Agriculture Applications Of Remote Sensing : Paddy Yield Estiamtion form Landsta-5 Thematic Mapper Data
2.4 Results and Discussions
The results show there is a linear relationship between each index and yield. The correlation coefficients obtained were 0.85, 0.55, 0.32 for NDVI, RVI (4/3) and RVI (5/3). This shows that NDVI correlates best with paddy yield as compared to other indices. This result is consistent with work done by Patel e.at (1985) who found that NDVI (4.3) have better correlation with paddy yield. Thus, the NDVI versus data have been selected for obtaining the yield estimation equation. The yield estimation equation is as follows.
YIELD (kg/ha) = 34.30 x (NDVIfield) - 237.85 (2)
The NDVI obtained from the satellite data was not similar to the NDVI in the field as given by the following relationship.
NDVIsat = NDVIfield + 0.1836 (3)
Therefore taking the above different in Eq (3) into account the yield estimation from the satellite data can be written as follows:
YIELD (kg/ha) = 34.30 x (NDVIsat - 0.1836) - 237.85 (4)
The average yield in the study are has been found to be about 6750 kg/ha by using the above techniques. However, the yield data from the Department of Agriculture indicate an approximate value of 5175 kg/ha for the same. The over-estimation in the satellite yield estimate of about 30% is due to the difference in the paddy phenology cycle at the time the satellite data were acquired (less matured) and the time the field measurements were taken (matured). Better accuracy could be obtained if the dates of the satellite data and the field data are the same or close to one another. The number of field samples were also not sufficient to improve the results of the regression analysis.
3.0 Conclusions
The NDVI values obtained from a combination of bands 3 and 4 of the Landsat-5 TM show better correlation with yield as compared to the RVI obtained from the combination of bands 3 and 4 the combination of bands 3 and 5. This is because the effects of soil and atmosphere have been considerably reduced in the NDVI. In conclusion, the paddy yield estimates. The error in the satellite yield estimate is due to the difference in the paddy phenology cycle at the time the satellite data were acquired and the time the field measurement were taken.
This study indicates the usefulness of satellite remote sensing techniques in deriving agriculture information over large areas in a cost effective manner that can benefit related agencies.
Acknowledgement
The authors wish to tanki Azhar Hj. Salleh, former postgraduates student at the faculty of surveying and Real Estate for his contribution in the study.
