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Present study was conducted in Uttarakhand state during rabi season for the year 2009-10 in which spectro-meteorological models were developed for predicting the yield of Lahi (Brassica campestris var. toria) crop. Crop management data for Lahi crop and cloud free LANDSAT-ETM+ images of path 145 and row 40 (containing Pantnagar and adjoining region...
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... was performed with the help of ENVI-4.8 software. The LANDSAT-ETM+ reflective image of 30 meter was considered as master image (base image) for registering the Google-Earth mosaicked image. Different feature objects such as road, river, field boundary etc. were identified in the Google-Earth image with the help of unscaled published map (Fig. 2) of the University farm and digitization was done with the help of ENVI-4.8 ...
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Citations
... The comparison between observed disease impact and the disease impact estimated by spectral meteorological model has been shown in fig. 3. The very high value of coefficient of determination (R 2 =0.99) suggests that spectral-meteorological model could prove to be very accurate in estimating the losses due to the disease in a very precise and timely manner. Wheat and mustard yield could be predicted well in advance with the help of site specific spectral meteorological yield models developed using derived NDVI and weather parameters Ranjan and Nain, 2015). ...
A remote sensing and modeling based approach has been used here to monitor stripe/yellow rust disease in wheat crop in U.S. Nagar district of Uttarakhand. The crop management data for wheat and cloud free crop season's LANDSAT-ETM + images of 145 th path and 40 th row for the period 2007-08 to 2011-12 were procured to develop a disease monitoring system. Remote sensing based spectral meteorological models were developed in order to prefigure the possible impacts of the disease on wheat yield. As a first step, DII (Disease Impact Index) was computed for all five years on the basis of weather driven wheat yield through CERES-Wheat crop simulation model embedded in DSSAT and the recorded district level wheat yield by the State Agriculture Department. The wheat yield variability on year to year basis has been calculated for both simulated and recorded wheat yield. Observed wheat yield ranged from 37.1q/ha to 39.6q/ha, whereas simulated yield by CERES-wheat model varied between 35.1q/ha to 41.2q/ha. The variability in the recorded wheat yield was compared with the variability in the simulated wheat yield. It was assumed that, variability other than the weather induced variability in the recorded wheat yield was due to the yellow rust disease. The DII ranged from-16.1 per cent to 0 per cent. The values of the disease impact index observed to be negative in most of the cases suggesting that with decreasing DII values, there might be reduction in wheat yield. These values of DII were considered as the observed values and were later related with remote sensing based indices and weather parameters to develop spectral meteorological models. NDVImax was regressed with DII and the remainders were calculated for all five years (2007-08 to 2011-12), which were then regressed with the weekly weather variables using forward selection approach in SPSS software so as to explain the variability in DII. The very high value of coefficient of determination (R 2 =0.99) suggested that spectral meteorological model proved to be very accurate in assessing the yellow rust/any biotic losses precisely and timely.
