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Poster Session
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Crop Yield Prediction in Command Area using Satellite Data
Correlation between yield and NDVI
The validity of crop yield models with satellite derived NDVI is determined by the strengths of association between the two variables included in the model. Hence it is essential to have understanding about the correlation existing between yield and NDVI at different phonological stages of crop for selecting appropriate date of pass to include in the model. NDVI statistics extracted from multidate satellite data representing panicle initiation, heading and ripening stages over CCE plots has been correlated with yield data. The results are presented in Table 1
Table 1 Correlation between yield and NDVI at different stages of paddy crop over CCE plots
| S.No. |
Date of satellite overpass |
Approximate Phonological stage |
Correlation between yield and NDVI |
| 1 |
05-04-93 |
Panicle initiation heading |
0.59 |
| 2 |
16.04.93 |
Panicle initiation heading |
0.85 |
| 3 |
27.04.93 |
Heading |
0.94 |
| 4 |
08.05.93 |
Ripending |
0.44 |
| 5 |
Time composited VI |
Just before heading or heading |
0.88 |
The correlation coefficients are found to be statistical significant with the NDVI of 16th April and 27th April which represents 'towards heading phase' of standing paddy crop. The correlations have become weaker with NDVI moving away from heading i.e. at panicle initiation or ripending. Hence, it is appropriate to select the satellite data representing either heading or just before heading phase. However, in practice, as single date may not represent the same physiological phase allover the study are due to differential crop calendar adopted by farmers. As a result the correlation coefficient is not applicable to all over the study area due to differential crop calendar adopted by farmers. Such problems can be overcome through time composition of multidate satellite data representing, in general, panicle initiation heading phases (Thiruvengadachari et al. 1994 and Jonna 1994). As a result maximum NDVI value is considered for each crop pixel, which normally occurs at heading phase of paddy. From Table1, it may be observed that the correlation with TCVI is also high and statistically significant.
Crop yield estimation with Yield -NDVI Model
TCVI is considered for yield model as it accounts for differences in crop calendar in the study area so that the model can be adapted for deriving the estimates over space and time. TCVI and yield over CCE plots are used to develop the yield model. Scatter diagram and regression line between yield and TCVI are shown in Fig 1
Figure 1 Relationship between yield and TCVI over CCE Plots
The estimated regression line is
Yield (kg/ha) = 42.23 TCVI - 3439.05
(R2 = 0.75)
Using the regression equation further yield estimates re obtained for smaller areal units such as distributary command by substituting mean NDVI as input parameter. The paddy yield has also been estimated for every pixel 72.5 mt. size, though in practice the estimates are better performed for any aerial units such as whole or part of distributary command to know the spatial yield variability across the command area. The yield estimates thus obtained have been validated through oral interviews with framers.
Thus, yield model derives a general applicable relationship between yield and NDVI which can be further to obtain estimates all over the study area. However, it may be noted that the reliability of such estimates depends upon the distribution pattern of input data i.e, the data o CCE. If CCE plots are well distributed all over the study area to represent differential crop condition then the resultant yield model would be unbiased. The sampling design adopted in existing directly related to yield, for stratification and subsequent sample selection and hence there is a need to improve the methodology by incorporating satellite derived crop area and crop condition information (NRSA 1993). In view of these limitations, the plots data from improved CCE is desirable for yield model.
Yield Prediction
The paddy yield model, developed based on CCE data and satellite data of 1992-93 rabi season has been used to predict the yield during 1993-94 rabi season. Satellite data of 16th April, 25t April and 02 May 1994 was co registered and time composited. The CCE data for rabi 1993-94 were collected and TCVI statistics was extracted for each plot as was done earlier done. Sing TCVI values, yield estimates are obtained for each plot. The comparison estimated yield and the actual yield obtained CCE is presented in Table 2
Table 2 Validity of yield estimates in 1993-94 Rabi Season
| Plot |
Estimated yield (Kgs/ha) |
Actual yield (Kgs/ha) |
% dev. from act. yield |
| 1. |
4922 |
4810 |
2.33 |
| 2. |
5345 |
5195 |
2.89 |
| 3. |
5001 |
5421 |
7.75 |
| 4. |
5852 |
6471 |
9.75 |
| 5. |
4669 |
4949 |
5.66 |
| 6. |
4796 |
4757 |
0.82 |
| 7. |
5852 |
6032 |
2.98 |
| 8. |
5809 |
6331 |
8.25 |
| 9. |
5598 |
5456 |
2.60 |
The adaptability of yield model developed with 1992-93 Rabi data to predict the yield of 1993-94 yield has thus been validated. Further, yield map showing pixel wise yield has been generated for both the seasons, using the same model, to study the spatial and temporal variations in yield over the command area, which helps temporal variations in yield over the command area, which helps identification of problem areas for effecting correlation measures.
Conclusions
In the absence of any syntematic procedures, to estimate the average yield of principal crops in irrigated command areas, the satellite data based crop yield models offer tremendous scope in command area management. The spatial yield information derived from yield model is vital to identify the problem areas in the command and to evaluate the performance of irrigation system. Once the model is established after continuous validation with the data of 2-3 seasons, the crop performance may be evaluated for historic seasons for which no yield information is now available, by adopting suitable normalization procedures for satellite data and crop calendar (Jonna et al. 1994). Such information highlights the spatial and temporal variations in crop yield in the command area which is useful in economic evaluation of irrigation projects for decision making in government policy matters.
Acknowledgements
Grateful thanks are due to Prof. B.L. Deekshatulu, Director and Dr. D.P. Rao, Associate Director, National Remote Sensing Agency, Hyderabad, India, for the valuable guidance and supported extended to this study. The valuable co-operation and support extended to this study. The valuable co-operation and support received from Shri M.L Lath, Commissioner (WM), Ministry of Water Resources, Government of India is also gratefully acknowledged. This study would not have been possible without the wholehearted support of Bhadra Project Engineers.
References
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