Bhattacharya B.K.,Forestry and Environment Group |
Mallick K.,Forestry and Environment Group |
Mallick K.,Lancaster University |
Nigam R.,Forestry and Environment Group |
And 2 more authors.
Agricultural and Forest Meteorology | Year: 2011
Accurate pre-harvest assessment of a staple food crop is an integral part of policy formulation in relation to food security issues. Here, two different approaches were attempted to estimate wheat yield using time series multi-year satellite (MODIS Aqua) optical-thermal data from a single earth observation (EO) mission. Surface energy budgeting was used to estimate evapotranspiration in terms of latent heat fluxes from net available energy and evaporative fraction to predict wheat yield over four agro-climate zones in semi-arid climate of Gujarat, India. Satellite based estimates of latent heat fluxes were found to show substantially less error with respect to the area-averaged heat flux measurements from LAS (large aperture scintillometer) as compared to measurements from BREB (Bowen Ratio Energy Balance) alone. The deviations in satellite based zonal CWU were found to have a strong correlation (r=0.71) with the deviations from zonal wheat yield. Among both the approaches, the radiation use efficiency (RUE) based approach produced better accuracy in the predicted yield with lower root mean square error (RMSE) of 390kgha -1 (14.8% of reported mean) and higher correlation coefficient (r=0.92) than the water use efficiency (WUE) based approach (RMSE 573kgha -1, 21.8% of reported mean; r=0.80). Uncertainties in the satellite based core inputs resulted into a net 10-12% error in predicted yield in case of RUE approach. Our demonstrative case studies recommend that the coupled use of satellite observations from multiple EO missions and radiative transfer simulation would be effective to make efficiency based approaches operationally viable for regional wheat yield forecasting in near real time. © 2011 Elsevier B.V.
Antony R.,Forestry and Environment Group |
Ray S.S.,Forestry and Environment Group |
Panigrahy S.,Forestry and Environment Group
Remote Sensing Letters | Year: 2011
Multi-angular narrowband compact high-resolution imaging spectrometer (CHRIS) on-board the project for on-board autonomy (PROBA) data of 18 March 2008 were used in this study to discriminate three different growth stages of wheat crop grown in the Central State Farm of Suratgarh, Rajasthan, India. Results showed that the off-nadir view angles performed better than nadir viewing for crop stage discrimination. Among all the off-nadir viewing angles, -55.37° view angle (in the backward-scattering direction) had the highest normalized distance between the crop stage classes. Based on the analysis, the five best bands were identified as 630, 660, 674, 705 and 712 nm for separating wheat at different stages. © 2011 Taylor & Francis.
Gupta P.K.,Forestry and Environment Group |
Dutta S.,Indian Institute of Technology Guwahati |
Panigrahy S.,Forestry and Environment Group
Water Resources Management | Year: 2010
Irrigated agriculture in many areas of the world is currently being practiced from multiple water sources such as precipitation, canal, wetlands, ground aquifer, etc. This study highlights the use of high temporal remote sensing data [IRS-1D; Wide Field Sensor (WiFS), 188-m resolution] to assess conjunctive water use pattern and its productivity in the 6 Main Canal command of Damodar Irrigation Project West Bengal, India. In this command three sources of water (canal water, groundwater and wetland) were used for the rice growing system during the summer season. A multi-date (ten dates, two bands) image stack was prepared. Using this image stack and an unsupervised classification (Fuzzy k-means) backed by space-time spiral curve (ST-SCs) technique, canal release and wetlands information was used to prepare irrigated classes (canal, groundwater or wetlands) map for summer 2000. ST-SCs have been used to analyze temporal WiFS data to continuously monitor class dynamics over time and space and to determine class separability (different types of irrigated-classes) at various time periods within the season. Results showed that the area under agriculture, non-agriculture and water were 81%, 18.5% and 0.5%, of the total area respectively. While, groundwater, canal water and wetland irrigated rice were 67.6%, 25.6% and 6.8%, respectively out of the total agriculture area. Classification results found to have more than 89.3% overall accuracy for broad land cover, while sub-classes of rice i.e. irrigated classes found have reasonably good accuracy of 85.7%. A productivity index (LAI/water-requirement) was also developed. Productivity index was high for the wetland and groundwater irrigated rice as compared to the rice irrigated through canal water. These results were weighed against the observed yield data. © Springer Science+Business Media B.V. 2009.