Statistics – Computation
Scientific paper
May 1993
adsabs.harvard.edu/cgi-bin/nph-data_query?bibcode=1993adspr..13..223g&link_type=abstract
Advances in Space Research, Volume 13, Issue 5, p. 223-232.
Statistics
Computation
Scientific paper
This paper deals with the utility of NOAA/AVHRR vegetation indices as agrometeorological growth parameters. The area chosen to the study was the Punjab and the Haryana states of India, both comprising districts primarily under wheat cultivation with 68-76% geographical area under agriculture and 89-96% agricultural area under wheat. Application of geometric correction to visible and near IR band images followed by computation of NDVI and RVI images yielded less error than the conventional method of correction to NDVI/RVI images. Images having far-side boundary pixel upto 38 degree scan angle did not cause significant error at district level statistics. RVI was more sensitive than NDVI from emergence to near jointing and maturity to senescence stages. Prior to jointing stage and after dough stage RVI can be transformed to NDVI, to enable its joint use with NDVI. The maximum and mean air temperatures based growing degree days (GDD) related well with the NDVI/RVI temporal profile over the entire crop growth cycle at 98-99% significance level. However, shapewise the maximum temperature based GDD was superior to mean temperature based GDD, whereas minimum temperature based GDD had statistically significant relationship only upto dough stage at 95% significance level. The integrated NDVI (INDVI) values computed for mid of late-tillering to jointing stage were significantly related to the yield (r-square = 0.867, at 99.34% significance level). When INDVI at this stage was coupled with that of mid of milking and dough - nearing maturity stages, the predictability of wheat yields increased (r-square = 0.891, at 99.32% significance level).
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