Operational semi-physical spectral-spatial wheat yield model development
Other literature type
Chaudhary, K. N.
Manjunath, K. R.
Ray, S. S.
Parihar, J. S.
(issn: 2194-9034, eissn: 2194-9034)
Spectral yield models based on Vegetation Index (VI) and the mechanistic crop simulation models are being widely used for crop
yield prediction. However, past experience has shown that the empirical nature of the VI based models and the intensive data
requirement of the complex mechanistic models has limited their use for regional and spatial crop yield prediction especially for
operational use. The present study was aimed at development of an intermediate method based on the use of remote sensing and the
physiological concepts such as the photo-synthetically active solar radiation (PAR) and the fraction of PAR absorbed by the crop
(fAPAR) in Monteith’s radiation use efficiency based equation (Monteith, 1977) for operational wheat yield forecasting by the
Department of Agriculture (DoA). Net Primary Product (NPP) has been computed using the Monteith model and stress has been
applied to convert the potential NPP to actual NPP. Wheat grain yield has been computed using the actual NPP and Harvest index.
Kalpana-VHRR insolation has been used for deriving the PAR. Maximum radiation use efficiency has been collected from literature
and wheat crop mask was derived at MNCFC, New Delhi using RS2-AWiFS data. Water stress has been derived from the Land
Surface Water Index (LSWI) which has been derived periodically from the MODIS surface reflectance data (NIR and SWIR1).
Temperature stress has been derived from the interpolated daily mean temperature. Results indicated that this model underestimated
the yield by 3.45 % as compared to the reported yield at state level and hence can be used to predict wheat yield at state level. This
study will be able to provide the spatial wheat yield map, as well as the district-wise and state level aggregated wheat yield forecast.
It is possible to operationalize this remote sensing based modified Monteith’s efficiency model for future yield forecasting with
around 0.15 t ha-1 RMSE at state level.