Estimation of daily vegetation coefficients using MODIS data for clear and cloudy sky conditions

ABSTRACT

Spatially distributed vegetation coefficients ($K_{\mathrm{v}}$) data with high temporal resolution are in demand for actual evapotranspiration estimation, crop condition assessment, irrigation scheduling, etc. Traditional remotely sensed based $K_{\mathrm{v}}$ data application gets hindered because of two main reasons i.e 1) spectral reflectance based $K_{\mathrm{v}}$ accounts only for transpiration factor, but fails to account for total evapotranspitration. 2) required optical spectral reflectances are available only during clear sky conditions, which creates gaps in the $K_{\mathrm{v}}$ data. Hence there is a necessity of a model which accounts for both transpiration and evpaoration factors and also for a gap filling method, which can produce accurate continuous quantification of $K_{\mathrm{v}}$ values. Therefore, in this study, different combinations of enhanced vegetation index (EVI), global vegetation moisture index (GVMI) and temperature vegetation dryness index (TVDI) have been employed in linear and non linear regression techniques to obtain the best model. To fill the gaps in the data, initially, temporal fitting of $K_{\mathrm{v}}$ values have been examined using Savitsky-Goley (SG) filter for 3 years of data (2012–2014), but this fails when sufficient high quality $K_{\mathrm{v}}$ values are unavailable. In this regard, three gap filling techniques namely regression, artificial neural networks (ANN) and interpolation techniques have been employed over Cauvery basin. Microwave polarization difference index (MPDI) has been employed in ANN technique to estimate $K_{\mathrm{v}}$ values under cloudy sky conditions. The results revealed that the combination of GVMI and TVDI using linear regression technique performed better than other combinations with correlation coefficient (r) and root mean square error (RMSE) values of 0.824 and 0.204 respectively. Furthermore, the results indicated that SG filter can be used for temporal fitting and for gap filling regression technique performed better than other techniques with the r and RMSE values of 0.68 and 0.25 for Berambadi station.

Aknowledgements

We would like to thank Prof. Muddu Shekar (Professor, Dept of Civil Engg, IISc, Bangalore) and his team for providing observed latent heat flux and other climatic variables data for the Berambadi site and also thank NASA Land Process Distributed Active Archive Center for rendering available MODIS LST, Reflectance and LULC data and ISRO’s MOSDAC for the supply of AWS data. We would like to thank GLDAS, Noah land surface model(LSM) community for providing latent heat flux and GCOM-W1 for rendering AMSR2 data.

Disclosure statement

No potential conflict of interest was reported by the authors.

Additional information

Funding

This work was supported by the Ministry of Earth Sciences [MOES/ATMOS/PP-IX/09];