
Pandey PC, Dadhwal VK, Sahai B, Kale PP, 1992. An optimal 
estimation technique for increasing the accuracy of crop