Use of Statistical Method to Remote Sensing Data for In-season Crop Growth Assessment

Farming is a risky business but, from the food security point of view, it is important that farmers continue to grow crops so that people get food to eat. Although natural calamities cannot be eliminated, its impact can be reduced through implementation of pro-active and pro-poor risk management policy programs. Remote sensing, with capabilities of synoptic coverage, multi-spectral and multi-temporal observations, is ideally suited for in-season monitoring the progress of crop. Normalized Differential Vegetation Index (NDVI) is the primary index for monitoring vegetation status and its temporal behavior captures the dynamic response of vegetation cover to prevailing physical conditions. The present study offers a methodology for making multiple inseason assessment of the crop growth vis-a-vis its normal performance. This is treated by use of conditional distribution. Present analysis reports the performance in deriving spectral maxima (Gmax) from complete profile of validation season and one which was derived from conditional mean approach. It was observed that in more than 90 % of the cases, the difference in Gmax was less than 3 %. Thus the performance of methodology can be termed as very good.